Appraisal and Evaluation of Energy
Utilization and Efficiency
in the Kingdom of Saudi Arabia
Volume 1
2014
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KAUST Industry Collaboration Program (KICP) Partners
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KAUST Industry Collaboration Program (KICP) Partner
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Acknowledgments
This 2013/2014 KICP strategic study, Appraisal and Evaluation of Energy Utilization and Efficiency in
Saudi Arabia: Supply and Demand Impacts, Business Opportunities, and Technological and Economic
Considerations, was led by Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), in
collaboration and with contribution from several other organizations. The King Abdullah University for
Science and Technology (KAUST) Industry Collaboration Program (KICP) and Economic Development
would like to extend their gratitude to all who contributed to this strategic study. Special thanks to the
following distinguished contributors:
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Dr. Naif Alabbadi, General Director, Saudi Energy Efficiency Center
Mr. Jean Michel Merzea, Vice President, Total Innovative Energies Solutions, Total
Mr. Saleh A. Al-Agili, VP, National Industrial Clusters Development Program (NICDP)
Dr. Ramzy Obaid, Professor of Electrical Engineering, King Abdulaziz University
Dr. Aqil Jamal, Researcher, Saudi Aramco
Dr. Muhammad Asif, Professor of Architectural Engineering, King Fahd University for Petroleum
and Minerals (KFUPM)
Dr. Raed Bkayrat, Vice President of Business Development, First Solar Inc.
Dr. Mohammed-Slim Alouni, Professor of Electrical Engineering, KAUST
Eng. Bertrand Rioux, Researcher, King Abdullah Petroleum Studies and Research Center
(KAPSARC)
Eng. Mohammed Al-Tamimi, Researcher, Research, Development, & Innovation, King Abdullah
City for Atomic and Renewable Energy (K.A.CARE)
Mr. Ahmed AlMohaimeed, Vice President & General Manager, Advanced Electronic Company
Eng. Aiman Baker, Utilities Manager, KAUST
Eng. Abdullah Al-Ghumaiz, Development Manager, Advanced Electronic Company
Eng. Waleed Al-Rumaih, General Manager, Imdad Energy
Mr. Guy Chaperon, CEO, Al Safwa Cement Company
Eng. Imad Albadry, General Manager, Al-Shurfa Restaurant
We also extend our sincere gratitude to the following organizations that provided meaningful input to
the study:
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Electricity & Cogeneration Regulatory Authority
National Grid Company
Saudi Electricity Company
University of Dammam
Volume 1
Preface
The Kingdom of Saudi Arabia is one of the few countries that have both abundant hydrocarbon
resources and high renewable energy potential. While it is well positioned in terms of energy security,
the Kingdom faces some major challenges: one of the highest energy consumption per capita rates in
the world, as well as a high rate of increase in energy consumption. Worldwide, improving energy
efficiency is considered to have the highest short-term payoff in reducing energy consumption. So, it
comes as no surprise that this topic was chosen for the annual KICP Strategic Study by the KAUST
Industry Advisory Board (KIAB) members. The findings of this year’s study are of critical importance
from the perspective of combatting anthropogenic climate change as well as stabilizing energy demand
in the face of population growth—without hampering economic prosperity.
This, KICP’s fourth annual strategic study is entitled, Appraisal and Evaluation of Energy Utilization and
Efficiency in Saudi Arabia: Supply and Demand Impacts, Business Opportunities, and Technological and
Economic Considerations. The study reviews and evaluates the current and future energy supply and
demand in the Kingdom. It involves:
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Assessment of the current energy waste in the industrial sector
Energy consumption audits in residential, commercial, and industrial sectors
Evaluation of smart grid technologies, including efficient integration of renewable energy
resources, adaptation, and regulations.
There are many options available to policy makers, scientists, and the private sector to improve energy
efficiency and flatten consumption. These options enable meeting the needs of the Kingdom’s growing
population, without inhibiting its robust economic growth. The KICP Strategic Study explores many of
the more promising ones, informing critical decision-making in both the public and private sectors.
This study estimates the potential energy that can be generated from waste heat in the three main
industrial sectors (saline water, petrochemicals, and power generation) is equivalent to 3,500 MWth.
Energy audits were conducted in select industrial sectors, including power, water, cement, paper,
petrochemicals, food, and textiles. The study concluded that up to 141 MW can be re-generated from
the current wasted heat, assuming efficiency rate between 10 percent and 25 percent were achieved.
By 2040, the energy efficiency in the most pessimistic (10 percent) and optimistic (25 percent) scenarios
is projected to translate to cumulative potential energy savings of 2,050 TWh and 4,413 TWh,
respectively.
Smart grid energy efficiency solutions were also considered in the scope of this study. Smart grid
technologies represent new business opportunities, integrating ICT solutions with legacy power
systems, power station control units, network distribution lines, transmission, and renewable energy.
Energy efficiency audits form the foundation of the report and represent the most accurate, credible
data collected in the Kingdom to date. These audits involved data collection both in the industrial sector
and on the household level, based upon load profile metering. The study does not stop at data
collection but presents further analysis, load profile evaluation, and proposed energy efficiency
measures and priorities to curtail consumption.
Renewable energy solutions, including solar (i.e., PV, CPV, and CSP), wind, geothermal, hydroelectric,
and wave energy were assessed in this report. Renewable energy applications in seawater desalination
and rooftop peak shaving case studies were also discussed and evaluated.
The benefits of applying the recommendations of the KICP Strategic Study are not limited to energy
savings and efficient capital utilization. The study identifies opportunities for strengthening the
Kingdom’s human capital base, enhancing academic research, and achieving “triple bottom line”
business outcomes.
Amin Shibani,
Vice President, Economic Development
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Executive Summary
Executive Summary
Introduction
Energy efficiency (EE) methodologies and strategic approaches represent a main economic priority for
the forthcoming decades worldwide. They may assist in sustainable economic future development,
especially for fast-growing economies, such as the Kingdom of Saudi Arabia (KSA), with high rates of
population growth. There should be a focus on specifics for each country’s economy in relation to
production and energy markets.
Motivated by the demanded diversification of the Saudi economy, this study aims to develop and
provide a comprehensive understanding of applicability of energy-saving technologies in the residential
and public sector, for commercial-industrial clients, and for main producers of waste energy. This study
builds upon a careful and sector-specific demand analysis and possible best available technology (BAT)–
based technology proposals by means of a sound and well-adapted EE regulation.
In parallel, the study focuses on the most up-to-date and country-specific opportunities to integrate
renewable energy (RE) resources such as photovoltaic (PV), solar-thermal, wind, and concentrating solar
power (CSP) technologies adapted for integration in decentralized commercial-industrial applications in
the KSA.
Volume 1 Summary
Chapter 1 Summary
Energy Market Economics: Energy Supply and Demand in the Kingdom of Saudi Arabia
from 2010 to 2040
1. The main aim of this substudy is to forecast a baseline balance scenario using a detailed analysis of
past energy and business data in the KSA economic and energy sector (from 1971 to 2009) with
demographic and economic data on the future of Saudi Arabia. The result is a baseline forecast up to
2040 for the next 30 years to allow the definition of priorities for EE measures in the respective sectors
of the kingdom until 2040.
2. The basis for the forecast is 1971 to 2009 data (38 years) to be extrapolated until 2040 for the next 30
years using time series analysis. This analysis is based on both population and number of households
and gross domestic product (GDP) and industry structure up to 2040, as projected by the United
Nations for population data and by the World Bank for economic data. We know for certain that
economic growth in Saudi Arabia depends heavily on the respective current and future oil price.
3. Considering the figures from the synoptic version of the energy flow analysis with the Sankey diagram
for Saudi Arabia in 2009, the main energy-saving potential can be clearly seen. Energy losses in the KSA
transformation segment total 72 Mtoe, which is about 43 percent of the entire input to the
transformation sector. This value is twice the losses from end use (only 35 Mtoe); therefore, any
priorities for energy savings should start in this area of the country’s energy balance. Within the Final
Energy Consumption category, the sectors of Transport (34 Mtoe) and Non-Energy Consumption (31
Mtoe) are individually twice as great as the Residential (15 Mtoe only) and the Industry (17 Mtoe)
sectors; therefore, any sector priority should be with Transport and Non-Energy Consumption. Within
the Final Energy Consumption, the losses in the Useful Energy Segment are about 35 Mtoe, which
equals approximately 53 percent losses during the final energy consumption.
4a. The main characteristics of the energy system of the KSA with reference to future energy
consumption are:
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Strong dependence on energy exports (crude oil)
Volatility of crude oil prices, which have extreme influence on the financial possibilities of the Saudi
economy
Government-owned electricity system
Subsidized energy supply for consumers
4b. The main characteristics of the electricity system of the KSA with reference to future energy
consumption are:
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Comparatively high age of power stations and turbines, which reduces the overall efficiency of the
KSA power system
Comparatively small generation units (43 percent of all thermal power plants [TPPs] are at capacity
of 251 MW and lower; there is a huge number of small generation units with 8 MW, 12 MW, or 25
MW capacity), which reduces the overall efficiency of the power system
Comparatively low efficiency of diesel generators, mainly simple-cycle gas turbine technologies with
an average efficiency rate of about 25 percent in 2009
Comparatively high distribution losses (9.6 percent transmission losses), which reduces the overall
efficiency of the power system
5. From the time series analysis, our team gained a more detailed understanding of the entire energy
system of Saudi Arabia for the year 2040. Final energy consumption will increase from 105 Mtoe in 2009
to about 425 Mtoe in 2040 at an annual growth rate of 4.3 percent, which is less than the foreseen
increase in GDP/capita of 7.2 percent annually (even not fully considering the expected increase in
population). Primary energy production in 2040 will be at the 2009 level and remain at 534 Mtoe, which
is the average production of the past 10 years. We did not receive indications that the energy
production will increase or decrease by 2040 in physical terms (bbl/day). An increase of real prices could
be foreseen, which will influence the turnover in monetary terms but not in physical terms. Primary
energy supply in the KSA will increase from 169 Mtoe in 2009 to 530 Mtoe by 2040 due to a heavy
increase in final energy consumption.
6. Finally, this increase in primary energy supply in KSA until 2040 will reduce the possibilities of oil
exports, which are currently at a level of 383 Mtoe (in 2009). For comparison, of course, the net exports
should be considered, which is 11 Mtoe less than the current exports, namely 372 Mtoe in 2009. For
2040, we see in the KSA a national production of energy at 534 Mtoe, while the total primary energy
supply is at 530 Mtoe. Therefore, there will be almost no crude oil net exports from the KSA to other
countries. As current GDP depends to a wide extent on oil production (92 percent of GDP originates in
the oil and petrochemicals industry), there will be quite a heavy influence on the wealth of the nation,
unless the KSA can increase oil production, which is currently not foreseen in the baseline forecast.
7. A direct comparison with other forecasting studies is not possible. Most of the other studies
(KAPSARC/IEEJC, ECRA/BRATTLE; SEEC/BAIN, KFUPM-SNC LAVALIN, and others like Saudi Aramco, ECRA,
KAPSARC, MOEP/MOPMR/MOWE, SEC, KACARE, and SEEC) concentrate exclusively on electricity, and
electricity is only 17 percent of total final energy consumption in the KSA. In addition, there are
different time horizons—2020, 2022, 2025, and 2030—but nearly no information going beyond 2032,
except for the TYNDALL study.
8. Concerning the reliability of forecast data, we see a comparatively high confidence level as standard
deviation is, for most variables, quite limited. The R2 values for the forecast of population (0.999 in
nonlinear forecast) and for GDP (0.994), Total Primary Energy Supply (0.984), Total Final Energy
Consumption (0.960), MW peak (0.997), and Total Electricity Output (0.993) are quite high.
9. Electricity consumption is expected to increase from 240 TWh/a in 2009 to about 850 TWh/a in 2040.
This increase will absorb a reasonable additional amount of energy production in the KSA, and its
influence on primary energy supply in the KSA is quite evident, as about 120 Mtoe will be required to
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supply the power stations with necessary fossil fuels (fossil fuel power stations increase from 57 GW to
about 87 GW in 2040).
10. Compared with neighbor countries (“GCC group”), and if energy consumption is measured against
population, Saudi Arabia with 7,500 kWh/capita remains much lower than Kuwait (17,500 kWh/capita)
and Dubai (13,000 kWh/capita). This may give indications that an increase to 185,000 MW capacity and
a respective power generation by 850,000 GWh/a would be technically possible.
11. The power supply capacity mix in Saudi Arabia for the period 2010 to 2040 in MW (baseline
forecast) will not meet the demand in 2040. We expect a considerable shortfall of about 44,000 MW
when calculating 185,000 MW electricity demand in 2040 against a planned capacity of 141,000 MW.
For the year 2032, we see from the analysis of trend data a difference between the 121,000 MW
planned power capacity compared with our forecasted value of 141,000 MW, which is a difference of
20,000 MW. As the 121,000 MW figure has been provided by the KSA government authorities, we have
filled the gap between peak load and peak capacity adding 64,000 MW to the capacity in 2032 until
2040. This additional capacity has been distributed between all types of power plants; as such, we see
an annual increase in oil and gas–fired power stations during this period of about 3,800 MW. The same
applies for wind (+570 MW/a), nuclear (+900 MW/a), CSP (+1,650 MW/a), and PV (+1,050 MW/a)
energy in order to meet the peak demand of 185,000 MW in 2040.
12. The transformation sector for oil and oil products will not require a large increase in transformation
capacities besides any continuous modernization and upgrades of production capacities. However, due
to the heavily increasing demand for electricity, the power sector will have to triple the 57,000 MW
installed capacity to 185,000 MW. This tripling will lead to additional capacities being constructed at
more than 4,200 MW per year. As we are looking at a period of three decades, in addition to any new
power production capacities, a large rehabilitation program covering the existing power plant capacities
has to be launched in parallel. This equals about an additional 1,500 MW per year to be rehabilitated, if
the current infrastructure of power plants older than 20 years in 2009 are replaced or rehabilitated over
the next three decades until 2040.
13. The system availability of RE—by international experience records—will not be comparable with
fossil and nuclear energy generation; therefore, the respective system reserve capacity should be
higher. Within our forecast modeling, we did apply 5,840 full load hours for fossil fuels; 6,000 hours for
nuclear fuels; 1,800 full load hours for wind; 2,200 full load hours for PV; and 3,500 hours for CSP
technology, due to expected storage capacities. These figures are considered very optimistic but only
allow the electricity demand of 850,000 GWh/a (KACARE) to be met at the national level. We also
calculated that all renewables (e.g., wind, PV, CSP) operate at peak load every day with their full
installed capacity and that there will be no maintenance/break during this peak load time to roughly
meet the electricity system demand, which seems to be very unrealistic. In this case, the installed
capacity should be even higher than the peak demand, but we have not been able to justify this within
this forecast. We just recognize and describe the forecasted system balance situation based on planned
installation capacities and existing international operation data.14. A first prioritization of technical and
organizational measures based on the forecasting results for Saudi Arabia for the period 2010 to 2040 is
drawn from the findings of different studies (SEEC/BAIN, ECRA/BRATTLE, CHATHAM HOUSE,
KAPSARC/IEEJ, KACST/AEA, KAPSARC, SEEC, Saudi Arabia Energy Efficiency Report, KACARE, TYNDALL,
and others). All of these studies have a different viewpoint; therefore, some measures overlap in nearly
all studies (for example, labeling), but some studies concentrate on specific measures (for example,
pricing structures). We have collected and reviewed the proposals for measures given and developed a
list of 40 technical measures and 70 organizational measures (40/70 List on Technical and
Organisational Energy Efficiency Measures in Saudi Arabia).
14. In the presented EE scenario, our team followed the measure of effectiveness (MoE) figures given
for EE improvements by 30 percent, as in most of the studies, but we still evaluate the situation as
“extremely difficult” for the Saudi economy even with this quite extended and ambitious improvement
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of 30 percent. Total final energy consumption will limit the increase in this scenario to 298 Mtoe, but
this still leads to a considerable decrease in oil exports from the current 383 Mtoe to 128 Mtoe, which is
only 33 percent of former oil exports (minus 67 percent).
15. As to the forecast for energy consumption and for “real” energy prices in 2040 (based on 2010
prices), we see two effects on the opportunity costs in 2040: A “real” price increase of about US$65,000
Million (Mio) and an increase due to additional consumption in 2040 compared with 2009, which is
US$446,000 Mio. Thus, national energy consumption will have, in total, opportunity costs of
US$635,302 Mio in 2040 and will absorb a huge percentage of national income; in this case, about 35.2
percent of the expected GDP.
16. We used a time series analysis in this forecasting model because the past energy data shows high R2
and the main influencing factors are also following this trend; therefore, there is no need for any
regression analyses. We expect that any detailed regression analyses will have similar results. Both
methods do not consider “loops” in their forecasting; for example, a currently high GDP growth leads to
high national energy demand and fewer exports, which in turn reduces GDP growth. This will reduce
national energy demand; therefore, some new and additional export possibilities, which increase GDP
again, will be required.
17. EE should be given highest priority in Saudi Arabia to ensure the current standard of living and
production. If EE cannot be realized on a remarkably higher scale compared with now, there will be
hardly any significant exports of oil in the future from 2040 onward. This clearly shows the urgency for
EE in the Kingdom. The KSA could then be considered a net-oil-importing country with a considerably
decreased living standard.
Chapter 2 Summary
Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of
Saudi Arabia
General Objectives
The objective of this chapter is to investigate the waste heat potential in the industries of the KSA.
The KSA industry was divided into five main sectors. Although the various sectors have been in the
process of liberalization for some years, there is still one large main player in each. Governmental
companies traditionally operate water production and power generation. A few companies dominate
the petrochemical and steel businesses, and one company manages the oil production and refinery
sector. The companies in the latter four sectors have very different conditions from all other industries
because of their export character, their size, and the difference in business character of the water and
power generation sector.
The refinery and oil production sector was not included in the scope of this study.
Other industries such as glass, food, and paper are summarized in this study under “other industries.”
For application of a proportional method to estimate the waste heat use potential compared with
international use potentials, this sector was subsectored.
A total waste heat use potential of about 3500 MWth was identified for the four sectors. More than 80
percent of this potential was found in the large industries: saline water, power generation, and large
petrochemicals. The waste heat use potential for “other industries” is about 650 MWth. The possible
power generation depends strictly on the available temperature.
There are four levels of waste heat reduction and use:
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Avoidance of waste heat generation
Direct process internal use
Use for chilling requirements
Transformation to power
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The fourth and highest level, which is also the most expensive, is additional electricity generation.
Below 350°C, this must be done by Organic Rankine cycle (ORC) processes; above 350°C, steam turbine
or engine cycles would also be relevant for the KSA. Using all waste heat of “other industries” would
provide a power potential of about 141 MWel.
Assuming an average heat-to-power efficiency ratio of 20 percent, the total waste heat losses
constitute a power potential of about 700 MWel.
For the different levels of waste heat use, detailed investigations including feasibility studies that take
into consideration individual conditions are necessary. For all possible technical measures, the very
special conditions associated with the climate and the low energy prices in the KSA must be taken into
account.
In addition, many of the measures require proper operation of the equipment, which means wellinstructed and motivated personal. The identification of best available operation, creation of key
performance indicators (KPIs), installation of monitoring systems, and development of incentive
systems are proven means to facilitate better operation with existing equipment.
In particular, the installation of external waste heat use equipment such as bottoming or topping cycles
usually complicates the process. Often, this is the main barrier. To overcome this barrier, awareness
programs or even better economic incentive systems may be advisable (e.g., tax reduction, funding
such as the German CHP funding law Kraft-Wärme-Kopplungs-Gesetz [KWKG], or power feed-in
regulations).
Specific Goal
Looking to the general energy situation and challenges in the KSA, the question is: Could the use of
waste heat in this sector also be a key to the energy challenges in the KSA?
The target of this report is to analyze the efficiency potential arising from any waste heat usage in the
industrial sector of the KSA.
The industrial energy sector in the KSA can be divided into five main sectors:
Sector 1: Water production
Sector 2: Power production
Sector 3: Refinery sector
Sector 4: Large petrochemical production
Sector 5: Other industries
The last sector could be divided further into subsectors.
This structuring of the KSA industry is in some ways different from other countries. One big difference is
the extreme climate, which makes water a very rare commodity. It is needed in larger quantities in the
KSA than in other countries. Due to the high ambient temperature and the arid conditions, all water
must be desalinated, which means it must be filtered on a molecular scale with very high pressure or
evaporated and then condensed.
Another big difference is the oil richness of the country. This wealth led to low energy prices followed
by very low awareness of efficiency issues. In addition, it led to growth in the secondary oil industry. A
large petrochemical complex was developed, which is a large exporter for polymers, plastics, and all
kinds of material made from hydrocarbons.
Results Achievable
Waste heat use in the KSA is (thermodynamically) more restricted, as the usable temperature potential
is smaller due to the usually high ambient temperatures. The term “waste heat use” is not always
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defined as gross or net or related to lower or upper heating value; it is always used in the correct
technical context within the frame of the accuracy of the report.
In this report, the word “potential” has been used mainly for the energy savings that could be achieved
compared with a reference status at the same quantities and qualities and without considering
technical progress by a different operation or modification of the equipment. With the term “technical,”
the potential is described as the maximum that can be realized under technical aspects, while the term
“economic” describes a measure that makes sense in terms of operation under economic, competitive
conditions.
The term “efficiency” in this report is not defined in strictly scientific terms but is used with a meaning
close to thermodynamic definition. Generally, it is understood as the ratio of output to input of energy
in a technical sense. For example, it is in the context differentiated between exergy and anergy shares
of energy, meaning electricity/power and heat, although this was not always described explicitly.
Chapter 3 Summary
Smart Grid Technologies
General Objectives
The Smart Grid chapter provides an overview of the possible technologies in this field. These
technologies can be used to realize energy management tasks and efficiency gains or to integrate
renewables like those described in the case study sections. All have the goal of making the total
electricity system more reliable and to save costs in electricity generation.
The technologies that will be described and evaluated are divided into the communication structure
and the components. The components include: Automated Metering Infrastructure (AMI), including
Smart Meters (SMs); On-Load Tap-Changer (OLTC); and reactive power control (RPC). Some of these
technologies can work totally decentralized as independent controllers (e.g., RPC) and must be
parameterized occasionally. Other components, such as the SMs, are designed to work in
communication networks for active power management. For example, they can be used to
communicate time variable tariffs or current power limits.
Based on the analysis of the technologies, recommendations for the integration of renewables in the
KSA should be made. The focus is on the effects on the power quality (maintaining voltage and current
boundaries).
Methodology
The methodology of the Smart Grid chapter is divided into two parts. The first part presents a metaanalysis on smart grid literature and projects from the past 10 years in Europe, primarily Germany.
Germany is currently the biggest market for smart grids, driven by the integration of a large number of
renewable generators. This chapter provides an overview on smart grid technologies, journals, and
recent European conferences, including projects. For the evaluation of the actual practical importance
of the described technologies, more than 100 real smart grid projects were evaluated according to the
applied technology, the lead structure, the stakeholders, and other criteria. The number of projects
dealing with a technology is an indicator for the importance of a technology.
The second part of this chapter is a case study on the effects of consumption and generation in lowvoltage distribution grids. A generalized grid topology of a reference grid for rural areas is investigated.
For the simulation time, profiles of loads and generators in the grid are necessary. To make the results
as general as possible, generalized household profiles are used. Cross-checks with measured profiles
from Chapter 4, Residential Metering, show similar profiles. As small-scale producers, PV plants are
installed. As positions and installed power of the PV plants are unknown, a probabilistic load flow
analysis solving 400 configurations is performed. The probabilistic load flow has been solved using the
open source software SimTOOL, which is a high-performing load flow simulator developed by
Fraunhofer ISE. SimTOOL calculates a combination of command-line–based load flow and controllers,
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allowing for the effect of renewables on the grids to be estimated. To mitigate the voltage change, an
OLTC is used for all the scenarios.
Key Results
Reviewing the current smart grid literature and projects, the most important technologies of a smart
grid are: OLTC; RPC; AMI, including SMs; and APC.
OLTC and RPC are technologies that can solve voltage problems in grids as independent controllers. The
OLTC does not raise the current, but it changes the voltage for the entire grid and facilitates the use of
the total range allowed by grid codes. RPC affects the voltage locally, but it raises the flowing currents.
However, there are technologies that might reduce or shift the load in the grid. One such technology is
APC, also called demand-side management. For efficient operation, this technology requires
information about the grid state. Typically, a centralized controller device distributes a signal for the
underlying systems (e.g., maximal power). This concept may rely on an AMI. The AMI is based on SMs
that send local measurements to a central point. With the knowledge of the current grid state, APC
works precisely.
To evaluate the importance of these technologies, European smart grid projects are reviewed. As the
knowledge of the system state is crucial to the smart grid, SMs are the most investigated technology.
With APC, all problems that can affect a grid might actually be solved; therefore, it is nearly as
important as SMs. The problem is that in distribution grids, the shift-able load is actually not big
enough. Assuming that customers’ comfort won’t be affected and PV plants are not switched off, not all
problems can be solved without storage.
In the case study, the effect of PV plants on distribution grids was evaluated. When the grid is not
reinforced, a high amount of PV plants in the analyzed distribution grid violate the upper voltage
boundary. To solve the voltage problems, an OLTC is installed in the grid. The OLTC is able to solve most
of the problems, eliminating the need for expensive grid reinforcement measures. Current list prices for
cables and OLTC show a cost savings by a factor of five while allowing for a large amount of PV to be
installed.
Chapter 4 Summary
Residential Metering
General Objectives
The energy demand of residential buildings in the KSA represents up to one-third of the total energy
consumption. The main residential energy consumers are electricity demand for air conditioning (AC),
domestic hot water production, and general household consumption such as lighting and mechanical
devices. Approximately 20 percent of Saudi households use electrical energy for cooking, while the rest
use natural gas. Although the electrical energy demand of residential buildings is a dominant part of the
country’s energy demand, the actual demand profile is not really known. As no monitoring of this
energy demand has been carried out so far, only simulated demand profiles that were derived from
certain general assumptions are available. To implement smart grid methods such as active power
management to integrate RE or to operate conventional power plants in a more efficient way, a good
understanding of household demand profiles and shift-able loads is essential. Thus, the target of this
task was to measure and analyze electricity demand profiles of residential buildings, which should be
compared with the standards in other countries. To achieve adequate results, as many as 100
residential buildings in Thuwal, Dammam, and Riyadh should be studied. To realize such measurements
in a foreign country, we were very pleased to have strong support from various stakeholders. The work
in this task can be split into the following parts: (1) monitoring and data acquisition and (2) statistical
and model-based data analysis.
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Methodology
To get reliable information about the actual energy demand of the residential sector, a monitoring
campaign was carried out. In a second step, the collected data gathered during this monitoring
campaign was analyzed. A model-based consideration of energy-saving potential within the residential
sector was conclusively demonstrated.
Monitoring and Data Acquisition
To obtain representative demand profiles, demand data normally is collected for 12 months. Because of
project constraints, acquiring data during a long time period was challenging. The project consortium
decided to use energy meters already installed at the King Abdullah University of Science and
Technology (KAUST). At Dammam University, energy meters had to be installed by the project team. At
KAUST, ADDAD-4 meters were installed, and at Dammam University, ADDAD-5 meters were installed.
Both meter types were produced by the Advanced Electronics Company (AEC). They are electronic
meters with internal registers that store power values for 1 month, with a time resolution of
15 minutes.
To understand and model the building energy system, it is important to gather data from big energy
consumers such as AC units separately. For this purpose, buildings in Dammam were equipped with an
additional submeter within the building’s junction box to measure the energy consumption of the
whole building except AC.
All installed meters are equipped with an optical serial interface. This interface was used to collect the
data manually using a notebook. An online collection of data using mobile networks was not planned.
This collected data were checked for plausibility, and faulty data were repaired. This task was realized
by Envidatec and is presented in detail in the report about data acquisition that is attached to the
report from Envidatec.
To create a model-based analysis, it is essential to correlate the power series with climate data. The
necessary data about ambient temperature and solar radiation were taken from the company
meteocontrol [SolarGIS].
Key Results
Residential Profiles in the KSA
The measurements acquired during the monitoring campaign showed an electricity consumption of as
much as 50 W/m2. Considering a typical living area of 500 m2, this yields a maximum power
consumption of 25 kW during one time step. This peak is typically reached in the early evening hours
and reduced to half during the night. Figure 1 shows that a typical daily profile has one peak. This peak
is roughly 4 hours after the daily peak of the ambient temperature, which leads to the assumption that
the peak in the daily profile correlates to the thermal capacity of the building. With higher ambient
temperatures, the room temperature stays in a normal range (near the set value) until the building’s
capacity is changed by the surrounding air. At night, the opposite occurs. Unfortunately, no room
temperatures were available to test this assumption.
It can also be stated that the profiles do not change considerably during the course of the week. No
change in weekend energy consumption can be detected.
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Figure 1: Weekly Load Profile
As described previously, the observed building profiles show a high correlation to ambient temperature
because the demand is driven mainly by AC. This was proven using the measurements with submeters,
allowing separation of AC demand from the total demand at households in Dammam. Figure 2 shows
the daily energy consumption of households and of AC alone, sorted by the daily mean ambient
temperature. It can be observed that the household energy consumption varies between 0.1 kWh/m2
and 0.15 kWh/m2 per day, depending on the ambient temperature. It is very likely that these
fluctuations are caused by varying user behavior.
Figure 2: Energy Consumption of Household and AC
The AC energy demand shows a strong correlation to temperature. During the measurement period,
the AC energy demand ranged from 0.05 kWh/m2 per day at a daily mean temperature of 22 °C to
0.55 kWh/m2 per day at a daily mean temperature of 34 °C. Finally, whereas AC constitutes 85 percent
of the total consumption in September and 70 percent in November, the monthly household
consumption is roughly constant at 3 kWh/m2 per month.
Recommendations for Energy Saving
The most important finding of the monitoring campaign is that AC systems account for a large part of
energy consumption within the residential sector. According to Figure 2, AC is responsible for 70
percent of the residential energy demand. Reducing the energy demand of the residential sector
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xv
directly leads to a reduction in AC energy demand. AC energy demand is affected by three factors that
have a direct influence on energy consumption:
•
•
•
The insulation of buildings has an impact on the demand for cooling. The better buildings are
insulated, the less they need to be cooled.
The indoor set temperature influences the cooling demand.
The efficiency of the AC unit has an impact on the electrical energy consumption required to
provide the requested cooling needs of the building.
Table 1 shows examples of these three energy-saving measures. It can be seen that an energy savings of
15 percent can be achieved simply by increasing the set temperature by 2 K. AC devices with an energy
efficiency ratio (EER) of three, which are state of the art for the residential sector, can reduce the
cooling demand by a factor of two. By far the biggest reduction, reaching 75 percent of the energy
demand for cooling, can be achieved by installing an insulation layer.
Table 1: Effects of Different Energy-Saving Measures on Cooling Demand Classified by Effort
Measure
Increase Tset by 2 K
Replace AC hardware (double EER)
Insulate with 7.5 cm polystyrene
Effect on Energy Demand
Effort
Minus 15%
Minus 50%
Minus 75%
None
Medium
High
In total, when looking on the savings experience in countries with similar housing census, business
structure, and climate, the residential sector should be able to easily save between 30 and 40 percent of
the consumed energy.
Chapter 5 Summary
Development of Industrial Energy Demand in Saudi Arabia
General Objectives
This chapter focuses on the industrial energy demand in Saudi Arabia and the energy saving potential
through the implementation of EE measures. As a result of the rapid population growth, an enormous
increase in energy consumption will be seen in the country. As industry has a fairly large share of the
energy demand in the KSA, the saving potential in that sector will have a positive effect on the overall
consumption. The present report aims to show potential development pathways and their effects on
domestic energy demand, carbon dioxide (CO2) emissions, and potential additional income from oil
exports. Therefore, the largest industry sectors—cement, steel, petrochemical, and desalination—are
analyzed in detail.
Methodology
To be able to analyze the effects of different EE measures on the Saudi energy demand in the future, a
projection of the future energy demand is required. The future energy demand is established based on
the development of the different sectors, presented in Chapter 1 of this study. The projection is based
on the EIA energy data for Saudi Arabia until 2009. To apply the saving potential, applicable EE
measures and their potential for the KSA are defined. This potential is based on previous chapters of the
present study as well as literature research. For each of the large industry sectors mentioned earlier,
the efficiency measures are analyzed separately. These sectors belong to the energy-intensive
industries because of their necessary energy consumption for certain production processes, making
them interesting for this analysis. Those EE potentials are defined individually for each sector. The EE
measures assumed in the low EE scenario are either easily applicable and economically viable or
necessary to reach the state-of-the-art level of technology. The efficiency potential can be defined as
the difference between the status quo in the KSA and the state of the art in industrialized countries
worldwide. For the evaluation of the savings, three scenarios are compared: the business as usual (BAU)
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scenario, a low EE scenario, and a high EE scenario. From the results of these scenarios, the amount of
energy saved can be projected until 2040. Accordingly, the CO2 emissions saved and the opportunity
costs from the saved fossil fuels are calculated. This calculation allows illustration of the effects of EE
measures not only on the energy demand but also on the economy and environment. Hence, the report
will give an indication of the potential range of effects that EE measures would have in the long term.
Analysis
To represent the industrial energy demand in the KSA, as mentioned earlier, the energy-intensive
industries, such as steel, petrochemical, and cement, were chosen. Within industrial energy
consumption, the largest energy demand stems from the desalination and the petrochemical sectors.
Looking closer at the cement industry, it can be inferred that applying EE measures will have a positive
effect on the industrial energy demand. The cement production capacity of Saudi Arabia nearly doubled
since 2005 and exceeded 50 Mio t in 2012, shared by 13 companies. Similar to the steep rise in
production capacity, the cement demand experienced a significant increase from 43 Mio t per year in
2010 to 49 Mio t in 2012, and was estimated to reach 52 Mio t in 2013 (Edwards, 2012). Both capacity
and demand are expected to increase further. Because most of the energy needed in the cement
manufacturing process is used to produce heat, the highest potential for EE is achieved by improving
the use of heat or in the avoidance of waste heat. According to the analysis, an efficiency potential of
15 percent for the high EE scenario is set. The low EE scenario assumes a retarded application of EE
measures and a lower implementation speed, which leads to an efficiency potential of 5 percent.
The steel sector had a steady growth of 6 percent per year between 2009 and 2011, increasing its crude
steel production from 4.7 Mio t to 5.3 Mio t (World Steel Association, World Steel in Figures, 2012, S.
11). However, crude steel production stagnated in 2012 with 5.2 Mio t (World Steel Association, World
Steel in Figures, 2013, S. 9). The steel sector is still expected to grow further. As steel is one of the most
energy-intensive industries, it is essential to try to reduce future energy demand of the sector. In the
analysis, an efficiency potential of 35 percent for the high EE scenario is identified. For the low EE
scenario, it is assumed that the transfer of production technology starts later and is carried out slower,
which reduces the EE potential to 10 percent for this sector.
The last detailed analysis was done on the petrochemical sector. The Saudi petrochemical sector is
largely dominated by SABIC. With an overall market share of more than 50 percent, it is the largest
domestic stakeholder. In the petrochemical industry, there is little room to improve the EE and CO2
emissions of the feedstock. Therefore, EE measures have to be applied in the production processes and
overall energy use. The analysis showed a potential efficiency increase of 17 percent in the low EE
scenario and 25 percent energy savings potential in the high EE scenario.
Having analyzed the different industries and defining the high- and low-efficiency potential, the three
main scenarios are developed. These three scenarios—the BAU scenario and the high- and lowefficiency scenarios—are compared in terms of energy consumption throughout the years until 2040,
the CO2 emissions caused by the three paths, and the opportunity costs created by saving fossil fuels.
Key Results
With this analysis and the demand projection from Chapter 1 of the present study, the scenario analysis
showed an energy savings potential of 10 percent in the low EE scenario. Cumulating the annual final
energy demand shows that until 2040, 2050 TWh could be saved. This results in a reduction in CO2
emissions of 2,200 billion (Bn) t. Furthermore, this would allow oil savings in the power-generation
sector, which could be exported. Considering this opportunity cost, additional revenues of US$544 Bn
could be realized. The high EE scenario shows potential energy savings of 20 percent, resulting in
4,413 TWh cumulated energy savings until 2040. This would enable a reduction in CO2 emissions of
3,460 Bn t. In addition, the analysis of opportunity cost indicates that additional revenues of US$784 Bn
could be generated from exporting saved oil.
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Chapter 6 Summary
Integration of Renewable Energy
General Objectives
The present report aims to analyze the economics of different RE sources in the KSA, and thus, enhance
the available information on country-specific aspects of each technology. A steadily rising demand for
energy and the limitations of fossil energy sources, along with decreasing prices for RE technologies and
increasing export prices, make RE sources more and more attractive for use in Saudi Arabia. However,
up to now, research and projects have mainly been dedicated to technical aspects of different RE
technologies in Saudi Arabia. For successful integration, economic aspects will be as important as
technical ones. This chapter, therefore, focuses on the economics of RE sources in Saudi Arabia.
Analysis
The three most promising technologies for the country—PV, CSP, and wind power—are discussed in this
chapter. Other RE sources, such as hydropower, tidal energy, and wave energy, have a comparatively
low potential in Saudi Arabia and, therefore, are not analyzed in this context.
Renewable Energy Potential
The potential of the PV, CSP, and wind power RE was assessed in this chapter. PV potential in Saudi
Arabia is abundant: Global horizontal irradiation is between 2,000 and 2,500 kWh/m². Theoretically, a
section of 2,400 km² would suffice to provide the electricity needed in the country, based on the gross
annual demand without taking into account differences in production and consumption time. The PV
rooftop capacity potential for residential and industrial buildings was estimated to be 16 GWp. Figure 3
shows the distribution of the potential within the country.
For the whole country, the potential is estimated to be 13.41 GWp on residential and 2.55 GWp on
industrial buildings. With this capacity, 3,743,412 MWh of electricity could be supplied annually,
constituting 17.7% of current energy demand. Since this analysis only considered roof-top potentials as
easily accessible areas for distributed generation, this confirms the abundance of solar resources in the
country.
The potential for CSP is similarly high: direct normal irradiation ranges between 1,200 and 2,800
kWh/m².
Saudi Arabia’s wind power potential is very low compared with international standards. The two most
suitable regions for the deployment of wind turbines are along the coasts of the Red Sea and the
Arabian Gulf. Average wind speeds in most of the country are between approximately 6.0 and 8.0 m/s
at hub height.
Levelized Cost of Energy
In this chapter, an analysis of the levelized cost of electricity (LCOE) assesses the economic potential of
RE technologies in Saudi Arabia. Figure 4 shows the results of the LCOE for rooftop PV, ground-mounted
PV, CSP, and wind power for different full-load hours in comparison with fossil energy costs with and
without opportunity costs.
In most areas of Saudi Arabia, annual full-load hours of ≥2,000 can be achieved for PV, resulting in an
LCOE of around 0.07 to 0.15 US$/kWh (ground mounted) and 0.09 to 0.18 US$/kWh (rooftop). The
LCOE for wind varies between 0.133 and 0.221 US$/kWh for 1,100 full-load hours and 0.091 and 0.143
US$/kWh for 1,800 full-load hours. CSP has the highest LCOE of all selected technologies. Depending on
the full-load hours, the LCOE ranges between 0.185 US$/kWh and 0.449 US$/kWh.
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Volume 1
Northern
Borders
1.0 km²
175 MWp
241 GWh/a
Al-Jouf
1.5 km²
253 MWp
365 GWh/a
Al-Qaseem
5.1 km²
838 MWp
1,308 GWh/a
Hail
1.9 km²
323 MWp
525 GWh/a
Tabouk
2.1 km²
356 MWp
548 GWh/a
Al-Madinah
Al-Monawarah
5.3 km²
873 MWp
1,357 GWh/a
Makkah Al-Mukarramah
24.0 km²
3,885 MWp
5,692 GWh/a
Al-Baha
1.5 km²
232 MWp
382 GWh/a
Jazan
3.7 km²
583 MWp
861 GWh/a
Aseer
6.9 km²
1,096 MWp
1,799 GWh/a
Region
Net roof area [km²]
Installable capacity [MWp]
Potential electricity generation [GWh/a]
Al-Riyadh
27.5 km²
4,514 MWp
6,610 GWh/a
Eastern Region
15.5 km²
2,560 MWp
3,494 GWh/a
Najran
1.7 km²
276 MWp
465 GWh/a
Figure 3: Net Roof Area, Installable PV Capacity, and Electricity Generation Potential by
Regions (map adapted from (Dalet, 2013))
Figure 4: LCOE of Renewable Energies Compared with Oil and Gas With Opportunity Costs
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xix
Case Studies
Three case studies are discussed in this chapter: a PV-driven, reverse osmosis (RO) desalination plant;
PV electricity supply for industry; and PV hybrid systems for remote applications.
The levelized water production cost (LWPC) for different plant configurations were calculated. The
LWPC for RO combined with PV proved to be higher than the LWPC for currently used Multi-Stage Flash
(MSF) plants. However, this calculation did not include the maintenance cost, fossil fuel prices, and
operation costs. If these additional costs are taken into consideration, the PV-powered RO plant may be
the more sustainable solution. Especially regarding growing fossil fuel prices and possible opportunity
costs, PV–RO will gain more importance.
The case study on the PV electricity supply for the industry shows the advantages of PV currently lie in
reducing climate gas emissions and daily demand peaks in the afternoon. Economically, PV and fossil
fuel electricity are equal, so future development in this area depends on the price of oil and the system
cost of PV.
The case study on PV–diesel hybrid systems shows that for off-grid areas, villages, or industrial sites, a
PV–diesel hybrid system solution can be economically profitable, depending on diesel price and solar
radiation. There is vast experience showing that PV–diesel hybrid systems are not only able to provide
sufficient electricity, but with respect to the environment, they are superior to pure diesel systems.
Key Results
The analysis of the currently existing rooftop potential in Saudi Arabia shows that the largest potential
exists in the regions of Riyadh and Makkah. For the whole country, the potential is estimated to be
13.41 GWp on residential buildings and 2.55 GWp on industrial buildings. With this capacity, 3,743,412
MWh of electricity could be supplied annually, constituting 17.7 percent of current energy demand.
Since this analysis only considered rooftop potentials as easily accessible areas for distributed
generation, this confirms the abundance of solar resources in the country.
For the successful introduction of renewable energies into a new market, economic aspects also play an
important role. Therefore, the LCOE was analyzed for all considered technologies. For PV, the LCOE
ranges between 0.09 and 0.18 US$/kWh, depending on system size and actual investment cost. The
LCOE of WECs is between 0.07 and 0.22 US$/kWh. For CSP, the LCOE ranges between 0.185 US$/kWh
and 0.45 US$/kWh, depending on the full-load hours. The higher investment cost for CSP, especially
when including storage, leads to these high values. However, they do not reflect the fact that storage
can easily be integrated, making CSP plants dispatchable electricity sources in contrast to wind and PV.
Volume 2 Summary
Energy Efficiency Audit Case Studies
General Objectives
The objective of this task is to establish the baseline of the current EE rating and to identify the areas for
improvement or energy savings in Saudi Arabia’s commercial buildings and industrial sectors. The focus
has been placed on medium-size clients with expected high specific energy consumption based on
typical samples for the existing regional economics and with a promising exemplary replication
potential. These were found mainly at three locations, including the West Coast area around the city of
Jeddah, the Central Economic area around the city of Riyadh, and the East Coast area around the cities
of Dammam, Dhahran, and Khobar. The original plan was to execute six to 10 energy audits in various
sectors. The following table describes the six facilities that were shortlisted from an original list of 30
facilities.
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Volume 1
Business
Sector
Facility/
Company
Business Status
Quality
Consumption
Level
Efficiency
Status
Commercial Services
Hospitals
Private-hospital,
Jeddah
Hotels
M-Hotel, Riyadh
Enmar Hotel
Jeddah
Private clinics and
surgery hospital, 300
staff
89 rooms, 210 beds,
120 staff, 12% admin
About 37 GWh/year Medium-size
200 rooms, 300–500
beds
About 3.9 GWh/a
Small-size innovative
constr. 15% admin
cost
210 rooms, 340 beds Medium-size,
About 5.8 GWh/a
Medium-size
weakly committed
standard constr. 12%
admin cost
240,000 m2, 400
Big-size, well
Operate own power No AC-reg, no VSD,
tenants, 240 staff
committed
n.y. PF correction
generation w/o
EnMS (ca 69 GWh)
Shopping malls A Mall in Jeddah,
KSA,
Corniche Jeddah
area
Restaurant
Al-Shurfa
1200 m2,
restaurant services 110 staff
Very EE
committed,
started EE-impl
Small-size, well
committed
Well committed
About 2.4 GWh/a
No AC regulation, no
VSD, no PF
correction
Very committed;
some data
inconsistency
About 159.7 GWh/a 1.5 Mio t/a,
105 kWh/ton
own power-gen w
heat recuperation
Industrial Sectors
Construction
industry
Cement factory
(medium-size,
3,6% country-dd)
ACP Lafarge private
300 staff
The agreed-upon business areas to be considered were commercial trade and services (e.g.,
restaurants, hotels, hospitals, shopping malls) and industrial production (e.g., cement production,
plastic production, and seawater desalination) as described in the preceding table. The stakeholders
agreed to concentrate on small and medium enterprise (SME) clients in the KSA—these being
representative of the actual economic development, and this sector avoided duplications of work with
the KSA oil and gas industries.
Specific commercial sites with rather high-energy consumption and with suitable SME size have been
commonly identified and selected with the assistance of national/local trade agencies and of MoCT and
with support of the KAUST/KICP-PM. The preselected, committed clients were visited and investigated
based on an in-depth EE audit. Resulting savings have been identified when comparing Saudi
consumption with international consumption standards.
The results of the energy audits were compared with German and world standards as well as to best
practices in the field of energy. The results of the energy assessments in Volume 2 are summarized in
Chapter 7: Study Findings and Conclusions, Recommendations, and Business Opportunities.
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Table of Contents
Volume 1
Executive Summary
Chapter 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—The
Necessity for Energy Efficiency in the Kingdom .............................................................. 1-1
1.1 Introduction ................................................................................................................... 1-1
Strategy and Outline of the Approach .................................................................................. 1-1
1.2 General Description of the Energy Situation in KSA in 2009 ............................................ 1-2
1.2.1 Energy System in Saudi Arabia ................................................................................. 1-2
1.2.2 Energy Data and Energy Balance Scheme for 2009 in Saudi Arabia ........................ 1-2
1.2.3 The Principles of an Energy Flow System Diagram Resulting in a Sankey
Diagram for Saudi Arabia ......................................................................................... 1-4
1.2.4 Sankey Diagram for the Saudi Energy System Based on the IEA Energy Balance
Scheme in 2009 ........................................................................................................ 1-4
1.3 Overview and Comparison of Current Energy Forecasting Studies in Saudi Arabia
until 2040 ...................................................................................................................... 1-7
1.3.1 KAPSARC-IEEJ Forecasting Study Report, 2011........................................................ 1-7
1.3.2 Saudi ECRA-Brattle Study (United States), 2011 ...................................................... 1-7
1.3.3 Saudi Energy Efficiency Center-Bain Consulting Study, 2013 .................................. 1-8
1.3.4 KFUPM-SNC Lavalin Study, 2007.............................................................................. 1-8
1.3.5 Tyndall Study, 2008 .................................................................................................. 1-8
1.3.6 Other Approaches from MOWE, MOEP, MOPMR, KAPSARC, Saudi Aramco,
SEC, ECRA, SEEC, UNDP, and the World Bank .......................................................... 1-8
1.4 Our Approach for a Baseline Forecast for the Entire Energy Sector in Saudi Arabia
until 2040 ...................................................................................................................... 1-9
1.4.1 General Approach for the Baseline Forecast ........................................................... 1-9
1.4.2 Influencing Factors for Energy Production, Transmission, and Energy
Consumption in Saudi Arabia in 2040 .................................................................... 1-14
1.4.3 Method Used to Forecast Energy Production, Transformation, and
Consumption in Saudi Arabia in 2040 .................................................................... 1-15
1.4.4 Energy Efficiency in Saudi Arabia in 2040 within the Baseline Forecast ............... 1-16
1.4.5 Resulting Energy Forecast in Saudi Arabia in 2040 ................................................ 1-17
1.4.6 Sankey Diagram for the Entire Energy Sector in Saudi Arabia in 2040 .................. 1-27
1.4.7 Reliability of the Power Generation and Power Transmission System in
Saudi Arabia for 2010 to 2040 ............................................................................... 1-27
1.5 Comparison of the Energy System of KSA with International and European
Standards and Benchmarks in Relation to Energy Efficiency until 2040 ........................ 1-29
1.5.1 Comparison of Other Countries’ Energy System Challenges with Those of
Saudi Arabia with Reference to Energy Efficiency ................................................. 1-29
1.5.2 Short Description of the Energy System of Neighboring Countries
Compared to Saudi Arabia ..................................................................................... 1-29
1.6 Priorities for Energy Efficiency in the Energy Sectors Based on New Technologies
in Saudi Arabia Through 2040 ...................................................................................... 1-29
1.6.1 Priority Sectors for Energy Efficiency Measures in Saudi Arabia ........................... 1-31
1.6.2 Priority Technologies for Energy Efficiency in Saudi Arabia .................................. 1-32
1.6.3 Priority Energy Efficiency Measures for Saudi Arabia ............................................ 1-35
1.6.4 Energy Efficiency Scenario for Saudi Arabia in 2040.............................................. 1-39
1.6.5 Influence of Renewable Energies on the Stability of the Saudi Electricity
System .................................................................................................................... 1-43
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1.6.6
Energy Costs and Opportunity Costs for the Entire Energy System in
Saudi Arabia in 2040 .............................................................................................. 1-44
1.7 Recommendations Based on Identified Shortages from the Current Energy
Balancing System and Its Forecasting Models in Saudi Arabia until 2040 ...................... 1-45
1.7.1 Providing Energy Data Compatible with Neighboring Countries and with
UN, IEA, and Eurostat Standards ........................................................................... 1-45
1.7.2 Ensuring a Continuous System for the Forecasting of Energy Production
and Consumption until 2040.................................................................................. 1-45
1.7.3 Developing and Publishing a National Energy Strategy Including
Renewable Energies and Energy Efficiency ........................................................... 1-46
1.7.4 Increased National Energy Consumption, Even in the Energy Efficiency
Scenario, Will Have Negative Influence on the National Economy ....................... 1-46
1.8 Summary ..................................................................................................................... 1-46
1.9 Baseline Forecast ......................................................................................................... 1-49
1.10 Literature ..................................................................................................................... 1-56
Chapter 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the
Kingdom of Saudi Arabia ................................................................................................ 2-1
Chapter Summary ................................................................................................................... 2-1
2.1 Introduction ................................................................................................................... 2-2
2.1.1 Energy Situation in KSA ............................................................................................ 2-2
2.2 Investigation of the Sectors............................................................................................ 2-5
2.2.1 Method .................................................................................................................... 2-5
2.2.2 Sector 1—Water Production .................................................................................... 2-5
2.2.3 Sector 2—Power Generation ................................................................................... 2-9
2.2.4 Sector 3—Refineries .............................................................................................. 2-13
2.2.4.1 General Process ..................................................................................... 2-13
2.2.4.2 Industry in KSA ....................................................................................... 2-14
2.2.5 Sector 4—Petrochemical (SABIC Affiliates, et al.) ................................................. 2-14
2.2.6 Sector 5—Other Industries .................................................................................... 2-16
2.2.7 Waste Heat Use–Relevant Industries .................................................................... 2-19
2.2.7.1 Subsector—Food ................................................................................... 2-20
2.2.7.2 Subsector—Sugar .................................................................................. 2-22
2.2.7.3 Subsector—Glass ................................................................................... 2-23
2.2.7.4 Subsector—Pulp and Paper ................................................................... 2-28
2.2.7.5 Subsector—Textile/Fiber ....................................................................... 2-32
2.2.7.6 Subsector—Construction Material ........................................................ 2-34
2.2.7.7 Subsector—FE Metals ............................................................................ 2-40
2.3 Waste Heat Use Potentials ........................................................................................... 2-40
2.3.1 Waste Heat Use Potential in the Desalination Sector ........................................... 2-40
2.3.2 Waste Heat Use Potential in the Power Generation Sector .................................. 2-40
2.3.3 Waste Heat Use Potential in the Refineries Sector ............................................... 2-41
2.3.4 Waste Heat Use Potential in the Petrochemicals Sector ....................................... 2-41
2.3.5 Waste Heat Use Potential in the Other Industries Sector ..................................... 2-41
2.3.5.1 Estimation by Correction Factors .......................................................... 2-41
2.3.5.2 Prioritization and Valuation of the Sectors ........................................... 2-45
2.3.6 Difference Between Large and Small Companies .................................................. 2-45
2.3.7 Difference in Technical and Economic Potential ................................................... 2-46
2.3.8 Necessary Investments .......................................................................................... 2-46
2.3.9 Influences of Growth and Change ......................................................................... 2-46
2.4 General EE Aspects....................................................................................................... 2-47
2.4.1 Proposed EE Technologies from Chapter 1............................................................ 2-47
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xxiii
2.4.2 Proposed EE Measures from Chapter 1 ................................................................. 2-48
2.4.3 Barriers ................................................................................................................... 2-50
2.5 Waste Heat Use Technologies ...................................................................................... 2-51
2.5.1 Usability of Power Generation Technologies......................................................... 2-51
2.5.2 Usability of Chiller Technologies ............................................................................ 2-51
2.6 Potentials and Demands .............................................................................................. 2-52
2.6.1 Possible Solutions and Business Potentials ........................................................... 2-52
2.6.1.1 Engineering ............................................................................................ 2-52
2.6.1.2 Improving the Condition of Equipment, Maintenance, and
Training .................................................................................................. 2-52
2.6.1.3 Direct Internal Use of Unavoidable Process Losses ............................... 2-53
2.6.1.4 Use of Unavoidable Process Losses for Chilling Purposes ..................... 2-53
2.6.1.5 Use of Unavoidable Process Losses for Power ...................................... 2-53
2.6.2 Demands for Research, Development, Pilot Plants, and Funding ......................... 2-53
2.7 Summary ..................................................................................................................... 2-54
2.8 Literature ..................................................................................................................... 2-55
Links ............................................................................................................................... 2-57
Chapter 3: Smart Grid Technologies ................................................................................................ 3-1
Chapter Summary ................................................................................................................... 3-1
Methodology ......................................................................................................................... 3-1
Key Results ............................................................................................................................ 3-1
3.1 Introduction ................................................................................................................... 3-2
3.2 Grid and Communication Layers .................................................................................... 3-3
3.2.1 Grid Layer ................................................................................................................. 3-3
3.2.2 Communication ........................................................................................................ 3-4
3.3 Smart Grid Technologies ................................................................................................ 3-6
3.3.1 Voltage Control—On-Load Tap-Changer ................................................................. 3-6
3.3.1.1 Control of Voltage at the Connection Point of the Substation................ 3-7
3.3.1.2 Control of Voltage at an Important Point in the Grid .............................. 3-8
3.3.1.3 Potential of the OLTC to Increase Grid Capacity ..................................... 3-9
3.3.2 Voltage Control—Reactive Power Control .............................................................. 3-9
3.3.2.1 Potential of RPC to Increase Grid Capacity .............................................. 3-9
3.3.3 Opportunities of Automated Metering Infrastructure ............................................ 3-9
3.3.3.1 Current Control—Smart Meter ............................................................... 3-9
3.3.3.2 Advanced Metering Infrastructure ........................................................ 3-10
3.3.3.3 Current Control—Active Power Control ................................................ 3-11
3.3.3.4 Distribution Grid Oriented ..................................................................... 3-11
3.3.3.5 Market Oriented .................................................................................... 3-11
3.3.3.6 Discussion .............................................................................................. 3-11
3.3.3.7 Potential of a Household to Shift Demand ............................................ 3-11
3.4 Smart Grid Projects ...................................................................................................... 3-12
3.4.1 Method of Meta-Analysis ...................................................................................... 3-12
3.4.2 Data Sources .......................................................................................................... 3-13
3.4.3 Results .................................................................................................................... 3-13
3.4.3.1 Active Smart Grid Projects ..................................................................... 3-13
3.4.3.2 Project Partners ..................................................................................... 3-14
3.4.3.3 Financial Support of Projects ................................................................. 3-14
3.4.3.4 Step 1: Stakeholder................................................................................ 3-14
3.4.3.5 Step 2: Controlled System ..................................................................... 3-15
3.4.3.6 Step 3: Lead Signal ................................................................................. 3-15
3.4.3.7 Step 4: Lead System Structure ............................................................... 3-16
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3.4.3.8 Step 5: Field Technology ........................................................................ 3-16
3.4.4 Evaluation Matrix ................................................................................................... 3-17
3.4.5 Evaluation of Saudi Smart Grid Status ................................................................... 3-17
3.5 Case Study of a Smart Grid ........................................................................................... 3-18
3.5.1 Smart Grid Modeling .............................................................................................. 3-18
3.5.2 Case Study Definition ............................................................................................. 3-18
3.5.2.1 Demand Household ............................................................................... 3-18
3.5.2.2 Grid Topology ........................................................................................ 3-19
3.5.3 Effect of Renewables ............................................................................................. 3-20
3.5.4 On-Load Tap-Changer ............................................................................................ 3-22
3.5.5 Economic Evaluation of Solutions .......................................................................... 3-23
3.5.5.1 Cost Parameters for Cables ................................................................... 3-23
3.5.5.2 Cost Parameters of Transformers.......................................................... 3-24
3.5.5.3 Costs of Grid Reinforcement ................................................................. 3-24
3.6 Recommendations ....................................................................................................... 3-24
3.7 Literature ..................................................................................................................... 3-25
Chapter 4: Residential Metering ...................................................................................................... 4-1
Chapter Summary ................................................................................................................... 4-1
Chapter Description .............................................................................................................. 4-1
Methodology ......................................................................................................................... 4-1
Monitoring and Data Acquisition .......................................................................................... 4-1
Statistical and Model-Based Data Analysis ........................................................................... 4-1
Key Results ............................................................................................................................ 4-2
4.1 Introduction ................................................................................................................... 4-4
4.2 Methodology ................................................................................................................. 4-5
4.3 Description of the Monitored Buildings .......................................................................... 4-5
4.4 Metering Equipment ...................................................................................................... 4-8
4.4.1 Metering Equipment at KAUST ................................................................................ 4-8
4.4.2 Metering Equipment at Dammam University .......................................................... 4-9
4.4.3 Data Acquisition in Saudi Arabia .............................................................................. 4-9
4.5 Data Basis .................................................................................................................... 4-10
4.5.1 Data Basis at KAUST ............................................................................................... 4-10
4.5.2 Data Basis at Dammam University ......................................................................... 4-11
4.6 Data Analysis ............................................................................................................... 4-12
4.6.1 Data Analysis at KAUST .......................................................................................... 4-12
4.6.2 Data Analysis at Dammam University .................................................................... 4-19
4.7 Modeling and Results and Recommendations .............................................................. 4-25
4.7.1 Residential Profiles in KSA ..................................................................................... 4-25
4.7.2 Modeling AC Demand ............................................................................................ 4-27
4.7.3 Recommendations for Energy Saving .................................................................... 4-29
4.7.4 Data Acquisition Experience and Recommendations ............................................ 4-29
4.7.5 KSA Profiles in Comparison to International Standards ........................................ 4-29
4.8 Literature ..................................................................................................................... 4-30
Chapter 5: Development of Industrial Energy Demand in Saudi Arabia ............................................ 5-1
Chapter Summary ................................................................................................................... 5-1
Chapter Description .............................................................................................................. 5-1
Methodology ......................................................................................................................... 5-1
Analysis ................................................................................................................................. 5-1
Results 5-3
5.1 Introduction ................................................................................................................... 5-3
5.2 Methodology ................................................................................................................. 5-3
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5.3
5.4
Current Industrial Energy Consumption in KSA ............................................................... 5-4
Energy Efficiency in Selected Sectors .............................................................................. 5-5
5.4.1 Cement Sector ......................................................................................................... 5-5
5.4.1.1 Cement Manufacturing Process .............................................................. 5-5
5.4.1.2 Energy-Efficiency Measures in the Cement Manufacturing Process....... 5-6
5.4.2 Steel Sector .............................................................................................................. 5-8
5.4.2.1 Production Steps of Steel ........................................................................ 5-8
5.4.2.2 Energy-Efficiency Measures in the Direct Reduction Steel Process ........ 5-9
5.4.3 Desalination ........................................................................................................... 5-11
5.4.4 Petrochemical ........................................................................................................ 5-14
5.5 Future Development of Energy Demand in KSA ............................................................ 5-15
5.5.1 Energy Demand ...................................................................................................... 5-15
5.5.1.1 Scenario 1: Business as Usual ................................................................ 5-15
5.5.1.2 Scenario 2: Low Energy Efficiency ......................................................... 5-16
5.5.1.3 Scenario 3: High Energy Efficiency......................................................... 5-16
5.5.2 Greenhouse Gas Emissions .................................................................................... 5-18
5.5.3 Opportunity Costs and Cost-Benefit Analysis ........................................................ 5-19
5.6 Emerging Business Opportunities With the Implementation of Energy Efficiency ......... 5-19
5.7 Conclusion ................................................................................................................... 5-20
5.8 Literature ..................................................................................................................... 5-21
Chapter 6: Integration of Renewable Energy ................................................................................... 6-1
Chapter Summary ................................................................................................................... 6-1
Chapter Description .............................................................................................................. 6-1
Analysis ................................................................................................................................. 6-1
Case Studies .......................................................................................................................... 6-3
6.1 Introduction ................................................................................................................... 6-4
6.2 Renewable Energy Technologies .................................................................................... 6-4
6.2.1 Photovoltaics............................................................................................................ 6-5
6.2.1.1 Technology Description ........................................................................... 6-5
6.2.1.2 Global Market ........................................................................................ 6-11
6.2.1.3 Technology Evaluation and Summary ................................................... 6-15
6.2.2 Concentrating Solar Power .................................................................................... 6-17
6.2.2.1 Technology Description ......................................................................... 6-17
6.2.2.2 Global Market ........................................................................................ 6-23
6.2.2.3 Technology Evaluation and Outlook ...................................................... 6-25
6.2.3 Wind ....................................................................................................................... 6-27
6.2.3.1 Technology Description ......................................................................... 6-27
6.2.3.2 Global Market ........................................................................................ 6-31
6.2.3.3 Technology Evaluation and Outlook ...................................................... 6-32
6.2.4 Hydroelectricity...................................................................................................... 6-33
6.2.4.1 Technology Description ......................................................................... 6-33
6.2.4.2 Global Market ........................................................................................ 6-35
6.2.5 Wave Energy .......................................................................................................... 6-35
6.2.5.1 Technology Description ......................................................................... 6-35
6.2.5.2 Global Market ........................................................................................ 6-37
6.2.5.3 Technology Evaluation and Outlook ...................................................... 6-37
6.2.6 Geothermal ............................................................................................................ 6-38
6.2.6.1 Technology Description ......................................................................... 6-38
6.2.6.2 Global Market ........................................................................................ 6-38
6.2.6.3 Technology Evaluation and Outlook for KSA ......................................... 6-38
6.2.7 Biomass .................................................................................................................. 6-39
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6.2.7.1 Technology Description ......................................................................... 6-39
6.2.7.2 Global Market ........................................................................................ 6-41
6.2.7.3 Technology Evaluation and Outlook ...................................................... 6-43
6.3 Renewable Energy Resources in Saudi Arabia .............................................................. 6-43
6.3.1 Wind Power Potential in Saudi Arabia ................................................................... 6-43
6.3.2 Solar ....................................................................................................................... 6-44
6.3.2.1 PV Potential in Saudi Arabia .................................................................. 6-44
6.3.2.2 CSP Potential in Saudi Arabia ................................................................ 6-46
6.3.3 PV Rooftop Potential in Saudi Arabia .................................................................... 6-46
6.3.3.1 PV Rooftop Potential on Residential Buildings ...................................... 6-47
6.3.3.2 PV Rooftop Potential on Industrial Buildings ........................................ 6-54
6.4 Economics of Electricity From Renewable Energies ...................................................... 6-56
6.4.1 Calculation of LCOE ................................................................................................ 6-56
6.4.2 Assumptions for the LCOE Calculation................................................................... 6-57
6.4.2.1 Assumptions for LCOE of PV Systems in Saudi Arabia ........................... 6-57
6.4.2.2 Assumptions for LCOE of Concentrating Solar Power ........................... 6-58
6.4.2.3 Assumptions for LCOE of Wind Power .................................................. 6-59
6.4.3 LCOE of Renewable Energy Technologies in Saudi Arabia ..................................... 6-60
6.4.4 Opportunity Cost ................................................................................................... 6-61
6.4.4.1 Savings Through Export ......................................................................... 6-61
6.4.4.2 CO2 Emission Reduction ........................................................................ 6-62
6.5 Case Studies for Renewable Energy in Saudi Arabia ..................................................... 6-64
6.5.1 Case Study: A PV-Driven RO Desalination Plant .................................................... 6-65
6.5.2 PV Electricity Supply for Industry ........................................................................... 6-66
6.5.3 PV Hybrid Systems for Remote Applications ......................................................... 6-70
6.5.3.1 Technology Description ......................................................................... 6-70
6.5.3.2 Typical System Configuration ................................................................ 6-70
6.5.3.3 Technology Comparison and Summary ................................................. 6-71
6.5.4 Business Opportunities .......................................................................................... 6-72
6.6 Conclusion ................................................................................................................... 6-72
6.7 Literature ..................................................................................................................... 6-74
Chapter 7: Study Findings and Conclusions, Recommendations, and Business Opportunities .......... 7-1
7.1 Category 1: Energy Market Economics ........................................................................... 7-2
7.2 Category 2: Energy Waste .............................................................................................. 7-3
7.3 Category 3: Smart Grid Technologies .............................................................................. 7-4
7.4 Category 4: Residential Metering ................................................................................... 7-5
7.5 Category 5: Industrial Demand ....................................................................................... 7-6
7.6 Category 6: Renewables Integration............................................................................... 7-7
7.7 Energy Efficiency Audit Case Study Findings ................................................................... 7-7
7.7.1 Key Findings of the Cement Industry ....................................................................... 7-8
7.7.2 Key Findings of Shopping Malls ............................................................................. 7-10
7.7.3 Key Findings of Hotels ............................................................................................ 7-12
7.7.4 Key Findings of Hospitals ....................................................................................... 7-13
7.7.5 Key Findings of Restaurants ................................................................................... 7-16
7.8 Potential Energy Savings for KSA from Case Studies ..................................................... 7-18
7.8.1 Methodology Used for Regional/National Extrapolation ...................................... 7-18
7.8.2 Cement Plants ........................................................................................................ 7-19
7.8.3 Shopping Mall ........................................................................................................ 7-19
7.8.4 Hotels ..................................................................................................................... 7-20
7.8.5 Hospitals................................................................................................................. 7-22
7.8.6 Restaurants ............................................................................................................ 7-22
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7.8.7 Concluded Savings Potential from Case Studies .................................................... 7-25
7.9 Energy Efficiency Study Recommendations .................................................................. 7-26
7.10 Business Opportunities (BO) Investigated by an In-Brief Analysis for Identified
Savings and RE Applications ......................................................................................... 7-30
Volume 2
Case Study Summaries
Case Study 1: Alsafwa Cement Plant, Jeddah, Saudi Arabia....................................................... CS 1-1
1.1 Introduction .............................................................................................................. CS 1-1
1.2 Summary of Energy-Efficiency Measurements........................................................... CS 1-1
1.2.1 Energy Audit ........................................................................................................ CS 1-1
1.2.2 ACP Energy Efficiency Proposed Measures......................................................... CS 1-2
1.2.2.1 Short-Term Measures ......................................................................... CS 1-4
1.2.2.2 Medium-Term Measures .................................................................... CS 1-5
1.2.2.3 Long-Term Measures .......................................................................... CS 1-6
1.3 Existing Status........................................................................................................... CS 1-7
1.3.1 Overview of Global Cement Plants ..................................................................... CS 1-7
1.3.2 ACP General Data ................................................................................................ CS 1-8
1.3.3 ACP Process Overview ........................................................................................ CS 1-8
1.3.4 Energy Supply and Consumption ........................................................................ CS 1-9
1.3.5 Existing Meters and Data Basis ......................................................................... CS 1-11
1.3.6 Energy Costs and Consumption ........................................................................ CS 1-14
1.3.7 Water Supply Consumption and Costs ............................................................. CS 1-20
1.3.8 Greenhouse Gas Emission Factors .................................................................... CS 1-20
1.3.9 Former Activities Regarding Energy Efficiency ................................................. CS 1-22
1.3.10 Planned Activities Regarding EE Issues ............................................................. CS 1-22
1.3.10.1 Precalcination with Alternative Fuel ................................................ CS 1-22
1.3.11 Who Benefits from EE? ..................................................................................... CS 1-23
1.4 Results .................................................................................................................... CS 1-23
1.4.1 Raw Mill Replacement by Vertical Mill ............................................................. CS 1-23
1.4.2 New Bag Filters Combined with VSD ................................................................ CS 1-24
1.4.3 Cooling and Waste Heat Technology ................................................................ CS 1-25
1.4.3.1 Process Cooling ................................................................................. CS 1-27
1.4.3.2 ORC Waste Heat Usage..................................................................... CS 1-27
1.4.3.3 Absorption Chiller with Waste Heat ................................................. CS 1-28
1.4.3.4 Increase the Internal Target Temperature ....................................... CS 1-30
1.4.4 Lighting .............................................................................................................. CS 1-30
1.4.5 Water Consumption .......................................................................................... CS 1-30
1.4.6 Optimization of Electrical Machines ................................................................. CS 1-31
1.4.6.1 Using Energy-Efficient Drives............................................................ CS 1-31
1.4.6.2 Using VSDs ........................................................................................ CS 1-32
1.4.6.3 Maintenance of Drives ..................................................................... CS 1-33
1.4.7 Reduction of Pressured Air for Bag-Filter Cleaning and VSD ............................ CS 1-33
1.4.8 Development and Implementation of an Energy-Monitoring System ............. CS 1-36
1.4.8.1 Measurement-Point Concept ........................................................... CS 1-37
1.4.8.2 Assessment of Investment and Savings ............................................ CS 1-38
1.4.9 Load-Management System ............................................................................... CS 1-39
1.4.10 Base-Load Reduction ........................................................................................ CS 1-39
1.4.11 Implementation of an EnMS ............................................................................. CS 1-39
1.4.12 Specification for the Purchase of Machinery and Equipment .......................... CS 1-41
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1.4.13 Sensitization of Employees ............................................................................... CS 1-41
Case Study 2: Al-Shurfa Restaurant, Riyadh, Saudi Arabia: A Traditional Middle-Class
Family Restaurant in the Riyadh City Center ....................................................... CS 2-1
2.1 Introduction .............................................................................................................. CS 2-1
2.1.1 Technical Assessment ......................................................................................... CS 2-2
2.1.2 Energy Efficiency Optimization Measures .......................................................... CS 2-2
2.1.3 Energy Efficiency Assessment ............................................................................. CS 2-4
2.2 Business Description ................................................................................................. CS 2-4
2.2.1 Al-Shurfa Restaurant Site Specifications ............................................................. CS 2-4
2.2.2 Estimated Climate Impact: Temperature and Humidity ..................................... CS 2-6
2.2.3 Building Construction Analysis ............................................................................ CS 2-6
2.3 Occupancy Rates, Power Consumption, and Outside Temperature Analysis .............. CS 2-7
2.3.1 Modeling of the Electricity Demand ................................................................... CS 2-8
2.4 Proposed Energy Efficiency Measures ..................................................................... CS 2-11
2.4.1 Short-Term Measures ....................................................................................... CS 2-11
2.4.2 Medium-Term Measures .................................................................................. CS 2-11
2.4.3 Long-Term Measures ........................................................................................ CS 2-11
2.4.4 Cost-Benefit Analysis of EE Measures ............................................................... CS 2-11
2.5 EE Legislation and Health and Safety Policy Issues................................................... CS 2-12
2.6 Impact Analysis on the Country’s Economy ............................................................. CS 2-13
Case Study 3: Enmar Hotel, Jeddah, Saudi Arabia ..................................................................... CS 3-1
3.1 Introduction .............................................................................................................. CS 3-1
3.2 Enmar Hotel Business ............................................................................................... CS 3-1
3.2.1 Proposed Energy Efficiency Measures ................................................................ CS 3-2
3.2.1.1 Short-Term Measures ......................................................................... CS 3-4
3.2.1.2 Mid-Term Measures ........................................................................... CS 3-4
3.2.1.3 Long-Term Measures .......................................................................... CS 3-5
3.2.1.4 Cost and Benefit Analysis of EE Measures .......................................... CS 3-5
3.2.1.5 Health and Safety Policy ..................................................................... CS 3-5
3.2.2 Existing Status ..................................................................................................... CS 3-6
3.2.2.1 Fact Sheet for Enmar Hotel ................................................................ CS 3-6
3.2.2.2 Energy Supply and Consumption ........................................................ CS 3-8
3.2.2.3 Greenhouse Gas Emission Factors.................................................... CS 3-18
3.2.3 Results ............................................................................................................... CS 3-18
3.2.3.1 Cooling Technology .......................................................................... CS 3-18
3.2.3.2 Lighting ............................................................................................. CS 3-23
3.2.3.3 Water Consumption ......................................................................... CS 3-24
3.2.3.4 Optimization of Electrical Machines ................................................. CS 3-27
3.2.3.5 Development and Implementation of an Energy Monitoring
System .............................................................................................. CS 3-28
3.2.3.6 Base Load Reduction ........................................................................ CS 3-30
3.2.3.7 Reactive Power Reduction ................................................................ CS 3-30
3.2.3.8 Implementation of an EnMS ............................................................. CS 3-31
3.2.3.9 Sensitization of Employees ............................................................... CS 3-33
3.2.3.10 Optimizing the Building Envelope .................................................... CS 3-33
3.2.3.11 Shading at Southern-Side Windows ................................................. CS 3-33
3.2.3.12 Establish Increased Roof Insulation .................................................. CS 3-33
Case Study 4: Case Study 4: M-Hotel, Riyadh, Saudi Arabia ....................................................... CS 4-1
4.1 Introduction .............................................................................................................. CS 4-1
4.2 Technical Fact Sheet.................................................................................................. CS 4-1
4.2.1 Proposed Energy Efficiency Measures ................................................................ CS 4-2
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4.2.2 Hotel Aspect and Opportunities ......................................................................... CS 4-3
Business Description ................................................................................................. CS 4-3
4.3.1 Location and Specifications ................................................................................ CS 4-4
4.3.2 Climate Impact—Temperature and Humidity .................................................... CS 4-5
4.3.3 EE Construction Analysis ..................................................................................... CS 4-6
4.3.4 Occupancy Rates, Power Consumption, and Outside Temperatures Analysis ... CS 4-7
4.3.5 Modeling of the Electricity Demand ................................................................... CS 4-8
4.3.6 Short-Term Measures ......................................................................................... CS 4-9
4.3.6.1 Medium-Term Measures .................................................................... CS 4-9
4.3.6.2 Long-Term Measures ........................................................................ CS 4-11
4.3.6.3 Cost and Benefit Analysis of EE Measures ........................................ CS 4-11
4.3.7 EE Health and Safety Policy Issues .................................................................... CS 4-11
4.3.8 EE Recommendations ....................................................................................... CS 4-12
Case Study 5: A Mall, Jeddah, Saudi Arabia ............................................................................... CS 5-1
5.1 Introduction .............................................................................................................. CS 5-1
5.1.1 Energy Efficiency Optimization Measures .......................................................... CS 5-2
5.1.1.1 Short-Term Measures ......................................................................... CS 5-5
5.1.1.2 Medium-Term Measures .................................................................... CS 5-5
5.1.1.3 Long-Term Measures .......................................................................... CS 5-5
5.2 Existing Status........................................................................................................... CS 5-6
5.2.1 Description and Specifications ............................................................................ CS 5-6
5.2.2 Energy Supply and Consumption ........................................................................ CS 5-6
5.2.2.1 Electrical Supply .................................................................................. CS 5-6
5.2.2.2 Existing Meters and Data Basis ......................................................... CS 5-12
5.2.2.3 New Prepaid Meters in 2014 ............................................................ CS 5-14
5.2.2.4 Energy Consumption ........................................................................ CS 5-14
5.2.2.5 Energy Cost ....................................................................................... CS 5-28
5.2.3 Greenhouse Gas Emission Factors .................................................................... CS 5-28
5.2.4 Who Benefits from Energy Efficiency?.............................................................. CS 5-28
5.3 Results .................................................................................................................... CS 5-29
5.3.1 Cooling Technology ........................................................................................... CS 5-29
5.3.1.1 Absorption Chiller with Waste Heat Usage ...................................... CS 5-32
5.3.1.2 Increasing the Internal Temperature ............................................... CS 5-33
5.3.1.3 Using Cold Night Air in Winter .......................................................... CS 5-33
5.3.1.4 Refrigerated Shelves ......................................................................... CS 5-33
5.3.1.5 Temperatures in Refrigerated Shelves and Freezers........................ CS 5-34
5.3.2 Lighting .............................................................................................................. CS 5-34
5.3.2.1 Lighting in the Mall ........................................................................... CS 5-34
5.3.2.2 Lighting in the Supermarket ............................................................. CS 5-35
5.3.2.3 Lighting in Smaller Stores ................................................................. CS 5-36
5.3.3 Water Consumption .......................................................................................... CS 5-36
5.3.3.1 Domestic Water ................................................................................ CS 5-36
5.3.4 Optimization of Electrical Machines ................................................................. CS 5-37
5.3.4.1 Using Energy-Efficient Drives............................................................ CS 5-37
5.3.4.2 Using Variable-Speed Drives ............................................................. CS 5-38
5.3.4.3 Maintenance of Drives ..................................................................... CS 5-38
5.3.5 Development and Implementation of an Energy Monitoring System .............. CS 5-38
5.3.5.1 Measurement Point Concept ........................................................... CS 5-39
5.3.5.2 Assessment of Investment and Savings ............................................ CS 5-40
5.3.6 Peak Load Management System ....................................................................... CS 5-41
5.3.7 Base Load Reduction ......................................................................................... CS 5-44
4.3
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5.3.8
5.3.9
5.3.10
5.3.11
5.3.12
Improvement of the Power Factor ................................................................... CS 5-44
Implementation of an Energy Management System ........................................ CS 5-45
Specification for the Purchase of Machinery and Equipment .......................... CS 5-47
Sensitization of Employees ............................................................................... CS 5-48
Optimizing the Building Envelope ..................................................................... CS 5-48
5.3.12.1 Shadowing at Southern Wall Windows and at Specific Southern
Roof Areas ........................................................................................ CS 5-48
5.3.12.2 Increasing Roof Insulation ................................................................ CS 5-49
Case Study 6: Case Study 6: Pilot Hospital, Jeddah, Saudi Arabia: Midsize, Traditional
Clinics and Hospital in the City-Coast Area, Jeddah ............................................. CS 6-1
6.1 Introduction .............................................................................................................. CS 6-1
6.1.1 Jeddah Hospital: Local Service Component ........................................................ CS 6-3
6.2 Business Description ................................................................................................. CS 6-4
6.2.1 Hospital Data ....................................................................................................... CS 6-4
6.2.2 Location and Construction Specifications ........................................................... CS 6-5
6.2.3 Climate Impact, Temperature, and Humidity Analysis ....................................... CS 6-6
6.2.4 Existing Supply Structure and Metering ............................................................. CS 6-7
6.2.5 EE Building Construction Analysis ....................................................................... CS 6-8
6.3 Occupancy Rates, Power Consumption, and Outside Temperature Analysis .............. CS 6-9
6.4 Modeling (LP Analysis) of the Electricity Demand .................................................... CS 6-10
6.5 Proposed Energy Efficiency Measures ..................................................................... CS 6-13
6.5.1 Short-Term Measures ....................................................................................... CS 6-13
6.5.2 Medium-Term Measures .................................................................................. CS 6-13
6.5.3 Long-Term Measures ........................................................................................ CS 6-13
6.5.4 Cost and Benefit Analysis for the EE Measures ................................................ CS 6-13
6.6 Environmental Impact and Health and Safety Policy ............................................... CS 6-13
6.7 Replication Case Basis Seen for Similar Hospital Service Clients in KSA .................... CS 6-15
6.8 Conclusion and Recommendation ........................................................................... CS 6-15
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List of Tables
Volume 1 Tables
Table 1-1:
Table 1-2:
Table 1-3:
Table 1-4:
Table 1-5:
Table 1-6:
Table 1-7:
Table 1-8:
Table 1-9:
Table 1-10:
Table 1-11:
Table 1-12:
Table 2-1:
Table 2-2:
Table 2-3:
Table 2-4:
Table 2-5:
Table 2-6:
Table 2-7:
Table 2-8:
Table 2-9:
Table 2-10:
Table 2-11:
Table 2-12:
Table 2-13:
Table 2-14:
Table 2-15:
Table 2-16:
Table 2-17:
Table 2-18:
xxxii
The Saudi Energy Balance Based on Data from the International Energy Agency
in Paris for 2009, in ktoe................................................................................................. 1-3
Overview of Main Assumptions Used for the 2040 Forecast of the Energy System
in Saudi Arabia (for details on data considered for the forecasting, see the
Appendix) ..................................................................................................................... 1-15
Comparison of R2 Values for Different Time-Series Analyses Between Data
Based on Linear and Nonlinear Extrapolation .............................................................. 1-16
Final Energy Consumption in Saudi Arabia According to Sector and Type of
Energy for 2009 and 2040, in Mtoe/a .......................................................................... 1-31
Forty Energy Efficiency Technologies for Saudi Arabia, According to Sector
Priorities ....................................................................................................................... 1-33
Seventy Energy Efficiency Measures for Saudi Arabia According to Sector
Priorities ....................................................................................................................... 1-37
Results of an Energy Efficiency Scenario for KSA in 2040 Compared to the
Baseline (BAU) .............................................................................................................. 1-41
Electricity Production Capacities (MWel), Electricity Production (GWh/year),
and Full-Load Hours per Year with and without Renewables Energy Production
in Saudi Arabia in 2040 ................................................................................................. 1-43
Estimated Electricity Costs in 2040 in Saudi Arabia for Generation and
Transmission of Electricity Depending on Energy Source (Fossil, Nuclear,
Renewables) ................................................................................................................. 1-44
Calculation of Opportunity Costs for Current and Future Energy Consumption
in Saudi Arabia until 2040 (Price and Consumption Effects) ........................................ 1-45
Variable Baseline Forecast 2010 to 2040 Time-Series Analysis Data ........................... 1-49
Variable Baseline Forecast 2010 to 2040 Main Data ................................................... 1-54
Water Production and Distribution by SWCC and IWWP............................................... 2-6
Desalination Plants and Water Allocation ...................................................................... 2-6
Projections for Energy Capacity and Consumption ........................................................ 2-9
ECRA Status of Permits and Licenses in 2012 ............................................................... 2-12
Refineries in KSA (Source: 53) ...................................................................................... 2-14
Distribution of Products by SABIC Affiliates (Source: 4) ............................................... 2-15
SABIC Affiliates’ Products and Production Rates.......................................................... 2-16
Industrial Distribution in KSA........................................................................................ 2-18
Distribution of Industrial Subsectors by Turnover ....................................................... 2-19
Food Production and Consumption in KSA and the GCC Countries ............................. 2-21
Food Consumption in KSA and Globally by Subsector, 2013 ....................................... 2-21
Sugar Production in KSA ............................................................................................... 2-23
Global Market for Flat and Container Glass, 2009–2017 ............................................. 2-24
Specific Energy Consumption by Glass Product and Technology ................................. 2-26
Glass Production in KSA, the Middle East, and European Union ................................. 2-27
Paper and Board Consumption Compared, 2010......................................................... 2-29
Pulp Imports to KSA by Country of Origin (%) .............................................................. 2-30
Consumption and Production of Tissue Paper in KSA and GCC (Mio t/y) .................... 2-31
Volume 1
Table 2-19:
Table 2-20:
Table 2-21:
Table 2-22:
Table 2-23:
Table 2-24:
Table 2-25:
Table 2-26:
Table 2-27:
Table 2-28:
Table 2-29:
Table 2-30:
Table 2-31:
Table 2-32:
Table 2-33:
Table 2-34:
Table 3-1:
Table 3-2:
Table 3-3:
Table 3-4:
Table 3-5:
Table 3-6:
Table 4-1:
Table 4-2:
Table 5-1:
Table 5-2:
Table 5-3:
Table 6-1:
Table 6-2:
Table 6-3:
Table 6-4:
Table 6-5:
Table 6-6:
Table 6-7:
Table 6-8:
Volume 1
Material Balance for Pulp and Paper in KSA and GCC .................................................. 2-31
Production of Chemical and Natural Fiber Globally, 2012 (Mio t/y) ............................ 2-33
Synthetic Fiber Production in KSA by Fiber Type (%) ................................................... 2-33
Specific Energy Consumption for Construction Materials (MJ/t)................................. 2-35
Distribution of Types of Cement in KSA (%) ................................................................. 2-35
Energy Consumption for Stone Production (MJ/m3) ................................................... 2-37
Distribution of Energy Input for Stone Production ...................................................... 2-37
KSA Market for Construction Materials, 2008 (Mio t/y) .............................................. 2-37
Energy Saving Potentials .............................................................................................. 2-42
Correction Factors for Population and Modernization ................................................ 2-44
Calculation of Correction Factor for Structural Differences in Industry ...................... 2-44
Waste Heat Use Potentials ........................................................................................... 2-45
Price of Equipment by Size and Temperature Level ..................................................... 2-46
EE Technologies Applicable in KSA by Priority ............................................................. 2-48
EE Measures Applicable in KSA by Priority ................................................................... 2-49
Typical Barriers Evaluated for Relevance in KSA .......................................................... 2-51
Results of “Smart Grid” Keyword Search (July 3, 2013) ............................................... 3-12
Evaluation Matrix of Smart Grid Field Technologies .................................................... 3-17
Nominal Power Will Be Distributed According to Likelihood of PV Units .................... 3-20
Cost Parameters for Possible Cable Types Separated to Hollow Price
(Aluminum base 0) and a Part for the Needed Aluminum US$253.38/100km
(Oct. 15, 2013) (Helukabel, 2013) ................................................................................ 3-23
Cost Parameters for Possible Transformer Types ........................................................ 3-24
Necessary Grid Reinforcement to Realize All Scenarios............................................... 3-24
Effects of Different Energy-Saving Measures on Cooling Demand Classified
by Effort .......................................................................................................................... 4-4
Effects of Different Energy-Saving Measures on Cooling Demand Classified
by Effort ........................................................................................................................ 4-29
Best Practice Energy Intensity for Portland Cement (95% Clinker) (Source:
Worrel, et al., 2008, pp. 24, 27)...................................................................................... 5-8
Best Practice Energy Intensity of a Direct Reduction Steelmaking Process
(Source: Worrel, et al., 2008, p. 14) ............................................................................. 5-11
Annual Energy Consumption for Selected Years and Each Scenario............................ 5-17
PV Power Plant Configurations: Examples at Two Locations With Comparable
Solar Irradiation (range, 2,000 to 2,100 kWh/m² global irradiation per year) ............... 6-9
Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of
Different PV Technologies ............................................................................................ 6-16
SWOT Analysis of CSP Technologies ............................................................................. 6-26
SWOT Analysis for Wind Power Technology in Saudi Arabia ....................................... 6-33
Number of Housing Units by Housing Type and Region (adapted from (General
Statistics and data authority, 2010)) ............................................................................ 6-48
Distribution of Living Space by Housing Type (from: “Study of the National
Housing Strategy in the Kingdom of Saudi Arabia” [SampleSurveyEn.pdf];
further bibliographic data not available)...................................................................... 6-48
Gross Roof Area per Region and Building Type ............................................................ 6-49
Average Roof Size per Building Type and Area Losses Owing to Wall Shadows .......... 6-50
xxxiii
Table 6-9:
Table 6-10:
Table 6-11:
Table 6-12:
Table 6-13:
Table 6-14:
Table 6-15:
Table 6-16:
Table 6-17:
Table 6-18:
Table 6-19:
Table 6-20:
Table 6-21:
Table 6-22:
Table 6-23:
Table 7-1:
Table 7-2:
Table 7-3:
Table 7-4:
Table 7-5:
Table 7-6:
Table 7-7:
Table 7-8:
Table 7-9:
Table 7-10:
Table 7-11:
Table 7-12:
Table 7-13:
Table 7-14:
Table 7-15:
Table 7-16:
Table 7-17:
Table 7-18:
Table 7-19:
xxxiv
Optimal Module Inclination and Resulting Area Losses Owing to Distances
Between Inclined Module Rows ................................................................................... 6-50
Net Roof Area Suitable for PV by Region and Building Type ........................................ 6-52
Installable Capacity on Residential Buildings in Saudi Arabia by Region and Building Type
...................................................................................................................................... 6-52
Annual Global Horizontal Irradiation (GHI) in the Capital of Each Region ................... 6-53
Potential Electricity Generation per Region and Building Type ................................... 6-53
Industrial Gross Roof Area, Net Roof Area, Installable PV Capacity, and
Potential Electricity Generation by Region .................................................................. 6-55
Financial Assumptions for PV LCOE Calculation ........................................................... 6-57
Assumptions for PV LCOE Calculation .......................................................................... 6-58
Financial Assumptions for LCOE Calculation of 100 MW CSP Plants (parabolic
trough) .......................................................................................................................... 6-58
Assumptions for CSP LCOE Calculation ........................................................................ 6-59
Financial Data Assumptions for Wind LCOE Calculation .............................................. 6-59
Technical Data for Wind LCOE Calculation ................................................................... 6-60
Energy Consumption of Different Desalination Technologies (Trieb, et al., 2007) ...... 6-65
Assumptions on the Reverse Osmosis Desalination Plant (Fichtner, 2011) ................. 6-65
RO Reference Plant for the Calculation of LWPCs (Fichtner, 2011) ............................. 6-66
Electricity Production Capacities (MWel), Electricity Production (GWh/y),
and Full-Load Hours per Year with and without Renewables Energy Production
in Saudi Arabia in 2040 ................................................................................................... 7-3
Possible Savings in Capitalizing on Waste Heat.............................................................. 7-4
Six Industrial and Commercial Sites Involved in EE Audit Case Studies ......................... 7-8
EE Measures Identified at Alsafwa Cement Plant and Expected Payback Times ........... 7-9
Overview of All Identified EE Measures at a Mall in Jeddah, KSA ................................ 7-11
Overview of the Energy-Saving Potentials for Medium-sized Enmar Hotel,
Jeddah........................................................................................................................... 7-12
Anticipated Energy-Savings Potential at M-Hotel in Riyadh ........................................ 7-13
Basic Technical Fact Sheet and Cited References for the Jeddah Hospital .................. 7-14
List of Proposed EE Measures at Jeddah Hospital ........................................................ 7-15
Potential EE Savings ...................................................................................................... 7-16
Basic Technical Fact Sheet and References for the Al-Shurfa Restaurant, Riyadh ...... 7-17
List of Proposed EE Measures for the Al-Shurfa Restaurant, Riyadh ........................... 7-18
Individual and National Savings Potentials of EE Measures in the Cement Sector ...... 7-19
Identified Efficiency Measures for a Large Pilot Shopping Mall in Jeddah and its
Replication Potential for All of KSA* ............................................................................ 7-20
Identified EE Measures for the Small M-Hotel in Riyadh and Its Regional
Replication Potential* .................................................................................................. 7-21
Efficiency Measures for a Mid-sized Hotel in Jeddah and Its Replication
Potential in Western Saudi Arabia* (estimated: 80 hotels by same size in KSA) ......... 7-22
Individual and Concluded National Savings Replication Potential of
EE Measures in Hospitals* ............................................................................................ 7-23
EE Measures for Mid-sized Restaurants and Their National Replication
Potential ....................................................................................................................... 7-24
EE Measures Applicable in KSA by Priority ................................................................... 7-26
Volume 1
Table 7-20:
Table 7-21:
Table 7-22:
Table 7-23:
Table 7-24:
Table 7-25:
Effects of Different Energy-saving Measures on Cooling Demand Classified
by Effort ........................................................................................................................ 7-28
Residential and HH Recommendations ........................................................................ 7-29
Potential EE Savings in the Residential Sector ............................................................. 7-30
System Power Capacity and Electricity Production for KSA ......................................... 7-32
Employment Figures that Could be Achieved by 2040 ................................................ 7-32
EE and RE Project Opportunities in KSA 2040 .............................................................. 7-33
Volume 2 Tables
Table CS 1-1:
Table CS 1-2:
Table CS 1-3:
Table CS 1-4:
Table CS 1-5:
Table CS 1-6:
Table CS 1-7:
Table CS 1-8:
Table CS 1-9:
Table CS 1-10:
Table CS 1-11:
Table CS 1-12:
Table CS 1-13:
Table CS 1-14:
Table CS 1-15:
Table CS 1-16:
Table CS 1-17:
Table CS 1-18:
Table CS 1-19:
Table CS 1-20:
Table CS 1-21:
Table CS 1-22:
Table CS 1-23:
Table CS 2-1:
Table CS 2-2:
Table CS 2-3:
Table CS 2-4:
Table CS 2-5:
Table CS 3-1:
Table CS 3-2:
Table CS 3-3:
Volume 1
Media Data for ACP and W-Company...................................................................... CS 1-1
Monthly ACP Consumption and Production Rate for 2012 ..................................... CS 1-2
Top Public Construction Firms in Saudi Arabia ........................................................ CS 1-2
EE Measures and Expected Payback Times ............................................................. CS 1-3
Power Consumption Savings Potential, Implementable Measures......................... CS 1-4
Price List for All Media Used at ACP ...................................................................... CS 1-18
Monthly Costs According to Staff Interviews ........................................................ CS 1-18
Water Usage at ACP ............................................................................................... CS 1-20
Ball Mill Replacement Only for Raw Mill and All Mills ........................................... CS 1-24
Bag Filter and VSD Replacement Effects................................................................ CS 1-24
Climate Data for Jeddah, 1961–1990 .................................................................... CS 1-25
Average Temperatures and Corresponding Estimated Percentage of Cooling
Consumption .......................................................................................................... CS 1-25
Assumption for Cooling Capacity of Split Units at ACP .......................................... CS 1-26
ACP Calculation Example for Implementing ORC into Cement Plants................... CS 1-28
Cooling Demand Assumption from Chapter 5 ....................................................... CS 1-29
Estimated Savings by Using Absorption Chillers .................................................... CS 1-29
Savings Attained by Increasing the Temperature .................................................. CS 1-30
Savings by Energy-Efficient Drives ......................................................................... CS 1-31
Average Power Demand of Known Drives and Fans without VSDs ....................... CS 1-32
Savings from Using VSDs ........................................................................................ CS 1-33
Effect of Pressured Air Reduction and a New VSD-Driven Compressor ................ CS 1-36
Parent Pneumatic Control ..................................................................................... CS 1-36
Estimated Cost Savings from Implementing an EnMS at ACP ............................... CS 1-41
Basic Technical Fact Sheet and References for the Al-Shurfa Restaurant,
Riyadh ...................................................................................................................... CS 2-2
Proposed EE Measures for the Al-Shurfa Restaurant, Riyadh ................................. CS 2-3
Summary of Cooling Demand for the Al-Shurfa Restaurant with 3,800 CDDs
for Riyadh, Likely Estimated to Be Approximately 9.2 MWh-th ............................. CS 2-7
Load Modeling for the Al-Shurfa Restaurant Power Demand by Sector and
Load Analysis............................................................................................................ CS 2-9
EE Proposals Identified for the Al-Shurfa Restaurant in Riyadh ............................ CS 2-12
Overview of Potential Savings ................................................................................. CS 3-2
Savings Potential ...................................................................................................... CS 3-3
Result of Modeling for Enmar Hotel Power Demand by Sector and Time-Load
Analysis, Sorted by Percentage ................................................................................ CS 3-9
xxxv
Table CS 3-4:
Table CS 3-5:
Table CS 3-6:
Table CS 3-7:
Table CS 3-8:
Table CS 3-9:
Table CS 3-10:
Table CS 3-11:
Table CS 3-12:
Table CS 3-13:
Table CS 3-14:
Table CS 3-15:
Table CS 3-16:
Table CS 3-17:
Table CS 3-18:
Table CS 3-19:
Table CS 3-20:
Table CS 4-1:
Table CS 4-2:
Table CS 4-3:
Table CS 4-4:
Table CS 5-1:
Table CS 5-2:
Table CS 5-3:
Table CS 5-4:
Table CS 5-5:
Table CS 5-6:
Table CS 5-7:
Table CS 5-8:
Table CS 5-9:
Table CS 5-10:
Table CS 5-11:
Table CS 5-12:
Table CS 5-13:
Table CS 5-14:
Table CS 5-15:
Table CS 5-16:
Table CS 5-17:
Table CS 5-18:
Table CS 5-19:
xxxvi
Official Prices for Electrical Energy ........................................................................ CS 3-12
Electricity Consumption and Cost of Enmar Hotel in 2010.................................... CS 3-13
Electricity Consumption and Cost of Enmar Hotel in 2011.................................... CS 3-13
Electricity Consumption and Cost of Enmar Hotel in 2012, and a Whole Mall
in Jeddah, KSA for Comparison .............................................................................. CS 3-13
Enmar Hotel Consumption Model 2012, Sorted by Percentage ........................... CS 3-15
Prices for Electricity in KSA .................................................................................... CS 3-18
Average Temperatures and Corresponding Estimated Percentage of Cooling
Consumption, Source: NOAA ................................................................................. CS 3-19
Air Chiller Types and Small Devices for the Enmar Hotel ...................................... CS 3-21
Calculated Number of Absorption Chillers for the Enmar Hotel and Cumulated
Waste Heat Demand from a Mall in Jeddah, KSA for Chillers and HW ProductionCS 3-22
Estimated Savings by Using Absorption Chillers .................................................... CS 3-22
Savings by Increasing the Target Temperature ..................................................... CS 3-23
Lighting Replacement by LED................................................................................. CS 3-24
Savings Potential for Lighting ................................................................................ CS 3-24
Electric Boiler Replacement for HW ...................................................................... CS 3-25
Savings by Using VSDs ............................................................................................ CS 3-28
Reactive Power Reduction ..................................................................................... CS 3-30
Savings Potential by Implementing an EnMS, Including Monitoring..................... CS 3-32
Basic Technical Fact Sheet and References of the Considered M-Hotel, Riyadh .... CS 4-1
Summarized Cooling Demand for the M-Hotel With 3,800 CDDs for Riyadh ......... CS 4-6
Load Modeling for the M-Hotel Power Demand by Sector and Load
Grouping .................................................................................................................. CS 4-8
EE Proposals Identified for the M-Hotel in Riyadh ................................................ CS 4-12
Overview of the Potential Savings ........................................................................... CS 5-2
Economic Figures of Measures ................................................................................ CS 5-3
Comparison of CO2 Emissions with and without Generators ................................. CS 5-8
Estimation of the Power Demand of the Entire Mall ............................................ CS 5-12
Electricity Consumption and Cost of Three Large Clients in 2012 ......................... CS 5-15
Electricity Consumption of Large Client No. 4 and Calculations for Total
Consumption in 2012 ............................................................................................. CS 5-17
Electricity Consumption of Four Large Clients in 2010 .......................................... CS 5-20
Electricity Consumption of Four Large Clients in 2011 .......................................... CS 5-22
Electricity Consumption of Four Large Clients in 2013 .......................................... CS 5-24
Climate Data for Jeddah, 1961–1990a (Source: NOAA, via Wikipedia) ................. CS 5-29
Average Temperatures and Corresponding Estimated Percentages of Cooling
Consumptiona ........................................................................................................ CS 5-29
Estimated Savings by Using Absorption Chillers .................................................... CS 5-32
Savings by Increasing the Temperature ................................................................. CS 5-33
Savings by Using Intelligent Light Controlling ........................................................ CS 5-34
Savings from Energy-Efficient Drives ..................................................................... CS 5-37
Savings from Using Variable-Speed Drives ............................................................ CS 5-38
Savings Potential by Implementing an Energy Monitoring System ....................... CS 5-41
Savings Potential by Implementing a Peak Load Management System ................ CS 5-44
Savings by Compensation of Reactive Power ........................................................ CS 5-45
Volume 1
Table CS 5-20:
Table CS 6-1:
Table CS 6-2:
Table CS 6-3
Table CS 6-4:
Table CS 6-5:
Table CS 6-6:
Volume 1
Savings Potential by Implementing an Energy Management System ................... CS 5-47
Technical Fact Sheet of the Pilot Hospital, Jeddah .................................................. CS 6-1
Proposed EE Measures at Pilot Hospital, Jeddah .................................................... CS 6-2
Technical Fact Sheet for the Pilot Jeddah Hospital .................................................. CS 6-4
The Summarized Cooling Demand for the Pilot Hospital with 3,900 CDD
for Jeddah City Has Been Estimated at Around 115.8 GWh-th Annually ................ CS 6-9
Modeling of the Annual Power Demand at Pilot Hospital by Sector, Capacity,
and Time ................................................................................................................ CS 6-11
EE Proposals Identified and Benefits Achievable for the Pilot Hospital
in Jeddah ................................................................................................................ CS 6-14
xxxvii
List of Figures
Volume 1 Figures
Figure 1-1:
Figure 1-2:
Figure 1-3:
Figure 1-4:
Figure 1-5:
Figure 1-6:
Figure 1-7:
Figure 1-8:
Figure 1-9:
Figure 1-10:
Figure 1-11:
Figure 1-12:
Figure 1-13:
Figure 1-14:
Figure 1-15:
Figure 1-16:
Figure 2-1:
Figure 2-2:
Figure 2-3:
Figure 2-4:
Figure 2-5:
Figure 2-6:
Figure 2-7:
Figure 2-8:
Figure 2-9:
Figure 2-10:
Figure 2-11:
Figure 2-12:
Figure 2-13:
Figure 2-14:
xxxviii
The Saudi Energy System with Energy Production (left), Transformation
(middle) and Consumption (right) for 2009, in Mtoe, Using a Standardized
Sankey Diagram Scheme ............................................................................................... 1-5
The Saudi Transformation Segment in 2009, in Mtoe .................................................. 1-6
Population Forecast for Saudi Arabia, 1971–2040 ..................................................... 1-11
GDP Forecast for Saudi Arabia, 1971–2040, in Billion Saudi Ryal............................... 1-12
Crude Oil Spot Price Brent Forecast for Saudi Arabia, 1971–2040, in 2011
US$/bbl ....................................................................................................................... 1-13
Total Final Energy Consumption in Saudi Arabia for the Period 1971–2040,
in Mtoe (Baseline Forecast) (The upper part shows the R2 for different types
of linear and nonlinear functions, and the lower part gives the trend for 2040.) ..... 1-19
Final Energy Consumption According to Sector for the Period 1971–2040,
in Mtoe (Baseline Forecast) ........................................................................................ 1-20
Total Primary Energy Supply for the Period 1971–2040, in Mtoe (Baseline
Forecast) ..................................................................................................................... 1-21
Total Energy Production for the Period 1971–2040, in Mtoe (Baseline
Forecast) ..................................................................................................................... 1-22
Total Electricity Output for the Period 1971–2040, in GWh/year (Baseline
Forecast) ..................................................................................................................... 1-23
Electricity Peak Load for the Period 1971–2040, in MW (Baseline Forecast) ............ 1-24
Power Supply Capacity Mix for the Period 2009–2032, in MW (Baseline
Forecast, with 121,000 MW in 2032) ......................................................................... 1-25
CO2 Emissions for the Period 1971–2040, in MtCO2/year (Baseline Forecast) ......... 1-26
Saudi Energy System with Energy Production, Transformation, and
Consumption in 2040, in Mtoe (Baseline Forecast).................................................... 1-28
Comparison of National Energy Consumption Data in Saudi Arabia with
World Countries and Neighboring Countries (Source: World Bank: World
DataBank: World Development Indicators. Washington, D.C., March 2013)............ 1-30
Results of an Energy Efficiency Scenario (−30 percent) on Final Energy
Consumption for KSA in 2040 Compared to the Baseline (BAU) ................................ 1-42
Energy Consumption in KSA (Source: 13) ..................................................................... 2-3
Energy Consumption—Total and Industry Share in KSA (Source: 13) .......................... 2-3
Consumption of Energy and Electricity in Industry in KSA (Source: 13) ....................... 2-4
Growth of Final Energy Consumption ........................................................................... 2-4
Method of Quantitative Waste Heat Use Potential Analysis........................................ 2-5
Desalination Plants and Water Pipelines (Source: 3).................................................... 2-6
Distribution of Desalination Train Size (Source: 3) ....................................................... 2-7
Distribution of Desalination Technology by Type (Source: 3) ...................................... 2-7
Specific Energy Consumption in Relation to Technology ............................................. 2-8
Desalination Performance 2012 (Source: 3) ................................................................. 2-8
Wastewater Generation 2010 (Source: 30) .................................................................. 2-9
Growth of Efficiency for Power Generation in KSA .................................................... 2-10
Growth of Electricity Generation by Technology Type in KSA .................................... 2-10
Operation Ranking by Merit Order Principle (Source: 60).......................................... 2-11
Volume 1
Figure 2-15:
Figure 2-16:
Figure 2-17:
Figure 2-18:
Figure 2-19:
Figure 2-20:
Figure 2-21:
Figure 2-22:
Figure 2-23:
Figure 2-24:
Figure 2-25:
Figure 2-26:
Figure 2-27:
Figure 2-28:
Figure 2-29:
Figure 2-30:
Figure 2-31:
Figure 2-32:
Figure 2-33:
Figure 2-34:
Figure 2-35:
Figure 2-36:
Figure 2-37:
Figure 2-38:
Figure 2-39:
Figure 2-40:
Figure 2-41:
Figure 3-1:
Figure 3-2:
Figure 3-3:
Figure 3-4:
Figure 3-5:
Figure 3-6:
Figure 3-7:
Figure 3-8:
Figure 3-9:
Figure 3-10:
Figure 3-11:
Figure 3-12:
Figure 3-13:
Figure 3-14:
Volume 1
Distribution of Power Generation by Producer (Source: 8)........................................ 2-11
Power Generation by Unit Size, SEC and Others ........................................................ 2-12
Electricity Production by Energy Type in KSA ............................................................. 2-13
Growth of SWCC Desalination Capacities ................................................................... 2-13
Energy Audit Questionnaire for KSA Study, 2013 ....................................................... 2-17
Distribution of “Other” Industry Sectors in KSA by Turnover ..................................... 2-18
Waste Heat Temperature Profiles by Industry Group ................................................ 2-20
Distribution of Food Subsectors in KSA (Source: 9) .................................................... 2-21
Typical General Sankey Diagram for Glass Production ............................................... 2-25
Typical Detailed Sankey Diagram for Glass Production Work (Source: 34) ................ 2-26
Glass Production in KSA .............................................................................................. 2-27
Scheme of Modernized Glass Manufacturing Control in a KSA Company.................. 2-28
Typical Sankey Diagram for Paper Production (Source: 32) ....................................... 2-29
Growth of Paper Production in Arab Countries .......................................................... 2-30
Growth of Textile/Fiber Production............................................................................ 2-32
Example of Fiber Production in KSA (Source: 57) ....................................................... 2-34
Typical Sankey Diagram for Cement Production (Source: 35) .................................... 2-35
Examples of Manufactured Stones (Source: 59) ........................................................ 2-36
Heating Equipment for Stone Production (Source: 25) .............................................. 2-36
Distribution of Construction Material Types in KSA ................................................... 2-38
Local Distribution of Construction Material Demand in KSA ...................................... 2-39
Growth of Cement Production in KSA (Source: 15, 26) .............................................. 2-39
Distribution of Energy Consumption by Industry in Germany, 2002.......................... 2-43
Distribution of Energy Consumption by Industry in KSA, 2012 .................................. 2-43
Changes in German Industry Sectors and Subsectors, 1998–2002 ............................ 2-47
Efficiency of ORC Processes (Source: 19).................................................................... 2-51
A One-Stage Absorption Chiller (Source: 61) .............................................................. 2-52
Electricity Grid Layers With Typical Connected Plants (picture by J. Messerly) ........... 3-4
Smart Grid Architecture Model (CEN-CENELEC-ETSI Smart Grid Coordination
Group, 2012) ................................................................................................................. 3-5
Most Technologies Can Be Categorized as Voltage Control and Active
Power Control, Which Are Strongly Dependent ........................................................... 3-6
OLTC with Controller (2013) ......................................................................................... 3-7
The Maximal Voltage Deviation of 10% Is Distributed Among the Voltage
Levels to 4%, 2%, and 4% for Medium-Voltage, Transformation, and LowVoltage Levels, Respectively ......................................................................................... 3-7
Voltage Distributions in a Low-Voltage Feeder ............................................................ 3-8
Smart Meter and a Web-Portal to Display Electricity Demand to the Customer ....... 3-10
The Peak of Normal Demand Is Shaved by APC.......................................................... 3-11
Smart Grid Categorization Methodology .................................................................... 3-12
Number of Active Smart Grid Projects per Year ......................................................... 3-13
Project Partners Within Smart Grid Projects per Year................................................ 3-14
Financial Support Forms of Smart Grid Projects per Year .......................................... 3-14
Stakeholders of Active Smart Grid Projects per Year ................................................. 3-15
Controlled System Within Smart Grid Projects Differentiated per Year .................... 3-15
xxxix
Figure 3-15:
Figure 3-16:
Figure 3-17:
Figure 3-18:
Figure 3-19:
Figure 3-20:
Figure 3-21:
Figure 3-22:
Figure 3-23:
Figure 3-24:
Figure 4-1:
Figure 4-2:
Figure 4-3:
Figure 4-4:
Figure 4-5:
Figure 4-6:
Figure 4-7:
Figure 4-8:
Figure 4-9:
Figure 4-10:
Figure 4-11:
Figure 4-12:
Figure 4-13:
Figure 4-14:
Figure 4-15:
Figure 4-16:
Figure 4-17:
Figure 4-18:
Figure 4-19:
Figure 4-20:
Figure 4-21:
Figure 4-22:
Figure 4-23:
Figure 4-24:
Figure 4-25:
Figure 4-26:
Figure 4-27:
Figure 4-28:
Figure 4-29:
xl
Used Lead Signals in Smart Grid Projects ................................................................... 3-16
Lead System Structure of Smart Grid Projects ........................................................... 3-16
Used Field Technologies in Smart Grid Projects Differentiated per Year ................... 3-17
Daily Variation in Peak Load and Temperature of Riyadh (Temperatures for
Riyadh, Sept. 9, 2006) (Abu-ebid & Alyousef, 2012)................................................... 3-19
Low Voltage Grid Topology ......................................................................................... 3-19
Nodal Voltage per Grid Connection Point Applying Household Load Profiles ........... 3-20
Power per Grid Connection Point for Scenarios With PV Units in Comparison
to Scenarios Without PV Units .................................................................................... 3-21
Nodal Voltage per Grid Connection Point Applying Household Load Profiles
and Probabilistic PV Units ........................................................................................... 3-21
Tap Positions per Configuration and Day ................................................................... 3-22
Nodal Voltage per Grid Connection Point Applying Household Load Profiles,
Probabilistic PV Units, and OLTC................................................................................. 3-23
Weekly Load Profile ...................................................................................................... 4-2
Energy Consumption of Household and AC .................................................................. 4-3
Monitored Buildings in the Kingdom of Saudi Arabia................................................... 4-6
Typical Residential Building at KAUST........................................................................... 4-7
Residential Buildings at Dammam University ............................................................... 4-7
Installation of the Energy Meters at KAUST ................................................................. 4-8
Electricity Meter Installation at KAUST ......................................................................... 4-9
Electricity Meter Installation at Dammam University .................................................. 4-9
Number of Houses Monitored at KAUST .................................................................... 4-10
Ambient Temperature at KAUST During the Monitoring Period [SolarGIS] ............... 4-11
Number of Monitored Houses at Dammam University .............................................. 4-11
Ambient Temperature at Dammam University During the Monitoring Period.......... 4-12
Average Normalized Electricity Consumption of All Buildings at KAUST.................... 4-13
Normalized Building Electricity Demand for Building 1 (left) and Building 2
(right) .......................................................................................................................... 4-13
Normalized Electricity Demand of Selected Buildings for Further Analysis ............... 4-14
Daily Load Profile in July ............................................................................................. 4-15
Weekly Load Profile in July ......................................................................................... 4-15
Daily Load Profile in September ................................................................................. 4-16
Weekly Load Profile in September ............................................................................. 4-17
Daily Load Profile in November .................................................................................. 4-17
Weekly Load Profile in November .............................................................................. 4-18
Mean Energy Consumption of All Monitored Buildings ............................................. 4-18
Monthly Energy Consumption and Ambient Temperature for the Selected
Buildings ...................................................................................................................... 4-19
Electricity Demand of the Whole Building: Main Meter (left) and Household
Only/Submeter (right)................................................................................................. 4-20
Power Demand of AC Only ......................................................................................... 4-20
Daily Load Profile of Buildings and AC in September ................................................. 4-21
Weekly Load Profile of Buildings and AC Units in September/October ..................... 4-21
Daily Load Profile of Buildings and AC in November .................................................. 4-22
Weekly Load Profile of Buildings and AC in November .............................................. 4-23
Volume 1
Figure 4-30:
Figure 4-31:
Figure 4-32:
Figure 4-33:
Figure 4-34 :
Figure 4-35:
Figure 4-36:
Figure 4-37:
Figure 4-38:
Figure 5-1:
Figure 5-2:
Figure 5-3:
Figure 5-4:
Figure 5-5:
Figure 5-6:
Figure 5-7:
Figure 5-8:
Figure 5-9:
Figure 5-10:
Figure 5-11:
Figure 5-12:
Figure 5-13:
Figure 6-1:
Figure 6-2:
Figure 6-3:
Figure 6-4:
Figure 6-5:
Figure 6-6:
Figure 6-7:
Figure 6-8:
Volume 1
Energy Consumption of Buildings and AC................................................................... 4-23
Daily Energy Consumption of Buildings and AC Clustered by Temperature .............. 4-24
Monthly Mean Energy Consumption of All Buildings at Dammam University ........... 4-24
AC Electricity Demand in Correlation to Ambient Temperature ................................ 4-25
Weekly Load Profile .................................................................................................... 4-25
Energy Consumption of Buildings and AC Ordered by Ambient Temperature .......... 4-26
Daily Energy Demand of AC ........................................................................................ 4-26
Energy Consumption at KAUST (left) and Dammam (right) ....................................... 4-27
Simulated Correlation Between Daily Ambient Temperature and Electricity
Demand for Different Energy-Saving Measures ......................................................... 4-28
Final Energy Distribution Including Desalination (Own Calculation Based on
Chapter 2) ..................................................................................................................... 5-2
Share of Energy Demand in KSA by Sector in 2011 and 2040 (as Projected in
Chapter 1) ..................................................................................................................... 5-5
Scheme of the Cement Manufacturing Process (Source: Sustain Consult, 2013,
p. 14) ............................................................................................................................. 5-6
Overview of Primary and Secondary Steel Production Processes
(Source: World Steel Association, 2008, p. 2)............................................................... 5-9
Example of the Realization of Direct Reduction in a MIDREX Process
(Source: APP, 2010, p. 3 Ch.3) ...................................................................................... 5-9
Shares of Installed Capacity (Column 2) in 2040 and Energy Consumed
(Column 3) by Technology Each Year for the Three Desalination Scenarios .............. 5-13
Production Steps for Main Products in the Petrochemical Sector (Source:
Alawi, 2011) ................................................................................................................ 5-14
Projected Development of Industrial Energy Demand by Sectors in the
BAU Scenario .............................................................................................................. 5-16
Projected Industrial Energy Demand in the Case of a Low-EE Application ................ 5-16
Projected Industrial Energy Demand by Sectors for a High-EE Deployment .............. 5-17
Comparison of the Projected Energy Demand in the BAU, Low-EE, and HighEE Scenarios ................................................................................................................ 5-18
Potential CO2 Emission Reduction in the Low-EE and High-EE Scenarios
Compared to the BAU Scenario .................................................................................. 5-18
Potential Opportunity Costs in the Low-EE and High-EE Scenarios ............................ 5-19
Net Roof Area, Installable PV Capacity, and Electricity Generation Potential
by Regions (map adapted from Dalet, 2013) ................................................................ 6-2
LCOE of Renewable Energies Compared to Oil and Gas With Opportunity
Costs .............................................................................................................................. 6-3
Exemplary c-Si-module Components (Fraunhofer ISE)................................................. 6-6
Left: Principle of a c-Si Cell (Solar Total Holding BV, 2011). Right: String of
Cells (Blue) Interconnected by Alloyed Copper Ribbon (red) (Fraunhofer ISE) ............ 6-6
Structure of a CIGS Cell (Paulson, 2004) ....................................................................... 6-7
Structure of a CdTe Cell (Paulson, 2004) ...................................................................... 6-7
Structure of a Triple a-Si Cell (Paulson, 2004) .............................................................. 6-7
Exemplary Arrangements of a PV Concentrator. Left: A Fresnel lens is used to
concentrate the sunlight to a small solar cell (Fraunhofer ISE). Right: Two
mirrors are used for concentration (SolFocus, 2011). Not shown is the
tracking part of the CPV system. .................................................................................. 6-8
xli
Figure 6-9:
Figure 6-10:
Figure 6-11:
Figure 6-12:
Figure 6-13:
Figure 6-14:
Figure 6-15:
Figure 6-16:
Figure 6-17:
Figure 6-18:
Figure 6-19:
Figure 6-20:
Figure 6-21:
Figure 6-22:
Figure 6-23:
Figure 6-24:
Figure 6-25:
Figure 6-26:
Figure 6-27:
Figure 6-28:
Figure 6-29:
Figure 6-30:
Figure 6-31:
Figure 6-32:
Figure 6-33:
Figure 6-34:
Figure 6-35:
xlii
Cost Breakdown of a Typical Rooftop PV System (100 kW) (unpublished data) ........ 6-10
Cost Breakdown of a Typical Ground-Mounted PV Power Plant (20 MW)
(unpublished data) ...................................................................................................... 6-11
Cost Breakdown of a Typical Ground-Mounted, PV, Thin-Film (CdTe) Power
Plant (20 MW) (unpublished data) ............................................................................. 6-11
Annual PV Module Shipments per Technology, From 2000 to 2011 (Navigant
Consulting) .................................................................................................................. 6-12
Global Annual PV Market 2000–2012. (For 2012, ROW figures are directly
integrated into those of the relevant regions.) MEA: Middle East and Africa;
APAC: Asia and Pacific. (EPIA, 2013) ........................................................................... 6-12
Worldwide PV Production Volume in 2011 Structured by Region (Photon
International, 2012) .................................................................................................... 6-13
Worldwide PV Cell Production. Over the last decade, the average growth rate
was above 50% per year. (Photon International, 2012) ............................................. 6-13
Historical Development of PV Module Prices Versus Cumulative Module
Production (PSE AG/Fraunhofer ISE; data based on Navigant Consulting and
EuPD module prices since 2006) ................................................................................. 6-14
PV System Price Development and Estimates for 2020. (Janzig, 2011;
Bank Sarasin, 2012; Fraunhofer ISE, 2012) ................................................................. 6-14
Installed CPV Power Worldwide (Fraunhofer ISE, 2012; Graph PSE AG, 2012) .......... 6-15
Schematic of Storage Tanks and Higher Electricity Output of a CSP Plant ................. 6-18
Schematic of a Parabolic Trough Plant With Integrated Two-Tank Thermal
Energy Storage (Solar Millennium, 2010) ................................................................... 6-18
Daily Operation for a Combined-Cycle Solar Plant With Storage System
(Valentina A. Salomoni, 2013) .................................................................................... 6-19
Parabolic Trough Collector: Aerial view of the Andasol plant (left) and working
principle (right) (Flagsol, 2013) ................................................................................... 6-19
Aerial View of Novatec Solar’s Puerto Errado 2 Collector Field (NOVATEC
Solar, 2013) ................................................................................................................. 6-20
The Gemasolar Solar Tower Power Plant (Fraunhofer ISE) ........................................ 6-21
Percentage Breakdown of Cost for CSP Technologies (unpublished data) ................ 6-23
Cumulated Installed Capacity of CSP Projects by Technology Since 1984
(Fraunhofer ISE) .......................................................................................................... 6-24
Project Pipeline of CSP and Technology Distribution (Fraunhofer ISE) ...................... 6-25
Maximum CSP Market Expectation for North Africa (estimated by
Fraunhofer ISE, considering all existing development plans [e.g., Moroccan
Solar Plan, Egypt’s RES strategy, Desertec projects]) ................................................ 6-25
Illustration of the Design of a Wind Energy Converter (Hau, 2003) ........................... 6-27
Schematic Illustration of Different Tower Types (Hau, 2003) .................................... 6-28
Cost Breakdown of a Wind Farm (calculation based on Hau (Hau, 2003)
and Blanco (Blanco, 2009)) ......................................................................................... 6-30
Cost Breakdown of a Wind Turbine With a Capacity of Approximately 2 MW
(calculation based on Blanco (Blanco, 2009)) ............................................................. 6-31
Global Wind Market Development Between 1996 and 2012 (GWEC, 2013) ............. 6-31
Installed Wind Power Capacities in the MENA Region ............................................... 6-32
Global Market Shares of Wind Turbine Manufacturers (Cleantech
magazine, 2012) .......................................................................................................... 6-32
Volume 1
Figure 6-36:
Figure 6-37:
Figure 6-38:
Figure 6-39:
Figure 6-40:
Figure 6-41:
Figure 6-42:
Figure 6-43:
Figure 6-44:
Figure 6-45:
Figure 6-46:
Figure 6-47:
Figure 6-48:
Figure 6-49:
Figure 6-50:
Figure 6-51:
Figure 6-52:
Figure 6-53:
Figure 6-54:
Figure 6-55:
Figure 6-56:
Figure 6-57:
Figure 6-58:
Figure 6-59:
Figure 6-60:
Figure 6-61:
Figure 6-62:
Figure 6-63:
Figure 6-64:
Figure 6-65:
Figure 6-66:
Figure 6-67:
Figure 6-68:
Figure 6-69:
Volume 1
Schematic Illustration of Hydroelectric Power Generation (Etrical, 2014) ................ 6-34
Pelton, Francis, and Kaplan Turbines (Voith Siemens Hydro Power
Generation, 2014) ....................................................................................................... 6-35
SDE Device (150 kW) (M. Fadaeenejad, January 2014) .............................................. 6-36
Oscillating Water Column Device (500 kW) (M. Fadaeenejad, January 2014) ........... 6-36
Archimedes Wave Swing Device (5–6 MW)(M. Fadaeenejad, January 2014) ............ 6-36
Oyster Device (31.5 MW) (M. Fadaeenejad, January 2014) ....................................... 6-36
Wave Dragon (11 MW) (M. Fadaeenejad, January 2014) .......................................... 6-37
Pelamis (0.75 MW): (M. Fadaeenejad, January 2014) ................................................ 6-37
Annual Average Wave Energy Flux (kW/m) of Wave Front (Ltd, 1990-1991) ............ 6-37
Schematic View of the Wide Variety of Bioenergy Routes (E4tech, 2009) ................ 6-39
Diagram From a Biomass Power Plant (AESI, 2012) ................................................... 6-40
Development Status of the Main Technologies for Producing Biofuels for
Transport From Biomass (E4tech, 2009) .................................................................... 6-41
Share of the Biomass in the World in the Primary Bioenergy Mix (IPCC, 2007)......... 6-41
Biomass to Energy Pathways (US Energy Information Administration, 2013) ........... 6-42
Main International Biomass for Energy Trade Routes (Junginger, 2008) ................... 6-42
Annual Average Wind Speed at 100m Height in Saudi Arabia (KACARE, 2013) ......... 6-44
Global Horizontal Irradiation in Saudi Arabia (GeoModelSOLAR, 2013) .................... 6-45
Map of the Saudi Grid (GENI, n.y.) .............................................................................. 6-46
Direct Normal Irradiation in Saudi Arabia (GeoModelSOLAR, 2013).......................... 6-47
Shadowed Areas on a Roof (top view and side view) With a 1m Balustrade
at a Sun Altitude of 30° in the East, South, and West ................................................ 6-50
Reductions From Gross Roof Area to Calculate the Net Roof Area Suitable
for PV Installations ...................................................................................................... 6-51
Net Roof Area, Installable PV Capacity, and Electricity Generation Potential
by Regions (map adapted from (Dalet, 2013)) ........................................................... 6-56
LCOE of Renewable Energy Technologies in Saudi Arabia.......................................... 6-60
Cost of Extraction and Opportunity Cost of Oil in KSA (Energy economics,
2013)(REUTERS, 2009) ................................................................................................ 6-61
Cost of Exploration, Production, and Liquefaction of LNG in KSA (Economides,
2005)(DoE, 2005) (DoE, 2005)(Foss, 2012) ................................................................. 6-61
LCOE of Renewable Energies Compared to Oil and Gas With Opportunity
Costs ............................................................................................................................ 6-62
Past Development of Installed Capacity of Oil and Gas Power Plants in KSA
and Future Development in the BAU Scenario ........................................................... 6-63
Historical Data for CO2 Emissions in KSA and Future Development for the
BAU Scenario .............................................................................................................. 6-63
Renewable Energy Scenario........................................................................................ 6-64
CO2 Emissions for the Renewable Energy Scenario ................................................... 6-64
LWPCs of Small and Large PV-Powered RO Plants ..................................................... 6-66
Electricity Generation of the Regarded Plant in Hourly Solution for One Year .......... 6-67
Electricity Generation of the Regarded Plant per Hour for One Week in Spring ....... 6-67
Comparison of Electricity Generation and Consumption Load Profiles.
(Orange: Scaled standard load profile for German manufacturing companies;
Green: Scaled idealized load profile of cotton factory) .............................................. 6-68
xliii
Figure 6-70:
Figure 6-71:
Figure 6-72:
Figure 7-1:
Figure 7-2:
Figure 7-3:
Figure 7-4:
Figure 7-5:
Figure 7-6:
Reduced Electricity Demand Owing to PV on a Working Day in April ........................ 6-69
Cost Breakdown of the CAPEX for the Regarded System ........................................... 6-69
Off-Grid PV–Diesel Hybrid Power System (WhoseSaleSolar, 2013) ........................... 6-71
Energy Consumption of HH and Ambient Temperature............................................... 7-5
Energy Consumption of HH and AC .............................................................................. 7-6
Anticipated Energy-Saving Potentials at ACP and Their Economic Value .................... 7-9
Anticipated Energy-Savings Potential at Enmar Hotel in Jeddah ............................... 7-12
Replication Potential for the Analyzed EA Business Sectors in KSA ........................... 7-25
Concluded Complex Findings by EU Sustainability Criteria ........................................ 7-34
Volume 2 Figures
Figure CS 1-1:
Figure CS 1-2:
Figure CS 1-3:
Figure CS 1-4:
Figure CS 1-5:
Figure CS 1-6:
Figure CS 1-7:
Figure CS 1-8:
Figure CS 1-9:
Figure CS 1-10:
Figure CS 1-11:
Figure CS 1-13:
Figure CS 1-12:
Figure CS 1-15:
Figure CS 1-14:
Figure CS 1-17:
Figure CS 1-16:
Figure CS 1-18:
Figure CS 1-19:
Figure CS 1-20:
Figure CS 1-21:
Figure CS 1-22:
Figure CS 1-23:
Figure CS 1-24:
Figure CS 1-25:
Figure CS 2-1:
Figure CS 2-2:
Figure CS 2-3:
xliv
Saving Potentials and Their Economic Value ........................................................... CS 1-3
Short-Term Measures .............................................................................................. CS 1-4
German Employee Survey Conducted in 2010 (Data Source: kfw Bank of
NRW [Germany], 2010) ............................................................................................ CS 1-5
Global Behavior of the Cement Market (Source: VDZ Germany) ............................ CS 1-7
Process Flow of a Cement Plant with Rotary Furnace (Source: ökobau.dat
from http://www.nachhaltigesbauen.de/baustoff-undgebaeudedaten/oekobaudat.html) ......................................................................... CS 1-9
Relative Consumption Rate 2012, ACP .................................................................. CS 1-10
The Engine Room Containing Five W-Company Motors ....................................... CS 1-10
Sankey Diagram of Energy Production .................................................................. CS 1-11
One of the Three Main Transformers .................................................................... CS 1-12
Power Plant 2012 Daily HFO Consumption (Below) and Power Generation
(Above)................................................................................................................... CS 1-13
The Control Room .................................................................................................. CS 1-14
Kiln Consumption in Liters/Hour ........................................................................... CS 1-15
HFO Consumption by ACP in Tons/Month............................................................. CS 1-15
Raw Mill Consumption in kWh .............................................................................. CS 1-16
cc Burner Consumption in Liters/Hour .................................................................. CS 1-16
CM1 Consumption in kWh ..................................................................................... CS 1-17
CM2 Consumption in kWh ..................................................................................... CS 1-17
Electricity Consumption at ACP in kWh/Month .................................................... CS 1-19
Monthly Water Consumption in Tons, 2012 ......................................................... CS 1-21
Tire Components (Source: VDZ Germany, 2013) ................................................... CS 1-22
Average Temperatures and Corresponding Estimated Percentage of Cooling
Consumption .......................................................................................................... CS 1-26
Waste Heat Use via ORC: A Simple Description..................................................... CS 1-28
Compressor Rooms ................................................................................................ CS 1-34
Start and Stop Times for All Grids .......................................................................... CS 1-35
The Plan-Do-Check-Act Circle of an EnMS ............................................................. CS 1-39
Front View of the Al-Shurfa Restaurant, Riyadh ...................................................... CS 2-1
Main Benefits of Implementing EE Proposals for the Al-Shurfa Restaurant,
Riyadh ...................................................................................................................... CS 2-3
Schematic of the Al-Shurfa Restaurant, Riyadh ....................................................... CS 2-5
Volume 1
Figure CS 2-4:
Figure CS 2-5:
Figure CS 2-6:
Figure CS 2-7:
Figure CS 3-1:
Figure CS 3-2:
Figure CS 3-3:
Figure CS 3-4:
Figure CS 3-5:
Figure CS 3-6:
Figure CS 3-7:
Figure CS 3-8:
Figure CS 3-9:
Figure CS 3-10:
Figure CS 3-11:
Figure CS 3-12:
Figure CS 3-13:
Figure CS 3-14:
Figure CS 4-1:
Figure CS 4-2:
Figure CS 4-3:
Figure CS 4-4:
Figure CS 4-5:
Figure CS 4-6:
Figure CS 4-7:
Figure CS 5-1:
Figure CS 5-2:
Figure CS 5-3:
Figure CS 5-4:
Figure CS 5-5:
Figure CS 5-6:
Figure CS 5-7:
Figure CS 5-8:
Volume 1
Metered Temperature Band and Humidity Data from Riyadh Airport,
September 2012 to September 2013 ...................................................................... CS 2-6
Business Data, Outside Temperature, and Monthly Power/Water
Consumption for the Al-Shurfa Restaurant, Riyadh, 2012 ...................................... CS 2-8
Power Consumption Shares for the Al-Shurfa Restaurant in Riyadh ...................... CS 2-9
Power Distribution Shares for the Al-Shurfa Restaurant in Riyadh ....................... CS 2-10
Saving Potentials in SR ............................................................................................. CS 3-2
Short-Term and Long-Term Measures, Savings, and Complexity of
Implementation ....................................................................................................... CS 3-3
Basic Ground Scheme of the Enmar Hotel, Jeddah, Provided by Management ..... CS 3-7
Monthly Relative Outside Temperature, Occupancy Rate, Power Consumption,
and Water Consumption at Enmar Hotel, Jeddah, for the Year 2012;
Consumption Data from a Mall in Jeddah, KSA Reporting and from a
Respective Mall in Jeddah, KSA Staff Interviews ..................................................... CS 3-8
Enmar Hotel Main Feeder ...................................................................................... CS 3-10
Power Generation a Mall in Jeddah, KSA (Hourly Values) ..................................... CS 3-11
Energy Balance of the Year 2012 for a Mall in Jeddah, KSA and the Distribution of
about 8 Percent to the Enmar Hotel ...................................................................... CS 3-14
Electrical Consumption in Percentage for 2012 for Enmar Hotel .......................... CS 3-16
Monthly Electricity Consumption of Enmar Hotel in 2012, Starting
September 1, 2012 (Left), Ending August 2013 (Right) ......................................... CS 3-17
Climate Data for Jeddah 1961–1990, Source: NOAA ............................................. CS 3-18
Average Temperatures and Corresponding Estimated Percentage of
Cooling Consumption............................................................................................. CS 3-19
Comparative Type of Larger Cooling Packages on the Roof of a Mall in
Jeddah, KSA ............................................................................................................ CS 3-20
A Mall in Jeddah, KSA and Enmar Hotel Using Waste Heat for HW and Absorption
Chillers ................................................................................................................... CS 3-26
The Plan-Do-Check-Act Circle of an EnMS ............................................................. CS 3-31
List of Proposed EE Measures for the M-Hotel, Riyadh........................................... CS 4-2
Main Benefits from EE Proposals for the M-Hotel, Riyadh...................................... CS 4-3
Basic Ground Scheme of the M-Hotel, Riyadh ........................................................ CS 4-4
Metered Temperature Band and Humidity Data at Riyadh Airport,
September 2012 to September 2013 ...................................................................... CS 4-6
Relative Business Data, Outside Temperature and Monthly Power/Water
Consumption for the M-Hotel, Riyadh 2012 ........................................................... CS 4-7
Power Consumption Shares for the M-Hotel, Riyadh.............................................. CS 4-9
Power Distribution Shares for the M-Hotel, Riyadh .............................................. CS 4-10
Short-Term Measures, Savings, and Complexity of Implementation ...................... CS 5-4
Long-Term Measures, Savings, and Complexity of Implementation ....................... CS 5-4
The Three 12-MVA Transformers ............................................................................ CS 5-7
One of the Two Generator Rooms Containing Nine Generators ............................. CS 5-7
Technical Specifications of the Generators ............................................................. CS 5-8
Generators in Generator Station 9, Its Sum, and the Overall Sum of All
18 Generators (Hourly Values) ................................................................................ CS 5-9
Generators in Generator Station 4 and Its Sum .................................................... CS 5-10
Sum of All Generators from Gate 4 and Gate 9 ..................................................... CS 5-11
xlv
Figure CS 5-9:
Figure CS 5-10:
Figure CS 5-11:
Figure CS 5-12:
Figure CS 5-13:
Figure CS 5-14:
Figure CS 5-15:
Figure CS 5-16:
Figure CS 5-17:
Figure CS 5-18:
Figure CS 5-19:
Figure CS 5-20:
Figure CS 5-22:
Figure CS 5-21:
Figure CS 5-23:
Figure CS 5-24:
Figure CS 5-25:
Figure CS 6-1:
Figure CS 6-2:
Figure CS 6-3:
Figure CS 6-4:
Figure CS 6-5:
Figure CS 6-6:
xlvi
Utility Meter in One of the RMUs .......................................................................... CS 5-13
One of the MDP Meters with Pulse Outputs ......................................................... CS 5-13
Feed-In into the Units, Also with Meters with Pulse Outputs ............................... CS 5-14
Electricity Consumption of the Large Clients, Other Clients, and Sum of a
Mall in Jeddah, KSA in 2012 ................................................................................... CS 5-18
Energy Balance of the Year 2012 for a Mall in Jeddah, KSA .................................. CS 5-19
Electricity Consumption of Four Large Clients in the Last 3.5 Years ..................... CS 5-26
Total Electricity Consumption of All Four Large Clients (Top, Stacked; Bottom,
Percentage Portions) ............................................................................................. CS 5-27
Average Temperatures and Corresponding Estimated Percentages of Cooling
Consumption .......................................................................................................... CS 5-30
Larger Cooling Packages on the Roof .................................................................... CS 5-31
Smaller Split Devices on the Roof .......................................................................... CS 5-31
Halogen Lamps in the Mall .................................................................................... CS 5-35
Intensive Lighting in the Supermarket ................................................................... CS 5-36
Sample Graphic of Peak Power Reduction ............................................................ CS 5-42
Sample Load Curve ................................................................................................ CS 5-42
Sample Graph of Peak Power Reduction (Magnified) ........................................... CS 5-43
The Plan-Do-Check-Act Circle of an Energy Management System ........................ CS 5-46
Thin Roof Insulation ............................................................................................... CS 5-49
Main Benefits from EE Proposals for the Jeddah Pilot Hospital .............................. CS 6-3
Metered Temperatures and Humidity Data at Jeddah Airport,
September 2012 Through September 2013 ............................................................ CS 6-7
Existing Supply Structure and Metering .................................................................. CS 6-7
Analytical Comparison of Relative Outside Temperature, Occupancy Rate,
and Monthly Power Consumption at the Pilot Hospital in Jeddah for 2012
(36.571 MWhel Total) ............................................................................................ CS 6-10
Analyzing the Annual Power Consumption Shares for the Pilot Hospital
in Jeddah, 2012 ...................................................................................................... CS 6-11
Analyzing the Daily/Weekly Power Consumption at the Pilot Hospital,
Jeddah .................................................................................................................... CS 6-12
Volume 1
Abbreviations
€
°C
a
ABB
AC
acc
ACHSI
ACM
ACP
ACPFP
ACS
ADB
AEC
AMI
APC
a-Siμc-Si
BAT
BAU
bbl
Bbbl
Bbbl/d
Bcm
Bcm/a
b/d
Bn
BO
Boe/d
BOO
BOS
cp
CAPEX
cc
CCGT
CDD
CDM
CDSI
CdTe
CEE
CFL
CHP
CI
CIGS
CIS
CM
Volume 1
EURO
Degree Centigrade
Year
Asea Brown Bovery
Air Conditioner/Conditioning
According
Australian Council for Healthcare Standards International
Associate for Computing Machinery
ALSAFWA Cement Plan
Australian Centre for Plant Functional Genomics
Absorption Chiller System
Asian Development Bank
Advanced Electronics Company
Automated Metering Infrastructure
Active Power Control
Amorphous-Microcrystalline Silicon
Best Available Technology
Business As Usual
Barrel
Billion Barrels
Billion Barrels per Day
Billion Cubic Meters
Billion Cubic Meters per Year
Barrels per Day
Billion
Business Opportunity
Barrels of Oil Equivalent per Day
Build-Own-Operate
Balance of System
Power Coefficient
Capital Expenditure
Cement-per-Clinker
Combined-Cycle Gas Turbine
Cooling Degree Days
Clean Development Mechanism
Central Department for Statistics and Information
Cadmium Telluride
Central and Eastern European
Compact Fluorescent Lamp
Combined Heat and Power
Confidence Interval
Copper Indium Gallium Selenide
Copper Indium Selenide
Cement Mill
xlvii
CNG
CO2
COC
COE
COP
CPV
c-SI
CSP
CT
DCS
Deg C
DFO
DG
DIN
DNA
DNI
DRI
DSM
EAF
EC
ECRA
EDD
EDP
EE
EER
EGS
EMS
EnMS
EnPI
EPC
EPI
EPIA
EQuIP
ESCO
ESD
EU
EUROSTAT
EV
EVA
FACTS
FOB
FS
GCC
GCP
GDP
xlviii
Compressed Natural Gas
Carbon Dioxide
Chamber of Commerce
Cost of Generating Energy
Coefficient of Performance
Concentrating Photovoltaics
Crystalline Silicon
Concentrating Solar Power
Current Transformers
Digital Control System
Degree Centigrade
Diesel Fuel Oil
Diesel Generator
Deutsches Institut für Normung
Designated National Authority
Direct Normal Irradiance
Direct-Reduced Iron
Demand-Side Management
Electric Arc Furnace
Energy Converter
Electricity and Cogeneration Regulatory Authority
Energy Data Development
Eight Development Plan
Energy Efficiency
Energy Efficiency Ratio
Enhanced Geothermal System
Energy Audit and Management System
Energy Management System
Energy Performance Indicator
Engineering, Procurement, and Construction
Energy Performance Indicator
European Photovoltaic Industry Association
Evaluation and Quality Improvement Program
Energy Services Company
EU Directive on Energy End-Use Efficiency and Energy Services
European Union
Statistical Office of the European Commission
Electric Vehicle
Ethylene Vinyl Acetate
Flexible AC-Transmission Systems
fFree-on-Board
Feasibility Study
Gulf Cooperation Council
Grid Connection Points
Gross Domestic Product
Volume 1
GE
GHG
GHI
GIZ
GJ/t
GNP
GT
HFO
HH
HRSG
HTF
HVAC
HVDC
HW
I&C
I/O
IAC
ICB
IEA
IEEJ
IPP
ISCC
ISE
ISO
IWPP
JCI
JCIA
JICA
KACARE
KAPSARC
KAUST
KFUPM
KIAB
KPI
KSA
kV
KWKG
LBNL
LCOE
LED
LFO
LNG
LOE
LPG
LTS
Volume 1
General Electric Company
Greenhouse Gas
Global Horizontal Irradiation
Gesellschaft für Internationale Zusammenarbeit GmbH
Gigajoule per Ton
Gross National Product
Gas Turbine
Heavy Fuel Oil
Household
Heat Recovery Steam Generator
Heat Transfer Fluid
Heating, Ventilation, Aand Air Conditioning
High Voltage DC
Hot Water
Instrumentation and Control
Input/Output
International Advisory Council
Incandescent Bulb
International Energy Agency
Institute of Energy Economics, Japan
Independent Power Producer
Integrated Solar Combined Cycle
Institute for Solar Energy Systems
International Organization for Standardization
Independent Water and Power Producer
Joint Commission International
Joint Commission International Accreditation
Japan International Cooperation Agency
King Abdullah Center for Atomic and Renewable Energy
King Abdullah Petroleum Studies and Research Center
King Abdullah University of Science and Technology
King Fahd University of Petroleum and Minerals
KAUST Industry Advisory Board
Key Performance Indicator
Kingdom of Saudi Arabia
Kilo Volt
Kraft-Wärme-Kopplungs-Gesetz
Lawrence Berkeley National Laboratory
Levelized Cost of Electricity
Light-Emitting Diode
Light Fuel Oil
Liquified Natural Gas
Level of Effort
Liquid Petroleum Gas
Long-Term Strategy
xlix
LWPC
Mboe/d
MDP
MED
MENA
min
Mio
MJ/m3
MoE
MOEP
MOPMR
MOWE
MSF
NAMA
NCV
NDA
NEEAP
NEEP
NG
NGO
NICDP
Nm3
NOAA
NRC
NWC
O&M
OECD
OEM
OLTC
OPEC
OPET
OPEX
ORC
PF
PFC
PV
PV-RO
R&D
RE
RMU
RO
RPC
SASO
SBC
SCADA
l
Levelized Water Production Cost
Million Barrels of Oil Equivalent per Day
Main Distribution Point
Multiple-Effect Desalination
Middle East and North Africa
Minute
Million
megajoules per Cubic Meter
Measure of Effectiveness
Ministry of Economy and Planning
Ministry of Petroleum and Mineral Resources
Ministry of Water and Energy
Multi-Stage Flash
National Appropriate Mitigation Actions
Net Calorific Value
Non-Disclosure Agreement
National Energy Efficiency Action Plans
National Energy Efficiency Program
Natural Gas
Nongovernmental Organization
National Industrial Clusters Development Program
Normal Cubic Meter
National Oceanic and Atmospheric Administration
National Research Council
National Saudi Water Company
Operation and Maintenance
Organisation for Economic Co-operation and Development
Original Equipment Manufacturer
On-Load Tap-Changer
Organization of Petroleum Exporting Countries
Organizations for the Promotion of Energy Technologies
Operational Expenditure
Organic Rankine cycle
Power Factor
Power Factor Compensation
Photovoltaic
Photovoltaic-Powered Reverse Osmosis
Research and Development
Renewable Energy
Ring Main Unit
Reverse Osmosis
Reactive Power Control
Saudi Standard Organization
Saudi Building Code
Supervisory Control and Data Acquisition
Volume 1
SCE
SEC
SEDC
SEEC
SEER
SEGS
SGAM
SGBC
SI
SIDF
SIEE
SM
SO2
SR
SWCC
SWOT
t
TFC
TL
TO
ToR
ToU
TPES
TPP
Tri-Gen
UAE
UN
UNDP
UNFCCC
UNSD
UNSNA
VSC
VSD
WACC
WE
WEC
Volume 1
Saudi Council of Engineers
Saudi Electricity Company
Solar Energy Development Center
Saudi Energy Efficiency Center
Strategic Energy and Economic Research
Solar Energy Generating System
Smart Grid Architecture Model
Saudi Green Building Council
System International of Units
Saudi Industrial Development Fund
Energy-Economic Information System
Smart Meter
Sulfur Dioxide
Saudi Riyal
Saline Water Conservation Corporation
Strengths, Weaknesses, Opportunities, and Threats
Ton (Metric)
Total Final Consumption
Team Lead
Time of Operation
Terms of Reference
Time of Use
Total Primary Energy Supply
Thermal Power Plant
Trigeneration
United Arab Emirates
United Nations
United Nations Development Program
United Nations Framework Convention on Climate Change
United Nations Statistics Division
United Nations System of National Accounts
Variable-Speed Controller
Variable-Speed Drive
Weighted Average Cost of Capital
Western Europe
Wind Energy Converter
li
1
Energy Supply and Demand in the Kingdom of
Saudi Arabia from 2010 to 2040 —
The Necessity for Energy Efficiency in the Kingdom
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Chapter 1: Energy Supply and Demand in the Kingdom of
Saudi Arabia from 2010 to 2040—The Necessity
for Energy Efficiency in the Kingdom
1.1 Introduction
Saudi Arabia is one of the largest consumers of electricity in the world, especially when considering
specific energy consumption figures (kWh/inhabitant as “per capita energy consumption” and kWh/m2
floor space as “per m2 energy consumption”). Contributing to energy efficiency in the country is an
important goal.
This report was designed to:
•
•
•
•
•
•
•
To analyze, comment on, and evaluate the different versions of energy forecasting studies in Saudi
Arabia for their availability and usefulness in overall energy forecasting of all energy carriers in KSA
up to 2040, which is not limited to electricity only and addresses shorter periods within the range of
2009 to 2040. This includes a comparison of different forecasting models, such as the SEEC-Bain and
Co. 2013 consulting study; the KAPSARC Institute of Energy Economics, Japan (IEEJ), 2011 study; the
ECRA-Brattle Group 2011 study from the United States; and other approaches from the Ministry of
Water and Energy (MOWE), the Saudi Electricity Company (SEC), the Saline Water Conservation
Corporation (SWCC), and Electricity & Cogeneration Regulatory Authority (ECRA) for 2009–2011, if
any, and presentation as a Sankey diagram.
To identify the effects of forecasting studies on energy balance, including primary energy balance
data and final energy balance data, based on demographic and economic data
To compare the energy system of Saudi Arabia with international, including European, benchmarks
To define, based on the outcomes of the above-mentioned studies, the priority energy saving and
energy efficiency potentials for the different energy sectors in Saudi Arabia, including the relevant
energy technologies
To define the influence of additional capacities from renewable energies (photovoltaic [PV] and
wind) on the forecasted load up to 2040, including reserve calculation and load shifting
To develop a methodology to transfer energy savings up to 2040 into “additional oil export
possibilities” and “avoided carbon dioxide (CO2) emissions”
To identify leaks and shortages from the current energy forecasting studies for future
improvements.
The assignment started in January 2013 and was concluded in May 2014.
Strategy and Outline of the Approach
To identify and define energy efficiency measures based on an energy baseline forecasting in Saudi
Arabia up to 2040, the following topics are discussed in this chapter:
•
•
•
•
Description of the specifics of the Saudi energy system and evaluation of current energy production
and consumption in Saudi Arabia in 2009 (Section 1.2)
Comparison of current energy forecasting schemes in KSA up to 2040, even though most of the
forecasting schemes are limited to a certain energy sector (i.e., electricity) and only go up to 2022,
2030, or 2032/2035 (Section 1.3)
A forecast of a baseline scenario using a detailed time-series analysis of past data in the energy
sector and some demographic and economic data on the future of Saudi Arabia, resulting in a
baseline forecast up to 2040, and concluding with the necessity for energy efficiency (Section 1.4)
Comparison of the energy system of Saudi Arabia with international, including European,
benchmarks in relation to energy efficiency up to 2040 (Section 1.5)
Volume 1
1-1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
•
•
Priorities for energy efficiency in the energy sectors based on new technologies in Saudi Arabia up
to 2040 (Section 1.6)
Leaks and shortages from the current energy forecasting models in Saudi Arabia up to 2040 (Section
1.7)
This information will enable understanding of the necessity and urgency for increased energy efficiency.
Reduced energy consumption in KSA by concentrating efforts on the most promising areas within the
energy sector compared to the baseline scenario up to 2040 will result in additional export possibilities
at world market prices.
1.2 General Description of the Energy Situation in KSA in 2009
1.2.1
Energy System in Saudi Arabia
The general situation of the energy system in Saudi Arabia can be characterized as follows:
•
•
•
•
•
Dependence on energy exports (energy oil and gas)
Power plants and other conversion technologies and their efficiencies, which are mainly simplecycle gas turbine technologies with an average efficiency rate of about 25 percent in 2009. With the
expansion of Saudi power generation capacities, this efficiency rate will increase gradually to
35 percent for the entire power production system, when planned investments in new power
stations will move to larger power stations and combined-cycle processes.
A background of main economic data and parameters in the Saudi energy system based on the
development and volatility of crude oil prices, which have extreme influence on the financial
possibilities of the Saudi economy
A government-owned electricity system
Subsidized energy supply for consumers.
1.2.2
Energy Data and Energy Balance Scheme for 2009 in Saudi Arabia
There is no national energy balance published by the Saudi government. Although the Statistical Office
has a unit for energy within its organization, it is not staffed at present. Instead, energy information is
distributed among many agencies. In addition, some information can be taken from the electric utilities
and the oil companies as well from the water desalination entities.
Therefore, only the balance schemes at international level, mainly the International Energy Agency (IEA)
and the United Nations (UN), can be used to describe energy production and energy consumption in the
country. The latest edition available is for 2009 and is published in thousand tons of oil equivalent (ktoe)
(Table 1-1). The reliability of energy data from IEA is limited without a national energy balance. IEA must
estimate from different sources the energy balance table for Saudi Arabia every year; this can lead to
mistakes in IEA figures, which affect the quality of the data, but have only limited effect on compiling a
time-series forecast. If KSA collects and publishes its own energy balance data, then this national source
should be considered a prime source.
On the production side, KSA is dependent on oil extraction because there are no other resources
currently available. The transmission sector is dominated by the refineries, which use the majority of
the Saudi primary energy consumption. The secondary energy produced in the transformation segment
is either exported or used on the domestic market for its own consumption processes, that is, in
industry, oil, and gas production, or the transport, commercial, or residential sectors.
The useful energy needs assessment for lighting, power, cooling, heating, hot water, and other uses
cannot be derived from the balance sheet.
1-2
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Table 1-1:
Supply and
Consumption
Production
Imports
Exports
Marine bunkers
Bunkers
b
b
Stocks
TPES
Transfers
Statistical
differences
Elec. plants
CHP plants
Heat plants
Gas works
Oil refineries
Coal
transformation
Liquefaction
plants
Other
transformation
Energy industry
own use
Losses
TFC
Industry
Transport
Other
Residential
Commercial and
public
Agriculture/
forestry
Fishing
Nonspecified
Nonenergy use
The Saudi Energy Balance Based on Data from the International Energy Agency in
Paris for 2009, in ktoe
Coal,
Peat
Crude
Oil
Oil
Natural
Geothermal, Biofuels
Products
Gas
Nuclear Hydro Solar, Other. Waste Electricity Heat
0
0
0
467,030
0
−318,51
4
0
10,979
−64,270
61,347
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3,840
152,356
−42,115
9,995
−2,494
−2,061
1,993
−55,853
44,297
0
0
0
0
61,347
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
528,377
10,984
−382,78
4
−2,494
−2,061
5,834
157,855
2,182
9,995
0
0
0
0
0
0
−17,147
0
0
0
−98,043
0
−15,315
0
0
0
96,352
0
-27,692
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18,669
0
0
0
0
0
0
0
0
0
0
0
−41,485
0
0
0
−1,691
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
−47
−5,942
−20,197
0
0
0
0
−1,346
0
−27,531
0
0
0
0
0
0
0
0
5,000
5,000
0
0
0
0
0
63,539
10,365
33,959
1,521
1,521
0
0
13,458
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
4
4
0
−1,545
15,778
2,087
0
13,691
8,672
4,551
0
0
0
0
0
0
0
−1,545
97,779
17,452
33,959
15,217
10,197
4,551
0
0
0
0
0
0
0
0
435
0
435
0
0
0
0
0
0
0
0
0
0
17,694
14,126
0
0
13,458
13,458
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
33
0
0
0
0
0
0
0
33
31,152
27,584
Petrochemical
feedstocks
TPES = total primary energy supply; CHP = combined heat and power.
a
Totals may not add up due to rounding.
b
International marine and aviation bunkers are included in transport for world totals.
Totala
Please note: The latest data available for the entire energy sector are data for 2009 based on
information from the IEA in Paris. Other data on electricity are available for 2011 and for part of 2012,
but these data cover only parts of the Saudi energy system. Because time-series data based on a
period of nearly 40 years are being used, we do not expect major changes in the data after publication
of the 2010 and 2011 data by IEA. Based on electricity data for 2010, 2011, and 2012, there are no
indications of any structural changes in the energy system of KSA.
Volume 1
1-3
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1.2.3
The Principles of an Energy Flow System Diagram Resulting in a Sankey
Diagram for Saudi Arabia
The energy flow system starts with the availability segment, where energy is made available via
extraction (production) or imports. If all exports are subtracted, the total primary energy consumption
is shown in a specific year for the region. The next segment is the transformation process with refineries
and power plants, where losses occur during the transformation process. Then the final energy
consumption, broken down into energy carriers and energy sectors, is presented in the flow system,
ending at useful energy.
A Sankey diagram (for Saudi Arabia in 2009, see Section 1.4) is usually used as a specific type of flow
diagram, in which the width of the arrows between the knots is proportional to the quantities of the
flows. These diagrams are typically used to visualize energy transfers between processes.
1.2.4
Sankey Diagram for the Saudi Energy System Based on the IEA Energy
Balance Scheme in 2009
All figures in this report refer to Mtoe/a. This stands for million tons of oil equivalent per year, which is
equivalent to 0.02019 Mboe/d, or million barrels of oil equivalent per day. For comparison, all figures
referring to Mtoe/a can be divided by 49.53 to produce the Mboe/d equivalent.
Figure 1-1 shows the Saudi energy system in the form of an aggregated Sankey diagram. The total
energy made available in Saudi Arabia in 2009 was 169 Mtoe, while the useful energy in the country
was 31 Mtoe. With “nonenergy consumption” of 31 Mtoe, the used energy in Saudi Arabia was 62 Mtoe
in 2009. This means that the overall efficiency of the Saudi energy system was approximately
37 percent.
Exports totaled 383 Mtoe in 2009; if energy imports are taken into consideration, the net exports were
372 Mtoe. However, the output of the transformation segment compared to the input was also
comparatively low. Altogether, the losses were 72 Mtoe, which is about 43 percent of total input to the
transformation segment. Also, the nonenergy use is greater than domestic and commercial use in the
country. Figure 1 shows the specific energy situation in Saudi Arabia compared with other countries.
Although there are huge natural reserves of fossil fuels within Saudi Arabia, these rich natural resources
should be used wisely and sustainably, allowing future Saudi generations to benefit from them.
Taking into consideration only the figures from the synoptic version of the energy flow analysis (Sankey
diagram) for Saudi Arabia in 2009, the main energy-saving potential can be seen from both Figure 1-1
and Figure 1-2:
Energy losses in the transformation segment totaled 72 Mtoe, 1 which is about 43 percent of the
entire input to the transformation segment and equates to twice the losses from end use (only 35
Mtoe). Therefore, any priorities for energy savings should start in this area of energy balance.
Within final energy consumption, the sectors of transport (34 Mtoe) and nonenergy consumption
(31 Mtoe) were individually twice as big as the residential (15 Mtoe) and industry (17 Mtoe) sectors.
Therefore, any sector priority should be transport, with an energy consumption of 34 Mtoe in 2009.
Nonenergy consumption 2 was quite high at 31 Mtoe in 2009, but this would not be considered a
loss because most of the products produced in this sector can be sold on world markets.
Within final energy consumption, the losses in the useful energy sector were about 35 Mtoe, which
equates to losses of about 47 percent during final energy consumption.
•
•
•
•
1
For details on the losses, refer to Figure 1–2, which gives more details on the transformation segment.
2
Nonenergy consumption is defined by fuels used for nonenergy purposes, such as raw materials for the manufacture of nonfuel
products, especially in the petrochemical industry, such as lubricants, greases, bitumen, white spirit, and industrial spirit for paint
manufacture and industrial cleaning purposes. (Source: IEA/EUROSTAT/OECD: Energy Statistics Manual, Paris, 2005, p. 29.)
1-4
Volume 1
Volume 1
528
Available
Energy
539
383
383
158
169
Statistical
Differences
Transfers
2
Figure 1-1:
69
13
10
Transformation
Input
Total
Exports
158
Gross Inland
Consumption
16
72
69
69
98
13
16
72
5
Energy
Losses
Residential
etc
15
34
Transport
17
Industry
25
5
35
12
9
10
31
Useful
Energy
Andreas Jahn, Berlin/Jeddah/Riyadh, April 2013, Version 26
Energy
Losses
34
17
15
31
31
Non-Energy
Consumption
Final Energy
Consumption
Petroleum
Products
13
Gas
16
Electricity
KAUST, KICP
The Saudi Energy System with Energy Production (left), Transformation (middle) and Consumption (right) for 2009, in Mtoe,
Using a Standardized Sankey Diagram Scheme
Source: International Energy Agency (IEA): Energy Balances of Non-OECD Countries, Paris 1013
1 Mtoe/a = 0.02019 Mboe/d = 7.36974 Mboe/a
Production
of Primary
Services
528
156
1
1
11
11
Bunkers,
Stocks
Total
Imports
Energy Flow Analysis for Saudi Arabia for the Year 2009 (Synoptic Version in Mtoe/a)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-5
1-6
17
28
98
15
Transformation
Losses
98
Oil
Refineries
60
Electricity
Plants
19
2
43
41
96
Oil
Products
Electricity
19
Energy Industry
Own Use
96
26
3
3
Energy Transportation
Losses
70
Oil Products
(Final Energy)
16
Electricity
(Final Energy)
Andreas Jahn, Berlin/Jeddah/Riyadh, April 2013, Version 5
70
16
KAUST, KICP Programme
Figure 1-2:
The Saudi Transformation Segment in 2009, in Mtoe
Note: IEA statistics and the corresponding balance sheet do not show how SWCC, which produces both electricity and desalinated water,
is incorporated within this balance sheet; this would require further investigation.
Source: International Energy Agency (IEA): Energy Balances of Non-OECD Countries, Paris 1013
1 Mtoe/a = 0.02019 Mboe/d = 7.36974 Mboe/a
115
Crude
Oil
15
Oil
Products
28
Natural
Gas
26
Energy Flow Analysis for for the Transformation Segment for Saudi Arabia in 2009 (in Mtoe/a)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
A closer look at the transformation part of the Saudi energy system shows the dimension and the
efficiency of the entire transformation segment. In 2009, there was an input of 60 Mtoe to the power
sector and 98 Mtoe to the refineries. The efficiency rate in the power stations is about 0.32 and in
refineries is about 0.98. Including distribution losses and own consumption, the figures decrease to 0.27
and 0.71, respectively.
The comparatively low efficiency figures in the power sector are based on a number of factors, namely:
•
•
•
•
Comparatively high age of power stations and turbines, which reduces the overall efficiency of the
power system
Comparatively small generation units (43 percent of all plants were at capacity of 251 MW and
below; the large number of small generation units at 8 MW, 12 MW, or 25 MW reduces the overall
efficiency of the power system
Comparatively low efficiency of diesel turbines (diesel engines used in Saudi Arabia often have very
low fuel efficiency, which reduces the overall efficiency of the power system)
Comparatively high transmission losses 3 (about 15.5 percent transmission losses when comparing
the exact figures of 2,891 Mtoe transmission losses to 18,669 Mtoe electricity production [the
figures in the Sankey diagram are rounded figures], which reduce the overall efficiency of the power
system).
For most of the sectors, energy-efficient equipment is not a priority as long as energy prices are at a
very moderate level compared to international standard prices. This leads to a supply of technical
equipment with lower energy efficiency; for example, a review of normal household equipment (in this
case, refrigerators) shows an energy consumption level at least twice the latest standard European
level.
This leads ultimately to higher primary energy consumption in Saudi Arabia, given the final energy
demand coming from population, industry, commerce, and transport.
1.3 Overview and Comparison of Current Energy Forecasting Studies
in Saudi Arabia until 2040
This section includes an overview of current state-of-the-art energy production and energy
consumption forecasting in Saudi Arabia from the present to 2040.
1.3.1
KAPSARC-IEEJ Forecasting Study Report, 2011
This study 4 is the most widely used energy system description within KSA for the energy sector. The
study was carried out by the IEEJ under the Japan International Cooperation Agency (JICA) program. The
comprehensive study gives an overview of the future energy system in Saudi Arabia to 2032, but is
restricted to the electricity sector.
1.3.2
Saudi ECRA-Brattle Study (United States), 2011
The Saudi ECRA Brattle study, 5 which is carried out on behalf of Saudi ECRA and published in Saudi
Arabia, concentrates on demand-side management (DSM) measures in the electricity sector; the
3
Transmission losses in Europe are usually far below 10 percent, depending on the specific country situation (e.g., production in
one part of the country with consumption in another part of the country, amounts of electricity imports and exports).
4
Japan International Cooperation Agency, Tokyo Electric Power Company, & IEEJ: The Master Plan Study for Energy
Conservation in the Power Sector in the Kingdom of Saudi Arabia, Final Report, Riyadh, on behalf of KAPSARC, February 2009;
and IEEJ: Energy and Macroeconomic Modelling for the Kingdom of Saudi Arabia, IEEJ Workshop, Khobar, May 2011.
5
Brattle Group: Bringing Demand-Side Management to the Kingdom of Saudi Arabia, Final Report on behalf of Saudi ECRA,
Riyadh, May 27, 2011.
Volume 1
1-7
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
calculations made are based on regression analyses. These calculations, which are mainly based on
experience gained in the U.S. electricity market with DSM instruments, will lead to an improvement of
the electricity sector in Saudi Arabia.
1.3.3
Saudi Energy Efficiency Center-Bain Consulting Study, 2013
The latest information is from the SEEC-Bain study, 6 which is currently not published in Saudi Arabia.
This study goes to 2030 but covers only the electricity sector. The calculations are based on regression
analyses.
1.3.4
KFUPM-SNC Lavalin Study, 2007
The King Fahd University of Petroleum and Minerals (KFUPM) and the consulting company SNC Lavalin
completed a study 7 in 2007 on the development of electricity generation and transmission for Saudi
Arabia. Within this study, forecasting is made until 2030.
1.3.5
Tyndall Study, 2008
The Tyndall 8 study from 2008 gives a complete overview of the Saudi energy system until 2050 based on
the Delphi method, but does not use time-series analyses. Therefore, the data from the Tyndall study
place more emphasis on quality level, whereas this energy forecast includes numerical data on the
future energy system in Saudi Arabia.
1.3.6
Other Approaches from MOWE, MOEP, MOPMR, KAPSARC, Saudi
Aramco, SEC, ECRA, SEEC, UNDP, and the World Bank
We have not seen any publications on the future of the energy system in Saudi Arabia by other
institutions. Other institutions in KSA produce their own forecasts, but either do not publish these
results or give summaries of these activities only and do not publish detailed data.
Saudi Aramco in Dhahran has its own forecasting mode and presents its results exclusively to the
management and the board of Saudi Aramco.
The ECRA of Saudi Arabia is working on a forecast of the electricity sector 9 until 2040.
The King Abdullah Petroleum Studies and Research Center (KAPSARC) has not published any data
and studies on the future energy system in KSA. There were some interviews by members of
KAPSARC to the press, presentations at conferences, 10 and some very limited information can be
seen on the KAPSARC homepage.
The Ministry of Economy and Planning (MOEP) has not published any data and studies on the future
energy system in KSA, but there are some indications on Saudi society and the Saudi economy
within its development strategy.
The Ministry of Petroleum and Mineral Resources (MOPMR) has not published any data and studies
on the future energy system in KSA.
The MOWE has not published any data and studies on the future energy system in KSA.
The SEC has carried out a 10-year forecasting for the electricity sector on power demand and power
capacity, but has not published it. 11
•
•
•
•
•
•
•
6
Currently no information is available on the results of this study, even interim reports.
7
KFUPM/SNC Lavalin: Development of Electricity Generation and Transmission Plan for Saudi Arabia, Electricity Demand
Forecast Study, Draft Final Report, Dammam/Riyadh, June 2007.
8
Al-Saleh, Y., Upham, P., & Malik, K.: Renewable Energy Scenarios for the Kingdom of Saudi Arabia, Tyndall Working Paper No.
125, Norwich, October 2008.
9
First results are expected for 2014.
10
KAPSARC: Review of National Energy Efficiency Initiatives, Saudi Energy Efficiency Workshop, Riyadh, Feb. 6, 2012.
No direct publication available; some information on SEC can be seen at: ECRA: Annual Statistics Booklet on Electricity and
Seawater Desalination Industries 2011, Riyadh, 2012.
11
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•
•
•
The Saudi Energy Efficiency Center (SEEC) in Riyadh has not published any data and studies on the
future energy system in KSA except for some publications on conferences. 12
The United Nations Development Program (UNDP) has not published any relevant data and studies
on the future energy system in KSA.
The World Bank has not published any relevant data and studies on the future energy system in
KSA.
1.4 Our Approach for a Baseline Forecast for the Entire Energy
Sector in Saudi Arabia until 2040
1.4.1
General Approach for the Baseline Forecast
During the evaluation of the different forecasting models used in KSA, we saw that the analyzed studies
differ not only for the forecasting period (there were time horizons of 2022, 2030, 2032, 2035, and
2050) but also with respect to research areas: Some studies concentrated only on electricity whereas
others looked at the entire energy sector, including electricity. Finally, the studies used both time-series
analyses and regression analyses for the forecasting of data.
We have concentrated on an easy-to-use forecasting model that covers the entire energy system to
2040 in KSA based on a business as usual (BAU) scenario. All forecasted data are based on a time-series
analysis from 1971 to 2009 and, therefore, no regression analysis was conducted. However, basic linear
data on future population and GDP growth until 2040 was used for the time-series analysis.
The method is implemented in a standard Excel spreadsheet system and the data are divided into nine
sections (see Table 1-11 and Table 1-12):
•
•
•
•
•
•
•
•
•
Section A includes all basic demographic and economic data.
Section B is the total energy production within Saudi Arabia.
Section C describes only the segment of energy production used within Saudi Arabia.
Section D is the total final energy consumption and is broken down into different energy sectors.
Section E shows the total final energy consumption and is broken down into different energy
carriers.
Section F concentrates on the main electricity data, especially the installed and used capacity of
electricity generation.
Section G shows the total electricity output in GWh/year.
Section H is a description of a 30-percent energy efficiency scenario.
Section I gives the CO2 emissions and additional export options due to reduced energy consumption
within Saudi Arabia.
The data included for the forecasting are based mainly on the following documents:
•
•
•
•
•
Adnan Ghosheh: Long-Term National Forecasts. Presentation by the Housing Project, no date
(probably December 2012).
IEA: Data Services: Energy Prices. Paris, February 2013.
IEA: Data Services: Summary Energy Balances. Paris, February 2013.
Köne, A. C., & Bükr, T.: Forecasting of CO2 Emissions From Fuel Combustion Using Trend Analysis. In:
Renewable and Sustainable Energy Reviews, 2010.
MOEP: Long-Term Strategy for the Saudi Economy. Riyadh, 2010.
12
Alabbadi, N. M.: Energy Efficiency Potential in the Building Sector, Saudi International Advanced Materials Technologies
Conference, KACST, Dec. 3–4, 2012; and Alabbadi, N. M.: Why Energy Efficiency? 4th Industrials Forum, Renewable Energy and
Energy Efficiency: Emerging Business Opportunities for the KSA, May 14–15, 2012.
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•
•
•
•
•
•
•
•
•
Saudi Arabia Launches Massive Renewable Programme with Hybrid FITs. In: Renewable Energy
World, May 15, 2012.
Saudi Arabia Plans $109 Billion Boost for Solar Power. In: Bloomberg Businessweek, May 11, 2012.
Economic Statistics Branch of the United Nations Statistics Division (UNSD), published at UNDATA
and based on World Bank: World Development Indicators. New York, February 2013.
Brattle Group: Bringing Demand-Side Management to the Kingdom of Saudi Arabia, Final Report.
Riyadh, May 27, 2011.
ECRA: Annual Statistical Booklet on Electricity and Sea Water Desalination Industries. Riyadh, 2011.
U.S. Energy Information Administration: Annual Energy Outlook 2013, Early Release Overview.
Washington, D.C., 2013.
United Nations Department of Economic and Social Affairs: Population Division, Population
Estimates and Projections Section. New York, February 2013.
United Nations: World Population Prospects, 2010 Revision, Volume 1: Comprehensive Tables. New
York, 2011.
U.S. Energy Information Administration (EIA): International Energy Statistics, Total Carbon Dioxide
Emissions From the Consumption of Energy.
The calculations provide an overview of all main energy figures for the period 2010–2040 based on data
from the period 1971–2009. Different results are not expected from using different forecasting
methods, and the results from this forecasting model can be described as robust, given available data.
Should population and gross domestic product (GDP) forecasts considerably change, the energy data
forecast should be updated to have the best forecasting results for 2040. With reference to newly
developed “varying coefficient models” and to scientific literature, the advantages of these models
apply particularly to nonlinear time-series analysis; for the purposes of this assessment, standard timeseries analysis is used.
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y = 0,5547x + 4,8558
R² = 0,997
9
9.4
Figure 1-3:
Population Forecast for Saudi Arabia, 1971–2040
9.8 10.5 11.2 11.9 12.5 13.2 13.8 14.4 15 15.6 16.1 16.6 17.1 17.6 18 18.5 18.8 19.2 19.5 19.9 20.2 21 21.8 22.5 23.3 24 24.7 25.4 26.1 26.8 27.4 28.1 28.7 29.3 29.9 30.5 31.1 31.7 32.3 32.9 33.5 34 34.5 35 35.5 36 36.5 37 37.5 38 38.5 38.9 39.2 39.6 40 40.3 40.7 41.1 41.4 41.8 42.2
1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
y = -7E-05x3 + 0,0075x2 + 0,3543x + 5,9466
R² = 0,9989
Population in Mio (median variant) within
Energy Forecasting (Baseline) Saudi Arabia 2010-2040
Series1 6.17 6.58 6.98 7.38 7.79 8.19 8.59
0
5
10
15
20
25
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1-11
1-12
y = 82,957x - 795,78
R² = 0,7989
1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
y = 0,0285x3 - 0,8292x2 + 12,966x + 554,79
R² = 0,9937
GDP in Bio SR (in 2005 prices) within
Energy Forecasting (Baseline) Saudi Arabia 2010-2040
Figure 1-4:
GDP Forecast for Saudi Arabia, 1971–2040, in Billion Saudi Ryal
Series2 316. 380. 467. 572. 579. 648. 693. 686. 752. 802. 840. 753. 694. 671. 634. 665. 640. 692. 693. 751. 819. 857. 857. 863. 864. 894. 917. 943. 936. 981. 987. 988. 1064 1120 1182 1219 1244 1297 1298 1364 1461 1545 1635 1730 1853 1984 2125 2276 2438 2650 2881 3131 3404 3700 3848 4002 4162 4328 4501 4682 4869 5064 5266 5477 5696 5924 6161 6407 6663 6930
-2000.0
-1000.0
0.0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
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1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Crude Oil Spot Price Brent (in 2011 USD/bbl) within
Energy Forecasting (Baseline) Saudi Arabia 2010-2040
2010
2011
Figure 1-5:
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
y = 3,7769x - 3,8767
R² = 0,7119
y = -0,0022x3 + 0,4256x2 - 4,9847x + 29,839
R² = 0,9079
2027
2028
Crude Oil Spot Price Brent Forecast for Saudi Arabia, 1971–2040, in 2011 US$/bbl
Series1 23.732 20.083 19.322 17.034 15.842 16.998 20.632 19.15 12.781 17.911 28.604 24.44 24.998 28.835 38.301 54.608 65.14 72.346 97.095 61.702 79.513 111.21
-100
0
100
200
300
400
500
600
700
2029
2030
2031
2032
2033
2034
2035
2036
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1.4.2
Influencing Factors for Energy Production, Transmission, and Energy
Consumption in Saudi Arabia in 2040
The influencing factors and variables of the energy system in Saudi Arabia to 2040 are similar to those
of other regions. These factors are usually as follows:
Population 13 and number of households to 2040
GDP 14 and industry structure to 2040. We know that, in addition to its industry diversification
program, economic growth 15 in Saudi Arabia depends on physical oil exports (in Mboe/d) and on
the respective oil price of the year (in US$/oil barrel [bbl]). Future economic growth will still depend
on this.
The oil price 16 itself. This is one of the major sources for the growth of the economy and, therefore,
for energy consumption. Here, we follow the approach of approximately US$163/bbl in 2040 based
on data from U.S. energy information. 17 The price range is between US$101/bbl and US$203/bbl. All
forecasted oil prices exclude inflation, so their price basis is 2010/2011. With inflation, the prices on
the markets will be considerably higher, but “real” prices excluding inflation will be within this
range. Floor space for office and residential buildings to 2040.
Other factors, such as numbers of cars, airline passengers, and students. Where there are data on
these variables available to 2040, they have been used. Data on either transport or education were
not available for this forecasting model; therefore, “population” and “GDP” represent the transport
sector, the education sector, and other sectors for which data to 2040 are missing. For example, as
population grows and the number of men between 18 and 30 years of age grows, and if GDP grows
as forecasted, the number of cars will increase and related energy consumption for fuel will also
steadily increase.
•
•
•
•
Most of these variables are known for 2010 and earlier, but for the future to 2040, only population data
are available via the UN population forecast model.
Table 1-2 gives an overview of the main assumptions used for the forecasting of energy consumption
data for the period to 2040 in Saudi Arabia.
13
Population forecasting was done on the basis of: UN, Department of Economic and Social Affairs, Population Division:
Population Estimates and Projections Section, New York, February 2013; and UN: World Population Prospects, 2010 Revision, Vol.
1: Comprehensive Tables, New York, 2011. The forecast does not take into account the estimated 7 million inhabitants in Saudi
Arabia who are not legally registered, which may cause the forecast to be conservative.
14
GDP growth is mainly based on World Bank and UNSD (Economic Statistics Branch) data, published at UNDATA: World
Development Indicators, New York, February 2013. The goal of doubling GDP per capita in Saudi Arabia is not considered (refer to
MOEP: Long-Term Strategy for the Saudi Economy, Riyadh, 2010) because this would require an annual increase of 7.2 percent
per year; and the forecast would be conservative.
15
About 92 percent of Saudi Arabia’s GDP currently depends on the world oil and petrochemicals exports.
16
NRC estimates the oil price in 2030 at an average of US$101.20 (in 2008 dollars). This is a median for the EIA annual energy
outlook forecast of US$51–203, the Deutsche Bank forecast of US$121, IEA forecast of US$77–90, IHS forecast of US$100, and
Strategic Energy & Economic Research Inc. (SEER) forecast of US$65–113. Source: Natural Resources Canada: Long-Term
Projections of World Oil Prices (Constant 2008 Dollars Per Barrel), Long-Term Outlook: Crude Oil Prices to 2030, Ontario, 2013 (all
data in US$/bbl). The World Oil Outlook 2012 published by OPEC gives a detailed overview of oil markets in the world and
forecasted oil consumption, but there is no information on future oil prices.
17
Source: EIA: Annual Energy Outlook 2013, Early Release Overview, 2013, p. 16 (forecast of crude oil prices, Brent spot crude oil
(US$/bbl in 2011 dollars).
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Table 1-2:
Overview of Main Assumptions Used for the 2040 Forecast of the Energy System in Saudi Arabia
(for details on data considered for the forecasting, see the Appendix)
No.
Assumptions made for the 2040 forecast
1
No relevant price increase in national energy prices (i.e., no significant
reductions in national subsidies)
No general disturbances on world and regional level (e.g., no military conflicts,
no civil wars in the Middle East)
Population development as foreseen in the national forecast (i.e., no
significant reduction in national birth rates, no significant changes in share of
non-Saudi population)
Economic development as foreseen in the national forecast (i.e., no significant
reduction in national birth rates, no significant changes in share of non-Saudi
population)
Oil price increases on world level until 2040 to US$163/bbl (in 2011 US dollars)
and no significant oil price increase or decrease in this period, except for some
stochastic changes
No significant price decrease in renewable energy investment costs (especially
PV and concentrating solar power [CSP] price decreases forecasted by Dii
GmBH, but especially for wind energy investment costs, which will be more or
less stabile until 2040)
No significant changes in national energy policy; continuation of current
energy efficiency initiatives in labeling, norms, and standards, and building
codes
2
3
4
5
6
7
1.4.3
Subsidies and prices
Conflicts
Population
Economic development
Oil prices
Energy efficiency
investment costs
Energy efficiency policy
Method Used to Forecast Energy Production, Transformation, and
Consumption in Saudi Arabia in 2040
Given the available data on energy production, transformation, and consumption, a regression analysis
could not be conducted, and time-series analysis was used to forecast the data to 2040. For example,
when looking in detail at the period 1990–2000, which was the period of the Gulf Wars, international oil
prices were comparatively low and GDP in Saudi Arabia was more or less constant. During this period,
final energy consumption increased, which means that even if the GDP were forecasted correctly, in this
case, a regression analysis would have led to forecast figures considerably different from the actual
figures measured.
A time series is a sequence of data points—in this scenario, final energy consumption, measured
typically on a yearly basis. Time series are usually plotted on line charts. Then a time-series analysis
method is used to analyze the data to obtain their characteristics. Time-series forecasting uses a model
to predict future values of final energy consumption for the period 2010–2040 based on previously
observed values for the period 1971–2009, using the lowest-average-square method. For the
interpretation of data, two parameters can be used:
•
•
A confidence interval (CI) is constructed across many separate data analyses of repeated
experiments; the proportion of such intervals that contain the true value of the parameter will
match the confidence level. The level of confidence of the CI would indicate the probability that the
confidence range captures this true value, usually a 95 percent CI.
The coefficient of determination, denoted as R2, is used in the context of prediction of future
outcomes on the basis of former years’ information. R2 is a number between 0 and 1.0. An R2 near
1.0 indicates that a time-series line fits the data well.
For this time-series analysis, the approach of nonlinear prolongation was followed because this type of
function has the best R2 values, almost always better than simple linear extrapolation. Log function also
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was tested, but these calculated values are not representative of past data, and it is not expected that
the energy data of KSA will follow this type of time series.
Table 1-3:
Comparison of R2 Values for Different Time-Series Analyses Between Data Based on Linear and
Nonlinear Extrapolation
R (linear)
R2
(nonlinear)
Population (median variant)
GDP (at constant 2005 prices)
0.997
0.799
0.999
0.994
Crude oil spot prices (Brent)
Total energy production
Total primary energy supply
Total final energy consumption
MW peak load
Total electricity output
0.908
0.345
0.971
0.941
0.912
0.969
not used
0.416
0.984
0.960
0.997
0.993
Variable (Baseline Forecast From 2010 to 2040)
2
Unit
Million
Billion Saudi
Ryal
2011 US$/bbl
Mtoe
Mtoe
Mtoe
MW
GWh/year
The time-series model is based on analyses for the last 40 years (1971–2010) and, in parallel, on
analyses for the last 10 years (2000–2010) because of structural changes seen in the energy sector
beginning around 2000. This forecast was developed starting with trend extrapolations for the main
influencing variables, namely, population, GDP, and floor space. A detailed investigation into the time
series from 1971 to 2010 showed nonlinear growth for these variables, and the forecasting of these
variables, especially the GDP, is predicted with nonlinear growth (“double the real GDP per capita in the
next 10 years” 18). Based on this nonlinear forecasting of GDP, time series was used for final energy
consumption to 2040. Here, the nonlinear trend with the x2 function gives lower results than the x3
function, but the most recent years from 2005 to 2010 are higher than this x2 function trend. Results
from the x3 function show extremely high values; therefore, 2040 final consumption was defined as a
median between x2 and x3 function values. From this calculation, nonlinear forecasting of the primary
energy supply for 2040 was performed. For the power sector, the peak load and the available capacity
were forecasted using nonlinear trend extrapolation, but even the last values for the years 2005–2010
are higher than the nonlinear trend. This can be seen as an indicator that electricity consumption and
peak load follow this nonlinear approach.
Based on the planned data for capacity extension with renewables and nuclear energy from K. A. CARE,
the power mix was calculated until 2032, then for the remaining period to 2040, because there was a
capacity of 20 GW missing compared to the forecasted electricity peak. All different types of electricity
production capacities were increased to allow for meeting the electricity demand at its peak in 2040. On
the basis of CO2 emission factors and the primary energy supply in Saudi Arabia, the total CO2 emissions
for Saudi Arabia were calculated to 2040.
1.4.4
Energy Efficiency in Saudi Arabia in 2040 within the Baseline Forecast
The transformation segment seems to have the largest potential for energy efficiency measures (in
absolute figures). The losses shown in the calculations for 2040 are mainly in the transformation
segment, but also, to an extent, in the consumption segment. The specific efficiency level in 2040 will
be on a comparatively high level. The remaining savings will be limited, because nearly all appliances
will have been replaced in the period 2010–2040 (except for the building stock) in the residential and
commercial sectors, where an average lifetime of at least 70 years is usually calculated (i.e., a
replacement rate of 1.4 percent per year). Therefore, no change in energy efficiency above the current
18
MOEP, Long-Term Strategy, Riyadh, 2012.
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trend is calculated in the baseline forecast. This means that energy efficiency in this forecast is already
included at the current level, and the forecast figures include the trend in energy efficiency for the next
30 years, taking into account the energy efficiency improvements achieved over the past 40 years.
1.4.5
Resulting Energy Forecast in Saudi Arabia in 2040
From the time-series analysis, we have a more detailed understanding of the entire energy system of
Saudi Arabia for the year 2040:
•
•
•
•
•
•
Final energy consumption will increase from 105 Mtoe in 2010 to about 425 Mtoe in 2040 at an
annual growth rate of 4.3 percent (Figure 1-6, Figure 1-7). This is less than the foreseen increase in
GDP per capita, which is 7.2 percent annually (without considering the expected increase in
population). 19
Primary energy supply in KSA will increase from 169 Mtoe in 2009 to about 530 Mtoe in 2040
(Figure 1-8).
Primary energy production in 2040 will be more or less at the 2009 level and remain at 534 Mtoe/a,
which is the average production of the last 10 years (Figure 1-9). There is no indication that energy
production will increase or decrease until 2040 in physical terms (bbl/d). An increase in real prices
can be foreseen, and this will influence the turnover in monetary, but not physical, terms.
Electricity consumption is expected to increase from 240 TWh/year in 2009 to about 850 TWh/year
in 2040. 20 This increase will absorb a reasonable additional amount of energy production in KSA,
and its influence on primary energy supply in KSA is evident, because about 120 Mtoe/a will be
required to supply the power stations with necessary fossil fuels (average daily consumption at
fossil fuel power stations will increase from 57 GW electricity capacity in 2010 to about 87.4 GW in
2040) (Figure 1-10).
The transformation segment for oil and oil products will not require a large increase in
transformation capacities other than any continuous modernization and upgrading of production
capacities; but the power segment, owing to the heavily increasing demand for electricity (Figure
1-11), will have to triple in this period from 57,000 MW to 185,000 MW. This will lead to additional
capacities being constructed of >4,200 MW/year. Because we are looking at a period of three
forthcoming decades, besides any new power production capacities, a large rehabilitation program
covering the existing power plant capacities must be launched in parallel, which is about an
additional 1,500 MW/year to be rehabilitated, if the current infrastructure of power plants with an
age of 20 years in 2009 is replaced or rehabilitated during the next three decades until 2040.
For 2032, analysis of trend data indicates a difference between planning of power capacity at
121,000 MW and the forecasted 141,000 MW—a difference of 20,000 MW. Based on the KSA figure
of 121,000 MW, the gap between peak load and peak capacity was filled by adding, in total, 61,000
MW to capacity between 2032 and 2040. This additional capacity has been distributed among all
types of power plants, resulting in an annual increase in oil- and gas-fired power stations during this
period of 3,800 MW. The same applies for wind energy (+570 MW/year), nuclear (+900 MW/year),
CSP (+1,650 MW/year), and PV (+1,050 MW/year) to meet the peak demand of 185,000 MW in
2040 (Figure 1-12).
19
3
2
In a first calculation of final energy consumption, the x curve was followed, which has an excellent R value of 0.9841; however,
3
calculations based on this x curve resulted in extreme values for primary energy consumption in KSA in 2040, and so the increase
3
2
was limited to a median version between the x and the x curve, which is somewhat arbitrary and requires additional detailed
discussion and update in the future.
20
The electricity consumption forecast was performed only on the mathematical approach of time-series analysis for 2040 based
2
on 1971–2009 data, which showed an R value of 0.9931. The same applies to the forecast of installed capacity for 2040, which is
2
based on a curve resulting in an R of 0.9970. Forecasting of final energy consumption and the forecasting of peak load and
electricity consumption were done independently because time-series analysis was used. These independently calculated data are
compatible for the 2040 energy system.
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•
•
•
•
•
Final energy consumption in 2040 will be about four times the energy consumption in 2009. At a
national level, this will require enormous investments in energy infrastructure. Besides additional
power stations, the transmission network will have to be upgraded in parallel and, to a certain
extent, the distribution networks in the cities and villages of KSA. All sectors will grow in this period,
with the largest increase seen in the commercial and governmental sector, whereas energy
consumption in industry increases moderately.
The increase in primary energy supply in KSA until 2040 will reduce the possibility of oil exports,
which were at 383 Mtoe in 2009. For comparison, the net exports are 11 Mtoe less than the current
exports (372 Mtoe in 2009). For 2040, national production of energy of 534 Mtoe/a and a total
primary energy supply of 530 Mtoe/a are forecasted. Therefore, there will be hardly any crude oil
net exports 21 from KSA to other countries. 22 Because current GDP depends to a large extent on oil
production (about 92 percent of GDP originates in the oil and petrochemicals industries), there will
be a profound influence on the wealth of the nation unless KSA can increase oil production, which is
not foreseen in the baseline forecast.
These results are based on mathematical time-series analysis; actual energy consumption in 2040
could be either higher or lower than the forecast figures. To judge the forecasting, the main
variables driving energy consumption in Saudi Arabia—population and GDP—must be evaluated.
The approach in this report uses only a conservative forecasting of population (no consideration of
the nonregistered population of 7 million nonlegal inhabitants) and GDP (no consideration of
doubling GDP per capita within the next decade). Finally, energy consumption of the nonregistered
population is not known; therefore, only a tendency in energy consumption can be given when
trying to include this group. Considering this, forecasted energy consumption is more likely to be
higher than lower.
The effects of decreasing household size and increasing average age on population are not
considered in the forecasting. A decrease in household size by keeping the population figures
constant is taken to result in an increase in energy consumption. Also, increasing average age
results in decreasing household size, and because of higher income and wealth of older people, a
tendency toward increased energy consumption is foreseen. This is not reflected in the
mathematical time-series analysis, and so the forecast values can be considered conservative.
Finally, energy consumption in KSA as forecasted in this model will result in heavily reduced oil
exports. This will reduce the national GDP and, therefore, limit energy consumption in the country.
The forecast indicates what the national level energy consumption will be in 2040, independent of
any decrease in oil export. The results clearly indicate the necessity for and urgency of energy
efficiency measures in KSA.
21
Looking ahead to 2040, a strong need for KSA to reduce its inland energy consumption is forecast, specifically, a need to
increase energy efficiency to allow the country to continue its current standard of living.
22
Based on the theory of system analysis, this extreme decrease in oil exports will result in a decrease in national GDP, and this
will limit any increase in energy consumption as calculated in this model. Therefore, the calculations were made based on the
assumption that GDP is growing as given in the model, and this will lead to the calculated energy consumption figures. If GDP
grows at a much lower rate than forecasted, energy consumption will increase more moderately and allow higher exports. It should
be noted this calculation is built on the assumption that there is no additional provision of conventional oil and gas or of shale gas
due to technical advancements that lead to additional exports in 2040 compared to 2010.
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26.8 30.7 33
35.9 34.9 35.9 37.3 41.5 43.3 45.9 43.1 47.5 47.1 51.2 53.1 55
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
33 35.9 34.9 35.9 37.3 41.5 43.3 45.9 43.1 47.5 47.1 51.2 53.1
30
31
32
33
35
36
37
55 57.9 63.1 65.1 72.4 74.8 80.9 85.7
39
40
94 99.6 105
38
99.6 105
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
Figure 1-6: Total Final Energy Consumption in Saudi Arabia for the Period 1971–2040, in Mtoe (Baseline Forecast)
(The upper part shows the R2 for different types of linear and nonlinear functions, and the lower part gives the trend for 2040.)
29 26.8 30.7
34
57.9 63.1 65.1 72.4 74.8 80.9 85.7 94
Total Final Energy Consumption (in Mtoe) within
Energy Forecasting (Baseline) Saudi Arabia 2010-2040
Series1 2.08 2.45 3.07 3.43 3.95 4.75 6.42 10.8 16.1 21.1 29.3 31.7 34.7
-50
0
50
100
150
200
250
300
350
400
450
y = 2,3218x - 6,0989
R² = 0,9407
y = 0,0322x2 + 1,0008x + 3,1483
R² = 0,96
y = 0,0036x3 - 0,1869x2 + 4,6388x - 10,042
R² = 0,9841
63
64
65
66
67
68
69
425
70
1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040
Total Final Energy Consumption (in Mtoe) within
Energy Forecasting (Baseline) Saudi Arabia 2010-2040
Series1 2.08 2.45 3.07 3.43 3.95 4.75 6.42 10.8 16.1 21.1 29.3 31.7 34.7 29
-100
0
100
200
300
400
500
600
700
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-19
1-20
Figure 1-7:
Final Energy Consumption According to Sector for the Period 1971–2040, in Mtoe (Baseline Forecast)
Industry (dark blue), Transport (red), Residential (green), Commercial (purple), Others (light blue)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Volume 1
Figure 1-8:
Total Primary Energy Supply for the Period 1971–2040, in Mtoe (Baseline Forecast)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Volume 1
1-21
Figure 1-9:
Total Energy Production for the Period 1971–2040, in Mtoe (Baseline Forecast)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-22
Volume 1
Figure 1-10: Total Electricity Output for the Period 1971–2040, in GWh/year (Baseline Forecast)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Volume 1
1-23
Figure 1-11: Electricity Peak Load for the Period 1971–2040, in MW (Baseline Forecast)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-24
Volume 1
Volume 1
Figure 1-12: Power Supply Capacity Mix for the Period 2009–2032, in MW (Baseline Forecast, with 121,000 MW in 2032)
Note that this power capacity mix will not meet the foreseen demand in 2040; we expect a significant gap based on the forecasted 185,000 MW electricity
demand in 2040
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-25
Figure 1-13: CO2 Emissions for the Period 1971–2040, in MtCO2/year (Baseline Forecast)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1-26
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1.4.6
Sankey Diagram for the Entire Energy Sector in Saudi Arabia in 2040
Based on the data received from the forecasting model, a Sankey diagram was constructed that shows
the main energy flows in Saudi Arabia in 2040. The data show the overwhelming influence of the oil
industry remains even over the next three decades.
However, due to increases in population and economic output of the Saudi society, the internal energy
consumption also increases, but much more than the oil production, which remains more or less at
2009 levels. The average internal energy consumption for the last 10 years was estimated as 534
Mtoe/a.
Due to population and GDP increases during the period 2009– 2040, the entire energy sector results in a
final energy consumption of 425 Mtoe/a. 23 This is attributed to industry (75 Mtoe/a), transport (148
Mtoe/a), and the residential sector (66 Mtoe/a).
A large increase in nonenergy consumption is expected, which will have a positive influence on the
Saudi economy in 2040. Based on the final energy consumption, the necessary transformation input
and gross inland consumption for 2040 was calculated using improved energy efficiency in the
transformation segment. This lowers the transformation and distribution losses to about 50 percent of
2009 data, which is “only” 155 Mtoe/a. As stated, an increase in oil production in KSA is not foreseen, so
the necessary gross inland consumption can only be provided using additional imports (46 Mtoe/a);
exports are not foreseen in this simulation.
The 2040 forecast figures are based on no population or economic adjustments; however, it is likely
that there will be some adjustment processes during the period 2010–2040, when net exports will drop
to zero. If, as a result, the population does not grow as planned and GDP decreases, final energy
consumption will not increase as calculated by the model. With these assumptions, the mathematical
formulas provide a realistic trend for 2040 if KSA takes no preventive measures. Results show the
necessity and urgency of preventive measures to prevent KSA from losing its position as the main net
oil-exporting country.
1.4.7
Reliability of the Power Generation and Power Transmission System in
Saudi Arabia for 2010 to 2040
There are, in general, two factors affecting the reliability of the energy system in KSA:
•
•
Power shortages during peak periods and peak hours, owing to high demand for electricity from all
consumer groups
Introduction of renewable energy into the national grid, which has limited power production
capacities, due to meteorological factors (e.g., wind speed, sun exposure)
The power transmission network has improved considerably in Saudi Arabia, which has increased
system stability in the electricity network, as has the addition of new power production units. In the 380
kV system, there are 15,360 km of transmission lines currently 24 and 59 transformer substations. 25 If
peak demand increases to 185,000 MW in 2040 from the current 51,000 MW, which is an increase of
263 percent, transmission capacity will need to be 2.5 times that of the current transmission system.
The same capacity increase applies for the 230 kV, 132 kV, 115 kV, and 110 kV system components.
23
This increase from 98 Mtoe in 2010 to 425 Mtoe in 2040 is about 4.34 times the original consumption, which seems a quite sharp
30
increase, but is “only” an annual increase of 5 percent per year (1.05 = 4.32).
24
ECRA: Annual Statistics Booklet on Electricity and Seawater Desalination Industries 2011, Riyadh, 2012, p. 40.
25
ECRA: Annual Statistics Booklet on Electricity and Seawater Desalination Industries 2011, Riyadh, 2012, p. 41.
Volume 1
1-27
1-28
534
Available
Energy
580
0
0
Total
Exports
580
580
580
0
0
298
57
Transfers
Statistical
Differences
Transformation
Input
Gross Inland
Consumption
70
155
425
57
70
Energy
Losses
155
109
23
52
39
43
134
Useful
Energy
Andreas Jahn, Berlin/Jeddah/Riyadh, April 2013, Version 5
Energy
Losses
23
66
148
Transport
75
Industry
Residential
etc
66
148
75
136
136
Non-Energy
Consumption
Final Energy
Consumption
155
298
298
Petroleum
Products
57
Gas
70
Electricity
KAUST, KICP
Figure 1-14: Saudi Energy System with Energy Production, Transformation, and Consumption in 2040, in Mtoe (Baseline Forecast)
Source: International Energy Agency (IEA): Energy Balances of Non-OECD Countries, Paris 1013
1 Mtoe/a = 0.02019 Mboe/d = 7.36974 Mboe/a
Production
of Primary
Services
534
580
5
0
46
46
Bunkers,
Stocks
Total
Imports
Energy Flow Analysis for Saudi Arabia for the Year 2040 (Synoptic Version in Mtoe/a)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1.5 Comparison of the Energy System of KSA with International and
European Standards and Benchmarks in Relation to Energy
Efficiency until 2040
Two indicators usually are used to compare the energy system of Saudi Arabia with the energy systems
of other countries: One indicator often used is energy consumption per capita and the other is energy
consumption per unit GDP. This section provides a comparison to other nations, including neighboring
countries, and regions.
1.5.1
Comparison of Other Countries’ Energy System Challenges with Those of
Saudi Arabia with Reference to Energy Efficiency
If energy consumption is measured against population, Saudi Arabia, at 7,500 kWh/capita, has much
lower energy consumption than the United States (9,500 kWh/capita) and Japan (8,000 kWh/capita) but
higher than the EU (6,100 kWh/capita), Russia (6,200 kWh/capita), and China (2,800 kWh/capita).
If energy consumption is measured against economic production, Saudi Arabia’s energy use, at 0.53
MWh/US$1,000 GDP, is lower than Russia (0.72 MWh/US$1,000 GDP) and China (0.71 MWh/US$1,000
GDP). On the other hand, this indicator is significantly higher than that of Japan (0.2 MWh/US$1,000
GDP) and the EU (0.19 MWh/US$1,000 GDP).
Both indicators show that there is room for additional energy efficiency. Other countries, such as those
of the EU, have lower energy consumption per capita and also lower energy consumption per unit GDP
than Saudi Arabia.
1.5.2
Short Description of the Energy System of Neighboring Countries
Compared to Saudi Arabia
If energy consumption is measured against population, Saudi Arabia, at 7,500 kWh/capita, has much
lower energy use than Kuwait (17,500 kWh/capita) and Qatar (14,500 kWh/capita) but higher than that
of Jordan (2,100 kWh/capita) and Egypt (1,800 kWh/capita). If energy consumption is measured against
economic production, Saudi Arabia’s, at 0.53 MWh/US$1,000 GDP, is lower than Egypt’s (0.65
MWh/US$1,000 GDP) and Syria’s (0.58 MWh/US$1,000 GDP). On the other hand, this indicator is
significantly higher than that of Jordan (0.5 MWh/US$1,000 GDP), Kuwait (0.42 MWh/US$1,000 GDP),
and Oman (0.33 MWh/US$1,000 GDP). Both indicators show that within the geographical region, there
is also some room for additional energy efficiency (especially with the increase in energy consumption
up to 2040). Other countries such as Jordan have lower energy consumption per capita and also lower
energy consumption per unit GDP. Finally, there is no trend for energy comparisons with neighboring
countries except that there will be a quite sharp increase in final energy consumption in KSA up to 2040.
1.6 Priorities for Energy Efficiency in the Energy Sectors Based on
New Technologies in Saudi Arabia Through 2040
A number of studies have been completed during the last 5 years on energy efficiency and energy
savings for KSA. In particular, there are studies on the electricity sector that concentrate on both
reduction (or limitation of increase) of electricity consumption and reduction (or limitation of increase)
of peak loads in the electricity system.
Volume 1
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Figure 1-15: Comparison of National Energy Consumption Data in Saudi Arabia with World Countries and
Neighboring Countries (Source: World Bank: World DataBank: World Development Indicators.
Washington, D.C., March 2013)
The discussion in this section is based on the outcomes of these studies and prioritizes technical and
organizational measures based on the forecasting results in Section 1.4 for the period 2010–2040. All of
these studies follow different approaches, either concentrating on sectors or on technologies. The main
studies are as follows:
Bain: Not available to this project team (March 2013)
Brattle Group 26: Concentrates in its very detailed analysis exclusively on DSM measures and only on
measures in the electricity sector
Chatham House 27: Concentrates on economic incentives for energy efficiency
Gesellschaft für Internationale Zusammenarbeit GmbH (German International Cooperation
GmbH)28: Gives a good overview of energy efficiency measures in the discussion of KSA
IEEJ 29: Can be considered the main study on energy conservation in KSA, but concentrates all
measures on the electricity sector exclusively
KACST/AEA 30: Gives a very good overview of the possibilities for energy efficiency in the various
sectors in Saudi Arabia but does not include the transport sector
KAPSARC 31: Has carried out a good overview of non-Saudi energy conservation policies and
efficiency programs.
•
•
•
•
•
•
•
26
Brattle Group: Bringing Demand-Side Management to the Kingdom of Saudi Arabia, Final Report. Riyadh, May 27, 2011.
27
Lahn, G., & Stevens, P.: Burning Oil to Keep Cool: The Hidden Energy Crisis in Saudi Arabia. Chatham House, December 2011.
28
Brinkmann, K., & Wenzel, K.: Energy Efficiency and Renewable Energies, Challenges and Training Needs in the Kingdom of
Saudi Arabia, Fact Finding Mission, Final Report. Riyadh, March/April 2011.
29
JICA, Tokyo Electric Power Company, & IEEJ: Master Plan Study for Energy Conservation in the Power Sector in the Kingdom
of Saudi Arabia, Final Report. Riyadh, February 2009.
30
Alyousef, Y., & Abu-Edid, M.: Energy Efficiency Initiatives for Saudi Arabia on Supply and Demand Sides. In: Energy Efficiency:
Z. Morvaj (Ed.), A Bridge to Low Carbon Economy. Rijeka/Shanghai, Mar. 16, 2012, p. 259ff.
1-30
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
•
•
MOWE 32: Sees a market for energy efficiency measures and expects opportunities for Saudi
companies in the energy efficiency business
Tyndall 33: Concentrates its energy efficiency measures on price reforms
The measures described in Section 1.6.2 and 1.6.3 are from the listed studies. Each of these studies has
a different viewpoint; therefore, some measures overlap (e.g., labeling), and some studies concentrate
on specific measures (e.g., pricing structures). The proposed measures were reviewed and a list of 40
technical measures and 70 organizational measures (40/70 List on Energy Efficiency in Saudi Arabia) was
developed (section 1.6.2 and 1.6.3).
1.6.1
Priority Sectors for Energy Efficiency Measures in Saudi Arabia
Priority sectors for energy efficiency usually can be identified in a country. For example, in Germany in
the 1990s, the industry sector consumed >30 percent of the country’s final energy consumption and
faced comparatively high energy costs. In California in the 1980s, heavily increasing household
electricity consumption pushed the electric utilities to construct many new power stations.
Based on 2010 data, however, the same is not true for Saudi Arabia: According to international
comparison, energy consumption is at an extremely high level in all sectors, and so no individual sector
(industry, transport, residential, commercial, or governmental) stands out as a priority.
Table 1-4 shows electricity is not the main energy source for final energy consumption in Saudi Arabia,
either in 2009 or forecasted in 2040. Electricity accounts for about 17 percent of the entire final energy
consumption for both years. Again, the two sectors with high public attention (industry and residential)
in terms of energy consumption will still be at half the level of the two main energy consumption
sectors, namely, transport and nonenergy production.
Table 1-4:
Final Energy Consumption in Saudi Arabia According to Sector and Type of Energy
for 2009 and 2040, in Mtoe/a
Final Energy Consumption According to Sectors and Type of Energy (in Mtoe)
Industry
sector
Transport
sector
Residential
etc. sector
Nonenergy
production sector
Total
2009
Petroleum products
15
34
1
18
68
Gas
–
–
13
13
Electricity
2
–
14
–
16
Other energy
––
–
–
–
–
Total
17
34
15
31
97*
2040
Petroleum products
65
148
6
79
298
Gas
–
–
–
57
57
Electricity
9
–
61
70
Other energy
–
–
–
–
–
Total
74
148
67
136
425*
*Total final energy consumption in the respective years for all sectors and all types of final energy.
31
97
425
KAPSARC: Review of National Energy Efficiency Initiatives. Saudi Energy Efficiency Workshop. Riyadh, Feb. 6, 2012.
32
Alawaji, S. H.: Market Development and Business Opportunities in the Power Sector of Saudi Arabia, Presentation given at the
nd
2 German-Arab Forum (GAEF). Berlin, 20-21 October 2011
33
Al-Saleh, Y., Upham, P., Malik, K.: Renewable Energy Scenarios for the Kingdom of Saudi Arabia. Tyndall Working Paper no
125. Norwich, October 2008
Volume 1
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
A more detailed analysis of final energy consumption data in the different sectors (e.g., industry)
requires data on energy consumption in the subsectors (e.g., steel industry), which are not available.
The difference in energy efficiency measures in Section 6.2 and 6.3 relates to the technology. All
measures in Section 6.2 are purely technical measures independent of any implementation
methodology. Measures in Section 6.3 are oriented toward the implementation of energy efficiency
activities regardless of any technologies used.
1.6.2
Priority Technologies for Energy Efficiency in Saudi Arabia
The proposals for priorities on energy efficiency technologies to be implemented in Saudi Arabia were
collected from the studies listed in Section 1.6.1. 34 Details on some of these technologies are given in
subsequent chapters of this report:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
Air conditioning cooling cycle improvement
Appliances’ efficiency to be improved (e.g., flat-screen television, liquid crystal display)
Only best available technologies allowed to enter into the market in Saudi Arabia
Batteries for peak load periods
Building management systems for commercial and residential buildings
Building standards and building codes tighter through shading, insulation, high-performance
windows, and highly efficient heating, ventilation, and cooling (HVAC) systems
Combined-cycle gas turbine (CCGT) for new power stations, cogeneration, trigeneration, district
cooling, modernization of existing power plants, and gas turbine and steam turbine conversion
to CCGT
Combined heat and power (CHP) cogeneration and trigeneration
Control systems with remote switching, developing new technologies
Desalination plants, in co-generation or as tri-generation, for the production of district cooling
Electric motors made more efficient
Farming: chicken production, energy efficiency technologies for the agriculture sector
Fuel cell technologies
Heat pumps for HVAC systems
HVAC retrocommissioning
Industrial sector: steel industry, cement industry, paper industry technology improvements
Insulation of buildings improved
Joint development and manufacturing of local solutions for energy efficient equipment with
international companies
Labels strictly installed and controlled
Light-emitting diode (LED) street lighting: lamps to be installed from a national production line
for LED and any other lighting purposes
Lighting efficiency: compact fluorescent lamps and LEDs for commerce, government, and
households
Load control
Manufacturing of new power plants and rehabilitation of generation equipment, transmission
equipment (transformers, cables, insulators), and distribution equipment
Micro- and small-scale CHP
34
Note that there is partial overlap both within the technologies and also between technologies and measures (see Section 6.3) to
increase energy efficiency in Saudi Arabia.
1-32
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The Necessity for Energy Efficiency in the Kingdom
25. Process engineering and process control
26. Pumps: energy efficient, speed control
27. Renewables used for any increase in energy demand in electricity (e.g., nuclear, CSP, centralized
PV, decentralized PV, geothermal energy, wind energy)
28. Peak load remote control for AC (obligatory for new equipment)
29. Saudi Building Code: strict implementation of building efficiency for new buildings
30. Smart buildings
31. Smart grids
32. Solar cooling
33. Solar desalination
34. Solar thermal and, according to region, solar heating
35. Solar thermal household boilers
36. Standards for equipment efficiency
37. Standby generation capacities
38. Storage of electricity: batteries and others
39. Storage of cooling capacity to reduce AC peaks
40. Transport: extension of trains for freight and passenger transport, hybrid cars, low-emission
cars, and others.
Table 1-5 gives an indication of these 40 priorities for Saudi energy policy in the field of new
technologies.
Table 1-5:
Forty Energy Efficiency Technologies for Saudi Arabia, According to Sector Priorities
No. Energy efficiency technologies for Saudi Arabia
1
Air conditioning, cooling cycle improvement
2
Appliance efficiency to be improved (flat-screen
television, liquid crystal display, and others)
Best available technologies only allowed to
enter into the market in Saudi Arabia
Batteries for peak load periods
3
4
5
6
7
8
9
10
11
12
13
Building management system for commercial
buildings and residential buildings
Building standards and building codes more
tight through shading, insulation, and highperformance windows, highly efficient HVAC
systems
CCGT for new power stations, cogeneration,
trigeneration, district cooling, modernization of
existing power plants, gas turbine and steam
turbine conversion to CCGT
CHP, cogeneration, trigeneration
Control systems with remote switching,
developing new technologies
Desalination plants, only in cogeneration or
trigeneration for the production of district
cooling
Electric motors, efficient electric motors
Farming: chicken production, energy efficiency
technologies for the agriculture sector
Fuel cell technologies
Volume 1
Industry
sector
Transport Residential,
sector
etc. sector
2
1
3
1
1
3
3
1
3
Transformation
sector
1
3
3
1
3
3
Nonenergy
use sector Total
1
8
5
9
5
6
5
2
3
6
3
3
3
3
2
2
3
3
6
4
3
5
2
2
2
1
1
1-33
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
No. Energy efficiency technologies for Saudi Arabia
14
Heat pumps for heating and cooling, ventilation
systems
15
HVAC retrocommissioning
16
Industrial sector: steel industry, cement
industry, paper industry, technology
improvements
Insulation of buildings, improved insulation
17
18
19
20
21
22
23
Joint development and manufacturing of local
solutions for energy-efficient equipment with
international companies
Labels, strictly to be installed and controlled
LED street lighting, lamps to be installed from a
national production line for LEDs and for any
other lighting purposes
Lighting efficiency: compact fluorescent lamps,
LED for commerce, government, and
households
Load control
Industry
sector
2
2
3
1
1
1
3
3
3
1
1
25
Process engineering and process control
3
26
Pumps: energy-efficient pumps, speed control
2
27
Renewables for any increase in energy demand
in electricity (nuclear, CSP, centralized PV,
decentralized PV, geothermal energy, wind
energy, and others)
Peak load remote control for air conditioning
(obligatory for any newly installed equipment)
Saudi Building Code: strict implementation of
building efficiency for new buildings
Smart buildings
31
32
33
34
Smart grids
Solar cooling
Solar desalination
Solar thermal and, according to region, solar
heating
35
36
37
38
39
40
Solar thermal household boilers
Standards for equipment efficiency
Standby generation capacities
Storage of electricity, batteries, and others
Storage of cooling capacity to reduce AC peaks
Transport: extension of trains for freight
transport and passenger transport, hybrid cars,
low emission cars, and others
Total
6
2
3
3
5
3
1
4
3
3
3
30
Nonenergy
use sector Total
3
24
29
2
Transformation
sector
3
Manufacturing of new power plants and
rehabilitation for generation equipment,
transmission equipment (transformers, cables,
insulators), distribution equipment
Micro- and small-scale CHP
28
Transport Residential,
sector
etc. sector
6
3
3
1
3
3
4
6
5
15
1
3
3
3
3
3
3
3
3
8
2
3
3
1
2
3
1
1
3
2
5
3
1
2
3
1
3
3
2
1
2
1
2
3
1
2
1
3
3
2
2
2
62
44
15
2
9
9
10
3
3
3
62
12
Note: Dark blue indicates very high energy savings potential, blue indicates high energy savings potential, and light blue
indicates limited energy saving potential.
1-34
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1.6.3
Priority Energy Efficiency Measures for Saudi Arabia
The proposals for priorities on energy efficiency measures to be implemented in Saudi Arabia were
collected from the studies listed in Section 1.6.1. 35 Details on some of these measures are given in
subsequent chapters of this report:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
Annual award system for energy efficiency solutions
Awareness raising for schools, universities, television, press, and others
Budget allocation to support the fields of science and technology in energy efficiency
Building energy management system for better information on energy consumption
Cash rebates for energy-efficient equipment
Clean development mechanism projects to be supported
Convoy designs for power plant units
Coordination and links between Saudi universities and industry
Culture of patenting and entrepreneurship in the field of energy efficiency
Curtailable load program
Customer education
Customer invoice support and customer system check
Daily demand and supply forecasting system
Demonstration projects for energy efficiency
Direct load control program
Educational system with lessons on energy efficiency
Energy audits, short term or on a detailed level, at governmental, commercial, and industrial
facilities
Energy bus with demonstration equipment for energy efficiency
Energy efficiency fund to finance investments in energy efficiency
Energy manager at large-scale consumers
Energy planning authorities (SEC, MOWE, ECRA, and others) to be strengthened to improve
energy efficiency
Energy service industry upgraded and promoted
Energy services companies
Engineering, procurement, and construction (EPC) contracts
Feed in tariff for renewable energies
Fuel standards, vehicle registration, increased registration fee, rebate for fuel-efficient vehicle
purchase (hybrid, plug-in hybrid, electric), fuel pricing, parking regulation, increase of public
transport (e.g., new metro lines and public buses/trains), changes in freight transport, modalshift, and new concepts in urbanization without separation of living, working/production,
education, and domestic needs (e.g., medical services, shopping)
Human resource development and energy efficiency within organizations
Incentives provided to purchase efficient appliances
Information campaigns on energy efficiency
Information material on energy efficiency
Information program on energy efficiency information and awareness
International cooperation on energy efficiency
35
Note that there is partial overlap both within the measures and also between measures and technologies (see Section 6.2) to be
used to increase energy efficiency in Saudi Arabia.
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The Necessity for Energy Efficiency in the Kingdom
33. International energy companies conducting research and development (R&D) in the energyefficiency sector, attracting companies and investment
34. Interruptible tariff program
35. Joint ventures, inviting leading renewable energy technology manufacturers into the country
36. Knowledge transfer, setting up more technological and knowledge-transfer joint ventures
37. Labeling of electrical household appliances
38. Labels: have SEC draft energy efficiency labels for all major classes of electrical appliances
39. Laws on energy efficiency
40. Leasing: an energy-efficient equipment leasing program
41. Market liberalization in the Saudi power sector
42. Minimum standards for new power stations, new cogeneration, new desalination plants
43. Monitoring and evaluation of energy efficiency measures and programs
44. National appropriate mitigation actions (NAMA) further developed
45. National energy efficiency action plans (NEEAP) update
46. Operation and maintenance improved with better standards, training, and supervision
47. Performance monitoring
48. Political support for energy efficiency as one of the main policy areas
49. Private-sector investment in electricity and water projects; increasing the role of the private
sector
50. Programs for promotion of energy efficiency
51. Promotion of an energy service industry
52. Low income electricity subsidies: reducing direct subsidies by replacing them with personal
subsidies
53. R&D activity in public and private sectors
54. R&D programs on energy efficiency
55. Revolving fund for energy efficiency investments
56. Reward system for energy-efficient equipment
57. Rewarding innovators and researchers for energy efficiency solutions
58. Saudi building code: strict implementation
59. SEEC support
60. Standardization and norms with Saudi Arabian Standards Organization (SASO) on energyefficient equipment (AC, refrigerators, lighting, and building insulation)
61. Subsidies reduced within 10 years
62. Support programs for energy-efficient equipment
63. Tariff restructuring
64. Tax incentives for energy-efficient investments
65. Time-of-use (ToU) tariffs for the residential sector
66. ToU tariff programs for major industrial and commercial customers
67. Training: technical, and managerial training through workshops and seminars (energy audits
with quick savings, detailed audits, energy efficiency financing, performance contracting, energy
efficiency technologies)
68. Vocational training on energy efficiency
69. Voluntary actions by industry and commerce supporting energy efficiency
70. Walk-through energy audits of governmental, commercial, and industrial facilities
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To prioritize organizational, legal, and economic energy measures according to sector specifics, Table
1-6 gives an indication of these 70 priorities for Saudi energy policy with regard to measures for energy
efficiency.
Table 1-6:
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Seventy Energy Efficiency Measures for Saudi Arabia According to Sector Priorities
Energy efficiency measures for Saudi Arabia
Award system for energy efficiency solutions
annually
Awareness raising: schools, universities,
television, press and others
Budgets allocation to support the fields of
science and technology in energy efficiency
Building energy management system for
better information on energy consumption
Cash rebates for energy-efficient equipment
Clean development mechanism projects to be
supported
Convoy designs for power plant units
Coordination and links between Saudi
universities and industry
Culture of patenting and entrepreneurship in
Saudi Arabia in the field of energy efficiency
Curtailable load program
Customer education
Customer invoice support and customer
system check
Daily demand and supply forecasting system
Demonstration projects for energy efficiency
Direct load control program
Educational system: lessons on energy
efficiency to be included
Energy audits, short term or on a detailed level
at governmental, commercial, and industrial
facilities
Energy bus with demonstration equipment for
energy efficiency
Energy efficiency fund to finance investments
in energy efficiency
Energy manager at large scale consumers
Energy planning authorities (SEC, MOWE,
ECRA, and others) to be strengthened to
improve energy efficiency for KSA
Energy service industry upgraded and
supported
Energy services companies
EPC contracts
Feed-in tariff for renewable energies
Fuel standards, vehicle registration, increase
registration fee, provide rebate to fuel
efficient vehicle purchase (hybrid, plug-in
hybrid, electric vehicle), fuel pricing, parking
regulation
Human resource development and energy
efficiency within organizations
Volume 1
Industry
sector
Transport Residential,
sector
etc. sector
2
Transformation
sector
Nonenergy
use sector Total
5
3
3
3
1
3
3
3
6
3
3
2
3
1
1
3
1
1
2
2
2
3
2
2
3
2
1
1
1
3
2
3
3
3
1
3
2
3
3
3
3
2
2
2
3
3
3
3
2
2
4
8
4
3
3
6
9
2
3
3
2
3
4
3
3
2
7
2
2
3
3
2
1
1
1
6
6
10
6
6
6
10
3
3
1
4
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The Necessity for Energy Efficiency in the Kingdom
No.
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
1-38
Energy efficiency measures for Saudi Arabia
Industry
sector
Incentives to purchase efficient appliances
Information campaigns on energy efficiency
Information material on energy efficiency
Information program on energy efficiency
information and awareness
International cooperation on energy efficiency
International energy companies that conduct
R&D in the energy efficiency sector, attracting
the companies and investment
1
1
Interruptible tariff program
Joint ventures, inviting leading renewable
energy technology manufacturers into the
country
Knowledge transfer and technological jointventure programs
Labeling of electric household appliances
Labels: have SEC draft energy efficiency labels
for all major classes of electrical appliances
Laws on energy efficiency
Leasing: an energy-efficient equipment leasing
program
3
Market liberalization in the Saudi power sector
Minimum standards for new power stations,
new cogeneration, new desalination plants
Monitoring and evaluation of energy efficiency
measures and programs
NAMA: further development
NEEAP update
Operation and maintenance: improved with
better standards, training, supervision
Performance monitoring
Political support for energy efficiency as one of
the main policy areas in KSA
Private sector investment in electricity and
water projects, increasing the role of the
private sector
Program promoting energy efficiency
Promotion of an energy service industry
Pro-poor electricity subsidies: reducing the
direct subsidies by replacing these subsidies
with personal subsidies
R&D activity in public and private sectors
R&D programs on energy efficiency
Revolving fund for energy efficiency
investments
Reward system for energy-efficient equipment
Rewarding innovators and researchers for
energy efficiency solutions
1
Saudi building code: strict implementation
SEEC support
1
2
Transport Residential,
sector
etc. sector
2
2
Transformation
sector
Nonenergy
use sector Total
3
3
2
1
1
3
2
1
1
1
1
1
1
1
1
1
1
1
3
1
1
3
1
1
5
7
2
7
5
5
9
5
0
1
1
1
1
1
5
3
2
7
5
3
2
1
1
3
1
1
1
1
1
1
1
1
1
3
3
1
1
1
1
1
1
1
2
2
2
1
1
1
2
5
6
1
1
5
6
3
15
3
3
3
3
3
1
3
3
3
1
3
1
3
3
15
1
1
1
1
1
3
3
2
1
1
1
1
1
2
2
2
1
1
2
3
2
2
2
6
2
4
10
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No.
60
61
Energy efficiency measures for Saudi Arabia
Standardization and norms with SASO on
energy-efficient equipment (AC, refrigerators,
lighting, and building insulation)
Subsidies reduction: reducing subsidies within
10 years
62
Support programs for energy-efficient
equipment
63
64
65
66
Tariff restructuring
Tax incentives for energy-efficient investments
ToU tariffs for residential sector
ToU tariff programs for major industrial and
commercial customers
67
Training: technical and managerial training
through workshops and seminars (energy
audits with quick savings, detailed audits,
energy efficiency financing, performance
contracting, energy efficiency technologies)
Vocational training on energy efficiency
Voluntary actions by industry and commerce
supporting energy efficiency
68
69
70
Walk-through energy audits of governmental,
commercial, and industrial facilities
Total
Industry
sector
Transport Residential,
sector
etc. sector
1
Transformation
sector
Nonenergy
use sector Total
4
3
1
1
1
1
3
3
1
3
3
3
3
9
1
3
6
8
2
2
2
2
2
2
2
2
2
3
3
3
93
29
3
3
3
97
44
91
10
6
12
Note: Dark blue indicates very high energy savings potential, blue indicates high energy savings potential, and light blue
indicates limited energy saving potential.
Besides prioritizing measures in the various economic sectors, a detailed analysis of the specific savings
per measure should be carried out. 36 Within this exercise, the effects of a combination of energy
efficiency measures will have to be calculated. Insulation of buildings, latest AC equipment, tariff
changes, and other measures have less energy savings in a combined approach than just adding
individual energy saving rates, because each measure reduces individual energy consumption and,
therefore, reduces the potential savings that additional measures could achieve.
1.6.4
Energy Efficiency Scenario for Saudi Arabia in 2040
There are a number of studies dealing with energy efficiency scenarios for KSA. They all differ with
respect to time horizon (2022, 2032, and up to 2050) and energy coverage (some studies are on the
entire energy sector, while most studies concentrate on the electricity sector). The main studies are the
following:
•
•
36
Tyndall study, 37 2008: no specific energy efficiency increase above the trend is anticipated for the
electricity sector and electricity demand increases to 850 TWh 38 by 2050
Chatham House study, 39 2011: indicates the need for energy efficiency
Based on data from other countires, this usually requires about 18 person-months and a minimum 9-month project period.
37
Al-Saleh, Y., Upham, P., & Malik, K.: Renewable Energy Scenarios for the Kingdom of Saudi Arabia. Tyndall Working Paper No.
125. Norwich, October 2008.
38
Al-Saleh, Y., Upham, P., & Malik, K.: Renewable Energy Scenarios for the Kingdom of Saudi Arabia. Tyndall Working Paper No.
125. Norwich, October 2008, p. 19.
39
Lahn, G., & Stevens, P.: Burning Oil to Keep Cool: The Hidden Energy Crisis in Saudi Arabia, Chatham House, December 2011.
Volume 1
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
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KACST and AEA study, 40 2012: wherein energy efficiency scenarios differ in energy consumption
reduction for 2030 between 5 percent and 16 percent of annual consumption
KAPSARC study, 41 2012: depending on the sector, subsector and technologies savings are calculated
at between 3 percent (for AC) and 50 percent (for building insulation)
KACST study, 42 2012: analyzes a reduction of 30 percent in energy demand (Saudi Primary Energy
Consumption) for 2028
IEEJ study, 43 2009: the “Advanced Scenario,” a reduction of 27 percent, is foreseen until 2035. 44 This
includes a number of policy assumptions for all sectors in Saudi Arabia: residential and
commercial, 45 CCGT power generation, industry (8 percent savings in factories), and transport
(hybrid vehicles). For the power sector, Tokyo Power and Electric Company and IEEJ 46 calculate with
an energy conservation rate of 25 percent up to 2030.
SEEC study, 47 via its Director General, Dr. Naif M. Allabadi, 2012: finds a 30 percent decrease in
energy consumption in sample buildings with excellent economic feasibility, while buildings
consume 80 percent of electricity
Saudi Arabia Energy Efficiency Report, 48 2012: a reduction in electricity intensity of 30 percent is
foreseen between 2005 and 2030
Brinkmann and Wenzel study, 49 2011: foresees saving potentials of up to 50 percent of energy
consumption.
•
•
•
•
•
•
•
In the energy efficiency scenario, the figures given for energy efficiency improvements of 30 percent are
followed, as in most of studies (this 30 percent shows up in different studies with different time
horizons; therefore, the 30 percent estimations are often used, although they are linked to different
periods). This 30 percent efficiency improvement has been followed in the forecast to 2040, but the
situation still is evaluated as “extremely difficult” for the Saudi economy, even with this limited
improvement of 30 percent. 50
40
Alyousef, Y., & Abu-Edid, M.: Energy Efficiency Initiatives for Saudi Arabia on Supply and Demand Sides, in: Energy Efficiency:
Z. Morvaj (Ed.), A Bridge to Low Carbon Economy, Rijeka/Shanghai, Mar. 16, 2012, p. 306.
41
KAPSARC: Review of National Energy Efficiency Initiatives, Saudi Energy Efficiency Workshop, Riyadh, Feb. 6, 2012, p. 9.
42
Alabbadi, N. M.: Why Energy Efficiency? 4th Industrials Forum, Renewable Energy and Energy Efficiency: Emerging Business
Opportunities for the KSA, May 14–15, 2012, p 3.
43
JICA, Tokyo Electric Power Company, and IEEJ: Master Plan Study for Energy Conservation in the Power Sector in the Kingdom
of Saudi Arabia, Final Report, Riyadh, February 2009.
44
IEEJ: Energy and Macroeconomic Modelling for the Kingdom of Saudi Arabia, IEEJ Workshop, Khobar, May 2011, p. 8.
45
For measures on building insulation, AC, energy efficiency in appliances, building management, LED street lighting, awareness,
and other measures, we refer to IEEJ: Energy and Macroeconomic Modelling for the Kingdom of Saudi Arabia, IEEJ Workshop,
Khobar, May 2011, p. 7.
46
JICA, Tokyo Electric Power Company, and IEEJ: Master Plan Study for Energy Conservation in the Power Sector in the Kingdom
of Saudi Arabia, Final Report. Riyadh, February 2009, p. 30.
47
Alabbadi, N. M.: Energy Efficiency Potential in the Building Sector, Saudi International Advanced Materials Technologies
Conference, KACST, Dec. 3–4, 2012, p. 27.
48
Anonymous: Saudi Arabia Energy Efficiency Report, latest update April 2012.
49
Brinkmann, K. & Wenzel, K.: Energy Efficiency and Renewable Energies, Challenges and Training Needs in the Kingdom of
Saudi Arabia, Fact Finding Mission, Final Report. Riyadh, March/April 2011, p. 10ff.
50
Another energy efficiency scenario is more aggressive, with energy savings of 76.94 percent of forecast energy consumption of
425 Mtoe in 2040 being reduced to the current level of 98 Mtoe (in 2009). This probably is not realistic because the main factors
involved in energy consumption as energy prices increase currently are not under discussion in KSA. Other options would be a
“lean-back scenario,” where almost no energy efficiency measures are implemented; this scenario appears unrealistic because at
least a certain level of energy efficiency improvements is required to continue to be an oil-exporting country. One alternative, an
“optimum energy efficiency scenario,” requires huge manpower efforts, because all energy efficiency measures possible in KSA will
have to be defined technically and economically to put them into an optimum order (i.e., putting energy efficiency measures with the
shortest payback period first).
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Total final energy consumption will decrease in this scenario to 298 Mtoe/a, but even this leads to a
considerable decrease in oil exports from the current 383 Mtoe to 128 Mtoe/a, which is only 33 percent
of former oil exports (down 67 percent).
Even if specific energy consumption for appliances could be reduced by 50 percent (in this case, on a
macro level), the energy consumption for refrigerators in KSA would decrease by 1 percent to
4.1 percent annually if there are no additional appliances entering the market in KSA.
Table 1-7 shows the baseline scenario (R2 = 0.9939) is better in the nonlinear trend than the energy
efficiency scenario (R2 = 0.9877).
Table 1-7:
Results of an Energy Efficiency Scenario for KSA in 2040 Compared to the Baseline (BAU)
Variable
Final energy consumption
Gross inland consumption
Net exports (compared to 383
Mtoe in 2009)
Trend 2040 (Baseline)
Energy Efficiency Scenario 2040
425 Mtoe
580 Mtoe
−46 Mtoe
298 Mtoe
406 Mtoe
128 Mtoe
Figure 1-16 shows the results of an energy efficiency scenario for KSA in 2040. Without any energy
efficiency measures, even a part of these savings can be achieved. Based on energy efficiency measures
implemented in other countries, even a reduction in energy consumption of only 3 percent at the
national level will require enormous efforts from the government, industry, and society, because the
energy system currently maintains the status quo. Energy-consuming devices are replaced on the basis
of technical lifetime (e.g., refrigerators are replaced after 15 years, cars after 12 years, buildings after 50
years); therefore, only a very small percentage of energy-consuming appliances are replaced every year
(in this case, only 6.7 percent of all refrigerators, 8.3 percent of all cars, and 2 percent of all houses).
In European countries, there is a discussion on the extent of savings in 2040 or 2050 if there are savings
of 30 percent to 50 percent in consumption now, and this is especially true for the CO2 emissions. In
Saudi Arabia, a 182 percent increase in energy consumption until 2040 is forecasted, even in the very
ambitious energy efficiency scenario.
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Figure 1-16: Results of an Energy Efficiency Scenario (−30 percent) on Final Energy Consumption for KSA in 2040 Compared to the Baseline (BAU)
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
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1.6.5
Influence of Renewable Energies on the Stability of the Saudi Electricity
System
The forecast for 2040 also includes (besides other energy carriers) data on electricity production and
consumption, and necessary power production capacity data (usually measured in MWel).
As seen in Table 1-8, the forecasted peak demand of 185,000 MWel can only be met if all generation
capacity is in the position to deliver its total installed capacity at full load during peak time (in other
words, no maintenance, no breakdown, full wind load, full sun exposure, and so forth).
From other countries, it is known that fossil- and nuclear-fueled power stations are considered to be at
about 5,000 to 6,500 full-load-hours/year, but renewables are usually considered at 1,800 to 2,200 fullload-hours/year, depending on meteorological and weather conditions.
Producing the forecasted 850,000 GWh/year exclusively on an oil/gas basis would require a total
electricity production capacity from oil/gas-fired power stations of only about 145,550 MWel,
considering 5,840 full-load hours. But the planned electricity production capacity is to be mixed with
planned and foreseen renewable energies, and this will not meet the forecasted peak demand of
185,000 MWel in 2040 at peak-load hours, because renewables could not always operate at full load
during this specific peak-load time and day.
Table 1-8:
Electricity Production Capacities (MWel), Electricity Production (GWh/year), and Full-Load Hours per
Year with and without Renewables Energy Production in Saudi Arabia in 2040
Electricity Capacity and Electricity Production in 2040 in Saudi Arabia with Renewables
MWel
Full-load
hours/year
Produced
GWh/year
Oil/gas
87,400
5,840
510,416
Calculation based on K. A. CARE, May
2012 forecast for 2032
Wind
13,800
1,800
24,840
Solar PV
24,400
2,200
53,680
CSP
38,200
3,500
133,700
Nuclear
21,200
6,000
127,200
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
2,000
–
849,836
Type of plant
A
Others*
Total production
–
185,000
B
Demand
185,000
850,000
–
164
–
164
Difference due to comparatively
low full-load hours of renewables
energies
B-A Necessary net-imports
Ratio of necessary net-imports to
total production
0.0
Source
Authors’ calculation
0.0
Note: This table shows the forecasted peak demand of 185,000 MWel can only be met if all generation capacity is in the
position to deliver its total installed capacity on full load during peak hours (i.e., no maintenance, no breakdown, full
wind load, full sun exposure, and so forth).
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Electricity Capacity and Electricity Production in 2040 in Saudi Arabia without Renewables
Type of plant
MWel
A
Oil/gas
Wind
Solar PV
CSP
Nuclear
Othersa
Total production
145,550
–
–
–
–
–
145,550
B
Demand
Difference
B-A Necessary net-imports
Ratio of necessary net-imports to
total production
Full-load
hours/year
5,840
1,800
2,200
3,500
6,000
2,000
Produced
GWh/year
Source
850,012
–
–
–
–
–
850,012
Authors’ calculation
185,000
850,000
Authors’ calculationb
39,450
39,450
–12
–12
27.1
0.0
a
Hydropower, geothermal, waste to energy, others.
To produce the forecasted 850,000 GWh/year exclusively on oil/gas base would require only a total electricity
production capacity from oil/gas-fired power stations at 145,550 MWel, but this will not meet the forecasted peak
demand of 185,000 MWel in 2040.
b
1.6.6
Energy Costs and Opportunity Costs for the Entire Energy System in
Saudi Arabia in 2040
Using the data from the Desertec Industrial Initiative, the total electricity production costs for 2040 has
been estimated for two scenarios: one with the use of renewables and the other without extensive use
of renewables. Table 1-9 shows that the expected difference among fossil fuel, nuclear fuel, and
renewables is not that large: The generation and transmission costs combined will approximately equal,
in 2010 prices, either €50/MWh or ≤€73/MWh, which is much higher than current price differences. In
total, the electricity costs (and, therefore, also the sales) are expected to be €54,231 million in 2040 (in
2010 prices) and will be about 3.9 percent of expected GDP in 2040, which is 6,930 billion Saudi Ryal or
equivalent to €1,386 billion.
Table 1-9:
Estimated Electricity Costs in 2040 in Saudi Arabia for Generation and Transmission of Electricity
Depending on Energy Source (Fossil, Nuclear, Renewables)
Electricity Costs in 2040 in Saudi Arabia (Generation and Transmission)
Type of plant
Oil/gas
Wind
Solar PV
CSP
Nuclear
Othersb
Total production
GDP Saudi Arabiac
Produced
GWh/year
Specific costs, in
€/MWha
510,416
24,840
53,680
133,700
127,200
–
849,836
73
50
50
50
50
50
Total annual costs,
in million euros
37,260
1,242
2,684
6,685
6,360
–
54,231
Source
Dii, Desert Power 2050, p 10
Dii, Desert Power 2050, p 7
Dii, Desert Power 2050, p 7
Dii, Desert Power 2050, p 7
Dii, Desert Power 2050, p 7
Dii, Desert Power 2050, p 7
1,386,080
3.9%
a
These data are calculated for Europe in 2050, but can be used as opportunity costs for Saudi Arabia (excluding
all subsidies) on a macroeconomic level.
b
Hydropower, geothermal, waste to energy, others.
c
Exchange rate between the Saudi Ryal (SR) and the euro is estimated for 2040 at 0.2 €/SR.
1-44
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The Necessity for Energy Efficiency in the Kingdom
Assuming that the energy consumption saved within Saudi Arabia can be used for additional exports,
the opportunity costs for the energy consumption level in 2040 was calculated. In this case, the current
(2009) primary energy supply is worth US$124 billion. As to the forecast for energy consumption and for
“real” energy prices in 2040 (in 2010 prices), two effects on the opportunity costs in 2040 are seen: on
the one hand, a “real” price increase of about US$65 billion, and on the other, an increase owing to
additional consumption in 2040 over that of 2009 amounting to US$446 billion. This means that
national energy consumption will have, in total, an opportunity cost of US$635,302 million in 2040 and
will absorb a substantial percentage of national income—about 35.2 percent of the expected GDP, in
this case.
Table 1-10: Calculation of Opportunity Costs for Current and Future Energy Consumption in Saudi Arabia
until 2040 (Price and Consumption Effects)
2009
Increase in PES, US$ billion
Price increase effect from 2009 to 2040, US$
billion
Current PES, US$ billion
PES, Mtoe
Additional PES, Mtoe
Oil price in 2011 US$/barrel
Price increase for oil price in 2011 US$/bbl
Conversion factor, Mtoe in barrel
Economic value of PES in US$ million
Total
PES = primary energy supply
2040
446
65
124
124
PES, in Mtoe, transferred to US dollars using current and
future oil export prices (opportunity costs) at 2011 price
level
157.9
529.9
157.9
372.0
106.60
162.68
56.08
7369740
7369740
124048
124048
65259
445994
124048
635302
1.7 Recommendations Based on Identified Shortages from the
Current Energy Balancing System and Its Forecasting Models in
Saudi Arabia until 2040
This section includes a list of deficits and leakages identified for KSA procedures in the energy sector in
comparison with other nations and international institutions working in the energy sector.
1.7.1
Providing Energy Data Compatible with Neighboring Countries and with
UN, IEA, and Eurostat Standards
Recommendation 1: Define within the Saudi government a public institution to develop a scheme for
energy data handling and publish on a yearly basis energy balances in a coherent system, preferably
similar to UN, IEA, and Eurostat standards, depending on the level of detail of the information to be
presented.
1.7.2
Ensuring a Continuous System for the Forecasting of Energy Production
and Consumption until 2040
Recommendation 2: Develop a system of regular updates of energy production and consumption data
for KSA up to 2040 as a reference basis for all actors in the Saudi energy market. This is especially
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required from the electricity and oil sectors because there a strong need is seen for additional
investments. The same applies to all investors in renewable energy technologies, because they must be
kept informed about the energy situation in Saudi Arabia over the next three decades. This should be
done regularly (e.g., an annual or biannual basis is better than a 5-year schedule).
1.7.3
Developing and Publishing a National Energy Strategy Including
Renewable Energies and Energy Efficiency
Recommendation 3: Develop the national energy strategy, especially the renewable energy strategy
and energy efficiency strategy. This will allow investors to join the Saudi energy markets more quickly,
while maintaining confidence in the general energy strategy of KSA.
1.7.4
Increased National Energy Consumption, Even in the Energy Efficiency
Scenario, Will Have Negative Influence on the National Economy
Recommendation 4: Develop a crash program with a main focus of reducing the increase in energy
consumption within Saudi Arabia until 2040.
The “normal” set of instruments within a conventional energy efficiency strategy will not stabilize the
Saudi economy in 2040. An energy efficiency scenario until 2040 has been calculated considering a
30 percent increase in energy efficiency, which is very ambitious, and still arrives at an increase in
national final energy consumption of 186 percent of current (2010) consumption.
This will allow Saudi Arabia to continue being an oil-exporting country, albeit at a lower level. Exports of
oil 51 in this energy efficiency scenario will, nevertheless, decrease by 66.57 percent from the current
383 Mtoe/a to 128 Mtoe/a.
All recommendations given are meant to improve the energy situation within KSA and, therefore,
improve the wealth and living standard of its population. This report indicates that with a conventional
energy efficiency strategy with savings of 30 percent compared to the baseline forecast, the standard of
living in Saudi Arabia cannot be kept at the current level, because a large part of oil production in Saudi
Arabia will not be available for export, being used for energy consumption within the country, instead.
1.8 Summary
1. The main aim of the chapter is to forecast a baseline scenario using a detailed analysis of past data in
the energy sector (from 1971 to 2009) with demographic and economic data on the future of Saudi
Arabia, and resulting in a baseline forecast for the next 30 years, through 2040, to allow the definition
of priorities for energy efficiency measures in the sectors of the Kingdom.
2. The basis for the forecast is 1971–2009 data (38 years), to be extrapolated to 2040 using time-series
analysis. This is based on (1) population and number of households and (2) GDP and industry structure
up to 2040 as projected by the UN for population data and the World Bank for economic data.
Economic growth in Saudi Arabia depends heavily on the current and future oil price.
3. The Sankey diagram for Saudi Arabia. Considering the figures from the synoptic version of the
energy flow analysis in the Sankey diagram for Saudi Arabia in 2009, the main energy-saving potential is
seen. Energy losses in the transformation segment total 72 Mtoe/a, which is about 43 percent of the
entire input to the transformation segment. This is twice the losses from end use (only 35 Mtoe);
therefore, any priorities for energy savings should start in this area of energy balance. Within the final
energy consumption, the sectors of transport (34 Mtoe) and nonenergy consumption (31 Mtoe) are
individually twice as great as the residential (15 Mtoe) and Industry (17 Mtoe) sectors; therefore, any
sector priority should be with Transport and Non-Energy-consumption sectors. Within final energy
51
Assuming there is no additional provision of conventional oil and gas or of shale gas, due to technical advancements that lead to
additional exports in 2040 compared to 2010.
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The Necessity for Energy Efficiency in the Kingdom
consumption, the losses in the useful energy sector are about 35 Mtoe, which equates to
approximately a 53 percent loss during final energy consumption.
4a. The main characteristics of the energy system of KSA with reference to future energy
consumption are:
•
•
•
•
Dependence on energy exports (crude oil)
Volatility of crude oil prices, which is very influential on the financial possibilities of the Saudi
economy
Government-owned electricity system
Subsidized energy supply for consumers.
4b. The main characteristics of the electricity system of KSA with reference to future energy
consumption are:
•
•
•
•
Comparatively high age of power stations and turbines, which reduces the overall efficiency of the
power system
Comparatively small generation units (43 percent of all plants are at capacity of ≤251 MW; there
are many small generation units with 8 MW, 12 MW, or 25 MW capacity), which reduce the overall
efficiency of the power system
Comparatively low efficiency of diesel turbines, mainly simple-cycle gas turbine technologies with
an average efficiency rate of about 25 percent in 2009
Comparatively high distribution losses (9.6 percent transmission losses), which reduce the overall
efficiency of the power system.
5. The increase in final energy consumption. From the time-series analysis, a more detailed
understanding of the entire energy system of Saudi Arabia for the year 2040 is gained. Final energy
consumption will increase from 105 Mtoe in 2009 to about 425 Mtoe in 2040 at an annual growth rate
of 4.3 percent, which is less than the foreseen increase in GDP/capita, which is 7.2 percent annually
(not considering the expected increase in population). Primary energy production in 2040 will remain at
the 2009 level, 534 Mtoe, which is the average production of the last 10 years. There is no indication
that energy production will increase or decrease by 2040 in physical terms (bbl/d). An increase in real
prices could be foreseen, and this will influence the turnover in monetary terms but not in physical
terms. Primary energy supply in KSA will increase from 169 Mtoe in 2009 to 530 Mtoe by 2040, owing to
a heavy increase in final energy consumption.
6. The increase in primary energy supply in KSA until 2040 will reduce the possibilities of oil exports,
which are currently (2009) at 383 Mtoe/a. For comparison, of course, the net exports should be
considered, which are 11 Mtoe less than the current exports, or 372 Mtoe in 2009. For 2040, national
production of energy is forecasted to be 534 Mtoe, while the total primary energy supply is 530 Mtoe.
Therefore, there will be almost no crude-oil net exports from KSA to other countries. 52 Because current
GDP depends to a large extent on oil production (92 percent of GDP originates in the oil and
petrochemicals industries), this will have a substantial influence on the wealth of the nation unless KSA
can increase oil production, which is not foreseen in the baseline forecast.
7. A direct comparison with other forecasting studies is not possible. Most of the other studies (i.e.,
IEEJ, Brattle, Bain, KFUPM-SNC Lavalin, Saudi Aramco, ECRA, KAPSARC, MOEP, MOPMR, MOWE, SEC,
K.A.CARE, SEEC) concentrate exclusively on electricity, and electricity is only 17 percent of total final
energy consumption in KSA. There are different time horizons—2020, 2022, 2025, and 2030—but nearly
no information going beyond 2032, except for the Tyndall study.
52
As previously noted, this calculation is built on the assumption that there is no additional provision of conventional oil and gas
and of shale gas owing to technical advancements that lead to additional exports in 2040 compared to 2010.
Volume 1
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8. Regarding the reliability of forecasted data, we see a comparatively high confidence level, as
standard deviation is, for most variables, limited. The R2 values for the forecast of population (0.999 in
nonlinear forecast) GDP (0.994), total primary energy supply (0.984), total final energy consumption
(0.960), MW peak (0.997), and total electricity output (0.993) are quite high.
9. Electricity consumption is expected to increase from 240 TWh/year in 2009 to about 850 TWh/year
in 2040. This increase will absorb a reasonable additional amount of energy production in KSA, and its
influence on primary energy supply in KSA is evident, as about 120 Mtoe/a will be required to supply
the power stations with necessary fossil fuels (average daily consumption at fossil fuel power stations
will increase from 57 GW electricity capacity in 2010 to about 87.4 GW in 2040).
10. The mix of power supply capacity in Saudi Arabia for the period 2010–2040 in MW (baseline
forecast) will not meet the demand in 2040. A considerable shortfall is expected of about 44,000 MW
when calculating 185,000 MW electricity demand in 2040 against a planned capacity of 141,000 MW.
For 2032, the analysis of trend data shows a disparity between the planning of power capacity at
121,000 MW and at the forecast of about 141,000 MW, a difference of 20,000 MW. Because the figure
of 121,000 MW is given by KSA government authorities, we have filled the gap between peak load and
peak capacity, adding a total of 64,000 MW to the capacity for 2032 through 2040. This additional
capacity has been distributed among all types of power plants; therefore, an annual increase in oil- and
gas-fired power stations of about 3,800 MW is seen during this period. The same applies for wind
energy (+570 MW/year), nuclear (+900 MW/year), CSP (+1,650 MW/year), and PV (+1,050 MW/year) to
meet the peak demand of 185,000 MW in 2040.
11. The installed capacity will triple. The transformation sector for oil and oil products will not require a
large increase in capacities besides any continuous modernization and upgrades of production
capacities, but the power sector, owing to the heavily increasing demand for electricity, will have to
triple the 57,000-MW installed capacity to 185,000 MW. This will lead to additional capacities being
constructed at >4,200 MW/year. Looking at the upcoming three decades, a large rehabilitation
program covering the existing power plant capacities must be launched in parallel with any new power
production capacities, which is about an additional 1,500 MW/year to be rehabilitated, if the current
infrastructure of power plants with ages of ≥20 years in 2009 are replaced or rehabilitated over the next
three decades.
12. The availability of renewable energies is not as high as that of fossil and nuclear energies. We have
calculated 5,840 full-load hours for fossil fuels and 6,000 full-load hours for nuclear fuels, compared to
1,800 full-load hours for wind; 2,200 full-load hours for PV; and, due to storage capacities, 3,500 fullload hours for CSP. These figures are very optimistic, but only these figures allow the electricity demand
of 850,000 GWh/year to be met at the national level. The calculations assume that all renewables
(wind, PV, CSP) operate at peak load-day with their full installed capacity and there is no maintenance
during this peak load-day to meet the electricity demand, which is unrealistic. In this case, the installed
capacity should be even higher than the peak demand, and this was not calculated in this forecast. The
situation is only identified and described.
13. Compared with neighboring countries (“a peer group”), and if energy consumption is measured
against population, Saudi Arabia, with 7,500 kWh/capita, has much lower energy consumption than
Kuwait (17,500 kWh/capita). This indicates that an increase to 185,000 MW and, by extension, 850,000
GWh/year, is possible. These data from the peer group give no evidence or argument against the
forecasted increase in energy consumption.
14. Initial prioritization of technical and organizational measures was based on the forecasting results
for Saudi Arabia for 2010–2040 from the findings of different studies (Bain, Brattle, Chatham House,
IEEJ, KACST/AEA, KAPSARC, SEEC, Saudi Arabia Energy Efficiency Report, K. A. CARE, Tyndall, and
others). All these studies have a different viewpoint; therefore, some measures overlap (e.g., labeling),
but some studies concentrate on specific measures (e.g., pricing structures). The proposals for these
measures have been reviewed and a list of 40 technical measures and 70 organizational measures
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The Necessity for Energy Efficiency in the Kingdom
(“40/70 List of Technical and Organizational Energy Efficiency Measures in Saudi Arabia”) was drawn
up.
15. A scenario of energy efficiency. In the energy efficiency scenario, the figure of 30 percent given for
energy efficiency improvements is seen in most of the studies, but the situation for the Saudi economy
is still evaluated as “extremely difficult” even with this quite extended and ambitious improvement of
30 percent. Total final energy consumption will limit the increase in this scenario to 298 Mtoe/a, but,
nevertheless, this leads to a considerable decrease in oil exports from the current 383 Mtoe/a to 128
Mtoe/a, which is only 33 percent of former oil exports (down 67 percent).
16. Opportunity costs in 2040. With regard to the forecast for energy consumption and “real” energy
prices in 2040 (based on 2010 prices), two effects on the opportunity costs in 2040 are seen: on one
hand, a “real” price increase of about US$65 billion and on the other, an increase owing to additional
consumption in 2040 over 2009 amounting to US$446 billion. This means that national energy
consumption will have, in total, opportunity costs of US$635,302 million in 2040, and will absorb a
large percentage of national income—about 35.2 percent of the expected GDP, in this case.
17. A time-series analysis. The method used in this forecasting model is a time-series analysis, because
the past energy data show a high R2, and the main influencing factors are also following this trend.
Therefore, there is no need for any regression analyses. Any detailed regression analyses would be
expected to have similar results. Both methods do not consider “loops” in their forecasting; for
example, a currently high GDP growth leads to high national energy demand and fewer exports, which,
in turn, reduce GDP growth. This will reduce national energy demand; therefore, some new and
additional export possibilities, which increase GDP again, will be required.
18. The highest priority should be given to energy efficiency in Saudi Arabia to ensure current living
standards. If energy efficiency cannot be realized on a larger scale, oil export levels will be low from
2040 onward.
1.9 Baseline Forecast
Table 1-11: Variable Baseline Forecast 2010 to 2040 Time-Series Analysis Data
Variable
(Baseline Forecast
Section from 2010 to 2040) Unit
Population (median inhabitants
A
variant)
Increase population inhabitants
Population (median Mio
variant)
Median age (median years
variant)
Household size
(Saudi population)
person/hh
GDP (current prices) US$
GDP (at constant
2005 prices)
Volume 1
SR
Source
United Nations: World Population Prospects, The 2010 Revision,
Volume 1: Comprehensive Tables, New York 2011, page 162f,
partly interpolated for the period 2010ff
Population year n minus population year (n-1)
Transferred to Mio
United Nations, Department of Economic and Social Affairs,
Population Division, Population Estimates and Projections Section,
New York February 2013, partly interpolated for the period 2010ff
Adnan Ghosheh: Long-term National Forecasts, Presentation
given by the Hausing Project, no date, page 8; for period 2030 to
2040 our estimation is no significant change of the 2030 rate.
Economic Statistics Branch of the United Nations Statistics
Division (UNSD), published at Undata and based on The World
Bank: World Development Indicators, New York February 2013
Economic Statistics Branch of the United Nations Statistics
Division (UNSD), published at Undata and based on The World
Bank: World Development Indicators, New York February 2013
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Variable
(Baseline Forecast
Section from 2010 to 2040) Unit
GDP (at constant
Bio SR
2005 prices)
Annual growth rate
for GDP (MOEP)
factor
Annual growth rate
for GDP (IEEJ)
factor
GDP per capita (at
SR/capita
constant 2005
prices)
Floor space for
Mio m2
residential buildings
B
C
D
1-50
Floor space per
capita
Crude oil spot prices
(Brent)
Crude oil price index
(2005=100)
Crude oil price index
(Brent, 2011 US$)
m2/capita
Total energy
production
Total energy
production
Total primary
energy supply
Total primary
energy supply
internal: annual
increase
Total final energy
consumption
Total final energy
consumption
ktoe
2011
US$/bbl
index
2011
US$/bbl
Mtoe
Source
Forecasted increase of GDP from 2010 to 2024 according to
Ministry of Economy and Planning (MOEP): Long-term Strategy for
the Saudi Economy, Riyadh 2010, Table 3.2 (Macroeconomic
Projections, Ministry of Economy and Planning), from 2025 own
calculation of GDP growth
Forecasted increase of GDP from 2010 to 2024 according to
Ministry of Economy and Planning (MOEP): Long-term Strategy for
the Saudi Economy, Riyadh 2010, Table 3.2 (Macroeconomic
Projections, Ministry of Economy and Planning)
Ministry of Water and Electricity (MOWE), prepared by Japan
International Cooperation Agency/Tokyo Electric Power
Company/The Institute of Energy Economics, Japan: The Master
Plan Study for Energy Conservation in the Power Sector in the
Kingdom of Saudi Arabia, Final Report (Summary), Riyadh,
February 2009, Table 3-2, GDP Growth Rate in the BAU Case, page
30, from 2030 own calculation based on previous period 2020–
2030
GDP on the basis of 2005 prices divided by population, both for
the past and for future values
Calculation based on data given by Mr. Ghosheh, Ministry of
Housing: National Housing Strategy, based on Annex (2), Housing
Model and Forecasts, Phase IV, Riyadh December 2011, increase
of 30% within next 30 years
For the period 2010 to 2040 own calculation using forecasted
floor space in m2 divided by forecasted population in Saudi Arabia
International Energy Agency (IEA): Data Services, Energy Prices,
Paris February 2013
International Energy Agency (IEA): Data Services, Energy Prices,
Paris February 2013
U.S. Energy Information Administration, Annual Energy Outlook
2013, Early Release Overview, 2013, page 16 (forecast of crude oil
prices), Brent spot crude oil (dollars per barrel), price basis 2011
US$
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
ktoe transferred to Mio toe
Mtoe
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
ktoe transferred to Mio toe
Mtoe
Own calculation
ktoe
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
ktoe transferred to Mio toe
ktoe
Mtoe
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Variable
(Baseline Forecast
Section from 2010 to 2040) Unit
internal: annual
Mtoe
increase
E
F
Industry
ktoe
Transport
ktoe
Residential
ktoe
Commercial and
public services
Others
ktoe
ktoe
Total final energy
consumption
Total final energy
consumption
Oil and oil products
ktoe
Gas
ktoe
Electricity
ktoe
Others
ktoe
MW installed
MW
Mtoe
ktoe
internal: annual
MW/a
increase
Annual additional
MW/a
installed capacity
(net)
MW installed oil/gas MW
additional capacity
from 2032
MW installed wind
Volume 1
MW
MW
Source
Own calculation based on annual increase for FEC to reach the
medium between two time-series 1971 to 2010 for forecasting
2040 final energy consumption with 2nd polynom and 3rd
polynom
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
Calculated as Total Final Consumption minus the consumption of
the sectors described above
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013
ktoe transferred to Mio toe
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2014
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2015
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2016
Calculated as Total Final Consumption minus the consumption of
the energy carriers described above
The Electricity, Co-Generation Regulatory Authority of Saudi
Arabia (ECRA): Annual Statistical Booklet on Electricity and Sea
Water Desalination Industries, Riyadh 2011, page 4 (Source for
the period 2001 to 2011), other data (1970 to 1987) Annual
Reports of the Saudi Arabia Monetary Agency, data from 1988 to
2005 by SNC-Lavalin: Development of Electricity Generation and
Transmission Plan for Saudi Arabia—Electricity Demand Forecast,
Draft Final Report, June 2007
Own calculation
To be detailed at a later stage
Calculated backwards as remaining rest of non-renewable
capacity from 2022 until 2032 as conventional O+G plus planned
renewables do not meet demand; therefore, an increase of O+G
capacity during this period is required.
Own calculation
Saudi Arabia Plans $109 Billion Boost for Solar Power, in:
Bloomberg Businessweek, May 11, 2012 and Saudi Arabia
Launches Massive Renewable Programme with Hybrid FITs, in:
Renewable Energy World, 15 May 2012, based on information
given by Maher Al-Odan and Khalid Al-Suman, King Abdullah City
for Atomic and Renewable Energy
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Variable
(Baseline Forecast
Section from 2010 to 2040) Unit
additional capacity MW
from 2032
MW installed
MW
nuclear
additional capacity
from 2032
MW installed CSP
MW
additional capacity
from 2032
MW installed PV
MW
MW
MW
additional capacity MW
from 2032
MW installed others MW
MW peak load
MW
internal: annual
increase
MW peak load
(Brattle/ECRA)
G
MW
Reserve at peak day MW
in MW
Reserve at peak day %
in %
Total electricity
GWh/a
output
internal: annual
increase
1-52
MW/a
Source
Own calculation
Saudi Arabia Plans $109 Billion Boost for Solar Power, in:
Bloomberg Businessweek, May 11, 2012 and Saudi Arabia
Launches Massive Renewable Programme with Hybrid FITs, in:
Renewable Energy World, 15 May 2012, based on information
given by Maher Al-Odan and Khalid Al-Suman, King Abdullah City
for Atomic and Renewable Energy
Own calculation
Saudi Arabia Plans $109 Billion Boost for Solar Power, in:
Bloomberg Businessweek, May 11, 2012 and Saudi Arabia
Launches Massive Renewable Programme with Hybrid FITs, in:
Renewable Energy World, 15 May 2012, based on information
given by Maher Al-Odan and Khalid Al-Suman, King Abdullah City
for Atomic and Renewable Energy
Own calculation
Saudi Arabia Plans $109 Billion Boost for Solar Power, in:
Bloomberg Businessweek, May 11, 2012 and Saudi Arabia
Launches Massive Renewable Programme with Hybrid FITs, in:
Renewable Energy World, 15 May 2012, based on information
given by Maher Al-Odan and Khalid Al-Suman, King Abdullah City
for Atomic and Renewable Energy
Own calculation
To be detailed at a later stage
The Electricity, Co-Generation Regulatory Authority of Saudi
Arabia (ECRA): Annual Statistical Booklet on Electricity and Sea
Water Desalination Industries, Riyadh 2011, page 26 (Source for
the period 2006 to 2020), other data (1970 to 1990) Annual
Reports of the Saudi Arabia Monetary Agency, data from 1990 to
2006 by SNC-Lavalin: Development of Electricity Generation and
Transmission Plan for Saudi Arabia—Electricity Demand Forecast,
Draft Final Report, June 2007
Own calculation
The Brattle Group: Bringing Demand-Side Management to the
Kingdom of Saudi Arabia, Final Report, Riyadh 27 May 2011, page
47 (for the years 2011–2021)
Own calculation
Own calculation
Source: International Energy Agency (IEA): Data Services,
Summary Energy Balances, Paris February 2013 (for the period
1971–2010)
GWh/a
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Variable
(Baseline Forecast
Section from 2010 to 2040) Unit
Total electricity
GWh/a
output
(IEEJ/KAPSARC)
H
I
Full load hours
Energy efficiency
scenario (-30%)
Total final energy
consumption (-30%)
Total primary
energy supply
(-30%)
MW peak load
(-30%)
MW installed (-30%)
Additional oil export
possibilities through
energy savings
achieved
Additional oil export
possibilities through
energy savings
achieved
CO2 emissions (IEA)
h/a
factor
Source
Ministry of Water and Electricity (MOWE), Kingdom of Saudi
Arabia, prepared by Japan International Cooperation
Agency/Tokyo Electric Power Company/The Institute of Energy
Economics, Japan: The Master Plan Study for Energy Conservation
in the Power Sector in the Kingdom of Saudi Arabia, Final Report
(Summary), Riyadh, February 2009, Table 3-5, Power Demand in
the BAU Case, page 31 (for the years 2015, 2020, 2025, 2030)
Own calculation
Energy efficiency scenario as described in the study report
Mtoe
Own calculation using above figures
Mtoe
Own calculation using above figures
MW
Own calculation using above figures
MW
Mtoe
Own calculation using above figures
Own calculation using above figures
Mio b/d
Only conversion of figures from Mtoe to Mio b/d using the factor
of 0.02019
Mio t CO2
CO2 emission factor
calculated on the
basis of IEA data
Mio t
CO2/Mtoe
total CO2 emissions
from energy
consumption (EIA
and Koene/Buekr)
Mio t CO2
International Energy Agency (IEA): Data Services, Summary Energy
Balances, Paris February 2013 for the period 1971 to 2010; for the
period 2011 to 2040, the emissions are calculated using the
emission factor based on the last 10-year average (2001–2010) for
projections on future multiplied with the forecasted “Total
Primary Energy Supply” of the respective year.
CO2 emission factors for the period 1971 to 2010 are calculated by
“CO2 emissions (IEA)” divided by “Total Primary Energy Supply.”
For the period 2011 to 2040, we use the 10-year average of the
period 2001 to 2010 as forecast, which is 2.434 t CO2 per toe.
U.S. Energy Information Administration (EIA): International Energy
Statistics, Total Carbon Dioxide Emissions from the Consumption
of Energy (Million Metric Tons) for the period 1980 to 2010; for
forecasted emission, we refer to Köne, A.C./Bükr, T.: Forecasting
of CO2 Emissions from Fuel Combustion Using Trend Analysis, in:
Renewable and Sustainable Energy Reviews, 2010, page 8.
Own calculation using above figures
total CO2 emissions Mio t CO3
from energy
consumption (-30%)
Avoided CO2
Mio t CO2
emissions through
energy savings
achieved
Volume 1
Own calculation using above figures
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Table 1-12: Variable Baseline Forecast 2010 to 2040 Main Data
Topic
Variable
Unit
Year 2010 Year 2040
General
Population
thereof Non-Saudi
population
Prices
SR/kWh
Household, price
for electricity from
0.05
0.05
SR/kWh
Household, price
for electricity up to
0.26
0.26
SR/kWh
0.12
0.12
Kingdom of Saudi Arabia, Ministry of
Industry and Electricity, Electricity Affairs
Agency: Electricity Service Tarif, October
2001, page 6
Kingdom of Saudi Arabia, Ministry of
Industry and Electricity, Electricity Affairs
Agency: Electricity Service Tarif, October
2001, page 6
Meeting protocol, location, date
US$/barrel
95.54
163.00
Dated 22.1.2013
US$/barrel
79.51
163.00
27,448,000 42,183,000
8,430,000 7,630,000 Forecast for 2040 based on national
target figure for 2024, Adnan Ghosheh:
Long-term National Forecasts,
Presentation given by the Housing
Project, no date, page 6
Households
households 4,485,000 8,271,000
GDP per capita
US$/person 49,723
164,294
4,643,151 7,898,151 Adnan Ghosheh: Long-term National
Occupied housing Units
units
Forecasts, Presentation given by the
Housing Project, no date (annual increase
of housing units 108500 per year as in
the past years, Scenario 1), page 3
2
m /flat
190
247
Floor space per
Data given by Mr. Ghosheh, Ministry of
Saudi household
Housing: National Housing Strategy,
based on Annex (2), Housing Model and
Forecasts, Phase IV, Riyadh December
2011, increase of 30% within next 30
years
2
m /flat
110
143
Floor space per
Data given by Mr. Ghosheh, Ministry of
non-Saudi
Housing: National Housing Strategy,
household
based on Annex (2), Housing Model and
Forecasts, Phase IV, Riyadh December
2011, increase of 30% within next 30
years
2
170
221
Average floor space m /flat
75% Saudi population, 25% non-Saudi
in the KSA
population; Source: Adnan Ghosheh:
Long-term National Forecasts,
Presentation given by the Housing
Project, no date, page 6
2
Floor space
Mio m
851
2,828
95
Oil dependence of in %
exports
92
Oil dependence of in %
budget revenues
Industry, price for
electricity
Oil price (Brent) in
January 2013
Oil price (Brent) in
2011 US$/bbl
1-54
inhabitants
inhabitants
Volume 1
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Topic
Variable
Unit
Year 2010 Year 2040
Cost for SEC for oil
used in power
sector
US$/barrel
4.00
4.00
The energy input for SEC into power
stations is heavily subsidized by KSA;
Saudi Aramco receives the price of 4
US$/barrel oil
Exchange rate
Exchange rate
Conversion barrel
to TOE
Conversion
barrel/day to TOE
Euro/SR
US$/SR
bbl/toe
0.20013
0.27000
7.36974
0.20013
0.27000
7.36974
(b/d)/toe
0.02019
0.02019
Dated 17.1.2013
Dated 17.1.2013
IEA/EUROSTAT/OECD: Energy Statistics
Manual, Paris 2005, page 73
Calculated using above factor divided by
365 days/year
Primary energy
consumption
Final energy
consumption
Oil production
Domestic energy
consumption
Mtoe
169
530
Mtoe
105
425
M b/d
M b/d
9.1
2.40
9.1
5.40
Almahnoud, page 1
AHK Newsletter, page 6
Electricity Installed capacity
Summer peak
Reserve capacity at
peak time
Reserve capacity at
peak time
Average used
Total electricity
consumption
Annual growth rate
(average)
MW
MW
MW
49,900
47,300
2,600
185,000
185,000
-
According to Al-Awaji
Almahnoud, page 1 for the year 2032
%
5.21
-
MW
GWh/a
22,000
240,067
82,000
850,000
%
4.3
4.3
Energy
Energy saving
Efficiency potential
Electricity load
saving potential
Electricity
consumption saving
potential
Efficiency of power
plants
Electrical Plants
(eff. to sec. energy)
Electrical Plants
(eff. to final energy)
Oil Refineries (eff.
to secondary
energy)
Oil Refineries (eff.
to final energy)
in %
-30.0
in %
-27.3
in %
-30.0
Others
Energy
Costs
Volume 1
110–80 GW according to Al-Awaji
in %
25-35
40-45
According to Al-Awaji
etha
0.31040
0.35
etha
0.26230
0.32
etha
0.98280
0.98
IEA: Energy Balance Saudi Arabia 2009,
Paris 2013
IEA: Energy Balance Saudi Arabia 2009,
Paris 2013
IEA: Energy Balance Saudi Arabia 2009,
Paris 2013
etha
0.71570
0.75
Electricity sales
Mio SR
Power sector needs Mio SR
for next 10 years
25873
75000
IEA: Energy Balance Saudi Arabia 2009,
Paris 2013
SEC, 2010, page 37
Aluwaji, MOWE, page 3
1-55
CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
1.10 Literature
Asea Brown Bovery: Trends in Global Energy Efficiency 2011, Country Reports, Saudi Arabia, 2012.
Alabbadi, N. M.: Energy Efficiency Potential in the Building Sector, Saudi International Advanced
Materials Technologies Conference. KACST, Dec. 3–4, 2012.
Alabbadi, N. M.: Why Energy Efficiency? Fourth Industrials Forum, Renewable Energy and Energy
Efficiency: Emerging Business Opportunities for the KSA, May 14–15, 2012.
Alawaji, S. H.: Market Development and Business Opportunities in the Power Sector of Saudi Arabia,
Presentation given at the 2nd German-Arab Forum (GAEF), Berlin, Oct. 20–21, 2011.
Al-Saleh, Y., Upham, P., & Malik, K.: Renewable Energy Scenarios for the Kingdom of Saudi Arabia,
Tyndall Working Paper No. 125, Norwich, October 2008.
Alyousef, Y., & Abu-Edid, M.: Energy Efficiency Initiatives for Saudi Arabia on Supply and Demand Sides,
in: Z. Morvaj (ed.), Energy Efficiency: A Bridge to Low Carbon Economy, Rijeka/Shanghai, Mar. 16, 2012.
Anonymous: Saudi Arabia Energy Efficiency Report, latest update April 2012.
Außenhandelskammer Saudi Arabien: Energieeffizienz: Saudi Arabien legt den Schalter um, in: AHK-SAReport, Newsletter March 2011, Riyadh, 2011.
Brattle Group: Bringing Demand-Side Management to the Kingdom of Saudi Arabia, Final Report,
Riyadh, May 27, 2011.
Brinkmann, K., & Wenzel, K.: Energy Efficiency and Renewable Energies, Challenges and Training Needs
in the Kingdom of Saudi Arabia, Fact Finding Mission, Final Report, Riyadh, March/April 2011.
Center for Strategies and International Studies: Saudi Arabia’s Energy Policy: A Disciplined Approach to
Forward Looking Policymaking, August 2012.
Central Intelligence Agency: The World Fact-Book, Energy, Country Consumption to the World,
Washington, D.C., 2011.
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH: Energy Efficiency and Renewable
Energies, Challenges and Training Needs in the Kingdom of Saudi Arabia, Riyadh, March/April 2011.
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH: Fact Finding Mission on Saudi
Energy Markets, Riyadh, 2011.
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH: Study Tour on Energy Efficient
Buildings, Damman, 2012.
Dii GmbH: 2050 Desert Power: Executive Summary, The Case for Desert Power, first edition, Munich,
June 2012.
Economic Statistics Branch of the United Nations Statistics Division (UNSD), published at UN Data and
based on The World Bank: World Development Indicators, New York, February 2013.
Economic Statistics Branch of the United Nations Statistics Division (UNSD), published at UN Data and
based on The World Bank: World Development Indicators, New York, February 2013.
Electricity, Co-Generation Regulatory Authority of Saudi Arabia (ECRA): Activities and Achievements of
the Authority in 2011, Riyadh, June 2012.
Electricity, Co-Generation Regulatory Authority of Saudi Arabia (ECRA): Annual Statistics Booklet on
Electricity and Seawater Desalinisation Industries 2011, Riyadh, 2012.
EUROSTAT: EU Energy in Figures, Statistical Pocketbook 2012, Luxembourg.
EUROSTAT: Yearly Energy Statistics for the Member States, Luxembourg.
1-56
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Ghosheh, A.: Long-Term National Forecasts, presentation given by the Housing Project, no date.
IEA, EUROSTAT, & OECD: Energy Statistics Manual, Paris, 2005.
Institute of Energy Economics, Japan: Energy and Macroeconomic Modelling for the Kingdom of Saudi
Arabia, IEEJ Workshop, Khobar, May 2011.
Institute of Energy Economics, Japan: Power Sector Analysis, IEEJ Workshop, Khobar, May 2011.
International Energy Agency: Data Services, Energy Prices, Paris, February 2013.
International Energy Agency: Data Services, Energy Prices, Paris, 2013.
International Energy Agency: Data Services, Summary Energy Balances, Paris, February 2013.
International Energy Agency: International Energy Balances, Paris.
Jahn, A., et al.: Energy Flow Analyses for All Member States of OLADE, Application of the EFLOW System
Used by EUROSTAT to All 26 Latin American Member States of the Organizacion Latinoamericana de
Energia (OLADE), Quito, 1993.
Jahn, A., et al.: Nutzenergiebilanzen Uruguay: Projektprüfung für die Gesellschaft für Technische
Zusammenarbeit (GTZ) (84 S.), Berlin/Montevideo, 1989.
Japan International Cooperation Agency, Tokyo Electric Power Company, & Institute of Energy
Economics, Japan: Master Plan Study for Energy Conservation in the Power Sector in the Kingdom of
Saudi Arabia, Final Report, Riyadh, February 2009.
Kampet, T., Jahn, A., et al.: Methodology on Energy Efficiency Measures Impact on National Energy
Balance in India, on behalf of Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH and
Ministry of Finance, IGEN Programme Ref. No. 81086046, New Delhi/Eschborn/Berlin, June 2007.
KEMA: Development of KPIs for the Electricity Sector in the Kingdom of Saudi Arabia, Targets and
Incentives Report, Arnhem, May 22, 2009.
King Abdullah Petroleum Studies and Research Center: Review of National Energy Efficiency Initiatives,
Saudi Energy Efficiency Workshop, Riyadh, Feb. 6, 2012.
King Abdullah Petroleum Studies and Research Center: Review of World Energy Conservation Studies,
Riyadh, no date.
King Fahd University of Petroleum and Minerals (KFUPM) & SNC Lavalin: Development of Electricity
Generation and Transmission Plan for Saudi Arabia, Electricity Demand Forecast Study, Draft Final
Report, Dammam/Riyadh, June 2007.
Kingdom of Saudi Arabia, Ministry of Economy and Planning (MOEP): Long-Term Strategy for the Saudi
Economy, Riyadh, 2010.
Kingdom of Saudi Arabia, Ministry of Housing: National Housing Strategy, Riyadh, Dec. 3, 2012
(unpublished).
Kingdom of Saudi Arabia, Ministry of Housing: National Housing Strategy, Annex 2, Housing Model and
Forecasts, Phase IV, Riyadh, December 2011.
Kingdom of Saudi Arabia, Ministry of Industry and Electricity, Electricity Affairs Agency: Council of
Ministers Decisions No. 169, of 11/8/1419 AH. Reorganisation and Restructuring of the Electricity Energy
Sector; and No. 170 of 12/7/1421 AH. Amendment of the Sale of Electricity Service Tariff, October 2001.
Kingdom of Saudi Arabia, Ministry of Water and Electricity (MOWE), prepared by Japan International
Cooperation Agency, Tokyo Electric Power Company,& Institute of Energy Economics, Japan: Master
Plan Study for Energy Conservation in the Power Sector in the Kingdom of Saudi Arabia, Final Report
(Summary), Riyadh, February 2009.
Volume 1
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
Kingdom of Saudi Arabia, Ministry of Water and Electricity (MOWE): Annual Report, 1431–1432 A.H.,
2010 A.D., Riyadh, 2012.
Köne, A. C., & Bükr, T.: Forecasting of CO2 Emissions From Fuel Combustion Using Trend Analysis, in:
Renewable and Sustainable Energy Reviews, 2010.
Lahn, G., & Stevens, P.: Burning Oil to Keep Cool: The Hidden Energy Crisis in Saudi Arabia, Chatham
House, December 2011.
Natural Resources Canada: Long-Term Projections of World Oil Prices (Constant 2008 Dollars Per Barrel),
Long-Term Outlook: Crude Oil Prices to 2030, Ontario, 2013.
NN: Table C5, Characteristics of the Existing Generation Units for the WOA Interconnected System, no
location, no date.
Organisation for Economic Co-operation and Development (OECD): Energy Balances for Developing
Countries, Paris.
Organization of Petroleum Exporting Countries (OPEC): World Oil Outlook 2012, Vienna, 2012.
Saline Water Conversion Corporation (SWCC): Annual Report No. 37, 1431H-1432H, Riyadh, 2012.
Saudi Arabia Launches Massive Renewable Program with Hybrid FITs, in: Renewable Energy World,
May15, 2012, based on information given by M. Al-Odan & K. Al-Suman, King Abdullah City for Atomic
and Renewable Energy.
Saudi Arabia Plans $109 Billion Boost for Solar Power, in: Bloomberg Businessweek, May 11, 2012.
Saudi Building Code National Committee (SBCNC): Saudi Building Code, Riyadh, no date.
Saudi Electricity Company (SEC): Annual Report 2010, Riyadh, 2011.
SNC-Lavalin International: Development of Electricity Generation and Transmission Plan for Saudi
Arabia: Electricity Demand Forecast Study, Draft Final Report, Riyadh, June 2007.
Solar Power: Final Bids for Plants within Three Months, in: Arab News, 24.2.2013, p. 15.
U.S. Energy Information Administration: Annual Energy Outlook 2013, Early Release Overview,
Washington, D.C., 2013, p. 16 (forecast of crude oil prices, Brent spot crude oil [dollars per barrel], 2011
US$).
U.S. Energy Information Administration: International Energy Statistics, Total Carbon Dioxide Emissions
From the Consumption of Energy (Million Metric Tons).
U.S. Energy Information Administration: Short-Term Energy and Summer Fuels Outlook, Washington,
D.C., Apr. 14, 2011.
United Nations, Department of Economic and Social Affairs, Population Division, Population Estimates
and Projections Section, New York, February 2013.
United Nations: World Population Prospects, 2010 Revision, Vol. 1, Comprehensive Tables, New York,
2011.
Volwahsen, A., Bröge, M., Jahn, A., et al.: Energy Flow Model for the Member States of the European
Community: Report for the Commission of the European Communities, Directorate-General for Energy,
DG XVII, Berlin, 1986.
Volwahsen, A., Jahn, A., et al.: Energia para Brasil: Resultados da Utilizacao do Modelo de Fluxo de
Energia com Dados do Balanco Energetico Brasileiro 1985 (Energy for Brazil: Results of Using the Energy
Flow Model with Data From the Brazilian Energy Balance 1985), Berlin/Rio de Janeiro, 1987.
World Bank: World DataBank: World Development Indicators (WDI), Washington, D.C., March 2013.
1-58
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CHAPTER 1: Energy Supply and Demand in the Kingdom of Saudi Arabia from 2010 to 2040—
The Necessity for Energy Efficiency in the Kingdom
The Electricity & Cogeneration Regulatory Authority (ECRA): www.ecra.gov.sa
Deutsche Gesellschaft Fur International Zusammenarbeit -GIZ : www.giz.de
King Abdulaziz City for Science and Technology (KACST): www.kacst.edu.sa
King Abdullah Petroleum Studies and Research Center :www.kapsarc.org
King Abdullah University of Science and Technology: www.kaust.edu.sa
King Fahd University of Petroleum and Minerals: www.kfupm.edu.sa
Ministry of Water and Electricity : www.mowe.gov.sa
Saudi Basic Industries Corporation: www.sabic.com
Saudi Aramco: www.saudiaramco.com
Saudi Electricity Company: www.se.com.sa
University Of Dammam : www.ud.edu.sa
Volume 1
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2
Appraisal and Evaluation of Energy Utilization and
Efficiency in the Kingdom of Saudi Arabia
CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Chapter 2: Appraisal and Evaluation of Energy Utilization and
Efficiency in the Kingdom of Saudi Arabia
Chapter Summary
For this study, KSA industry was divided into five main sectors. Although the various sectors have been
in the process of liberalization for some years, there is still one large main player in each. Water
production and power generation are traditionally operated by governmental companies. The
petrochemical and steel businesses are dominated by a few companies, but the oil production and
refinery sector is managed by one company. The companies in the latter four sectors have different
conditions from all other industries because of their export character, their size, and the difference in
business character of the water and power generation sector.
The refinery and oil production sector was not included in the scope of this study.
Other industries such as glass, food, paper, etc., are summarized in this study under “other industries.”
For application of a proportional method to estimate the waste heat use potential in comparison to
international use potentials, this sector was subsectored.
A total waste heat use potential of about 3,500 MWth was identified for the four sectors. More than
80 percent of this potential was found in three large industrial sectors, including saline water,
petrochemicals, and power generation. The waste heat use potential for “other industries” is about 650
MWth. The possible power generation depends on the available temperature.
There are four levels of waste heat reduction and use:
•
Avoidance of waste heat generation
Direct process internal use
Use for chilling requirements
•
Transformation to power.
•
•
The fourth and highest level, which is also the most expensive, is additional electricity generation.
Below 350 °C this must be done by Organic Rankine Cycles (ORC) processes; above 350 °C steam turbine
or engine cycles would also be relevant for KSA. Using all waste heat of “other industries” would
provide a power potential of about 141 MWel.
Assuming an average heat to power efficiency ratio of 20 percent, the total waste heat losses constitute
a power potential of about 700 MWel.
For the different levels of waste heat use, detailed investigations including feasibility studies that take
into consideration individual conditions are necessary. For all possible technical measures, special
conditions associated with the climate and low energy prices in KSA must be taken into account.
In addition, many of the measures require proper operation of the equipment. The identification of best
available operations, creation of key performance indicators (KPIs), installation of monitoring systems,
and implementation of incentive systems are proven means to facilitate optimal operation with existing
equipment.
In particular, the installation of external waste heat use equipment like bottoming or topping cycles
usually complicates the process. Often this is the main barrier. To overcome this barrier, awareness
programs or economic incentive systems may be advisable (e.g., tax reduction, funding like the German
CHP funding law Kraft-Wärme-Kopplungs-Gesetz [KWKG], or power feed-in regulations).
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
2.1 Introduction
Considering the energy situation in KSA, the question is: Can the use of waste heat in industry be of
significant benefit?
The aim of this report is to analyze the efficiency potential arising from waste heat use in the industrial
sector of KSA. The industrial energy sector in KSA can be divided into five main sectors:
•
•
•
•
•
Sector 1: Water production
Sector 2: Power production
Sector 3: Refinery
Sector 4: Large petrochemical production
Sector 5: Other industries
The last sector can be further subdivided.
The structure of KSA industry is different from other countries. One big difference is the extreme
climate, which makes water a rare commodity. Because of the high ambient temperature and arid
conditions, much water must be desalinated, which means it must be filtered on a molecular scale with
very high pressures or evaporated and then condensed.
Another big difference is the country’s oil riches. This led in the past to low energy prices, which were
accompanied by a low awareness of efficiency issues. It also led to growth in the secondary oil industry;
a large petrochemical complex was developed that is the largest exporter for polymers, plastics, and
every kind of material made from hydrocarbons.
Waste heat use in KSA is further restricted because the usable temperature potential is smaller because
of the high ambient temperature.
The term “waste heat use” in this report is not always defined as gross or net, or related to lower or
higher heating values. However, it is always used in a correct technical context in each part of this
report.
The word “potential” is used in this report to refer to the energy saving that could be achieved
compared to a reference status at the same quantities and qualities and without considering technical
progress by a different operation or modification of the equipment. “Technical” potential is the
maximum that can be realized under particular technical conditions, and ”economic” potential is a
measure that makes sense under economic, competitive conditions.
The term “efficiency” in this report is not defined strictly scientifically but is instead closer to a
thermodynamic definition. Generally, efficiency is understood as the ratio of output to input of energy
in a technical sense. For example, it is in the context differentiated between exergy and anergy share of
energy means electricity/power and heat although it is not always described explicitly.
2.1.1
Energy Situation in KSA
(Source: 13)
Energy growth in KSA is tremendous, as shown in Figure 2-1. This comes especially from the growing
petrochemical industry, from growth of population, and from changes in lifestyle such as the
tremendous growth of cooling capacities for air conditioning (AC) and food cooling in recent years in all
fields of public and residential life. Final energy consumption shown in Figure 2-1 reflects all types of
losses.
2-2
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
total and final energy
consumption in Mtoe
200
150
100
50
0
1985
1990
1995
final
Figure 2-1:
2000
2005
2010
total
Energy Consumption in KSA (Source: 13)
Electricity growth in industry is much lower than the growth of total energy demand. Energy
consumption for electricity production respecting the average efficiency of power production in KSA is
shown in Figure 2-2.
share of final energy
consumption in Mtoe
60
50
40
30
20
10
0
1985
1990
1995
Industry
Figure 2-2:
2000
2005
2010
total
Energy Consumption—Total and Industry Share in KSA (Source: 13)
Although growth of electricity in industry is low, the growth of general energy consumption in industry
is high, as shown in Figure 2-3.
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energy consumption in Mtoe
CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
16
14
12
10
8
6
4
2
0
1985
1990
1995
2000
industrial energy consumption
Figure 2-3:
2005
2010
electricity
Consumption of Energy and Electricity in Industry in KSA (Source: 13)
share of final energy
consumption in Mtoe
Final energy demand in industry is growing with the same velocity as the electricity demand of
households, as shown in Figure 2-4.
60
50
40
30
20
10
0
1985
Without transport
1990
1995
Household&Agriculture
Figure 2-4:
2005
2000
2010
Industry
Growth of Final Energy Consumption
For Figures 2-1 through 2-4, it is not clear if the demand for water desalination is counted as industry in
the statistics, but it is likely not because the desalination plants are all operated as combined heat and
power (CHP) plants.
Although the growth of industry is nearly constant, energy demand is growing tremendously, perhaps
reflecting independent self-generation. The much lower growth of electricity may be because of the
growth of independent, decentralized generation.
2-4
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
2.2 Investigation of the Sectors
2.2.1
Method
For Sectors 1 to 4, data were used from a variety of sources: business reports, generally accessible
sources, meetings at saline water and power-generation industries, knowledge from former GIZ
projects, and personal contacts.
For Sector 5, “other industries,” there are very few available data. Therefore, the method used in this
study, depicted in Figure 2-5, is based on the proportional transfer (“rule of three”) of available figures
from Germany/Europe and other countries to KSA, accounting for the differences between KSA and the
database by using correction factors. An exception was the steel company Hadeed, which is an affiliate
of the petrochemical SABIC group. In that case, as for the other SABIC affiliates, high energy efficiency
(EE) awareness was assumed.
Sector 1-4:
Sector 5:
•
Water production (SWCC and private companies)
•
Power production (SEC and private companies)
•
Refineries (SaudiAramco): not in focus of this study
•
Large Petrochemical production (SABIC et al.)
Industrial complex
• subsector share (market, production)
• age of equipment
• population
Comparison with German, EU or international
• Keyfigures
• Sums, percentages
• Specific indicators
Kingdom of Saudi Arabia
Waste Heat Use Potentials in Industry
Figure 2-5:
Method of Quantitative Waste Heat Use Potential Analysis
For a correct allocation for the proportional method, the main characteristics of Sector 5, “other
industries,” must be similar.
2.2.2
Sector 1—Water Production
(Sources: 3, 11, 12, 30, 43, 44)
Because there are few wells with potable water, almost no rain, and an extremely warm climate, a
significant portion of the country’s water must be desalinated from seawater and pumped to the cities,
as shown in Figure 2-6.
KSA is a country of about 27 million people who are highly concentrated in a few cities along the east
and west coasts.
Two large industry centers (Yanbu and Al-Jubail), which were founded a few decades ago, are relevant
for water- and energy-related investigations.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Figure 2-6:
Desalination Plants and Water Pipelines (Source: 3)
Besides some smaller desalination plants, desalination can be allocated to the cities as shown in Table
2-2.
Because of its importance, in the past all water production and distribution was done by the stateowned company SWCC. Today, however, as shown in Table 2-1, roughly 30 percent is produced by
private desalination plants (independent water and power producers [IWPP]), and the privatization
process in this sector will increase this share. The private plants are mainly all new, though nearly
50 percent of all desalination capacity is not older than 10 years.
Table 2-1:
Water Production and Distribution by SWCC and IWWP
Mio m³/d
State-owned SWCC:
881 (2010)
IWPP:
420 (2010)
Table 2-2:
Desalination Plant
Flow
Desalination Plants and Water Allocation
City Region
Mio m³/y
Yanbu
Shuaibah
Al-Khobar
Al-Jubail (Gulf coast)
Jeddah
118
100
132
334
132
Madinah
Makkah, Jeddah
Dammam
Riyadh
Makkah, Jeddah
Distance*
Height Difference**
km
M
225
80
32
800
85
20
20
20
200
20
*Transport length between desalination plant and consumer city.
**Maximum geodetic pumping height along transport way.
2-6
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
The distribution of desalination train size is shown in Figure 2-7.
Figure 2-7:
Distribution of Desalination Train Size (Source: 3)
All thermal desalination plants are operated in cogeneration mode and mainly only at full load. The
electricity produced by SWCC in parallel is delivered to SEC.
The major share of water production is done by the thermal technologies Multi-Stage Flash (MSF) and
Multi-Effect Distillation (MED), as shown in Figure 2-8.
MSF+MED
Reverse Osmosis
Figure 2-8:
Production Share
84 %
16%
Distribution of Desalination Technology by Type (Source: 3)
Thermal desalination means that all water has to be evaporated and then condensed or distilled.
To minimize the waste heat equipment, the operation pressure starts from 0.125 MP and is reduced
step by step below atmosphere, though evaporation at low temperatures is possible.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
The energy necessary for thermal desalination is:
Thermal heat
MSF
MED
Mechanical power
250–400 MJ/t 4.5 kWhel/t
250–400 MJ/t 1.5 kWhel/t
The heat can be reused, but only at a level below 100° C. A typical dependency of consumption and
equipment is shown in Figure 2-9.
Figure 2-9:
Specific Energy Consumption in Relation to Technology
The performance of the large desalination plants in 2012, both scheduled and actual, is shown in Figure
2-10. Assuming that for each location the water was produced with the newer unit, the total weighted
consumption would be 361 kJ/kg.
energy consumption in MJ/t
700.00
600.00
500.00
400.00
300.00
Weighted
average
200.00
100.00
0.00
scheduled
actual 2012
Figure 2-10: Desalination Performance 2012 (Source: 3)
2-8
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
In mechanical desalination (reverse osmosis), molecular filtering takes place at very high pressures.
Final energy demand in industry is growing much faster than in households.
Large potential would have also a higher water recycling rate. Saving water will influence the energy
demand for desalination and the demand for transport, especially for Riyadh. The potential can be seen
in Figure 2-11, which shows that reused water adds up to about 10 percent.
Water demand was 5.8 Mio m³/d in 2011 (Source: 14).
100.00
90.00
80.00
Share in %
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
potable water
consumed
generated
wastewater
wastewater
treated
reused water
Figure 2-11: Wastewater Generation 2010 (Source: 30)
2.2.3
Sector 2—Power Generation
(Sources: 1, 5, 8, 60)
The main target for power generation is to meet the fast-growing demand for electrical power,
especially the peak demand, in KSA. Depending on different assumptions, domestic demand in KSA will
eventually meet the domestic production of oil and gas. Whether or not a problem will arise, as
described in the scenario shown in Table 2-3, depends on further assumptions (Source: 5).
Table 2-3:
Projections for Energy Capacity and Consumption
2010
2020
77,430
Capacity planning
MWel
49,138
Consumption
MWel
88,550
Independent of these various influences, all publications conclude that improvements need to be made
to satisfy growing demand.
Proposed solutions include:
•
•
•
Erection of large, high-efficient thermal, base-load units for different fuels (types and amounts of
proper fuel)
Erection of large combined-cycle units for efficiencies of more than 55 percent
Construction of a common Gulf Cooperation Council (GCC) power grid (Kuwait, Qatar, Bahrain, KSA,
UAE, and Oman)
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
•
•
•
•
•
Improvement of the national grid (electrification of 1,126 villages [more than 10
houses][10 percent] with 44,000 people [1.6 percent] by means of a five-year plan)
Unbundling of generation, transmission, and distribution
Use of smart grid technologies and energy management
Auditing and use of all efficiency measures
Performing additional research.
Power generation has grown tremendously in recent years. As shown in Figure 2-12, average efficiency
rose from 27 percent in 1990 to 31 percent in 2010. A large impetus for this development came from
the introduction of large combined-cycle plants, as shown in Figure 2-13.
32
efficiency in %
31
30
29
28
27
26
1985
1990
1995
2000
2005
2010
efficiency
Figure 2-12: Growth of Efficiency for Power Generation in KSA
50
electric capacity in GW
45
40
35
30
25
20
15
10
5
0
1985
1990
1995
steam
gasturbine
2000
2005
2010
cogeneration
Figure 2-13: Growth of Electricity Generation by Technology Type in KSA
As in recent years, when the operation of the units followed the merit order principle, as shown in
Figure 2-14, it can be assumed that the operation of small, older units could be further reduced,
thereby increasing average efficiency.
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All units
2007
All
unitsfrom
in 2007
Figure 2-14: Operation Ranking by Merit Order Principle (Source: 60)
Yet, especially at peak times, many older, smaller, lower-efficiency units that are mainly gas turbines in
open-cycle design are operated. Peak times can be assumed to account for 10 percent of the time and
about 20 percent of the production, which means about 10 GW.
Waste heat use measures are mainly targeted to the older plants; payback times for such measures will
be prolonged.
Like water production, the electricity sector is in a phase of privatization of the former 100-percent
state-owned SEC, which started in 2004. Today about 25 percent of electricity is produced by non-SEC
companies, as shown in Figure 2-15. About 10 of these companies are mostly private water producers.
2% 2% 2% 1% 0%
0%
0%
2% 2%
SEC
SWCC
Jubail Water&Electricity
Shuiabah Water & Electricity
5%
Tihamah Power Generation
9%
Marafiq(Yanbu)
Shuqaiq Water & Electricity
Saudi Aramco
Arabian Rabigh Water & Electricity
75%
Saudi Cement
Jubail Power
Aman Modern energy
Figure 2-15: Distribution of Power Generation by Producer (Source: 8)
The privatization and unbundling of generation, transmission, and distribution is accompanied by the
Electricity & Cogeneration Regulatory Authority (ECRA) in Riyadh. ECRA must issue permits and licenses
for all water and power production facilities. Permits issued in 2012 are shown in Table 2-4.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Table 2-4:
ECRA Status of Permits and Licenses in 2012
Permits Issued Holding License Exemption from License
Electricity generation
Cogeneration
Desalination
Desalination
MWel
MWel
m³/d
m³/d
1,000
12,310
1,702,000
720,000
57,767
14,141
1,716,100*
1,500
942
40
*Not including Saudi Aramco
In addition to stationary power generation, some mobile units generate 899 MWel. These will be
decreased by enlarging the transmission and distribution network in the coming years.
Age distribution of the generating units is similar to that for the desalination plants: Roughly 50 percent
of the units are less than 10 years old.
Power generation by unit size, comparing the SEC and other companies, is shown in Figure 2-16.
electric generation capacity in GW
16
14
12
10
08
06
04
02
00
0-5
6-10
11-20
SEC
21-25
26-30
31-35
35-50
Others
Figure 2-16: Power Generation by Unit Size, SEC and Others
Many of the large modern plants are also gas-fired, combined-cycle units, which have generally higher
efficiencies, as shown in Figure 2-17. That also positively affects overall efficiency.
2-12
Volume 1
2,500
190000
185000
2,000
180000
175000
BTU / Year
1,500
170000
1,000
165000
160000
500
155000
00
150000
2007
Gas
HFO
2008
Diesel
2009
SEC Electricity Production in GWh/year
CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
2010
2011
Crude
SUM
SEC production
Figure 2-17: Electricity Production by Energy Type in KSA
The decrease in production of water in recent years, especially at SWCC (see Figure 2-18), also means
that smaller, older, and more inefficient desalination plants are operated less.
water production capacity in 1000 t/day
4.00
3.80
3.60
3.40
3.20
3.00
2.80
2.60
2.40
2.20
2.00
2000
2002
2004
2006
2008
2010
2012
Figure 2-18: Growth of SWCC Desalination Capacities
2.2.4
Sector 3—Refineries
(Sources: 4, 53)
2.2.4.1
General Process
Refining oil is an energy-intensive process. Energy costs are usually 50 percent of the total costs. This
may be different in KSA, where the refinery operating company also owns the oil. Most of the energy is
used as steam and heat for heating processes. Electrical energy is mainly used for moderate pumping.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
2.2.4.2
Industry in KSA
In KSA, few very large companies are operating in the oil export business, as shown in Table 2-5.
Saudi Aramco operates 10 refineries. One refinery is operated by Petro Rabigh; some ownership is
shared with international oil companies. Totally 590,000 t/d could be produced.
Table 2-5:
Location
2.2.5
Refineries in KSA (Source: 53)
Operator
Riyadh
Saudi Aramco
Capacity
thousand m³/d
19
Start Operation
Rabigh
Jeddah
Petro Rabigh
Saudi Aramco
64
16
1989
1968
Ras Tanura
Yanbu
Yanbu
Jubail
Saudi Aramco
Saudi Aramco
Saudi Aramco/Exxon
Saudi Aramco/Total
87
35.8
64
64
1941
1983
later than 1990
later than 1990
YASREF
Jazan
Jazan
Jazan
SUM
Saudi Aramco/Sinopec
Saudi Aramco
Saudi Aramco/Shell
Saudi Aramco/Total
64
64
48.5
64
590.3
later than 1990
later than 1990
later than 1990
later than 1990
1974
Sector 4—Petrochemical (SABIC Affiliates, et al.)
In addition to Saudi Aramco, which operates all refineries and oil export facilities such as pipelines,
tanks, etc. of KSA and is the largest oil exporter in the world, the second-largest export company is the
SABIC group, which produces various types of petrochemical products, especially chemical-based
products. The company was founded in 1976 according to Royal Decree No M/66.
SABIC is 70 percent owned by the government and 30 percent owned by other investors. At SABIC
40,000 employees generate sales revenue of 189 Bn SR (or about 45 Mio €).
The SABIC affiliate, Hadeed, is the largest and only steel producer in KSA.
The business sectors of SABIC companies are:
•
•
•
•
•
•
•
Chemicals
Performance chemicals
Innovative plastics
Polymers
Ethylene glycol
Fertilizers
Metals.
The distribution of products by SABIC affiliates is shown in Table 2-6.
2-14
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50
Ar-Razi
Al-Bayroni
Yanpet
Ibn Sina
Sadaf
Saudi Methanol Company
Al-Jubail Fertilizer Company
Saudi Yanbu Petrochemical Company
National Methanol Company
Saudi Petrochemical Company
Shrouq
Ibn Rushd
Safco
Saudi Kayan
Saudi Arabian Fertilizer Company
Saudi Kayan Petrochemical Company
50
Al-Jubail
x
x
x
x
x
x
ethanolamine
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
acetone
x
polycarbonate (PC)
x
x
ethoxylate
x
butanol
Al-Jubail
Al-Jubail
Yanbu
x
x
x
x
x
x
natural detergent
35
42.99
47.26
Al-Jubail
low density polyethylene (LLDPE)
Al-Jubail
x
x
Styrene
Saudi Japanes Acrylonitrile Company
Al-Jubail
x
x
dichloride
Al-Jubail
x
x
Caustic soda
50
Al-Jubail
x
2-ethyl hexanol
Yanbu
x
chemical grade methanol
Al-Jubail
Yanbu
Yanbu
x
x
Chemicals
Saudi Industrial Fiber Company
70
high density polyethylene (HDPE)
51.95
x
Ethylene glycol (mono, di, tri)
Sharq
Yansab
Yanbu National Petrochemical Company
Ethylene glycol (EG)
x
aromatics
Kemya
50
50
Gas
National Industrial Gases Company
x
x
gases
Al-Jubail
linear alpha olefines
x
x
urea
70
x
x
ammonia
Eastern Petrochemical Company
50
50
Gas
National Industrial Gases Company
x
x
compound fertilizer
Al-Jubail
x
x
phosphate
Al-Jubail Petrochemical Company
50
Ibn Al-Baytar
National Chemical Fertilizer Company
Al-Jubail
x
olefines
Al-Jubail
x
liquid fertilizer
71.5
80
75
United
100
100
Polyproylene
Saudi European Petrochemical Company Ibn Zahr
Sabcat
x
Methyl-tert-butylether MTBE
Jubail United Petrochemical Company
Sukuk
SABIC Industrial Catalyst Company
Ethylene
x
Polyethylene
x
Butene-1
SABIC Sukuk Company
Al-Jubail
Polystyrene
Al-Jubail
Butene-2
100
VCM
100
Benzene
Petrokemya
Acrylonitrile Butadiene Styrene (ABS)
Hadeed
Propylene
Arabian Petrochemical Company
shares 2012
%
Distribution of Products by SABIC Affiliates (Source: 4)
Butadiene
Saudi Iron and Steel Company
Subsidiary
Table 2-6:
CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
oxygenates
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
The main products and their production rates are shown in Table 2-7.
Table 2-7:
SABIC Affiliates’ Products and Production Rates
Polymer
Ethylene glycol
Fertilizer
Chemicals
Metals
1.2 Mio t
6.46 Mio t
6,600 t
600,000 t
5.6 Mio t
Because of the company’s sustainability awareness and because the energy price has a major share in
many of the products, SABIC follows an internal EE policy in both product development and production
itself. Attempts are made to lower temperatures as much as possible in all processes.
In 2012 SABIC received approval for four EE projects from Saudi Arabia’s National Designated Authority
for the Clean Development Mechanism (CDM). One of these was the new CO2–urea conversion facility
at the SAFCO V plant.
SABIC’s EE program states that both water and energy consumption will be reduced by 25 percent by
2025 from the 2010 baseline.
The group follows a “Best Operation Practice” program, in which experiences between the different
affiliates are exchanged. In addition, a KPI system was installed for continuous process monitoring.
While SABIC holds about 50 percent of the petrochemical production in KSA, private companies like
NATPET share the rest.
It is assumed that NATPET, which also operates in the global market under conditions of international
competition, has highly developed EE awareness.
2.2.6
Sector 5—Other Industries
Final energy consumption in industry is influenced by:
•
•
•
•
•
Product quantities (e.g., masslows, pieces)
Products and their qualities
Technologies
Price structures
Other elements (e.g., climatic impacts).
To estimate waste heat use potential, relevant industries’ market, products, production, and equipment
must be understood. Therefore, an analysis of the local conditions in the different KSA subsectors was
necessary.
The results for the relevant industries in KSA are described in subsequent chapters. This analysis gives a
good overview of the industrial subsectors in KSA.
In general, waste heat use for power generation and waste heat use for internal process must be
differentiated.
As most of the production in this subsector is not exported, a correlation between the population,
domestic demand, GDP, and production is assumed.
The sector “other industries” was divided into subsectors, which were analyzed with regard to:
•
•
•
2-16
General process
Characteristics in KSA
Industry in KSA.
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In particular, these parameters were tracked:
•
•
•
•
•
Number of companies
Product
Depth of production
Size and output
Age of production equipment.
Because there is no general register of companies in KSA industry and there is no record of comparable
figures for the different subsectors, it was decided to generate a list with waste heat use–relevant
production data by means of a “Heat and Power Questionnaire,” as shown in Figure 2-19.
Figure 2-19: Energy Audit Questionnaire for KSA Study, 2013
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
A list of 751 companies was assembled from diverse sources, with small differences in:
•
•
•
•
Subsectorizing
Base year (e.g., founding, start operation)
Status of the company (e.g., in operation, factory planned)
Balancing limits (e.g., energy contained in material, food, and beverages).
From the list, which covers about 15 percent of all companies in KSA, 102 companies were selected to
address the developed questionnaire. Only existing and producing companies in a waste heat–relevant
sector were considered.
The total number of registered enterprises in KSA is reported at 5,043. This figure contains all types of
enterprises (e.g., trade, offices, crafts, small and large production facilities).
Table 2-8 shows the distribution between refineries and basic chemicals and “other” industries in
another published analysis.
Table 2-8:
Industrial Distribution in KSA
Number
Turnover
97
1,908
€50 Bn
€28 Bn
Refineries and basic chemicals
Other
Thus, “other industries” accounts for 40 percent of all companies in KSA by number and 62 percent by
turnaround. The distribution by turnover in this analysis is shown in Figure 2-20.
Rubber &
plastics
11%
Chemicals
3%
Basic metals
36%
Pulp & paper
7%
food & sugar
38%
Textile
5%
Figure 2-20: Distribution of “Other” Industry Sectors in KSA by Turnover
Because response to the distributed questionnaire was limited, it was decided to transfer data from
other countries with the use of correction factors.
Analysis Results
Our analysis allowed us to summarize the characteristics of industry in KSA:
•
•
•
2-18
There are a few very large groups like Saudi Aramco and SABIC.
Large companies like SABIC, NATPET, et al., are expanding rapidly.
There was enormous growth (by a factor of 6) from 1995 to 2011.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
•
•
•
•
•
•
•
Industrial cities like Al-Jubail and Yanbu (strategic for the energy-intensive petroleum industry) have
been established.
There are many young and small companies.
Many companies or groups are operating in more than one sector.
Many companies are very diversified in products.
Many of the smaller companies are family driven.
The largest concentration of companies (815) is in the construction materials subsector (based on a
published study of 2,005 companies).
The development of industry in different sectors is highly dynamic because of growth and changes
(e.g., in lifestyle).
It was concluded that the equipment of the companies must be assumed to be mostly modern.
Turnover of the waste heat–relevant production is €78 billion (2011).
The distribution for the subsectors by turnover is shown in Table 2-9.
Table 2-9:
Distribution of Industrial Subsectors by Turnover
Coke and refined petroleum products
Metals
Food
Beverages
Others
2.2.7
64%
13%
7%
7%
9%
Waste Heat Use–Relevant Industries
The different waste heat use–relevant subsectors of the “other industries” sector were analyzed for
comparability. The influences and main aspects for each single industry are described in the context of
the process, the neighboring countries of KSA in the economic region (e.g., GCC), and history.
Regarding the temperature potential of waste heat, two industry groups can be identified, as shown in
Figure 2-21. High waste heat temperatures come from melting processes (e.g., glass, steel, minerals).
The low temperature waste heat potentials come mainly from drying and cooking processes (e.g., paper
making, sugar, textile bleaching).
The following sectors were determined to be not relevant for waste heat use:
•
•
•
•
•
•
•
Transport
Trade, sales, and service
Mechanical manufacturing
Research
Engineering
Waste treatment
Injection-moulded production.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
High temperature WHR
High temperature
Bottoming
cycles
WHR,
Bottoming cycles
FE-Metals
(Blast Furnace Gas)
FE M e ta l s
LowLow
temperature
temperature
Topping cycles
Topping cycles
FE M e ta l s
Chemicals
Minerals
Refineries
Glass
Pulp&paper
Non FE-Metals
Food
Textile <100°C
Figure 2-21: Waste Heat Temperature Profiles by Industry Group
2.2.7.1
Subsector—Food
(Sources: 9, 49, 50)
General Process
There are two distinct types of food production:
•
•
Fresh agri-processed (e.g., fruits and vegetables, marine, grains, dairy)
Processed (e.g., packaged foods, including dairy, meat, poultry, bakery; frozen, chilled, canned food;
oils; cereals; beverages).
Characteristics in KSA
The turnover in the food sector in the GCC was US$25.8 billion in 2010; of this, KSA’s share was US$16.8
billion (Source: 9).
Because of climatic conditions that cause a shortage of water and arable land in the GCC, 80 percent to
90 percent of food is imported. As a result, KSA, UAE, Qatar, and Kuwait have been buying farmland
abroad for some years.
Domestic food production and consumption in KSA and the GCC countries is detailed in Table 2-10.
Because population growth has increased steadily for many years, there are more young people in KSA
compared to the demographics of Europe. Increasing average income causes changes in food behavior,
especially an increasing in demand for (Source: 9):
•
•
•
•
2-20
Fast, prefabricated food and retail distribution (urbanization; fast-paced, young lifestyles)
More carbohydrate-rich, less protein-rich foods (increased per capita income)
More health food (concerns of diabetes and other diseases)
Halal food.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Table 2-10: Food Production and Consumption in KSA and the GCC Countries
PRODUCTION:
Agri
Dairy
Meat
wheat, tomatoes, melons, dates, citrus, mutton, chicken, eggs
milk, cheese, cream, fats, yoghurts (30% of KSA demand)
poultry (60% of GCC demand); red meat (40% of GCC demand)
CONSUMPTION:
Population (Mio)
Food, except beverages (Mio t)
Consumption (kg/y/capita)
KSA
27
40.6
619 (2013)
GCC
40.6
52 (2015)
960 (2013)
Global
7,000
KSA
3.3
2.1
GCC
1.1 (average)
Global
CONSUMPTION TRENDS:
Population growth (%/y)
Food market growth (%)
The distribution of food subsectors in 2013 is shown in Figure 2-22.
Figure 2-22: Distribution of Food Subsectors in KSA (Source: 9)
Compared to global figures, as shown in Table 2-11, it can be assumed that there will be changes in the
next years, especially in the milk and meat subsectors, and that cereal consumption probably will
decrease (Source: 9).
Table 2-11: Food Consumption in KSA and Globally by Subsector, 2013
Cereals
Vegetables and fruits
Milk and milk products
Meat
Others
SUM
Volume 1
KSA
KSA
Global
Consumption
Mio t/y
%
%
kg/y/capita
15,722
6,936
3,948
1,673
2,197
30,476
42.7
25.8
15.0
5.8
10.7
46
20
23
11
–
171
72
85
40
–
368
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Industry in KSA
The principal food companies in KSA for the different food subsectors are (Source: 9):
•
•
•
•
Agri-processed
− Savola
− Al Jouf
− Tabuk Agriculture
Dairy
− Almarai
− National Agriculture Dev. Co.
− Saudi Dairy & Foodstuff Co.
Processed/Frozen
− Halwani Bros.
− Food Products Co.
Meat, Fish, Poultry
− Saudi Fisheries
− Jazan Development Co.
− Alyoum (brand of Almarai).
Three food companies in KSA generate most of the total revenue of the food sector (Source: 9), as
shown in the following.
Almarai: This company accounts for 71 percent of the KSA food market.
Dairy
Cheese and butter
Bakery
Fruit juice
Long-life dairy
Infant nutrition
Percentage of turnover
45.7
18.5
11.9
10.8
9.5
(start of plant at Al Kharj in 2011).
Savola (with Afia, an affiliate): This company produces edible oils and sugar and is also engaged in food
retail and the plastics sector.
Nalco: This company operates as a U.S. subsidiary in the food/beverages, paper, oil, energy, mining,
hospitals, healthcare, chemicals, water treatment, and consulting sectors.
Jazadco: This company specializes in bottled water, producing 46 Mio/year.
2.2.7.2
Subsector—Sugar
(Source: 58)
General Process
The sugar subsector accounts for a large share of food production. Like beverages, it can be handled as
a separate part of the food subsector.
The global trend in the sugar business is to move the “raw to final sugar” production step to the
destination regions because bulk transport is cheaper than packaged transport.
Characteristics in KSA
IN KSA, all sugar (1.45 Mio t/y) is imported, as of 2013. Sugar is not exported.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
More than 80 percent of the sugar in KSA is handled by United Sugar Company (USC), a subsidiary of
SAVOLA, which also operates sugar factories in Egypt.
The first domestic brand in KSA was the white sugar brand, “Al-Osra,” a brand name of USC. Today
70 percent of the total retail sugar market in KSA is covered by this brand (Source: 58).
Sugar production in KSA is shown in Table 2-12.
Table 2-12: Sugar Production in KSA
Sugar Refineries
1997 USC
2005 USC
2011 USC
2015 USC
Omega Sugar Refining
Status
Jeddah
planned
Jeddah
planned
coplanned
Location
1,370
Shokna
1,370
Jeddah
Dammam
t/d
greenfield
2,500
extension
1,370
greenfield
Type
Product
greenfield
white sugar
extension
Industry in KSA
USC Jeddah (Source: 58)
Savola Sugar Division was founded in 1997–1998.
The aim at that time was to turn KSA into a self-supplied sugar market. The action was initiated by the
Al-Yamamah Economic Offset program.
The Jeddah factory is a standalone cane sugar refinery producing EEC No. 2 product.
The process is operated with dispensed affination, conventional carbonatation, and granular-activated
carbon processes. The operation in a water-deficit region and away from a berth required installation of
a long raw sugar conveyor, desalination plant, and AC condensers. In general, the production line is
identical to that used in U.S. and European sugar factories.
USC Shokna (Source: 58)
The equipment supplier for this facility is the Tate & Lyle Company. The automation is realized via
Profibus PA and AS-I from Siemens. Control is arranged in a central engineering workstation and a
central control room. Control is also supplemented by options for local operation because this type of
process handling is easier for junior personnel. Therefore, the latest standards regarding product quality
and efficiency must be assumed in regards to engineering and installed technology.
2.2.7.3
Subsector—Glass
(Sources: 7, 16, 34, 37, 38)
The glass subsector can be further divided according to type, as follows:
•
•
•
•
Container glass (e.g., food and beverages; flacons for perfumery, cosmetics, and pharmacy)
Flat glass (e.g., building, automotive, solar-energy glass, safety glass)
Glass fiber (e.g., fiber-reinforced polymers or glass-reinforced plastics)
Others.
Glass production can also be subdivided according to application, as follows:
•
•
•
•
•
Packaging (e.g., jars for food, bottles for drinks, flacon for cosmetics and pharmaceuticals)
Tableware (e.g., drinking glasses, plates, cups, bowls)
Housing and buildings (e.g., windows, facades, conservatory, insulation, reinforcement structures)
Interior design and furniture (e.g., mirrors, partitions, balustrades, tables, shelves, lighting)
Appliances and electronics (e.g., oven doors, cooktops, TV, computer screens, smartphones)
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
•
•
•
•
•
Automotive and transport (e.g., windscreens; backlights; lightweight, reinforced structural
components of cars, aircrafts, ships)
Medical technology, biotechnology, life science engineering, optical glass
Radiation protection from X-rays (radiology) and gamma rays (nuclear)
Fiber optic cables (e.g., phones, TV, computer)
Renewable energy (e.g., solar-energy glass, wind turbines).
Generally glass consumption is related to population growth, but behavior, development, and other
factors can also play a role.
The use of glass is often influenced by fashion and technical development, as in architecture. For
example, a current trend is moving away from reflective glass to clear glass on facades.
Today the consumption of glass in KSA is about 7 kg/year/capita. In comparison, consumption in the
European Union is about 2 kg/y/capita, mainly depending on different trends in the recycling rate,
plastic containers, building sector, and so forth.
Container glass generally loses market share continuously in the face of plastic applications.
The average price of glass is about 400 €/t.
The global market for flat glass and container glass from 2009 to 2017 is described in Table 2-13.
Table 2-13: Global Market for Flat and Container Glass, 2009–2017
Flat glass
Demand
2009
Growth
LCD global
Solar panels
m²
T
€
%/y
m²
m²
Container glass
€
2010
2011
2012
2013
2016
2017
9.2 Bn
52 Mio
22 Bn
4–5
467 Mio
120 Mio
22 Bn
It can be concluded that the solar glass market is a niche market. To install a production line for solar
glass, which is a white glass, the demand of a 400 MW/y flat glass and 250 MW/y parabolic troughs can
be taken.
General Process
A common step in all glass-making processes is that the prepared material has to be melted at high
temperatures.
Because of its liquid behavior at high temperatures, glass can be:
•
•
•
•
Poured
Blown
Pressed
Moulded.
Process glass-making steps are as follows:
•
•
•
2-24
Different types of sand, recycled glass, and additives are melted at around 1,500–1,700 °C.
The material is shaped.
The product is cooled.
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Ensuring specific properties such as higher mechanical strength, resistance to breakage, or the ability to
filter selected optical waves requires further processing, including:
•
•
•
•
•
•
Coating
Tempering at 600° C
Laminating
Coloring
Patterning
Mirroring.
The use of recycled glass reduces necessary energy by about 25 percent.
In manufacturing, production capacities of up to 850 t/d melted glass are usual. On average today, a
150,000 t/y production rate can be considered the minimum capacity for profitability of a new
production line.
Typical energy demands for glass manufacturing are shown in Figure 2-23.
Figure 2-23: Typical General Sankey Diagram for Glass Production
It is usual to preheat combustion air by flue gas heat exchangers.
The distribution of losses by electrical work is shown in more detail in Figure 2-24. The two Sankey
diagrams show that the big losses and specific energy consumption depend on the product and
technology, as described in Table 2-14.
The necessary furnaces are fired by gas, and the lifetime of such furnaces today is 12 to 15 years.
Therefore, the process-dependent specific energy consumption is frozen for years once the technology
is chosen.
The energy demand for glassmaking can be reduced by using recycled glass, especially if green and
brown glass is recycled. As a rough estimate, each 10 percent recycling share saves about 3 percent of
energy.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Figure 2-24: Typical Detailed Sankey Diagram for Glass Production Work (Source: 34)
Table 2-14: Specific Energy Consumption by Glass Product and Technology
Specific Energy Consumption (MJ/t)
Cross-fired furnace with air preheating
Cross-fired furnace with air preheating
Cross-fired furnace with air preheating
Regenerative end-fired furnace
Regenerative furnace
Fuel oxygen-fired furnace
Furnace with recuperative air preheating
Furnace with recuperative air preheating
container glass
flat glass
television screen glass
container glass
container glass
container glass
tableware glass
glass fiber glass
4,200
6,300
8,300
3,800
5,000
3,300
6,700
4,300
Characteristics in KSA
Glass production in KSA is mainly oriented towards packaging, especially in the food and beverage
business (Source: 37), as shown in Table 2-15.
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Table 2-15: Glass Production in KSA, the Middle East, and European Union
Production
Container glass
Beverages
Food
Perfume
Flat glass
Glass fiber
Other
Container glass
Number of plants
KSA
Middle East
wt%
wt%
wt%
wt%
wt%
wt%
wt%
100
<5
EU
EU
60
56
29
15
30
6
4
170
The KSA glass subsector is balanced and has no import/export.
Industry in KSA
The SAGCO Company located in Jeddah specializes in the production of bottles. The plant has been
expanded through the years (Source: 38), with capacity rising from 60 t/d in 1985 to 650 t/d (or 180,000
t/y of flint, green, and amber glass) in 2012. Glass production in KSA is shown in Figure 2-25.
Figure 2-25: Glass Production in KSA
During the modernizations, the technologies were also updated. Figure 2-26 shows the updated control
structure after the last modernization.
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Figure 2-26: Scheme of Modernized Glass Manufacturing Control in a KSA Company
2.2.7.4
Subsector—Pulp and Paper
(Sources: 17, 45, 47)
General Process
The general process for pulp and papermaking consists of two steps: the pulping and the papermaking.
Pulping is the “cooking” of cellulose mixed with recycled paper.
Papermaking is characterized mainly by a dewatering and drying process.
The paper industry is usually subdivided into the production of:
•
•
•
Office paper
Corrugated paper (packaging)
Tissue or hygienic paper.
The typical Sankey diagram for paper production is shown in Figure 2-27.
Typical measures for fuel savings in paper manufacturing are:
•
•
•
•
•
•
•
2-28
Condensate cooling to 85 °C or below and optimization of feedwater preheating
Process optimization (e.g., higher dewatering rate at the sieve dryer)
Reduction of wrecking restarts by raising water supply temperatures
Reduction of heat demand by direct heat recovery at the consumer (e.g., by machine air heat
exchanger at the paper machine)
Reduction of power consumption by speed control and technical optimization measures at the
single consumer (e.g., motors, fans, pumps, and new technologies)
Use of wet waste gas at ultra-low temperatures (below 50 °C) by ORC cycles
Reduction of water losses.
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Figure 2-27: Typical Sankey Diagram for Paper Production (Source: 32)
A possible value for continuous reduction in energy consumption in a German paper mill is 2.5
percent/y.
Many of the measures are quite specific, depending on the paper machine, which is usually adjusted
and optimized not by the manufacturer but by the paper company itself.
The specific consumptions of energy is highly dependent on the product, its quality, recycling rate,
wastewater handling, age of the paper machine, and other influences. For tissue paper, 0.34 kg
steam/kg paper and 41 kW/t paper are usual values.
Usually paper factories have their own steam generation with several boilers, some of which are also
used to burn production residues.
Characteristics in KSA
Paper and board consumption in KSA, the GCC, and globally in 2010 is shown in Table 2-16 (Source: 47).
Table 2-16: Paper and Board Consumption Compared, 2010
Consumption
Consumption
Volume 1
Mio t/y
kg/y/capita
KSA
GCC
Global
1.1
39.6
3.5
400
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The following are facts about the paper market in the Middle East:
•
•
•
•
•
The market volume is US$10 billion (Middle East), assuming a price of US$300/ton.
Middle East paper production meets 50 percent of local demand.
In KSA the recovery rate is about 28 percent, which is very low (Source: 47).
UAE is the leader in paper processing in the GCC region (Source: 54).
Water consumption is 3–5 m³/t paper (Source: 47).
The growth rate of paper consumption will be tremendous in the coming years. While the demand for
printing paper decreases worldwide, the demand for tissue and hygienic paper as packaging material
will grow rapidly. The growth rate for paper production in Arab countries is shown in Figure 2-28.
Mio t/y
50
Arab countries
38
29
MiddleEast
18
3.5
GCC
2010
2020
Figure 2-28: Growth of Paper Production in Arab Countries
Pulp is imported 100 percent in KSA. As of 2006, imports of hardwood and softwood pulp amounted to
0.055 Mio t/y, compared to 0.208 Mio t/y for the GCC countries. The distribution of pulp imports by
source is shown in Table 2-17.
Table 2-17: Pulp Imports to KSA by Country of Origin (%)
USA
Brazil
Far East (i.e., Indonesia, South Korea, Singapore, and Malaysia)
Canada
Sweden
Russia
30
25
20
15
9.5
0.5
Investigations of the use of domestic resources have been undertaken to reduce import dependence.
The Agricultural Research Centre at King Abdulaziz University initiated the development of natural
resources for pulp production, such as fiber production from date palms. It was found that although
general lingo-cellulose from date palms is usable for pulp production, timber is still preferred. Also,
most of the pulp is used for the fluting medium, whereas only 30 percent is used in liner.
Recycling. Although the recycling rate of 70,000 t/y is low, KSA exports waste paper to India (Source:
56). A new recycling mill began the production of 0. 400 Mio t/y in KSA in 2005 (Source: 56).
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Corrugated Paper. The consumption and production of corrugated paper in the GCC countries is
1.3 Mio t/y and 1.0 Mio t/y, respectively. The growth of corrugated paper use in the region and in KSA
has been dramatic. This is mainly influenced by:
•
•
•
•
Economic growth
Consumer behavior
Environmental awareness
Packaging industry.
Tissue Paper. In 2002 the consumption of tissue paper in KSA was 4.7 kg/y/capita and growing at a rate
of 39 percent per year. The growth of consumption and production of tissue paper in KSA and the GCC
is shown in Table 2-18.
Table 2-18: Consumption and Production of Tissue Paper in KSA and GCC (Mio t/y)
Consumption
1998
2005
2012
Production
2005
2012
KSA
GCC
0.072
0.110
0.030
0.254
0.421
0.105
0.299
0.549
The consumption of tissue paper is usually high when cotton is not available.
The material balance for the pulp and paper industry in KSA and the GCC is shown in Table 2-19 (Source:
47).
Table 2-19: Material Balance for Pulp and Paper in KSA and GCC
Hard and soft pulp import
Consumption paper
Wood-free paper
Publication paper
Tissue paper
Other paper and board
SUM
KSA
Recycled
GCC
Mio t/y
0.055
(2005)
0.130
0.140
0.110
0.120
0.500
Mio t/y
0.208
Mio t/y
0.090 (45%)
0.155
0.260 (50%)
Industry in KSA
In KSA there are three to five paper mills in operation. Their equipment came mainly from France,
which is one of the leading countries for superior-quality paper and relevant machinery in the world.
The main paper companies in KSA are Obeikan and SPMC.
Obeikan (Source: 53): This company is operating at a current annual production capacity level of 0.170
Mio t/y. A rebuild of the paper machine is currently taking place. Capacity after the rebuild will be 0.220
Mio t/y.
The investment is aligned with Obeikan’s strategy to keep the mill up to date in technology to stay
competitive.
The 3.4-m-wide (wire) PM 1 produces white lined chipboard in the basis weight range of 180–450 g/m²
at the design speed of 600 m/min.
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SPMC: This company is the main tissue paper mill in KSA. It was established in 1991–1992 and has been
expanded (Source: 55). Production has increased from 0.070 Mio t/y in 1992 to 0.125 Mio t/y in 2012.
The factory produces for the domestic market in KSA. Diverse subprocesses have the flexibility to
produce different grades of tissue and hygienic paper products.
2.2.7.5
Subsector—Textile/Fiber
(Sources: 46, 48, 52, 4)
In KSA fiber production is a part of the petrochemical complex and could not clearly be separated from
this.
General Process
It is customary to subdivide the textile/fiber subsector according to origin and end use, as follows:
•
•
•
•
•
•
Agro
Geo
Nonwovens
Automobile
Hometech
Oecotech
•
•
•
•
•
•
Sports
Clothing
Industrial
Packaging
Construction
Medical-protective.
There are three subprocesses for textile/fiber production:
•
•
•
Wet spinning (rayon), which uses a coagulating medium
Dry spinning (acetate and triacetate), in which the polymer is contained in a solvent that evaporates
in the heated exit chamber
Melt spinning (nylons and polyesters), in which the extruded polymer is cooled in gas or air and
then sets.
Polymers are processed as long fibers or batched and cut.
Characteristics in KSA
Figure 2-29 shows that production of synthetic fibers has been increasing while classical textile
production remains nearly constant.
60
50
40
30
20
10
0
1988
1990
1992
Wool
1994
1996
cotton
1998
2000
Cellulosis
2002
2004
2006
synthetic
Figure 2-29: Growth of Textile/Fiber Production
In 2012 total global demand for fiber was 85 Mio t/y. Total production of chemical and natural fiber in
2012 is shown in Table 2-20.
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Table 2-20: Production of Chemical and Natural Fiber Globally, 2012 (Mio t/y)
Chemical fiber:
Chemical
Cellulose
56
12.2
Natural fiber:
Cotton, linen, wool
23.3
The consequences of market development are falling prices and a reduction in agricultural production.
The distribution of synthetic fiber production in KSA according fiber type is shown in Table 2-21.
Table 2-21: Synthetic Fiber Production in KSA by Fiber Type (%)
Polyester
Cellulose
Polyamid
Acrylics
PVC
Rayon (Viscose)
Other
Nylon
Share
Trend
74
9
5
4
4
3
1
0
high growth
low growth
constant
Global textile/fiber industry facts and trends include:
•
•
•
•
•
Synthetic production shifted from the United States/Europe to Asia.
Synthetic production in Eastern Europe was nearly stopped.
Cellulose production in Asia is growing rapidly but remains constant in the United States/Europe.
Enthusiastic prognoses exist for carbon-fiber production as a light construction material. Carbonfiber is a highly interesting commercial product. Actual global carbon-fiber capacity is 0.035 Mio t/y
(and estimated for 2015 to be 0.060 Mio t/y)
The price of carbon-fiber is about 15,000€/t (of which 50 percent is raw material, 30 percent is
energy, and 20 percent is manpower).
KSA is one of the largest producers of synthetic fiber basic material. A number of secondary industry
companies exist, which are mainly producing for export. Many of them are operating with injection
moulding, and most of the equipment is modern. Waste heat use potential must be assumed,
therefore, to be low.
The textile demand for the housing subsector is growing parallel to the building sector.
Fiber production in KSA is shown in Figure 2-30.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Figure 2-30: Example of Fiber Production in KSA (Source: 57)
Industry in KSA
The main KSA-based synthetic fiber and semi-finished material production companies are:
•
•
•
SABIC affiliates (operate plants with 6,000 t/y)
MATTEX
ETEX
2.2.7.6
•
•
•
SAAF Advanced Fabrics
NATPET
Glass Fiber Technologies.
Subsector—Construction Material
(Sources: 8, 10, 15, 22, 23, 24, 25, 26, 28, 29, 33, 35, 36)
General Process
The process steps in civil building are: (1) construction material bulk transport; (2) construction material
production; and (3) construction material transport.
Specific energy consumption for important construction materials are shown in Table 2-22 (Sources: 24,
36).
Cement is a powder of alumina, silica, lime, iron oxide, and magnesium oxide burned together in a kiln.
The resulting clinker is finely pulverized (milled) and used as an ingredient in mortar and concrete.
The production process is highly energy-intensive: 30 percent to 40 percent of the production costs are
for fuel. In 2012 a total of 50 Mio t was produced in KSA. Of that, 41 percent was bagged for resale and
59 percent consisted of bulk, ready mix, cement blocks, and precast. The distribution of types of cement
in KSA is shown in Table 2-23.
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Table 2-22: Specific Energy Consumption for Construction Materials (MJ/t)
Mortar as gypsum, lime
Clay
Wood
Steel
Cement
Normal concrete
Lightweight concrete
Glass
Plastics (PVC, PU, silicones)
Aluminum
Natural stones
Artificial stones
Bricks and ceramics of quick potters’ earth
Other metals (copper, lead)
Varnishes/paintings, adhesives, isolation material
diverse
3
60
835
diverse
990
3,600
21,600
46,800
259,200
diverse
diverse
diverse
diverse
Table 2-23: Distribution of Types of Cement in KSA (%)
Ordinary Portland cement (OPC)
Sulphate-resistant Portland cement (SRC)
Pozollan cement
Oil cement (specially for Saudi Aramco)
73
20
6
1
Advanced technology has been adopted in new cement plants, and many older companies run
modernization programs (for instance, Arabian Cement, the oldest cement company in KSA, installed a
membrane surface filtration unit).
A typical Sankey diagram of energy use in cement production is shown in Figure 2-31.
Figure 2-31: Typical Sankey Diagram for Cement Production (Source: 35)
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Concrete. Precast products make up about 30 percent of the concrete market. Key raw materials are
limestone, clay, chalk, marl, and others.
Stones. Modern civil design demands production of artificial stones (bricks). In KSA, about 20 Mio t/y of
cement is processed to stones. Figure 2-32 shows some examples of manufactured stones.
The binding and maturing process is accelerated by high temperatures and high pressure. Ingredients
are typically: (1) lime, 43 percent; (2) sand (earth wet), 46 percent; (3) additives (e.g., stone), 2 percent;
and (4) water, 11 percent.
Figure 2-32: Examples of Manufactured Stones (Source: 59)
Lightweight concrete is not actually a concrete but a lime sandstone. This material is a mixture of quick
lime, cement, and aluminum powder (e.g., Ytong). Sometimes it is reinforced with steel profiles.
The density is low due to its porous nature (0.7–0.9 t/m³). The very low heat transfer coefficient is also
based on the porous structure, which has made it very attractive in building efficiency discussions in
recent years.
Other brick types are made of clay, mud, and ceramics.
The production of stones is done in autoclaves. The process is therefore intermittent. Heating
equipment for stone production is shown in Figure 2-33.
Figure 2-33: Heating Equipment for Stone Production (Source: 25)
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Depending on the heating procedures, specific energy consumption can cover a wide range.
Energy consumption for stone production is detailed in Table 2-24.
Table 2-24: Energy Consumption for Stone Production (MJ/m3)
Conventional brick
Lightweight concrete (0.6)
Gas concrete stone
Light brick
Roof tiles
Lightweight concrete (0.4)
Lime sandstone
Pumice concrete stone
4,000
1,970
1,960
1,555
1,400
1,315
1,100
810
One ton of lime sandstone means about 333 stones, which equals roughly the demand for two flats.
The distribution of energy input for stone production steps is shown in Table 2-25.
Table 2-25: Distribution of Energy Input for Stone Production
Oil, gas, coal
Power
Total
Used in the Further Manufacturing
773 MJ/t
71 MJ/t
369 MJ/t
34 MJ/t
Process improvement measures include storage of steam between batch processes and using deloading
steam for feedwater preheating.
Characteristics in KSA
The market for construction materials in KSA in 2008 is shown in Table 2-26.
Table 2-26: KSA Market for Construction Materials, 2008 (Mio t/y)
Natural stones
Mud
Blocks, bricks
Concrete
Total
0.008
0.007
0.605
2.265
2.922
In 2012, the market in KSA for cement stones was 20 Mio t; for cement it was 49 Mio t.
The distribution of construction materials in KSA by type is shown in Figure 2-34.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Mud
0%
Stone
0.00
Block / Brick
8%
Other
0%
Concrete
92%
Other
Stone
Mud
Block / Brick
Concrete
Figure 2-34: Distribution of Construction Material Types in KSA
The demand for construction material in KSA is evenly distributed. As Figure 2-35 shows, no local
concentration can be identified.
For marbles and stones, the market volume expressed as turnover in KSA was US$2.322 billion in 2010,
US$2.406 billion in 2011, and US$2.486 billion in 2012. By comparison, total volume in the GCC in 2012
was US$4.87 billion (Source: 22).
In KSA, the price of lightweight stones (0.6) is US$120/t and cement is US$31/t; by comparison, the
price of cement in Germany is US$27/t. In Germany, the market for lightweight concrete is 2.1 Mio t/y;
the market for lime sandstone is 6.0 Mio t/y; and energy consumption is 700 GWh/y.
The following are relevant facts for the KSA cement market and cement production:
•
•
•
•
•
•
•
•
2-38
In 2009 and 2010 construction projects across the region were put on hold.
In 2011 growth returned to 14.2 percent.
Cement prices in KSA are stabile as a result of governmental controls.
Fuel costs and bulk are highly subsidized.
The cement industry’s gross margin in KSA is about 51.8 percent.
Cement production is lower than demand in KSA.
For 2013 about 16 production lines were planned for projects adding up to a turnover of about
US$2.9 billion, but not all are commissioned yet.
The usual cement production per line is 1.2–1.5 Mio t/y.
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Figure 2-35: Local Distribution of Construction Material Demand in KSA
The growth of cement demand and production in KSA is shown in Figure 2-36.
70000
in 1000 t/year
60000
50000
40000
30000
20000
10000
0
2002
2004
2006
cement production
2008
2010
2012
2014
2016
demand in KSA prognosed
Figure 2-36: Growth of Cement Production in KSA (Source: 15, 26)
Industry in KSA
There are about 815 (as of 2005) companies in the entire building sector. Among these, several produce
construction materials, especially stones. Most of them are newer plants. New plants are mostly state
of the art. The manufacturers of the equipment for stone production in KSA include Thyssen Krupp and
SLS Düsseldorf.
It is assumed that cement production plants younger than 7 years have no potential for waste heat use.
In 2012 there were 37 lines for stone production in KSA; of these, 17 were installed between 2005 and
2010. It can be assumed that between 2011 and 2012, six new production lines were installed.
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CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
This means that about 60 percent are less than 7 years old. The remaining 14 plants may have a
potential for waste heat use.
Estimation of waste heat potential. Stones are produced usually as Autoclaved Aerated Concrete (AAC)
or Autoclaved Light Concrete (ALC). In the batch process, which operates at 1.2–1.6 MPa and 180–
200 °C, the product is processed and dried for about 12 hours.
The total energy demand for the process is about 12 MJ/t, used mainly as steam condensing from h”
(1.4 MPa) = 2,789 kJ/kg to h’ (1.4 MPa) = 830 kJ/kg. This means that 30 percent of the input energy of
12 MJ/t is potential, which leaves as condensate at a temperature of about 200 °C. For stone production
in KSA, which reaches a total of 20 Mio t/y, this adds up to 20 GWh/y, or 2.5 MWth (assuming
8,000 operating hours/y).
Because this is an intermittent process and the steam coming from the process cannot be used directly
because of the particulates, the steam can be used only indirectly via a heat exchanger that reduces the
maximum temperature to 180 °C.
Therefore, the better solution, which is already realized in KSA’s modern equipment, is direct use for
preheating with reuse integrated as an internal process.
GIZ Energy Audit at Alsafwa Cement Company, Jeddah
Within this project, GIZ performed an energy audit at the Alsafwa Cement Company in Jeddah in
October 2013. Alsafwa Cement Company is 25 years old and produces SRC, OPC, and Portland
Pozzolanic cement. Energy consumption for the production of 5,000 t/d is 450 t/d HFO direct and
150 t/d HFO for power, which is burned in Wärtsila diesel engines. The specific energy consumption
results in 4,400 MJ/t. The detailed results of the audit are described in the last chapter of this study.
2.2.7.7
Subsector—FE Metals
In KSA the Hadeed Company, an affiliate of SABIC, is the only large producer of steel.
It must therefore be assumed that EE awareness is as high as at the petrochemical and other affiliates
of SABIC. The modern central district cooling system, which was installed in 2012 by Hadeed Technical
Management, testifies to the validity of this assumption. Also, Hadeed recently installed a wastewater
treatment facility.
SABIC also adopted a “best practices” program that is applied at all companies. In addition, Hadeed
engaged an external consultant to document the CO2 footprint and provide an energy audit.
Hadeed produces 3.3 Mio t/y long products and 2 Mio t/y flat products. All products are delivered to
the domestic market (Source: 4).
The waste heat potential based on a specific consumption of about 835 MJ/t is estimated to be
10 percent.
2.3 Waste Heat Use Potentials
2.3.1
Waste Heat Use Potential in the Desalination Sector
For the desalination sector, it is assumed that production of about 650 Mio t/y (typical for the older
MSF plants) with a specific consumption of 360 MJ/t has a waste heat use potential of 3 percent, which
results in a waste heat potential of 371 MWth.
2.3.2
Waste Heat Use Potential in the Power Generation Sector
For the power generation sector, it is assumed that the oldest 30 percent of the units (about 50 Mio
MWhth of primary energy) are available for waste heat use, which have a potential of 1,712 MWth,
assuming 20 percent efficiency. This comes from the fact that many gas turbines are operated in open
cycles.
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2.3.3
Waste Heat Use Potential in the Refineries Sector
The refineries were outside the scope of this study. The potential of the refineries was therefore not
investigated in this study.
However, based on a production of 590,000 t/d, a specific energy consumption of 4,460 kJ/kg, and a
reduction potential of 2 percent, there is a waste heat use potential of 19,219 MWth, which would be
by far the largest potential of all sectors.
2.3.4
Waste Heat Use Potential in the Petrochemicals Sector
The companies in the petrochemical sector are very international and compete in the world market.
It is therefore assumed that their EE level is high. A reduction potential of 3 percent is assumed for the
sector. For the total SABIC production of 8 Mio t, which is 50 percent of the total KSA production, this
adds up to a waste heat potential of 350 MWth. The non-SABIC companies that provide the other
50 percent of production are estimated to have a potential of 4 percent, which adds up to 467 MWth.
2.3.5
Waste Heat Use Potential in the Other Industries Sector
The following facts are relevant for appraisal of the potential of waste heat use in this sector:
•
•
There has been significant industrial growth, especially in the last 10 years.
Nearly all machinery is bought new.
Therefore, it must be assumed that the actual efficiency factors of the industrial equipment may be
better than in power generation.
Therefore, only machinery and processes installed before 2000–2005 may have a potential for
improvement by waste heat use.
2.3.5.1
Estimation by Correction Factors
Because it was not possible to analyze energy use in industrial plants directly during the preparation of
this study, the proportional transfer of results from a Norwegian-German study considering different
conditions was undertaken. The results of this study for waste heat use potential are split into two
temperature sections:
•
•
Greater than 140 °C:
60–140 °C:
316 PJ (12 percent of the industrial final energy)
60 PJ
This result is equivalent to a potential of about 10,000 MWth heat generation capacity.
Although these figures cover especially large companies, it is assumed that they are also valid for
smaller and medium companies. The figures are the base for the model calculation.
German final energy consumption in industry is 2,530 PJ, which accounts for 26.85 percent of the total
consumption.
The technical energy saving potential is about 25 percent, which is distributed as shown in Table 2-27.
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Table 2-27: Energy Saving Potentials
Recovery
Adjustment to demand
Machines
Design drive
Efficiency drive
Mechanical losses
Transmission
Galvanic processes
Efficiency lightning
Burning/baking processes
Thermal processes greater than 500
Thermal processes 200‐500 °C
Thermal processes less than 200 °C
Other thermal processes
Heating
Consumption 2002
electricity %
fuel %
2.16
0.00
16.31
0.00
7.66
0.00
16.31
0.00
16.31
0.00
16.31
0.00
16.31
0.00
2.56
0.00
1.17
0.00
0.60
45.47
0.90
1.55
0.00
1.79
0.27
13.59
2.40
18.30
0.73
19.31
Saving Potential
electricity %
fuel %
7.69
9.39
9.57
3.06
1.02
1.02
0.82
9.09
57.14
16.67
5.24
0.00
19.23
0.00
10.00
0.00
9.65
4.17
13.03
4.55
19.14
From the waste heat temperature profiles for the different subsectors, correction factors for power
production from waste heat are defined as follows:
•
•
•
•
•
•
•
•
•
Chemicals semi-finished
Refineries
Pulp and paper
Food and sugar
Textiles
FE metals
Non-FE metals
Glass
Minerals and construction materials
1.57
1.00
0.51
0.42
0.06
1.30
0.10
0.44
1.07
It can be assumed that for waste heat temperatures below 350 °C only ORC processes are applicable
and that for waste heat temperatures above 350 °C steam processes will be applied.
The distribution by power generation of these two processes will be 35 percent for the ORC processes
and 65 percent for the steam processes.
For the proportional transfer, the distribution of the German and KSA industry sectors are compared in
Figure 2-37 and Figure 2-38.
Both distributions are corrected by the refineries sector, and the figures of KSA are also corrected by
the petrochemical sector, which means in particular that companies like SABIC affiliates or NATPET are
not included. That the sector is still large for KSA may depend on the fact that a share of textile fiber is
included in the chemistry sector.
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Figure 2-37: Distribution of Energy Consumption by Industry in Germany, 2002
chemicals
(e.g. fiber,
but except
SABIC)
22%
construction
material
28%
paper &
pulp
15%
glass
6%
non iron
metals
4% iron&steel textile
7%
2%
food
16%
Figure 2-38: Distribution of Energy Consumption by Industry in KSA, 2012
To calculate the transfer factor from German (Source: 21) to KSA figures, it was assumed that the
subsector for construction materials is relatively double that in Germany and the non-iron subsector is
half.
The GDP, which is about US$700 billion for KSA, was not used as a correction factor because for KSA it is
strongly interrelated with the oil sector. Although the KSA export quota of 56 percent is similar to
Germany’s 52 percent, KSA’s GDP is dependent on the oil sector and therefore not comparable.
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The correction factors for population and age distribution of the equipment are shown in Table 2-28.
Table 2-28: Correction Factors for Population and Modernization
Population
(Assumed that industry correlates with population)
Modernization (equipment age greater than 10 years)
(Lifetime assumed 25 years)
Germany
KSA
Factor
80 Mio
27 Mio
0.34
60%
10%
0.17
The calculation of the correction factor for structural differences in the industry sectors is shown in
Table 2-29.
Table 2-29: Calculation of Correction Factor for Structural Differences in Industry
Industrial Sectors/Subsectors
Chemicals (e.g., fiber)
Refineries*
Pulp and paper
Food and sugar
Textiles
FE metals
Non-FE metals
Glass
Minerals and construction materials
Sector factor for structural differences
Germany
KSA
Process
share %
share %
potential %
36
0
13
15
3
6
8
6
13
21
0
15
16
2
7
4
6
28
0.94
5
0
10
10
25
20
20
10
*Refinery subsector is not part of this study.
Therefore, the overall transfer factor = 0.053
This means that a waste heat potential in Germany must be corrected by this factor to get the potential
in KSA.
The industrial waste heat use potential in Germany is about 10,000 MWth.
The industrial waste heat use potential for KSA for the sector “other industries” is calculated then as
528 MWth.
It is assumed that there is only negligible potential for the direct use of waste heat in KSA because of
climate conditions. Therefore, only generation of coldness or power can be seen as realistic.
If an efficiency of 20 percent to 30 percent is assumed, the waste heat use potential of 528 MWth heat
means an electrical output of about 106–159 MWel.
From the investigation it is assumed that about 35 percent of the waste heat is at a temperature level
below 350 °C and the rest will be above.
For calculation of the technical electric potential, the following assumptions were made:
•
•
•
2-44
35 percent must be realized with ORC processes (t less than 350 °C)
65 percent can be realized with steam processes (t greater than 350 °C)
This assumption leads to the technical potential total of
37 MWel
104 MWel
141 MWel.
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2.3.5.2
Prioritization and Valuation of the Sectors
The results described in the previous sections were prioritized and are shown in Table 2-30.
Table 2-30: Waste Heat Use Potentials
Priority
1
2
3
4
5
6
7
8
9
10
11
Power generation
Petrochemicals (non‐SABIC)
Food and sugar
Water production
Petrochemicals (SABIC)
Pulp and paper
Glass
Construction materials
‐ Cement
‐ Stones
‐ Concrete
FE metals
Non-FE metals
Textile
Consumption
Production
kg/y/capita
Mio t/y
619
270
7
50 Mio MWhth
8
9
651
8
0.5
0.21
50
20
2.3
5.6
0
0
Savings
MJ/t
%
MWth
46000
15000
360
46000
45000
8500
30
4
10
5
3
10
10
1712
467
428
371
350
71
56
330
700
10
5
835
10
52
22
0
15
0
0
Although many of the data had to be assumed, some conclusions can be drawn.
It can be summarized that the energy supply sector “power generation” itself still has the largest
potential for energy saving by waste heat use because there are still a large number of open cycles in
operation without any heat use. This waste heat has a temperature of about 500 °C, and the potential is
quite high. Whether it would be more economical to use this waste heat or to replace the older units
should be investigated. Also a large potential exists in the petrochemicals and water production sectors.
The third-largest sector for waste heat use potential may be the food and beverage sector. This sector is
large; needs a large amount of energy, especially for cooling; is decentralized; and may therefore
require a detailed investigation.
These first four sectors/subsectors add up to more than 90 percent of all the potential in KSA industry.
2.3.6
Difference Between Large and Small Companies
For the assessment of potentials in the different sectors and subsectors, various “weak” influences were
identified in addition to the factors that could be calculated. One of the major differentiations could be
the size of the company.
The history, development, size, and share of GDP (i.e., economic importance for the country) are
different for small and large companies in KSA.
Large companies in KSA, such as SABIC and its affiliates, Saudi Aramco, SWCC, or SEC, are strongly
interconnected with governmental institutions.
From preparation of the study it can be assumed that smaller, distributed, and non-international
companies have a relatively larger waste heat use potential.
Large companies with many specialized departments that are members of international groups and that
are operating in strong international competition have a lower process potential but are a larger
multiplier by virtue of larger production.
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Thus, the group of large companies has the larger absolute potential. In fact, 85 percent of the waste
heat use potential can be allocated to this group.
Industries with smaller or only a few companies have the smaller absolute potential for the country; in
terms of investments that will be needed for EE measures, however, this group will be more attractive
because the individual potential seems higher. Usually the investment process also works faster at
smaller companies.
2.3.7
Difference in Technical and Economic Potential
The economic potential is lower than the technical potential depending on prices, assumptions for
depreciation, and lifetime.
In an example from Germany in 2008, a technical potential of 25 percent at a depreciation of eight
years resulted in an economic savings potential of 14 percent, or 56 percent of the technical potential.
Usually, for small companies economic potential will be 81 percent of technical potential; for large
companies it will be 56 percent of technical potential.
Also it must be seen that the energy costs in many of these sectors are only a minor share of the
production costs. In Germany, for example, with the exception of energy-intensive sectors, energy costs
were only 2 percent of the production costs on average in 2004. In KSA it may be even less due to low
energy prices. This can be seen as a barrier because production management may be less willing to use
additional or more complex technologies and components.
With the conservative assumption that 50 percent of the technical potential of 141 MWel can be
announced as economic, 70.5 MWel is a realistic figure for the economic potential of waste heat use in
KSA.
2.3.8
Necessary Investments
Realization of the economic potential would require additional equipment for the waste heat use in
KSA. Specific prices of such equipment depend mainly on size and temperature level, as shown in Table
2-31.
Table 2-31: Price of Equipment by Size and Temperature Level
Low temperature level
High temperature level
SUM
18.5 MWel
52 MWel
US$/kW Investment
Mio US$
2,800–4,200
2,200–3,300
51.8–77.7
114.4–171.6
166.2–249.3
The estimation shows that if all potentials are used, there will be an investment range of US$150–250
Mio for installation of waste heat use equipment.
2.3.9
Influences of Growth and Change
To estimate the technical and economic potentials and possible investment sums, many assumptions
were necessary. A wide range of results is characteristic for such investigations. For the total economic
efficiency potential (power and heat) in industry, for example:
•
•
•
A study from Steiermark, Austria, resulted in 15 percent.
A 2002 Prognos study in Germany resulted in 25 percent
A global analysis resulted in 50 percent.
Differences in studies may also occur as a result of the changes in industry structure, as shown for
Germany from 1998 to 2002 in Figure 2-39.
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glass
primary zinc
secondary copper
primary copper
secondary aluminum
primary aluminum
Non FE metals moulding
Non FE metals prefabricated
steel moulding
rolled steel
oxygen steel
pig iron
limestone kiln
cement stones
fine ceramics
bricks
dairy
sugar
paper
detergent
soda salt
PVC
Soda
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
Olefine
Change in production 1998 - 2002 in %
CHAPTER 2: Appraisal and Evaluation of Energy Utilization and Efficiency in the Kingdom of Saudi Arabia
Figure 2-39: Changes in German Industry Sectors and Subsectors, 1998–2002
The figures calculated for KSA are lower for several reasons:
•
•
•
64 percent of KSA industry is refineries, which were excluded from the study.
Because of climate conditions, direct heat use will be the exception.
Installed production equipment in KSA is relatively modern.
2.4 General EE Aspects
Besides waste heat use, a number of different EE measures for operation and technologies installation
are known. These average technologies are applicable to nearly each company. Most well known are
the generation of pressurized air; lighting, preheating, and rewarming processes; and speed control
drives. In addition, a number of principles for initiation and acceleration of projects are proposed. These
have been the subject of several studies in KSA during recent years.
2.4.1
Proposed EE Technologies from Chapter 1
In Chapter 1 of this study, the different EE technologies applicable in KSA were listed, summarized, and
evaluated in three priority classes, as shown in Table 2-32.
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Table 2-32: EE Technologies Applicable in KSA by Priority
Priority High
3.
5.
7.
8.
11.
16.
21.
24.
25.
27.
28.
36.
37.
Best Available Technologies only allowed to enter newly into the market in Saudi Arabia
Building Management System for commercial and residential buildings
CCGT for new power stations, co-generation, trigeneration, district cooling, modernization of existing
power plants, GT and ST conversion to CCGT
CHP, co-generation, tri-generation
Electric motors, efficient electric motors
Industrial sector, steel industry, cement industry, paper industry, technology improvements
Lighting efficiency: compact fluorescent lamps, light-emitting diodes for commerce, government, and
households
Micro- and small-scale CHP
Process engineering and process control
Renewables for any increase in energy demand in electricity (nuclear, CSP, centralized PV, decentralized
PV, geothermal energy, wind energy, and others)
Peak load remote control (“Rundsteueranlagen”) for AC (obligatory for any newly installed equipment)
Standards for equipment efficiency
Standby generation capacities
Priority Medium
1.
6.
9.
12.
14.
17.
26.
38.
AC, cooling cycle improvement
Building standards and building codes more tight through shading, insulation and high-performance
windows, highly efficient heating, cooling and ventilation systems
Control systems with remote switching, developing new technologies
Farming, chicken production, energy-efficient technologies for the agriculture sector
Heat pumps for heating and cooling, ventilation systems
Insulation of buildings, improved insulation
Pumps, energy-efficient pumps, speed control
Storage of electricity, batteries, and others
Priority Low
2. Appliances efficiency to be improved (flat-screen television, LCD, and others)
18. Joint development and manufacturing of local solutions for energy-efficient equipment with international
companies
19. Labels, strictly to be installed and controlled
22. Load control
30. Smart buildings
31. Smart grids
39. Storage of cooling capacity to reduce AC peaks
2.4.2
Proposed EE Measures from Chapter 1
In Chapter 1 of this study, various EE measures applicable in KSA were also summarized and evaluated
in three priority classes, as shown in Table 2-33.
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Table 2-33: EE Measures Applicable in KSA by Priority
Priority High
4.
10.
15.
19.
20.
22.
23.
24.
34.
49.
51.
63.
66.
69.
70.
Building Energy Management System for better information on energy consumption
Curtailable load program
Direct load control program
Energy Efficiency Fund to finance investments in EE
Energy manager at large-scale consumers (as in Europe, where most industrial companies have a
specialized engineer responsible for all energy consumption within the company)
Energy service industry, support to upgrade and promote a Saudi energy service industry
Energy Services Companies (ESCO)
EPC contracts
Interruptible tariff program
Private sector investment in electricity and water projects, increasing the role of the private sector
Promotion of an energy service industry
Tariff restructuring
Time-of-use tariff programs for major industrial and commercial customers
Voluntary actions by industry and commerce supporting EE
Walk-through energy audits of governmental, commercial, and industrial facilities
Priority Medium
1.
9.
14.
21.
25.
31.
39.
47.
56.
59.
67.
68.
Award system for EE solutions annually
Culture of patenting and entrepreneurship in Saudi Arabia in the field of EE
Demonstration projects for EE
Energy planning authorities (SEC, MOWE, ECRA, and others) to be strengthened in achieving better EE
for KSA
Feed in tariff for renewable energies
Information, a program of EE information and awareness
Law on EE
Performance monitoring
Reward system for energy-efficient equipment
Saudi Energy Efficiency Center, support to the SEEC
Training, technical and managerial training through workshops and seminars (energy audits with quick
savings, detailed audits, EE financing, performance contracts)
Vocational training on EE
Priority Low
3.
6.
8.
12.
27.
28.
29.
32.
33.
35.
37.
40.
41.
Budgets allocation to support the fields of science and technology in EE
CDM projects to be supported
Coordination and links between Saudi universities and industry
Customer invoice support and customer system check
Human resource development and EE within organizations
Incentives, provisions of incentives to purchase efficient appliances
Information campaigns on EE
International cooperation on EE
International energy companies that conduct R&D in the EE sector, attracting companies/buying shares
Joint ventures, inviting leading renewable energy technology manufacturers into the country
Labeling of electric household appliances
Leasing, an energy-efficient equipment leasing program
Market liberalization in the Saudi power sector
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42.
45.
46.
48.
50.
53.
54.
55.
57.
58.
60.
Minimum standards for new power stations, new co-generation, new desalination plants
NEEAP update
Operation and Maintenance (O&M), improvement of O&M, better standards, training, supervision
Political support for EE as one of the main policy areas in Saudi Aramco KSA
Programs for promotion of EE
R&D activity in both public and private Saudi sector
R&D programs on EE
Revolving fund for EE investments
Rewarding innovators and researchers for EE solutions
Saudi Building Code, strict implementation of
Standardization and norms with SASO on energy-efficient equipment (ACs, refrigerators, lighting, and
building insulation)
From these proposals the technologies and measures pertaining to waste heat use were concentrated
in the following lists.
Technologies:
•
•
•
•
•
•
•
•
Only best available technology regarding efficiency allowed to enter market
Efficient electric motors
Lighting efficiency
Auditing process engineering and control
Standards for equipment regarding efficiency
AC
Insulation of buildings
Energy-efficient pumps.
Measures:
•
•
•
•
•
•
Energy manager at large-scale consumers
ESCOs
Tariff restructuring
Walk-through energy audits of industrial facilities
Performance monitoring
Technical and management training.
Besides the direct use of waste heat, the proposed measures will result in energy savings and avoiding
fuel consumption, which leads in most cases to an additional generation of power or coldness.
2.4.3
Barriers
The use of waste heat and the introduction of EE measures and technologies usually face some barriers.
Barriers to an energy-saving action or its realization can include:
•
•
•
•
•
Information (e.g., missing knowledge)
Legal reasons (e.g., permits)
Financial (e.g., additional investment)
Organizational formal (e.g., hierarchies)
Organizational motivation driven.
In Table 2-34, the barriers listed in a European study have been evaluated for relevance in KSA.
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Table 2-34: Typical Barriers Evaluated for Relevance in KSA
Barriers:
Relevance for KSA
• Structural financial, information and operational heat logistic barriers
(the local and time-dependent coherence of heat supply and demand
and the information status about heat use) technologies and sources
less (no direct heat use)
• Production safety
important
• Payback expectations
less (peak load, efficiency driven)
Supports:
• Increasing energy prices
no support on medium term
• Distribution of energy management systems
possible
• Image reasons
less important
• Announcement of energy-responsible persons
possible
2.5 Waste Heat Use Technologies
> 350 °C
80–350 °C
< 80 °C
Steam turbine
ORC process
internal preheating
absorption chiller
adsorption chiller
2.5.1
Usability of Power Generation Technologies
ORC efficiency in %
Identified waste heat below 350 °C can be used for low-temperature power cycles (ORC processes). As
temperatures on the cooling side are high in KSA, the cycle efficiencies will be below the European
values shown in Figure 2-40.
40
35
30
25
20
15
10
5
0
0
50
100
150
200
250
300
ORC efficiency
Figure 2-40: Efficiency of ORC Processes (Source: 19)
For cases with temperatures above 350 °C, steam cycles with waste heat recovery boiler and steam
turbine are possible.
While for Europe an economic use for below 85–120 °C (Germany) is known, for KSA about 115–150 °C
(only thermodynamic considered) must be assumed.
2.5.2
Usability of Chiller Technologies
Another possibility would be the operation of absorption and adsorption chillers for coldness
production. A one-stage absorption chiller, for example, is shown in Figure 2-41.
There are several manufacturers of such chillers, mainly in the Far East, especially Japan.
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One million small absorbers (18–75 kW) are produced
annually, 800,000 of them by Electrolux in Germany,
Sweden, Hungary, and Switzerland. Of the 8,600 large
absorbers
produced
annually,
Japan
produces
2,800 absorbers less than 200 kW and 3,600 absorbers
greater than 200 kW. The rest (700) are produced in the
United States by Trane, Carrier, and York. Japanese chiller
manufacturers include Mitsubishi, Sanyo, Kawasaki, Ebara,
Hitachi, Yazaki, Daikin, and Takuma.
If cooling devices can be installed, for 500 kW cooling
power, 700 kWth heat is needed at a temperature level
above 100 °C.
Figure 2-41: A One-Stage Absorption Chiller
(Source: 61)
The lower limit for an efficient and economical installation is about 100 kW coldness.
2.6 Potentials and Demands
2.6.1
Possible Solutions and Business Potentials
Overall, a waste heat use potential of about 3,500 MWth was identified for the four sectors in KSA.
More than 80 percent of this potential was found in three large companies—SWCC, SEC, and SABIC—
and large petrochemicals. The “other industries” sector has a potential of about 650 MWth.
Possible power generation is dependent on the available temperature. Assuming an average heat to
power efficiency of 20 percent, the waste heat losses translate into a power potential of about
700 MWel.
2.6.1.1
Engineering
For the different levels of waste heat use, detailed investigations including feasibility studies
considering individual conditions are necessary. For all possible technical measures, the special
conditions arising from the climate and low energy prices in KSA must be taken into account.
For a breakdown of the identified potential into specific investments, several feasibility studies and
detailed engineering solutions are necessary.
Business potentials. Typically engineering companies and efficiency audit companies are the right
partners for identifying potentials. Independent ESCO solutions may also be of interest. These
companies are often specialized in specific components such as drives, heat exchangers, etc.
2.6.1.2
Improving the Condition of Equipment, Maintenance, and Training
Waste heat often means that process losses are too high. The best and most economical solution is to
avoid or reduce the losses directly. For example, one can:
•
•
•
•
•
•
•
•
Optimize existing air or feedwater preheating
Reduce convective or radiation losses by improving isolation
Equalize flow distributions
Avoid or minimize load changes and fluctuations
Minimize leakages (e.g., at dampers, valves, flanges, casings)
Avoid fouling of heating surfaces by material properties, soot blowers, etc.
Reduce pressure drops (e.g., by routing, surface smoothing, avoidance turbulences)
Reduce all types of water losses.
The necessary measures can include operational measures, maintenance or repair of equipment, or
replacement of equipment.
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Also many of the measures require proper operation of the equipment. The identification of best
available operations, creation of KPIs, installation of monitoring systems, and implementation of
incentive systems are proven means to facilitate optimal operation with existing equipment.
Business potentials. Depending on maintenance processes, there are business potentials for training
and increasing the knowledge, awareness, and organization of the company’s own personnel. Reorganization of maintenance (e.g., outsourcing service activities to independent providers) can also be a
solution.
The purchase of original spare parts (OEM)—qualitatively better spare parts with better properties such
as material or lower tolerances—may create business potentials for spare parts dealers and providers.
2.6.1.3
Direct Internal Use of Unavoidable Process Losses
The second level for use of waste heat potential could be identification of direct process internal use for
process steps where heat is needed at a lower temperature level. If such steps are not obvious,
processes can be analyzed by the pinch point method, which shows heat demand and heat potential at
the smallest temperature difference. Such measures could include:
•
•
•
•
•
•
•
Condensate supercooling to 85 °C or below
Optimization or split into steps of air or feedwater preheating
Higher dewatering rates for drying processes to reduce steam or drying medium losses
Reduction and shortening of startup procedures
Direct heat recovery at consumer such as air or water cooling heat exchanger
Reduction of power consumption by installation of efficiency-classified drives
Speed control for drives of large consumers like motors, fans, and pumps.
Business potentials. Use of this category of waste heat requires specialized knowledge of the process
and installed technology. Business potentials are therefore restricted to companies that are specialized
for the particular technology. Often it is only possible to engage the original manufacturer.
2.6.1.4
Use of Unavoidable Process Losses for Chilling Purposes
If direct use within the process is not possible, the third level of waste heat use is via an absorption
chiller for AC or process cooling purposes.
Business potentials. The use of unavoidable heat for chilling processes offers business potentials,
especially for manufacturers of absorption chillers. Energy prices in KSA are still so low that absorption
chillers compete with conventional electric chillers. Possible solutions depend on the availability of the
waste heat with respect to the operation mode of the plant, especially if the generation is continuous or
periodical, such as after-batch processes and the cooling demand.
2.6.1.5
Use of Unavoidable Process Losses for Power
The fourth level of waste heat use, which is also the most expensive, could be additional electricity
generation. Below 350 °C this must be done by ORC processes; above 350 °C steam turbine or engine
cycles would also be relevant for KSA. Using all waste heat of the “other industries” sector would
provide a power potential of about 141 MWel.
Business potentials. The installation of additional power generation equipment using waste heat
usually demands specialists. Therefore there is a business potential for low-temperature ORC processes
or waste heat steam cycles, in particular the integration of a generation of auxiliary power into the
public net.
2.6.2
Demands for Research, Development, Pilot Plants, and Funding
In KSA many projects at all universities and institutes, as well as in large companies, are started with the
aim of investigating the water and power sector under the special conditions of KSA. Many of them also
involve the use of renewables.
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For waste heat use as for EE measures, domestic research and development do not seem to be
considered urgent because most of the solutions are available or state-of-the-art on the market.
In particular, the installation of external waste heat use equipment such as bottoming or topping cycles
is usually problematic. Often this is the main barrier. To overcome it, awareness programs or economic
incentive systems such as tax reduction, funding similar to the German CHP funding law (KWKG), or
power feed-in regulations may be needed.
2.7 Summary
For this study, KSA industry was divided into five main sectors. Although the various sectors have been
in the process of liberalization for some years, there is still one large main player in each. Water
production and power generation are traditionally operated by governmental companies. The
petrochemical and steel businesses are dominated by a few companies, but the oil production and
refinery sector is managed by one company. The companies in the latter four sectors have different
conditions from all other industries because of their export character, their size, and the difference in
business character of the water and power generation sector.
The refinery and oil production sector was not included in the scope of this study.
Other industries such as glass, food, paper, etc., are summarized in this study under “other industries.”
For application of a proportional method to estimate the waste heat use potential in comparison to
international use potentials, this sector was subsectored.
The subsectoring was necessary to analyze if the particular business is generally comparable with the
international sector business regarding products, processing, and boundary conditions. Correction
factors were defined for the different waste heat use–relevant industry subsectors.
A total waste heat use potential of about 3,500 MWth was identified for the four sectors of water,
power, petrochemical, and others. More than 80 percent of this potential was found in three large
companies—SWCC, SEC, and SABIC—and large petrochemicals. The “other industries” sector has a
potential of about 650 MWth.
The possible power generation is dependent on the available temperature. Assuming an average heat
to power efficiency of 20 percent, the waste heat losses translate into a power potential of about
700 MWel.
Waste heat often means that process losses are too high. The best and most economical solution is to
avoid or reduce the losses directly. For example, one can:
•
•
•
•
•
•
•
•
•
Optimize existing air or feedwater preheating
Improve isolation to reduce convective or radiation losses
Install blades to equalize flow distributions
Equalize operation to avoid or minimize load changes and fluctuations
Minimize leakages (e.g., at dampers, valves, flanges, casings)
Avoid fouling of heating surfaces by soot blowers or cleaning devices
Replace heat exchanger components with worse material properties
Install lean routing and liners to reduce pressure drops (e.g., by routing, surface smoothing,
avoidance turbulences)
Reduce water loss.
The necessary measures can be operational measures, maintenance or repair of equipment, or
replacement of equipment.
Direct process internal use for process steps where heat is needed at a lower temperature level is also a
solution. If such steps are not obvious, processes can be analyzed by the pinch point method, which
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shows heat demand and heat potential at the smallest temperature difference. Such measures could
be, for example:
•
•
•
•
•
•
•
Condensate supercooling to 85 °C or below
Optimization or split into steps of air or feedwater preheating
Higher dewatering rates for drying processes to reduce steam or drying medium losses
Reduction and shortening of startup procedures
Direct heat recovery at consumer such as air or water cooling heat exchanger
Reduction of power consumption by installation of efficiency-classified drives
Speed control for drives of large consumers like motors, fans, pumps.
If direct use within the process is not possible, the third level of waste heat use is via an absorption
chiller for AC or process cooling purposes.
The fourth level, which is also the most expensive, could be additional electricity generation. Below
350 °C this must be done by ORC processes; above 350 °C steam turbine or engine cycles would be
relevant for KSA. Using all waste heat of the “other industries” sector would provide a power potential
of about 141 MWel.
For the different levels of waste heat use, detailed investigations including feasibility studies
considering the particular conditions are necessary. For all possible technical measures, the special
conditions arising from the climate and low energy prices in KSA must be taken into account.
In addition, many of the measures demand proper operation of the equipment. The identification of
best available operations, creation of KPIs, installation of monitoring systems, and implementation of
incentive systems are proven means to facilitate optimal operation with existing equipment.
In particular, the installation of external waste heat use equipment such as bottoming or topping cycles
is usually problematic. Often this is the main barrier. To overcome it, awareness programs or economic
incentive systems like tax reduction, funding similar to the German CHP funding law (KWKG), or power
feed-in regulations may be needed.
2.8 Literature
1. N.N.: SEC Annual Reports up to 2012.
2. N.N.: Ministry of Water & Electricity (MOWE). Annual Reports up to 2012.
3. N.N.: SWCC Annual Reports up to 2012.
4. N.N.: SABIC—Annual Report. 2012.
5. N.N.: Personal information at SEC planning department, Riyadh , 2011.
6. Matara, W.: Modeling the Saudi energy economy and its administered components: The Murphya,
F. et al.:KAPSARC energy model. KAPSARC, Temple University, 2013.
7. N.N.: Glass Market Intelligence Report: Report 1—2013, Market Intelligence and Forecasts Series,
Ispy Publishing Ltd., 2013.
8. N.N.: The Saudi Industry. Ventures Middle East, 2011.
9. N.N.: GCC Food Industry. Alpen Capital, June 2011.
10. Al-Nagadi, M.: Saudi Arabia—Concrete Construction Industry—Cement-Based Materials and Civil
Infrastructure (CBM&CI). Ministry of Urban and Rural Affairs, Riyadh, 2012.
11. Danoun, R.: Desalination Plants: Potential impacts of brine discharge on marine life, The Ocean
Technology Group, 2011.
12. N.N.: The GCC Power & Desalination 2012 Report: An in-depth outlook of the GCC Power &
Desalination market up to 2020. MEED insight, 2012.
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13. N.N.: Saudi Arabia: Energy efficiency report. Country Reports from ABB, 2012.
14. Al-Ibrahim, A.M.: Electricity Demand Forecast in KSA. Kaust Solar Energy, May 2010.
15. Al-Nagadi, M.: Saudi Arabia –Concrete construction industry-cement based materials and civil
infrastructure , CBM-CI Workshop, Karachi, 2010.
16. N.N.: Anlagenbezogene CO2-Minderungspotenziale in der Glasindustrie. Bayrisches Landesamt für
Umweltschutz, ECH Heidelberg, August 1997.
17. Brunner, F. et al.: Gesamtenergieanalyse bei Perlen Papier AG. Bundesamt für Energie, Juni 2008.
18. N.N.: Volkswirtschaftliche Aspekte einer ambitionierten Effizienzstrategie.
19. Tänzer, G.: Industrielle Abwärme-Ergebnisse einer Potenzialstudie für Deutschland. Institut für
Zukunfts Energie Systeme, September 2011.
20. N.N.: Veolia Water is chosen to build one of the world’s largest desalination plants in Saudi Arabia.
Veolia Environment, October 2013.
21. N.N.: Potenziale der industriellen Effizienzsteigerung. Prognos, 2007.
22. N.N.: The Saudi Construction Industry. Ventures Middle East, January 2011.
23. Brandstaetter, R.: Industrielle Abwärmenutzung. Sachverständigenbüro, October 2008.
24. Hahne, K.: Energieeffizientes Vorgehen im Bauprozess. Bundesinstitut für Berufsbildung BiBB, 2013.
25. Laing, D. et al.: Energieeffizienz in der Baustoffindustrie. DLR, 2013.
26. Espey, R.: Baustoffhersteller in Saudi Arabien bauen Kapazitäten aus. German Trade & Invest April
2013.
27. N.N.: Energiebedarf in der Industrie. Zentrum Ressourceneffizienz, Düsseldorf 2009-2013.
28. Christ, M. et al.: Energie aus dem Verborgenen. Thermische Speicher in der Industrie. DLR
Nachrichten 120, 2012.
29. N.N.: Beispieldatenblatt Porenbeton. Fraunhofer-Institut für Bauphysik. Holzkirchen, 2012.
30. N.N.: National Water Company. Ministry of Water & Electricity, Annual Report 2010.
31. N.N.: Scottish Energy Study. AEA Technology, 2006.
32. N.N.: Sustainable for nature and mankind. Bundesministerium für Lamd und Forstwirtschaft,
November 2010.
33 Schu, R.: Niedertemperatur-Tunneltrockner zur optimierten Wertstoffgewinnung.
Abfallforschungstage, Juni 2008.
34. Sardehspande, V.: Model based energy benchmarking for glass furnace. Energy Conversion &
Management, 2007.
35. Atmaca, A. et al.: Decreasing the Specific Energy Consumption and Emissions in a Rotary Kiln in
Cement Industry. The Clute Institute, 2013.
36. Koroneos, C. et al.: Exergy analysis of cement production. International Journal Exergy, Vol.2 No 1,
2003.
37. N.N.: GCC glass industry set for unprecedented growth. Albawaba business, 2013.
38. N.N.: Gulf Glass Market Intelligence Report. Ispy Publishing Limited, 2013.
39. Pehnt, M. et al.: Die Nutzung industrieller Abwärme: technisch-wirtschaftliche Potenziale und
energiepolitische Umsetzung. FKZ 03KSW016A und B. Institut für Energie- und Umweltfoschung
Heidelberg, 2010.
40. Taenzer, G. : Industrielle Abwärme—Ergebnisse einer Potentialstudie für Deutschland. IZES Institut
fuer Zukunftsenergiesysteme, Fenne, 2011.
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41. Sandbakk, M.: Enovas satsing mot industrien. Norske Energis arsmote, 2010.
42. N.N.: Energieeinspar- und -effizienzpotentiale. Endbericht. IFEU, Fraunhofer ISI, Prognos, GWS, 2010.
43. Sommariva, C.: Desalination Management and Economics. Mott MacDonald, 2004.
44. Osman, A. H.: SWCC MSF Desalination Plants-current status and future prospects. 6th Saudi
Engineering Conference, KFUPM Dhahran, 2002.
45. Sherif, S. H. : Physico-Chemical Characterization of some Saudi Lignocellulosic Natural Resources.
Environmental & Arid Land Agriculture, Vol 21(2010) No. 2.
46. N.N.: Anstieg der globalen Chemiefaserproduktion 2012. Melliand Textilberichte 2010, No 2, page
62.
47. N.N.: Paper Arabia 2012. Saudigazette, July 16, 2012.
48. N.N.: Advanced Fabrics. SAAF, 2012.
49. N.N.: Web Information by United Sugar Company, Jeddah, 2013.
50. N.N.: Saudi Arabia food consumption. Web information, 2011.
51. N.N.: Review of National Energy Efficiency Initiatives. KAPSARC Saudi Energy Efficiency Workshop,
February 2012.
52. Almahmoud, J.: Research Notes from the Kingdom. International Resource Journal, January 2013.
53. N.N.: www.pulpandpaperonline.com/Doc/metso-machine-rebuild.
54. N.N.: www.pssmagazine.com.
55. N.N.: www.saudipaper.com.
56. N.N.: The first tissue paper study of the Arab world. www.alghader.com/product.
57. N.N.: advanced Fabrics (SAAF).www.saafnw.com
58. N.N.: www.zwaya.com, April 2013.
59. N.N.: www.alc.en.alibaba.com.
60. Wenzel, K. et al.: Energy Efficiency and Renewable Energies: Challenges and Training Needs in the
Kingdom of Saudi Arabia. Fact Finding Mission GIZ, 2010.
61. N.N.: York absorption chillers. Mannheim, 2010.
Links
Alpen Capital Investment Banking
www.alpencapital.com
Ministry of Commerce & Industry
www.commerce.gov.sa
Ministry of Petroleum & Minerals
www.mopm.gov.sa
Saudi Arabian General Investment Authority (SAGIA)
www.sagia.gov.sa
Saudi Industrial Development Fund (SIDF)
www.sidf.gov.sa
Saudi Industrial Property Authority (Modon)
www.modon.gov.sa
Royal Commission for Jubail and Yanbu (RCJY)
www.rcjy.gov.sa
Human Resources Development Fund (HRDF)
www.hrdf.org.sa
www.researchandmarkets.com
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Smart Grid Technologies
CHAPTER 3: Smart Grid Technologies
Chapter 3: Smart Grid Technologies
Chapter Summary
The Smart Grid chapter provides an overview of the possible technologies in this field. These
technologies can be used to realize energy management tasks and efficiency gains or to integrate
renewables like those described in the case study sections. All have the goal of making the total
electricity system more reliable and saving costs in electricity generation.
The technologies that will be described and evaluated are divided into the communication structure
and the components. Components are Automated Metering Infrastructure (AMI), including Smart
Meters (SM); On-Load Tap-Changers (OLTC); and Reactive Power Controls (RPC). Some of these
technologies can work totally decentralized as independent controllers (e.g., RPC) and must be
parameterized occasionally. Other components, such as the SM, are designed to work in
communication networks for active power management. For example, they can be used to
communicate time variable tariffs or current power limits.
Based on the analysis of the technologies, recommendations for the integration of renewables in the
Kingdom of Saudi Arabia should be made. The focus here is on the effects on the power quality
(maintaining voltage and current boundaries).
Methodology
The methodology of the Smart Grid chapter is divided into two parts. The first part presents a metaanalysis on smart grid literature and projects from the past 10 years in Europe, primarily Germany.
Germany is currently the biggest market for smart grids, driven by the integration of a large number of
renewable generators. For the overview on smart grid technologies, journals and recent European
conferences, including projects, were evaluated (see Section 4.7). For the evaluation of the actual
practical importance of the described technologies, more than 100 real smart grid projects were
evaluated according to the applied technology, the lead structure, the stakeholders, and other criteria.
The number of projects dealing with a technology is an indicator for the importance of a technology.
The second part of this chapter is a case study on the effects of consumption and generation in low
voltage distribution grids. A generalized grid topology of a reference grid for rural areas is investigated.
For the simulation time, profiles of loads and generators in the grid are necessary. To make the results
as general as possible, generalized household profiles are used. Cross-checks with measured profiles
from Chapter 4 show similar profiles. As small-scale producers, photovoltaic (PV) plants are installed.
Since positions and installed power of the PV plants are unknown, a probabilistic load flow analysis
solving 400 configurations is performed. The probabilistic load flow has been solved using the opensource software SimTOOL, which is a high-performing load flow simulator developed by Fraunhofer ISE.
SimTOOL calculates a combination of command line based load flow and controllers, allowing for the
effect of renewables on the grids to be estimated. To mitigate the voltage change, an OLTC is used for
all of the scenarios.
Key Results
Reviewing the current smart grid literature and projects, the five most important technologies of a
smart grid are OLTC; RPC; AMI, including SMs; and APC.
On the one hand, OLTC and RPC are technologies that can solve voltage problems in grids as
independent controllers. The OLTC does not raise the current, but it changes the voltage for the entire
grid and facilitates use of the total range allowed by grid codes. RPC affects the voltage locally, but it
raises the flowing currents.
On the other hand, there are technologies that may reduce or shift the load in the grid. The actual
technology is APC, also called Demand Side Management. For efficient operation, this technology
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requires information about the grid state. Typically a centralized controller device distributes a signal
for the underlying systems (e.g., maximal power). This concept may rely on an AMI. The AMI is based on
SMs that send local measurements to a central point. With the knowledge of the current grid state, APC
works precisely.
For the evaluation of the importance of these technologies, European smart grid projects are reviewed.
Since the knowledge of the system state is crucial to the smart grid, SMs are the most investigated
technology. With APC, all problems that can affect a grid might actually be solved; therefore it is nearly
as important as SMs. The problem is that in distribution grids, the shiftable load is actually not big
enough. Assuming that customers will not be influenced in their comfort and PV plants are not switched
off, not all problems can be solved without storage.
In the case study, the effect of PV plants on distribution grids was evaluated. When the grid is not
reinforced, a high amount of PV plants in the analyzed distribution grid violate the upper voltage
boundary. To solve the voltage problems, an OLTC is installed in the grid. The OLTC is able to solve most
of the problems, eliminating the need for expensive grid reinforcement measures. Current list prices for
cables and OLTC show a cost savings by a factor of five while still allowing for a large amount of PV to be
installed.
3.1 Introduction
Global changes of the energy supply sector to try to minimize the use of fossil fuels often leads to rising
shares of renewable—and fluctuating—energy production. With 22.9 percent of its electric
consumption covered by wind and sun in 2012 (BMU), the German electric supply leads this process.
Germany’s electric supply is in a transformation. This change is creating many challenges, especially for
distribution grids that connect these generators and need reinforcement before transmission is
extended. The rise of renewable energies was boosted by financial incentives of the renewable energy
law. To date, this law has been adopted by 65 countries worldwide (www.eeg-aktuell.de, 2012).
Therefore, energy systems in the whole world are changing right now. Additional consumers, such as
electric vehicles (EV) and heat-pumps, intensify this change.
In general there are two ways to deal with the transformation process. Electric energy consumption has
grown all over the world. The usual procedure to solve problems in the low voltage grid was to
reinforce and extend the grid. That means either replacing the transformer or strengthening the lines.
With progress in communication and information technology, other possibilities arise. These
possibilities are in general called smart grid technologies. Which solution is more economical is
determined on a case-by-case basis, which is why new technologies must be evaluated.
Based on typical structures of electricity grids and hierarchies for control, the Smart Grid Architecture
Model (SGAM) for communication will be described in Section 3.2. The first part of the chapter explains
the design principle of electric grids. The second half of the chapter outlines the overlying
communication and control model.
This leads to important smart grid technologies in Section 3, which will be explained in detail. There are
two main categories: voltage control technologies and APC. Voltage control technologies help to keep
voltage in the operation boundaries. APC technologies prevent the overloading of grid components and
help balance overall production and demand.
Section 3.4 characterizes smart grid projects. In the first part, a method for smart grid characterization
is explained. The second half of the section shows the classification results. This section also yields
several conclusions. First, it evaluates the acceptance of the different technologies. Second, the future
use of the technologies can be determined. Third, the question of who is using the specific technologies
is answered. In the last part of Section 3.4, an evaluation matrix is presented to provide a brief overview
of the impact a technology has on certain problems, if applied.
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In Section 3.5, one representative low voltage distribution grid is reviewed with several load scenarios.
Starting from the classic load case, various PV scenarios using probabilistic load flow are examined.
Evaluations of how OLTCs can help to avoid reinforcement follow. At the conclusion, recommendations
for Saudi Arabia are drawn.
3.2 Grid and Communication Layers
The physical grid and the communication infrastructure are organized in different layers. To gain an
understanding of the whole grid, it is important to have an overview of both. In the first part of this
section, the physical grid layers and their tasks are explained. In the second part the communication
infrastructure, which is based on the physical grid, is described.
3.2.1
Grid Layer
In general, grid layers are divided into two parts, as shown in Figure 3-1: a transmission layer and a
distribution layer. The transmission layer is used to transport energy over long distances with low losses
and to connect big power plants to the grid. The distribution layer supplies small units with energy or
connects medium and small generators to the energy system.
The transmission layer consists of the extra high voltage level and a portion of the high voltage level.
Typically voltage levels above 220 kV belong to the transmission layer, whereas the 110 kV level is part
of the distribution grid. The transmission grid is very reliable, as it is designed following the n-1
principle. The n-1 principle states that grid functionality must not be affected by the outage of any
connected utility, no matter which utility it is (Heuck, Schulz, & Dettmann, 2010). Sometimes this is
overlain by some point-to-point connections with very high voltage level or DC connections. There are
high voltage DC (HVDC) connections from offshore wind to the land because AC transmission causes too
much capacitive losses in the lines from the surrounding water. On land HVDC connections are
economical for distances above 750 km. For this reason, China and Russia use the technology to
transport power from big generators to cities.
For optimal grid operation, the transmission system is highly automated with many methods to control
the grid. To facilitate control of voltage and load-flows there are conventional utilities like transformers
with in-phase regulation, regulation in quadrature, and reactive power compensation units. In addition
to the conventional utilities, there are newer technologies called FACTS (for Flexible AC-Transmission
Systems) that facilitate a more efficient use of the grid (Schwab, 2012). So transmission grids can be
considered smart already, which extends into the lower voltage levels through cost reduction.
The lower half of Figure 3-1 displays the distribution grid. The distribution grid consists of the low
voltage level, the medium voltage level, and the high voltage level up to 110 kV. The overall line length
of the distribution system exceeds the length of the transmission system by far. Originally the
distribution grid was built to distribute energy from big central plants or interchange stations to
households and industrial customers. With the rise of renewable energies, new tasks for the
distribution grids arise. They connect decentralized plants, collect the generated power, and
redistribute it to customers or transport it to the transmission layer.
A low penetration of renewable energies would not affect the distribution grid. Higher penetrations
necessitate either grid reinforcement, which is generally expensive for the distribution system operator
(DSO), or the use of new technologies. Those new technologies and their strengths and weaknesses are
described in Section 3.3. With very high penetrations, transmission grids must be extended to
guarantee energy exchange between regions.
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Figure 3-1:
3.2.2
Electricity Grid Layers With Typical Connected Plants (picture by J. Messerly)
Communication
Communication is crucial to the smart grid. Therefore this section describes the SGAM for
communication, which is shown in Figure 3-2. The model consists of three dimensions.
The first dimension consists of the electrical energy conversion chain, which includes all instances that
are needed for energy production, transmission, distribution, and consumption.
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The second dimension represents the SGAM zones. In general the zones are used to aggregate and
reduce information. The different zones are defined as:
•
•
•
•
•
•
The Process Zone gathers the energy conversion chain.
The Field Zone protects, controls, and monitors utilities.
The Station Zone aggregates the data from the Field Zone.
The Operation Zone hosts the power system control for the respective domains (e.g., energy
management systems and virtual power plants).
The Enterprise Zone offers commercial and organizational processes, services, and infrastructures
for enterprises, such as asset management, billing, and logistics.
The Market Zone represents the market operations that are possible in the energy conversion
chain, such as energy trading.
Figure 3-2:
Smart Grid Architecture Model (CEN-CENELEC-ETSI Smart Grid Coordination Group, 2012)
The third dimension is built by the Interoperability Layers. Interoperability is very important to the
smart grid because many of its advantages are related to communications. The Component Layer
consists of the first two dimensions. That means it represents the whole physical grid. The
Communication Layer describes the protocols and mechanisms for the interoperable exchange between
components. The Information Layer describes the information that is being used and exchanged
between the underlying Component Layer and the overlying Function Layer. The Function Layer
describes functions and services that are used in a smart grid. The Business Layer shows the business
view on information exchange and can be used to represent regulatory and economic structures. It
helps to show business models and specific projects (CEN-CENELEC-ETSI Smart Grid Coordination Group,
2012).
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Figure 3-2 illustrates the hierarchical relations between transmission system, centralized generation,
distribution grid, and decentralized generation. Whereas the first two elements are connected by realtime communication, in the last decade these technologies spread to distribution systems and made
them smart. This is described in the following sections.
3.3 Smart Grid Technologies
This section presents the most important technologies that are used in smart grids. Many of these
technologies, such as OLTCs, are used in transmission grids but are redesigned for distribution grids.
These technologies enable new grid control strategies and serve as an alternative to conventional grid
reinforcement or can help to adapt renewable generation and demand. This section focuses on the
distribution grid because it is the biggest part of a typical electrical energy system with regard to line
length, costs (Hosemann, 1988), and potential for control strategies of decentralized generators, which
are typically connected to the distribution grid.
In general a system operator is responsible for two tasks. The first task is to keep the voltage in the
tolerance band that is defined by the grid codes. The second task is to ensure that the grid components
are not overloaded by the current flow. Figure 3-3 shows the two main categories of smart grid
technologies. They are strongly interdependent. Because flowing active currents directly change the
voltage level and vice versa, the magnitude of flowing current is determined by voltage. In general,
these technologies decrease both voltage and current problems. They are separated into categories by
their main effects.
Figure 3-3:
Most Technologies Can Be Categorized as Voltage Control and Active
Power Control, Which Are Strongly Dependent
Efficient options for voltage control are the OLTC described in Section 3.1 and the RPC described in
Section 3.2. Technologies related to APC are described in Section 3.3. The fourth described technology
is SM. It provides a convenient way to access the household load profiles for DSOs and consumers as
explained in Section 2.3 and is part of the APC. These smart grid technologies support the main tasks a
grid operator must perform.
3.3.1
Voltage Control—On-Load Tap-Changer
The OLTC is a well-known technology. To date, it has been used only in the high voltage level, because
available technologies were expensive. Because the low voltage grid is in a transformation process
resulting from increased decentralized generation, the OLTC technology is becoming important for low
voltage substations. An example of a new OLTC, which fits in the same housing as a conventional
substation, is shown in Figure 3-4.
More specific reasons for the need to transform the low voltage grid follow. On the one hand, there is
the challenge of connecting renewable energies to the low voltage grid. Historically, the low voltage
grids were designed for energy consumption (von Oehsen, Saint-Drenan, Stetz, & Braun, 2011). Since
the low voltage grid has a resistive characteristic, the feed-in of active power causes a significant rise of
voltage (Kerber G. , 2010). On the other hand, energy consumption becomes more complex and
additional consumers like EVs will be connected. In the low voltage grid the voltage limits are violated
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usually before current limits are violated (von Oehsen, Saint-Drenan, Stetz, & Braun, 2011). Hence,
instead of reinforcing cables and transformers, voltage control is an option to avoid high investments.
Figure 3-4:
OLTC with Controller (2013)
For a detailed understanding of voltage problems in low voltage grids, an overview on regulatory
background and planning strategies is necessary. In general, European grid voltage may fluctuate ±10
percent around the nominal voltage (DKE, 2010). Usually voltage is controlled at the transformer from
high voltage to medium voltage to decouple the distribution grid and keep voltage near the nominal
voltage.
The allowed 10 percent of deviation needs to be shared between medium and low voltage levels as well
as the transformer for distribution grid planning. The typical assumptions taken by German DSOs
(Annegret-Cl. Agricola, 2012) are displayed in Figure 3-5. For planning purposes low voltage may
fluctuate only around 4 percent of the nominal voltage. Hence there is significant potential to extend
the voltage range of 6 percent by using an OLTC.
Figure 3-5:
3.3.1.1
The Maximal Voltage Deviation of 10% Is Distributed Among the Voltage Levels to 4%, 2%, and 4%
for Medium-Voltage, Transformation, and Low- Voltage Levels, Respectively
Control of Voltage at the Connection Point of the Substation
The simplest way to configure an OLTC is to hold the voltage at the low voltage connection point on the
nominal voltage. Voltage control at the connection point keeps the voltage in the whole grid in the
allowed limits. The effect of this strategy is shown in Figure 3-6.
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Figure 3-6:
Voltage Distributions in a Low-Voltage Feeder
At the connection point to medium voltage, the voltage can range ±4 percent around the nominal
voltage. A substation without an OLTC operates at a fixed transmission ratio between the medium and
low voltage sides, so the allowable 4 percent variation on the medium voltage side transmits through to
the low side. The substation transformer itself can introduce another ±2 percent variation in voltage. In
the worst case there is only the remaining tolerance (±4 percent) for the low voltage grid. If there is
high energy production (consumption) the voltage will violate the limits when there is no OLTC,
especially at locations distant from the substation. With the use of an OLTC, voltage is held steady at
the nominal voltage and voltage problems do not occur. By changing taps, the OLTC adjusts its own
transformer ratio to counteract voltage fluctuations on the medium voltage side and to compensate for
its own varying voltage losses. Assuming that the number of transformer taps is sufficient for the
needed transmission ratio the OLTC can hold the low voltage at the connection point in a tighter
tolerance band than ±6 percent.
If the consumption or production structure of the grid is well known, a more advanced approach can be
used. In most cases the grid is mainly driven by consumption, and production is negligible. Therefore it
is a good choice to configure the OLTC in a way that holds voltage at a higher level—for example at 1.02
p.u. Using this setting, the possible voltage drop is larger and allows for a higher load without voltage
problems. Moreover, distribution losses will be reduced by lower currents for the same power.
3.3.1.2
Control of Voltage at an Important Point in the Grid
An even more sophisticated approach is to control the voltage at critical points in the grid. The control
method described in the prior paragraph is simple and inexpensive because there is no need for
communication. It controls the voltage at the connection point of the transformer. In a typical low
voltage grid, this is the most unlikely point to have voltage problems. A more targeted solution would
be to control the voltage at a critical node. Usually, the most critical point in a grid is at the end of a
feeder. In Figure 3-5 that would be at the position of U5. When voltage reaches the limits of the allowed
band, the OLTC can change the voltage at the low voltage side of the transformer. Using this approach,
communication between the transformer and the measurement at the critical node must be
established. Since communication technology is commonly expensive and may cause faults, the use of
this approach needs to be evaluated on a case-by-case basis.
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3.3.1.3
Potential of the OLTC to Increase Grid Capacity
As noted by Hinz and Sojer (2012), the ability to increase the potential to integrate renewable energies
with an OLTC is calculated to be between a factor of 2 and 8. In urban grids when renewable energies
are connected close to the transformer, the capacity is raised by a factor 2. In rural grids it may rise by a
factor 4. When there are long feeders to which the plant must be connected, the OLTC may increase the
capacity of the grid by a factor of 8. In Bülo and Geibel (2012), load flow calculations are done for a real
grid configuration. The increase of grid capacity by an OLTC is calculated to a factor of 2.6 to 3.1.
3.3.2
Voltage Control—Reactive Power Control
Another possibility to solve voltage problems is RPC. In contrast to OLTC, RPC is a decentralized
solution. It does not change voltage in the entire low voltage grid. It selectively decreases the effect
consumers or producers have on the voltage when necessary. For example, a PV plant feeds in active
power and causes a significant voltage rise. The RPC interferes by feeding in reactive power to constrain
voltage rise.
For RPC to be effective as proposed in VDE (2011), the power electronics that are needed for PV plants
or EVs must be oversized by about 11 percent, assuming a power factor cosφ of 0.9, due to higher
currents. That implies that originators of voltage problems must pay the price for voltage support
because grid modifications are needless.
Although this technique offers many advantages, it is not yet widely used because of DSO prejudices
against it. First, feeding in reactive power causes higher currents. This means more thermal stress for
lines and transformers. In general this is correct, but the maximum current rise for the unit is just 11
percent. When RPC is dependent on the actual voltage, mean current rise may be lower.
Another concern is the voltage oscillations caused by several inverters in the same grid. In Witzmann
and Esslinger (2012), this is tested under real conditions and no oscillations are observed, even without
damping resistance between the inverter connection points.
The last DSO concern is that voltage-dependent RPC interacts with the tap-changer of the HighVoltage/Medium-Voltage transformer. Witzmann and Esslinger (2012) state that this is not problematic
because time constants of the controllers differ significantly.
3.3.2.1
Potential of RPC to Increase Grid Capacity
In Biener (2013), the potential to increase grid capacity is investigated. Depending on grid structure and
the chosen method of reactive power, control grid capacity is increased by a factor of 2.1 to 2.3. Bülo
and Geibel (2012) again derive an increase of grid capacity from the factor of 1.5 to 2.2.
3.3.3
Opportunities of Automated Metering Infrastructure
In this section, the Smart Meter as a crucial element of the AMI is described. In general this is a
conservative definition of an SM, which implies just the core functionality. In the second part of this
section, a system of SMs is described in which SMs use their communication abilities to contribute to an
AMI. In the third part of this section, APC is described. This technology is a direct gain of the use of SMs.
3.3.3.1
Current Control—Smart Meter
Large industry consumers are typically obligated to measure their load profile and not just their
aggregated consumption, like residential consumers do. The load profile is electronically submitted to
the utility. The utility buys the electricity according to this demand profile for which a next day
prognosis is calculated. Energy-metered consumers are forecasted by a typical demand profile. By this
means, the load forecast can be improved. An improved load forecast promotes more economical
operations because less secondary reserve power is needed.
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For an efficient low voltage grid design, load profiles of households also have to be measured. For
this purpose a Smart Meter can be used, as shown in Figure 3-7. Using SMs can save time required to
gather the measurement data. The SM can also be used to disconnect customers from the grid for
nonpayment of bills.
Figure 3-7:
Smart Meter and a Web-Portal to Display Electricity Demand to the Customer
The right side of Figure 3-7 shows a web portal that displays the electricity demand to encourage
energy savings. Additionally, it helps to detect applications that consume standby power.
SM data can be used in future operation of the distribution system. Today distribution grids are
constructed with a wide safety margin. Currently there are no other options because without sufficient
measurements, the state of the system can only be estimated.
3.3.3.2
Advanced Metering Infrastructure
This subsection focuses on the benefits that SMs offer when the measuring and communication
functionality contributes to an AMI.
Low voltage grids do not currently have measuring units because costs outweigh benefits. With the
introduction of SMs, low voltage grids are equipped with a very dense measuring infrastructure. This
measuring infrastructure strengthens the impact of the technologies described in this section.
An OLTC (see Section 3.3.1) with no measurements in the grid needs to rely either on the voltage, the
current, or a combination of both at its connection point. However, the purpose of the OLTC is to
counteract the most critical voltage deviations, which are generally not at the connection point of the
transformer. With a dense metering infrastructure, critical points may be identified and the OLTC may
act when it is necessary (Dallmer-Zerbe & Wille-Haussman, 2014).
Looking at the RPC, the gains by the AMI are similar to the gains by the OLTC. RPC works in general
without an AMI, but with complete information over the whole grid, a centralized controller may be
used. The centralized controller uses the RPC more efficiently than decentralized solutions. Through this
method, it avoids losses and requires less parameterization (Biener, 2013).
In the following subsection, the role of the AMI in an APC system is described. Using an AMI raises the
full potential of APC. Without a complete monitoring solution, it would be too critical to grid safety to
use the potential that APC offers.
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3.3.3.3
Current Control—Active Power Control
In electricity grids there are usually electrical loads that have a window in which they need to consume
energy to fulfill their function. The exact time when they start and finish consuming power is randomly
distributed within that window. For example, a refrigerator needs to keep its temperature in a certain
range. There is only a short time frame when it can shift its consumption. Several applications (home
and industrial) like heating devices with storages or washing machines offer a window to shift load (von
Roon, Gobmaier, & Huck, 2010). This results in a determined amount of power that can be shifted.
3.3.3.4
Distribution Grid Oriented
By shifting energy from peak time into valleys, grid reinforcement in the distribution level can be
avoided due to the decreased maximal power. As Figure 3-8 shows, the idea is to shave the peaks in
consumption and production in the grid. The grid must be designed for peak demand or production.
When the peak is reduced, the benefit for the DSO is that grid reinforcement can be avoided and grid
operation is more efficient by way of lower currents.
Figure 3-8:
3.3.3.5
The Peak of Normal Demand Is Shaved by APC
Market Oriented
Another option for the cumulated shiftable power is to use APC as an extension of secondary reserve
power. Using shiftable power as secondary reserve power provides two possible benefits. Either other
secondary reserve power units can be replaced or the shiftable power can extend the conventional
secondary reserve. While the use as replacement for reserve power implies a cheaper operation of the
whole system, the use as additional reserve power contributes to safer system operation. The
preferable method is dependent on the particular system.
3.3.3.6
Discussion
In general, APC offers much potential to support the grid. The economic use strongly depends on the
effort needed for the communication infrastructure. In the easiest case, the loads and production units
can be controlled by a broadcasted signal (e.g., ripple control). But with the availability of Internet
technologies, much more detailed versions are available to control active power of generators and
loads. If there is an incentive to provide shiftable power to the grid, the provided power needs to be
measured and the meter needs to send this information to a central metering system. The preceding
paragraph described the AMI, which describes a metering infrastructure based on SMs. This
infrastructure may be used for this purpose.
3.3.3.7
Potential of a Household to Shift Demand
It is difficult to determine an absolute figure for the energy that may be shifted in a household. Nabe et
al. (2009) determined that around 10 percent of the total household demand may be shifted in a day.
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3.4 Smart Grid Projects
Smart grid has become a buzzword for projects within field electricity grids. Unfortunately, there is no
exclusive definition for the term “smart grid.” Most definitions include the use of information and
communication technology to improve the efficiency, reliability, economics, and sustainability of the
electricity grid in comparison to conventional grid planning and operation. For example the European
Technology Platform for Smart Grids defines it as (SmartGrids, 2010):
A SmartGrid is an electricity network that can intelligently integrate the actions of all users
connected to it—generators, consumers and those that do both—in order to efficiently deliver
sustainable, economic and secure electricity supplies.
Table 3-1 displays the results of a keyword search for “smart grid.” The large number of projects labeled
as “smart grid” calls for a systematic characterization and categorization. The following sections provide
the developed method of meta-analysis, present the data sources, and evaluate results of the metaanalysis.
Table 3-1: Results of “Smart Grid” Keyword Search (July 3, 2013)
3.4.1
Search Engine
Hits
Google.com
Google Scholar
SciVerse/ScienceDirect
IEEE Xplore
73,000,000
440,000
16,394
8,078
Method of Meta-Analysis
The developed method characterizes smart grid projects in five steps. Each step is associated with a
corresponding question and is numbered as shown in Figure 3-9. For each step all possible answers are
defined for later categorization.
Figure 3-9:
Smart Grid Categorization Methodology
Step 1: Who is the potential stakeholder of the developed system?
Typical answers: company, customer, DSO, TSO, service provider.
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The differentiation between company and customers is done based on the developed products. If the
products are targeted for residential households, the stakeholder is defined as customer.
Step 2: Which system is controlled?
Typical answers: distribution grid, EV, home, storage.
Step 3: Is the optimization addressing grid or economic issues?
Typical answers: grid oriented, economic oriented, no optimization.
Step 4: Where is the optimization located or are optimization tasks split up?
Typical answers: centralized, decentralized, decentralized hierarchic, centralized hierarchic.
A lead system structure is characterized as decentralized hierarchic if the optimization tasks are split up
and the decentralized optimization part is more important.
Step 5: Which field technology is implemented in the controlled system?
Typical answers: APC, SMs, virtual power plant, OLTC.
3.4.2
Data Sources
The main data sources that are used in this meta-analysis are the reports of the Joint Research Center
of the European Commission (Giordano, Gangale, & Fulli, Smart Grid projects in Europe: lessons learned
and current developments, 2011)(Giordano, Meletiou, Covrig, Mengolini, Ardelean, & Fulli, 2012). In
addition, intern projects of the Fraunhofer Institute for Solar Energy Systems (ISE) are evaluated.
Twenty-four projects were discarded due to insufficient documentation. In total, 86 smart grid projects
are evaluated.
3.4.3
Results
Results are structured into meta-information in the first sections and the evaluation of the method’s
five questions.
3.4.3.1
Active Smart Grid Projects
As a basis for further investigation, the absolute number of active smart grid projects is evaluated. As
displayed in Figure 3-10, the first smart grid projects were launched in 2004. Because 2004 and 2005
provide only one smart grid project each, the subsequent evaluation of projects starts in 2006. At this
time, the term “smart grid” became popular. The number of projects increased to a peak of 70 active
projects in 2011. New research initiatives were founded, such as the E-Energy project and the Smart
Cities Project. The decline after 2011 is due to the timeframe of the study this investigation is based on.
The study includes only projects that were started before 2012. A large number of projects are
scheduled to begin during 2014.
Figure 3-10: Number of Active Smart Grid Projects per Year
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3.4.3.2
Project Partners
Subsequent to the absolute number of smart projects, the focus is moved to the participating project
partners. Figure 3-11 shows the participating institutions. This provides an overview of what
organizations are interested in smart grid technologies. The majority of the project partners are private
enterprises, primarily DSOs. DSOs have a strong interest in smart grid technologies because they have
to deal with the transformation of the power system. Research institutes and universities are on the
same level, which illustrates that smart grids are still a topic of research. DSOs and research institutes or
universities often team up for specific projects.
Figure 3-11: Project Partners Within Smart Grid Projects per Year
3.4.3.3
Financial Support of Projects
Another sign of interest in developing a technology for the grid is the funding of projects. Figure 3-12
shows the most common financial support forms. Other financial support forms, such as funds and
cross-financed projects, are neglected. The highest proportion of smart grid projects is financed by
country government, followed by intern research and industry-funded projects. The fewest projects are
funded by the European Union. The distribution is similar to the distribution of all active smart grid
projects per year.
Figure 3-12: Financial Support Forms of Smart Grid Projects per Year
3.4.3.4
Step 1: Stakeholder
The first question within the smart grid categorization is who are the potential users of the developed
systems. The answers are shown in Figure 3-13. The majority of the stakeholders are DSOs, followed by
customers or end users. Customers represent a large part of stakeholders because of the product
design and acceptance issues. There is only a small portion of transmission system operator projects, as
it is not directly their concern. The increase of smart grid projects over the years is due to more DSO
and customer-oriented projects.
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Figure 3-13: Stakeholders of Active Smart Grid Projects per Year
3.4.3.5
Step 2: Controlled System
The second question within the smart grid categorization is which system is controlled. Figure 3-14
depicts the five most common controlled systems. Because DSOs are the primary project leaders, the
distribution grid is the most important system. This is followed by economic/balancing systems and
homes. Economic/balancing systems are important because they directly yield business cases, and the
financial results can be compared. Homes are evaluated because of the changing role of residential
consumers due to the rising importance of demand-side management and measurements within the
distribution grid (e.g., by SMs). Next are EVs. Because the effect of many EVs on the distribution grid is
unknown, investigations are necessary. EVs temporarily need high power, which might lead to a change
of grid topology. In the last place are storages. Currently, grids are designed for peak demand or peak
production. Cheap storages foster the idea of decoupled generation and demand. This implies an
optimized grid capacity usage that is improbable from an economical point of view.
Figure 3-14: Controlled System Within Smart Grid Projects Differentiated per Year
3.4.3.6
Step 3: Lead Signal
In the last step, the controlled systems were defined. This part answers the question, to which objective
are the systems optimized. A system can either be controlled to serve the grid (grid-oriented
optimization) or to generate cost savings or revenue (economic-oriented optimization). Grid-oriented
optimization means to balance consumption and production locally and to shift power as a result of grid
constraints. Economic-optimized orientation focuses on optimization to economic objectives. This
means to earn or to save as much money as possible with the consumed or produced power. Gridoriented optimization is used a bit more often than economic-oriented optimization. Projects having no
optimization are rare. Results are presented in Figure 3-15.
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Figure 3-15: Used Lead Signals in Smart Grid Projects
3.4.3.7
Step 4: Lead System Structure
The preceding subsection described for which objective systems are optimized. Either they are
optimized to minimize the maximal grid load or for economic advantages of the system owner. This
section investigates the structure of the lead system. In Figure 3-16 the use of the different structures is
shown. Electrical energy systems need to be extremely robust. Decentralized systems can implicitly
handle the loss of any single unit so the decentralized structure is clearly dominant. Market-based
optimizations also tend to use decentralized solutions because it is only necessary to send a price signal
to the single units. The reaction on the price is then completely decentralized. The investigation period
found that decentralized-hierarchical, centralized, and no optimization structures are about the same
level of importance. A very uncommon structure is centralized-hierarchical.
Figure 3-16: Lead System Structure of Smart Grid Projects
3.4.3.8
Step 5: Field Technology
As the last part of the investigation, the distribution of field technologies is observed. The results are
shown in Figure 3-17. APC and SM clearly dominate the field technologies within the entire time frame.
APC is usable for grid optimizations as well as for economic optimizations, making it the focus of many
investigations. SM can be used as a gateway for APC applications, to test customer acceptance, and for
marketing issues. OLTC are rarely investigated because the technology and its effects are well
documented. It is more a problem of cost and of down-scaling the technology from highvoltage/medium-voltage transformers to medium-voltage/low-voltage transformers. In addition, smart
grid projects primarily deal with system solutions, but the OLTC is more of a single technical solution.
RPC is a power electronic solution to voltage problems. DSOs suspect interaction with tap-changers and
voltage oscillation, although there are studies providing contrary evidence (Witzmann & Esslinger,
2012). That is the reason why it is not so well investigated in smart grid projects.
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Figure 3-17: Used Field Technologies in Smart Grid Projects Differentiated per Year
The concept of the Virtual Power Plant is the bundling of small generators for easier market access. It is
rarely investigated because it already works well. Lichtblick (2013) provides an example of a successful
project.
3.4.4
Evaluation Matrix
The different field technologies can also be classified by their effect on the electricity grid. Table 3-2
shows the effects of the technologies on the line usage rate and voltage stability. Acceptance and costs
are also estimated.
Table 3-2: Evaluation Matrix of Smart Grid Field Technologies
Field Technology
Line Usage Rate
Voltage
Acceptance
Costs
Height of Effect:
3.4.5
Active Power
Control
+++
+++
+++
++
Good: +++
Smart Metering
Virtual Power
Plant
On-Load
Tap-Changer
Reactive Power
Control
+
0
++
++
0
0
+++
++
++
++
+++
+
++
+
+++
Fair: ++
Poor: +
No Effect: 0
Negative: -
Evaluation of Saudi Smart Grid Status
In this subsection the knowledge base of smart grids in Saudi Arabia is evaluated. This task is performed
by reviewing papers from the recent “Saudi Arabia Smart Grid 2013” conference. The papers addressed
four main topics:
•
•
•
•
Economic aspects, such as energy trading and asset management
Future plant concepts
Whole energy systems
Indirect energy grid concepts, such as improving residential energy efficiency by providing
information about energy efficient buildings to local companies.
The economic portion of the papers concentrates on the one hand on efficient grid building and
administration (Mostafa, El Belawy, & El-Latif Badr, 2013). Since the grid exists to serve customers with
cheap electricity, it is important to evaluate the opportunities created by new technologies to
administer and to build grids efficiently. On the other hand, there are investigations of opportunities to
earn money with the grid. Grids may be used to transport power from one country to another. With the
rise of fluctuating energy producers like renewable energies, opportunities to generate revenue
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increase (Al-Hamad & Al-Ebrahim, 2013). When power is produced in one country where it is not
needed it may be transported to another country and consumed there. By balancing supply and
demand over different countries, energy does not need to be wasted or banked in expensive stores.
This is equivalent to the extensions of Germany and the European network ENTSO-E to allow energy
exchange driven by market rules and not by grid restrictions. With this, a win-win situation is created,
and the system comes closer to an optimum and gets more to a copper plate scenario.
With the rise of renewable energies, new possibilities to build power plants become more economical.
In the papers, different optimizations to combine heat plants with renewable plants were investigated
(Hafeez Ansari & Al-Awami, 2013)(Pazheria, Othmanb, & Malikc, 2013). With this arrangement, the
security of supply is not to be affected but emissions of greenhouse gases can be reduced.
Technologies concerning energy transport itself were discussed in depth (Dolezilek & Schweitzer,
2013)(Heyrman, Abdallh, & Dupré, 2013). The most important technologies are Demand Side
Management, System Protection, and Renewable Integration. The importance of Demand Side
Management was discussed in subsection 3.3.3. With technologies facilitating economical and secure
communication, new protection concepts arise. The improvement of protections enables a more
reliable grid. A key task of future grids that has been addressed by many of the studied papers is to
integrate renewable energies.
3.5 Case Study of a Smart Grid
To decide which of the technologies is most suitable for Saudi Arabia, it is necessary to evaluate them
against a real-world case study using smart grid technologies. Before the actual investigation starts, a
paragraph explains the modeling of smart grids. After that the basic case study is defined based on
sample daily load flows (Abu-ebid & Alyousef, 2012)(Al-Alawi & Islam). The effects of renewables are
evaluated via probabilistic power flow. Qualitative low voltage grid analysis applying an OLTC is
executed.
3.5.1
Smart Grid Modeling
The software SimTOOL is used to model smart grid technologies. This is an open-source steady-state
load-flow calculation tool developed by Fraunhofer ISE in (Wille-Haussmann, 2011). It is cross-validated
with the well-known tool PowerFactory and provides powerful grid simulation. For input values,
SimTOOL needs both the grid load and the production in the grid for each connection point. Moreover
the grid data needs to be available to set up a mathematical model of the grid. With these input values,
SimTOOL calculates the resulting voltages at the nodes using the “gaus-seidel algorithm.” With the node
voltages, power and current flows are calculated. Hence all relevant grid states are known.
In this case study an OLTC is simulated. The OLTC is used to control the voltage. Therefore if a violation
of a voltage limit is recognized, the state of the OLTC is changed. The switching of an OLTC results in a
small change of the grid. In the end the mathematical model of the grid is rebuilt and a new load-flow
calculation is done. This process will be repeated until the OLTC is at the limit of its switcher or the
voltage no longer violates the boundaries.
The following sections describe how the needed input values are derived.
3.5.2
Case Study Definition
The basic case study is defined by demand of the households and grid topology. The case study
describes a distribution grid supplying a couple of households. Afterwards it will be upgraded with
decentralized PV.
3.5.2.1
Demand Household
The demand of a typical household is derived by the daily peak variation in peak load of Riyadh as
presented in Figure 3-18, which is scaled by peak power of one residential household of 9.3kW
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according to Shaahid and Elhadidy (2008). Taking identical profiles for all grid connection points in the
grid is a simplification and hence neglects simultaneous factors.
Figure 3-18: Daily Variation in Peak Load and Temperature of Riyadh (Temperatures for Riyadh, Sept. 9, 2006)
(Abu-ebid & Alyousef, 2012)
3.5.2.2
Grid Topology
A rural grid is set up according to Kerber and Witzmann (2008) and von Oehsen et al. (2011). Due to the
higher peak loads of households in Saudi Arabia, the cable length between two grid connection points
and the voltage levels is adjusted. It is shown in Figure 3-19. The feeders (Vn= 380 V) are coupled with a
transformer (SN =50 kVA) to the medium voltage grid (Vn=13.8kV). These voltage levels are most
common according to Saudi distribution code ((SEC), 2008). Each feeder supplies seven grid connection
points (GCP): 1 to 7 and 8 to 14, respectively. The GCP are connected equidistantly with 35 m of cable
(type NAYY 4x150 mm2).
Figure 3-19: Low Voltage Grid Topology
The results of a power flow analysis are shown in Figure 3-20 via box plot applying the household
profiles on each GCP. In addition, the voltage boundaries of ±3 percent are marked in orange color. At
no time are these boundaries surpassed. The nodal voltage decreases with the distance to the
transformer to a minimum of almost 0.97 p.u. The spread of the nodal voltage due to the daily load
variation is 0.008 p.u.
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Figure 3-20: Nodal Voltage per Grid Connection Point Applying Household Load Profiles
3.5.3
Effect of Renewables
In this project a probabilistic power flow approach is implemented. This method is commonly used to
evaluate future effects, like integration of distributed PV, in electricity grids. The nominal powers Pnom of
the plants are listed in Table 3-3 and will be selected based on the possibility of occurrence p according
to characteristic clustered values based on real low voltage grids (von Oechsen, Saint-Drenan, & Stetz,
2011). Four hundred configurations of Pnom of the plants will be simulated. All PV plants and household
loads are connected symmetrically to all three phases of the feeder.
Table 3-3: Nominal Power Will Be Distributed According to Likelihood of PV Units
Pnomin kW
p in %
3.4
30.08
7.3
31.5
12.1
13.17
23.2
18.21
53
7.04
The change of the appended power per GCP is displayed in Figure 3-21. Depending on the size of the PV
unit, the nodes show either load or generation characteristics.
Results of the probabilistic power flow analysis are displayed in Figure 3-22 via box plot. The upper
voltage boundary is surpassed at several times. Due to the decentralized generation, the nodal voltage
increases with the distance to the transformer up to a maximum of 1.055 p.u. at GCP 14. Nevertheless
the mean nodal voltage per GCP is around 0.98 p.u. and the third quartile below 1 p.u. Hence most of
the PV configurations suffice for the voltage boundary. The spread of the nodal voltages per GCP
increases by a factor 10 to 0.083 p.u. in comparison to only load profiles in Figure 3-20.
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Figure 3-21: Power per Grid Connection Point for Scenarios With PV Units in Comparison to
Scenarios Without PV Units
Figure 3-22: Nodal Voltage per Grid Connection Point Applying Household Load Profiles and
Probabilistic PV Units
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3.5.4
On-Load Tap-Changer
One possible solution to control the nodal voltage is the OLTC as explained in sub-section 3.3.1. The
OLTC is implemented in the case study and changes the transmission ratio of the transformer by the
voltage of the low voltage side in 1.5 percent steps. There are four settings for voltage down
regulation—tap positions -1 to -4—and four settings for up regulation—tap position 1 to 4. The
standard transmission ratio is defined by tap position 0. To control voltage more efficiently, the OLTC
measures the voltage at feeder end corresponding to GCP 7 and 14. In case of OLTC control based on
the voltage at the beginning of the feeder, the voltage deviation in the feeder due to the PV units is
detected less with a decreased efficiency.
Results of the probabilistic power flow analysis, including households, PV units, and OLTC, are shown in
Figure 3-23 via carpet plot and box plot. Figure 3-23 presents tap positions of the OLTC for all
configurations per day. During night time, voltage up regulation takes place with tap positions of 2,
because without PV generation the power flow is characterized by the household loads. With sunrise
the voltage up regulation is attenuated and switches to voltage down regulation up to 3 percent of the
nominal voltage corresponding to tap position -2. The potential of the voltage regulation is not
exploited completely since not all possible tap positions are used. Hence the OLTC control algorithm
voltage deadband could be adjusted, leading to an increased number of OLTC switch operations. The
number of switch operations correlates with the life expectancy of the OLTC. Therefore it should be
minimized to operate the OLTC economically.
Figure 3-24 evaluates the nodal voltages via boxplot for OLTC operation, applying demand of the
households and PV units. The maxima of the nodal voltage increase with the distance to the
transformer, whereas the minima show only weak relation. The maximum of the nodal voltage
decreases by 0.02 p.u. to 1.035 p.u. in comparison to Figure 3-22 due to the OLTC. The spread is
decreased by a factor of 1.4 to 0.061 p.u. The influence of the PV units is reduced by the OLTC.
Nevertheless for extreme PV configurations with multiple 53MW units in one feeder, the voltage
boundary is surpassed slightly. In usual cases the voltage is controlled sufficiently, preventing or
avoiding conventional grid reinforcement.
Figure 3-23: Tap Positions per Configuration and Day
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Figure 3-24: Nodal Voltage per Grid Connection Point Applying Household Load Profiles,
Probabilistic PV Units, and OLTC
3.5.5
Economic Evaluation of Solutions
Economic analysis follows the technical evaluation of solutions. In this section the OLTC will be
compared to conventional grid extensions. The necessary cost parameters for cables and transformers
will be presented first. This is the basis for calculation costs for grid reinforcement.
3.5.5.1
Cost Parameters for Cables
This case study assumes that grid reinforcement is done by using underground cables, so costs can be
separated out for installation and for the cable itself. The cable is split into the hollow price (aluminum
base 0), which contains manufacturing of the cable, and an additional price for the conducting material
(Aluminum: US$253.38/100km [October 15, 2013]). Table 3-4 shows cost parameters for this case
study.
Table 3-4: Cost Parameters for Possible Cable Types Separated to Hollow Price (Aluminum base 0) and a Part
for the Needed Aluminum US$253.38/100km (Oct. 15, 2013) (Helukabel, 2013)
Cable Type
NAYY-J 4x 50 SE
NAYY-J 4x 95 SE
NAYY-J 4x120 SE
NAYY-J 4x150 SE
NAYY-J 4x185 SE
NAYY-J 4x240 SE
NAYY-J 4x300 SE
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Hollow Price
[$/km]
Al
[$/km]
Cost Al
[$/km]
Cost
[$/km]
11469,16394,19349,23181,27053,35460,41410,-
580,1102,1392,1740,2146,2784,3480,-
1470,2792,3527,4409,5438,7054,8818,-
12939,19186,22876,27590,32491,42514,50228,-
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Planning a new cable for grid reinforcement also must account for burying the cable, which depends on
the surface to be built. Costs range from US$40/m without any surface up to US$250/m in urban areas.
For the study we use an average value of US$130/m. Therefore the costs of US$130,000/km have to be
added to the cable costs that are presented in Table 3-4.
3.5.5.2
Cost Parameters of Transformers
Transformers are offered in different sizes. Table 3-5 shows transformers that are typically used with
their associated general costs. Because OLTCs are a new product for low voltage substations, initial
selling prices for their use are difficult to define. Manufacturers suggest that an OLTC costs twice that of
a conventional transformer. To replace the 160 kVA transformer in this case study with an OLTC would
result in roughly US$20,000. All of the presented prices consider only the transformer itself and do not
account for housing costs. This is valid because OLTCs use the same housing as conventional
transformers.
Table 3-5: Cost Parameters for Possible Transformer Types
Transformer Type
160 kVA
400 kVA
630 kVA
3.5.5.3
Price [US$]
10000,15000,25000,-
Costs of Grid Reinforcement
To estimate the costs for reinforcement in this case study, we applied an algorithm for grid extension
into the probabilistic load flow. This algorithm analyzes the voltage profile of the two feeders. If one of
the voltages violates the ±3 percent voltage criteria, the grid is extended stepwise by the cable with the
next greater diameter. This process will be repeated either until no voltage violation occurs or all cables
are replaced by the biggest cable.
The result presents the maximal grid reinforcement to realize all of the 400 scenarios studied in the
probabilistic load flow. Table 3-6 shows the necessary length of cables to be re-created in the case
study. This results in a total cost for grid extension of US$84,000 (installation costs: US$64,000; cable
costs: US$20,000), which is higher by a factor of 4 than the expected costs of replacing the transformer
with an OLTC.
Table 3-6: Necessary Grid Reinforcement to Realize All Scenarios
Cable Type
NAYY-J 4x 50 SE
NAYY-J 4x 95 SE
NAYY-J 4x120 SE
NAYY-J 4x150 SE
NAYY-J 4x185 SE
NAYY-J 4x240 SE
NAYY-J 4x300 SE
To Install [m]
0m
0m
0m
0m
140 m
246 m
105 m
3.6 Recommendations
The analysis reinforces that smart grids are a global topic, especially in Europe, where this market is
showing robust growth. This is mainly driven by the need to integrate renewables. Simulations on one
typical Saudi grid demonstrated that, due to their natural load behavior, low voltage grids can connect
large numbers of PV. Cost efficiency can be increased with OLTCs. This chapter shows that decentralized
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generators can be connected in low voltage grids. The effects on higher voltage levels could not be
investigated because grid data were not available for the study.
Besides the grid integration, it is important to maintain the global balance of demand and generation.
For this purpose we suggest over the long term the use of incentives, like variable tariffs, to motivate
for load shifting and to allocate storages (thermal and electric).
3.7 Literature
(SEC), S. E. (2008). The Saudi Arabian Distribution Code. Electricity & Co-generation Regulatory
Authority.
(2013, 06 26). Retrieved from www.rheinhausen.com
Abu-ebid, M., & Alyousef, Y. (2012). Energy Efficiency Initiatives for Saudi Arabia on Supply and Demand
Sides, Energy Efficiency - A Bridge to Low Carbon Economy. Retrieved September 3, 2013, from InTech:
http://www.intechopen.com/books/energy-efficiency-a-bridge-to-low-carbon-economy/energyefficiency-initiatives-for-saudi-arabia-on-supply-and-demand-sides
Al-Alawi, A., & Islam, S. (n.d.). ESTIMATION OF ELECTRICITY DEMAND FOR REMOTE AREA POWER
SUPPLY SYSTEMS INCLUDING WATER DESALINATION AND DEMAND SIDE MANAGEMENT MODELS.
Centre for Renewable Energy and Sustainable Technologies Australia.
Al-Hamad, M. Y., & Al-Ebrahim, A. A. (2013). Peak Load Opportunities Trading Model Forecasted for
Bahrain and Kuwait. Saudi Arabia Smart Grid 2013.
Annegret-Cl. Agricola, B. H. (2012). Ausbau- und Innovationsbedarf der Stromverteilnetze in Deutschland
bis 2030. Berlin: dena.
Biener, W. (2013). Regelungsoptionen im Verteilnetz durch verteilte Erzeuger.
BMU. (n.d.). Retrieved July 23, 2013, from http://www.erneuerbare-energien.de/diethemen/datenservice/erneuerbare-energien-in-zahlen/erneuerbare-energien-im-jahr-2012/
Bülo, T., & Geibel, D. (2012). Spannungshaltung in aktiven, intelligenten Niederspannungsnetzen. VDEKongress 2012. Berlin ∙ Offenbach: VDE VERLAG GMBH .
CEN-CENELEC-ETSI Smart Grid Coordination Group. (2012, November). Smart Grid Reference
Architecture.
Dallmer-Zerbe, K., & Wille-Haussman, B. (2014). Verteilnetzplanung mit dezentralen Blindleistungsregler
und rONT. Zukünftige Stromnetze für erneuerbare Energien. Berlin.
DKE. (2010). Merkmale der Spannung in öffentlichen Elektrizitätsversorgungsnetzen. Berlin: Beuth.
Dolezilek, D. J., & Schweitzer, S. (2013). Practical Applications of Smart Grid Technologies. Saudi Arabia
Smart Grid Conference.
Giordano, V., Gangale, F., & Fulli, G. (2011). Smart Grid projects in Europe: lessons learned and current
developments.
Giordano, V., Meletiou, A., Covrig, C. F., Mengolini, A., Ardelean, M., & Fulli, G. (2012). Smart Grid
projects in Europe: Lessons learned and current developments - update 2012.
Hafeez Ansari, M. A., & Al-Awami, A. T. (2013). Wind Thermal Generation Scheduling with Fuzzy Genetic
Optimization. Saudi Arabia Smart Grid.
Helukabel. (2013). http://www.helukabel.de. Retrieved October 15, 2013, from :
http://www.helukabel.de/opc/workarea/suppliers/STD/documents/pdf/ks/1KS_32301_en.pdf
Heuck, K., Schulz, D., & Dettmann, K.-D. (2010). Elektrische Energieversorgung. Wiesbaden: Teubner.
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Heyrman, B., Abdallh, A. A., & Dupré, L. (2013). An efficient program for modeling, control and
optimization of hybrid renewable-conventional energy systems . Saudi Arabia Smart Grid Conference.
Hinz, A., & Sojer, M. (2012). Spannungsgeregelte Ortsnetzstationen zur Verbesserung der
Netzintegration von erneuerbaren Energien. VDE Kongress 20120 Stuttgart. Berlin ∙ Offenbach: VDE
VERLAG GMBH .
Hosemann, G. (1988). Energietechnik Band3:Netze. Berlin: HÜTTE.
Kerber, G. (2010). Aufnahmefähigkeit von Niederspannungsverteilnetzen für die Einspeisung aus
Photovoltaikkleinanlagen. München.
Kerber, G., & Witzmann, R. (2008). Statistische Analyse von NS-Verteilungsnetzen und Modellierung von
Referenznetzen. ew, S. 22-26.
Lichtblick. (2013, 07 4). LichtBlick-SchwarmStrom. Retrieved 07 04, 2013, from
http://www.lichtblick.de/schwarm-strom/
Mostafa, G. D., El Belawy, S. A., & El-Latif Badr, M. A. (2013). Management of Electric Distribution
Networks Planning Equipped with distributed generation . Saudi Arabia Smart Grid.
Nabe, C., Beyer, C., Brodersen, N., & Schäfer, H. (2009). Einführung von last-variablen und zeitvariablen
Tarifen.
Pazheria, F. R., Othmanb, M. F., & Malikc, N. H. (2013). Benefits of High Renewable Penetration on
Environment. Saudi Arabia Smart Grid Conference.
Schwab, A. J. (2012). Elektroenergiesysteme. Heidelberg: Springer.
Shaahid, S., & Elhadidy, M. (2008). Economic analysis of hybrid photovoltaic–diesel–battery power
systems for residential loads in hot regions—A step to clean future. Renewable and Sustainable Energy
Reviews, pp. 488–503.
SmartGrids, E. T. (2010). Strategic Deployment Document for Europe’s Electricity Networks of the
Future.SMARTGRIDS.eu.
VDE. (2011). Erzeugungsanlagen am Niederspannungsnetz - Technische Richtlinien für den Anschlus und
Parallelbetrieb von Erzeungsanlagen am Niederspannungsnetz. Berlin: VDE Verlag.
von Oechsen, A., Saint-Drenan, Y.-M., & Stetz, T. (2011). Vorstudie zu Integration großer Anteile
Photovoltaik in die elektrische Energieversorgung. Kassel: Fraunhofer Institut für Windenergie und
Energiesystemtechnik (IWES).
von Oehsen, A., Saint-Drenan, Y.-M., Stetz, T., & Braun, M. (2011). Vorstudie zur Integration großer
Anteile Photovoltaik in die elektrische Energieversorgung. Kassel: Fraunhofer Institut für Windenergie
und Energiesystemtechnik (IWES).
von Roon, S., Gobmaier, T., & Huck, M. (2010). Demand Side Management in Haushalten. München:
Forschungsstelle für Energie.
Wille-Haussmann, B. (2011). Einsatz der symbolischen Modellreduktion zur Untersuchung der
Betriebsführung im “Smart Grid”. phd.
Witzmann, R., & Esslinger, P. (2012). Studie Q(U). TUM: TU München.
www.eeg-aktuell.de. (2012). http://www.eeg-aktuell.de/. Retrieved 07 04, 2013, from http://www.eegaktuell.de/das-eeg/
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CHAPTER 4: Residential Metering
Chapter 4: Residential Metering
Chapter Summary
Chapter Description
The energy demand of residential buildings in the Kingdom of Saudi Arabia represents up to one-third
of the total energy consumption (see Chapter 1). The main components of residential energy
consumption are electricity demand for air conditioning (AC), domestic hot water production, and
general household consumption such as lighting and mechanical devices. Approximately 20 percent of
Saudi households use electrical energy for cooking, while the rest use natural gas. Although the
electrical energy demand of residential buildings is a dominant part of the country’s energy demand,
the actual demand profile is not fully known. Because no monitoring of this energy demand has been
carried out so far, only simulated demand profiles derived from certain general assumptions are
available. To implement smart grid methods such as active power management to integrate renewable
energies or to operate conventional power plants more efficiently, a thorough understanding of
household demand profiles and shiftable loads is essential. Thus, the goal of this task was to measure
and analyze electricity demand profiles of residential buildings, which should be compared to the
profiles in other countries. To achieve adequate results, as many as 100 residential buildings in Thuwal,
Dammam, and Riyadh should be studied.
The work in this task can be split into the following parts: (1) monitoring and data acquisition and (2)
statistical and model-based data analysis.
Methodology
To obtain reliable information about the actual energy demand of the residential sector, a monitoring
campaign was carried out and the collected data was analyzed. A model-based consideration of energysaving potential within the residential sector was conclusively demonstrated.
Monitoring and Data Acquisition
To obtain representative demand profiles, demand data normally is collected for 12 months. Because of
project constraints, acquiring data during such a time period was challenging. The project consortium
decided to use energy meters already installed at the King Abdullah University of Science and
Technology (KAUST). At Dammam University, energy meters were installed by the project team. At
KAUST, ADDAD-4 meters were installed, and at Dammam University, ADDAD-5 meters were installed.
Both meter types were produced by the Advanced Electronics Company. They are electronic meters
with internal registers that store power values for 1 month, with a time resolution of 15 minutes.
To understand and model the building energy system, it is important to gather data from big energy
consumers, such as AC units, separately. For this purpose, buildings in Dammam were equipped with an
additional submeter within the building’s junction box to measure the energy consumption of the
whole building except AC.
All installed meters are equipped with an optical serial interface. This interface was used to collect the
data manually. An online collection of data using mobile networks was not planned. This collected data
were checked for plausibility, and faulty data were repaired.
To create a model-based analysis, it is essential to correlate the power series with climate data. The
necessary data about ambient temperature and solar radiation were taken from the company
meteocontrol [SolarGIS].
Statistical and Model-Based Data Analysis
Fraunhofer ISE analyzed the data collected in the monitoring campaign. First, the plausibility of the
measurements was checked. To do so, action carpet plots were created, and the whole time series was
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checked visually. Faulty data were neglected. During this step, all power data were normalized to the
living area of the buildings to allow a comparison of different buildings. A further clustering of the
houses was originally planned. No detailed data about the characteristics of the buildings were
available.
The following analyses were carried out on the mean data of the selected buildings at each site so that
the results would show the typical behavior per region of the evaluated building type:
•
•
•
•
Comparison of daily and weekly profiles of different months
Comparison of daily and weekly profiles with the ambient temperature
Correlation of temperature and power on different timescales (15 minutes, 1 day, 1 month)
Modeling of the electrical energy consumption for the specific cooling demand.
These analyses targeted improved understanding of typical demand profiles in the Kingdom of Saudi
Arabia. If a submeter was installed, the demand of the AC also was evaluated.
Key Results
In this section, the main findings on residential profiles are presented and observed challenges and
recommendations are summarized.
Residential Profiles in KSA
The measurements acquired during the monitoring campaign showed an electricity consumption of up
to 50 W/m2. Considering a typical living area of 500 m2, this yields a maximum power consumption of
25 kW during one time step. This peak is typically reached in the early evening hours and reduced to
half during the night. Figure 4-1 shows that a typical daily profile has one peak. This peak is roughly 4
hours after the daily peak of the ambient temperature, which leads to the assumption that the peak in
the daily profile correlates to the thermal capacity of the building. With higher ambient temperatures,
the room temperature stays in a normal range (near the set value) until the building’s capacity is
charged by the surrounding air. At night, the opposite occurs. Room temperatures were not available to
test this assumption.
Figure 4-1:
Weekly Load Profile
The profiles do not change considerably during the course of the week. No change in weekend energy
consumption can be detected.
As described previously, the observed building profiles show a high correlation of AC demand to
ambient temperature, because the demand is driven mainly by AC. This was proven using the
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measurements with submeters, allowing separation of AC demand from the total demand at
households in Dammam. Figure 4-2 shows the daily energy consumption of households and of AC alone,
sorted by the daily mean ambient temperature. The household energy consumption varies between 0.1
kWh/m2 and 0.15 kWh/m2 per day, depending on the ambient temperature. It is very likely that these
fluctuations are caused by varying user behavior.
The AC energy demand shows a strong correlation to temperature. During the measurement period,
the AC energy demand ranged from 0.05 kWh/m2 per day at a daily mean temperature of 22 °C to 0.55
kWh/m2 per day at a daily mean temperature of 34 °C. Finally, whereas AC constitutes 85 percent of the
total consumption in September and 70 percent in November, the monthly household consumption is
roughly constant, at 3 kWh/m2 per month.
Figure 4-2:
Energy Consumption of Household and AC
Recommendations for Energy Saving
The most important finding of the monitoring campaign is that AC systems account for a large part of
energy consumption within the residential sector. According to Figure 4-2, AC is responsible for 70
percent of the residential energy demand. Reducing energy demand in the residential sector directly
reduces AC energy demand. AC energy demand is affected by three factors that have a direct influence
on energy consumption:
•
•
•
The building insulation has an impact on the demand for cooling. The better that buildings are
insulated, the less they need to be cooled.
The indoor set temperature influences the cooling demand.
The efficiency of the AC unit has an impact on the electrical energy consumption required to
provide the requested cooling needs of the building.
Table 4-1 shows examples of these three energy-saving measures. An energy savings of 15 percent can
be achieved simply by increasing the set temperature by 2 K. AC devices with an energy efficiency ratio
(EER) of 3, which are state of the art for the residential sector, can reduce the cooling demand by a
factor of 2. By far the biggest reduction, reaching 75 percent of the energy demand for cooling, can be
achieved by installing an insulation layer.
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Table 4-1: Effects of Different Energy-Saving Measures on Cooling Demand Classified by Effort
Measure
Increase Tset by 2 K
Replace AC hardware (double EER)
Insulate with 7.5 cm polystyrene
Effect on energy demand
Effort
minus 15%
minus 50%
minus 75%
none
medium
high
Data Acquisition Experience and Recommendations
After reviewing the data of the existing meters at KAUST, we detected unreasonably high consumptions
of more than 10 MWh per day for some households. These extremely high figures were caused by
different current transformer factors within the meter: one for the display and another for the internal
register. Furthermore, the batteries for the energy supply of the internal clock were empty, which
caused a strong time shift for a large number of energy meters at both monitored sites. Those issues
made it necessary to do comprehensive correction and reengineering.
During monitoring, it also became apparent that manual data collection leads to long intervals (typically
more than a month) in detecting errors. In addition, the installation of submeters inside the buildings
made it necessary to get access to each household for data collection, which required administrative
coordination and cost. These issues could be solved by online monitoring, as described by Envidatec.
The collected measurements show the expected results, but for accurate modeling, correlation to
additional indoor data (e.g., room temperature, inhabitants) is essential. Most of these data were not
available for this study because of confidentiality.
Further monitoring of the household sector could include these steps:
•
•
•
•
•
Select objects with collaborative users to improve collection of internal data.
Critically review the metering structure: It might be better to install a well-known meter.
If at all possible, install a remote data access.
Set a realistic goal for the number of buildings to be monitored.
Consider setting up metering of several buildings, including submeters for AC and domestic hot
water, other big consumers, and indoor temperature.
KSA Profiles in Comparison to International Standards
The energy demand for AC shows a direct correlation between ambient temperature, the set room
temperature, and the volume to be cooled. Because of the very different climate conditions in Saudi
Arabia and Europe, the resulting load profiles are not comparable to European standards.
Consequently, a comparison with European standards will be omitted in the following discussion, which
concentrates on the remaining household demand.
The results showed electricity consumption for households (without AC) of 3 kWh per square meter and
per month, assuming a living area of 500 m2 for a five-person household. This yields a yearly
consumption of 18 MWh, which includes production of domestic hot water. Typically, one person has a
demand of 1 MWh [KIT] for domestic hot water. For the described household, this yields a yearly
demand of 13 MWh.
4.1 Introduction
It is assumed that the demand profile in the Kingdom of Saudi Arabia is dominated by AC units. To
quantify their effect, residential buildings were monitored and analyzed. The household monitoring at
different locations in Saudi Arabia was designed to provide a reliable estimation of residential electrical
consumption and its daily load pattern. Moreover, influences such as ambient temperatures and the
day of the week were analyzed. Up to now, demand profiles were based on general assumptions, such
as load patterns and a continuous ratio between day and night consumption. There is great interest in
verifying these assumptions to improve existing demand forecasts and establish a basis for active power
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management over the long term. In the future, this knowledge will also be necessary to integrate
renewable energy technologies in the Kingdom.
To achieve these objectives, 34 households were monitored—11 residential homes at KAUST and 19
households on the residential campus of Dammam University. The data acquisition lasted from July 1 to
November 30, 2013. The electrical demand was monitored and analyzed to obtain an average load
pattern for each region.
4.2 Methodology
The buildings that were monitored are typical family houses in the residential areas of KAUST and
Dammam University. Monitoring these buildings generated a stock of data that represents the
electricity consumption profile of the residential building sector in the Kingdom of Saudi Arabia. As
residential energy demand in Saudi Arabia consists of electrical energy only, monitoring the electrical
energy equates to monitoring the complete residential energy demand. This characteristic is a
significant difference when comparing Saudi Arabia with northern European countries. In northern
European countries, heating plays a major role in residential energy consumption. Therefore, various
energy sources, such as oil, gas, or electricity, are used.
Energy meters were installed in selected KAUST and Dammam University residential buildings. The
energy meters store the electrical load profile at a high time resolution of 15-minute intervals. The
stored data of those energy meters were collected manually and processed. In the buildings at
Dammam University, additional submeters were installed. The submeters allow measurement of the
electricity demand of the AC units separately.
The process of the metering brought to light various challenges in obtaining electricity profiles with a
high time resolution. At the beginning of the measurement period, huge time shifts due to sliding clocks
could be detected. Additionally, incorrect current transformation factors caused extremely high values.
These challenges led to extended fieldwork, carried out by Envidatec.
To analyze the energy consumption accurately and thus obtain a more detailed view of the residential
electrical load pattern, climate data on the selected sites were acquired from GeoModel Solar. The
climate data were gathered using the SolarGIS method.
The raw data from the energy meters were processed and consolidated into a single file containing the
values for all meters at one site. After all the data were in one file with a continuous timestamp, the
values were analyzed in further steps.
Normalization of the individual electricity demands was done by dividing those values by the amount of
square meters of the monitored buildings. The normalized values were analyzed in daily and weekly
load profiles. The ambient temperatures for the different sites were also integrated in the load profiles.
A monthly energy consumption profile was created for the two sites. Finally, a graph was created, in
which the mean energy consumption of the buildings is clustered by the ambient mean temperature.
Correlation analyses give initial ideas for a building model in Saudi Arabia that is extended by the
modeling of the electricity demand for cooling.
4.3 Description of the Monitored Buildings
To get a complex stock of data for the analyses, the monitored buildings were selected based on
location and form. The monitored buildings were typical family houses in the residential areas of KAUST
and Dammam University. The two locations were chosen according to different climate zones in the
Kingdom of Saudi Arabia. Figure 4-3 shows the location of the monitored buildings.
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KAUST
Dammam University
Source: Google Maps.
Figure 4-3:
Monitored Buildings in the Kingdom of Saudi Arabia
KAUST is located in the city of Thuwal, which is approximately 85 kilometers north of Jeddah on the
west coast of Saudi Arabia. “Unlike other Saudi Arabian cities, Jeddah retains its warm temperature in
winter, which can range from 15 °C at dawn to 28 °C in the afternoon. Summer temperatures are
extremely hot, often breaking the 43 °C mark in the afternoon and dropping to 30 °C in the evening.
Rainfall in Jeddah is generally sparse, and usually occurs in small amounts in November and December”
[Wiki].
Dammam University is located between the cities of Dammam and Al Khubar on the east coast of Saudi
Arabia. Dammam also retains its warm temperatures in winter, which can range from 10 to 22 °C. The
temperature drops to as low as 0 °C. Summer temperatures are very hot and break the 40 °C mark and,
on occasion, the 50 °C mark. Average summer temperatures, however, usually range between 40 and
45 °C [Wiki].
The residential buildings at KAUST measured in the monitoring campaign offer a living space of 491 to
541 m2 (Figure 4-4). This living area includes two levels. All buildings were constructed in 2010 and are
made of concrete.
The main electrical energy consumers in the buildings are domestic water heaters and AC units. The
electrical domestic water heaters have a maximum energy demand of 4 kW. Whereas the typical
demand for domestic hot water can be regarded as approximately 1 MWh per person per year, the AC
demand is highly sensitive to the ambient temperature. To evaluate these dependencies, a modelbased analysis of the measurement data was carried out.
The AC is a split-unit system. The outside unit contains the condenser, the compressor, and two fans.
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Figure 4-4:
Typical Residential Building at KAUST
The monitored buildings at Dammam University offer a living area of roughly 500 m2 on two levels
(Figure 4-5). All buildings were constructed in the 1970s and are made of concrete.
Figure 4-5:
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As at KAUST, the main electrical energy consumers in the buildings are the domestic water heaters and
the AC.
The AC in the Dammam University residences is also a split-unit system, in which the outside unit
contains the condenser, the compressor, and two fans. The technical data on the AC systems are not
known.
4.4 Metering Equipment
All monitored buildings were equipped with an energy meter measuring the electrical energy
consumption of the whole building. The meters at the two sites are described in the following sections.
4.4.1
Metering Equipment at KAUST
The buildings at KAUST were equipped with ADDAD-4 meters manufactured by the Saudi Advanced
Electronics Company. The meters were installed in the main energy supply box outside each building,
containing the main circuit breakers, the current transformers, and the meter itself (Figure 4-6).
Figure 4-6:
Installation of the Energy Meters at KAUST
Current transformers installed at the main power cables delivered a value for the actual electrical load
to the energy meter. The energy meter stored the average load value in kilowatts every 15 minutes.
Those values were stored internally in the meter in a ring buffer for a maximum period of 4 weeks. At
the end of those 4 weeks, the stored data had to be downloaded manually to a personal computer.
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After 4 weeks the meter would overwrite the first value saved in the storage, so data gaps would be
created. The data transmission between the energy meter and personal computer was realized with an
optical probe. Figure 4-7 is an illustration of the electricity meters at KAUST.
Figure 4-7:
4.4.2
Electricity Meter Installation at KAUST
Metering Equipment at Dammam University
The buildings at Dammam University were equipped with ADDAD-5 meters manufactured by the Saudi
Advanced Electronics Company. They were operated in the same way as the ADDAD-4 meters at KAUST.
The main meters were installed in the main energy supply cable of each building. To provide a detailed
view of the energy consumption of the AC, a submeter was installed (see Figure 4-8). This meter
measured the energy consumption of the building without the AC. The difference between readings of
the two meters describes the energy consumption of the AC.
Figure 4-8:
4.4.3
Electricity Meter Installation at Dammam University
Data Acquisition in Saudi Arabia
Creating a definitive database for this study was not possible. At the beginning of the planning phase,
the goal was to have 100 buildings integrated in the program. Those buildings would have been located
at three different sites in Saudi Arabia: 35 houses at KAUST, 30 houses at the diplomatic quarter in
Riyadh, and 35 houses at Dammam University. Ultimately, we used 15 buildings at KAUST and 19
buildings at Dammam University.
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4.5 Data Basis
4.5.1
Data Basis at KAUST
This section first describes the available data source for the measured households at KAUST. The second
part shows the climate data.
Household Measurement
The graph in Figure 4-9 gives an overview on the number of buildings that were monitored at KAUST
during the period from July to November 2013.
Figure 4-9:
Number of Houses Monitored at KAUST
The number of houses that were monitored during the measuring campaign was not constant. The
measuring started with only two houses at the beginning of July. On July 21, six more houses were
added. The number then increased to 11 houses that were monitored during the period from August 23
until November 21. Data for most of the buildings could not be collected after November 21. Since it is
not possible to draw valid conclusions from just two or three households, these times were skipped. For
the final analysis we considered the period of July 21 to November 21, 2013.
Climate Data
To evaluate correlations to climatic conditions, data from meteocontrol [SolarGIS] were merged to the
consumption profiles. Figure 4-10 shows the ambient temperatures for KAUST during the monitoring
period.
The temperature range in this period is from 20 to 42 °C. The period with the highest temperatures lasts
from the beginning of the monitoring period in July until the beginning of December. During this time
the temperature starts at above 25 °C in the night time. After sunrise the temperature rises quickly to
30 °C at 06:00. The temperature then rises to approximately 40 °C by 15:30. After 15:30 the
temperature decreases to 30 °C at 16:30. After sunset the temperature decreases to 25 °C. The period
with lower temperatures starts in October. During this period the temperature in the night time is
approximately 20 °C. After sunrise, the temperature rises more slowly than in the hot period. It reaches
approximately 30 °C at 11:00. After this time it slowly decreases until it again reaches 20 °C after sunset.
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Figure 4-10: Ambient Temperature at KAUST During the Monitoring Period [SolarGIS]
4.5.2
Data Basis at Dammam University
This section first describes the available data source for the measured households at Dammam
University. The second part shows the climate data.
Household Measurement
Figure 4-11 gives an overview on the number of buildings and meters that were monitored at Dammam
University. The green graph indicates the number of main meters, which were integrated in the
monitoring campaign. The orange graph indicates the number of submeters, measuring the energy
demand of the households separately from the AC.
Figure 4-11: Number of Monitored Houses at Dammam University
The monitoring period for the buildings at Dammam University was from September through November
2013. The number of main meters monitoring the total electrical energy consumption of the buildings
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continuously increased to 16 buildings in total. On September 10 two submeters were installed,
measuring the electrical energy demand of the households, excluding AC. This number increased to five
buildings on October 11. On November 12 seven additional submeters were installed. Therefore, data
for the maximum number of 12 submeters and 16 main meters are only available for a period of 8 days.
Climate Data
To evaluate correlations to climatic conditions, data from meteocontrol [SolarGIS] were merged to the
consumption profiles. Figure 4-12 shows the ambient temperatures for Dammam University in the
regarded period.
Figure 4-12: Ambient Temperature at Dammam University During the Monitoring Period
The temperature range in this period is from 20 to 42 °C [SolarGIS]. The period with the highest
temperatures lasts from the beginning of the monitoring period in September until the beginning of
October. During this time the temperature starts at above 25 °C in the night time. After sunrise the
temperature rises quickly to 30 °C at 06:00. The temperature then rises to approximately 40 °C by
15:30. After 15:30 the temperature decreases to 30 °C at 16:30. After sunset the temperature decreases
to 25 °C. The period with lower temperatures starts in October. During this period the temperature at
night is approximately 20 °C. After sunrise, the temperature rises more slowly than in the hot period. It
reaches approximately 30 °C at 11:00. After this time it slowly decreases until it again reaches 20 °C
after sunset.
4.6 Data Analysis
4.6.1
Data Analysis at KAUST
This section first describes the measurement and presents a plausibility check of the data. It then
describes how, based on this, data were selected for further analysis.
Plausibility Check of Measurement
As a first step, the overall energy consumption of all houses at KAUST was analyzed. All of the living
areas are similarly sized, so the arithmetic mean of all the monitored buildings was used. Figure 4-13
displays the normalized electricity demand in kW/m2 in a carpet plot.
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Figure 4-13: Average Normalized Electricity Consumption of All Buildings at KAUST
Figure 4-13 displays the average normalized electrical power demand per square meter of all monitored
buildings within the period from July 2 to November 30, 2013, in kW/m2. The power demand of the
individual buildings ranges from 0 to 0.1 kW/m2. The highest energy consumption values appear from
10:00 to 22:00.
Because of the low number of meters, as discussed in Section 5.1, faulty data at the beginning and the
end were expected. This can be proven by a visual plausibility check using Figure 4-13. Consequently,
these parts were not used for further analysis.
However, measurements of all buildings are of the same quality. In the first half of July, measurement
values of only two buildings were available. The more buildings and data sets that can be integrated
into the calculation, the more reliable the data become. The timespan from mid-July until mid-August
shows no clear demand profile, which could be caused by the absence of residents during Ramadan.
This behavior is mainly seen in two buildings (1 and 2), which are shown in Figure 4-14 as carpet plots.
Figure 4-14: Normalized Building Electricity Demand for Building 1 (left) and Building 2 (right)
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The graphs for the two buildings show the challenges that were faced in trying to obtain reliable data of
domestic consumption profiles. The left diagram, for example, shows a jump in consumption of almost
0.04 kW/m2. This can only be explained by the inhabitants moving out or by measurement problems.
Because of this, the most plausible data were selected for further analysis.
Selected Measurements
Finally, to calculate the average energy consumption of the buildings, the four buildings shown in Figure
4-15 were selected. For these four buildings, monitoring data were available for the maximum period,
and they generally look plausible. There is no presence of unrealistic steps or gaps within the energy
consumption of these buildings.
Although all buildings are located in the same area, have similar types of inhabitants, and have similar
shape, the amplitude and form of the profiles look quite different. In the following section, these four
buildings will be analyzed in detail.
Figure 4-15: Normalized Electricity Demand of Selected Buildings for Further Analysis
Daily and Weekly Profiles for KAUST
In this section, daily and weekly power consumption profiles of different seasons are presented and
analyzed.
The electrical power demand is at its minimum at 03:15, when it reaches a low of 0.025 kW/m2. This
value describes the arithmetic mean value for the power demand within the time period from 03:00
until 03:15. Until 07:00 the power demand is still below 0.03 kW/m2. Beginning at 07:15 the curve rises
to 0.041 kW/m2 at 10:30. From 11:00 to 13:00 there is a reduction of the energy demand of 0.005
kW/m2. At 13:15 the power demand reaches its peak value at 0.048 kW/m2. After 13:15 the curve
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decreases rather continuously until 22:00. Comparing the behavior of the power demand with the
temperature curve shows a clear correlation between the two. The demand curve is shifted from the
temperature curve by approximately 3.5 hours. Furthermore, the demand curve indicates that there is a
high base load caused by the buildings. A power demand of 0.027 kW/m2, even at night, is extremely
high.
Figure 4-16 shows the demand profile of the selected houses over the course of 1 day in July.
Figure 4-16: Daily Load Profile in July
Figure 4-17 shows the demand profile of the selected houses for 1 week in July.
Figure 4-17: Weekly Load Profile in July
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The electricity demand ranges between 0.0225 kW/m2 during the night and 0.04428 kW/m2 during the
day. The average base load in the night reaches 0.03 kW/m2. The demand profile shows a clear
correlation between the temperatures during the whole week. Still, a time shift of approximately 3.5
hours between the power and temperature curves can be obtained. The correlation between
temperature and power can be seen clearly at night. Lower temperatures lead to decreased
consumption of electrical energy. A correlation to the type of day—work day or weekend—can hardly
be observed.
Figures 4-18 through 4-21 show the demand profile and the temperature for a typical day or week in
the months of September, when the temperature reaches its maximum, and November, when the
temperature reaches its minimum, during the monitoring period.
When focusing on the base load, the behavior of the power demand curve in September, shown in
Figure 4-18, is similar to the one in July. Focusing on the daytime, the peaks caused by the high daily
temperatures are substantially higher than in July. Starting at 0.0288 kW/m2 at 06:15, the electricity
demand rises to 0.0544 kW/m2 at 15:00.
Figure 4-18: Daily Load Profile in September
The weekly energy demand shown in Figure 4-19 is similar to that described in Figure 4-17 for July.
There is a strong correlation between temperature and power demand. The type of day (work day or
weekend) has no impact on the power demand of the buildings. It can be calculated that the mean
value is 0.01 kW/m2 higher in September than in July. Furthermore, the positive and negative amplitude
ranges are larger than in July.
On the last 2 days of the monitored week, the maximum and the average daily temperatures are lower
than in the rest of the week. This leads to a reduction of the energy demand during those 2 days.
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Figure 4-19: Weekly Load Profile in September
Figure 4-20 displays the load profile of the four selected buildings on a typical day in November. Even in
this month, the power demand is highly sensitive to the temperature curve. It can be calculated that the
base load in November is 0.02 kW/m2 during the night, 0.01 kW/m2 smaller than in July and September.
Furthermore, it is can be seen that the peaks during the daytime are less pronounced than in the time
period from July to September.
Figure 4-20: Daily Load Profile in November
Figure 4-21 shows the weekly load profile in November. The base load is mostly still above 0.02 kW/m2.
The peak level is about 0.03 kW/m2, and the average daily demand reaches 0.03 kW/m2. The load
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profile is still primarily influenced by the temperature. The type of day (work day or weekend) does not
influence the load profile.
Figure 4-21: Weekly Load Profile in November
Figure 4-22 shows the mean energy consumption of the four selected buildings during the entire
monitoring period. Additionally, the mean temperatures for each day were calculated. The days were
subsequently ordered by the mean temperature level.
Figure 4-22: Mean Energy Consumption of All Monitored Buildings
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The mean daily energy consumption ranges from 0.048 kWh/m2 per day to 1.038 kWh/m2 per day. The
average daily temperature ranges from 26.6 to 35.3 °C. By assembling the days according to the mean
daily temperature, a clear correlation between average daily temperature and mean daily energy
consumption can be obtained. The linear best-fit lines of average temperature and mean energy
consumption are parallel.
Monthly Energy Consumption
To obtain an arithmetic mean value for the energy consumption per month, we used a calculation that
creates an arithmetic mean value for the consumption of the four houses for every measured quarterhour. In a second step, the monthly energy sums were calculated. Figure 4-23 shows the monthly
energy consumption of the building stock at KAUST, related to the average ambient temperature during
the monitoring period.
The monthly energy consumption ranges from 21.7 kWh/m2 in November to 28.4 kWh/m2 in summer.
As expected, a clear correlation between energy consumption and ambient temperature can be
observed.
Figure 4-23: Monthly Energy Consumption and Ambient Temperature for the Selected Buildings
4.6.2
Data Analysis at Dammam University
In the first step, the overall energy consumption of all houses at Dammam University was analyzed,
then an arithmetic mean value for all of the monitored buildings was calculated. Figure 4-24 (left side)
displays this value in a carpet plot. In addition, the arithmetic mean value for all of the submeters
measuring the energy consumption of the building only, without the consumption of AC, is shown in
Figure 4-24 (right side). The difference between main and submeter represents the energy consumption
of AC. The arithmetic mean of this value is shown in Figure 4-25.
Figure 4-24 (left side) displays the average electrical power demand of all monitored buildings within
the period from September 1 to November 25. The power demand of the individual buildings ranges
from 0 to 0.06 kW/m2. The highest energy consumption appears from 09:00 to 22:00.
In the beginning of September there is still a high electrical load, especially in the daytime. Even in the
night time, there is a base load that never falls below 0.04 kW/m2. Starting in mid-September, the base
load decreases to approximately 0.02 kW/m2. During this time of the year the peak loads in the daytime
also appear smaller than during the 2 weeks prior. Starting on October 10, the power demand again
decreases. The power demand does not reach more than 0.03 kW/m2, even in the daytime. Around
October 15 and from October 20 until November 20, there is again a power demand in the daytime
reaching 0.04 kW/m2.
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Figure 4-24: Electricity Demand of the Whole Building: Main Meter (left) and Household Only/Submeter (right)
Figure 4-25: Power Demand of AC Only
Comparing the behavior of the electricity demand shown in Figure 4-24 (left side) with the ambient
temperatures in Dammam shown in Figure 4-12, it can be deduced that the load profile of the buildings
is strongly sensitive to the ambient temperatures.
Figure 4-24 (right side) shows the power demand of the households only. The main loads appear during
October in the late evening hours. Starting in November, the main load appears in the afternoon hours.
In general, the electricity demand caused by the households ranges between 0 and 0.2 kW/m2. The
power demand of the households is not influenced by the temperature. Furthermore, no influence
caused by the day of the week or the monthly profile can be identified.
Subtracting the measured values of the submeter from those of the main meter gives the electricity
demand of AC. Comparing the load profile with the ambient temperatures shown in Figure 4-12, it is
clearly visible that the load profile is only influenced by the ambient temperature conditions.
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Daily and Weekly Profiles at Dammam University
In this section, daily and weekly profiles of different seasons are presented and analyzed. Figure 4-26
shows the arithmetic mean daily load pattern for the households and the AC electricity demand of all
buildings monitored at Dammam University starting on September 28. Figure 4-27 shows the weekly
load profile of buildings and AC units in September and October. Curves are presented together with
the ambient temperature.
Figure 4-26: Daily Load Profile of Buildings and AC in September
The energy consumption of the households ranges between 0.0032 kW/m2 during the night and 0.01
kW/m2 in the evening hours. The household electricity demand is influenced by the power demand of
the domestic water heater, lighting, and cooking. Lighting and cooking are influenced by the user
behavior. The household electricity demand curve rises at 06:30 to 0.0078 kW/m2 when the occupants
get up. There is a second peak visible at 11:00 when the electricity demand in the kitchen rises. The
peak value of 0.01 kW/m2 is reached at 21:00, when lighting plays a major role.
Figure 4-27: Weekly Load Profile of Buildings and AC Units in September/October
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Focusing on the electricity demand of AC, it can be observed that the load curve starts at 0.015 kW/m2
at 00:00. During the night hours a mean base load of 0.0125 kW/m2 can be detected. At 07:15 the
electricity demand of AC starts to rise to 0.0018 kW/m2. The peak level of AC electricity demand occurs
at 17:45 with a value of almost 0.03 kW/m2.
Focusing on the weekly load profile, a clear relation between energy consumption of AC and the
temperature profile can be detected. The base load of the AC electricity demand, even at night, is
generally higher than 0.01 kW/m2. No connection between day of the week and electricity demand,
either for households or AC, can be identified. A correlation can be seen between household energy
consumption and AC energy consumption.
There is no accurate information about the actual physical connection of the main meter and the
submeter. It must be assumed that subunits such as fans or additional AC units are also represented in
the household electricity consumption.
Figure 4-28 displays the load profiles and the temperatures on a typical day in November. The base load
of AC is still almost 0.01 kW/m2. During the day, the amplitude is much smaller than in September. The
household electricity demand does not differ from that in September.
Figure 4-28: Daily Load Profile of Buildings and AC in November
Focusing on the weekly load profile, a clear relation between AC energy consumption and the
temperature profile can be detected. As long as the temperature levels are above 24 °C, the base load
of the AC electricity demand, even at night, is higher than 0.01 kW/m2.
On November 15 the ambient temperature falls below 24 °C. Starting at this time, the AC electricity
demand falls below 0.01 kW/m2.
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Figure 4-29: Weekly Load Profile of Buildings and AC in November
Figure 4-30 displays the energy consumption of households and AC during the entire monitoring period
at Dammam University. The values represent the mean energy consumption for all buildings during 1
day. The daily household energy consumption is nearly constant. The consumption of AC power
correlates to the ambient temperature.
Figure 4-30: Energy Consumption of Buildings and AC
Figure 4-31 shows the mean energy consumption of the four selected buildings during the whole
monitoring period. Additionally, the mean temperatures for every day were calculated. The days were
subsequently arranged by the mean temperature level.
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Figure 4-31: Daily Energy Consumption of Buildings and AC Clustered by Temperature
The mean daily energy consumption of households plus AC ranges from 0.175 kWh/m2 per day to 0.7
kWh/m2 per day. The average daily temperature ranges from 21.8 to 33.5 °C. By ordering the days
according to the mean daily temperature, a clear correlation between average daily temperature and
mean daily energy consumption can be obtained.
As shown in Figure 4-32, the energy consumption of households is nearly constant at 3.4 kWh/m2
during the entire monitoring period. The energy consumption of AC ranges from 15.2 kWh/m2 in
September, to 9.7 kWh/m2 in October, to 6.5 kWh/m2 in November.
Figure 4-32: Monthly Mean Energy Consumption of All Buildings at Dammam University
Figure 4-33 illustrates the correlation between ambient temperature and AC electricity demand. The
electricity demand is shifted 3.5 hours versus the temperature.
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Figure 4-33: AC Electricity Demand in Correlation to Ambient Temperature
4.7 Modeling and Results and Recommendations
4.7.1
Residential Profiles in KSA
The measurements acquired during the monitoring period showed an electricity consumption of up to
50 W/m2. Considering a typical living area of 500 m2, this yields a maximum power consumption of
25 kW during one time step.
This peak is typically reached in the early evening hours and reduced to half during the night. Figure
4-34 shows weekly load profile, and Figure 4-35 shows that a typical daily profile has one peak. This
peak is roughly 4 hours after the daily peak of the ambient temperature. This leads to the assumption
that the peak in the daily profile correlates to the thermal capacity of the building. With higher ambient
temperature, the room temperature stays in a normal range (near the set value) until the building’s
capacity is charged by the surrounding air. At night this is reversed. Room temperatures were not
available to test this assumption.
The profiles do not change considerably during the course of the week. No change in weekend energy
consumption can be detected.
Figure 4-34 : Weekly Load Profile
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As described previously, the observed building profiles show a high correlation of AC demand to
ambient temperature, because the demand is driven mainly by AC. This was proven using the
measurements with submeters, allowing separation of AC demand from the total demand of
households in Dammam. Figure 4-35 shows the daily energy consumption for households and of AC
alone, sorted by the daily mean ambient temperature. It can be observed that the household energy
consumption varies between 0.1 kWh/m2 and 0.15 kWh/m2 per day, independent of the ambient
temperature. It is very likely that these fluctuations are caused by varying user behavior.
Figure 4-35: Energy Consumption of Buildings and AC Ordered by Ambient Temperature
The AC energy demand shows a strong correlation to temperature. During the measurement period,
the AC energy demand ranged from 0.05 kWh/m2 per day at a daily mean temperature of 22 °C to
0.55 kWh/m2 per day at a daily mean temperature of 34 °C. The average AC energy consumption was
0.28 kWh/m2 per day, and the average household energy consumption was 0.12 kWh/m2 per day.
During the monitoring period, AC was responsible for 70 percent of the total energy consumption of
households.
Figure 4-36 shows the correlation between daily energy consumption and average daily ambient
temperature.
Figure 4-36: Daily Energy Demand of AC
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The average daily AC energy consumption starts at 0.1 kWh/m2 per day and goes up to 5.5 kWh/m2 per
day.
As shown in Figure 4-37, the total energy consumption of the buildings at KAUST and Dammam
University differs from each other. The mean energy consumption of all buildings at KAUST in
September was about 28 kWh/m2. At the same time the mean energy consumption of the buildings at
Dammam reached 18.6 kWh/m2. The mean energy consumption at Dammam in October and November
was significantly lower than at KAUST. Finally, AC accounted for 85 percent of the total energy
consumption in September and 70 percent in November. The monthly household consumption, at
3 kWh/m2 per month, was roughly constant.
Figure 4-37: Energy Consumption at KAUST (left) and Dammam (right)
4.7.2
Modeling AC Demand
Because AC accounts for the biggest energy demand, this also has the greatest potential for increasing
energy efficiency. This section models this segment of energy consumption. The energy demand for
cooling a building can be roughly estimated using the following formula [Baer]:
where: Qc = cooling demand in Wh
𝑄𝑐 = 𝑢 ∗ 𝐴 ∗ (𝜗𝑎 − 𝜗𝑖 ) ∗ 𝑇
u = heat transfer coefficient in W/(m2*K)
A = heat transferring surface in m2
ϑa = ambient temperature in K
ϑi = indoor temperature in K
T = length of time interval in hr
The electrical energy needed to provide cooling can be calculated by multiplying by the EER using the
following formula [Baer]. The parameter EER defines the ratio of produced cold to the necessary
electrical energy.
where: Qc = cooling demand in Wh
EER = energy efficiency ratio
𝑄𝑐 = 𝑒𝑒𝑟 ∗ 𝑊𝑒𝑙
Wel = electrical energy demand in Wh
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For the calculation in Figure 4-38, the heat-transferring surface of the buildings was assumed to be
750 m2. With the energy consumption shown in Figure 4-35, an indoor set temperature of 20 °C, and an
assumed AC EER of 1.5, the heat-transferring coefficient of the buildings is 2 W/(m2*K). Assuming that
the walls and ceilings of the buildings are made out of concrete, this value is realistic.
Decreasing the AC electrical energy demand can be achieved by insulating the buildings. With this
measure, using an insulation layer of 7.5 cm of polystyrene insulation decreases the energy-transferring
coefficient to 0.5 W/(m2*K). The energy-saving effect of this insulation is shown in Figure 4-38.
Figure 4-38: Simulated Correlation Between Daily Ambient Temperature and Electricity
Demand for Different Energy-Saving Measures
Figure 4-38 also shows the impact of increasing the indoor set temperature by 2 K and using more
efficient AC units on energy consumption.
The solid green line in Figure 4-38 indicates the modeled energy demand at its current status (see
Figure 4-36) in relation to the daily ambient temperatures. The dashed green line indicates the energy
consumption with no additional insulation but when the indoor set temperature is raised by 2 °C to
22 °C. This measure results in a reduction of the energy consumption by 15 percent at an ambient
temperature of 28 °C. A far greater reduction in energy consumption can be achieved by installing
insulation. The solid red line indicates the AC energy consumption when the whole building is insulated
with a layer of 7.5 cm polystyrene insulation. Owing to the reduced cooling demand of the building, the
AC electrical energy demand decreases by 75 percent in relation to consumption with no insulation at
28 °C ambient temperature. Further raising the indoor temperature by 2 °C is indicated by the dashed
red line. A further reduction of the energy consumption by 6 percent can be achieved.
The violet line indicates the reduction in energy consumption when the building is insulated and
equipped with an AC unit that has an EER of 3 instead of 1.5. With this measure, the energy
consumption can be decreased by an additional 10 percent.
The solid blue line indicates the energy consumption when there is no additional insulation but the AC
unit has an EER of 3. If this is the only measure, the cooling consumption is reduced by 50 percent. AC
devices with an EER of 3 are state of the art for the residential sector.
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4.7.3
Recommendations for Energy Saving
The most important finding of this monitoring campaign is that AC systems account for a large part of
energy consumption within the residential sector. According to Figure 4-35, AC is responsible for 70
percent of the residential energy demand. Reducing the energy demand of the residential sector
directly leads to a reduction in AC energy demand. AC energy demand is affected by three factors that
have a direct influence on energy consumption:
•
•
•
The building insulation has an impact on the demand for cooling. The better that buildings are
insulated, the less they need to be cooled.
The indoor set temperature influences the cooling demand.
The efficiency of the AC unit has an impact on the electrical energy consumption required to
provide the requested cooling needs of the building.
Table 4-2 shows examples of these three energy-saving measures. An energy savings of 15 percent can
be achieved simply by increasing the set temperature by 2 K.
Table 4-2: Effects of Different Energy-Saving Measures on Cooling Demand Classified by Effort
Measure
Increase Tset by 2 K
Replace AC hardware (double EER)
Insulate with 7.5 cm polystyrene
4.7.4
Effect on energy demand
Effort
minus 15%
minus 50%
minus 75%
none
medium
high
Data Acquisition Experience and Recommendations
In reviewing the existing meters at KAUST, we detected high consumptions of more than 10 MWh per
day for some households. This was due to different current transformer factors within the meter: one
for the display and another for the internal register. Also, the batteries buffering the internal clock were
empty, and at interruptions of supply the time stamp was in some cases greatly delayed. This required
some reengineering work.
In addition, the process of manual data collection leads to long intervals (typically more than a month)
to detect errors. Installing submeters within the building requires access to the household for data
collection, which leads to administrative cost and coordination. This can be solved by online monitoring.
The measurements collected manually showed the expected results, but for accurate modeling,
correlation to additional indoor data (e.g., room temperature, inhabitants) is essential. Most of these
data were not available for this study because of confidentiality.
Further monitoring of the household sector could include these steps:
•
•
•
•
•
Select objects with collaborative users to improve collection of internal data.
Critically review the metering structure: It might be better to install a well-known meter.
If at all possible, install a remote data access.
Set a realistic goal for the number of buildings to be monitored.
Consider setting up metering of several buildings, including submeters for AC and domestic hot
water, other big consumers, and indoor temperature.
4.7.5
KSA Profiles in Comparison to International Standards
The energy demand for AC shows a direct correlation between ambient temperature, the set room
temperature, and the volume to be cooled. Because of the very different climate conditions in Saudi
Arabia and Europe, the resulting load profiles are not comparable to European standards.
Consequently, a comparison with European standards was omitted from this discussion.
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The results showed electricity consumption for households (without AC) of 3 kWh per square meter and
per month, assuming a living area of 500 m2 for a five-person household. This yields a yearly
consumption of 18 MWh, which includes production of domestic hot water. Typically, one person has
demand of 1 MWh [KIT] for domestic hot water. For the described household, this yields a yearly
demand of 13 MWh.
4.8 Literature
[Baer]
Baerhr Thermodynamics, Springer-Verlag Berlin, 2000.
[google]
www.maps.google.com, accessed Jan. 24, 2014.
[KIT]
www.energiedetektive.kit.edu/index.php/Energiebedarf_f%C3%BCr_Warmwasser, accessed
Jan. 24, 2014.
[SolarGIS] GeoModel Solar s.r.o., M. Marecka 3, Bratislava, Slovakia, http://solargis.info
[Wiki]
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www.wikipedia.com, accessed Jan. 24, 2014.
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5
Development of Industrial Energy Demand
in Saudi Arabia
CHAPTER 5: Development of Industrial Energy Demand in Saudi Arabia
Chapter 5: Development of Industrial Energy Demand in
Saudi Arabia
Chapter Summary
Chapter Description
This chapter focuses on the industrial energy demand in the Kingdom of Saudi Arabia (KSA) and the
energy-saving potential through implementing energy efficiency (EE) measures. Because of rapid
population growth, an enormous increase in energy consumption will be seen in the country. Because
industry represents a fairly large share of the energy demand in KSA, the saving potential in this sector
will have a significant positive effect on overall consumption. This report shows potential development
pathways and their effects on domestic energy demand and carbon dioxide (CO2) emissions, as well as
potential additional income from oil exports. Therefore, the largest industry sectors (i.e., cement, steel,
petrochemical, and desalination) are analyzed in detail.
Methodology
To analyze the effects of different EE measures on Saudi energy demand in the future, a projection of
future energy demand is required. Future energy demand is assessed based on the development of the
different sectors presented in Chapter 1. The projection is based on EIA energy data for KSA until 2009.
To apply the saving potential, applicable EE measures and their potential for KSA are defined. This is
based on previous chapters of this study as well as literature research. For each of the large industry
sectors mentioned previously, efficiency measures are analyzed separately. These sectors belong to
energy-intensive industries because of their necessary energy consumption for certain production
processes, making them interesting for this analysis. Those EE potentials are defined individually for
each sector. The EE measures assumed in the low-EE scenario are either easily applicable and
economically viable or necessary to reach the state-of-the-art level of technology. The efficiency
potential can be defined as the difference between current conditions in KSA and those in industrialized
countries worldwide. For the evaluation of savings, three scenarios are compared: the business-as-usual
(BAU) scenario, a low-EE scenario, and a high-EE scenario. From the results of these scenarios, the
amount of energy saved can be projected until 2040. Accordingly, the CO2 emissions saved and the
opportunity costs from the saved fossil fuels are calculated. This allows illustrating the effects of EE
measures not only on the energy demand but also on the economy and environment. Hence, the report
will give an indication of the potential range of effects that EE measures would have in the long term.
Analysis
To represent industrial energy demand in KSA, as mentioned above, the energy-intensive industries of
steel, petrochemical, and cement were chosen. Desalination was also included because KSA has a
shortage of fresh water, which requires desalinating seawater. This process is very energy intensive and
contributes significantly to Saudi energy consumption. Industrial energy demand is distributed as shown
in Figure 5-1. As can be seen, the largest energy demand stems from the desalination and
petrochemical sectors. Further energy-intensive industries are building materials, including cement and
steel. Thus, those four sectors are reviewed more closely and serve as a basis for scenario development.
In this figure, cement falls under the category “building material,” which was adopted from Chapter 2.
Looking closer at the cement industry, it can be inferred that applying EE measures will have a positive
effect on industrial energy demand. The cement production capacity of KSA nearly doubled since 2005
and exceeded 50 million t in 2012, shared by 13 companies. Similar to the steep rise in production
capacity, cement demand experienced a significant increase from 43 million t per year in 2010 to
49 million t in 2012 and was estimated to reach 52 million t in 2013 (Edwards, 2012). Both capacity and
demand are expected to increase further. Because most of the energy needed in the cement
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manufacturing process is used to produce heat, the highest potential for EE is achieved by improving
the use of heat or avoiding waste heat. According to the analysis, an efficiency potential of 15 percent
for the high-EE scenario is set. The low-EE scenario assumes a delayed application of EE measures and a
lower implementation speed, which leads to an efficiency potential of 5 percent.
Figure 5-1:
Final Energy Distribution Including Desalination (Own Calculation Based on Chapter 2)
The steel sector had steady growth of 6 percent per year between 2009 and 2011, increasing its crude
steel production from 4.7 million t to 5.3 million t (World Steel Association, 2012, p. 11). However,
crude steel production stagnated in 2012 with 5.2 million t (World Steel Association, 2013, p. 9). The
steel sector is still expected to grow further. Because this is one of the most energy-intensive industries,
reducing future energy demand is essential. In the analysis, an efficiency potential of 35 percent for the
high-EE scenario is identified. For the low-EE scenario, it is assumed that the transfer of production
technology will be delayed and developed more slowly, which reduces the EE potential to 10 percent
for this sector.
For the desalination sector, which represents around 36 percent of energy consumption, and with
Multi-Stage Flash (MSF) desalination plants being dominant, the methodology of the analysis is
different. For the EE potentials, it was assumed that the components are not developed to be more
efficient, but that an exchange of the technologies is pursued. For this analysis, three scenarios were
identified: (1) the BAU scenario; (2) the “efficient new plants” scenario—the increase in the amount of
water desalinated is the same as in the BAU scenario, the share of technologies does not change, and
new plants have high EE; and (3) the “efficient Reverse Osmosis (RO)” scenario—the increase in the
amount of water desalinated is the same as in the BAU scenario and all new plants are highly energyefficient RO desalination plants. Results show that for the overall scenario calculation, it can be
assumed that for the low-EE scenario, 10 percent energy savings from the efficient new plants scenario
is applied. For the high-EE scenario, 30 percent energy savings from the efficient RO scenario is used.
The last detailed analysis was performed on the petrochemical sector. The Saudi petrochemical sector is
largely dominated by SABIC. With an overall market share of more than 50 percent, it is the largest
domestic stakeholder. In Figure 5-1 above, it is referred to as “Chemistry.” In the petrochemical
industry, there is little room to improve the EE and CO2 emissions of the feedstock. Therefore, EE
measures have to be applied in the production processes and overall energy use. The analysis showed a
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potential efficiency increase of 17 percent in the low-EE scenario and 25 percent energy savings
potential in the high-EE scenario.
Having analyzed the different industries and defined the high-EE and low-EE potential, the three main
scenarios are developed. These three scenarios are compared in terms of energy consumption
throughout the years until 2040, CO2 emissions caused by the three scenarios, and opportunity costs
created by saving fossil fuels.
Results
With this analysis and demand projection from Chapter 1, the scenario analysis showed an energy
savings potential of 10 percent in the low-EE scenario. Cumulating the annual final energy demand
shows that until 2040, 2,050 TWh could be saved. This results in a reduction in CO2 emissions of
2,200 billion t. Furthermore, this would allow saving oil in the power generation sector so that the oil
could be exported. Considering this opportunity cost, additional revenues of US$544 billion could be
realized.
The high-EE scenario shows potential energy savings of 20 percent, resulting in 4,413 TWh cumulated
energy savings until 2040. This would enable a reduction in CO2 emissions of 3,460 billion t. In addition,
the analysis of opportunity cost indicates that additional revenues of US$784 billion could be generated
from exporting saved oil.
5.1 Introduction
Based on the energy demand projections presented in Chapter 1, it is possible to say that because of
KSA’s enormous growth in population, energy consumption will increase considerably. As discussed
previously, if the current trends continue, KSA will consume large amounts of the oil produced in the
country that could otherwise be used for export. Thus, the following report is dedicated to potential
energy savings in Saudi industry.
Because of the rapid expansion of KSA’s industry, it could be assumed that much of the manufacturing
equipment is relatively new and hence very energy efficient. However, plant visits and energy audits
have identified significant EE potentials mainly from improvements to inefficient energy conversion
chains and efficiency improvements in the generation and use of heat. The high need for cooling makes
the efficient generation and use of heat a very important issue for Saudi industry if a sustainable energy
strategy is to be followed.
This report shows potential development pathways and their effects on domestic energy demand, CO2
emissions, and potential additional income from oil exports. The scope of the work includes a review of
the largest industry sectors in the country, their current energy consumption, and applicable EE
measures. For this, best practices in different countries were analyzed and used as benchmarks. From
this review, potential development pathways of industrial energy demand are shown and used to
illustrate the existing energy savings potential. Furthermore, they will allow potential macroeconomic
effects such as a reduction in CO2 emissions and a freeing of oil for export, thus generating additional
revenues.
5.2 Methodology
Because a projection of future industrial energy demand for different levels of EE penetration is
required in this task, the applied methodology comprises literature research, a collection of secondary
data, and an analysis of scenarios.
To define applicable EE measures and their potential for KSA, data collection is required. This is based
on the tasks discussed in previous chapters as well as literature research. For each of the large industry
sectors—cement, steel, desalination, and petrochemical—efficiency measures are analyzed individually.
These sectors are chosen because they represent a large share of KSA’s industry. Furthermore, they all
belong to energy-intensive industries because of their necessary energy consumption for certain
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production processes. In most industrial statistics, desalination is not counted as an industry sector but
as a public service. For this study, desalination was included because it plays a major role related to
energy consumption and the potential for energy savings. Because the power generation sector has
been excluded from this analysis, the interdependencies between saving energy in the desalination
process and its effect on resulting power generation of combined power and water plants are beyond
the scope of this study.
To determine realistic and applicable EE potentials, we examined currently employed technologies in
KSA, current state-of-the-art technologies in other countries, and best practice technologies in terms of
energy demand.
Those EE potentials are defined individually for each sector. The EE measures assumed in the low-EE
scenario are either easily applicable and economically viable or necessary to match current state-of-theart technologies. “Current state-of-the-art technologies” is defined as the energy intensity that can be
found in most industrialized countries. Therefore, potential energy savings can be determined as the
difference between current specific energy demand in KSA and current specific energy demand in
countries such as Germany. For the high-EE scenarios, the energy consumption of current best practice
examples is used as a benchmark. This includes not only reducing the energy demand of currently used
processes but also considering alternative manufacturing solutions. The results of the data analysis,
current level of energy demand, and potential energy savings for each scenario are presented
separately for each sector in Section 4. How the potentials are derived and which values are used for
the scenario development are explained in detail.
Using the outcomes of the steps described above, a scenario analysis is conducted. Based on the energy
demand projection presented in Chapter 1, development for individual sectors is determined using the
distribution of energy share presented in Section 3. For the individual scenarios, energy savings
potentials are applied to each sector respectively, leading to different pathways of demand
development. Cumulating the demand of the individual sectors gives the total industrial energy demand
from today until 2040. With the projected energy savings, the potential for CO2 emission reductions and
opportunity cost can be calculated for each scenario. This allows illustration of the effects of EE
measures not only on the energy demand but also on the economy and environment. Hence, the report
will give an indication of the potential range of effects that EE measures would have in the long term.
5.3 Current Industrial Energy Consumption in KSA
To analyze current and future energy demand, KSA’s industrial sector “industry” is defined as all
manufacturing industries in the country. The pure provision of services is excluded because the energy
intensity is low in the services sector, making it negligible for the following analysis. The main energy
demand stems from energy-intensive industries such as steel, cement, or petrochemical companies. In
addition, energy demand for desalination was added to the analysis because of the high energy needs
of the process and the expected rise in desalination capacity. Because KSA is short on natural water
supply, a large share of potable water comes from seawater desalination. In addition, with a steadily
growing population, water demand will increase further, along with energy requirements to fulfill water
needs. Now and in the future, desalination plays a major role in the energy consumption of KSA.
Total energy consumption is displayed in Figure 5-2. This projection shows significant growth in energy
demand, assuming that current framework conditions and population trends maintain. Figure 5-2
illustrates the current distribution of energy demand by sector as well as the shares projected for 2040
in Chapter 1. Transport and industry are expected to grow in absolute values and relative numbers.
In publically available statistics, desalination is not considered part of the industrial sector; however, for
this study the energy demand of all desalination plants is added to the industrial energy demand. This
leads to a total energy consumption of 940 TWh of the examined industries.
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Saudi industry has experienced considerable growth in the last several years. For the future, an average
growth rate of 4.3 percent is expected (Alyousef, 2012). Figure 5-2 illustrates the development of
energy demand distribution as projected in Chapter 1.
2011 vs 2040
Figure 5-2:
Share of Energy Demand in KSA by Sector in 2011 and 2040 (as Projected in Chapter 1)
Industrial energy demand is distributed as shown in Figure 5-1. As can be seen, the largest energy
demand stems from the desalination and petrochemical sectors. Further energy-intensive industries are
building materials industries such as cement and steel. Thus, those four sectors are reviewed more
closely in the next section for potential EE measures and energy savings. This serves as a basis for the
scenario development in Section 5.
5.4 Energy Efficiency in Selected Sectors
To analyze EE potentials in KSA, this study concentrates on four large sectors of Saudi industry: cement,
steel, desalination, and petrochemical.
These sectors are generally quite energy intensive and thus represent a major share of the industrial
energy demand, as mentioned in the previous section. In the following subsections, different EE
measures and their applicability to Saudi industry are described.
5.4.1
Cement Sector
The cement production capacity of KSA nearly doubled since 2005 and exceeded 50 million t in 2012,
shared by 13 companies. Similar to the steep rise in production capacity, cement demand experienced a
significant increase from 43 million t per year in 2010 to 49 million t in 2012 and was estimated to reach
52 million t in 2013 (Edwards, 2012). Both capacity and demand are expected to increase further.
Because the cement industry is highly energy intensive, EE in this sector is an essential part of the
energy strategy to meet KSA’s rising energy demands.
5.4.1.1
Cement Manufacturing Process
The main components of cement are limestone/chalk and clay or its natural mixture in the form of
limestone marl. The clay also can be replaced by flue ash or sand. These raw materials are mainly
exploited by blasting in quarries, where they are then crushed to gravel.
Because all raw materials have a natural origin, their chemical composition varies. Therefore, depending
on the actual and desired chemical composition of raw materials, the gravel has to be homogenized in a
blending bed. After homogenization, the raw material is ground in the raw mill. Before grinding, the
material can be dried by the waste heat of the rotary kiln if its moisture is too high. Because the Saudi
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climate is very arid, this is not necessary in most cases (Holcim Foundation for Sustainable Construction,
2011, p. 44).
Subsequent to grinding, the raw meal can be homogenized again before being transported to the
burning process. During the transport to the rotary kiln, the raw meal is preheated to 800 °C by cyclone
preheaters using the exhaust gases of the kiln. In the rotary kiln, the raw meal is burned at 1,450 °C to
sinter the meal, which leads to a product consisting of round, differently sized particles, called clinker.
Leaving the rotary kiln, the clinker is cooled to 100–300 °C by a clinker cooler and stored in the clinker
silo, which offers the opportunity for further homogenization. Together with other main components,
the clinker is ground in a cement mill, which can be a ball mill, a rolling mill, a vertical roller mill, or
several of these mills in succession. The finished cement is stored in silos, from where it can be
packaged or directly loaded, depending on the later mode of transport (Verein Deutscher Zementwerke
e.V., 2006, p. 5ff). Figure 5-3 shows an overview of the process.
Raw Material
Quarrying and
Crushing
Raw Meal
Homogenization
and Storage
Blending Bed
Drying and
Grinding
Electrostatic
Filter
Burning
Cyclone
Preheater
Rotary Kiln
Quarry
Crusher
Raw Mill
Clinker
Cement
Homogenization
and Storage
Clinker Silo
Grinding
Storage
Loading
Other Main Components
Clinker
Sulfate Agent
Cement Mill
Solid Matter
Figure 5-3:
5.4.1.2
Gas
Scheme of the Cement Manufacturing Process (Source: Sustain Consult, 2013, p. 14)
Energy-Efficiency Measures in the Cement Manufacturing Process
Most of the energy needed in the cement manufacturing process is used to produce heat. Therefore,
the highest potential for EE lies in the improved use of heat or in the avoidance of waste heat. Waste
heat can be reduced by precalcination, which reduces the length of the rotary kiln and diminishes the
radiation losses of the kiln (Sustain Consult, 2013, p. 28).
Waste heat also can be used to preheat and partly calcinate the raw meal in cyclone preheaters, which
is done by four cyclone preheaters in a common cement plant. The usage of the preheaters is limited by
the moisture of the raw materials because the exhaust gases of the kiln must be warm enough to dry
the raw materials during the grinding process. In regions with low raw material moisture content,
drying in the grinding mill is not necessary. Therefore, up to six preheaters can be installed (Holcim
Foundation for Sustainable Construction, 2011, p. 38). Each additional preheater can lower energy
demand by 80–100 kJ/kg clinker (Allplan GmbH, Verein deutscher Zementwerke e.V., 2010, p. 32).
(Sustain Consult, 2013, p. 28).
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A possibility for using waste heat more efficiently is using highly efficient satellite coolers with an
efficiency of up to 80 percent in the clinker cooling process. To avoid the loss of energy during clinker
cooling, heat is recovered by the clinker cooler. By increasing the efficiency of the recovery process,
more heat can be used for preheating processes (Allplan GmbH, Verein deutscher Zementwerke e.V.,
2010, p. 32f). A further possibility for using waste heat is cooling with absorption refrigerators (Allplan
GmbH, Verein deutscher Zementwerke e.V., 2010, p. 41).
By installing a water-steam cycle that uses the heat of the kiln’s flue gases, the clinker cooler, and
residual heat in the denitrification plant, it is possible to power a turbine that generates electricity,
which can supply one-third of the cement plant’s electricity demand (Sustain Consult, 2013, p. 28). If
waste heat is only available at lower temperature levels, electricity can be generated via an organic
rankine cycle (ORC), which can use the energy of the clinker coolers’ exhaust air, exhaust gases of the
heat exchanger prior to the rotary kiln, or exhaust gases of the chlorine bypass. The ORC has an
electrical efficiency of 15–23 percent. A main advantage of the electricity production from waste heat
compared to the direct use of heat is that electricity generation is independent of the plant’s current
energy demand. Generated electricity could be fed into the electricity grid, while heat is lost if it is not
needed (Allplan GmbH, Verein deutscher Zementwerke e.V., 2010, p. 36f).
Because clinker production is the most energy-intensive process of cement production, the substitution
of clinker contributes to reducing the specific energy intensity of cement. In many cases, parts of the
clinker can be replaced by slag sand or unburned limestone (Sustain Consult, 2013, p. 29).
Electrical energy accounts for only about 10 percent of energy demand in the cement production
process (Sustain Consult, 2013, p. 26). Nevertheless, considering an efficiency of around 30–35 percent
of electric power generation and the primary energy consumption involved, the electricity saving
potentials should still be examined. The preparation of raw material makes up about 35 percent of the
total electricity demand, burning and cooling of clinker 22 percent, and grinding and loading 38 percent.
Most of this energy is needed in the grinding processes. By replacing ball mills with vertical roller mills,
20 percent–30 percent of the electrical energy can be saved in raw material preparation. The same
effect occurs for the cement grinding process. However, ball mills are necessary to ensure an optimal
distribution of grain size. Energy consumption can be reduced by pregrinding the cement in a roller
press and optimizing ball size composition in the ball mill (Allplan GmbH, Verein deutscher
Zementwerke e.V., 2010, p. 44ff).
Another measure to reduce the consumption of conventional primary energy is using alternative fuels.
This is very common in German cement plants, where around 60 percent of total energy consumption
stems from alternative fuels (Sustain Consult, 2013, p. 17) such as municipal solid waste, sewage sludge,
or drilling waste, which are used to fuel the rotary kiln instead of fossil fuels (Lichtenberg, 2012, p. 19).
The necessary drying of the alternative fuels can be done by exhaust gases of the clinker process
(Sustain Consult, 2013, p. 44).
The energy intensity (final energy) for clinker production of best practice cement plants today is below
3,100 kJ/kg clinker (Holcim Foundation for Sustainable Construction, 2011, p. 13): 3,070 kJ/kg clinker;
(Worrel, et al., 2008, p. 24)]: 3,014 kJ/kg clinker. Best practice for cement with the highest energy
intensity (Portland cement, 95 percent clinker) is 2,930 kJ/kg cement (final energy). Other types of
cement may have lower energy intensities, such as fly ash cement (65 percent clinker) with 2,060 kJ/kg
cement or blast furnace slag cement (35 percent clinker) with 1,680 kJ/kg cement.
Table 5-1 gives an overview of best practice energy intensity for each production step of Portland
cement in terms of final energy and primary energy consumption.
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EE potential for the cement industry in
the scenarios in Section 4.1.1 is based on
the specific energy demand of the
clinker production in Austria (3,600 kJ/kg
clinker) (Allplan GmbH, Verein deutscher
Zementwerke e.V., 2010, p. 18) because
no comparable value is available for KSA.
As best practice cement production,
a cement plant in the Mexican desert
is regarded with a specific energy
demand of 3,070 kJ/kg clinker (Holcim
Foundation for Sustainable Construction,
2011, p. 13). The comparison of these
values leads to an efficiency potential of
15 percent for the high-EE scenario. The
low-EE scenario assumes a delayed
application of EE measures and a lower
implementation speed, which leads to
an efficiency potential of 5 percent.
5.4.2
Steel Sector
Table 5-1: Best Practice Energy Intensity for Portland Cement
(95% Clinker) (Source: Worrel, et al., 2008, pp. 24, 27)
Production Step
Raw material preparation
Solid fuels preparation
Clinker making
Cement grinding
325 cement
425 cement
525 cement
625 cement
Total
325 cement
425 cement
525 cement
625 cement
Final Energy
Consumption
(kJ/kg cement)
Primary Energy
Consumption
(kJ/kg cement)
70
3
2,790
220
10
2,940
60
60
70
70
170
190
210
220
2,923
2,923
2,933
2,933
3,340
3,360
3,380
3,390
The steel sector in KSA experienced steady growth of 6 percent per year between 2009 and 2011,
increasing its crude steel production from 4.7 million t to 5.3 million t (World Steel Association, 2012, p.
11). However, crude steel production stagnated in 2012 with 5.2 million t (World Steel Association,
2013, p. 9). Nevertheless, the steel sector is expected to grow further. Because this sector is one of the
most energy-intensive industries in KSA, it is essential to limit its future energy demand. The following
section begins with an overview of the production steps of steel (Section 5.4.2.1) before explaining EE
measures (Section 5.4.2.2).
5.4.2.1
Production Steps of Steel
The production of steel can be divided into different processes. Primary steel production has to be
differentiated from iron ore/pig iron and secondary production by recycling steel scrap, which has s
lower energy intensity because no reduction of iron ore is necessary. Primary production can be done
by different processes. From a global perspective, production via blast furnace is the most common
method. Widespread alternatives for this process exist in the direct reduction of iron ore or in the smelt
reduction, both combined with an electric arc furnace (EAF), which is also used for the secondary
production of steel from scrap (Stahl-Informations-Zentrum, 2009, p. 2). Figure 5-4 gives an overview of
primary and secondary steel production processes. Direct reduction will be explained in more detail
because it is the most common process in the Middle East and North Africa (MENA) region, accounting
for 90 percent of steel production (Chadwick, 2012, p. 154). In KSA, direct reduction accounts for
96 percent (World Steel Association, 2013, p. 9 and 19) because of the region’s supply of natural gas,
which is required as feedstock for the process (IEAGHG, 2013, p. 4).
In the direct reduction of steel, lump ore or pelletized iron ore is reduced in a shaft furnace with a
reduction gas. The reduction gas is produced in a reformer where steam and natural gas react to
hydrogen and carbon monoxide (IEAGHG, 2013, p. 4). The iron ore is charged into the top of the shaft
furnace and passes downward during the reduction process. In contrast to blast furnaces, the ore
remains in a solid state during the whole process. The reducing gas moves in the opposite direction of
the pelletized ore so that the oxygen content decreases continuously with the ore moving downward
until the direct-reduced iron (DRI) is removed at the bottom of the shaft furnace (APP, 2010, pp. 3, Ch.
3). There are several possibilities for the technical realization of this process. Figure 5-5 shows an
example for the realization of direct reduction in a MIDREX process.
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Figure 5-4:
Overview of Primary and Secondary Steel Production Processes
(Source: World Steel Association, 2008, p. 2)
Leaving the shaft furnace, the DRI, also known as iron sponge, is mainly used in the EAF process, where
it is melted with steel scrap at temperatures up to 1,800 °C. The final product of this process is crude
steel. In KSA, total crude steel production is cast in continuous casting processes (World Steel
Association, 2013, p. 9 and 11). Depending on its later use, the casting process is followed by a hot or
cold rolling process (Stahl-Informations-Zentrum, 2009, p. 3).
Figure 5-5:
5.4.2.2
Example of the Realization of Direct Reduction in a MIDREX Process (Source: APP, 2010, p. 3 Ch.3)
Energy-Efficiency Measures in the Direct Reduction Steel Process
EE in the direct reduction steel process begins with the preparation of raw material, which can be
sintering of lump ore or pelletizing of pellet ore. The main focus is on the recovery of waste heat that
can be used to preheat the air for the burners in the sinter plant or to generate steam that drives a
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turbine to produce electricity (APP, 2010, p. 17). A further possibility lies in the use of multislit burners
in the ignition furnace, which can reduce the necessary heat input by 30 percent (APP, 2010, p. 25).
Raw material preparation is followed by direct reduction in a shaft furnace. Here again, the production
of electricity by waste heat is a possibility to increase EE (Worrel, et al., 2008, p. 13). A further possibility
is to load the hot DRI directly into the EAF instead of letting it cool. The warmer the DRI, the less energy
is needed by the EAF. With each 100 °C increase in temperature, the EAF can save 20 kWh/t liquid steel
(72 MJ/t). In practice, possible charging temperatures are 600 °C and higher, which means energy
savings of at least 120 kWh/t liquid steel (0.43 GJ/t) can be obtained (Metius & Kopfle, 2009).
To decrease the energy intensity of the electric arc, furnace oxygen fuel burners can be installed, which
substitute electricity by burning oxygen and hydrocarbon fuels (APP, 2010, p. 80). Because the heating
is done directly by primary energy, the primary EE can be increased. Instead of converting primary
energy into electricity with energy losses of about 63 percent and then reconverting it to heat, where
again losses occur, the heat is used directly to melt the iron. An even wider approach is to use only
primary energy in the form of gas and oil to melt scrap metal and to limit electricity use to superheating
the melt by a conventional EAF. It is estimated that, compared to a normal EAF, primary energy
intensity could be reduced by 32 percent with this technology (APP, 2010, p. 85).
Another potential for energy saving in the EAF is preheating scrap metal (e.g., by the off-gas of the EAF)
by a continuous charging system (APP, 2010, p. 81ff). The off-gas heat also can be used for other
preheating applications (e.g., to generate hot water or steam) which is needed in other parts of the
steel mill (APP, 2010, p. 91). By controlling the composition of the furnace’s off-gas, it is possible to
optimize the injection of oxygen, carbon fuel, and the electrical power input. This allows energy
reductions of 0.2 GJ/t crude steel (APP, 2010, p. 88). Energy savings of 2.5–3 percent in the EAF can be
achieved by slag foaming. The injection of oxygen, carbon, burnt lime, and burnt mag-lime into the slag
results in its foaming. As the slag foam submerges the electrodes of the furnace and the arc, heat
radiation to the furnace shell is decreased, while the transfer of heat from the arc to the liquid bath is
increased (APP, 2010, p. 89f).
In nearly all steel plants in KSA, crude steel production is followed by a continuous casting process (see
Section 5.4.2.1). At this stage of production, energy can be saved by casting steel close to the product’s
finished dimensions (e.g., by thin-slab casting), which reduces the energy needed in the rolling process.
A further potential for energy savings is process integration. If steel is passed in one heat through crude
steel production, casting, and hot rolling, losses from the cooling and reheating processes are avoided
(APP, 2010, p. 94ff). Generally, wherever waste heat in a process occurs, usage in downstream
processes for the preheating of media or materials should be considered (SABIC, 2012, p. 61). Because
the energy intensity of secondary steel production is much lower than for primary steel production,
another option is to increase the share of scrap steel in the crude steel production as far as technically
possible. To ensure a sufficient supply of steel scrap, increasing the share of scrap steel in crude steel
production should be examined if the domestic steel recycling potential is already optimized.
Table 5-2 gives an overview of the energy intensity in each production step for a best practice direct
reduction steelmaking process. The process is calculated with 60 percent DRI and 40 percent steel
scrap. It uses oxygen and carbon injection, while fuel injection is limited because of the high share of
DRI. Furthermore, waste heat is used in the direct reduction process to produce electricity, which
reduces the primary energy consumption by 1.2 GJ/t final product. The process does not include scrap
preheating, which could reduce energy consumption further (Worrel, et al., 2008, p. 13f).
Assumptions for the steel sector in the scenarios in Chapter 5 are based on the comparison of today’s
best practice process with today’s global average direct reduction process. To be comparable to the
global average energy intensity of 28.3–30.9 GJ/t crude steel (World Steel Association, 2008, p. 2), the
energy consumption of hot rolling has to be subtracted from the total energy consumption of the best
practice process. Furthermore, it should be noted that the scrap share of the best practice process is
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40 percent, so energy consumption of the EAF must be adapted to 100 percent DRI. This leads to an
energy intensity of 6.2 GJ/t for the EAF (based on a specific energy consumption [primary energy] of
5.5 GJ/t for 100 percent scrap in the EAF (Worrel, et al., 2008, p. 15f)), resulting in a best practice
energy intensity (primary energy) of 18.5 GJ/t crude steel. Compared with the energy consumption of
today’s direct reduction steel production (28.3 GJ/t), an EE potential of about 35 percent for the highEE scenario is identified. For the low-EE scenario, it is assumed that the transfer of production
technology is delayed and developed more slowly, which reduces the EE potential to 10 percent for this
sector.
Table 5-2: Best Practice Energy Intensity of a Direct Reduction Steelmaking Process
(Source: Worrel, et al., 2008, p. 14)
Production Step
Raw material preparation
Ironmaking
Steelmaking
Casting
Hot rolling
(alternative processes)
Total
5.4.3
Process
Sintering
Pelletizing
Direct Reduction
Electric Arc Furnace
Continuous Casting
Hot Rolling—Strip
Hot Rolling—Bars
Hot Rolling—Wire
HR—Strip
HR—Bars
HR—Wire
Final Energy Consumption Primary Energy Consumption
(GJ/t final product)
(GJ/t final product)
1.9
0.6
11.7
2.5
0.1
1.6
1.8
2.1
18.4
18.6
18.9
2.2
0.8
9.2
5.9
0.1
2.2
2.4
2.9
20.4
20.6
21.1
Desalination
Because of the limited amount of natural resources of potable water in KSA, the country depends on
desalination for potable water production. Local demand is mainly met by thermal desalination
technologies, which consume large amounts of energy. Therefore, desalination technologies are
responsible for a high share of the country’s total energy consumption. To understand the EE potential
in Saudi industry, the potential of EE in the desalination sector must be examined more closely.
There are three main types of desalination technologies: MSF, Multiple Effect Distillation (MED), and
RO. MSF and MED are thermal desalination technologies, and RO is classified as a membrane
technology. Thermal desalination technologies are based on evaporation and distillation: If seawater is
heated, the water evaporates, leaving behind salt. The produced steam can then be condensed and the
distillate may be used for industrial purposes or, after treatment, as potable or irrigation water.
In an MSF plant, seawater is heated by steam to temperatures of about 110 °C. Then the water flows
into a first box, also called “stage,” where it partly evaporates. The formed steam is condensed at the
outside of tubes, which transport the feed water. The remaining saltwater flows into the next stage,
which operates at a lower pressure and therefore again causes flash evaporation. The feed water is
preheated in this process, resulting in lower energy consumption.
The MED process can be described as follows: Heated steam flows into an evaporator, also called an
“effect.” In addition, seawater is inserted into this effect, where it absorbs the heat from the steam and
partly evaporates. The heated steam condenses and flows back to the heat source. The vapor that is
gained from the seawater is directed to a second effect, where it serves as a heat source for the
following seawater. By passing the heat to the feed seawater, the vapor condenses and is collected as
distillate. The process may be continued in several effects. Because each additional evaporator recovers
the heat from the previous effect, the efficiency of the plant increases with a growing number of
effects. For this process to work, each effect needs to have lower pressure than the previous effect.
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RO as a membrane distillation technology is driven by a different force. Osmosis is a natural
phenomenon based on the tendency of solutions to balance concentration differences: When plain
water and saltwater are put in a container separated by a semipermeable membrane (i.e., water
molecules can cross the membrane but salt particles cannot), water will flow from the plain water side
to the saltwater side. This effect creates osmotic pressure. For the purpose of desalination, this process
is reversed: Pressure is applied to the saltwater side, forcing the water to flow through the membrane
to the plain water side. The amount of pressure needed increases with increasing salinity.
MSF is the most energy-consuming desalination technology of these three main commercial
technologies. MSF plants are common because the technology is mature and has been on the market
for a long time; therefore, economies of scale made it a cost-efficient and large-scale technology. MED
technology allows desalination with a smaller energy consumption compared to MSF, but for a long
time the process involved complications that were overcome only in the late 1990s when EE became
more important. Since then, MED desalination plants have been increasing worldwide because of their
lower energy consumption. RO plants have been growing steadily in the last 30 years. For RO
technology, electricity is the only energy source needed, compared to the mainly heat-driven MSF and
MED plants. RO desalination has lower overall energy consumption; however, more complex
pretreatment is needed to avoid membrane abrasion. Countries in which EE is a major concern—mainly
countries that have limited fossil fuel resources—increasingly use electricity-driven RO desalination
plants.
Currently, most desalination plants in KSA are MSF plants (about 69 percent). MED technology is used
by 15 percent of desalination plants; the remaining 16 percent are based on RO (see Chapter 2). This
composition corresponds to the large amounts of fossil resources and long history of seawater
desalination in the country.
Regarding MSF and MED plants, a higher efficiency can be achieved by adding more stages/effects to
the desalination plants. This measure, however, is only applicable when a new plant is built; integration
in an already existing desalination plant would require reconstruction. The same is true for RO plants,
where EE can be improved by installing different membrane types or making changes to pretreatment
processes.
EE in the desalination sector can be achieved by installing more efficient MSF and MED plants or
installing the most energy-efficient desalination technology, namely RO desalination.
Three scenarios were developed for the estimation of EE potential in the Saudi desalination sector:
1. The BAU scenario: The shares of technologies used for desalination in the future are the same
as today (69 percent MSF, 15 percent MED, and 16 percent RO). The energy consumption of the
technologies does not change.
2. The “efficient new plants” scenario: The increase in the amount of water desalinated is the
same as in the BAU scenario. The share of technologies does not change. New plants have high
EE.
3. The “efficient RO” scenario: The increase in the amount of water desalinated is the same as in
the BAU scenario. All new plants are highly energy-efficient RO desalination plants.
For these three scenarios, estimations of current and future energy consumption were conducted. The
results are shown in Figure 5-6. The amount of desalinated water was projected from the amount of
water desalinated in 2011, assuming that demand growth will be proportional to population growth.
Some sources state that demand might increase at a higher rate in the future (Elayan (2008): 10 million
m³ per day in 2020 as compared to 4.4 million m³ projected with the population increase).
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16%
15%
150
MED electricity
MSF electrictiy
RO electricity
2039
2037
2035
2033
2031
2029
2027
2025
2023
2021
2019
2017
2015
MSF heat
RO
GWhth
MED heat
100
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
10%
2015
46%
2013
44%
-
2011
69%
2013
50
2011
2 „Efficient new plants“
3 „Efficient RO“
Figure 5-6:
MED
50
-
69%
MSF
100
2013
15%
GWhth
1 BAU
16%
150
2011
GWhth
CHAPTER 5: Development of Industrial Energy Demand in Saudi Arabia
150
100
50
-
Shares of Installed Capacity (Column 2) in 2040 and Energy Consumed (Column 3) by Technology
Each Year for the Three Desalination Scenarios
Also, information on the EE of desalination plants in KSA is rare. It was therefore assumed that the
efficiency of the plants is comparable to desalination plants worldwide. The calculations do not consider
plants going offline because no information on the age and lifetime of existing plants is available. Even
though there are assumptions in the calculations, the results can give a first indication on the energysaving potential in the Saudi desalination sector.
Figure 5-6 shows the shares of each technology of the installed desalination capacity in 2040 and energy
consumption for desalination by technology for the three scenarios. Regarding energy consumption in
2011, it can be seen that heat demand for MSF desalination accounts for about 70 percent of the
desalination energy consumption.
The shares of technologies are equal in the BAU scenario and the “efficient new plants” scenario, but
the desalination energy consumption in 2040 can be reduced by almost 10 percent compared to the
BAU scenario. This is because Scenario 2 includes more energy-efficient desalination plants.
In Scenario 3, desalination energy consumption increases only by about 10 percent between 2011 and
2040. This is because new, highly efficient RO plants are included in this scenario. RO will hold a share of
44 percent of installed capacity in 2040. The energy savings potential compared to the BAU scenario will
be about 30 percent.
The shown energy savings potentials will be applied to the scenarios for the industry energy demand
developed later. This means that for the low-EE scenario, the 10 percent energy savings from the
“efficient new plants” scenario is applied; for the high-EE scenario, the 30 percent energy savings from
the “efficient RO” scenario is used.
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5.4.4
Petrochemical
The Saudi petrochemical sector is largely dominated by SABIC. With an overall market share of more
than 50 percent, it is the largest domestic player. Internationally, SABIC achieved a recognizable
position in the global market, benefitting from low domestic oil prices compared to the world market.
The company’s main products are polymers, chemicals, and fertilizers (Alawi, 2011).
Figure 5-7 gives an overview of the process chain and main products of the petrochemical industry.
Light blue indicates the field where MENA companies are most active. In the plastics category,
polypropylene and ethylene are the main products. Ethylene is obtained using the steam-cracking
process and naphtha as a substrate. Naphtha is converted into hydrocarbons with shorter chains. Main
products of this process are hydrogen, methane, ethylene, and propene. Because of the amount of
energy required to split the long-chain hydrocarbon, this process is rather energy intensive. However,
compared to other methods, it proved to be more efficient and is thus state-of-the-art technology.
Polypropylene is obtained by polymerization of propene, being one of the products of the steam
cracker (Alawi, 2011).
In the petrochemical industry, there is little room to improve the EE and CO2 emissions of the feedstock.
Therefore, EE measures have to be applied in the production processes and overall energy use. Also,
training employees and raising EE awareness are crucial steps in realizing EE measures in this sector.
Figure 5-7:
Production Steps for Main Products in the Petrochemical Sector (Source: Alawi, 2011)
EE measures in the petrochemical sector are either dedicated to improving the manufacturing process
itself or implementing the reuse of waste heat. As the example above demonstrates, using an energy
management system to monitor energy consumption in different buildings and stages of the
manufacturing process contributes significantly to reducing energy demand.
According to Saygin, et al., 2009, the main areas identified for energy savings in the Saudi petrochemical
sector are combined heat and power (CHP) integration, recycling, and energy recovery and heat
savings. Similar to the EE measures in the steel and cement sectors, these measures aim to reduce
overall energy demand and reuse waste heat. CHP integration is always beneficial compared to
separate heat and electricity generation because it has a higher efficiency. Globally, the average share
of CHP in the petrochemical sector is 10 percent–25 percent. Because no data are available for KSA, it is
assumed that the Saudi petrochemical sector does not differ considerably from the global average.
However, Saygin, et al., 2009 indicate that often regulatory barriers are the main obstacles for the
implementation of CHP and that a clear political direction can lead to very high shares of CHP.
Recycling and energy recovery can be realized by mechanical recycling, feedstock recycling, and energy
recovery. The most common approach is mechanical recycling because it helps reduce the amount of
steam cracking, which is one of the most energy-intensive processes in the value chain. Alternatively,
energy recovery, incineration, and landfilling can be used to enhance the recycling share.
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According to SABIC’s sustainability strategy, it plans to reduce its energy consumption by 25 percent by
2025. Current specific energy consumption equals 5 MWh/t of sold product (SABIC, 2013). Introducing
an energy management system in one of its manufacturing plants in KSA proved to be very efficient and
saved 10.3 GWh of energy within its first year alone. Because it was the first in the MENA region to
introduce such a system, this can be taken as a pilot project for other plants.
Because of its leading role in the sector and efforts in its sustainability strategy, it is assumed that a
potential energy savings of 25 percent can be considered an ambitious EE target for the whole sector.
Current actions, such as improving the energy consumption of the Al Bayroni plant, target a 15 percent
cut in energy demand. Those identified EE potentials and targets are in accordance with the findings of
Saygin, et al., 2009, who stated that a potential EE of 22.7 percent for the Saudi petrochemical sector
compared to best practice. However, they found that European countries such as Germany and France
consumed even less energy than what they defined as the best practice. Therefore, EE potentials might
even be higher than the stated 22.7 percent. This confirms that SABIC’s objectives are quite realistic. For
this report, the following potentials are assumed:
•
•
17 percent energy savings potential in the low-EE scenario.
25 percent energy savings potential in the high-EE scenario.
Those potentials can be transferred to companies in the sector because they will have a longer time
span than SABIC to implement the necessary measures. Because SABIC already published the savings
potential, it can be concluded that other companies in the sector might also be able to reduce their
energy consumption by roughly the same share, especially as technologies develop over time. Thus, EE
of new plants might even be higher in the future.
5.5 Future Development of Energy Demand in KSA
In the following subsections, different scenarios are presented. The results are discussed and a
conclusion on their potentials for CO2 emission reductions and opportunity cost is drawn.
5.5.1
Energy Demand
In this section, the scenarios, which were developed based on the data presented in Sections 3 and 4,
are presented. Based on the BAU scenario, a low- and a high-EE scenario were defined. Comparing
those to the BAU scenario enables conclusions to be drawn on potential effects of EE measures, their
potential for CO2 emission reductions, and resulting opportunity costs.
5.5.1.1
Scenario 1: Business as Usual
For the BAU scenario, the development of total energy demand from the task discussed in Chapter 1
was considered. As stated in Section 3, the industry is expected to grow by 4.3 percent per year on
average. This leads to a total energy consumption of 940 TWh in 2040.
The share of electricity compared to the total industrial energy demand ranged between 17–21 percent
in the past. Maintaining a share of 29 percent leads to an electricity consumption of 162 TWh/a in 2040
and a heat demand of 779 TWh/a.
Because desalination plays a major role in the industrial sector in KSA, it is necessary to add it to the
industrial final energy distribution and the EE potential scenarios. Adding the desalination sector, and
according to the industrial distribution from Chapter 2, as can be seen in Figure 5-8, the total final
energy consumption of the industry reaches 988.7 TWh in 2040.
Figure 5-8 shows the projected development of industrial energy demand for the BAU scenario. As can
be seen, on average the total energy demand is expected to grow by 4.6 percent annually. This leads to
an energy demand of only 988 TWh in 2040, compared to 402 TWh in 2011. For a sustainable future
energy supply, realizing EE measures will be essential.
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Final industry energy consumption in TWh
BAU scenario
1000
900
800
700
600
500
400
300
200
100
0
Desalination
Figure 5-8:
5.5.1.2
Building material
Steel
Food
Paper and Pulp
Non iron metals
Glass
Chemistry
Textile
Projected Development of Industrial Energy Demand by Sectors in the BAU Scenario
Scenario 2: Low Energy Efficiency
For the low-EE scenario, it was assumed that those measures that are easy to realize or especially
effective are implemented, whereas very ambitious measures and targets are not included. Figure 5-9
shows the development in the low-EE scenario for individual industry sectors. It can be seen that this
scenario leads to a cumulated energy demand reduction of 2,050 TWh compared to the BAU scenario
during the considered time span. This starts with savings of 40 TWh in the first years and increases to
98 TWh in 2040. The cumulated energy savings of 2,050 TWh amount to around 10 percent savings
potential. Energy demand for the desalination and petrochemical sectors especially significantly
decreases over time. However, because Saudi industry is expected to grow considerably, total energy
demand is still expected to increase. Although an increase in EE of 5 percent is considered in the
building materials sector, its total energy demand still grows steadily.
Final industry energy consumption in TWh
Low EE scenario
1000
900
800
700
600
500
400
300
200
100
0
Desalination
Building material
Figure 5-9:
5.5.1.3
Steel
Food
Paper and Pulp
Non iron metals
Glass
Chemistry
Textile
Projected Industrial Energy Demand in the Case of a Low-EE Application
Scenario 3: High Energy Efficiency
The high-EE scenario shows the potentials of realizing ambitious EE measures and targets. Those include
considering best practice examples as the benchmark to be reached within the regarded period. Figure
5-10 shows the projected development for the high-EE scenario. It can be seen that in contrast to the
BAU and low-EE scenarios, total industrial energy consumption in 2040 does not exceed 800 TWh.
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Regarding cumulated energy savings, these exceed 4,400 TWh compared to BAU. This illustrates the
enormous potential that exists for energy savings in Saudi industry.
For desalination, a significant shift in production technology towards a higher share of RO desalination
is assumed, as was presented in Section 5.4.3. This leads to an enormous reduction of the desalination
sector’s energy demand. In 2040, desalination consumes 137 TWh, in contrast to 196 TWh in the low-EE
scenario and 218 TWh in the BAU scenario.
Final industry energy consumption in TWh
High EE scenario
1000
900
800
700
600
500
400
300
200
100
0
Desalination
Building material
Steel
Food
Paper and Pulp
Non iron metals
Glass
Chemistry
Textile
Figure 5-10: Projected Industrial Energy Demand by Sectors for a High-EE Deployment
Table 5-3 and Figure 5-11 enable a comparison of total industrial energy demand of the different
scenarios. The energy savings potential increases with time, leading to an increasing gap between the
EE scenarios and BAU scenario.
Table 5-3: Annual Energy Consumption for Selected Years and Each Scenario
Annual Energy Consumption
BAU scenario
Low-EE scenario
High-EE scenario
Unit
2011
2040
TWh
TWh
TWh
402.92
364.32
315.80
988.69
892.66
787.49
Implementing the efficiency measures presented in Section 4 will result in notable energy savings and
significant reductions in domestic energy demand in KSA. The presented final annual energy
consumption in Figure 5-11 equals energy savings of 10 percent (low-EE scenario) and 20 percent
(high-EE scenario) compared to the BAU scenario.
Figure 5-11 shows a year-by-year comparison of the scenarios. It should be noted that the high-EE
scenario is not the most progressive approach. There is still room for more efficiency measures because
not all sectors have been analyzed in great detail. Looking at the total energy demand in 2040, even in
the high-EE scenario, KSA still faces sizable energy demand in the industrial sector. This means that very
large investments for increased demand should be undertaken. If power park technologies are not
adapted, large thermal power plants will have to be installed, leading to a further increase in fossil fuel
consumption. As will be discussed in later chapters, an RE development could considerably reduce the
domestic demand for fossil fuels.
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Final industry energy consumption in
TWh
CHAPTER 5: Development of Industrial Energy Demand in Saudi Arabia
1000
900
800
700
600
500
400
300
200
100
0
Total Industry BAU
Total industry low EE
Total industry high EE
Figure 5-11: Comparison of the Projected Energy Demand in the BAU, Low-EE, and High-EE Scenarios
5.5.2
Greenhouse Gas Emissions
The scenarios developed and shown above also have similar potentials for greenhouse gas emissions
reduction. The potential for CO2 emission reduction is calculation based on the energy saved in the lowand high-EE scenario. Figure 5-12 shows the potential annual savings in greenhouse gas emissions.
Because the Saudi mix consists of 88 percent oil and 12 percent gas, in addition to the specific emissions
of 859,000 kg/GWh from oil and 518,000 kg/GWh from gas, respectively, CO2 savings could be
calculated accordingly. The savable amount of CO2 in the low-EE scenario amounts to 107 million t; in
the high-EE scenario, it amounts to 166 million t. Analogous to energy savings, potential CO2 savings
increase with increasing energy demand reduction. The potential shown here is based on the current
Saudi power generation mix.
CO2 emission reduction in million tons
180
160
140
120
100
80
60
40
20
0
Total CO2 savings from low EE scenario
Total CO2 savings from high EE scenario
Figure 5-12: Potential CO2 Emission Reduction in the Low-EE and High-EE Scenarios
Compared to the BAU Scenario
There is an enormous potential to reduce greenhouse gas emissions by cutting industrial energy
demand. Considering that the developed scenarios only account for enhancing EE and do not include
supply side potentials, realizing this potential will be a crucial step in a long-term EE and low-carbon
strategy.
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5.5.3
Opportunity Costs and Cost-Benefit Analysis
Because of the EE measures and resulting energy savings, the country will require less energy for
industry. Therefore, saved energy is seen as opportunity costs. All fuel that is not used for energy
generation can be exported, allowing for revenues based on the world market’s fuel price. In this
section, the opportunity costs for the two scenarios are presented.
Figure 5-13 shows the annual amount of additional revenues that could be generated if saved fuel were
exported on the global market.
Opportunity Costs in Million US$
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
Low EE Opportunity costs
High EE Opportunity costs
Figure 5-13: Potential Opportunity Costs in the Low-EE and High-EE Scenarios
Oil prices are constantly increasing, and KSA is one of the world’s main oil exporters. If local energy use
can be decreased, exports can be increased correspondingly.
Based on the savings from the low-EE scenario, up to US$25 billion can be realized in 2040 alone. A
rough estimate results in cumulative revenues of US$544 billion in the considered period. The high-EE
scenario can generate up to US$36 billion in the year 2040, and totaled from 2011, KSA has opportunity
costs of around US$784 billion.
This analysis shows that from the macroeconomic perspective, the implementation of EE measures and
reduction of energy demand has many advantages. Not only could KSA reduce its CO2 emissions but it
could also generate considerable additional revenues.
However, because energy prices in KSA are fairly low, economic viability of EE measures will be the
highest obstacle for their implementation. To increase interest in EE measures, they have to be
profitable from a company’s point of view (i.e., comparing necessary investment cost to current energy
cost). Investment costs differ considerably for the different measures. Therefore, a detailed economic
analysis lies beyond the scope of this study.
5.6 Emerging Business Opportunities With the Implementation of
Energy Efficiency
With the implementation of EE measures, a certain set of services and goods will be required. Detailed
examples of applicable products can be found in the energy audits of this study. For Saudi companies,
this offers a range of considerable business opportunities. In the following, exemplary business models
are described.
EE measures for industrial consumers can be classified into building- and process-related measures. One
business model will be the implementation of building-related EE measures such as insulation. This is
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not restricted to industrial clients and could be offered for residential buildings. Improving EE of
buildings and consulting homeowners and businesses has become a major aspect of the construction
sector. Based on this, an overview of the essential skills recommended for the construction industry will
be given as they have been identified in research for the sample market of Germany (Mohaupt, et al.,
2011):
•
•
•
•
•
•
•
•
Ability to view the complete picture
Coordination of different subsections within the construction sector
Knowledge of materials and their efficiency
Analysis of energy-saving potential: knowledge about efficiency measures, potential of different
materials
Economic knowledge: installation cost, lifecycle cost
Communication and consulting skills
Logistics and construction planning
Financing knowledge: advise and support the client in planning and evaluating different financing
opportunities.
Another business opportunity will be offering energy audits and analyzing a company’s energy
consumption to derive the best mix of EE measures available. In addition to the skills mentioned above,
this will require profound knowledge in production processes and technologies as well as energy flows
within production lines. This necessitates an understanding of heat flows, electricity use, and the energy
requirements of individual production steps.
According to Mohaupt, et al., 2011, building renovation already constitutes nearly 80 percent of the
German construction sector. Based on this, it has been stated that an investment of 1 billion € creates
16,500 jobs. A more detailed prognosis of employment and job creation cannot be found in the
literature because the analysis and realization of EE measures is spread between a variety of subsectors
and thus impede a quantification of employment effects. Because the necessary tasks in this field show
a strong global resemblance, this can be used as an indicator for job creation potential in KSA. As
energetic renovation is not a sector on its own, the majority of business opportunities can be found
within already existing business sectors, especially in construction. Although special knowledge is
necessary, the broad knowledge of general construction engineers and architects should always be the
base. Thus, to create business opportunities within the country, training and qualification of
professionals and on-the-job training should be considered.
5.7 Conclusion
This report examines the current state of manufacturing technologies in KSA as well as best practice
technologies in the steel, cement, petrochemical, and desalination sectors. From this analysis,
applicable EE potentials are analyzed. With those values, three scenarios are developed, showing the
future energy demand if business as usual continues (BAU scenario), if the most accessible options are
realized (low-EE scenario), and if very ambitious EE measures are implemented (high-EE scenario).
For the steel sector, the analysis showed that compared to state-of-the-art technologies, energy savings
of 10 percent could be realized in KSA. Compared to best practice technologies, 25 percent could be
achieved. In the cement sector, similarly, 5–15 percent could be achieved. In the petrochemical sector,
potential exists for energy savings in the range from 17 percent of current demand to 25 percent when
aiming at best practice technologies and future improvement of manufacturing technologies. For the
desalination sector, different pathways were determined for future plant installations. Because KSA has
one of the largest demands for desalinated water in the world and demand is expected to rise,
additional desalination capacities will have to be developed. Assuming that future plants will be more
efficient and current technologies will remain until 2040, energy savings of 9.1 percent can be achieved
compared to the BAU scenario. Considering other desalination technologies with lower-energy
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intensity, such as RO, the savings potential reaches as much as 30 percent compared to the BAU
scenario.
With this analysis and the demand projection presented in Chapter 1, the scenario analysis showed an
energy savings potential of up to 10 percent in the low-EE scenario. Cumulating the annual final energy
demand shows that until 2040, 2,050 TWh could be saved. This results in a reduction in CO2 emissions
of 2,200 billion t. Furthermore, this would allow saving oil in the power generation sector for exporting.
Considering this opportunity cost, additional revenues of US$544 billion could be realized.
The high-EE scenario shows potential energy savings of 20 percent, resulting in 4,413 TWh cumulated
energy savings until 2040. This would enable a reduction in CO2 emissions of 3,460 billion t. In addition,
the analysis of opportunity cost indicates that additional revenues of US$784 billion could be generated
from exporting saved oil.
This demonstrates that EE measures in industry could contribute significantly to meeting Saudi energy
needs in the future. However, analysis in other countries showed that main barriers for the realization
of EE potentials are investment costs for EE measures as well as regulatory barriers. Therefore, to
realize the identified potentials, EE in the industry should be part of an overall energy strategy of KSA.
Recommendation/Conclusion
1
2
3
4
5
6
7
8
9
10
11
Considerable energy savings could be reached; the high-EE scenario showed potential energy savings of
20%, resulting in 4,413 TWh cumulated energy savings until 2040.
Opportunity cost in the range of US$700 billion could be generated by saving fossil fuels at the analyzed
amount for the considered industry sectors.
CO2 savings of up to 3,460 billion t could be achieved by implementing EE measures in industry.
For desalination, up to 30% of primary energy could be saved by opting for a higher share of RO.
Compared to global standards, the cement sector could save up to 15%.
In the petrochemical sector, there is potential for energy savings in the range from 17% of current demand
to 25% when aiming at best practice technologies and future improvement of manufacturing technologies.
For the steel sector, the analysis showed that compared to state-of-the-art technologies, energy savings of
10% could be achieved. Compared to best practice technologies, energy savings of 25% could be achieved.
Closing the gap of available demand data for KSA’s industry would significantly improve the analysis and
facilitate the planning of policies to promote EE.
EE standards should become obligatory to accelerate adaption of EE measures.
Energy audits already proved that the analyzed EE potential is realistic -> replications necessary to achieve
savings.
First EE projects should be presented publicly to function as a role model for other Saudi companies.
5.8 Literature
Agency, I. a. t. O. N. E., 2010. Projected Costs of Generating Electricity 2010 Edition.
Al Asmakh, 2012. Qatar Real Estate Report Q4, Qatar: s.n.
Alawi, A., 2011. Saudi Petrochemical Sector, Riyadh: Aljazira Capital.
Allplan GmbH, Verein deutscher Zementwerke e.V., 2010. Energieeffizienz der österreichischen
Zementindustrie. [Online] Available at: http://www.zement.at/downloads/energieeffizienzanalyse.pdf.
[Zugriff am 19 11 2013].
Alyousef, Y. A.-e. M., 2012. Energy Efficiency Initiatives for Saudi Arabia on Supply and Demand Sides. In:
D. Z. Morvaj, Hrsg. Energy Efficiency—A Bridge to Low Carbon Economy. Riyadh: INTECH, p. 344.
Anon. kein Datum National Action Plan for EE Sector Collaborative on EE Office Building Energy Use
Profile; load curve based on USA, s.l.: s.n.
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CHAPTER 5: Development of Industrial Energy Demand in Saudi Arabia
APP, A. P. P. f. C. D. a. C., 2010. The State-of-the-Art Clean Technologies (SOACT) for Steelmaking
Handbook, Second Edition. [Online] Available at:
http://asiapacificpartnership.org/english/soact2nd.aspx. [Zugriff am 12 11 2013].
Bailey, D. & Wright, E., 2003. Practical SCADA for Industry. s.l.: Elsevier.
Bank, S., 2011. Solarwirtschaft: Hartes Marktumfeld—Kampf um die Spitzenplätze, Study of Sarasin
Bank., s.l.: s.n.
Bhandari, R. a. S. I., 2009. Grid parity analysis of solar photovoltaic systems in Germany using experience
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CHAPTER 6: Integration of Renewable Energy
Chapter 6: Integration of Renewable Energy
Chapter Summary
Chapter Description
The present report aims to analyze the economics of different renewable energy sources in the
Kingdom and, thus, enhance the available information on country-specific aspects of each technology. A
steadily rising demand for energy and the limitations of fossil energy sources, along with decreasing
prices for renewable energy technologies and increasing export prices, make renewable energy sources
more and more attractive for use in Saudi Arabia. However, up to now, research and projects have
mainly been dedicated to technical aspects of different renewable energy technologies in Saudi Arabia.
For successful integration, economic aspects will be as important as technical ones. This chapter,
therefore, focuses on the economics of renewable energy sources in Saudi Arabia.
Analysis
Renewable Energy Technologies
The three most promising technologies for the country—photovoltaics (PV), concentrating solar power
(CSP), and wind power—are discussed in this chapter. Other renewable energy sources, such as
hydropower, tidal energy, and wave energy, have a comparatively low potential in Saudi Arabia and,
therefore, are not analyzed in this context.
PV comprises many different types of technology, including crystalline, thin-film, and concentrating PV
(CPV). The first two represent a large market share, but CPV is projected to gain increasing importance
in the future. The PV market has grown rapidly in the last decade; the global installed capacity is now
around 30 GW annually. The market is dominated primarily by the European Union, while the Middle
East and North Africa (MENA) region only contributes a small share of the global market. But because
North Africa lies in the earth’s “sun belt” and module prices are decreasing while fuel prices are
increasing, the PV market certainly contains a huge potential. EPIA (EPIA, 2011b) projects capacities of
60 to 250 GW by 2020 and 260 to 1,100 GW in 2030 for the sun belt countries.
Of CSP technologies, parabolic trough, linear Fresnel, and solar tower have the highest market shares.
CSP has also seen a rapid market growth in the last decade: By the end of 2012, a cumulated CSP
capacity of 2,650 MW operated in different electricity markets. Furthermore, 2,000 MW are under
construction and will be commissioned by 2013. In addition, a large number of CSP projects of >16 GW
have been announced and are in different planning stages in countries from Australia and China to
North Africa and Europe. The United States, however, is expected to become the largest market for CSP
in the medium term. With increasing PV and wind growth in the United States, the questions of grid
stability and the importance of storage possibilities will receive more and more attention, favoring CSP.
In the field of wind technology, technology diversion is not as broad as for the solar technologies.
Modern wind energy converters (WEC) usually use a design with a horizontal axis and three rotor
blades, using the lift principle. A typical WEC consists of a rotor, a drive yawing, tower, and foundation,
as well as electrical components. In 2012, the global installed wind power capacity reached 282.5 GW,
representing an annual average growth rate of about 22 percent during the last decade. The wind
market has experienced a global shift from European markets, especially Denmark, Germany, and Spain,
towards American and Asian markets. Within the MENA region, installed capacities are limited. In Saudi
Arabia, no wind turbines are installed.
Renewable Energy Potential
The potential of the three described renewable energy technologies was assessed in this chapter. Saudi
Arabia’s wind power potential is very low compared to international averages. Average wind speeds in
most of the country are between approximately 6.0 and 8.0 m/s. The two most suitable regions for the
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deployment of wind turbines are along the coasts of the Red Sea and the Arabian Gulf. The region with
the highest annual wind speeds is Yanbu, with annual mean wind speeds >4 m/s at a height of 10 m.
PV potential in Saudi Arabia is abundant: Global horizontal irradiation is between 2,000 and 2,500
kWh/m². Theoretically, a section of 2,400 km² would suffice to provide the electricity needed in the
country, based on the gross annual demand without taking into account differences in production and
consumption time. The potential for CSP is similarly high: direct normal irradiation ranges between
1,200 and 2,800 kWh/m². The PV rooftop capacity potential for residential and industrial buildings was
estimated to be 16 Gap. Figure 6-1 shows the distribution of the potential within the country.
Northern
Borders
1.0 km²
175 MWp
241 GWh/a
Al-Jouf
1.5 km²
253 MWp
365 GWh/a
Al-Madinah
Al-Monawarah
5.3 km²
873 MWp
1,357 GWh/a
Makkah Al-Mukarramah
24.0 km²
3,885 MWp
5,692 GWh/a
Al-Baha
1.5 km²
232 MWp
382 GWh/a
Jazan
3.7 km²
583 MWp
861 GWh/a
Figure 6-1:
Al-Qaseem
5.1 km²
838 MWp
1,308 GWh/a
Hail
1.9 km²
323 MWp
525 GWh/a
Tabouk
2.1 km²
356 MWp
548 GWh/a
Aseer
6.9 km²
1,096 MWp
1,799 GWh/a
Region
Net roof area [km²]
Installable capacity [MWp]
Potential electricity generation [GWh/a]
Al-Riyadh
27.5 km²
4,514 MWp
6,610 GWh/a
Eastern Region
15.5 km²
2,560 MWp
3,494 GWh/a
Najran
1.7 km²
276 MWp
465 GWh/a
Net Roof Area, Installable PV Capacity, and Electricity Generation Potential by Regions
(map adapted from Dalet, 2013)
Levelized Cost of Energy
In this chapter, an analysis of the levelized cost of electricity (LCOE) assesses the economic potential of
renewable energy technologies in Saudi Arabia. Figure 6-2 shows the results of the LCOE for rooftop PV,
ground-mounted PV, CSP, and wind power for different full-load hours in comparison to fossil energy
costs with and without opportunity costs.
In most areas of Saudi Arabia, annual full-load hours of ≥2,000 can be achieved for PV, resulting in an
LCOE of around 0.07 to 0.15 US$/kWh (ground mounted) and 0.09 to 0.18 US$/kWh (rooftop). The
LCOE for wind varies between 0.133 and 0.221 US$/kWh for 1,100 full-load hours and 0.091 and 0.143
US$/kWh for 1,800 full-load hours. CSP has the highest LCOE of all selected technologies. Depending on
the full-load hours, the LCOE ranges between 0.185 US$/kWh and 0.449 US$/kWh.
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Figure 6-2:
LCOE of Renewable Energies Compared to Oil and Gas With Opportunity Costs
Case Studies
Three case studies are discussed in this chapter: a PV-driven, reverse osmosis (RO) desalination plant;
PV electricity supply for industry; and PV hybrid systems for remote applications.
The levelized water production costs (LWPC) for different plant configurations were calculated. The
LWPC for RO combined with PV proved to be higher than the LWPC for currently used multistage flash
(MSF) plants. However, this calculation did not include the maintenance cost, the fossil fuel prices, and
the operation costs. If these additional costs are taken into consideration, the PV-powered RO plant
may be the more sustainable solution. Especially regarding growing fossil fuel prices and possible
opportunity costs, PV–RO will gain more importance.
The case study on the PV electricity supply for the industry shows the advantages of PV currently lie in
reducing climate gas emissions and daily demand peaks in the afternoon. Economically, PV and fossil
fuel electricity are equal, so future development in this area depends on the price of oil and the system
cost of PV.
The case study on PV–diesel hybrid systems shows that for off-grid areas, villages, or industrial sites, a
PV–diesel hybrid system solution can be economically profitable, depending on diesel price and solar
radiation. There is vast experience showing that PV–diesel hybrid systems are not only able to provide
sufficient electricity, but with respect to the environment, they are superior to pure diesel systems.
Results
The analysis of the currently existing rooftop potential in Saudi Arabia shows that the largest potential
exists in the regions of Riyadh and Makkah. For the whole country, the potential is estimated to be
13.41 GWp on residential buildings and 2.55 GWp on industrial buildings. With this capacity, 3,743,412
MWh of electricity could be supplied annually, constituting 17.7 percent of current energy demand.
Because this analysis considered only rooftop potentials as easily accessible areas for distributed
generation, this confirms the abundance of solar resources in the country.
For the successful introduction of renewable energies into a new market, economic aspects also play an
important role. Therefore, the LCOE was analyzed for all considered technologies. For PV, the LCOE
ranges between 0.09 and 0.18 US$/kWh, depending on system size and actual investment cost. The
LCOE of WECs is between 0.07 and 0.22 US$/kWh. For CSP, the LCOE ranges between 0.185 US$/kWh
and 0.45 US$/kWh, depending on the full-load hours. The higher investment cost for CSP, especially
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when including storage, leads to these high values. However, they do not reflect the fact that storage
can easily be integrated, making CSP plants dispatchable electricity sources in contrast to wind and PV.
6.1 Introduction
With an increasing population, the Kingdom of Saudi Arabia faces a steadily rising demand for energy.
Currently used energy sources are mainly oil and gas, which are limited. In addition, their export is the
main source of income for the kingdom. Therefore, anticipating future needs, options need to be
considered, such as reducing the amount of domestic oil consumption to ensure steady income from oil
exports. This can be realized by enhancing energy efficiency. Another option, considering Saudi Arabia’s
abundant solar resources, would be renewable energy technologies, which do not require oil or gas to
generate electricity. Thus, renewable energies enter the discussion quickly as a potential energy source
that can replace fossil fuel-based energy generation.
With several countries in the world having already installed large amounts of renewable energy
technologies and dedicated themselves to transforming their energy systems towards a renewable
energy supply, risks of integrating those energy technologies have decreased significantly. Furthermore,
prices also have declined considerably in recent years, making them more and more attractive in the
economic context. Often, these two approaches are regarded as two sides of the same coin: One should
always consider both to reach an optimal energy system structure and a reduction of greenhouse gas
emissions. Therefore, especially combined with energy efficiency measures, renewable energy
technologies present an interesting opportunity for Saudi Arabia not only to free some oil for export but
also to reduce domestic carbon dioxide (CO2) emissions. Previous chapters of the present study have
already been dedicated to potential energy efficiency measures applicable to the Saudi context.
Recently, plans to build a domestic photovoltaic (PV) industry, as well as to increase local solar
electricity generation, have been announced. With K.A. CARE being responsible for the introduction of
renewable energies into the Saudi electricity generation portfolio, a clear statement in favor of
integrating renewable energies has been made.
However, up to now, research and projects have mainly been dedicated to technical aspects of different
renewable energy technologies in Saudi Arabia. A network of weather measurement stations has been
established to develop a detailed solar and wind atlas of the country. For successful integration,
however, economic aspects will be as important as technical ones. Because energy is broadly
recognized as one of the basic requirements for modern living standards, governments have to ensure
that the energy supply remains secure and at payable levels. Therefore, the economics of all available
technologies should be analyzed to detect which technology is already profitable in which application
and in which cases additional regulations might be necessary.
The present report aims to analyze the economics of different renewable energy sources in the
Kingdom and, thus, enhance the available information on country-specific aspects of each technology.
For this, first the renewable energy technologies—wind power, PV, and concentrated solar power
(CSP)—are presented. For each technology, a market overview is given. The energy conversion is
explained and the specific Saudi resources for each technology are described. For PV, the technical
rooftop potential in Saudi Arabia is assessed in detail, giving the installable capacity on Saudi residential
and industrial buildings in each region. For all technologies, system cost and levelized cost of electricity
(LCOE) are calculated. Furthermore, opportunity cost and CO2 emissions are analyzed and discussed.
The report concludes with an outlook on potential useful applications of renewable energy
technologies.
6.2 Renewable Energy Technologies
In this chapter, the different renewable energy technologies are described, with the focus on wind, PV,
and CSP technology, because they have the potential to be installed and used in KSA. Other
technologies, like hydropower, tidal energy, and wave energy, are not described in detail. Sources such
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as the EIA also confirm that Saudi Arabia has no hydropower in the electricity mix. This is due to the
nature of the Gulf region, a desert landscape without lakes or rivers. The seas around Saudi Arabia do
not have the potential of tidal energy or wave energy, because they are closed seas (i.e., the Gulf and
the Red Sea).
In the following, the promising technologies for Saudi Arabia are described in detail and the other
technologies are mentioned briefly.
6.2.1
Photovoltaics
By the use of PV technologies, solar radiation can be transformed directly into usable electricity.
Decreasing prices caused a remarkable rise in global installed capacity and production, resulting in a
global PV capacity of >100 GWp today and an expected further rise in installations in the forthcoming
years. Because of the historical development, PV technologies are classified according to their
corresponding cell technology: crystalline silicon (c-Si) refers to mono–, multi–, and ribbon c-Si; thin-film
technologies include cadmium telluride (CdTe), amorphous-microcrystalline silicon (a-Siμc-Si), copper
indium gallium selenide (CIGS), and copper indium selenide (CIS). Concentrating PVs (CPV) represent
various technologies that concentrate the irradiation before directing it to the PV cell. Furthermore,
there are technologies such as organic PV that have not yet been commercialized on a large scale. This
chapter gives an overview of the PV market, typical plant configurations, and an introduction to the
considered technologies.
6.2.1.1
Technology Description
Crystalline Silicon Photovoltaic
C-Si modules have been in use for the last 25 years, which makes it a proven and reliable technology for
harvesting solar radiation. Because of overcapacity and the need to decrease the manufacturing cost in
the c-Si industry, new cell and module concepts have been launched in recent years, which aim to lower
the electricity generation cost and increase efficiency.
Today, conventional c-Si modules have product manufacturers’ warranties of between 5 and 10 years,
and electrical performance warranties of up to 25 years. This has set a de facto standard, which all
module manufacturers have to fulfill (i.e., newcomers are required to produce robust and reliable
modules to be successful in the market).
The most important concepts of c-Si solar cells are mono- and polycrystalline cells. Monocrystalline cells
consist of single-crystal wafer cells with a potentially higher efficiency and higher production costs.
Polycrystalline cells are produced from cast square ingots.
The typical c-Si module consists of the components shown in Figure 6-3. The important components are
solar cells, solar glass, an encapsulant such as ethylene vinyl acetate (EVA), a back sheet, a junction box,
and a frame. These items are assembled in a module assembly line. Because of the high competition in
the PV market, many manufacturers opt for the complete integration of all production steps to avoid
additional retail and distribution costs.
The conventional c-Si cell functions like a flat semiconductor diode, that is, it allows the energy to pass
only in one direction. Usually, the light-exposed semiconductor zone is made of n-doped silicon, while
the light-averted semiconductor zone is made of p-doped silicon (Figure 6-4). To absorb as many
photons as possible, an antireflection coating is applied to the cell surface. The front contacts, usually
made of an aluminum or silver alloy, are screen printed on the cell surface. The fine contacts that
collect the charge are called fingers. Bus bars, broader contacts, are intended for interconnector wire
soldering. In general, the backside of the cell is totally covered with a backside contact made of
aluminum.
Typically cells are interconnected with a flat copper wire. Modules using these cells have average
module efficiencies of about 15 percent. The record laboratory cell efficiency of monocrystalline PV
cells is 25 percent.
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Figure 6-3:
Exemplary c-Si-module Components (Fraunhofer ISE).
2 mm
Figure 6-4: Left: Principle of a c-Si Cell (Solar Total Holding BV, 2011). Right: String of Cells
(Blue) Interconnected by Alloyed Copper Ribbon (red) (Fraunhofer ISE)
Compared to thin-film technologies, crystalline PV modules have significantly higher efficiencies, which
allow for installation of more electrical power on a predefined area than common thin-film
technologies. Because of the module setup and long experience with the module materials, crystalline
solar modules are expected to guarantee a longer lifetime compared to the moisture-sensitive, thin-film
modules.
Installed modules do not require intensive maintenance service, so the variable costs are significantly
lower compared to CSP technologies.
In recent years, PV module prices have decreased dramatically. While the average net price for a monoor polycrystalline module produced in Germany was about 2 €/Wp in January 2011, in May 2013, the
price dropped below 0.80 €/Wp. Due to this high price decline, experts expect increasing demand in the
near future. Compared to household electricity prices (approximately 25 ct/kWh), newly installed
rooftop systems and large PV systems already reach grid parity in Germany.
While the thin-film technologies always have a vertically integrated production line, the cell and module
production can be located separately for crystalline solar system production, which allows a flexible and
small-scale manufacturing startup. Improved cost-effectiveness usually requires a vertical integration of
production up to cell production or even beyond, and high-volume production to enable scale effects.
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Thin-Film Photovoltaics
Thin-film modules are currently used in 14 percent of worldwide PV installations. It has gained
importance due to highly competitive prices. The theoretic efficiency of thin-film modules lies between
30 percent for simple junction cells and 39 percent for tandem cells. Triple junction cells have an even
higher theoretic efficiency. Efficiencies of thin-film modules available on the market range between
10 percent and 16 percent, depending on the technology.
Like the c-Si technology, thin-film PV uses the PV effect to convert solar irradiation into electricity. In
contrast to c-Si, the selected materials allow thinner semiconducting layers, because of the material
properties. The structure of a single-junction, thin-film cell can be seen in Figure 6-5 and Figure 6-6;
Figure 6-7 shows a triple-junction cell. Tandem and triple-junction cells are characterized by the number
of p-n junctions. In the case of a tandem cell, two p-n junctions and, hence, two sets of semiconducting
p-n layers, exist; triple-junction cells dispose of three layers. Because the semiconductor has a natural
doping, a second material with the opposite doping is necessary to achieve a proper p-n junction.
Common materials are amorphous silicon, often combined with other layers of microcrystalline silicon,
CdTe or CIGS. The thin films of the conducting materials of front and back contact are deposited on a
glass substrate. To protect the module against external conditions, it is encapsulated with a foil and
another glass layer, and closed with a frame. The schematic layout of different thin-film technologies is
illustrated in Figure 6-5, Figure 6-6, and Figure 6-7. The front contact usually consists of a layer of
indium tin oxide. Combined with another oxide layer, it is also known as transparent conducting oxide
film. The back contact usually is a metal alloy, such as molybdenum, nickel, or silver.
Figure 6-5: Structure of a CIGS
Cell (Paulson, 2004)
Figure 6-6: Structure of a CdTe
Cell (Paulson, 2004)
Figure 6-7: Structure of a Triple aSi Cell (Paulson, 2004)
Because the n and p layers have a significantly reduced thickness compared to c-Si, less material is
required for production and, therefore, production costs decrease. Moreover, production is less energy
intensive and thus reduces production costs, as well as the energy payback time.
Thin-film power plants are always mounted on fixed structures, because their efficiency and low price
do not justify the higher investment of a tracked system. This makes thin-film technologies very
attractive with respect to investment and maintenance expenditure. They also provide a smaller
temperature coefficient, meaning a smaller efficiency reduction with rising temperatures. However, the
long-term stability of thin-film modules, as well as their recycling, is still subject to scientific research
and discussion.
Concentrating Photovoltaics
In a CPV system, an optical element focuses the incoming light onto a small solar cell, where it is
converted into electrical energy. This arrangement allows a reduction of the comparatively expensive
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semiconductor surface by a factor of 10 to 1,000. Silicon solar cells are used in concentrator systems
with low concentration factors of up to 100. Highly efficient III–V multijunction solar cells are used in
systems with a high concentration ratio.
The basic principle of CPV is illustrated in Figure 6-8. Comparably low-cost concentrating optics, such as
lenses or mirrors, reduce the area of the expensive solar cell and/or of the modules that house them,
which reduces the overall system costs significantly. This allows use of highly efficient but rather
expensive solar cells. To keep the solar cell within the focus of the lens or mirror throughout the day,
CPV requires a tracking system. Because of the concentrating optics, CPV systems use only direct solar
radiation. This can be compensated for by longer sunlight exposure of the cells during the day, because
the tracking leads to high-capacity factors and high energy yields.
solar radiation
lens
solar cell
heat transport
Figure 6-8: Exemplary Arrangements of a PV Concentrator. Left: A Fresnel lens is used to concentrate the
sunlight to a small solar cell (Fraunhofer ISE). Right: Two mirrors are used for concentration (SolFocus, 2011).
Not shown is the tracking part of the CPV system.
Thus, the governing principle of CPV is that sunlight is concentrated onto a PV cell. Yet, CPV systems can
follow a wide variety of designs, which can be categorized by the concentration factor, the method of
concentration, and the tracking system. The concentration factor can be low (2 to 100) or high (>300).
The concentrating elements may be based on reflection, refraction, or other forms of optical
manipulation. The tracking system can be single axis or double axis.
CPV power plants usually combine several CPV systems. This offers a high level of flexibility in plant size
and plant layout. Most CPV systems (in particular, systems with lenses) do not require cooling water.
Cooling water is required only for some systems (e.g., large mirror optics). By using the waste heat (e.g.,
for desalination or steam production), the overall system efficiency can be significantly increased. The
optimization of such CPV thermal systems is ongoing. In general, low quantities of cleaning water are
necessary for all CPV systems.
The most important benefit of CPV is the possibility of using highly efficient solar cells. Particularly high
efficiencies are reached in high-concentration systems, in which III–V multijunction solar cells have
become standard. These solar cells achieve efficiencies >40 percent and lead to module efficiencies of
around 30 percent and air conditioner (AC) system efficiencies >25 percent. Significant improvements
towards system efficiencies >30 percent are expected. Such values cannot be achieved by singlejunction one-sun (i.e., nonconcentrating) PV technology. Another advantage of III–V multijunction solar
cells in CPV systems is their lower dependence on temperature. In general, the efficiency of a solar cell
decreases with increasing temperature. This decrease is lower for III–V multijunction solar cells than for
conventional Si-based or thin-film solar cells.
The tracker unit of most CPV systems has a comparably small supporting stand, which leads to minimal
land coverage, and the land around the tracked CPV unit is not permanently shadowed. This enables
dual land usage (e.g., as pasture). In addition, the plant layout is flexible, because several independent
tracker units are combined. Thus, the requirements for site geography are reduced.
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Most CPV systems do not require cooling water (in particular, lens-based systems), which is an
advantage in areas where water is scarce. Other CPV systems (e.g., large mirror systems) use active
water cooling. For sites with sufficient water availability, such systems offer the chance to use waste
heat (e.g., for water desalination or steam production). This significantly increases the overall system
efficiency.
A possible drawback of CPV systems is their higher complexity compared to conventional, nontracked
PV. In addition, CPV has a shorter track record compared to Si-based PV or CSP, which can complicate
bankability. Yet, several large plant constructions in recent years should help prove reliability, expected
energy yields, and availability in the field, which help increase the confidence of investors. This is also
shown by the recent announcement of the construction of CPV plants with >100 MW installed capacity.
Another point to note is that CPV systems only use the direct part of the solar spectrum for electricity
generation. Therefore, CPV is best suited for areas with high direct irradiation. Tracking leads to higher
capacity factors and a higher availability compared to nontracked PV. Because of the system complexity
and the necessity of tracking, CPV is suited for medium or large PV installations rather than for small
ones. In addition, CPV should be sited in open areas or on large-area flat roofs rather than on inclined
roofs.
Typical Power Plant Configuration
Generally, there are two options to mount PV power plants: The modules can be placed on a fixed
mounting system that is made of steel, aluminum, or wood; or the modules can be mounted on a
tracking system that follows the path of the sun. Single-axis trackers turn on one axis so that the PV
panels follow the sun’s daily path from east to west on a horizontal axis or in a fixed tilt. Dual-axis
systems additionally tilt on a vertical axis to follow the sun from sunrise to sunset.
Tracked systems can, depending on their location, achieve up to 35 percent higher yields than fixed
tilted systems. Table 6-1 summarizes the key data of two exemplary plants. These fixed tilted and
single-axis tracked systems represent two of the largest power plants worldwide. Even though the
tracked system has an installed capacity of 46 MWp compared to 60 MWp for the fixed system, it
produces 6 GWh more electricity per year than plant 1, because of the use of trackers.
Trackers generate higher investments and make additional maintenance necessary. Therefore, this
configuration is more likely to pay off in southern regions with high direct solar insolation. While
module prices are constantly falling, financing this additional expenditure in northern areas of Europe is
becoming less economically attractive.
Table 6-1:
PV Power Plant Configurations: Examples at Two Locations With Comparable Solar Irradiation
(range, 2,000 to 2,100 kWh/m² global irradiation per year)
Mounting
Installed capacity
Annual production
Number of modules
Location
Implementation
Date of completion
Installation time
Plant 1
Plant 2
Fixed tilted
60 MWp
87 GWh
270,000
Olmedilla de Alarcón, Spain
Nobesol
2008
16 months
Single-axis tracker
46 MWp
93 GWh
260,000
Amareleja, Portugal
Acciona
2008
13 months
In a CPV power plant, an adequate number of CPV systems are combined. Depending on the CPV
technology, each unit usually has a power between 5 kW and 60 kW. Therefore, the size of a CPV power
plant can be scaled from a capacity of few kWp to several hundreds of MWp.
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One advantage of the CPV technology is the modular character that allows an individual scaling of the
final PV system, depending on the customer’s needs. PV systems can be used as on-grid or off-grid
systems, which makes them applicable for both central power generation and local power generation.
Cost Breakdown of Typical PV Plants
In this section, the cost of different PV systems is compared. Figure 6-9 shows the cost breakdown of a
typical rooftop PV power installation with 100 kW. The modules account for about one-half of the
overall system cost, even though this share has decreased strongly in recent years due to the modules’
price drop. The inverter is the second most costly component, followed by the mounting structure and
indirect costs such as project development and insurance. Because of the increased share of balance-ofsystem (BOS) cost in the overall PV system cost, this part is now the focus of further price reductions.
Grid
connection
4%
Installation
1%
Cables
1%
Mounting
structure
11%
Indirect
costs
8%
Module
44%
Inverter
31%
Figure 6-9:
Cost Breakdown of a Typical Rooftop PV System (100 kW) (unpublished data)
Figure 6-10 shows the cost breakdown of a typical ground-mounted PV power plant of about 20 MW.
Because of the larger size, the specific cost for BOS components is smaller than that of rooftop systems,
while the price of the modules is usually not much lower. Therefore, the share of the module cost is
slightly higher for the ground-mounted system. Grid connection is more complicated because the
system needs to be connected to a higher voltage level, resulting in a slightly higher share on the total
system cost. In addition, civil works are necessary for the foundation of the plant, which is not required
for rooftop systems.
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Grid connection
Installation 5%
0%
Indirect
Civil works
costs
5%
7%
Cables
2%
Mounting
structure and
foundation
9%
Module
53%
Inverter
19%
Figure 6-10: Cost Breakdown of a Typical Ground-Mounted PV Power Plant (20 MW) (unpublished data)
Figure 6-11 shows the cost breakdown for a typical ground-mounted, thin-film (CdTe) power plant.
Because the efficiency of thin-film modules is lower than that of c-Si modules, a larger amount of
module area is needed to achieve the same installed capacity. This leads to a higher amount of
mounting structure needed and, thus, results in a higher cost share of the mounting structure.
Grid connection
Installation 4%
0%
Indirect
Civil works
costs
4%
7%
Cables
4%
Mounting
structure and
foundation
14%
Module
49%
Inverter
17%
Figure 6-11: Cost Breakdown of a Typical Ground-Mounted, PV, Thin-Film (CdTe) Power Plant
(20 MW) (unpublished data)
6.2.1.2
Global Market
The global PV installations now claim a cumulative installed capacity of >100 GW. Because PV can be
installed on rooftops as well as be ground mounted, it offers a variety of solutions at each scale. As
Figure 6-12 illustrates, c-Si (mono- and polycrystalline) is the most common technology, with a market
share of 85 percent, whereas all thin-film technologies combined reach a share of 14 percent. Within
the thin-film technologies, CdTe clearly dominates the market. The remaining 1 percent constitutes
third-generation PV, such as organic or dye-sensitized solar cells.
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Figure 6-12: Annual PV Module Shipments per Technology, From 2000 to 2011 (Navigant Consulting)
In the last two years, the global installed capacity was around 30 GW annually. The European Union
holds the largest share of installations, with a cumulative installed capacity of about 70 GW (70 percent
of total cumulative installation) (EPIA, 2013) (Figure 6-13). In Germany and Spain, the contribution of PV
energy to electricity generation has reached >3 percent of total electricity generation on average per
year (EPIA, 2011). On the other hand, the PV markets in Africa and the Middle East remain small. But
because North Africa lies in the earth’s sun belt and module prices are decreasing while fuel prices are
increasing, the PV market certainly contains a huge potential. According to the European Photovoltaic
Industry Association (EPIA), “The PV potential of the sun belt countries could range from 60 to 250 GW
by 2020 and from 260 to 1,100 GW in 2030, representing 27 percent to 58 percent of the forecast
global installed PV capacity by then” (EPIA, 2011b). To enable this transformation to renewable energy,
governmental incentives and constant efforts of all stakeholders are necessary. Nevertheless, especially
in sunny regions, grid parity is expected within the next few years.
Figure 6-13: Global Annual PV Market 2000–2012. (For 2012, ROW figures are directly integrated into those of
the relevant regions.) MEA: Middle East and Africa; APAC: Asia and Pacific. (EPIA, 2013)
China’s PV installations have been growing extensively in the last two years. Although China’s market
share in terms of annual installed PV capacity has developed only in the last two years, the Chinese
industry accounts for 47.8 percent of PV cell and module production (Figure 6-14 and Figure 6-15). The
other main regions for production are Taiwan, Germany, and Japan. PV production in Africa and the
Middle East still remains below the 1 percent mark. So far, there is no PV production in Saudi Arabia.
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Figure 6-14: Worldwide PV Production Volume in 2011 Structured by Region (Photon International, 2012)
Figure 6-15: Worldwide PV Cell Production. Over the last decade, the average growth rate was above 50% per
year. (Photon International, 2012)
Current price trends show tremendous reductions in module and cell prices, which are also displayed in
the learning curve of PV module prices shown in Figure 6-16. The gained experience supported the
significant improvements in production cost, making PV more and more attractive compared to
conventional energy sources and bringing it closer to grid parity.
Since 2008, PV system prices have dropped from >4.05 US$/Wp (c-Si) to <1.35 US$/Wp (i.e., the market
has seen a price reduction of >66 percent). This will foster further investments in PV power plants and
raise its attractiveness for investors. With dropping silicone prices, the cost advantage of thin-film
modules is decreasing significantly; further efficiency improvements must keep up with competing c-Si
technologies.
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Figure 6-16: Historical Development of PV Module Prices Versus Cumulative Module
Production (PSE AG/Fraunhofer ISE; data based on Navigant Consulting and EuPD module prices since 2006)
The price decrease derives from new manufacturing concepts, economies of scale, and increasing cell
efficiencies, which lead to higher module power. All technologies experienced significant improvements
in module efficiencies, which enhance the energy yield and technology attractiveness. In the last two
years, falling prices also have been caused by market overcapacity and price dumping by Chinese
manufacturers. This has led to PV system prices that no longer can be explained with a reasonable
learning curve (Figure 6-17). For the future, therefore, it is expected that prices will decline at a small
rate only until they get back to the long-term learning rate of 19 percent (Figure 6-17).
Figure 6-17: PV System Price Development and Estimates for 2020. (Janzig, 2011;
Bank Sarasin, 2012; Fraunhofer ISE, 2012)
CPVs have played a minor role in the PV industry for >25 years, but over the last few years, the CPV
manufacturing capacity has significantly grown to currently about 200 to 250 MWp/year (Depuydt,
2011), since more and more companies have entered the market (Figure 6-18). The main reasons for
this increased interest in CPV technology are the following (SRA, 2011):
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•
•
•
PV applications have grown to a scale that makes large PV power plants achievable. The PV power
plant market is increasing at sites with high direct normal irradiance (DNI), such as the
southwestern United States or the Mediterranean areas, where water scarcity may be a difficulty
for CSP.
Solar cells made of III–V semiconductor compounds have already reached efficiencies >40 percent,
CPV modules have reached efficiencies 30 percent, and full-scale systems have reached >25 percent
AC operating efficiencies. CPV systems with efficiencies of 35 percent to 40 percent can be
expected. The high efficiencies are of crucial importance to achieve low costs per kWh.
Although CPV technology has the highest conversion efficiencies compared to other PV
technologies, experience with the manufacturing of CPV systems is comparatively lacking. However,
this experience, as well as experience in operating CPV systems, is being gained and, thus, is helping
increase investor confidence.
Figure 6-18: Installed CPV Power Worldwide (Fraunhofer ISE, 2012; Graph PSE AG, 2012)
In general, decreasing module prices and rising oil prices will very likely ensure further PV market
growth worldwide (Jäger-Waldau, 2011). However, startup companies that have to expand their
production often have limited financial resources and, therefore, are struggling in the current market
environment. This situation is expected to continue for the next few years. On the other hand, the
falling module and system prices will generate a broader market, which opens possibilities for more
industry growth, at least for those companies that are able to expand and reduce their costs at the
same time. The competitive environment in the PV industry makes it difficult for new, smaller
companies to enter the market.
6.2.1.3
Technology Evaluation and Summary
General advantages of PV technologies are their low water consumption and the possibility of gridconnected as well as off-grid installation. Moreover, current price reductions have enabled PV to almost
reach grid parity in sunny regions. All technologies have very low emissions during their lifecycle, such
as noise and gases. The energy payback time ranges between one and three years, depending on the
technology and location (Fthenakis, 2011). Many companies are able to install PV systems. Table 6-2
presents a strengths, weaknesses, opportunities, and threats (SWOT) analysis of PV technologies.
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Table 6-2:
Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of Different PV Technologies
Photovoltaic
Strengths
c-Si
• Operating systems for
>20 years
• High module efficiency
reducing area-related
costs
• Standard semiconductor
materials and technology
use
• Si harmless and
abundantly available
• Robust, easy handling
installation
Thin film
• Lower temperature
coefficient improving
annual system
performance ratio
• Low material
consumption
• Low manufacturing cost
• Robust, easy handling
and installation
• Shortest energy payback
time
CPV
• High efficiency
• High energy yield in
regions with high DNI
• Smaller area per MW
than flat plate
Weaknesses
Opportunities
Threats
• Energy- and capital- • Modular plant
• Limits in cost
intensive
configuration allows
reduction
production (except
very small (few Wp) • High
at module level)
to multi-MWp plants
competition
• Installation is
rooftop- and groundmounted
• Silicon consumption
is reduced
• Lowest efficiency of • Modular plant
• Long-term
all PV technologies
configuration allows
reliability not
very small (few Wp)
proven
• Raw materials
to multi-MWp plants • Shortage of
possibly subject to
availability
• Installation is
semiconductor
rooftopand
ground(e.g., In, Te)
• Higher land
mounted
consumption
• Use of
environmentally
• Higher BOS-cost
hazardous
elements
• High direct
radiation needed
• Tracking required
• Higher cost per Wp
compared to flatplate PV
• Low production
capacities and few
existing MWp scale
installations
• Large cost reduction • Expensive highpotential
precision tracker
• High energy yield per • Short period of
m²
field reliability
experience
• High growth rate for
CPV market expected • Reliability and
power rating
• Utility-scale
standards rather
installations in the
new or still
pipeline
under
• Materials widely used
development
(glass, plastics, steel)
An economic storage system for large-scale applications is lacking. Therefore, energy generation is
limited to daytime and has its peak at midday. In sandy regions, module cleaning is strongly
recommended to maintain performance, because dust layers significantly reduce the energy output of
the modules.
PV systems have a modular character that allows individual scaling of the PV system according to the
customer’s needs, from the Wp to the GWp range. They can be used as on-grid or off-grid systems,
allowing both central power generation and installation in remote rural areas. Also, rooftop, groundmounted, or building-integrated installation is possible. Depending on the technology, installation in
regions with very high radiation, as well as installation in less sunny regions, is possible. Thin-film
technologies cope best with a higher share of indirect radiation, whereas CPV can only convert direct
solar radiation. In addition, the temperature coefficient of thin-film modules shows an inferior
efficiency loss with rising temperature than crystalline modules. Installed modules require relatively
little maintenance, which applies especially to fixed-mounted systems. Tracked PV systems have a
slightly elevated need for maintenance, because the movable parts have a higher risk for damage or
failure.
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The market is competitive and a variety of companies have experience in the installation and operation
of all kinds of PV plants. Operating experience has existed for almost 25 years for c-Si technology.
However, there is less experience with thin-film PV, and CPV power plants have been installed for only a
few years. Although CPV promises high energy yields and high system efficiencies, long-term stability
and performance has not been proven yet. However, existing field experiences, as well as accelerated
aging tests, are promising.
With regard to production, thin-film PV is the less energy-intensive technology (Fthenakis, 2011). All
technologies require highly automated production lines and, thus, a skilled workforce that can operate
the machines. Thin-film PV and CPV require less semiconductor material than c-Si. However, because of
its lower efficiency, thin-film PV leads to a higher need for BOS components and CPV always requires a
tracked installation. Therefore, BOS cost might have a larger share than in c-Si power plants.
An advantage of thin-film PV modules over flat-plate technologies is their substantially reduced need
for raw materials compared to c-Si. The production is less material and energy intensive. Nevertheless,
the deployed raw materials, such as cadmium, are often rare or potentially toxic and, therefore, both
the availability and environmental impacts have been an issue of public discourse. Cadmium has been
claimed to be safely encapsulated and, consequently, no danger should occur (Fthenakis, 2004). The
availability of the materials still is an economic issue, especially with falling prices for silicon.
6.2.2
6.2.2.1
Concentrating Solar Power
Technology Description
Introduction
The basic concept of concentrating solar thermal power plants is to convert solar radiation into
electricity. The process goes through the following steps:
1. Concentration of solar radiation using reflective optics (i.e., mirrors)
2. Absorption of the radiation in a receiver
3. Conversion of heat to electricity in a heat engine driving an electric generator
For this process, a reflecting surface is required to focus the solar radiation either onto a line (linefocusing systems) or onto a point (point-focusing systems). Typical line-focusing systems are parabolic
trough collectors and linear Fresnel collectors. Typical point-focusing systems are solar tower power
plants and parabolic dish systems.
In the present chapter, the market environment for CSP is discussed, typical plant configurations are
presented, and details on the various CSP technologies are provided.
Dish-Stirling technology is not covered, because of its currently small share and relevance in the world
market, making it not as applicable to the Saudi market as the other technologies.
Typical Power Plant Configurations
Because of their dispatchability, solar thermal power plants can be integrated more easily into the
electrical grid than many other renewable energy technologies, which is a significant advantage. Even a
CSP system without storage has relatively good stability and time constants of several minutes. The
option of dispatchable generation also exists through the availability of storage or by hybridization of
solar power plants in a CSP plant. Therefore, they often feature thermal energy storage tanks. The
possibility of storing thermal energy for a certain time is an advantage for the supply management of
electricity. In times of high thermal productivity, the excess energy can be stored and dispatched later,
either to cover high-demand peaks or to supply constant base load, both of which can also correspond
to a higher feed-in-tariff in some scenarios and are, therefore, a direct economic benefit. The
integration of a storage system requires a larger solar field if the rated power output is to remain the
same, because not only the turbine has to be supplied during times of high irradiation but also the
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storage must be charged. However, the additional investment is compensated by the higher amount of
produced electricity (Figure 6-19).
Solar
Field
Solar
Field
+
+
Solar
Field
+
St orage
+
Pow er
Block
=
Pow er
Block
=
Elect ricit y
Out put
Elect ricit y Out put
Figure 6-19: Schematic of Storage Tanks and Higher Electricity Output of a CSP Plant
There are different technical options to store energy. Currently, molten salt storage is the only
commercially available and feasible storage technology for CSP plants with large storage capacities. In
addition to their application in parabolic trough power plants, they are used in solar tower power plants
such as the Spanish Gemasolar plant by Torresol Energy (Figure 6-20).
Figure 6-20: Schematic of a Parabolic Trough Plant With Integrated Two-Tank Thermal
Energy Storage (Solar Millennium, 2010)
Another advantage of CSP plants is that they can be run and combined with other fuels (e.g., natural
gas). In such a plant, the heat transfer fluid (HTF) can be heated either by the sun or with gas. Hence,
the plant could theoretically be operated 24 hours per day (Figure 6-21).
An example of a plant in which one part of the thermal energy comes from the sun and another part
comes from fossil fuels is the integrated solar combined cycle (ISCC) concept. In an ISCC plant, the solar
field acts as an additional heat source for the steam turbine of a gas/steam combined-cycle power
plant. This concept was implemented in the ISCC plant in Ain Beni Mathar, Morocco, and in Kuraymat,
Egypt. Another hybridization method is the approach of the Masdar SHAMS-I plant. In this plant, a gas
booster heater superheats the steam coming from the solar evaporator to a higher temperature of 540
°C. With this hot steam, higher thermodynamic efficiencies can be reached at the turbine without
needing a very high gas supply, especially during lower irradiation periods.
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Figure 6-21: Daily Operation for a Combined-Cycle Solar Plant With Storage System
(Valentina A. Salomoni, 2013)
Parabolic Trough Technology
Currently, the commercially most developed CSP technology is the parabolic trough technology. In this
system, parabolic mirrors are mounted on a supporting structure and reflect the incoming sunlight onto
an absorber tube, in which a thermal fluid is pumped through to harvest the energy. The parabolic
shape is often composed of multiple mirrors. The trough segments form rows 100 to 150 m long. The
rows follow the sun through a one-axis movement by a tracking device. This allows the focal line to stay
on the absorber. The parallel collector rows can be oriented arbitrarily, but a north–south orientation is
considered optimal. In Figure 6-22, the working principle of a parabolic trough collector is shown, as
well as an aerial view of the Andasol power plant, where the parallel collector rows can be seen,
including the 50 MWel power block and the two molten salt tanks of the storage system with a capacity
of 7.5 full-load hours.
Figure 6-22: Parabolic Trough Collector: Aerial view of the Andasol plant (left) and working
principle (right) (Flagsol, 2013)
Thermal oil usually is used for HTF, but steam and molten salts are being studied. Parabolic troughs
using HTF, however, are not yet commercially viable because they are not as mature as the thermal oil
technology. Their maturity is similar to other tower or Fresnel concepts. The temperature limit for
thermal oil is currently 390 °C (including a safety margin). Other HTFs may be used up to 560 °C, but
neither the direct generation of superheated steam (target operating temperature, 480 °C at 120 bar)
nor the use of molten salt (target operating temperature, 560 °C) has been examined in large parabolic
trough fields.
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When the HTF in the collector is heated, it goes either through a steam turbine to generate steam or is
directed to heat storage. The generated wet or, preferably, superheated steam is directed to an electric
turbine generator. In some cases, gas- or oil-driven backup heating systems are used to enable 24-hour
operation.
Parabolic trough collectors have a long history but became technically mature with the construction of
the nine solar energy generating systems (SEGS) power plants in Kramer Junction, CA, United States.
The SEGS plants were built between 1985 and 1991 and have a combined power rating of 354 MW (14
to 80 MW each). They are still in operation (electricity production of >10 TWh since 1985) and, thus,
have demonstrated the high potential of this technology.
Linear Fresnel Technology
The linear Fresnel reflector technology uses long horizontal segments of parallel mirrors to reflect and
focus sunlight onto the fixed absorber at a height of several meters (3 to 15 m). The different mirror
rows are individually tracked according to the position of the sun (Figure 6-23).
Figure 6-23: Aerial View of Novatec Solar’s Puerto Errado 2 Collector Field (NOVATEC Solar, 2013)
The Fresnel technology is regarded as a lower-cost alternative for solar steam production and power
generation compared to parabolic trough technology. The main advantages of this technology are:
•
•
•
•
•
•
•
Inexpensive, nearly flat mirrors and simple tracking system
Fixed absorber tube with no need for flexible high-pressure joints or thermal expansion bellows
Fixed receiver irradiated from underneath, which is favorable for direct steam generation
Lower pumping costs due to reduced flow resistance (no joints, expansion bellows in the receiver
line)
Efficient land use because the collector mirrors can be placed close to one another
Low wind load because the primary mirrors are smaller and mounted horizontally
Higher local content possible in the erection of the solar field.
A disadvantage of this technology is the lower efficiency compared to parabolic troughs, which has to
be compensated for by lower investment costs in the solar field. The main factor affecting the efficiency
is that the linear Fresnel collector is a horizontal collector (similar to the tower). Therefore, the
irradiation on the collector is low for low sun-elevation angles. Currently, no economically feasible
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large-scale storage for steam is available, but several developments are ongoing. Therefore, linear
Fresnel plants are installed and operated without storage.
Cost reductions may come from economies of scale and design optimization of the collector. Lower
operation and maintenance costs also offer potential savings. For example, an automated cleaning
robot for the flat mirrors with extremely low water consumption has already been developed. Largescale solar power plants are under construction and in planning. An increasing number of companies
develop commercial collectors and first demonstration projects have been installed.
Solar Tower Technology
A solar tower plant uses a point-focusing receiver instead of a line-focusing receiver, as with the
parabolic trough and linear Fresnel technology. A large number of flat mirrors, called heliostats, track
the sun and focus the solar irradiation onto one receiver, which absorbs the incoming light and
transforms it to heat. To be visible for the large number of heliostats in the solar field, the receiver is
mounted high above on a tower (Figure 6-24). A wide range of tower technologies with different
maturity stages is being investigated. For the heliostat field, two configurations are possible: a more or
less symmetric arrangement of heliostats around a 360° receiver or an asymmetric arrangement in
which the mirrors are to the north (in the northern hemisphere) or the south of the tower.
Water/steam, molten salt, or air is used in the various concepts as HTF.
Figure 6-24: The Gemasolar Solar Tower Power Plant (Fraunhofer ISE)
In most of the current power plants, the collected heat of the receiver drives a water- or steam-based
thermodynamic cycle to generate electric power. In that case, the heat is either transferred by an HTF
(e.g., molten salt) or the receiver directly produces water or steam. The first commercial solar tower
plant (PS10 in Spain) uses water as the HTF and generates saturated steam to power its turbine. It has a
capacity of 11 MWel and a small thermal energy storage capacity of 20 MWhth in the form of steam
drums. In 2009, a larger version of the same concept, called PS20, with a capacity of 20 MWel, started
operation. Both towers are operating only with saturated steam at a relatively low temperature of
about 260 °C. The company eSolar promotes a similar concept for superheated steam. Its first
demonstration plant, Sierra Sun Tower, located in California, also started operating in 2009 and has a
capacity of 5 MW. The company BrightSource pursues a similar concept and has built a pilot plant called
Solar Energy Development Center (SEDC) in Israel, which started operation in 2008. Sierra and SEDC
generate superheated steam in the receiver (400 to 450 °C). The higher temperatures of superheated
steam may lead to higher power block efficiencies but also may lead to higher heat losses in the
receiver and necessitate the use of more expensive materials. A large plant of this type, with a nominal
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capacity of 123 MW, is under construction in California: the Ivanpah plant by the company
BrightSource. It was due for completion in 2013.
The other option is using molten salt in a tower as a heat transfer and storage medium. The first
objective of this technology is to operate at high temperatures to increase the power block efficiency.
The second objective is to facilitate storage integration. Because the HTF is also the storage medium,
integration of a two-tank system is relatively simple. The company Torresol recently commissioned a
plant of this type in Spain, called Gemasolar, with a nominal capacity of 20 MW and a storage capacity
of 15 full-load hours. A large plant of this type, with a nominal capacity of 110 MW, is under
construction in Nevada in the United States: the Crescent Dunes project by the company SolarReserve.
It is due for completion in 2014.
Alternatively, a solar tower plant can be used to heat compressed air in the receiver to power a gas
turbine (Buck, 2008). In this case, the receiver replaces the combustion chamber of a conventional gas
turbine. In the long run, high solar efficiencies with a combined cycle, (i.e., a combined gas and steam
turbine cycle) are possible.
Several solar tower systems of multi-megawatt size are under construction or in a testing phase. A few
commercial projects with 10 MW and 20 MW are in operation. The cost of electricity is not well
documented. Nevertheless, depending on the heliostat field costs, it may become lower than with
parabolic trough systems, because the potentially higher operating temperature allows solar tower
plants to reach working fluid temperatures up to 1,100 °C. The high temperature allows the operator to
run the thermodynamic process of the steam turbine with a slightly higher efficiency compared to
parabolic trough and linear Fresnel technology, where lower temperatures are achieved. However, the
temperature ranges of steam (currently 250 °C for saturated steam and 450 °C for superheated steam)
and molten-salt-tower technologies have the same limitation for all concentrator technologies: Because
of instability of the salts, the maximum temperature is 560 °C. Only air/gas as HTF has the potential to
reach 1,100 °C. One advantage of a solar tower plant is the centralized heat generation at the receiver,
which makes piping within the solar field unnecessary. Therefore, the solar field is easier to construct
and maintain. The flat shape of the mirrors also contributes to a cost reduction in the solar field
because they require less manufacturing knowledge. However, because of the spatial distribution of the
heliostats, an automated cleaning system is much more difficult to realize than for systems where the
mirrors have a more linear orientation. This is especially the case for linear Fresnel collectors, where
automatic cleaning machines can drive along a 100-m row of linear Fresnel mirrors, compared to
cleaning heliostat entities individually.
Cost Breakdown of Typical CSP Reference Plants
In this section, the costs of the parabolic trough, linear Fresnel, and solar tower plants are compared.
The costs are based on data from previous studies done by Fraunhofer ISE (Fraunhofer ISE/ISI, E&Y,
2011) and internal data. Figure 6-25 shows the share of costs of the different major components of a
solar plant.
The most relevant category in terms of cost is the equipment of the solar field and the HTF. Most of
these components are very specific in their design and specifically developed for the CSP industry (e.g.,
the receiver tube or parabolic mirrors). Solar field and HTF systems are responsible for the largest
portion of the investment for parabolic trough and solar tower technology; whereas, the share of the
solar field investment is considerably lower for the linear Fresnel technology. This is because linear
Fresnel plants use simple technology as much as possible (e.g., flat mirrors and water as the HTF).
The most relevant category in terms of local value added is the labor costs affiliated with site
preparation and assembling of the solar field. Experience shows that the unskilled labor tasks in this
category are usually handed to companies employing a local workforce. Labor expenditures for site
preparation, civil works, and solar field assembly, therefore, can be expected to have full impact on the
local market.
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Figure 6-25: Percentage Breakdown of Cost for CSP Technologies (unpublished data)
The thermal storage system accounts for 10 percent to 15 percent of the total system costs. This is a
relatively small portion compared to the costs of the solar field and explains why most of the CSP plants
that are up and running in Spain include a thermal storage facility in their system design. The additional
costs for the storage system are more than compensated by the higher use of the steam turbine, which
then runs throughout the evening or even during the whole night.
Costs that arise from the conventional power plant components are mainly influenced by the capacity
of the turbine. The respective technology that is applied influences these costs only to a minor degree.
The costs of project management (engineering, procurement, and construction) and project
development were derived from available data of similar projects and applied under the assumption
that these costs are dependent on the total project size but are not proportional.
6.2.2.2
Global Market
The market growth of renewable energies and the market push for CSP technologies in locations with
high direct sun radiation accelerate the technology deployment of CSP in sun belt countries like Saudi
Arabia. With the SEGS plants’ commercial experience of >25 years in California, many new CSP plants
are operating today or are under construction in the Spanish and U.S. market. Figure 6-26 shows the
path of cumulated CSP capacity in the world market from 1984 to today. By the end of the construction
of the SEGS plants in California in 1991, 354 MW of CSP capacities had been installed in the Mojave
Desert. In the following years, the technology application stagnated for a period of 16 years due to the
lack of financial support mechanisms for this technology. But at the same time, technological progress
was accomplished by some new demonstration plants in the United States and Spain. Starting with
Nevada One (64 MW) and Andasol1 (50 MW) in 2006 and 2007, respectively, a new period of CSP
installations increased the worldwide capacity to 1,600 MW by the end of 2011. Although parabolic
trough technology represents the majority of all projects, the first larger solar tower and linear Fresnel
power plants are being installed. In Figure 6-26 the market development over each of the last four years
for smaller technologies is presented.
Since 2006, >30 new projects have been implemented in the Spanish and U.S. electricity markets. In
countries like Australia, Algeria, Morocco, Egypt, and India, first projects could be implemented on a
large scale. Parabolic trough technology is particularly widely developed, with a large share of
integrated storage solutions. Solar tower projects (>50 MW) in Spain and the United States, and linear
Fresnel (30 MW) in Spain also have their first completed, commercial projects.
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Figure 6-26: Cumulated Installed Capacity of CSP Projects by Technology Since 1984 (Fraunhofer ISE)
The global project pipeline of CSP projects increased under the framework of extensive support
incentives, like feed-in tariffs in Spain or tax credits in the United States (Figure 6-26 and Figure 6-27).
Concessional financing by multilateral banks helped to finance the first projects in North Africa. By the
end of 2012, a cumulated CSP capacity of 2,650 MW operated in different electricity markets.
Furthermore, 2,000 MW are under construction and were expected to be commissioned by 2013. In
addition, >16 GW of CSP projects have been announced and are in different planning stages in countries
from Australia and China to North Africa and Europe. But the largest market in the medium term is
expected to be the United States, if the recent trend to switch from CSP projects to PV projects remains
limited. With increasing PV and wind growth in the United States, the questions of grid stability and the
importance of storage possibilities will receive more and more attention, favoring CSP. At the moment,
there are no incentives for CSP with storage in the United States.
As parabolic trough plants gained the status of a commercially bankable technology, due to the longterm operation in California, this technology has the highest share of new projects worldwide (up to
16,000 MW), mainly in the Spanish and U.S. market. But some large projects with central receiver
technology (e.g., solar towers) will develop in the United States (up to 4,000 MW), as well. In Spain, a
first CSP Fresnel project with 30 MW is under construction. At the same time, development of a Fresnel
plant with >200 MW is ongoing in Australia. In contrast, technological and economic barriers affected
the Dish-Stirling development in the United States, and many projects have been canceled or switched
to PV. In total, the market is expected to grow to >22 GW by 2015 or 2016.
However, threats to stronger market development exist due to two important reasons:
1. Unexpected slow market development in the United States and financial barriers have limited
the cost reduction of CSP technology and its penetration in the world market.
2. Competitive PV costs in the U.S. market have resulted in a considerable switch of CSP projects
to PV projects. Because of the large global PV market size of 26 GW, newly installed capacity by
2011 and a fall of system prices well below 2,000 €/kWp, the produced electricity by PV became
cheaper compared to CSP.
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Figure 6-27: Project Pipeline of CSP and Technology Distribution (Fraunhofer ISE)
The expected installations up to 2020 in the MENA region are presented in Figure 6-28, which indicates
the positive outlook for CSP in all countries. Currently, Saudi Arabia is undeniably emerging as the
country of highest opportunity for CSP. In the MENA region, many other countries have a technical
potential for CSP similar to Saudi Arabia. However, because of the stability and financial advantage of
Saudi Arabia, the attention of the solar market is diverting towards Saudi Arabia. In addition to the
international attention, the country is putting much effort in seizing the opportunity to enter the CSP
market. With a new 25 GW solar thermal power plant, the largest project in the MENA pipeline, Saudi
Arabia is becoming more and more the center of CSP developers.
Figure 6-28: Maximum CSP Market Expectation for North Africa (estimated by Fraunhofer ISE, considering all
existing development plans [e.g., Moroccan Solar Plan, Egypt’s RES strategy, Desertec projects])
6.2.2.3
Technology Evaluation and Outlook
Table 6-3 presents a SWOT analysis of CSP technologies. Parabolic trough is the dominating CSP
technology at the moment, a situation that will probably remain unchanged for some time to come. The
biggest advantage of parabolic trough is that it is a long-term proven technology. Some parabolic trough
plants have been operational for 25 years. Through integration of a storage system, the capability of
supplying dispatchable power has been demonstrated. This results in good bankability, which is an
important factor when developing CSP or other large-scale projects. Also, the manufacturing processes
for parabolic troughs, in terms of mass production and standardized components, are more advanced
compared to those of the other CSP technologies, especially given the large number of parabolic trough
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plants currently under construction or in planning. However, because of its advanced state of
development, further cost reduction potential for parabolic trough technology might not be as high as
for linear Fresnel or solar tower technology. Parabolic trough technology includes many sophisticated
components (e.g., parabolically bent mirrors, flexible joints, vacuum receivers). Because of their high
technological standard, these components are also more expensive compared to the other CSP
technologies and mostly provided by only a few companies. With a further growth of the CSP market,
more companies might specialize in the production of these parts, thus increasing the market
competition and, ultimately, lead to lower component costs.
Table 6-3:
Solar
Strengths
thermal
Parabolic • Commercially used for
trough
25 years
• Large industry
capacity and
experiences
• Standardization of
design
• Mass production of
components
Fresnel
• Flat mirrors
• Efficient land use
• Power block similar to
parabolic trough
• Direct steam
generation
• Superheated soon
expected
Solar
• Flat mirror systems
tower
• High concentration
• High temperature
potential
SWOT Analysis of CSP Technologies
Opportunities
Threats
• Large number of
• Cost reductions
announced projects
not satisfactory
• Scalability up to 300
MW
• Higher temperatures for
other HTF under
research
• First project of 30 MW
ongoing
• Upscaling to 300 MW
• Lower efficiency
is not
compensated by
lower costs
• Higher efficiencies
possible by
temperatures. ≤900–
1,000 °C (with air as
HTF)
• Combined cycle with
HTF air
• High cost reduction
possible by cheap mirror
production
• Problems with
storage systems
• Durability of
receiver
technologies
• Many competing
concepts
• Upscaling difficult
Weaknesses
• Mirrors and receiver
technologies are of
high standard and
cost
• Temperature of HTF
(oil) is currently
limited to 400 °C
• Flexible joints are
costly
• Lower optical
efficiency
• Small industry
capacity
• Lower optical
efficiency
• Little commercial
experience
• Restricted tower
height to 80–120 m
• Limited maximum
achievable
temperature
because of current
HTFs
The increase of market share is one of the main challenges the other two main CSP technologies have to
face—they are currently underrepresented in the CSP market. With only a few plants in operation, both
the linear Fresnel and solar tower technologies must demonstrate that they can be competitive with
parabolic trough in the coming years to increase their market shares. Linear Fresnel has the advantage
of a simpler and lighter construction, using, for example, a stationary absorber, flat mirrors, fewer
structural materials, and a simpler tracking mechanism. An additional advantage of the linear Fresnel
technology is that it is capable of direct steam generation in the field. Hence, higher temperatures and,
therefore, higher efficiencies of the thermal cycle can be achieved. Furthermore, the steam generated
in the solar field can be fed to the power block directly without requiring expensive and loss-afflicted
heat exchangers.
The overall efficiency of linear Fresnel collectors, however, is lower than the parabolic trough collectors
(10 percent to 30 percent depending on location, layout, design, and so forth). Also, cost-effective
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storage integration for direct steam generation has not been demonstrated in the field so far, but some
promising concepts are being developed. The use of molten salt as HTF is already a reality in solar tower
plants. Combined with a high concentration factor, temperatures up to 560 °C can be reached, making it
one of the main technology advantages. With alternative HTFs (air or particles), even higher
temperatures of up to 1,100 °C are possible. These concepts, however, are still in the demonstration
phase. The solar tower’s use of flat mirrors and the absence of costly mirror field piping also provide
good opportunities for cost reduction and low LCOE. In addition to its low commercial experience, solar
tower technology faces challenges with its receiver technology, which is not yet optimized.
Furthermore, field sizes for one tower are restricted by the optical accuracy of heliostats at a large
distance and also by the height of the receiver tower.
6.2.3
Wind
The use of wind power has a long history. All concepts for wind turbines convert the kinetic energy of
the wind into rotating energy, which is then either converted to electric energy or used directly to drive
mills or similar heavy equipment.
6.2.3.1
Technology Description
Structure and Functioning of Modern Wind Energy Converters
Modern wind energy converters (WECs) usually are designed with a horizontal axis and three rotor
blades, using the lift principle. Figure 6-29 shows the structure of a common WEC. It consists of the
following elements:
•
•
•
•
•
Rotor with blades, aerodynamic brake, and
hub
Drive chain including the rotor shaft, rotor
bearing, gearbox (if present), and generator
Yawing with the yaw bearing and yaw drive
between the nacelle and the tower
Tower and foundation
Electrical components for the control and grid
connection.
The rotor including the blades converts wind
energy into mechanical rotation energy. This
energy is then converted into electrical energy,
using the generator within the nacelle. However,
as Betz stated in his law, the maximum proportion
of the usable wind energy amounts to 59 percent,
or, otherwise stated, the maximum power
coefficient (cp) of a wind turbine equals 0.59.
Because of additional losses, modern WECs reach
a cp of 0.5. Those losses are mainly due to profile,
tip, and spin losses, as well as the efficiency of the
generator (Gasch, et al., 2007)(Hau, 2003).
For onshore WECs, the foundation usually consists
of concrete and steel. If possible, so-called shallow
foundations are used. In case of softer soils, deep
foundations are required. Those are usually pile
foundations.
Two basic tower concepts are currently in the
market. First, the tower can be made of a steel
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Figure 6-29: Illustration of the Design of a Wind
Energy Converter (Hau, 2003)
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CHAPTER 6: Integration of Renewable Energy
structure. Second, the tower can be made of concrete, which is commonly a precast concrete tower
that is delivered in ready-built segments to the site. In this case, the tower is composed of individual
concrete segments, which are assembled on the site to ring segments. These are then placed atop one
another, glued, and stressed. Generally, the last section, where the nacelle is mounted, is a steel
section. In Figure 6-30, different tower types are illustrated. In general, higher hub heights can be
reached with a concrete tower (Gasch, et al., 2007), which is preferable, as the wind speed increases
and turbulence decreases with growing distance from the earth’s surface (Hau, 2003). As the generated
power of a turbine rises with the cube of the wind speed (i.e., an increase in wind speed of 10 percent
leads to a 33 percent rise in the power output), higher hub heights directly lead to a significantly
increased power output (Hau, 2003)(Gasch, et al., 2007). Because the tower is around 25 percent to 30
percent of the WEC’s cost, the tower height is always a compromise between technical and economic
factors. At sites with very strong winds, hub heights will not be as high, because an economically
sufficient energy yield can be achieved at lower heights and a lower tower means reduced project cost.
At sites with lower annual wind speeds, a tendency for taller towers is observed, because the resulting
higher hub height allows reaching sufficient full-load hours to achieve economic feasibility.
The rotational speed of a WEC is about 6 to
20 rpm. To ensure that the electricity
generated by the WEC has the same
frequency as the grid electricity, there are
two options: gear drive and direct drive. The
first uses a gear box to reach the necessary
rotational speed of 500 to 1,800 rpm at the
generator. This has the disadvantage of
energy loss due to friction, more noise
emissions,
and
higher
maintenance
requirements. The second requires a
multipole synchronous generator and
frequency converter to reach grid frequency.
Because of the number of poles, this
generator is larger and heavier, so a larger
nacelle and rotor are needed as well. This
Figure 6-30: Schematic Illustration of Different Tower Types
can be compensated for by generators with
(Hau, 2003)
permanent magnets. However, these require
rare earth elements (Schoßig, 2011).
The rotor blades are usually produced of glass-fiber-reinforced plastics, but coal fiber and partial
application of aluminum also are considered with growing blade lengths. When wind speeds exceed the
rated speed, an overload has to be prevented. This is realized by either a pitch or stall control. The pitch
control varies the pitch angle of the blade and, therefore, controls the emerging lifting force. The stall
control uses a specific blade design that leads to stall once a certain wind speed is reached. Pitch
control currently is the dominant concept in commonly used WECs, because it allows operation at
higher wind speeds and a more variable control.
Recently, systems have been developed that allow the provision of ancillary services by wind turbines.
In Germany, these are already mandatory to support grid stability and to reduce the weakening effects
of fluctuating electricity feed-in.
Typical Power Plant Configuration
Wind turbines can be installed as single turbines, but often several turbines are combined at a wind
farm. Usually, wind farm layout development is conducted after the site is selected. The development
requires considering existing project constraints, such as maximum and minimum installed capacity, site
boundaries, and environmental and logistical constraints.
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An individual site assessment is essential for each project, because the WEC’s position within the wind
farm significantly affects the overall energy yield of the farm. Generally, the minimum distance between
two WECs is a multiple of the rotor diameter. Furthermore, the internal wake effect has to be assessed
to avoid underperformance of certain turbines. The wake effect refers to wind deceleration behind a
turbine as energy is converted to electricity by the WEC. The wind needs a certain distance to increase
its velocity again. Within this wake, with reduced velocity, the kinetic wind energy is lower. Therefore, if
a wind turbine is placed in this area, it would generate considerably less energy than a turbine at a
farther distance. The wake effect is also influenced by the topography and roughness of the ground.
Thus, simulation programs are used to determine the ideal turbine position within a wind farm to
achieve the highest possible energy yield. If the selected site has a pronounced preferred wind
direction, distance along this direction will be larger (i.e., the turbines can be installed in lines behind
each other). Without a preferred wind direction, an ideal farm layout would use equidistant positioning
of the turbines (Kaltschmitt, 2010).
Depending on the existing grid situation, the individual WEC or wind farm is connected to the grid via a
substation where the injected energy is transformed to the grid’s voltage level, which usually is medium
or high voltage. The operation of wind farms can then be controlled and monitored remotely. Each WEC
has a Supervisory Control and Data Acquisition (SCADA) system that collects the operational data, such
as energy production, power, wind speed, frequency, power factor, or current. These data can be used
to monitor the performance, detect problems, invoice the client buying the electric energy, and
schedule maintenance, to name the main activities. It also offers a number of control functions to the
client; these, however, are limited by the manufacturer of the turbines, which usually supplies the
SCADA as well (Hau, 2003). For the SCADA to function, all WECs must be connected by fiber-optic cables
to the communication system, because fiber optics have the highest rates of data transmission, the
highest data capacity, and are least prone to external noises (Bailey & Wright, 2003). Those are laid with
the electric cables in the cable ducts.
Logistics Assessment
Logistics for wind farms require profound knowledge and specialized equipment, because almost all
parts are heavy or oversized cargo because of their dimensions, which place a number of requirements
on the logistics, particularly the blades, with their lengths between 25 and 50 m each; tower segments;
and the nacelle, with a weight of around 60 tons.
For transport, all roads must be able to accommodate oversized and heavy cargo. Therefore, access
roads have to be constructed or widened in many cases to be suitable for the heavy machinery, the
transport vehicles, and the crane. Moreover, the dimensions of the blade, nacelle, and tower segments
require that the curve radii be not too narrow and the slopes not too steep. Usually, heavy transports
only go up a slope of 10 percent. However, some good wind sites are on top of a mountain ridge with
very steep and narrow access roads, which constitute a special challenge for the transport of the
machinery and WECs. In many cases, the accessibility of potential wind sites is even more difficult in
less-developed countries, determining the profitable limit for the WEC site (Hau, 2003). For
international projects, the WECs not only have to be transported from the plant to the site but also
often have to be shipped to the respective country, discharged from the ship, loaded into a storage area
in the port, and then reloaded onto the trucks for transport. For this kind of operation, a certain port
size and equipment with sufficient capacity are required. Furthermore, the port access must allow
transporting the large parts out of the port to the storage area, which must be large enough to store all
parts.
Electricity Generation
Electricity generation depends on the availability of good wind resources. The fluctuating nature of
wind leads to fluctuating wind energy output. Depending on the wind site, there might be a certain
generation pattern, such as higher wind speeds in the afternoon or a variation in wind speeds during
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the season. However, one cannot state generally applicable generation curves, as is possible for PV or
CSP.
As experience has been gained with wind technology, however, the technology has been developed
further. Now, at excellent sites, almost 3,000 full-load hours can be reached. At offshore wind farms,
even 4,000 full-load hours have been reached. The achievable electricity generation not only depends
on the wind speed but also on turbulence intensity. If gusts of wind reach speeds above the rated wind
speed, the WEC switches off. Depending on the turbine type, the rated wind speed ranges between 25
and 30 m/s.
In the case of Saudi Arabia, Rehman and Ahmad (2004) assessed wind speeds and, thus, generation
patterns, in different regions of Saudi Arabia. From the results it can be concluded that the electricity
generation pattern tends to have a diurnal peak in the afternoon and a seasonal peak in June and July,
or no pronounced seasonal peak at all. In conclusion, at several locations in Saudi Arabia, wind power
shows a considerable conformity, with demand peak during summer months. Thus, it might significantly
contribute to cover the rising electricity demand in the Kingdom.
Cost Breakdown of Typical Wind Farms
Figure 6-31 and Figure 6-32 give an overview on the cost structure of wind farms. It can easily be seen
that the WEC accounts for the largest investment share, with almost 77 percent. The turbine cost
usually includes the turbine itself, as well as the logistic cost and that of the electrical components. A
detailed breakdown is shown in Figure 6-32. The foundation and civil works include road works on the
access roads, as well as all necessary civil works on site. The grid connection consists of all electrical and
grid components required and, if necessary, a substation. Project management includes insurance,
administration, and financing costs. Because no large wind farms have been installed yet in Saudi
Arabia, the cost data are based on international price data and global experience from installed wind
parks. Because WEC is very capital intensive, labor and installation costs only play a minor role in the
procurement of wind farms. The operation and maintenance costs are fairly low, with 0.03 US$/kWh.
Figure 6-31: Cost Breakdown of a Wind Farm (calculation based on Hau (Hau, 2003) and Blanco (Blanco, 2009))
Looking at the turbine cost breakdown in Figure 6-32, it can be seen that tower and blades play the
major roles in investment cost. This is due to their material intensity and which materials are used: steel
or concrete and glass fiber. Moreover, the blade production is still hardly automated and, thus, quite
labor and knowledge intensive.
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3.44%
26.3%
0%
10%
Tower
22.20%
20%
Blades
30%
Rotor
40%
Gearbox
8.65%
50%
Generator
12.91%
60%
5.23%
9.56%
70%
Electrical works
80%
11.71%
90%
Control System
100%
Other
Figure 6-32: Cost Breakdown of a Wind Turbine With a Capacity of Approximately 2 MW
(calculation based on Blanco (Blanco, 2009))
6.2.3.2
Global Market
In 2012, the global installed wind power capacity reached 282.5 GW, representing an annual average
growth rate of about 22 percent during the last decade. Figure 6-33 shows the development of global
wind power installations during that time. It can be seen that annual installations reached 45 GW in
2012. Since 2009, China has been the largest wind power market in terms of cumulated installed
capacity and the second largest market in terms of annual installation rates. It is closely followed by the
United States, which has an installed capacity of 60 GW and had 13 GW newly installed capacity in 2012
(GWEC, 2013).
The wind market has experienced a global shift from European markets, especially Denmark, Germany,
and Spain—locations of the first WEC manufacturers—towards American and Asian markets. Those
tend to be geographically larger, with the United States and China as markets in North America and
Asia. Installation rates mostly have been driven by support for renewable energies. Denmark, pioneer in
wind power technology and one of the first countries with high installed capacities, still is the country
with the highest share of wind power generation in the total energy mix (GWEC, 2013).
Annual installed wind power [GW]
45
40
Global annual installed
wind capacity
Global cumulative installed
wind power
35
300
250
200
30
25
150
20
100
15
10
Cumulative wind power [GW]
50
50
5
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
0
Figure 6-33: Global Wind Market Development Between 1996 and 2012 (GWEC, 2013)
Within the MENA region, installed capacities are limited. The largest wind market presently is Egypt,
with an installed capacity >500 MW, as illustrated in Figure 6-34. In MENA countries not mentioned in
the graph, there are no wind turbines installed. For Saudi Arabia, this is the case as well.
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Total installed wind capacities [MW]
CHAPTER 6: Integration of Renewable Energy
600
500
400
300
200
100
0
Tunisia Ethiopia
Egypt Morocco
Iran
Cape
Verde
Other
Figure 6-34: Installed Wind Power Capacities in the MENA Region
The turbine manufacturer industry was initially rooted in Europe, especially Denmark and Germany.
Vestas, a Danish manufacturer, still is the market leader. However, in recent years, Asian
manufacturers, in particular, such as Sinovel, Goldwind, Suzlon, United Power, and Mingyang
WindPower, have gained market shares (Figure 6-35).
Vestas
13%
Others
21%
Sinovel
9%
Mingyang
WindPower
4%
Goldwind
9%
Siemens
6%
Gamesa
8%
Guodian United
Power
7%
Suzlon
7%
GE
8%
Enercon
8%
Figure 6-35: Global Market Shares of Wind Turbine Manufacturers (Cleantech magazine, 2012)
The WEC supply market is well developed. Because of the capital-intensive manufacturing process and
required knowledge for blade production, market entry for new suppliers has proven rather difficult.
6.2.3.3
Technology Evaluation and Outlook
In contrast to the presented solar technologies, there is less technological variance in wind power
converter concepts. Although there are turbines with and without gear and synchronous and
asynchronous generators, the effect of technological differences on the actual energy generation
pattern is fairly low. For WECs, there is a mature global market that has proven reliability and cost
competitiveness in many locations. Table 6-4 shows a SWOT analysis of current wind power technology.
WECs are already able to provide system services that support a stable grid operation. Current trends
show that many manufacturers tend to improve their WECs to achieve higher efficiencies at lower wind
speeds. This will allow making locations with lower wind speeds commercially viable for wind farms.
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Saudi Arabia might especially benefit from this development, because most locations have rather
limited wind resources. Therefore, improved WEC concepts will allow profitable wind farms to be
installed at more locations. Because wind farms are already able to provide grid services, they can
support stable grid operation. In addition, no harmful or toxic elements are used and WECs can be
dismantled rather easily at the end of their lifetime.
Table 6-4:
SWOT Analysis for Wind Power Technology in Saudi Arabia
Strengths
Wind
Weaknesses
• Commercially used for >25
• Limited locations
years
with high wind
speed in Saudi
• Very cost efficient
Arabia
• High efficiency
•
High
fluctuation in
• Supported grid services to
electricity
support stable grid operation
generation
• Wide international experience,
• Very thorough site
mature technology
assessment
• No harmful or toxic elements
necessary
used
• Electricity production during
the night possible
Opportunities
Threats
• Grid stability is
• Reduction of CO2
emissions
perceived as a
problematic issue
• Expectation of
depending on
further cost
project size,
reductions
despite technical
• Integration of local
possibilities of
value creation
providing grid
• Improvement of
services
efficiency at lower
•
Grid extension
wind speeds
might be necessary
For Saudi Arabia, an obvious disadvantage of wind power is that there are few adequate locations with
high wind speeds (see Chapter 3.1). Combined with the very high solar radiation, it can be assumed that
wind will be used only as a complement to solar electricity generation but might not become the
leading technology in the Kingdom. However, the complementary nature of wind and solar, as well as
the long-term experience in wind power, are strong advantages of wind power. Wind can generate
electricity at any time, including at night, depending on wind resources, whereas solar is limited to day
time. In many countries, wind power is already cost competitive with conventional power plant
technologies and provides a significant share of electricity. Installation of WECs usually allows an easy
integration of local value creation during the construction and operation phase. Depending on the
market size, local value creation might even be enhanced by locally producing WEC components such as
steel towers. Although wind farms are able to provide grid services and are used for this purpose, the
prejudice that wind farms lead to an unstable grid is very common in new markets. Decisionmakers
tend to exclude wind farms, because of these prejudices, posing an obstacle to implementing new
projects that has to be considered at early planning stages.
Considering the long-term experience that exists on the international market and the currently low
energy costs of wind power, wind power should be considered as part of the Saudi technology mix,
despite the identified threats and weaknesses cited in Table 6-4.
6.2.4
Hydroelectricity
Hydroelectricity means the generation of electricity by the use of hydropower. It has a high level of
reliability, high efficiency, and relatively low costs, and it is and will be the major renewable electricity
generation technology worldwide for a long time (IEA, 2012).
6.2.4.1
Technology Description
Structure and Functioning
Hydroelectricity is using the energy from the natural water cycle. River water is stored in a reservoir by
a dam. It is led through a large pipe (penstock) by sluice gates (Figure 6-36). At the penstock’s end, the
water flow makes a turbine spin, activating a generator to produce electricity. For further transmission,
the electricity is transformed to a higher output voltage in the transformer and fed into the grid. Behind
the turbine, the water is released from the power station.
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Figure 6-36: Schematic Illustration of Hydroelectric Power Generation (Etrical, 2014)
The extracted water power depends on the volume and on the height difference between the point of
flow from the source and the turbine (head). These two factors depend on climate factors such as
rainfall, as well as terrain characteristics (IEA, 2011).
Hydroelectric Power Stations
Generally, a distinction is made for hydroelectricity between run-of-the-river power stations, storage
power stations (impoundment dam), and pumped storage power stations. Storage power stations
(impoundment dam) are the most common type of hydroelectric power plant. A dam is used to store
river water in a reservoir. This reservoir can realize a steady output flow over natural fluctuations.
Depending on the reservoir size, it can retain months or even years of average inflows and can also
provide flood protection and irrigation services (IEA, 2012).
Run-of-the-river hydroelectric stations take the energy mainly from the available flow of the river. They
have a small or no reservoir capacity and no storage, or just a limited amount. Here, the water reservoir
is called pondage. The water flow is strongly dependent on weather factors.
Pumped-storage stations are used to supply high peak demands. They are used as large-scale grid
energy storage by putting water reservoirs at different elevations. During hours of low electricity
demand, water is pumped to a higher water basin and is released again through turbines during hours
of high electricity demand. Pumped storage currently represents 99 percent of on-grid electricity
storage (EPRI, 2010).
The world’s largest power plant in terms of installed power is the Three Gorges Dam in China. It has 26
turbines with a single-unit capacity of 700 MW, total installed capacity of 18,200 MW, and annual
power production of 84.68 TWh (Chincold, 2011).
Turbine Types
To generate electricity, there are different turbine types available. Turbines can be classified into
reaction turbines and impulse turbines. Impulse turbines use the kinetic energy of the water jet rather
than pressure change. The main impulse turbine is the Pelton turbine. The Pelton turbine is used for
high head and small flows (IEA, 2012).
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Reaction turbines use the pressure change due to the height difference to generate electricity in
combination with a generator. The most common type is the Francis turbine, which accommodates a
wide range of heads (20 to 700 m). Another reaction turbine is the Kaplan turbine, which is installed for
low heads but large flows. A schematic illustration of these three turbine types is shown in Figure 6-37
(IEA, 2012).
Figure 6-37: Pelton, Francis, and Kaplan Turbines (Voith Siemens Hydro Power Generation, 2014)
6.2.4.2
Global Market
Hydropower is contributing 16 percent (about 3,500 TWh in 2010) of the worldwide electricity
generation and about 85 percent of global renewable electricity. The largest electricity generation takes
place in China (694 TWh in 2010) and Brazil (403 TWh in 2010). In some countries, the share of
hydropower is nearly 100 percent (e.g., Albania and Paraguay (IEA, 2012)).
The World Energy Council sees hydropower potential already exploited to a high degree in Europe and
North America and still of significant potential in Latin America, Asia, and particularly in Africa. Average
levelized costs of hydroelectricity globally range for small hydroelectric plants (<10 MW) between 19
and 314 US$/MWh and for large hydroelectric plants (>10 MW) between 24 and 302 US$/MWh (World
Energy Council, 2013).
Technology Evaluation and Outlook for KSA
Hydroelectricity is the most common renewable power source, but it relies on nature’s water cycle.
Saudi Arabia has no natural rivers running into the sea. Because of a lack of lakes and rivers, nearly 70
percent of freshwater needs are supplied by water desalination. In addition, water is obtained from
underground water resources via drilled wells. To regulate surface water and control flooding, >300
dams are installed in KSA, with a reservoir capacity of about 1,300 million m3. However, these dams
cannot be used for electricity generation because of a lack of water inflow (Ouda, 2013). Therefore,
successful use of hydroelectricity is missing in Saudi Arabia (Aljarboua, 2009). In the future, seawater
could be used in pumped-storage plants in Saudi Arabia. A first plant with a turbine size of 30 MW and a
head of 136 m was installed in Okinawa Island, Japan, in 1999 (Deane, 2010).
6.2.5
6.2.5.1
Wave Energy
Technology Description
Wave-power devices are based on ocean surface waves, which are generated by wind passing over the
surface of the sea. The wave power is determined by its length, speed, and density. This energy could
be used for electricity generation, water desalination, or pumping of water. The first wave-energy
converter (wave EC) was patented in 1799 (S. Lindroth, 2011) and the first experimental wave farm
opened in 2008 in Portugal (Lima, September 23, 2008).
Many factors, including the method used to capture the energy of waves, the location, and the power
take-off system, categorize a wave-power device. About 200 different wave-energy devices are
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currently in development and testing (J. Hayward, 2012). In this study, the wave ECs will be classified
according to three different locations.
Shoreline Wave Energy Converters
“Shoreline energy converters are located entirely on shore. Shoreline devices have the advantages of
easier installation and maintenance. Furthermore, these devices do not require deep-water moorings
and long underwater electrical cables. These types of Wave Energy Converters are close to the national
electricity grid. However, low wave power in shallow water is one of the essential disadvantages for
shore mounted devices” (M. Fadaeenejad, January 2014).
Figure 6-38 shows an SDE device and Figure 6-39 illustrates the parts and water flow within the
shoreline wave ECs. It can be seen how the rising water level pushes the air stored underneath the
device through the wells turbine.
Figure 6-38: SDE Device (150 kW) (M.
Fadaeenejad, January 2014)
Figure 6-39: Oscillating Water Column Device (500 kW) (M.
Fadaeenejad, January 2014)
Near-Shore Wave Energy Converters
The near-shore wave ECs convert the wave energy from the near shore and are directly installed at the
seabed. Figure 6-40 and Figure 6-41 show different examples of installed near-shore wave ECs.
Figure 6-40: Archimedes Wave Swing Device (5–6
MW)(M. Fadaeenejad, January 2014)
Figure 6-41: Oyster Device (31.5 MW) (M.
Fadaeenejad, January 2014)
Offshore Wave Energy Converters
Offshore devices offer large energy fluxes with predictable conditions, but need more maintenance,
because of their components and to underwater electrical cables. Figure 6-42 shows the functional
scheme of offshore wave ECs.
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Figure 6-42: Wave Dragon (11 MW) (M. Fadaeenejad, January 2014)
6.2.5.2
Figure 6-43: Pelamis (0.75 MW): (M.
Fadaeenejad, January 2014)
Global Market
The wave power resources worldwide are estimated at around 2 TW (A. Saket, 2012). North America,
South America, Western Europe, Japan, South Africa, Australia, and New Zealand have significant wave
energy potential. Figure 6-44 shows the wave energy flux at various locations around the world.
Figure 6-44: Annual Average Wave Energy Flux (kW/m) of Wave Front (Ltd, 1990-1991)
6.2.5.3
Technology Evaluation and Outlook
Wave energy has a high power potential and will play a key role for sustainable development in the
coming years. Compared to wind and solar energies, power extraction from wave energy is predictable
(A. Angelis-Dimakis, 2011) and continuous over the course of a day (about 90 percent of the time
compared to 20 percent to 30 percent for wind and solar) (M.N. Sahinkaya, 2009). For instance, a farm
of 20 Oyster units could produce enough energy to power 9,000 homes (Hawai, 2005). Therefore, wave
energy appears to be one of the promising energy sources for some countries.
For Saudi Arabia, the annual average wave energy flux is about 17 kW/m (Ltd, 1990-1991). A massive
wave energy project in Saudi Arabia would not be practical, mainly because of its peninsular geography.
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6.2.6
6.2.6.1
Geothermal
Technology Description
Geothermal energy is using the thermal resources from the earth’s interior. Potential energy is stored in
either hot rock or reservoirs of steam and/or hot water. Depending on the characteristics of the well or
other means that produce hot fluids of steam, there are basically three different types of turbine design
operating in geothermal power plants (Goldstein, 2011).
If the well provides vapor of 240 to 300 °C, it can be directly piped into the steam (condensing) turbine
and produce electricity. This so-called dry-steam plant is the simplest way of using geothermal energy,
and makes it the pioneer technology in this branch. Power plants using this technology have been
producing electricity for 100 years.
A second approach, also using a steam-condensing turbine, exploits liquid-dominated reservoirs with
temperatures >150 °C. In “flash-steam” power plants, hot water is pumped out of the ground and
depressurized into steam before it powers a turbine.
In the third approach, called binary-cycle plants, reservoirs with low- and intermediate-temperature
geothermal fluids of 70 to 170 °C are depleted. These plants are more complex, because the geothermal
fluid is passing through a heat exchanger that heats another working fluid with a lower boiling point.
Typically, these are organic Rankine cycle units, or sometimes Kalina cycle power plants.
Because of concern about pollution (hydrogen sulfide) and dehydration of the well, a new generation of
power plants tries to recycle the pumped water and inject it into the well again. This makes the whole
process even more complicated than just using binary-cycle plants, because a proper knowledge of the
fracture network underground has to be obtained to guide the water-flow well between injection and
production wells.
The so-called enhanced geothermal systems (EGS) are still in the demonstration and experimental
stages (UCSUSA, 2009)(Joseph N. Moore, 2013)(Goldstein, 2011).
6.2.6.2
Global Market
The International Geothermal Association reported an online geothermal production capacity of 11.25
GWe in 2011, which resulted in an annual energy production of 69,370 GWh (IEA-GIA, 2013). About onefourth of this capacity, as well as the energy production, are originated in the United States, followed by
the Philippines, Indonesia, and Mexico (Goldstein, 2011). Nevertheless, geothermal energy accounts for
only 0.3 percent of the worldwide electricity production. In Iceland, however, geothermal energy
provision accounts for 66 percent of the primary energy supply and 27.3 percent of power generation
(IEA-GIA, 2013).
Installation costs range between 50 and 300 US$/MWhe for steam-condensing turbines, binary-cycle
plants cost 50 to 100 US$/MWhe, and EGS reaches 100 to 300 US$MWhe (IEA, 2011).
Actual growth is generated by exploiting wells not deeper than 3 km. Despite that, however, the highest
potential for geothermal energy is in the untapped thermal resource underlying most continental
regions at depths varying from 3 to 10 km (Joseph N. Moore, 2013).
6.2.6.3
Technology Evaluation and Outlook for KSA
Currently, geothermal energy is not used for electricity production in KSA (Shafiqur Rehman, 2005). But
the Saudi government has included geothermal energy in its renewable energy development plan,
constituting an increase in its renewable energy production capacity to 54 GW in 2032, in which
geothermal should account for 1 GW (KA-CARE, 2012)(GRC, 2013).
The major resources for geothermal energy are located at the west coast of the country, where the Red
Sea Rift separates the African from the Arabian tectonic plates (Caglan, 2012).
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This result is confirmed by an analysis of Landsat red-green-blue images. It also states that the Wadi AlLith is the most promising geothermal system in western Saudi Arabia. Its tectonic activity and
structural regime, as well as its reservoir temperature of 136 °C (Ain Al Harrah), make it the most
promising target for geothermal exploitation in Saudi Arabia (Mohamed T. Hussein, 2013)
What makes the subject of geothermal energy very interesting to Saudi Arabia (besides being a
relatively new technology, especially if it comes to drilling >3 km deep and exploiting very hot
reservoirs), is that Saudi companies have the technical experience in exploration and drilling, sea water
treatment, and injection and reservoir management, due to decades of operations in the oil and gas
industry (Aljuhani, 2012).
6.2.7
Biomass
Biomass refers to all biological materials derived from living or recently living organisms, which may
become a source of energy by combustion, after methanization or further chemical transformations.
Different biomass sources can be used to produce energy such as:
•
•
•
•
•
•
Forestry crops and residues
Agricultural crops and residues
Sewage
Municipal solid waste
Animal residues
Industrial residues.
6.2.7.1
Technology Description
Different processes can be used to convert raw biomass feedstock into a final energy product (Figure
6-45). Several conversion technologies have been developed to provide the energy service required
(e.g., heat, power, transport fuel).
Figure 6-45: Schematic View of the Wide Variety of Bioenergy Routes (E4tech, 2009)
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Biomass power plants, via direct combustion, produce heat or electricity, or can be used in combined
heat and power plants (Figure 6-46). They are fueled by agricultural and forestry wastes, such as corn,
straw, wheat straw, rice husks, and wood waste. To improve efficiency, the biomass power plants can
also be used in cofiring in combination with fossil fuels.
Figure 6-46: Diagram From a Biomass Power Plant (AESI, 2012)
The power plant size depends on biomass availability and is installed in a way that enables reduced rail
and shipping costs.
Bioethanol (starch and sugar crops) and biodiesel (oil crops, residual oils, and fats) are deployed in
several countries in the transport sector and are qualified as first-generation biofuels.
The second-generation technologies should use lignocellulose feedstock but are still immature and
need further development.
Further investment on oils produced from algae could improve the efficiency and sustainability of
bioenergy chains. This future generation could avoid food price increases and possibly indirect land-use
change, which are the problems of the current generation. A schematic illustration of these different
technologies is shown in Figure 6-47.
Landfill gas is generated during the natural process of bacterial decomposition of organic material
contained in municipal solid-waste landfills and can be used to produce electricity, heat, and fuel. The
main purposes of these projects are to treat methane gas so it can be used for electricity or upgraded to
pipeline (EPA, 2012).
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Figure 6-47: Development Status of the Main Technologies for Producing Biofuels for
Transport From Biomass (E4tech, 2009)
6.2.7.2
Global Market
This section discusses the share of the bioenergy mix (Figure 6-48) held by global primary biomass
demand (Figure 6-49).
Figure 6-48: Share of the Biomass in the World in the Primary Bioenergy Mix (IPCC, 2007)
In 2012, biomass contributed 10 percent of the global primary energy supply to reach approximately 55
EJ (IEA, 2012). It is the fourth largest source of energy in the world, following oil, coal, and natural gas.
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With 46 EJ, heating represents the majority of biomass use, and just 4.5 EJ of biomass primary energy is
consumed for electricity generation (IEA, 2012). In 2012, 6 percent to 7 percent of total global primary
energy was used for traditional biomass (wood fuels, agricultural byproducts, and dung burned for
cooking and heating purposes) compared to 3 percent to 4 percent for modern biomass the same year
(IEA, 2012).
Figure 6-49: Biomass to Energy Pathways (US Energy Information Administration, 2013)
This energy is mainly located in countries where production of organic waste is important (e.g., North
America and Western Europe) (Figure 6-50). With >30.7 percent of the global production, the United
States is the largest producer of electricity from biomass, followed by Germany and Brazil (Monde,
2009).
Figure 6-50: Main International Biomass for Energy Trade Routes (Junginger, 2008)
In industrialized countries, the total contribution of modern biomass is, on average, only about 3
percent of total primary energy, and consists mostly of heat-only and heat and power applications.
Many countries have targets to significantly increase biomass use, because it is seen as a key
contributor to meeting energy and environmental policy objectives.
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6.2.7.3
Technology Evaluation and Outlook
With its harsh, dry climate, its extreme temperatures, and its mostly uninhabited, sandy deserts, only
0.11 percent of the total land area is permanent cropland in Saudi Arabia (CIA, 2014). Given that small
share of cropland and the absence of forests (0 percent of total land area) (World Bank, 2014), Saudi
Arabia is ineligible for the use of most of biomass technologies.
The only biomass technology that Saudi Arabia could benefit from is the conversion of waste to energy
(Muhammad Sadiq Munfath Khan, 2013). Biomass is part of the national plan to reach a renewable
energy production capacity of 54 GW in 2032, with biomass accountable for 3 GW (KA CARE, 2012).
Because of its vast urbanization, its rapid industrialization, and a very high population growth rate,
Saudi Arabia is experiencing increased pollution and waste generation. The daily waste generation per
capita in the Kingdom is estimated to increase to 1.8 kg, which is considerably higher than its
neighboring countries (Zafar, 2013).
Given the high per capita waste production, the high degree of urbanization (>82 percent of the
population lives in urbanized areas) (CIA, 2014), and the low cost of biomass energy (e.g., 380 Saudi
Riyals/MWh for biomass, 1,080 Saudi Riyals/MWh for PV) compared to other renewable energy
technologies in Saudi Arabia (Muhammad Sadiq Munfath Khan, 2013), wastes-to-energy technologies
are an adequate means of electricity production.
Because of the high share of organic waste (70.16 percent) and its moisture content of 38.45 percent,
biological conversion (i.e., biochemical processes like anaerobic digestion and fermentation) seems to
be the most suitable technology for Saudi Arabia. This can lead to a potential biogas production of
3,420.50 million/m³/y if 70 percent of the organic waste is collected for biological conversion purposes
(Muhammad Sadiq Munfath Khan, 2013).
6.3 Renewable Energy Resources in Saudi Arabia
This section presents an overview of renewable energy resources in Saudi Arabia. Data, literature, and
previous studies were examined. A short discussion of wind and solar potentials in the Kingdom are
given; other renewable energies, like hydropower, and wave and tidal energies are mentioned only
briefly, because Saudi Arabia has such a dry climate. According to the focus of the KICP 2012/2013
annual study on energy efficiency and industrial energy supply, the potential for rooftop installation of
PV modules will be assessed in more detail. This is because PV is the most suitable technology for onsite electricity supply.
As discussed in the previous section, it can be concluded that hydroelectricity, geothermal, and wave
energies do not contribute to a significant renewable energy potential in Saudi Arabia and, therefore,
are not discussed in further detail.
6.3.1
Wind Power Potential in Saudi Arabia
Saudi Arabia’s wind resource has not been fully characterized. Several studies have been conducted to
determine the wind power potential in KSA. For example, Rehman et al. (2003) assessed various
locations in Saudi Arabia, their annual wind speeds, and the performance of different types of wind
turbines. They found that the two most suitable regions for the use of wind turbines are along the
coasts of the Red Sea and Arabian Gulf. According to Rehman et al., the region with the highest annual
wind speeds is Yanbu, with annual mean wind speeds >5 m/s at a height of 10 m, the equivalent of an
annual mean wind speed >6.8 m/s at a height of 80 m.
In a second analysis, Rehman and Ahmad (2004) demonstrated that the diurnal wind speed pattern
tends to have a peak in the afternoon. Most locations have lower wind speeds in winter (i.e., from
November until March). This corresponds well with the lower domestic electricity demand in that
period.
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The studies also showed that 2,000 full-load hours can be reached at good wind locations. However,
locations with 800 full-load hours also were assessed. Because the annual wind speed variation has not
been fully published, it can be assumed that with further technology developments, full-load hours at
the assessed locations can be increased. The determined value of 2,000 full-load hours indicates mean
annual wind speeds at hub height of around 6.5 m/s. Those are at the best available wind sites in Saudi
Arabia, corresponding to International Electrotechnical Commission wind class II. As stated, those are
located along the coast at the Red Sea and special research focus has been on Yanbu.
In 2013, an Internet wind atlas was developed that shows the wind speeds in KSA (Figure 6-51).
Average Wind
Speed (m/s)
Figure 6-51: Annual Average Wind Speed at 100m Height in Saudi Arabia (KACARE, 2013)
Annual average wind speeds in most of Saudi Arabia were between approximately 6.0 and 8.0 m/s that
year. The higher wind speeds ≥8.0 m/s occur in the northeast and central regions, as well as near
mountains in the western region. In October 2013, a pilot wind-monitoring mast 10-m tall was installed
on K.A.CARE’s city site outside of Riyadh (KACARE, 2013).
6.3.2
Solar
In this section, the solar potential is examined for PV in general, then for CSP, and then a detailed
analysis is presented of the rooftop PV potential of residential and industrial buildings in Saudi Arabia.
6.3.2.1
PV Potential in Saudi Arabia
The potential for PV technologies in Saudi Arabia is mainly dependent on the solar resources available
in the country. For PV, the global horizontal irradiation (GHI) is the relevant indicator. The GHI is the
total amount of radiation received by a certain area. It includes the DNI from the sun, as well as diffuse
irradiance.
The Red Sea area has a high solar resource potential. Figure 6-52 depicts the distribution of GHI in Saudi
Arabia. It ranges from 2,000 kWh/m² in the coastal region of the south and the northeast to about
2,500 kWh/m² in the south of the country’s central plateau.
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As Saudi Arabia is largely covered by deserts, space is not the primary constraint to the amount of PV
systems that could be installed. The magnitude of the PV potential in Saudi Arabia can be determined
by a rough estimation: With an average GHI of 2,200 kWh/m² on each square kilometer of land, roughly
85 GWh of electricity can be produced (considering module efficiency, performance ratio, and soil
losses [see Section 6.3.3], resulting in a conversion to electricity of 12 percent). Saudi Arabia currently
has an overall electricity demand of about 200 TWh/y (IEA, 2012, p. 54). Theoretically, a section of
2,400 km² would suffice to provide the electricity needed in the country, based on the gross annual
demand without taking into account differences in production and consumption time. The Rub' al Khali
desert alone has an area of 647,500 km², of which a large amount is on Saudi state property. Therefore,
it can be concluded that the PV potential is a multiple of the country’s energy demand.
Figure 6-52: Global Horizontal Irradiation in Saudi Arabia (GeoModelSOLAR, 2013)
However, not all regions are sufficiently connected to the grid, because of the absence of inhabitants in
desert areas (Figure 6-53). In these places, high grid connection costs may impede the installation of PV
systems. It can be assumed, therefore, that large PV systems first will be located in areas with high
irradiation and high grid connection proximity, such as on the southern plateau of Saudi Arabia.
Typically, PV roof installations are located in the areas where grid connection is already available or as
remote off-grid systems. The potential of PV roof installations is estimated in Section 6.3.3 of this study.
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Figure 6-53: Map of the Saudi Grid (GENI, n.y.)
6.3.2.2
CSP Potential in Saudi Arabia
The potential for CSP technologies in Saudi Arabia depends mainly on the solar resources available in
the country. For CSP, the DNI is the relevant indicator because CSP power plants concentrate direct light
to a receiver. Figure 6-54 shows the distribution pattern of the DNI in Saudi Arabia. It ranges from
<1,200 kWh/m² in the coastal region of the south to about 2,800 kWh/m² in the northwest of the
country. The DNI, on average, is higher than in most other countries in the Middle East and North
Africa. The DNI can be calculated through a simple equation, based on a clear sky model. To be more
accurate, a cloud index can be derived and added (Franz Trieb, 2009). However, it is preferable to do
on-site measurements for a more accurate result.
Like PV, CSP power plants, in most cases, are bound to grid connection and, therefore, will be located in
desert areas close to the existing grid to avoid high grid connection costs.
6.3.3
PV Rooftop Potential in Saudi Arabia
In this study, the solar resources of residential and industrial buildings’ rooftop areas are estimated.
Solar resources on open spaces are not regarded, because of missing information about the size of
available areas and their suitability for PV. For this assessment, manufacturing facilities are considered
as industrial (General Statistics and data authority, 2013). Furthermore, the solar potential is
differentiated among the 13 administration districts of Saudi Arabia. This allows for a more detailed
conclusion on the distribution of solar potential in different areas and cities.
The available PV potential for residential buildings is estimated by using the methodology developed to
calculate the rooftop potential of the building type discussed in Section 6.3.3.1. The methodology is
based on the number of buildings per region, the distribution of living space by housing type, and the
average number of floors of buildings. Thereby, the built-up area is calculated, which can be regarded
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as similar to the gross roof area, because of the high percentage of flat roofs in Saudi Arabia.
Subtracting areas unsuitable for PV from the gross roof area results in the net roof area.
Figure 6-54: Direct Normal Irradiation in Saudi Arabia (GeoModelSOLAR, 2013)
For industrial buildings (see Executive Summary), the built-up area is calculated via the number of
employees of each region, assumptions on the average floor space per employee, and the average
number of floors per building. By assuming typical area losses for industrial buildings, the net roof area
is calculated.
In both cases, the installable capacity is calculated from the net roof area, the efficiency of PV modules,
and the module surface, for which the optimal inclination for each region is determined. Finally, the
annual GHI of each region is used to calculate the potential electricity generation.
6.3.3.1
PV Rooftop Potential on Residential Buildings
To calculate the roof area of residential buildings, the estimation is based on KSA’s 2010 housing census
(General Statistics and data authority, 2010). The number of housing units in each administrative area is
given and separated into six housing types: apartment, floor in traditional house, floor in villa, villa,
traditional house, and other housing types (Table 6-5). According to “Socio-Economic Factors in the
Present and Future KSA Housing Market” (SocioEconomicEN.pdf; bibliographic data not available), the
housing type “other housing” consists of spaces in nonresidential buildings, like garages or warehouses,
as well as spaces in residential buildings without use (e.g., below stairs or on roofs). Hence, this housing
type is already included in the other five housing types or is not relevant for the solar rooftop potential.
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Table 6-5:
Number of Housing Units by Housing Type and Region (adapted from (General Statistics and data
authority, 2010))
Region
Al-Riyadh
Makkah Al-Mukarramah
Al-Madinah Al-Monawarah
Al-Qaseem
Eastern Region
Aseer
Tabouk
Hail
Northern Borders
Jazan
Najran
Al-Baha
Al-Jouf
Sum
Other
117,744
41,758
13,417
19,448
22,413
17,611
5,813
5,468
2,580
8,612
8,090
3,731
4,926
271,611
Apartment
434,533
719,305
158,064
43,925
287,402
88,530
61,762
13,380
9,810
26,793
23,776
23,298
20,670
1,911,248
Number of housing units
Floor in a
traditional
house
Floor in a villa
11,812
159,612
19,035
48,532
3,351
9,620
2,793
25,245
8,760
35,624
5,607
34,991
1,835
10,320
1,012
5,754
1,252
6,087
2,876
7,763
1,156
6,786
2,591
7,666
1,437
4,451
63,517
362,451
Villa
299,243
100,888
26,149
59,410
161,911
58,526
15,112
24,404
14,381
19,902
12,308
8,943
23,328
824,505
Traditional
house
132,819
399,322
98,570
51,752
103,175
130,800
38,314
44,205
8,598
133,679
33,234
28,998
15,364
1,218,830
To calculate the floor space by housing type and by region from the number of housing units, the
distribution of living space by housing type in Table 6-6 is adopted, whereby the mean of each floor size
class is used for calculation.
Table 6-6:
Distribution of Living Space by Housing Type (from: “Study of the National Housing Strategy in the
Kingdom of Saudi Arabia” [SampleSurveyEn.pdf]; further bibliographic data not available)
Distribution of living space, percent
Floor area, m²
<50
50–99
100–149
150–199
200–249
250–299
300–349
>350
Total, percent
Mean, m²
Apartment
Floor in a
traditional
house
25.0
74.5
124.5
174.5
224.5
274.5
324.5
400.0
3
32
39
16
7
3
0
0
100
6
34
24
20
9
6
1
2
100
Floor in a
villa
Villa
Traditional
house
6
34
24
20
9
6
1
2
100
0
3
7
13
22
18
16
21
100
6
41
29
13
10
1
0
0
100
It is assumed that the large majority of buildings in Saudi Arabia are equipped with flat roofs, so that the
built-up area of the houses is almost equal with the gross roof area. To get the built-up area for each
housing type by region, it is necessary to know the average number of floors for each housing type.
According to file plugin-SocioEconomicEN.pdf (p21ff), villas and traditional houses are estimated to
have two floors. For the housing types “floor in traditional house” and “floor in villa,” an average of 2.2
floors is assumed based on the “Study of the National Housing Strategy in the Kingdom of Saudi Arabia”
(SampleSurveyEn.pdf, p. 6; further bibliographic data not available). The same source states an average
of 6.4 housing units for apartment buildings. The assumption that the medium Saudi apartment building
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has two apartments on each floor leads to a mean value of three floors for this building type. With the
given data and presented assumptions, the gross area of the different housing types is calculated.
Table 6-7 shows the results according to region and building type. It becomes obvious that the regions
of Al-Riyadh and Makkah Al-Mukarramah make up the highest share of the gross roof area: 26.7
percent and 24.6 percent of the total roof area, respectively. The building type “villa” accounts for >38
percent of the roof area, while 27.6 percent is located on apartment buildings and 24.5 percent on
traditional houses.
Table 6-7:
Gross Roof Area per Region and Building Type
Gross roof area by building type, m²
Region
Al-Riyadh
Makkah AlMukarramah
Al-Madinah AlMonawarah
Al-Qaseem
Eastern Region
Aseer
Tabouk
Hail
Northern Borders
Jazan
Najran
Al-Baha
Al-Jouf
Sum
Apartment
Floor in a
traditional
house
Floor in a
villa
Villa
Traditional
house
Sum
18,108,000
29,975,000
760,000
1,224,000
10,263,000
3,121,000
40,152,000
13,537,000
7,705,000
23,167,000
76,987,000
71,023,000
6,587,000
215,000
619,000
3,509,000
5,719,000
16,648,000
1,830,000
11,977,000
3,689,000
2,574,000
558,000
409,000
1,117,000
991,000
971,000
861,000
79,647,000
180,000
563,000
361,000
118,000
65,000
81,000
185,000
74,000
167,000
92,000
4,085,000
1,623,000
2,291,000
2,250,000
664,000
370,000
391,000
499,000
436,000
493,000
286,000
23,306,000
7,971,000
21,725,000
7,853,000
2,028,000
3,274,000
1,930,000
2,670,000
1,651,000
1,200,000
3,130,000
110,630,000
3,002,000
5,986,000
7,588,000
2,223,000
2,565,000
499,000
7,755,000
1,928,000
1,682,000
891,000
70,710,000
14,607,000
42,541,000
21,741,000
7,606,000
6,832,000
3,309,000
12,226,000
5,081,000
4,513,000
5,261,000
288,375,000
To quantify the suitable area for PV installations (net roof area), unusable areas for PV installations are
subtracted from the gross roof area. A large number of Saudi buildings have a balustrade around the
roof enclosing the walkable part of the roof. For low sun altitudes, the balustrade causes shadows on
the roof that diminish the electricity output of PV modules in the shadowed area. Because a PV
installation in a frequently shadowed area is not suitable for PV, those areas are subtracted from the
gross roof area. In other words, only areas that are irradiated at a sun altitude of 30° are taken into
account. Because there are buildings with balustrades 2m high, as well as buildings without balustrades,
an average height of 1 m is assumed for the calculation. At a sun angle of 30°, the shadow of a 1-m wall
reaches a length of 1.73 m. Therefore, the resulting shadowed area on the roof is left out when
installing PV modules, as shown in Figure 6-55. The shadows occur at the eastern, southern, and
western sides of the roof area, because of the course of the sun during the day and throughout the
year. Compared to the average roof size of each building type, this distance leads to a PV area reduction
of 42 percent for apartment buildings, 40 percent for “floor in traditional house” buildings, “floor in
villa” buildings and villas, as well as 58 percent on traditional houses, if the roof is assumed to be square
(Table 6-8).
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Irradiation
East
Shadow
1.73m
30°
Balustrade
North
Roof
South
East
House
West
West
Figure 6-55: Shadowed Areas on a Roof (top view and side view) With a 1m Balustrade at a Sun Altitude of 30°
in the East, South, and West
Table 6-8:
Average Roof Size per Building Type and Area Losses Owing to Wall Shadows
Average roof area, m²
Area loss due to balustrade shadow, percent
Apartment
125
42
Floor in a
traditional house
140
40
Floor in a
villa
140
40
Villa
134
40
Traditional
house
58
58
Because most houses in Saudi Arabia have flat roofs, PV modules have to be mounted with an
inclination to optimize the energy output from the incident irradiation. The optimal module inclination
depends on the latitude of the location and is calculated with PVGIS software (European Commission,
Joint Research Centre Institute for Energy and Transport, 2012) for the capital of each administration
district. Depending on the inclination angle of the modules, a certain distance between the module
rows has to be kept to avoid mutual shadowing of the rows. This space cannot be used for electricity
generation, so the corresponding area has to be subtracted from the remaining area. The results are
shown in Table 6-9
Table 6-9:
Optimal Module Inclination and Resulting Area Losses Owing to Distances
Between Inclined Module Rows
Region
Al-Riyadh
Makkah Al-Mukarramah
Al-Madinah Al-Monawarah
Al-Qaseem
Eastern Region
Aseer
Tabouk
Hail
Northern Borders
Jazan
Najran
Al-Baha
Al-Jouf
6-50
Optimal inclination angle
24°
22°
25°
25°
25°
20°
27°
26°
28°
18°
20°
19°
28°
Losses due to distances between rows, percent
44
41
45
45
45
39
47
46
48
36
39
37
48
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Parts of the remaining roof area are shadowed by staircases and elevator shafts, or cannot be used
because the space is required for ventilation pipes. Therefore, a further 5 percent is deducted from the
roof area. Air conditioners and water tanks can be mounted in the shaded area between the balustrade
and the PV modules. This is not possible for satellite dishes, because the balustrade hinders the
required reception of the satellite signal. Therefore, another 5 percent must be subtracted. Figure 6-56
displays the reductions from the gross roof area, as described. The resulting losses are considerably
higher than in countries such as Germany. The approach is rather conservative to avoid overestimating
the existing potential.
20-35%
Gross
roof area
Net roof area
2.1-3.6%
18.4-24.5%
Satellite dishes,
ventilation pipes,
shadows of staircases
and elevator shafts
100%
Distances between
module rows
40-58%
Shadows of
balustrade wall
Figure 6-56: Reductions From Gross Roof Area to Calculate the Net Roof Area Suitable for PV Installations
Table 6-10 gives an overview of the resulting net roof area by region and building type. The results are
similar to the gross roof area, with Al-Riyadh, Makkah Al-Mukarramah, and the Eastern Region as
regions with the largest roof areas. Villas are the building type with the largest roof area, followed by
apartment buildings. Because the losses due to balustrade walls are very high for traditional houses,
this building type makes up only 19 percent of the net roof area, compared to 24.5 percent of the gross
roof area.
For the calculation of the installable capacity, the module inclination has to be considered, because this
increases the area compared to the covered roof area. To calculate the module surface, the net roof
area for each region is divided by the cosine of the optimal inclination angle (Table 6-9). Based on the
available module area, the installable capacity can be calculated. Hereby, a module efficiency of 15
percent is assumed, which is equal to a specific capacity per area of 150 Wp/m². The results for the
regions and building types are shown in Table 6-11. The sum of the resulting installable capacity for
residential buildings in Saudi Arabia is 13.4 GWp.
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CHAPTER 6: Integration of Renewable Energy
Table 6-10: Net Roof Area Suitable for PV by Region and Building Type
Net roof area by building type, m²
Region
Apartment
Al-Riyadh
5,382,000
Makkah Al-Mukarramah
9,283,000
Al-Madinah Al-Monawarah
1,918,000
Al-Qaseem
533,000
Eastern Region
3,488,000
Aseer
1,191,000
Tabouk
720,000
Hail
159,000
Northern Borders
112,000
Jazan
376,000
Najran
320,000
Al-Baha
320,000
Al-Jouf
236,000
Sum
24,038,000
Floor in a
traditional
Floor in villa
house
233,000
3,154,000
392,000
999,000
65,000
186,000
54,000
489,000
170,000
690,000
120,000
751,000
34,000
192,000
19,000
109,000
23,000
111,000
64,000
174,000
25,000
146,000
57,000
168,000
26,000
81,000
1,282,000
7,250,000
Villa
12,197,000
4,285,000
1,044,000
2,373,000
6,466,000
2,591,000
579,000
955,000
540,000
919,000
545,000
404,000
877,000
33,775,000
Traditional
house
1,654,000
5,181,000
1,203,000
631,000
1,259,000
1,769,000
449,000
528,000
99,000
1,887,000
450,000
401,000
176,000
15,687,000
Sum
22,620,000
20,140,000
4,416,000
4,080,000
12,072,000
6,423,000
1,974,000
1,771,000
885,000
3,420,000
1,485,000
1,350,000
1,396,000
82,032,000
The electricity generation is the result of the product of module surface, annual irradiation per surface,
efficiency, and performance ratio reduced by losses due to shadowing and sand and dust on modules.
This calculation can be summarized in the following formula:
where:
WE,R
AMod, R
IR
η
PR
L
=
=
=
=
=
=
WE,R = AMod,R ∙ IR ∙ η ∙ PR ∙ (1 − L)
Annual electricity generation of the region
Module surface of the region
Annual irradiation of the region
Module efficiency
Performance ratio
Electricity losses due to dirt and shadowing
Table 6-11: Installable Capacity on Residential Buildings in Saudi Arabia by Region and Building Type
Installable capacity by building type, kWp
Region
Al-Riyadh
Makkah Al-Mukarramah
Al-Madinah Al-Monawarah
Al-Qaseem
Eastern Region
Aseer
Tabouk
Hail
Northern Borders
Jazan
Najran
Al-Baha
Al-Jouf
Sum
6-52
Apartment
883,649
1,501,743
317,456
88,219
577,218
190,130
121,156
26,552
19,031
59,315
51,062
50,787
40,099
3,926,416
Floor in a
traditional
house
38,325
63,406
10,738
8,950
28,071
19,213
5,743
3,204
3,875
10,158
3,961
9,012
4,448
209,103
Floor in a
villa
517,868
161,662
30,826
80,895
114,153
119,898
32,300
18,218
18,841
27,420
23,253
26,662
13,777
1,185,773
Villa
2,002,622
693,170
172,832
392,670
1,070,149
413,645
97,558
159,375
91,812
144,996
86,989
64,156
148,933
5,538,906
Traditional
house
271,558
838,205
199,040
104,501
208,338
282,431
75,566
88,198
16,770
297,543
71,761
63,555
29,967
2,547,432
Sum
3,714,020
3,258,186
730,891
675,235
1,997,930
1,025,317
332,323
295,547
150,329
539,432
237,026
214,172
237,223
13,407,631
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CHAPTER 6: Integration of Renewable Energy
The annual irradiation per square meter in each region is given by Meteonorm (Meteotest
Genossenschaft, 2010), by the GHI of its capital, and is listed in Table 6-12. The annual irradiation
ranges from 2,043 kWh/(m²a) in the eastern region to 2,521 kWh/(m²a) in Najran.
Table 6-12: Annual Global Horizontal Irradiation (GHI) in the Capital of Each Region
Region
Al-Riyadh
Makkah Al-Mukarramah
Al-Madinah Al-Monawarah
Al-Qaseem
Eastern Region
Aseer
Tabouk
Hail
Northern Borders
Jazan
Najran
Al-Baha
Al-Jouf
Capital
Riyadh
Makkah
Madinah
Buraidah
Dammam
Abha
Tabouk
Hail
Arar
Jazan
Najran
Al-Baha
Sakakah
GHI, kWh/(m²a)
2,193
2,194
2,326
2,337
2,043
2,459
2,306
2,438
2,061
2,213
2,521
2,472
2,165
Because of the very high temperatures in Saudi Arabia’s desert regions, the common performance ratio
of 0.84 (Reich, et al., 2012) has to be reduced by 10 percentage points (Ibrahim, et al., 2009) and is
calculated with 0.74 in the present case. A second problem of PV in desert regions is that the module
surface becomes covered with dust and sand. Without cleaning of the modules, the energy generation
of the modules can be reduced by 35 percent after one year (Ibrahim, et al., 2009). To avoid high losses,
monthly cleaning of the modules is considered for further calculation. It is assumed that the reduction
of energy generation is about 10 percent after one month, so the average monthly loss is 5 percent.
Because parts of the installed modules are shadowed at least for some hours of the day, another 5
percent is subtracted. The result of this calculation is a cumulated potential electricity generation of
19.90 TWh/y on residential buildings in Saudi Arabia. The results for the individual regions and building
types are displayed in Table 6-13. It can be seen that the highest electricity generation can be achieved
for the building type “villa.” The highest regional potential can be found in Al-Riyadh and Makkah AlMukarramah. The two regions together make up >50 percent of the electricity generation potential.
Table 6-13: Potential Electricity Generation per Region and Building Type
Region
Apartment
Al-Riyadh
1.293.918
Makkah Al-Mukarramah
2.200.176
Al-Madinah Al-Monawarah
493.040
Al-Qaseem
137.696
Eastern Region
787.683
Aseer
312.225
Tabouk
186.625
Hail
43.225
Northern Borders
26.200
Jazan
87.647
Najran
85.972
Al-Baha
83.830
Al-Jouf
57.975
Sum
5.796.212
Volume 1
Building type, MWh/a
Floor in a
Floor in a
Traditional
traditional house
villa
Villa
house
56.118
758.309 2.932.419
397.639
92.895
236.848 1.015.551
1.228.039
16.677
47.876
268.425
309.128
13.969
126.264
612.896
163.110
38.306
155.776 1.460.347
284.303
31.550
196.893
679.273
463.799
8.847
49.754
150.276
116.399
5.216
29.658
259.453
143.581
5.335
25.937
126.396
23.087
15.011
40.517
214.254
439.665
6.669
39.150
146.462
120.822
14.875
44.009
105.896
104.904
6.431
19.918
215.324
43.326
311.899 1.770.910 8.186.972
3.837.803
Sum
5.438.402
4.773.510
1.135.146
1.053.936
2.726.415
1.683.742
511.900
481.133
206.956
797.094
399.074
353.514
342.973
19.903.795
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6.3.3.2
PV Rooftop Potential on Industrial Buildings
Similar to the roof area calculation for residential buildings in the preceding section, the methodology
and results for industrial buildings are presented in this section. The roof area of industrial buildings is
calculated for the manufacturing sector in each region. The branches included in the manufacturing
sector are as follows (General Statistics and data authority, 2010):
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Manufacture of food products
Manufacture of beverages
Manufacture of tobacco products
Manufacture of textiles
Manufacture of wearing apparel
Manufacture of leather and related products
Manufacture of wood and of products of wood and cork
Manufacture of paper and paper products
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of products and pharmaceutical preparations
Manufacture of rubber and plastics products
Manufacture of other nonmetallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer, electronic, and optical products
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles, trailers, and semi-trailers
Manufacture of other transport equipment
Manufacture of furniture
Other manufacturing.
To estimate the roof area, the number of employees per region is used (General Statistics and data
authority, 2013) (Table 6-14).
To determine the floor area from the number of employees, the average floor area per employee
(specific floor space) is estimated, which takes into consideration office buildings as well as production
and storage buildings in this sector. Because of the lack of Saudi data on this topic, the present
calculation assumes a specific floor area of 76 m² per employee as an estimate (average based on
Schlomann et al (Schlomann, et al., 2009)). To calculate the built-up area from the floor area, an
average of 1.5 floors is assumed. Corresponding to the assumption for residential buildings, the built-up
area is assumed as equal to the gross area of flat roofs, which leads to the gross roof area for each
region, as shown in Table 6-14. As for residential buildings, the highest gross roof area is available in the
region of Al-Riyadh.
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Table 6-14: Industrial Gross Roof Area, Net Roof Area, Installable PV Capacity, and Potential
Electricity Generation by Region
Region
Employees
Gross roof
area, m²
Net roof
area, m²
Installable
capacity,
kWp
Potential electricity
generation,
MWh/y
Al-Riyadh
200,361
10,152,000
4,872,000
799,941
1.171.346
Makkah Al-Mukarramah
152,887
7,746,000
3,874,000
626,673
918.127
Al-Madinah Al-Monawarah
36,166
1,832,000
862,000
142,606
221.482
Al-Qaseem
41,333
2,094,000
985,000
162,980
254.387
142,592
7,225,000
3,397,000
562,255
767.264
16,712
847,000
441,000
70,466
115.716
Tabouk
6,038
306,000
138,000
23,254
35.820
Hail
6,982
354,000
163,000
27,202
44.284
Northern Borders
6,498
329,000
146,000
24,749
34.072
Jazan
9,946
504,000
274,000
43,229
63.878
Najran
9,265
469,000
245,000
39,066
65.774
Al-Baha
4,081
207,000
110,000
17,466
28.829
Al-Jouf
4,074
206,000
91,000
15,517
22.434
636,935
32,271,000
15,598,000
2,555,404
3.743.412
Eastern Region
Aseer
Sum
Compared to residential buildings, the area losses of industrial buildings are smaller. The main reason
for this is that industrial buildings usually do not have a balustrade. Moreover, no satellite dishes are
assumed for this building type. The losses caused by the necessary module distances are the same as
for residential buildings (Table 6-14). Furthermore, losses for elevator shafts, ventilation pipes, water
tanks, and air conditioners are accounted for with 15 percent of the remaining roof area. The resulting
net roof area is shown in Table 6-14. The installable capacity for industrial buildings is calculated with
the same module efficiency (15 percent) as the capacity of residential buildings, resulting in potentially
installable capacity of 2.6 GWp. The assumptions for the potential electricity generation are the same as
those for residential buildings. The annual irradiation of each region given in Table 6-12, a module
efficiency of 15 percent, a performance ratio of 70 percent, and losses due to dirt (5 percent) and
shadowing (5 percent) of 10 percent are assumed. The regional distribution of the resulting energy
generation potential of 3.74 TWh/y is given in Figure 6-57. Here again, the two regions with the highest
potential, Al-Riyadh and Makkah Al-Mukarramah, make up >50 percent of the possible electricity
generation. Table 6-14 gives an overview of the total PV potential (residential plus industrial) in Saudi
Arabia.
Figure 6-57 gives an overview of the total PV potential, residential plus industrial, in Saudi Arabia. The
total capacity that could be installed is 15.96 GWp. The map clearly shows that the highest potential
currently exists in the most populated regions of the Kingdom. Compared to the current total electricity
demand, rooftop PV alone could contribute a share of 17.7 percent. Rooftop installation can be done
easily and can also enhance citizen participation because installing an own PV system generally
increases the awareness of energy consumption. Considering the already existing potential and taking
into account the increase in inhabitants and, thus, residential buildings, tapping the potential of rooftop
PV might prove very beneficial to energy system development.
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Northern
Borders
1.0 km²
175 MWp
241 GWh/a
Al-Jouf
1.5 km²
253 MWp
365 GWh/a
Al-Qaseem
5.1 km²
838 MWp
1,308 GWh/a
Hail
1.9 km²
323 MWp
525 GWh/a
Tabouk
2.1 km²
356 MWp
548 GWh/a
Al-Madinah
Al-Monawarah
5.3 km²
873 MWp
1,357 GWh/a
Al-Riyadh
27.5 km²
4,514 MWp
6,610 GWh/a
Makkah Al-Mukarramah
24.0 km²
3,885 MWp
5,692 GWh/a
Al-Baha
1.5 km²
232 MWp
382 GWh/a
Aseer
6.9 km²
1,096 MWp
1,799 GWh/a
Jazan
3.7 km²
583 MWp
861 GWh/a
Region
Net roof area [km²]
Installable capacity [MWp]
Potential electricity generation [GWh/a]
Eastern Region
15.5 km²
2,560 MWp
3,494 GWh/a
Najran
1.7 km²
276 MWp
465 GWh/a
Figure 6-57: Net Roof Area, Installable PV Capacity, and Electricity Generation Potential
by Regions (map adapted from (Dalet, 2013))
6.4 Economics of Electricity From Renewable Energies
6.4.1
Calculation of LCOE
The calculation of LCOE is carried out with the net present value method. The expenses for investment
and operation thereby are included in the calculation during the lifetime of the plant. All cost data are
calculated to the costs for the year 2012 in U.S. dollars. The total costs over the lifetime include the
investment and operation costs. The sum of all expenses is divided by the sum of the electricity output.
The following formula is used for the LCOE calculation of newly installed projects (ISE, 2012):
𝐿𝐶𝑂𝐸 =
where:
6-56
LCOE
I0
At
Mel
I
N
T
𝑛
A𝑡
(1
+ 𝑖)𝑡
𝑡=1
𝑛
M𝑒𝑙
�
(1
+ 𝑖)𝑡
𝑡=1
𝐼0 + �
in US$/kWh
=
investment in US$
=
annual total costs in US$
=
annual electricity output (kWh/year)
=
interest rate (discount rate)
=
economic lifetime in years
=
year of operation (1, 2,…n)
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The electricity output is discounted hereby, because it represents the future income generated by
selling the electricity. The annual total costs contain the fixed and variable operation costs for the
projects, maintenance, service replacements, and insurance. The share of profit sharing and profit
margin is considered in the discount rate by using the weighted average cost of capital (WACC) method.
It depends on the equity ratio, the profit sharing, the debt ratio, and the profit margin. Therefore, the
formula for the annual total costs in the calculation of LCOE (ISE, 2012) is:
Annual total costs =fixed operational costs +variable operating costs +(residual value, dismantling of
system)
With the described method, the LCOEs are calculated. The results are described in the following.
The LCOE method allows different technologies to be compared on a cost basis but does not represent
the actual electricity price that, for example, an independent power producer would charge when
selling renewable electricity. The seller’s price can only be defined by additional input parameters and
the specific project cost. LCOEs are not sufficient to determine a feed-in tariff either. Self-consumption,
tax laws, and realized incomes for the owner make it more difficult to calculate a feed-in tariff from the
LCOE results. Furthermore, the calculation of LCOEs does not consider the value of generated electricity
within an energy system in a certain hour of the year.
6.4.2
6.4.2.1
Assumptions for the LCOE Calculation
Assumptions for LCOE of PV Systems in Saudi Arabia
The financial assumptions for the calculation of LCOE of PV systems in Saudi Arabia are displayed in
Table 6-15. Two sizes of PV systems are examined: a large ground-mounted PV system with an installed
capacity of 20 MWp and a small rooftop PV system with an installed PV capacity of <10 kWp. For each
system size, an investment range is considered and used to calculate the possible range of LCOE in
Saudi Arabia. The range takes into account that project costs may vary due to specific project
conditions, chosen manufacturer, and others. Therefore, the investment range is between 1.69 and
2.34 US$/Wp for rooftop PV and between 1.17 and 1.82 US$/Wp for ground-mounted systems.
The costs are based on internal data and were calculated using data on local labor costs. For a PV
system in Saudi Arabia, from the data displayed in Table 6-15, the calculated WACC is 7.5 percent.
Table 6-15: Financial Assumptions for PV LCOE Calculation
Unit
Value
CAPEX – PV rooftop
US$/Wp
CAPEX – PV ground mounted
US$/Wp
OPEX
Insurance cost
Equity ratio
Profit sharing
Debt ratio
Profit margin
US$/kWp/y
% CAPEX/y
%
%
%
%
Low: 1.69a
Average: 2.02
High: 2.34
Low: 1.17
Average: 1.49
High: 1.82
40c
0.3d
36e
12e
64e
5e
CAPEX: capital expenditure; OPEX: operational expenditure.
a
Author’s calculation based on (ISE, 2012); b(EuPD, 2012); c(ISE calculation, 2012); d(Kurokawa, 2003);
e(Mansour, 2011).
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All other assumptions to calculate the LCOE are listed in Table 6-16. For specific energy yield, annual
full-load hours of 1,500, 2,000, and 2,500 are assumed to cover the complete range of available solar
irradiation at different locations in Saudi Arabia. Thus, the calculated LCOE will show the achievable
range for all locations, depending on the specific irradiation at the site. The lifetime is set to 25 years,
which is a commonly expected value. Because of aging of the cell and materials, a degradation of 0.2
percent per year is taken into account. Currently, inverters still have to be overhauled or replaced after
12 years. This is reflected in the cost assumption “inverter overhaul” and “residual value inverter.”
Table 6-16: Assumptions for PV LCOE Calculation
Lifetime
Degradation
Inverter overhaul
Residual value inverter
Energy production, low
Energy production, average
Energy production, high
a
Unit
Value
Years
% kWh/kWp/y
% of investment after 12 years
% of project cost
kWh/kWp/y
kWh/kWp/y
kWh/kWp/y
25a
0.2a
15a
10a
1,500
2,000b
2,500
(ISE, 2012); b(DLR, 2005)
6.4.2.2
Assumptions for LCOE of Concentrating Solar Power
The investment data, including financial data, are given in Table 6-17, and other assumptions for the
LCOE calculation are displayed in Table 6-18. The chosen CSP technology is parabolic trough and the
plant size is set to 100 MW, including eight-hour storage. The LCOE are calculated for parabolic trough,
because it is the most mature technology in terms of market penetration and cost. The specific capital
expenditure (CAPEX) ranges between 6,760 and 8,580 US$/kW depending on specific project conditions
and constraints.
Table 6-17: Financial Assumptions for LCOE Calculation of 100 MW CSP Plants (parabolic trough)
Unit
Value
CAPEX—100 MW CSP plant
US$/MW
OPEX
Insurance cost
Equity ratio
Profit sharing
Debt ratio
Profit margin
US$/kWh
% of CAPEX/y
%
%
%
%
Low: 6.760a
Average: 7.02a
High: 8.58a
0.03b
0.75c
36d
12d
64d
5d
OPEX: operational expenditure.
a
(IRENA, 2012); b(ISE, 2012); c(Trieb, 2005); d(Bank, 2011)
If the installed power of the CSP plant decreases to 50 MW, the specific CAPEX increases by about 14
percent (Agency, 2010), to 7,850 US$/kW. On the other hand, the specific CAPEX decreases by 20
percent (Agency, 2010), to 5,600 US$/kW, if the installed CSP capacity is doubled, compared to a 100
MW CSP plant. Because of the uncertainty of cost projections, the specific cost variations of the
different sizes of CSP plants are used for the 100 MW CSP plant analyzed in this study. Operational
expenditures (OPEX) are set to 0.03 US$/kWh. In addition, insurance costs (0.75 percent of CAPEX) are
higher than for PV, due to the more complex system of a CSP plant including the hot water production
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via parabolic trough and the power block. With the equity/debt ratio and equity/debt return (Table
6-18), the WACC is assumed as 7.5 percent for a CSP plant in Saudi Arabia.
Table 6-18: Assumptions for CSP LCOE Calculation
Capacity of CSP plant
Storage size of CSP plant
Lifetime
Degradation
Full-load hours
a
Unit
Value
MW
h
y
%/y
h/y
100a
8a,b
25b
0.2b
Low: 2,000
Average: 3,300a,b
High: 4,050
Author’s assumption; b(ISE, 2012)
The lifetime of a CSP plant is set to 25 years and the degradation equals those of PV systems with 0.2
percent of kWh/kW/y. A CSP plant achieves full-load hours of 3,300 to 4,050 h/y with a storage size of
eight hours and different operation of the storage. Therefore, on average, the CSP plant can supply
about nine hours of electricity at full load every day. According to Fraunhofer ISE (2012), with DNI of
2,000 kWh/m²/y, the total electricity generation in one year is set to 330 GWh. To demonstrate the
achievable range of LCOE depending on the operation mode of the CSP plant, LCOEs for 2,000, 3,300,
and 4,050 full-load hours are calculated.
6.4.2.3
Assumptions for LCOE of Wind Power
The assumptions necessary to calculate the LCOE of WECs are summarized in Table 6-19 and Table 6-20.
Table 6-19 includes the financial data. As can be seen, investment costs range between 1300 and 2300
US$/kW. Especially for wind, the price range is highly related to the site and the chosen WEC; WECs
with high towers and larger rotor diameter are more material intensive and usually have higher
investment cost. Costs also differ with concrete and steel prices, which are often purchased locally and
may vary significantly.
Table 6-19: Financial Data Assumptions for Wind LCOE Calculation
CAPEX, high value
CAPEX, average value
CAPEX, low value
OPEX
Insurance cost
WACC
a
Unit
Value
US$/kW
US$/kW
US$2012/kW
US$/kWh
% of CAPEX/y
%
1,300a,b,c
1,600a,b
2,300a,b
0.02a
0.75a
7.5a,d
(ISE, 2012); b(IEA, 2012); c(IRENA, 2012); author’s assumption
Because of the existing wind conditions in Saudi Arabia, LCOE are calculated for 1,100, 1,800, and 2,500
full-load hours (Table 6-20). The assumptions on the specific energy yield are based on the wind
resource assessment conducted by Rehman et al. (2003). Based on the wind measurements, the fullload hours given by Rehman et al. (2003) were adapted to recent technology development, achieving
more full-load hours at the same wind speeds, especially at wind sites that would not be classified as
excellent. The lifetime is set to 25 years. Experience has already proven that WECs are able to operate
even longer. Based on international experience, it can be stated that degradation does not apply to
WECs.
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Table 6-20: Technical Data for Wind LCOE Calculation
Lifetime
WEC
Degradation
Specific Energy production
a
Unit
Value
Years
Capacity, MW
Hub height, m
% kWh/kWp/y
kWh/kWp/y
25a
2.5
80
0a
Low: 1,100a,b
Average: 1,800a,b
High: 2,500a,b
(ISE, 2012); b(Rehman, et al., 2003)
6.4.3
LCOE of Renewable Energy Technologies in Saudi Arabia
Based on the presented assumptions, LCOEs have been assessed for the selected technologies. Figure
6-58 displays the results for all technologies at different, exemplary locations.
The LCOEs of PV systems vary between 0.186 and 0.112 US$/kWh for rooftop systems and between
0.113 and 0.09.2 US$/kWh for ground-mounted systems. At first, this seems quite high compared to
local oil-powered electricity. Taking into account the excellent solar resources in most locations in Saudi
Arabia, annual full-load hours of ≥2,000 can be achieved. Hence, LCOEs reach around 0.10 US$/kWh
(ground mounted) and 0.12 US$/kWh (rooftop).
Although Saudi Arabia does not have the best wind sites from a global perspective, there are quite a
few locations with good wind conditions. At sites with annual mean wind speeds >6 m/s at hub height,
>2,000 full-load hours can be achieved. This leads to LCOEs between 0.07 and 0.221 US$/kWh. At lower
hub heights or wind speeds, LCOEs vary between 0.221 and 0.133 US$/kWh for 1,100 full-load hours
and between 0.091 and 0.143 US$/kWh for 1,800 full-load hours. At excellent wind sites, allowing 2,500
full-load hours, LCOEs of 7.0 can be reached. Even in the case of high investment costs, LCOEs do not
exceed 0.109 US$/kWh.
Figure 6-58: LCOE of Renewable Energy Technologies in Saudi Arabia
CSP has the highest LCOE of all selected technologies. Depending on the full-load hours, the LCOEs
range between 0.449 US$/kWh and 0.185 US$/kWh. The higher LCOEs of CSP are due to significantly
higher investment costs, which are caused by the number of components required and the complex
technology and knowledge necessary for production and installation of the components. However, CSP
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has the advantage that energy generation is dispatchable and storage can be integrated easily. There is
a significant difference between plants with 2,000 full-load hours compared to 4,050 full-load hours.
With 2,000 full-load hours, the storage capacity is hardly used and, thus, the higher investment cost for
the storage has to be refinanced by fewer kWh.
6.4.4
6.4.4.1
Opportunity Cost
Savings Through Export
The Saudi electricity system relies highly on oil- and gas-fired power plants. By increasing the share of
renewable electricity generation, oil and gas can be saved.
Given that Saudi Arabia is one of the main exporters of oil, the saved oil from nonproduced electricity
can present a large opportunity cost if this oil is to be exported. With a spot price of 106.94 US$/barrel
(Energy economics, 2013), Saudi Arabia can make additional revenues of up to 100 US$/barrel. Figure
6-59 shows the cost of extraction, capital expenditure, and the opportunity costs of saved oil (i.e., not
used for domestic energy production but for export) in KSA.
Figure 6-59: Cost of Extraction and Opportunity Cost of Oil in KSA (Energy economics, 2013)(REUTERS, 2009)
The same concept can be applied to gas production and export. Assuming an export price of 15
US$/MBtu and exploration and liquefaction expenditures of 2 US$/MBtu, Figure 6-60 shows the
opportunity cost that can be saved from exporting the liquefied natural gas (LNG) instead of using it to
generate own electricity.
Figure 6-60: Cost of Exploration, Production, and Liquefaction of LNG in KSA (Economides, 2005)(DoE, 2005)
(DoE, 2005)(Foss, 2012)
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The costs of exploration, production, and the liquefaction process need to be subtracted from the
export price of LNG free-on-board (FOB) in Saudi Arabia to achieve the actual opportunity costs for
saving natural gas. This results in opportunity costs for saving natural gas of about 8 to 13 US$/MBtu.
The following section directly compares the renewable energy alternative with fossil fuels, including the
presented opportunity costs.
Because of the price increase of fossil fuels in recent decades, Saudi Arabia can achieve high income by
exporting oil and gas. Currently, these fossil resources are used to a large extent for national electricity
production. If electricity is provided by renewable energy technologies, fossil resources can be reserved
for exports. To take the opportunity cost of exporting fossil resources into account when comparing the
cost of electricity production, the lost revenue from not exporting oil or gas is included in the cost of
fossil-based electricity production. Applying the LCOE method to oil and gas power plants, this leads to
the LCOE shown in Figure 6-61. It becomes clear that considering the opportunity cost of oil, PV already
is a competitive technology in KSA, and the same applies to wind power.
Concluding, it can be said that especially PV and wind farms are already considerable energy
alternatives for Saudi Arabia. Although their LCOE seems to be higher than the LCOE of electricity from
local oil- or gas-fired power plants, considering the opportunity costs clearly shows that renewable
energies might prove very beneficial in the Saudi environment. The demand scenarios developed in
Chapters 1 and 5 of this project demonstrated that it will be crucial to reduce domestic oil production to
maintain revenues from oil export.
6.4.4.2
CO2 Emission Reduction
In this section, Saudi Arabia’s CO2 emissions are analyzed from 1971 until 2050. As previously stated in
the study, Saudi Arabia relies mostly on oil for its energy production. In addition to oil, 12 percent of the
energy comes from gas power plants. Gas production emits 518,000 kg of CO2 per GWh
(Forschungsstelle für Energiewirtschaft e.V., 2010). Burning oil for energy generation causes emissions
of 859,000 kg/GWh (Forschungsstelle für Energiewirtschaft e.V., 2010).
Figure 6-61: LCOE of Renewable Energies Compared to Oil and Gas With Opportunity Costs
According to the results of Chapter 1, there are two main scenarios that are analyzed in this section.
The first scenario is the business as usual (BAU) scenario. In this scenario, the main electric generation
stems from oil and gas power plants; 53 percent is generated by oil plants. In the scenarios, an expected
installed capacity of 185 GW is forecasted to cover an expected primary energy consumption of 4,948.5
TWh.
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The second scenario, defined as the renewable energy scenario, includes 97.6 GW of renewable energy
generation capacity in 2050, representing 38 percent of the total generated energy.
Based on the BAU scenario defined in Chapter 1 (Figure 6-62) and the specific CO2 emissions stated
previously for the different power plants, CO2 emissions of electricity generation are displayed (Figure
6-63). If the 53 percent of electricity generation continues to come only from oil and 47 percent from
gas, by the year 2050, around 850 TWh will be generated through conventional power plants. In this
scenario, the total emissions can reach 594 million tons of CO2 emissions in 2050.
200
installed capacity [GW]
180
160
140
120
100
80
60
40
20
0
1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040
oil plants
gas plants
Figure 6-62: Past Development of Installed Capacity of Oil and Gas Power Plants in KSA and
Future Development in the BAU Scenario
Figure 6-63: Historical Data for CO2 Emissions in KSA and Future Development for the BAU Scenario
If the renewable energy scenario is implemented, almost 324 TWh are generated by renewable
energies (Figure 6-64). According to this scenario, 13.8 GW of wind, 21.2 GW of nuclear energy, 38.3
GW of CSP, and 24.4 GWp of PV will be installed in 2050. This will save a large amount of emissions in
the country. If the rest of the 850 TWh is generated from conventional resources, as it is today (57
percent from oil and 43 percent from gas), a total of around 227 million tons of CO2 can be saved per
year.
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200
installed capacity [GW]
180
160
140
120
100
80
60
40
20
0
1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040
oil/gas
wind
nuclear
CSP
PV
others
Figure 6-64: Renewable Energy Scenario
Assuming that Saudi Arabia would participate in emissions certificate trading, the potential for
additional revenues would be good. In recent years, CO2 prices have been ranging between 4 and 22
US$/tCO2. For that reason, the average value of 13.3 US$/tCO2 is assumed in this calculation.
In the renewable energy scenario analysis, it was calculated that around 147 million tons of CO2 can be
saved through reduced oil generation and around 78 million tons of CO2 from reduced gas generation
(Figure 6-65). Considering an average price of 13.3 US$/tCO2, an annual revenue of around US$3,010
million can be reached by selling CO2 certificates.
Figure 6-65: CO2 Emissions for the Renewable Energy Scenario
6.5 Case Studies for Renewable Energy in Saudi Arabia
After having analyzed the technical rooftop potential for PV on different building types and having
assessed the LCOE and opportunity cost, two specific PV applications will be examined in more detail.
The first application is a PV-driven RO desalination plant. Because the Kingdom has a very dry climate
and scarce water resources, water desalination is an important business in the country. At the moment,
desalination is mostly driven by thermal processes powered by fossil fuel plants. However, in the future,
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other technologies, like RO, might be an option, because they can be powered easily by PV. Therefore,
in a case study, the technical and economic aspects of such an application will be analyzed.
The second case study examines a cotton factory that decides to install a PV system on its roof. This
case will illustrate the cost of PV as well as the amount of energy that can potentially be substituted.
6.5.1
Case Study: A PV-Driven RO Desalination Plant
There are three main technologies for water desalination: MSF, multiple-effect desalination (MED), and
RO. RO has the lowest energy consumption, with an electrical energy demand between 2.5 and 7
kWh/m³ and no thermal energy requirements. Both MSF and MED have an electrical consumption
similar to or slightly lower than RO, but they have thermal energy requirements (Table 6-21). A simple
system setup is possible by combining PV electricity generation with an RO desalination plant.
Therefore, such a system was selected for the case study.
Table 6-21: Energy Consumption of Different Desalination Technologies (Trieb, et al., 2007)
Technology
MSF
MED
RO
Energy consumption
Thermal, kWhth/m3
70–92
40–108
–
Electric, kWhel/m3
3–5
1.5–2.5
2.5–7
Technically, the complexity of running the RO plant by a PV system is not high. The only alteration
compared to conventional operation would be the power source of the plant. Instead of having the
plant connected to the grid to get electricity, the PV system can supply the electricity for the plant. A
high use rate of the desalination plant reduces the cost per unit of water produced. Therefore, 24-hour
operation is the most economic solution for the RO plant. Because PV electricity is generated only
during the day, a PV- and grid-connected RO plant was chosen for this case study. The reference RO
plant has a capacity of 100,000 m³/d and is assumed to have an electricity requirement of 4.3 kWh/m3
(Fichtner, 2011). Using satellite solar data of Saudi Arabia, it is calculated that an 80 MW PV plant with
1,840 full-load hours per year (corresponding to an availability of 94 percent) can theoretically supply
the annual demand of the reference RO desalination plant, disregarding time-related differences in
supply and demand. According to this calculation, to supply a 100,000-m³/d desalination plant, the cost
of the 80 MW PV plant would very roughly be US$130 million.
The assumptions for the RO desalination plant are summarized in Table 6-22.
Table 6-22: Assumptions on the Reverse Osmosis Desalination Plant (Fichtner, 2011)
Operation
360
days
Operation
Specific electricity consumption
Plant capacity
Amount of electricity required per day
Amount of electricity required
PV installed capacity
24
4.3
100,000
430,000
154,800,000
84.1
hours
kWh/m3
m3/d
kWh/d
kWh/y
MW
The electricity costs for the RO plant are the LCOE as calculated in Section 6.4. The calculation of the
LWPC is similar to the calculation of the LCOE and, therefore, the net present value method is used.
The cost assumptions for the calculation of the LWPC are defined in the following. A data overview is
given in Table 6-23.
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Table 6-23: RO Reference Plant for the Calculation of LWPCs (Fichtner, 2011)
CAPEX (200,000 m³/d)
CAPEX (100,000 m³/d)
OPEX without electricity costs
Electricity demand
Lifetime, y
Availability, %
Equity ratio, %
Equity return, %
Debt ratio, %
Interest on debt capital, %
WACC, %
1,180 US$/(m³/d)
1,320 US$/(m³/d)
0.36 US$/m³
4.3 kWh/m³
25
94
36
12
64
5
7.5
The average LWPC in 2013 was 1.10 US$/m³ with a range of 1.06 to 1.18 US$/m³ for small plants and
1.02 to 1.16 US$/m³ for large plants (Figure 6-66). The share of the CAPEX is 30 percent; the OPEX
without and with electricity is 33 percent and 37 percent, respectively. With the variation and resulting
range of the PV LCOE of the CAPEX, the LWPC for a small and a large PV–RO desalination plant also has
a similar range. Regarding costs of similar desalination plant sizes, but with an MSF technology (Figure
6-66), the short-term water production costs from RO are higher.
According to the LWPC from PV–RO and the costs of a conventional MSF plant, the MSF plant is more
cost-effective at the current stage. However, if the maintenance cost, the fossil fuel prices, and the
operation costs are taken into consideration, the PV-powered RO plant will be the more sustainable
solution. Because the fossil fuel prices are increasing rapidly and the availability is not guaranteed, the
safer choice is to have the most valuable resource, water, from a reliable power source. In addition, as
mentioned previously, the opportunity costs from oil and gas will have a large effect on the economy
and feasibility of the desalination plants.
Figure 6-66: LWPCs of Small and Large PV-Powered RO Plants
6.5.2
PV Electricity Supply for Industry
To show an example for the application of PV systems on industrial buildings, a case study for a fictional
cotton factory located in the region of Makkah Al Mukarramah is conducted. The company has 520
employees and a flat roof area of 26,350 m². Its total annual electricity consumption reaches 41,700
MWh. The installable PV capacity (i.e., the PV potential of the factory) is calculated based on the
assumptions given in Section 6.3.3. For the PV modules, an optimal inclination angle of 22° is assumed.
To avoid shadowing the modules, they are positioned in equally distanced rows. This leads to an empty
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area between the individual rows, which results in a remaining area for PV of 59 percent of the total
roof. Furthermore, construction on the roof (e.g., elevator shafts, ventilation pipes, water tanks, air
conditioners) causes losses of 15 percent. These assumptions result in an area of 13,180 m² available for
PV, which is equal to a module area of 14,210 m². Considering a module efficiency of 150 Wp/m² (equal
to 15 percent at standard test conditions), an installable PV capacity of 2,132 kWp is calculated.
Regarding the electricity generation of the installed PV system, an irradiation data set from Meteonorm
(Meteotest Genossenschaft, 2010) is taken as a basis for the calculation. The annual electricity
generation is calculated for every hour using the hourly irradiation, the available module area, the
module efficiency of 15 percent, a performance ratio of 74 percent, and losses due to shadowing (5
percent), as well as a loss of 5 percent due to dust and sand on the modules. The resulting annual
generation is displayed in Figure 6-67. Figure 6-68 shows the weekly generation for one week in spring.
The cumulated annual electricity generation equals 3,123 MWh.
To determine an actual economic value of electricity generation, it is necessary to know whether it can
be consumed directly or sold to the grid operator. Because the latter option does not exist in Saudi
Arabia, the present case study only looks at the self-consumption of PV electricity generation. To selfconsume the electricity, it is necessary that generation and consumption match in time. Thus,
generation and consumption have to be compared for each hour. For the consumption, the factory’s
load profile is required.
Figure 6-67: Electricity Generation of the Regarded Plant in Hourly Solution for One Year
Figure 6-68: Electricity Generation of the Regarded Plant per Hour for One Week in Spring
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Because actual load profiles of Saudi companies are not publically accessible, two load profiles are
scaled to the consumption of the regarded factory. The first profile is a standard load profile for German
manufacturing companies. It is chosen because the temporal course of the electricity consumption
within a working week is assumed to be comparable to Saudi factories. Nevertheless, there is a
difference between the seasons. While the German electricity consumption rises in winter, caused by
heating and additional lighting, the Saudi consumption peaks in summer, when demand for cooling is
highest. Therefore, the standard load profile of German companies is adapted to the Saudi conditions
by shifting it by half a year. Then demand in summer is higher, while it is lower in winter. The second
profile is a profile of a cotton factory in the MENA region. It shows a continuous consumption during
each month with variable use by the company in the different months of the year (Figure 6-69).
Figure 6-69: Comparison of Electricity Generation and Consumption Load Profiles.
(Orange: Scaled standard load profile for German manufacturing companies; Green: Scaled idealized load
profile of cotton factory)
As can be seen in Figure 6-70, the electricity generation of the installed system does not exceed the
consumption in any hour of the year, either for profile 1 or for profile 2. This means that for both cases,
all the electricity produced by the PV system is self-consumed by the factory. Therefore, a feed-in of
surplus electricity to the grid is not required, nor is it necessary to dump energy. The electricity
generation suffices to supply 7.5 percent of the total electricity demand. Thus, the factory’s external
electricity supply can be reduced. Because of the factory’s high energy intensity in this case, PV can
contribute only a small amount of the necessary electricity. Nevertheless, it should be considered that
the share of self-supply from PV can be higher for less energy-intensive facilities. This can help them
become more independent of the grid supply and avoid outages. The recently introduced industrial
electricity tariff includes a higher rate from 12:00 a.m. until 5:00 p.m., when overall demand, and thus
grid load, reaches its daily peak. This tariff only applies for companies with digital meters. Because the
solar irradiation and, thus, PV electricity generation also peaks during this time, PV can reduce the
demand of high-priced electricity and contribute to peak shaving of the company’s load curve. Figure
6-70 shows the reduced demand of electricity from the grid for the regarded factory on a working day
in April. The electricity, which has to be supplied by the grid in the hours with the highest electricity
price (12:00 a.m. to 5:00 p.m.), is diminished by 11.4 percent, because of self-supply by PV.
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Figure 6-70: Reduced Electricity Demand Owing to PV on a Working Day in April
At this point, the question of the cost of self-supply arises, which can be answered by calculating the
LCOE. The LCOE calculation is based on the assumptions made in Section 6.4.2.1. With a rather large
installable capacity of 2,132 kWp, the CAPEX is considered to be 1.69 US$/Wp, whereby a quantity
break is supposed with increasing system size. Figure 6-71 shows a cost breakdown of the total CAPEX
of US$3,600,000. Furthermore, electricity production is calculated to be 3,123 MWh/y, as stated.
Ensuing from these assumptions, the LCOE is 0.1485 US$/kWh.
Figure 6-71: Cost Breakdown of the CAPEX for the Regarded System
From a macroeconomic perspective, the LCOE of PV not only must be compared to the electricity price,
but also to the opportunity cost of fossil fuel electricity generation. To calculate the opportunity cost for
electricity generation by fossil fuel, the amount of oil and gas required to produce 1 MWh electricity has
to be determined. Assuming that grid electricity is generated with the average Saudi energy mix, 53.3
percent is produced by oil power stations and 46.7 percent by gas power stations (GIZ, 2013). With an
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efficiency of 40 percent for gas plants (UBA, 2009) and 35 percent for oil plants (author’s estimation),
for 1 MWh electricity, 1,168 kWh gas and 1,523 kWh oil are required. This energy amount corresponds
to 0.932 of barrel oil plus 3.99 MBtu gas per MWhel. Based on the assumptions in Section 6.4.4, the
opportunity cost of oil is 100 US$/barrel and the opportunity cost of gas is 13 US $/MBtu, which leads to
an opportunity cost of electricity of 145 US$/MWhel (oil: 93.3 US$/MWh; gas: 51.7 US$/MWh).
Compared with the LCOE of the regarded system (0.1485 US$/kWh), the opportunity cost of fossil fuel
electricity (0.145 US$/kWh) is only 0.0035 US$/kWh lower. This means that both possibilities—installing
PV or electricity supply by the grid—are nearly equal from the macroeconomic perspective. The better
option depends highly on the export price of oil and on the system cost of PV, which are expected to
decrease further after the current phase of market consolidation.
Being economically equal to fossil fuel electricity, one advantage of PV is that it does not emit CO2. A
substitution of the named 3,123 MWh grid electricity by PV reduces the electricity demanded from the
grid to the same amount. Considering the electricity mix stated above and specific CO2 emissions of 776
kg/MWh for the electricity production of oil and 469 kg/MWh for gas (Forschungsstelle für
Energiewirtschaft e.V., 2010), the PV system of the regarded factory leads to a CO2 reduction of 1,976
tons/y. Of this, a CO2 reduction of 1,292 tons stems from oil and 684 tons from gas.
The case study shows that the advantages of PV currently lie in the reduction of climate gas emissions
and daily demand peaks in the afternoon. Economically, PV and fossil fuel electricity are equal; thus, the
future development on this question depends on the development of the oil price and the system cost
of PV.
6.5.3
PV Hybrid Systems for Remote Applications
Rural communities or remote industries are often not connected to the national grid because of
unusually high grid connection costs. In Saudi Arabia, those isolated communities mostly use diesel
generators (DG) to provide power (Bawah, et al., 2013). The globally rising fuel costs for the DG and the
rising concern about greenhouse gas emissions are leading to a search for alternative or advanced
solutions to combine the existing DG with renewable energy technologies.
6.5.3.1
Technology Description
As shown in this report, renewable energy, especially PV, has enormous potential in Saudi Arabia. PV
and DG can be combined into so-called PV–diesel hybrid power systems. The advantages of a PV–diesel
hybrid power system are lower maintenance costs, lower fuel consumption, and lower CO2 emissions
compared to a pure diesel system. Lower maintenance costs result from less maintenance needs for PV
arrays and inverters compared to the DG. Combining DG and PV is reasonable, because DG can balance
PV fluctuations and PV arrays can easily be integrated into the existing DG power system. The
disadvantages of a PV–diesel hybrid power system are the higher investment costs for the PV array and
the additional systems, such as inverters. The payback time of the PV array and additional system
components depends on the radiation zone, the installation dimensions, and the number of DG
operation hours.
6.5.3.2
Typical System Configuration
The typical system configuration of a PV–diesel hybrid power system is shown in Figure 6-72. The
system contains a PV array, one or more diesel generators, one or more inverters for a grid connection,
and optional battery storage.
The system controller guarantees optimal system operation by ensuring that the DG operates near its
optimal point to enable maximum diesel savings. The solar power not consumed during the day can be
stored in batteries and then used in high-demand hours or at night, when no solar generation takes
place. For the battery banks, usually lead accumulators are used because they have low costs and high
availability. However, new technologies, such as lithium-ion batteries, are being used more often as
their price decreases.
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Figure 6-72: Off-Grid PV–Diesel Hybrid Power System (WhoseSaleSolar, 2013)
PV produces direct current electricity, so the electricity needs to be transformed into alternating
current to provide electricity to the consumption side. Therefore, one or more inverters (depending on
the size of the PV array) need to be installed between the consumer and the PV panels.
Hybrid PV–diesel power systems have been successfully installed with and without battery storage all
over the world. IBC Solar, for example, installed a 1.5 MW PV–diesel hybrid system in Malaysia. This
system supplies nine islands with electricity. It includes a 6.8-MWh battery bank to ensure a reliable
electricity supply (Solar, 2013). At a chrome mine in South Africa, a decentralized 1-MW PV system was
integrated into an existing diesel system by Solea AG. The annual savings are approximately 450,000 L
of diesel fuel (AG, 2013).
6.5.3.3
Technology Comparison and Summary
Various studies have calculated the cost of generating energy (COE) for hybrid PV–diesel-battery
systems. For a hybrid system containing a 80-kWp PV system, a 175-kW diesel system, a battery storage
of 3 h average load, and a diesel price of 0.1 US$/L, the COE comes to 0.149 US$/kWh (Shaahid &
Elhadidy, 2007). Rehman and Al-Hadhrami (2010) calculated the COE for a PV–diesel battery system
with 21 percent solar penetration and a diesel price of 0.2 US$/L to be 0.219 US$/kWh. In comparison,
the COE of diesel-only systems is likely to be around 0.044 US$/kWh (Rehman, et al., 2007). A sensitivity
analysis showed that at a diesel price of 0.6 US$/L, the COE of hybrid systems comes close to that of the
diesel-only system. At >0.6 US$/L, hybrid systems become more economic than the diesel-only system
(Rehman & Al-Hadhrami, 2010).
It can be summarized that for off-grid areas, villages, or industrial sites, a PV–diesel hybrid system
solution can be economically profitable, depending on diesel price and solar radiation. There is vast
experience showing that PV–diesel hybrid systems are not only able to provide sufficient electricity but
also, environmentally, are superior to pure diesel systems.
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6.5.4
Business Opportunities
As shown in the chapters on the potential of renewable energy, economic analysis, and the case
studies, renewable energies could be used in various applications in the Kingdom. The potential for
solar energy is especially remarkable, even when only considering rooftop areas for PV. With K.A.
CARE’s renewable energy targets and a PV industry to be established in Saudi Arabia, a wide range of
business opportunities will emerge. For companies to consider PV or PV-hybrid systems as attractive
energy solutions, businesses dedicated to the optimal design and installation of such plants will be
required. Furthermore, independent consultants will be necessary who review and confirm solar or
wind measurements. Companies could also consider selling renewable energy–based electricity to the
customer, instead of just installing the system. This would require the system to be directly competitive
in domestic prices with other energy sources, which is not yet the case.
However, the basic requirements for such business opportunities to become viable are that a
functioning market of solar and wind power use exist. This involves necessary framework conditions,
such as regulations about whether those plants may feed excess energy into the grid. Other aspects are
raising awareness about the overall issue of reducing energy demand and greenhouse gas emissions. To
take the first hurdle, supporting and promoting first movers should be considered. Furthermore, adding
targets for small-scale and/or remote renewable energy applications to the existing renewable energy
targets would directly open opportunities and make renewable energies more significant. Once the
necessary framework is set and awareness exists, there will be a number of business opportunities
along the renewable energy value chains. This is especially true if local component production will
further reduce domestic renewable energy system prices.
6.6 Conclusion
This chapter contains an extensive techno-economic analysis of the renewable energy technologies of
wind power, PV, and CSP. To assess the country-specific economics, first an introduction to PV, wind
power, and CSP was given, as well as an overview on their current global market status. The general
potential of wind and solar resources in the Kingdom was presented and discussed. Generally, it can be
said that the potential for PV is evenly distributed in the country and is very high. For wind, the highest
potential can be found at the Red Sea. In the global context, Saudi Arabia’s wind energy potential is
rather limited. The resources for CSP are generally good, except for a few locations at the eastern coast,
where the DNI is lower because of humidity and dust particles.
The analysis of the current rooftop potential in Saudi Arabia showed that the largest potential exists in
the regions of Riyadh and Makkah. For the whole country, the potential is estimated to be 13.41 GWp
on residential and 2.55 GWp on industrial buildings. With this capacity, 3,743,412 MWh of electricity
could be supplied annually, constituting 17.7 percent of current energy demand. This analysis only
considered rooftop potentials as easily accessible areas for distributed generation, confirming the
abundance of solar resources in the country.
For the successful introduction of renewable energies into a new market, economic aspects play an
important role. Therefore, the LCOE was analyzed for all considered technologies. For PV, the LCOE
ranges between 0.09 and 0.18 US$/kWh, depending on system size and actual investment cost. The
LCOE of wind energy converters lies between 0.07 and 0.22 US$/kWh. For CSP, the LCOE ranges
between 0.19 US$/kWh and 0.45 US$/kWh, depending on the full-load hours. The higher investment
cost for CSP, especially when including storage, leads to these high values. However, they do not reflect
that storage can easily be integrated, making CSP plants dispatchable electricity sources in contrast to
wind and PV.
Opportunity cost and CO2 emissions of the current mix of energy technologies were analyzed and
compared to renewable energy technologies. Analysis of the opportunity costs of gas and oil showed
that cost of electricity for oil-powered plants would be superior to PV and wind if opportunity costs are
integrated. For gas, costs, including opportunity cost, would be at the same level as PV and wind costs.
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Thus, in the bigger picture, PV and wind already are considerable alternatives to oil- and gas-based
power plants in Saudi Arabia. Integrating renewable energies into the domestic energy mix would be
very beneficial for reducing CO2 emissions. In the renewable energy scenario analysis, it was calculated
that around 147 million tons of CO2 emissions could be saved by reducing oil generation and around 78
million tons of CO2 emissions could be eliminated by reducing gas generation. Participating in CO2
certificate trading could render additional revenues by selling the certificates for the saved emissions.
To get a better picture of potential applications of renewable energies and their specific technical and
economic characteristics, two case studies were presented. The first examined an RO water
desalination plant powered by a PV plant. In contrast to other water desalination technologies, RO is
purely driven by electricity; other technologies require heat, as well. The case study showed that water
costs from 1.06 to 1.18 US$/m³ for small plants and from 1.02 to 1.16 US$/m³ for large plants.
Compared to conventional technologies, the costs are slightly higher. However, a PV–RO plant has the
advantage that no heat is required and, thus, no fossil fuels have to be burned and no CO2 emissions
occur.
The second case study examined a PV system on a factory roof in the region Makkah Al-Mukarramah.
The installable capacity amounted to 2,132 kWp and led to an annual electricity generation of 3,123
MWh. The company was able to self-consume the generated electricity at all times of the year. During
the generation peak in the afternoon, the company could reduce its demand from the grid at peak time
(12:00 a.m.-5:00 p.m.) by 11.4 percent. With a considered investment of 1.69 US$/Wp, the electricity
costs of the PV system are calculated to be 0.1485 US$/kWh. Because this is superior to grid electricity
prices, a merely economic motivation might not be sufficient to increase PV penetration. However, if
opportunity costs are considered, electricity costs reached the same level. In that case, electricity
generated from PV costs the same as electricity generated by the fossil fuel-based electricity mix in
Saudi Arabia. The presented system would reduce CO2 emissions by 1,976 tons/y, of which 1,292 tons
would stem from oil and 684 tons from gas.
Concluding, this study analyzed the specific benefits of renewable energies in Saudi Arabia, as well as
current costs and economics of each renewable technology. It showed that future integration of
renewable energies will not only reduce greenhouse gas emissions but also be economically reasonable
on the macroeconomic level. Future research should be dedicated to the question of how to tap the
widely available rooftop potential, especially on industry buildings. Current energy tariffs are still
inferior to PV electricity costs, making it necessary to establish additional regulations to make it
attractive from the commercial perspective. Nevertheless, with K.A. CARE’s plans already established,
the Kingdom is sending a strong signal in favor of introducing renewable energies into the local energy
supply.
1
2
3
4
5
6
7
8
Recommendation/Conclusion
Saudi Arabia has enormous solar potential, but rather limited potential for wind and biomass, and hardly
any potential for hydropower and geothermal power.
On the macroeconomic level, PV and wind power are already competitive when compared to conventional
power generation, including opportunity cost.
There is a rooftop potential of 15.96 GWp for PV alone, the majority being located in the region of Riyadh.
On the microeconomic level, current framework conditions hinder deployment of renewable energy for
most companies, because investments cannot be reimbursed by the amount of energy savings.
PV suits industrial demand patterns and can reduce peak-demand of individual consumers by >10 percent,
as shown in the case study for an industrial client.
Comparison of electricity generation technologies should be done on the macroeconomic level to reveal real
costs, rather than neglect “hidden” costs such as subsidies, because the calculation of opportunity costs for
oil and gas are significantly different than domestic prices.
PV-hybrid systems are economically viable, stand-alone solutions for remote areas or large companies.
The existing goals for large-scale renewable energy plants should be replenished with targets for small-scale
applications, such as hybrid systems or renewable energy for self-supply to tap the identified rooftop
potential.
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Study Findings and Conclusions, Recommendations,
and Business Opportunities
CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Chapter 7: Study Findings and Conclusions,
Recommendations, and Business Opportunities
As the Kingdom enjoys significant economic and population growth, it continues to accelerate the rate
of energy consumption within the residential and industrial sectors, which poses a challenge to future
competitiveness and economic growth.
This study determines how energy is being consumed now, how demand can be reduced through
energy-efficiency measures, and how renewable energy (RE) sources can be integrated into the current
energy delivery model to drive further efficiencies. The findings, conclusions, recommendations, and
business opportunities of the study are discussed in this chapter.
•
•
•
Findings and Conclusions
− Energy Market and Economics
− Energy Waste
− Smart Grid Technologies
− Residential Metering
− Industrial Energy Demand
− Renewables Integration
− Energy Efficiency Audits: Case Studies
Recommendations
Business Opportunities
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
7.1 Category 1: Energy Market Economics
•
•
•
•
•
•
•
•
•
•
•
7-2
Considering the figures from the synoptic version of the energy flow analysis with the Sankey
diagram for Saudi Arabia in 2009, the main energy-saving potential can be clearly seen. Energy
losses in the KSA transformation segment total 72 million tons of oil equivalent (Mtoe), which is
about 43 percent of the entire input to the transformation sector. This is twice the losses from end
use (only 35 Mtoe); therefore, any priorities for energy savings should start in this area of the
country’s energy balance. Within the figures for final energy consumption, the sectors of transport
(34 Mtoe) and nonenergy consumption” (31 Mtoe) are both individually twice as great as the
residential (15 Mtoe) and the industry sectors (17 Mtoe) sectors; therefore, any sector priority
should be with transport and nonenergy consumption. Within the final energy consumption figures,
the losses in the useful energy segment are about 35 Mtoe, which equals approximately 53 percent
losses in final energy consumption. Low efficiency of diesel turbines, mainly simple cycle gas turbine
technologies with an average efficiency rate of about 25 percent in 2009
High distribution losses (9.6 percent transmission losses), which reduces the overall efficiency of the
power system
Comparatively small generation units (capacity of ≤251 MW) make up 43 percent of all thermal
power plants. There is a large number of small generation units with 8 MW, 12 MW, or 25 MW
capacity, which reduces the overall efficiency of the power system.
From the time-series analysis, our team gained a more detailed understanding of the entire energy
system of Saudi Arabia for the year 2040. Final energy consumption will increase from 105 Mtoe in
2009 to about 425 Mtoe in 2040, at an annual growth rate of 4.3 percent, which is less than the
foreseen increase in GDP/capita of 7.2 percent annually (even without fully considering the
expected increase in population).
The primary energy supply in KSA until 2040 will reduce the possibilities of oil exports, which were
at a level of 383 Mtoe in 2009. For comparison, the net exports should be considered, which are 11
Mtoe less than the export of 372 Mtoe in 2009. For 2040, national production of energy in KSA is
534 Mtoe, while the total primary energy supply is at 530 Mtoe.
The R2 values for the forecast of population (0.999 in nonlinear forecasts) and for GDP (0.994), total
primary energy supply (0.984), total final energy consumption (0.960), MW peak (0.997), and total
electricity output (0.993) are quite high.
Electricity consumption is expected to increase from 240 TWh/y in 2009 to about 850 TWh/y in
2040. This increase will absorb a reasonable additional amount of energy production in the KSA,
and its influence on primary energy supply in the KSA is quite evident because about 120 Mtoe will
be required to supply the power stations with necessary fossil fuels (fossil-fuel power stations
increase from 57 GW to about 87 GW in 2040).
Saudi Arabia has 7,500 kWh/capita consumption levels. This is considered to be among the highest
in the world. If consumption continues at the same level, power generation is forecasted to require
up to 185 GWh/y by 2040.
The power supply capacity mix in Saudi Arabia for the period 2010 to 2040 in MW (baseline
forecast) will not meet the demand in 2040. A considerable shortfall of about 44,000 MW is
expected when calculating 185,000 MW electricity demand in 2040 against a planned capacity of
141,000 MW.
RE implementation and use are necessary to meet the 2040 power generation demand of 185 GW.
The influence of renewables is depicted in Table 7-1
The expected energy efficiency improvement is expected to be between 20 percent and 30 percent,
based on many Electricity and Cogeneration Regulatory Authority (ECRA)/King Abdullah Petroleum
Studies and Research Center (KAPSARC), King Abdulaziz City for Science and Technology (KACST),
and Saudi Energy Efficiency Center (SEEC) studies. This is in line with results found in this study.
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•
•
Total final energy consumption will limit the increase in this scenario to 298 Mtoe, but this still leads
to a considerable decrease in oil exports from the current 383 Mtoe to 128 Mtoe, which is only 33
percent of former oil exports (minus 67 percent).
Regarding the forecast for energy consumption and “real” energy prices in 2040 (based on 2010
prices), two effects on the opportunity costs in 2040 are seen: a “real” price increase of about
US$65,000 million (Mio) and an increase of US$446,000 Mio due to additional consumption in 2040
compared to 2009. This means that national energy consumption will have total opportunity costs
of US$635,302 Mio in 2040 and will absorb a large percentage of national income—in this case,
about 35.2 percent of the expected GDP.
The influence of renewables on the energy section in the Kingdom is depicted in Table 7-1.
Table 7-1:
Electricity Production Capacities (MWel), Electricity Production (GWh/y), and Full-Load Hours per
Year with and without Renewables Energy Production in Saudi Arabia in 2040
Electricity Capacity and Electricity Production in 2040 in Saudi Arabia with Renewables
MWel
Full-load
hours/y
Produced
GWh/y
Oil/gas
87,400
5,840
510,416
Wind
13,800
1,800
24,840
Solar PV
24,400
2,200
53,680
CSP
38,200
3,500
133,700
Nuclear
21,200
6,000
127,200
Type of plant
Source
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Calculation based on K. A. CARE, May
2012 forecast for 2032
Others
–
2,000
–
A
Total production
185,000
849,836
B
Demand
185,000
850,000
Authors’ calculation
Difference due to comparatively
–
164
low full-load hours of renewables
energies
B−A Necessary net-imports
–
164
Ratio of necessary net imports to
0.0
0.0
total production
CSP = concentrating solar power; PV = photovoltaic
Note: This table shows the forecasted peak demand of 185,000 MWel can only be met if all generation capacity is in the
position to deliver its total installed capacity during full-load during peak hours (i.e., no maintenance, no breakdown, full
wind load, full sun exposure, and so forth).
7.2 Category 2: Energy Waste
In total, a provisional waste-heat potential usage of about 3,500 MWth has been identified for the four
economic sectors in KSA, namely: water, power, petrochemical, and other. More than 80 percent of this
potential has been identified in the large companies, saline water, power generation, and large
petrochemicals. Analysis of the sector “other—more commercial—industries,” identified a savings
potential of about 650 MWth, with good chances for fast implementation and replication in KSA via
instruments, such as EE promotion and energy services companies (ESCO) service support.
The possible power generation from waste heat is strictly dependent on the existing temperature level
for ensuring efficient, interim steam production. Assuming an average heat-to-power efficiency rate of
about 20 percent, the waste-heat losses represent a power generation potential of about 700 MWel
capacity to be used for the company production process according to demand.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
The savings for this task have been achieved following the savings methodology procedures shown in
Table 7-2.
Table 7-2:
Business sector
Thermal power plant
sector
Saline water
Petrochemicals
Other SME industry
Possible Savings in Capitalizing on Waste Heat
Production/consumption
analyzed
Savings capacity defined
Possible savings, %
WHR potentials
A minimum of ~40
SHR potentials
WHR
25–35
15–20
25–30
SHR = steam-heat reduction; SME = small- and medium-sized enterprises; WHR = waste-heat reduction
7.3 Category 3: Smart Grid Technologies
Reviewing the current smart-grid literature and projects, the five most important technologies of a
smart grid are OLTC; reactive power control; AMI, including smart meters; and active power control.
OLTC and reactive power control are technologies that are able to solve voltage problems in grids as
independent controllers. The OLTC does not raise the current, but it changes the voltage for the whole
grid and allows the use of the total range allowed by grid codes. Reactive power control affects the
voltage locally, but it raises the flowing currents.
There are technologies that may reduce or shift the load in the grid, such as the active power control
(also called demand-side management). For efficient operation, this technology needs information
about the grid state. Typically, a centralized controller device distributes a signal for the underlying
systems (e.g., maximum power). This concept may rely on an AMI. The AMI is based on smart meters,
which send local measurements to a central point. With the knowledge of the current grid state, active
power control works precisely.
To evaluate the importance of technologies, European smart-grid projects are reviewed. Because the
knowledge of the system state is crucial to smart-grid technology, smart meters are the most
investigated technology. With active power control, all problems that can occur to a grid might actually
be solved; therefore, it is nearly as important as smart meters. The problem is that in distribution grids,
the shiftable load is actually not big enough. Assuming that customers’ comfort is to remain at an
acceptable level and photovoltaic (PV) plants are switched on, not all problems can be solved without
storage.
In the case study, the effect of medium-size PV plants on distribution grids is evaluated. When the grid
is not reinforced, a high number of PV plants in the analyzed distribution grid can result in
consequences of upper-voltage boundary violations. To solve the voltage (i.e., stabilization) problems,
an OLTC is installed in the grid. The OLTC is able to solve most of the problems; hence, no expensive
grid-reinforcement measures need to be taken. Using current list prices for cables and OLTC (for the
European Union [EU]) for this case study, a cost savings, by a factor of five, can be observed, while a
huge amount of PVs still can be installed.
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7.4 Category 4: Residential Metering
The measurements showed electricity consumption of up to 50/m2. Taking a typical living area of 500
m2, this leads to a maximum power consumption of 25 kW during one time step. This peak is typically
reached in the early evening hours and reduced by one-half during the night. Figure 7-1 shows that a
typical daily profile has one peak. This peak is shifted roughly 4 hours later against the ambient
temperature.
LPs for residential buildings show a high correlation to the ambient temperature, as the demand is
driven mainly by AC. This has been proven using the measurements with submeters, allowing
separation of AC demand from the Dammam site. It can be observed that the HH consumption
independently ranges between 0.1 kWh/m2/d and 0.15 kWh/m2/d in the ambient temperature.
The AC demand shows a strong correlation to the outside temperature. During the measurement
period, the AC demand between June and December 2013 ranged from 0.05 kWh/m2/d (corresponding
to 9.15 MWh/y) at daily mean temperatures of 22 °C, to 0.55 kWh/m2/d (corresponding to 91.2
MWh/y) at a mean temperature of 34 °C.
ambient temperature
30 per. Mov. Avg. (mean electricity demand)
electricity demand in kW/m2
0.07
45
40
35
30
25
20
15
10
5
0
0.06
0.05
0.04
0.03
0.02
0.01
0
temperature in °C
mean electricity demand
Figure 7-1: Energy Consumption of HH and Ambient Temperature
Finally, the AC accounted for 85 percent of the total consumption in August/September 2013 and 70
percent in November. The monthly HH base consumption remained roughly constant during the
metering period, with an average of 3 kWh/m2/mo.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
energy consumption in kWh/(m2 * day)
0.8
ambient temperature
energy consumption air condition
0.7
35
0.6
30
0.5
25
0.4
20
0.3
15
0.2
0.1
0
40
temperature in °C
energy consumption household
10
days ordered by temperature
5
0
Figure 7-2: Energy Consumption of HH and AC
In total, a provisional savings potential of about 12,500 MWel has been roughly identified for the three
key economic regions in KSA considered, namely, the West coast, Central Riyadh, and East coast areas.
More than 60 percent of this savings potential has been identified for the bigger (500 m2) houses as
representative for the KSA city outskirt areas.
Analysis of the HH sector revealed a savings potential of about 7,500 MWel, which has a good chance of
fast implementation and replication in KSA via instruments such as a sound tariff setting, EE technology
promotion, and potential ESCO EE service support.
This will allow drawing conclusions on potential macroeconomic effects, such as a reduction of CO2.
7.5 Category 5: Industrial Energy Demand
•
•
•
•
•
7-6
Applying EE measures to different industries in KSA will have a positive effect on the industrial
energy demand. Considerable energy savings could be reached. The high energy-efficiency scenario
showed potential energy savings of 20 percent, resulting in 4,413 TWh of cumulated energy savings
until 2040.
In the cement industry, an efficiency potential of 15 percent for the high EE scenario is set. The low
EE scenario assumes a slower application of EE measures and a lower implementation speed, which
leads to an efficiency potential of 5 percent.
In the steel industry, an efficiency potential of 35 percent for the high EE scenario is identified. For
the low EE scenario, it is assumed that the transfer of production technology starts later and is
carried out more slowly, which reduces the EE potential to 10 percent for this sector.
In the petrochemicals industry, there is a potential efficiency increase of 17 percent in the low EE
scenario and 25 percent energy savings potential in the high EE scenario. Opportunity cost in the
range of US$700 billion could be generated by saving fossil fuels at the analyzed amount for the
considered industry sectors.
CO2 savings of up to 3,460 billion tons could be achieved purely by implementing EE measures in
the industry.
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7.6 Category 6: Renewables Integration
In Saudi Arabia, PV locations mainly record 2,000 full-load hours annually, and more can be achieved for
PV, resulting in a levelized cost of electricity (LCOE) of around 10 US¢/kWh (ground mounted) and 12
US¢/kWh (rooftop). Regarding the wind potential of the country, the LCOE for wind varies between 22.1
and 13.3 US¢/kWh for 1,100 full-load hours and between 9.1 and 14.3 US¢/kWh for 1,800 full-load
hours. CSP has the highest LCOE of all selected technologies. Depending on the full-load hours, the LCOE
ranges between 44.9 US¢/kWh 18.5 US¢/kWh.
Three case studies were conducted in the present task: a PV-driven reverse osmosis (RO) desalination
plant, PV electricity supply for industry, and PV-hybrid systems for remote applications. The levelized
water production costs (LWPCs) for different plant configurations were calculated. The LWPC for RO
combined with PV proved to be higher than the LWPC for currently used multistage flash plants.
However, this calculation does not include the maintenance cost, the fossil-fuel prices, and the
operation costs. If these additional costs are taken into consideration, the PV-powered RO plant may be
the more sustainable solution. Especially because of increasing fossil-fuel prices and possible
opportunity costs, PV-RO will gain more importance.
The case study on the PV electricity supply for the industry showed that the advantages of PV currently
lie in the reduction of climate gas emissions and daily demand peaks in the afternoon. Economically, PV
and fossil-fuel electricity are equal; the future development on this question depends on the
development of the oil price and the system cost of PV.
The case study on PV-diesel hybrid systems showed that for off-grid areas, villages, or industrial sites, a
PV-diesel hybrid system solution can be economically profitable, depending on diesel price and solar
radiation. There is vast experience showing not only that PV-diesel-hybrid systems are able to provide
sufficient electricity but with respect to environmental aspects, they are superior to pure diesel
systems.
The analysis of the currently existing rooftop potential in Saudi Arabia showed that the largest potential
exists in the regions of Riyadh and Makkah. For the whole country, the potential is estimated to be
13.41 GWp on residential and 2.55 GWp on industrial buildings. With this capacity, 3,743,412 MWh of
electricity could be supplied annually, constituting 17.7 percent of current energy demand. Because this
analysis only considered rooftop potentials as easily accessible areas for distributed generation, this
confirms the abundance of solar resources in the country.
The LCOE is analyzed for all considered technologies. For PV, the LCOE ranges between 9 and 18
US¢/kWh, depending on system size and actual investment cost. The LCOE of wind energy plants lies
between 7 and 22 US¢/kWh. For CSP, the LCOE ranges between 19 US¢/kWh and 45 US¢/kWh,
depending on the full-load hours.
On the microeconomic level, current legal framework and policy regulations are needed to start the
deployment of RE technologies. REs generally suit (smaller) industrial demand patterns and can reduce
peak load of individual consumers by >15 percent. By comparison with internationally proven RE site
planning and operation experience, positive employment aspects could be identified for respective KSA
engineers and consultants.
7.7 Energy Efficiency Audit: Case Study Findings
Table 7-3 lists all industrial and commercial buildings that were considered for the energy efficiency
audit. The sectors studied include services (restaurants, hotels, hospitals, and shopping malls) and
industrial production (cement production and plastic production). Small and medium enterprise (SME)
clients in the Kingdom were preferred for the study, being representative of the actual economic
development and to avoid duplication of work with the KSA oil and gas industries.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-3:
Six Industrial and Commercial Sites Involved in EE Audit Case Studies
Location
Riyadh
Business
Facility/
sector
company
Business status
Commercial services
Private hospital, Private clinics and
Hospitals
Jeddah
surgery hospital,
300 staff
Hotels
M-Hotel, Riyadh 89 rooms, 210 beds,
120 staff, 12%
administration
Enmar Hotel,
210 rooms, 340
Jeddah
beds
Jeddah
Quality
Consumption
level
Efficiency status
Very EE
~37 GWh/y
committed,
implemented EE
Size: small
~3.9 GWh/y
Well committed
Size: medium
200 rooms, 300–500
beds
Size: small
Innovative construction,
15% administrative cost
Size: medium
~5.8 GWh/y
Size: medium
Weakly
Standard construction,
committed
12% administrative cost
Generates own No AC regulation, no
Size: large
VSD yet power factor
Well committed power without
EnMS (~69 GWh) correction
A Mall in
240,000 m2, 400
Jeddah, KSA,
tenants, 240 staff
Corniche Jeddah
area
No AC regulation, no
Restaurant Al-Shurfa
1,200 m2,
Well committed ~2.4 GWh/y
restaurant
VSD, no power factor
110 staff
services
correction
Industrial sectors
Constructio Cement factory ACP Lafarge, private Very committed, ~159.7 GWh/y,
1.5 Mtons/y,
n industry (medium size, 300 staff
some data
105 kWh/ton
Generates own power
inconsistency
3.6% countrywith heat recuperation
dd)
ACP = Alsafwa Cement Plant; dd = degree days; EnMS = energy management system; VSD = variable speed drive.
Shopping
malls
7.7.1
Key Findings of the Cement Industry
The biggest energy shares are used to treat and process the raw material and for burning heavy fuel oil
(HFO) for clinker production. Because of the high optimization potential of 21.94 percent for electrical
demand, the investigation focused more on this than on the HFO consumption element. Nevertheless,
because of self-generated power, the HFO is influenced directly by implementation of EE measures
regarding electricity demand.
The following data show all investigated energy-savings potential for the pilot ACP client, illustrated in
Table 7-4 by consumption savings and in Figure 7-3 for cost savings.
The data and figures presented in Table 7–4 and Figure 7–3 give an overview of all analyzed and
investigated energy-saving measures for the ACP factory and clearly demonstrate the technicaleconomical challenge and the practical possibility of integrating measures for EE improvement in
normal business plans of Saudi commercial, industrial clients by today’s operating conditions.
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Table 7-4:
Proposed EE
improvement
measure in
sector
EPC
EPC
EPC
EPC
EPC
EPC
EPC
EPC
EPC
Total, SR
Total, EUR
EE Measures Identified at Alsafwa Cement Plant and Expected Payback Times
Description of EE
measures (savings)
ORC waste-heat usage
(30%)
VSD for fans and other
drives (25%)
New bag-filter systems
combined with VSD (30%)
Raw mill replacement by
vertical mill (16%)
Reduction of pressurized
air for bag-filter cleaning
and VSD (25%)
Implementation of an
EnMS (3%)
Reduction of the
temperature by 1 °C
Parent control for both
pressurized air grids (12%)
Absorption chillers for split
unit replacement (92%)
Consumption
Consumptio
with EEn in baseline improvement
situation,
project,
kWh/y
kWh/y
Potential
physical
savings,
kWh/y
Potential
cost
savings,
SR/y
159,687,202
111,781,041
47,906,160
4,790,616
32.35
26,046,720
19,535,040
6,511,680
651,168
3.84
10,128,384
7,089,869
3,038,515
303,852
3.36
31,610,880
26,553,139
5,057,741
10,115
8,128,512
7,293,404
835,108
83,511
7.78
159,687,202
154,896,585
4,790,616
1,107,937
0.18
1,314,720
1,222,690
92,030
9,203
0.54
8,128,512
7,348,175
780,337
78,034
1.92
1,314,720
105,178
1,209,542
70,221,730
14,044,346
120,954
7,155,389
1,431,078
54
Expected
payback
*, no.
years
EE = energy efficiency; EnMS = energy management system; EPC = electrical power consumption; EUR = euro;
ORC = Organic Rankine Cycle; SR = Saudi Ryal; VSD = variable-speed drive.
*Assumption is 5 percent savings and includes additional water savings for payback calculation.
Figure 7-3: Anticipated Energy-Saving Potentials at ACP and Their Economic Value
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
7.7.2
Key Findings of Shopping Malls
The selected Mall in Jeddah, KSA business complex, as a typical shopping center in Jeddah, represents a
majority of strong commercial-trade clients, being supplied and served through the operations
department of the mall management, including a big (Danube Co.) hypermarket, a medium-sized hotel,
a medium-sized office building, and about 500 tenant subcontractors within a business service area of
240,000 m2 total within a three-floor building complex. This type of supermall structure was
constructed in 2007 and seems to be a typical one for the bigger city areas in all the considered KSA
business regions.
The EA negotiations with a Mall in Jeddah, KSA company started in March 2013 and were concluded
contractually in early May. From the beginning, the mall management showed a clear commitment to
EE behavior and understanding of the business advantages of their announced implementation. Several
site visits were made to learn the business structure and the functional and logistical relationships
between the mall management and all settled business clients.
A Mall in Jeddah, KSA was designed between 2005 and 2006, the construction was finished in 2007, and
it is in its fifth year of operation. The building construction frame is mainly composed of industrially
manufactured concrete plates, with steel frames and roofs of glass and (low-insulated) metal plates,
which still represent generally international, state-of-the-art construction quality.
The biggest energy consumption share at a Mall in Jeddah, KSA comprises electricity used for cooling,
ventilation, and lighting inside and outside the different mall buildings. Because of the high
consumption share (about 45 percent from total power consumption), the optimization potential of the
heating, ventilation, and air conditioning (HVAC) subsector inside a Mall in Jeddah, KSA LP had been the
strong focus for the team’s EE investigations, focusing mainly on all aspects (business and technology)
of this specific electricity-consumption sector.
The specific electricity consumption of a Mall in Jeddah, KSA complex was reported to be around 290
kWh/m2/y for the total inner service area; this seems reasonably good by international comparison,
having similar climate conditions, although there is room for several EE improvement measures, as this
report (and Table 7–5) demonstrates.
Nevertheless, because a Mall in Jeddah, KSA has recently started its own power generation, the savings
impact of some EE measures may be negatively influenced by direct comparison with actually
subsidized fuel power during the implementation.
The data presented in Table 7-5 give an overview of all analyzed and investigated energy-saving
measures for a Mall in Jeddah, KSA. When comparing the energy consumption of international shopping
centers, it is remarkable that its energy performance indicators are very different, mainly due to size,
climate conditions, and business profile. When making an international comparison, the investigated
energy consumption levels are in general accordance with several studies in similar climate regions; a
range of 250 to 500 kWh/m2/y, occasionally reaching maximum values of 600 kWh/m2/y, have been
reported.
The location of the shopping center and the direction it faces seem to affect energy consumption less
than the construction type, the glassing share, the selected inside-temperature regime, and the number
and type of tenant clients, and, finally, the number of shopping visitors. Scandinavian shopping centers,
for example, are in the same energy-consumption range as Turkish or Indian shopping centers (200
kWh/m2 to 400 kWh/m2). The heat energy demand in northern countries (about 4,000 heating degree
days) generally correspond to the cooling energy demand (3,900 cooling degree days in KSA) in
southern countries, as for most KSA.
Because of the very different construction of shopping centers, a comparison is difficult. According to
several studies, an energy-efficient shopping center has a yearly consumption of 250 kWh/m2 to 270
kWh/m2. An EE objective of modern shopping centers in several studies in Europe and India is to reach
the target of 150 kWh/m2/y. The combination of new 2×9 power generators and absorption chillers for
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using waste heat would be a good opportunity for realization of energy savings. From the beginning, the
mall management showed a high awareness of EE issues and a strong willingness to invest in feasible EE
measures.
Table 7-5:
Measure
Short-term measures
Energy monitoring system
(complete)
Energy management system
according to ISO 50001
Energy-saving concept for all
involved parties
Intelligent lighting controlling
system
Increasing internal
temperature (in zones or
complete)
Controlling inside temperature
in dependence on the outside
temperature
Peak load management system
Glass doors at refrigerated
shelves in supermarket
Periodical maintenance of the
drives
Medium-term measures
Installation PF-condensors for
cosphi >0,9 from PF=0,76
Absorption chillers working
with waste heat
Shadowing for sunlightexposed parts of the building
Frequency converters at the
large drives
Long-term measures
Changing lamps to LED
Increasing the roof insulation
Changing inefficient drives
Overview of All Identified EE Measures at a Mall in Jeddah, KSA
Possible energy
savings in the
respective area
5%–10% of
overall energy
5%–10% of
overall energy
5%–10% of
overall energy
15%–25% of
lighting energy
5%–10% of
cooling energy
Savings
used for
calculation
Estimated
average
savings in
SAR/year
Operative costs
Investment (OPEX) (Difference
costs
to existing
Payback
(CAPEX)
technology)
Time,
SAR
SAR
year
7.50%
1,333,194
400,000
20,000
0.32
7.50%
1,333,194
250,000
25,000
0.21
7.50%
1,333,194
200,000
15,000
0.16
20.00%
1,066,555
250,000
2,500
0.24
7.00%
622,157
1,000
0
0.00
4%–7% of cooling
energy
5.50%
488,838
25,000
0
0.05
none (saves only
costs)
30%–50% of
cooling energy of
refrigerator
shelves
3%–5% of energy
for the drives
0.00%
200,000
200,000
15,000
1.08
40.00%
71,104
100,000
5,000
1.48
4.00%
53,328
0
100,000
1.88
1.123.857
654.000
0
0,57
17.50%
1,555,393
8,000,000
0
5.14
17.50%
1,555,393
10,000,000
20,000
6.44
25.00%
333,298
2,000,000
0
6.00
40.00%
2,133,110
15,000,000
0
7.03
10.00%
888,796
7,500,000
0
8.44
7.50%
99,990
2,000,000
0
20.00
35-40%
depending on
existing PF, 1000
kVAr
10%–25% of
cooling energy
10%–25% of
cooling energy
20%–30% of
energy for the
drives
30%–50% of
lighting energy
5%–15% of
cooling energy
5%–10% of
energy for the
drives
37%
CAPEX = capital expenditure; ISO = International Organization for Standardization; LED = light-emitting diode;
OPEX = operational expenditure; refr = refrigerator; PF = power factor; savg = saving.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
7.7.3
Key Findings of Hotels
Table 7-6 and Figure 7-4 provide information and an overview of the identified potential energy-saving
measures at a medium-sized (220 rooms) Enmar Hotel in Jeddah and for the small M-Hotel (89 rooms)
in Riyadh city.
Table 7-6:
Proposed EE
improvement
measure in sector
EPC
EPC
EPC
EPC*
EPC
EPC
EPC, kVArh/y
Total, SR
Total, EUR
Overview of the Energy-Saving Potentials for Medium-sized Enmar Hotel, Jeddah
Description of EE
measures for cost
estimate
Absorption chillers for
air chiller replacement
Electric boiler
replacement
LED technology instead
of halogen lights
Implementation of an
EnMS
Increase the target
temperature by 1 °C
VSD for large drives
Reduction of reactive
power
Consumption
Consumption
with EEPotential
in baseline improvement physical
situation,
project,
savings,
kWh/y
kWh/y
kWh/y
Potential
cost
savings,
SR/y
Expected
payback,
no. years
4,168,021
344,731 3,823,291
988,321
5.95
647,281
647,281
167,322
2.99
86,566
10,008
76,558
19,790
7.42
5,786,271
5,496,958
289,314
74,788
3.34
4,168,021
5,786,271
3,876,260
5,688,003
291,761
98,268
75,420
25,402
0.01
1.82
4,948,023
2,802,484 2,145,539
5,226,472
1,045,294
184,874
1,351,043
270,209
2.7
EE = energy efficiency; EnMS = energy management system; EPC = electrical power consumption; EUR = euro;
LED = light-emitting diode; SR = Saudi Ryal; VSD = variable-speed drive.
*Assumption is 5 percent savings and includes additional water savings for payback calculation
The two hotels use a majority of seven EE measures, preferably increasing the energy handling
specifically for the power supply inside the considered hotels.
The specific EE measure for air-chiller replacement covers the whole air-conditioned part of the hotel.
Figure 7-4 illustrates the results for better understanding and comparison.
Figure 7-4: Anticipated Energy-Savings Potential at Enmar Hotel in Jeddah
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The indicated savings often cannot be simply added together, because they influence each other. For
example, a reduced operation time of lighting reduces the savings by new lighting technology, because
of reduced consumption through replacement of respective lamps. The respective savings potential
analysis is described more in detail in the EA report.
In general, it should be noted that most of the database has been acquired via telephone interview and
a short on-site visit to the Enmar Hotel, and there were restrictions on data collection.
The anticipated energy savings potential at M-Hotel in Riyadh had a theoretic volume of up to 56
percent from total hotel consumption and ranged from exchanging old lightbulbs to introduction of an
advanced energy management system (EnMS). This represented cost savings up to 25,000 €/y for the
hotel’s own trigeneration units, enabling savings with an actual payback between 5 and 10 years.
From the beginning, the management at M-Hotel showed a high awareness of EE issues and a strong
willingness to invest in feasible EE measures. Energy savings identified at the small-sized M-Hotel in
Riyadh city are illustrated in Table 7-7.
Table 7-7:
Anticipated Energy-Savings Potential at M-Hotel in Riyadh
Physical Cost
CO2-red
Payback
Savings Savings
ton
Considered Consumption Sector Electricity
1 Exchange ICB lamps for energysaving LED lamps with same lighting
2 AC operation better adapted to
outside temperature and hospital
occupation
3 Installation of PF suitable PF
compensation unit by 136 kVA-4 for
achieving cosphi >0.95
4 VSD inverter load regulation for all
big (elevator+ pump) motors, pilot
for 3 elevator motors by 3.5 kW
5 a) Energy Management System
(EnMS) for main hotel building
6 b) EnMS for new AC package units
6 Solar thermal roof or window
shading HW collectors for repl
electr. sanitary HW preparation
7 Install trigeneration unit by 50–70
kW-el/55, 45-80 kW-th
EE Measure
Replace 200 ICB with cap 60W
by 10W LED lamps
AC optimization via PLC
programming tool per main AC
unit
PF compensation via new
condenser bank
VSD installations at four
elevator motor drives
35% savable by optimized
system
Replace 30 splitting AC by four
central AC-chillers
Install at min 40 m2
collectors/bdg by 2 sqm
MWh/a EUR/a
CO2/a
Years
25.000
1,100
18
4.55
78.000
3432
55
3.50
1,060.8 11668,8
750
1.20
11.200
493
8
1.70
204.8
9009
145
1.55
144.0
6336
102
2.21
56.0
2464
40
8.1
Install a pilot 50 kW-el, 45 kWth
403
10.4
570.0 25080
trigeneration system for power
generation, HW, and for cooling
TOTAL
MWh
2,149.8 59,583
1.520
AC = air conditioning; bdg = building; EE = energy efficiency; gen = generation; HW = hot water; ICB =
incandescent bulb; LED = light-emitting diode; PF = power = power factor; PLC = programmable logic controller;
TG = trigeneration; VSD = variable-speed drive.
7.7.4
Key Findings of Hospitals
Jeddah hospital represents a typical, traditional mid-sized, upscale medical facility with about 300
patient rooms (about 600 beds) and several service facilities, such as a blood bank, its own laundry,
several operation facilities, and specific treatment facilities, such as a specific X-ray station and
computed tomography and magnetic resonance magnetic resonance tomography diagnostics (Table
7-8).
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-8:
Basic Technical Fact Sheet and Cited References for the Jeddah Hospital
Item
Description
Type of hospital
Technical size
Clinics/hospital/college/gym
3 buildings, 5–7 floors, massive
construction, insulated roof
Power consumption,
2012
Specific power
consumption
Water consumption,
2012
Specific water
consumption
HW preparation
Clinics patients/mo
Cited references:
Howard, Jeffrey
HVAC handbook
UBA study
Prof. M. Kubessa
HTWK-Leipzig
World Bank/IFC
EC OPET-CS network
Volume
Characterization
Mid-size
~600 employees over
3 shifts
36,571
90 bed-rooms, 300 beds
36,800 m2
115,000 m3
MWh
50,000
kWh/bed/y
~50% above total EU
level
96,800
m3/y
161
m3/bed/y
Seems oversized
Exclusively from power
50,000 (summer) and 60,000
(winter)
55,000/mo (1,800/d)
Occupancy rate
How to make an energy audit
Recknagel-Sprenger-Schramek
Energy efficiency in hospitals
Collected commercial-industrial
energy benchmarks
Stanford University
Oldenburgverlag
Web search analysis
BEA Energy agency
publication, No 1289/2001
2009
2008 edition
2009 edition
2006 edition
Handbook on energy efficiency
benchmarks
Collected public–commercialindustrial energy benchmarks
Publication of John Wiley &
Sons
Publication of EC DG
TREN/OPET network
2009 edition, updated
in 2011, 2013
1999 edition, updated
in 2005, 2010
Jeddah hospital is a private medical-service entity that has been in operation since 1978, during the first
prospering phase of KSA economy. It is in line with the Saudi Harmonized Commodity Description and
Coding System standard, defining minimum standards for healthcare and regional city clinics’ services.
The hospital comprises three main buildings, which were erected (or refurbished) in three main phases,
beginning in 1978, continued in 1985, and completed in 2004/2005.
The latest modernization (reconstruction) phase ended in 2005, just before the implementation of the
Saudi building energy code (SBC), which aimed to establish regionally adapted construction and building
climate energy protection standards and define minimum levels of heating/cooling insulation and
efficient operation of new private and public buildings in KSA.
The estimated thermal cooling demand of the hospital was, in accordance with the SBC monitoring
procedure and using 3,800 cooling degree days (CDD) for the specific climate demand, calculated to be
about 340 MWhth annually, corresponding to an occupied building volume of the three main buildings
at around 70,000 m3.
To adapt the expected savings results as close as possible to the international consumption standard,
the following International Organization for Standardization (ISO) standards were respected: ISO 9000
for sound management organization, ISO 14000 for environmental preparedness, and ISO 50001 for
sound energy management; best available technologies (BATs) were applied for alternative EE proposals
in the monitored client facility only. Due to steadily rising power consumption, the hospital management
had already installed an energy manager, who still needs technological EE support for load monitoring
and sound technology advice. Table 7-9 shows the list of proposed EE measures for the hospital
(including implementations observed in early 2014 [e.g., light-emitting diode [LED] lighting, power
factor [PF] correction adoption].
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Table 7-9:
List of Proposed EE Measures at Jeddah Hospital
EE Proposals Identified for the F-Hospital in Jeddah
1
2
3
4
5
6
7
8
Considered consumption sector electricity
Exchange ICB lamps by energysaving LED lamps
AC operation better adapted to
outside temperature and hospital
occupation
Upgrade of existing PF
compensation for achieving
cosphi >0.9 in Building B
VSD inverter load regulation of all
big (elevator and pump) motors,
expl of 29 elevator motors by 3.5 kW
a) EMS for internal Pediatric Building
(B) temperature regime useful (18K
design temp)
b) EMS for main old building (A)
seems useful
c) EMS for new Building C (sister
home, gym) seems useful
Solar-thermal roof or windowshading HW collectors for repl
electric sanitary HW preparation
PV roof (and/or wall shading)
installation w direct HVAC feeding
per building
Install trigeneration unit by 200–300
kW-el
EE measure
Replace 500 ICB w cap
60W by 10W LED
AC-optimization via PLC
pogr tool p main AC unit
Physical
Cost
savings savings CO2 red Payback
kWh/a
EUR/a ton CO2/a years
62,500
2.750
44
1.82
330.000
13,200
233
0.91
PF optimization via PLC
tool for four main feeders
555,600
22,224
393
0.63
VSD installations at
motor-drive supply board
71.456
2.858
51
2.13
Analysis and design of
hospital sector-specific
demand
35% savable by
optimizing system
Replace 40 splitting AC by
four central AC chillers
Install at min 40 m2
collectors/building by 2
m2
Install at min 40 m2 PV
panels by 1 m2 (4 kW)
227,500
9,100
161
1.32
341,250
13,650
241
1.03
252,000
10,080
178
1.39
56,000
2,240
40
8.9
10,000
400
7
9.0
3,420,000 136,800
2.418
1.9
Install a pilot 250 kW-el
trigeneration system of
power generation, HW,
cooling
TOTAL
5.326.306 213.302
3.766
CO2 = carbon dioxide; ICB = incandescent bulb; PLC = programmable logic controller; PF = power factor; VSD =
variable speed drive; HW = hot water; PV = photovoltaic
The investigated specific power consumption for medical services has been estimated at 14,000
kWh/patient/y; for hospital services, 27,000 kWh/bed/y; for college boarding, 9,000 kWh/bed/y; and
the reported specific water consumption was about 2.7 m3/bed/y in total. The main benefits achievable
from implemented EE measures were detected in the electricity consumption sector of hospital
operation.
Because of the existing main supply structure for electricity applications, an increased specific demand
for cooling, ventilation, HW preparation, and lighting had been analyzed at Jeddah Hospital during the
site visits.
The identified savings proposals may assist in a technically more efficient hospital operation and/or an
increased number of treated patients, because of reduced service times in hospital premises and
apartments.
Volume 1
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-10: Potential EE Savings
Business sector
Sector-1, 160 AC operation
Sector-2, 29 elevators
Sector-3, 2 mn water boilers
Sector-4, Lighting
Sector-5, Trigeneration
Other sectors
TOTAL
Production/consumptio
n analyzed (%)
Extractable savings
identified, %
EER to be checked
Possible sector savings,
%
20–30
25–30
45
25
25–35
14.6
EER = energy efficiency ratio.
From the beginning, Jeddah Hospital management showed a high awareness of EE issues and a strong
willingness to invest in feasible EE measures. Some conditions of ISO 50001 (e.g., installed energy
manager) have already been done by hospital management.
7.7.5
Key Findings of Restaurants
The Riyadh branch of the Al-Shurfa Restaurant represents a middle-class, traditional Arabian service
facility with about 400 seats in larger dining rooms or in 15 separate rooms for guests. It is open for
service about 16 h/d. The restaurant has been in operation since 2005, just before the publication of
the new SBC, defining minimum standards for heat and cooling insulation and efficient operation of
new private and public construction.
The estimated cooling demand of the Al-Shurfa Restaurant, Riyadh, was in accordance with SBC
monitoring procedure and applied 3,800 CDDs for the specific climate cooling demand. To adapt the
expected savings results as close as possible to international consumption standards, the following ISO
standards were followed: ISO 9000 for sound management organization, ISO 14000 for environmental
preparedness, and ISO 50001 for sound energy management; BATs were applied for alternative EE
proposals in the monitored client facility, only.
The estimated thermal cooling demand of the restaurant was in accordance with the SBC monitoring
procedure and used 3,800 CDDs for the specific climate demand, calculated to be about 9.4 MWhth
annually, corresponding to an occupied building volume of the three main service floors of around
12,000 m3, as shown in Table 7-11.
The described technical and economical production and consumption patterns characterize the AlShurfa Restaurant in Riyadh as a well positioned and active market player in the restaurant service
business, representing an energy- and water-intensive facility with locally rather high specific energy
and water consumption and consequent demand in EE consultancy.
The investigated specific power consumption for restaurant services has been estimated to be about
500 kWhel/m2/y for the restaurant services and a reported specific water consumption of about 0.350
m3/seat/d, on average.
These figures emphasize EA investigations regarding which part of the different restaurant services
would have the highest specific consumption levels and, consequently, the biggest demand for a
successful EE analysis and potential sustainable EE investments.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-11: Basic Technical Fact Sheet and References for the Al-Shurfa Restaurant, Riyadh
Item
Description
Volume
Characterization
Type of restaurant
Technical size
Multi-facility restaurant
1 main building, 2 floors,
massive construction,
noninsulated walls and roof
2.463
Maximum 400 seats
9,600 m2
11,200 m3
Mid size
~110 employees over 3
shifts
Power consumption,
2012
510
Specific power
consumption
Water consumption 2012 4,920
50
Specific water
consumption
HW preparation
Guests/mo
Cited references:
ISI Fraunhofer
Howard, Jeffrey
HVAC handbook
BINE study
Prof. M. Kubessa
HTWK-Leipzig
World Bank/IFC
EC OPET-CS network
Exclusively from power
5,000 (summer) and 4,000
(winter)
MWh/y
kWh/m2/y
m3/y
L/guest/d
~40% above mean EU
level
Own water wells,
normal consumption
76%/bed day
Occupancy rate
Commercial facilities energy
demand
How to make an energy audit
Recknagel-Sprenger-Schramek
Energy efficiency in
restaurants
Collected commercialindustrial energy benchmarks
BINE Karlsruhe edition
2010
Stanford University
Oldenburgverlag
Web-search-analysis
2009
2008 edition
Karlsruhe, 2010 edition
Handbook on energy
efficiency benchmarks
Collected public-commercialindustrial energy benchmarks
Publication of John Wiley &
Sons
Publication of EC DG
TREN/OPET network
2006 edition
BEA Energy agency
publication, No 1289/2001
2009 edition, updates
in 2011, 2013
1999 edition, updated
in 2005 and 2010
EE Proposals for Al-Shurfa Restaurant
Three groups were analyzed (short, medium, and long term) within seven pilot EE measures, all in the
electricity consumption sector; electricity makes up about 95 percent of the restaurant’s energy
demand. Table 7-12 presents proposed EE measures for the restaurant.
The main benefits achievable from implemented EE measures were detected in the electricity
consumption sector of the restaurant operation. Due to the existing main supply structure for electricity
applications at the Al-Shurfa Restaurant, an increased specific demand for cooling, ventilation, HW
preparation, and lighting had been analyzed at the restaurant previously; some reasons could be
identified during several site visits and inspections.
The biggest savings for the Al-Shurfa Restaurant, with a rather short payback time, could be achieved
for the PF compensation measures (1.1 years), when assuming a virtual tariff for reactive power by 50
percent from active power.
The identified savings proposals may assist in a technically more efficient restaurant operation and/or
an increased number of interested guests and business due to reduced service costs and increased
services in the restaurant premises.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-12: List of Proposed EE Measures for the Al-Shurfa Restaurant, Riyadh
Considered Consumption Sector electricity
1
2
3
4
5
6
7
Exchange ICB lamps for energy-saving LED
lamps (100 ICB by 60W w LED by 10W)
AC package (9x30 kW) operation better
adapted to outside temperature and
hospital occupation
Installation of PF compensation for
achieving cosphi >0.9 (existing cosphi
assumed to be 0.76)
EE Measure
Replace old ICBs with new LED
lamps
AC optimization via PLC
programming tool per main
feeder
PF compensation with
condenser unit (87,6 kVA-r)
installation at main SEC cable
feeder
VSD inverter load regulation of all big (water VSD installations at motor
pump) motors, example for 8 pumping
supply board
motors by 2.5 kW
Solar-thermal roof or window shading HW
Install at min 20 m2 collectors
collectors for sanitary HW preparation
by 2 sqm
PV roof (and/or) wall-shading installation
Install at min 40 m2 PV panels
with direct HVAC feeding of specification AC by 1 sqm
units per building
Install trigeneration unit by 50 kW-el/55
Install a pilot 50 kW-el
kWth to replace SEC-power import and 10
trigeneration and connect to
electric HW boilers (2.5 kW-el) with usage of HW and AC supplies
a new heat buffer (1000I) for permanent
HW supplies
TOTAL
MWh-el
Physical
Savings
Cost
Savings Payback
kWh/a
EUR/a
Years
12500
562.5
2.1
263250
11.846
1.1
353000
15.405
1.15
29000
1220
2.15
24000
960
5.2
10000
420
8.3
472500
15800
4.43
1.101
To adapt the expected savings results as close as possible to the international consumption standard,
the following ISO standards were followed: ISO 9000 for sound management organization, ISO 14000 for
environmental preparedness, and ISO 50001 for sound energy management; BATs were applied for
alternative EE proposals in the monitored client facility, only.
7.8 Potential Energy Savings for KSA from Case Studies
For this study, clients from certain business areas (restaurants, hotels, shopping malls, and hospitals)
plus industrial production companies (cement production and plastic production) were contacted. SME
clients in the Kingdom were focused on because they are representative of actual economic
development and to avoid duplicating work of the KSA oil and gas industries.
Specific commercial and industrial sites corresponding generally to SME size with rather high energy
consumption and with tested commitment were selected with the assistance of national/local trade
agencies and by the assistance of KAUST-PM. The sites were visited and business data investigated on
the basis of an in-depth EE audit; respective savings have been identified when compared with the
international consumption standard.
The savings results achieved per location will be used to assess and estimate the potential energy
savings more on a regional and national KSA level through a simplified replication procedure.
7.8.1
Methodology Used for Regional/National Extrapolation
The extrapolation of calculated savings collected from the individual client sites to the regional/national
level was conducted, depending on comparability of resources used and business structures executed
via simple replication factors, using specific energy savings per unit of product or similar measure.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
The final participants in the study were one mid-sized cement plant, one larger shopping mall, a midsized hospital, two hotels, and a mid-sized restaurant in Riyadh and another in Jeddah city. Within these
audits, significant potentials to improve the individual EE could be identified for each company. Results
were taken from different energy audits also done for this project. These identified the main savings
potential for different components and resources used.
For each of the analyzed sectors, a multiplication factor was derived to calculate the efficiencies on the
national level. These factors differ from sector to sector and are based on open-source references and
reports. In cases such as the cement sector, the saving potential was calculated for each kWh per year
per ton; therefore, it had to be multiplied by the total, annual, national production capacity. Other
examples are the hotels or restaurants, which had to be multiplied by similar units. A total energysaving potential was calculated from the analyzed sectors for Saudi Arabia.
7.8.2
Cement Plants
For the assessed mid-sized ACP cement plant, an individual savings potential of 70.2 GWh/y has been
identified. The plant management reported an annual cement production capacity of 1.5 Mio t/y.
When considering the national production capacity for cement of 51.1 Mio t/y and receiving a specific
savings potential of 47.4 kWh/t produced cement, the national savings potential was extrapolated to be
2,470 GWh/y. Table 7-13 presents details of the savings potential.
Table 7-13: Individual and National Savings Potentials of EE Measures in the Cement Sector
EE measure
ORC waste-heat usage
VSD for motor drives
New bag-filter system
Raw mill replacement
New control of air pressure
Implement EnMS
Reduced temperature
Modification of air pressure
Implementing use of
absorption chillers
TOTAL
Individual energy-savings
potential
Specific energy-saving
potential
National savings
potential
kWh/y
47,906,160.00
6,511,680.00
3,038,515.00
kWh/y/t cement
32.3
4.4
2.1
GWh/y
1,684.00
228.90
106.81
5,057,740.00
835,107.00
4,790,616.00
92,030.00
780,337.00
1,209,542.00
3.4
0.6
3.2
0.1
0.5
0.8
70,221,727.00
47.38
177.79
29.36
168.40
3.24
27.43
42.52
2,468.44
EnMS = energy management system; ORC = Organic Rankine Cycle; VSD = variable-speed drive.
7.8.3
Shopping Mall
Malls represent a rather typical service-business area within the whole country, differing by size and
trading structure in accordance with population modal split and existing income structure, and having
specific impact and close relationships with the fast-growing population and its increasing net income
volume.
Table 7-14 gives an overview of the EE measures applicable to a hotel in Riyadh with about 90 rooms.
The energy audit of this large mall in Jeddah with about 500 clients (and a high share of its own power
generation) presented a remarkably different picture than energy audits from other malls, probably
because of a different cooling technology. Therefore, for the shopping malls, an (upper) national
replication potential was assumed, because location, size, and climate conditions may have a
remarkable impact on the actual energy consumption of shopping malls.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-14: Identified Efficiency Measures for a Large Pilot Shopping Mall in Jeddah and its Replication
Potential for All of KSA*
Individual savings
potential
Regional savings
potential
Short-term
Energy monitoring system (complete)
EnMS according to ISO 50001
Energy-saving concept for all involved parties
Intelligent lighting controlling system
Increasing internal temperature (in zones or complete)
Controlling inside temperature relative to outside temperature
PF load management system
Glass doors at refrigerated shelves in Danube market
Periodic maintenance of the motor drives
Subtotal
Medium-term
Absorption chillers working with MT waste heat
Shadowing for sunlight-exposed parts of the building
Frequency VSD converters for large motor drives
Subtotal
Long-term
Changing ICB-lamps to LED lamps
MWh/y
5,157.4
6,157.4
5,157.4
4,125.9
2,406.8
1,891.1
6,000
275.1
206,3
30,477.4
GWh/y
128.94
129.2
128.9
103.2
60.2
47.3
125.5
6.9
5.2
761.94
6,017.0
5,017.0
1,289.4
13,323.3
150.4
150.4
32.2
333.8
Increasing the mall’s roof insulation
Replace inefficient motor drives
Subtotal
TOTAL
3428.3
386.8
12,077.0
55,877.7
EE measure
8251.9
206.3
86.0
9.7
301.92
1.396,94
EE = energy efficiency; EnMS = energy management system; ICB = incandescent bulb; LED = light-emitting diode;
PF = power factor; VSD = variable-speed drive.
*Estimate: 20 of 80 malls of similar size.
Most of the existing shopping malls were designed (and partly constructed) before the new SBC was
issued in 2010. Therefore, only minimum standards of heat insulation, using shadow-protection and
natural air ventilation, were considered during planning and implementation in some instances. Thus,
some of the existing mall construction recognizes an overestimated cooling demand, especially during
summer and in the front area facing the sun.
7.8.4
Hotels
Hotels represent a rather typical service-business area inside the whole country, having a specific
impact and close relationship with the fast-growing construction and infrastructure development
industries in KSA.
Table 7-15 gives an overview of the EE measures applicable to the M-Hotel in Riyadh, with about 90
rooms. In parallel, the energy audit made of a mid-sized hotel in Jeddah (about 220 rooms) showed a
remarkably different picture than the energy audit described above, probably partly due to a different
cooling technology. For details, compare Table 7-14 and Table 7-15.
Hence, for the hotels, a more regional replication potential was assumed, because climate variations
and travel purposes of hotel guests seem to have a remarkable influence on the actual energy
consumption.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-15: Identified EE Measures for the Small M-Hotel in Riyadh and Its Regional Replication Potential*
EE measure
Replace 500 old ICBs with new LED lamps
AC optimization via spec. PLC programming tool per main
feeder
PF compensation with installation of condenser unit (136
kVA-r) at main SEC cable feeder
VSD installations at motor supply board, thermal
VSD installations at motor supply board, electrical
Install a minimum of 20 m2 × 2 m2 collectors
Install a minimum of 40 m2 × 1 m2 PV panels
Install a 50 kWel trigeneration unit and connect to HW and AC
supplies, thermal
Install a 50 kWel trigeneration unit and connect to HW and
AC-supplies, electrical
TOTAL
Individual savings
potential
Regional savings
potential
MWh/y
12.2
45.2
GWh/y
0.85
3.16
231.76
16.22
1.01
29.0
24.0
10.0
225.0
0.07
2.03
1.68
0.70
15.75
175.0
12.25
753.17
52.72
AC = air conditioning; HW = hot water; ICB = incandescent bulb; LED = light-emitting diode; PF = power factor; PLC
= programmable logic controller; SEC = Saudi Electric Company; VSD = variable-speed drive.
*Estimate: 70 hotels of the same size expected.
The M-Hotel in Riyadh represents a typical, small but highly sophisticated hotel, with traditionally good
services and a reasonable price–value relationship, suitable mainly for businessmen.
The hotel exercises a very specific connection to the Saudi Electric Company (SEC) grid, using a separate
LV-analogous power meter for each individual hotel room. Thus, the maintenance engineer has a full
and rather detailed overview of the specific load-behavior of guests for room location and for
occupation impact at each room.
The hotel was constructed during 2006/2007, before the inauguration of the new SBC in 2007/2008.
The M-Hotel management is aware of the cost impact of wasted electrical energy, especially through
controlled lighting of apartments, dedicated HW boiler operation in the apartments, and through advice
to guests on possibilities for AC energy savings via slightly increased design temperature used for AC
control inside the rooms. A corresponding message was given to guests in the hotel regularly about
possible savings, especially by the more demand-oriented AC operation.
The estimated specific energy consumption per bed (19.800 kWh/bed/y) and about 390 kWh/m2/y per
about a 10,000-m2 service area are both in the upper range of small-sized family hotels with similar
cooling/heating degree-day standard (3,800 CDDs), showing an acceptable international consumption
standard.
Table 7-16 gives an overview of the EE measures for a mid-sized hotel in Jeddah.
The Enmar Hotel in Jeddah was constructed in 2009/2010, just before the inauguration of the new SBC.
Until now, there has not been secondary legislation in KSA that directly coupled the construction license
with a certified energy-consumption standard of new commercial buildings.
The hotel management seems to be aware of the cost impact of wasted electrical energy, especially
through controlled lighting of apartments, dedicated HW boiler operation in the apartments, and
through advice to guests on possibilities for AC energy savings via a slightly increased design
temperature used for AC control inside the apartment.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-16: Efficiency Measures for a Mid-sized Hotel in Jeddah and Its Replication Potential in Western Saudi
Arabia* (estimated: 80 hotels by same size in KSA)
EE measure
Absorption chillers for air-chiller replacement
Electric boiler replacement
LED lighting instead of ICB and halogen lights
Implementation of an EnMS
Increase AC target temperature by 1 °K
VSD for large motor drives
Compensation of reactive PF
TOTAL
Individual savings
potential
MWh/y
3,820
650
80
290
290
100
2,150
7,370.00
Regional savings potential
GWh/y
305.86
51.78
6.12
23.15
23.34
7.86
171.81
589.92
AC = air conditioning; EnMS = energy management system; ICB = incandescent bulb; LED = light-emitting diode;
PF = power factor; VSD = variable-speed drive.
*Estimated: 80 hotels of same size in KSA.
The estimated specific energy consumption per bed (21.300 kWh/bed/y) and per m2 service area (~320
kWh/m2/y) are both in the upper range of mid-sized family hotels with an acceptable international
consumption standard.
7.8.5
Hospitals
The analyzed hospital in Jeddah represents a rather typical, middle-class construction with reputable
clinics and medical nursery services just in the center of Jeddah city. The annual energy-savings
potential of 5,383 MWhel is shown in Table 7-17. There exists a minimum of about 80 comparable
hospitals in the country with a similar construction frame and comparable annual consumption plus
savings potential as calculated in Table 7-17. Summing up all the potential savings gives a national
savings potential of about 427 GWh/y. This would represent about 0.5 percent of KSA’s total
consumption in 2011.
The hospital management seems to be aware of the cost impact of wasted electrical energy, especially
through controlled lighting of floors and rooms, dedicated HW boiler operation for the hospital rooms,
and through advice to patients and guests on possibilities for AC energy savings via a slightly increased
design temperature used for AC control inside the hospital rooms and the office rooms.
The estimated specific energy consumption per bed (22.500 kWh/bed/y) and per m2 service area (~290
kWh/m2/y) are both in the upper range of medium-sized, private hospitals, showing an acceptable
international consumption standard.
The country-wide replication of potential savings in medium-size hospitals could be investigated for the
only accessible consumption data on the private hospital sector and, therefore, deliver a limited
contribution of all savings possibilities at hospitals in KSA.
7.8.6
Restaurants
The analyzed restaurant, Al-Shurfa, is located in the heart of Riyadh city, in a neighborhood near
Madina road and represents a mid-sized (300 seats, two floors, and a service area of about 3,000 m2)
family restaurant with different food and other services inside and outside the restaurant.
The Al-Shurfa Restaurant in Riyadh was constructed in two main phases between 1980 and 2008,
shortly before the inauguration of the new SBC was established in 2009/2010. Until now, there has not
been secondary legislation in KSA that directly coupled the construction license with a certified energyconsumption standard of new commercial and service buildings. In particular, the roof insulation of the
Al-Shurfa Restaurant seems to represent a poor insulation condition against direct and indirect solar
heat.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-17: Individual and Concluded National Savings Replication Potential of EE Measures in Hospitals*
Consumption sector
Exchange of 500 ICB lamps by energy-saving
LED lamps (replacing 60-W ICB with 10-W LED
bulbs
AC-package operation better adapted to
outside temperature and hospital service
occupation
Upgrading of existing PF compensation for
achieving cosphi >0.9 (existing cosphi assumed
to be 0.75; see Appendix)
VSD inverter load regulation for all big
(elevator and pump) motors (e.g., 29 12.5 kW
elevator motors).
EMS design and implementation for the main
three hospital buildings
Solar thermal roof or window shading HW
collectors for sanitary HW preparation
PV roof- and/or wall-shading installation with
direct HVAC feeding of specification AC units
per building
Install 250–300 kWel trigeneration unit to
replace two electric HW boilers (2×150 kWel)
used as a heat buffer, and one bigger, 300kWth AC-package unit by absorption cooling
(efficiency is very design dependent) for
permanent lobby acclimatization
TOTAL
EE measure
Replace old ICBs with new LED lamps
Individual
savings
potential
MWh/y
62.5
Regional
energy
savings
potential
GWh/y
5.0
AC optimization via PLC
programming tool per main feeder
330.0
26.4
PF optimization (and automation)
using a specific PLC tool per main
SEC cable feeder
VSD installations at motor supply
board
560.0
44.8
32.6
28.42
Detail analysis of sector specific
demand; design of suitable EMS
logistics adapted to spec. building
functions, supply and install adapted
remote EMS-control system
Install a minimum of 20 m2 × 2 m²
collectors
Install a minimum of 40 m2 × 1 m² PV
panels
750.0
65.00
24.0
1.92
10.0
0.80
1,500.0+
1,975.0
278.0
5382.7
426,2
Install a 250-kWel trigeneration unit
and connect to HW and AC supplies
AC = air conditioning; EMS = energy management system; HVAC = heating, ventilation, and air conditioning; HW
= hot water; ICB = incandescent bulb; LED = light-emitting diode; PF = power factor; PLC = programmable logic
controller; PV = photovoltaic; SEC = Saudi Electric Company; VSD = variable-speed drive.
*Estimate: about 80 hospitals of the same size.
The restaurant management seems to be aware of the cost impact of wasted electrical energy,
especially through controlled lighting of floors and rooms, dedicated HW boiler operation for the
restaurant kitchen and guest rooms, and through advice to guests on possibilities for AC energy savings
via a slightly increased design temperature used for AC control, especially inside the restaurant dining
rooms and offices.
The estimated specific energy consumption per guest (2.500 kWh/guest/y) and per m2 service area
(about 290 kWh/m2/y) are both in the upper range of mid-sized family restaurants, showing an
acceptable international consumption standard.
When considering the occupation and climate impact of similar restaurants by size and service
structure, the national energy-saving potential for this type of food service could be estimated. As seen
in Table 7-18, the national savings potential for this type of medium-sized open restaurant reaches
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
nearly 700 GWh/y. This shows the remarkable savings potential that could be tapped by rather simple
individual EE measures.
Table 7-18: EE Measures for Mid-sized Restaurants and Their National Replication Potential
Consumption sector: electricity
Exchange of ICB lamps by energy- saving
LED lamps (100 60-W ICB replaced with
10-W LED bulbs
AC-package (2×25 kW) operation for lobby
better adapted to outside temperature
and service occupation
Installation of PF compensation for
achieving cosphi >0.9 (existing cosphi
assumed to be 0.76; see Appendix)
Installation of PF compensation for
achieving cosphi >0.9 (existing cosphi
assumed to be 0.76; see Appendix)
VSD inverter load regulation of all big
(elevator and pump) motors (e.g., 3 3.5
kW elevator motors)
Solar thermal roof or window shading HW
collectors for sanitary HW preparation
PV roof- and/or wall-shading installation
with direct HVAC feeding of spec AC units
per building
Install a cogeneration unit of 50–55 kWel
to replace SEC-power import and 10
electric water boilers (2.5 kWel), using a
new heat buffer (1000 l) for permanent
HW supplies
TOTAL
EE measure
Replace old ICBs with new
LED lamps
AC optimization via spec. PLC
programming tool per main
feeder
PF compensation with
installation of condenser unit
(136 kVA-r) at main SEC cable
feeder: ACs
PF compensation with
installation of condenser unit
(136 kVA-r) at main SEC cable
feeder: electrical
VSD installations at motor
supply
Energy savings of National savings
one restaurant,
potential,
MWh/y
GWh/y
12.2
8.30
122.2
83.10
149.0
101.32
680.0
462.41
29,000.00
19.72
Install at a minimum of 20 m2
× 2 m2 collectors
Install at a minimum of 40 m²
× 1 m2 PV-panels
24,000.00
16.32
10,000.00
6.80
Install a 50-kWel trigeneration
unit and connect to HW and
AC-supplies: thermal
225.00
0.15
Install a 50-kWel trigeneration
unit and connect to HW and
AC-supplies: electrical
175.00
0.12
1,026.8
698.23
AC = air conditioning; HVAC = heating, ventilation, and air conditioning; HW = hot water; ICB = incandescent bulb;
LED = light-emitting diode; PF = power factor; PLC = programmable logic controller; PV = photovoltaic; SEC =
Saudi Electric Company; VSD = variable-speed drive.
The investigated savings potential in the Al-Shurfa premises mainly was concerned with the rather light
and noninsulated construction of service apartments, especially on the second floor; thus strongly
depending on running AC cooling during the full-service time (~14 h). The investigated possible
dissemination of analyzed EE country savings similar to the Al-Shurfa restaurant mainly have shown a
rather high possibility for replication due to size, customer behavior, and technology used.
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7.8.7
Concluded Savings Potential from Case Studies
The concluded energy-savings potential, collected from seven different EE measures when conducting
the six EA case-study reports in three different KSA regions, are presented in Figure 7-5.
In conclusion, if successfully implemented, the identified, replicated EE measures for the case studies in
this study could save a total of about 6 TWhel energy annually (5.9 percent from country total), resulting
in >18 TWhth primary energy annually by actual system generation efficiency when being implemented
by one step.
Potential cumulated energy savings (GWh) derived from 7 pilot cases 2012
3,000
2,500
2,000
1,500
1,000
500
0
Restaurant
Hotel Jeddah
Hotel Riyadh Shopping Mall Cement Plant SWCC-Jeddah
Hospital
Figure 7-5: Replication Potential for the Analyzed EA Business Sectors in KSA
The investigated list of cumulated EE measures per sector could be considered as a first step for a
national EE roadmap for KSA to be able to support the transition to a more efficient and sustainable
energy system, and assisting in a serious limitation of the future growth in system energy (electricity)
demand.
These cumulated savings represent a theoretical potential that normally could not be mobilized
completely by one step within a limited period of time (e.g., 1 year). Some of these proposals, when
considering different applied energy resources such as electricity (for driving and cooling), heat for
water boilers, and liquefied petroleum gas (LPG) for cooking, may be added to each other as they
comply with technologically different energy resources.
Others, like the technologically different EE proposals, which are based on the same applied type of
energy, mainly electricity, have to be considered and analyzed (from consumption behavior) differently
and should implement specific mobilization factors, like the load factor, time of operation, and social
impacts, like family composition, terms of use, and type of equipment installed.
Taking this into consideration, a careful customer analysis has to be made before any serious,
extractable, practical savings potential could be identified. This is best done by sector.
Comparing this theoretical savings potential with a practical implementation experience in developed
countries of EU and the United States, about 10 to 20 years have to be considered as realistic before full
implementation is realized. This means that only about 1.5 percent to 2 percent of extractable savings
could be achieved annually.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
7.9 Recommendations
•
•
Prioritize technical and organizational measures based on the forecasting results for Saudi Arabia
for 2010–2040 from the findings of different studies (Bain, Brattle, Chatham House, IEEJ,
KACST/AEA, KAPSARC, SEEC, Saudi Arabia Energy Efficiency Report, K. A. CARE, Tyndall, and others).
The study highly recommends implementing the technical and organization measures outlined in
Table 7-19.
Table 7-19: EE Measures Applicable in KSA by Priority
High Priority
4.
10.
15.
19.
20.
22.
23.
24.
34.
49.
51.
63.
66.
69.
70.
Building an energy management system for better information on energy consumption
Curtailable load program
Direct load-control program
Energy efficiency fund to finance investments in EE
Energy managers at large-scale consumers (as in Europe, where most industrial companies have a
specialized engineer responsible for all energy consumption within the company)
Energy service industry: support to upgrade and promote a Saudi energy service industry
Energy services companies
EPC contracts
Interruptible tariff program
Private sector investment in electricity and water projects, increasing the role of the private sector
Promotion of an energy-service industry
Tariff restructuring
Time-of-use tariff programs for major industrial and commercial customers
Voluntary actions by industry and commerce supporting EE
Walk-through energy audits of governmental, commercial, and industrial facilities
Medium Priority
1.
9.
14.
21.
25.
31.
39.
47.
56.
59.
67.
68.
Annual award system for EE solutions
Culture of patenting and entrepreneurship in Saudi Arabia in the field of EE
Demonstration projects for EE
Energy planning authorities (SEC, MOWE, ECRA, and others) to be strengthened in achieving better EE for
KSA
Feed-in tariff for REs
Information: a program of EE information and awareness
EE laws
Performance monitoring
Reward system for EE equipment
Saudi Energy Efficiency Center, support to the SEEC
Training: technical and managerial training through workshops and seminars (energy audits with quick
savings, detailed audits, EE financing, performance contracts)
Vocational training on EE
Low Priority
3.
6.
8.
12.
27.
28.
7-26
Budget allocation to support the fields of science and technology in EE
CDM projects to be supported
Coordination and links between Saudi universities and industry
Customer invoice support and customer system check
Human resource development and EE within organizations
Incentives: provisions of incentives to purchase efficient appliances
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
29.
32.
33.
35.
37.
40.
41.
42.
45.
46.
48.
50.
53.
54.
55.
57.
58.
60.
•
•
•
•
•
Information campaigns on EE
International cooperation on EE
International energy companies that conduct R&D in the EE sector, attracting companies/buying shares
Joint ventures, inviting leading RE technology manufacturers into the country
Labeling of electric HH appliances
Leasing: an energy-efficient equipment leasing program
Market liberalization in the Saudi power sector
Minimum standards for new power stations, new cogeneration, new desalination plants
NEEAP update
Operation and maintenance: improvement of operations and maintenance, better standards, training,
supervision
Political support for EE as one of the main policy areas in Saudi Aramco KSA
Programs for promotion of EE
R&D activity in both public and private sector
R&D programs on EE
Revolving fund for EE investments
Rewarding innovators and researchers for EE solutions
SBC: strict implementation
Standardization and norms with SASO on EE equipment (ACs, refrigerators, lighting, building insulation)
Detailed investigations of the different levels of waste-heat use, including feasibility studies
considering individual conditions, are necessary in the industrial sector in the Kingdom. For all
possible technical measures, the special conditions arising from the climate and low energy prices in
KSA must be taken into account.
For a breakdown of the identified potential of specific investments, several feasibility studies and
detailed engineering solutions are necessary.
To become an efficient tool for EE implementation support to smaller industries, the planned ESCO
and audit providers should use regional structures and work on latest BAT standards, maybe on the
basis of a revolving investment fund to better control the expected paybacks according to the
updated demand. The possible usage of the identified waste-heat energy potential strongly
depends on the production technologies applied and their respective demand in steam, heat, and
HW, structured by volume and temperature level. This represents a key business opportunity in the
energy sector in KSA.
Business Potential I. Improve industrial equipment, maintenance, and staff training and inspection
procedures. Such steps help minimize losses and improve efficiency. For example:
− Optimize existing air or feedwater preheating.
− Reduce convective or radiation losses by improving isolation.
− Equalize flow distributions.
− Avoid or minimize load changes and fluctuations.
− Minimize leakages (e.g., at dampers, valves, flanges, casings).
− Avoid fouling of heating surfaces by material properties, soot blowers, and so forth.
− Reduce pressure drops (e.g., by routing, surface smoothing, avoidance turbulences).
− Reduce all types of water losses.
Business Potential II. The use of unavoidable heat for chilling processes offers business potentials,
especially for manufacturers of absorption chillers. Energy prices in KSA are still so low that
absorption chillers compete with conventional electric chillers. Possible solutions depend on the
availability of the waste heat with respect to the operation mode of the plant, especially if the
generation is continuous or periodic, such as after-batch processes and the cooling demand.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
•
•
•
•
•
Business Potential III. The installation of additional power generation equipment using waste heat
usually demands specialists. Therefore, there is a business potential for low-temperature ORC
processes or waste-heat steam cycles; in particular, the integration of auxiliary power generation
into the public net.
Electricity generation from waste heat <350 °C must be done by ORC processes; at >350 °C, steam
turbine or engine cycles would also be relevant for KSA. Using all waste heat of the “other
industries” sector would provide a power potential of about 141 MWel.
Establishment of pilot project platforms and energy assessment programs. Saudi universities can
participate in research and development in the various sectors, such as renewables, energy audits,
smart grid technologies, and waste heat. The programs can be established with partnership of key
players in KSA and sponsored by the private or public sectors.
Implementation of smart grid and decentralized power-generation sector. This allows for overall EE
improvement, reduce losses, and enables integration of RE into the power network. The smart grid
implementation represents one of highest priority areas of academic research, business
opportunities, and return on the investment.
The most important result of the residential monitoring campaign is that there is a large share of
energy consumption for the AC systems. According to metering results, AC is responsible for
roughly 70 percent of the residential energy demand. Reducing the energy demand of the
residential sector directly leads to a reduction of AC energy demand. This can be achieved via three
different factors that have a direct influence on the energy consumption:
− The outside insulation of the residential buildings has a strong impact on the demand of
cooling. The better the buildings are insulated, the less they need to be cooled.
− The desired indoor set temperature and the designed ventilation air flow influence the
residential cooling demand.
− The efficiency of the AC units (energy efficiency ratio [EER]) has a strong impact on the electrical
energy consumption needed to meet the cooling needs of the building.
− Replacing inefficient HH base equipment, like old ICB bulbs and refrigerators
Table 7-20 shows examples for these five basic, energy-saving measures. It can be seen that an
energy saving of 15 percent can be achieved with the simple effort of increasing the indoor set
temperature by 2K. AC devices with an EER of 3, which are state of the art for the residential sector,
can reduce the cooling demand by a factor of 2.
Table 7-20: Effects of Different Energy-saving Measures on Cooling Demand Classified by Effort
Measure
Increase set temperature by 2 °K
Insulate ceiling/walls with 7.5-cm
polystyrene layer
Replace AC hardware
(double EER)
Replace 30 ICB lamps/HH with
LEDs
Replace old refrigerators with
modern devices
Effect on HH
electricity
demand
Savings effect on regional
electricity demand,
MWh/HH/y
Estimated
effort
Estimated
payback, y
−15%
−30%
4.5
12
Low
Medium
0.3
~4
−50%
20
High
2.4
−75%
3.75
Medium
0.9
− 50%
2×400 kWh/HH/y
High
4.3
KSA Profiles in Comparison to International Standards
The demand for AC is in direct correlation to the ambient temperature, the set room temperature,
the existing humidity, and the volume to be cooled. Due to the very different climate conditions in
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
KSA, this is not easily comparable to European standards. Consequently, this will not be addressed
in the following detailed discussion, which concentrates on the remaining household demand.
Nevertheless, the residential metering for around 45 houses at the KAUST settlement and DU
compound in Dammam confirmed a consumption share of 60 percent to 70 percent for HVAC
purposes.
The specific metering results showed electricity consumption for the considered houses (without
AC) of about 3 kWh/m2/mo, resulting in an average total electricity consumption by 18 MWh
annually for the assumed residential house with a living space of 500 m2 for a five-person HH. This
also included production of domestic HW. Typically, one person has a demand of 1 MWh/y for
domestic HW. For the described household size, this leads to an additional yearly power demand of
5–13 MWh. The remaining HH demand sectors are, on average, washing (3 percent), cooking (2
percent), and lighting (2.5 percent), if the house has a centralized AC cooling supply.
For comparison, a typical German family of three has (by German state statistics) an annual
electricity consumption of 4 MWhel. This is roughly one-third of the sample Saudi HH. One reason
for this may be the different type of energy used for the living areas by a German family (about 50
percent for heating with gas or oil) and a typical Saudi family and the resulting higher amount for AC
(70 percent–80 percent with electricity).
Table 7-21: Residential and HH Recommendations
1
2
3
4
5
6
7
8
9
10
•
The residential sector represents nearly 50% of total KSA power consumption and about 15% of KSA total
primary energy demand.
The residential sector demand is split into two main groups: HVAC loads (about 75%) and HH demand
(25%), with a single, maximum, daily peak for all week days of about a 15% energy share.
On the microeconomic level, current legal framework conditions hinder deployment of RE technologies for
the residential demand sector.
RE generation (e.g., PV) generally suits demand patterns of smaller residences and can reduce the peak
load of remote settlement areas by >15%.
The regulatory framework in KSA should be adapted to ensure understanding and viability of specific pilot
RE projects, enforcing maximum replication.
Comparative assessment of remote electricity generation technologies should be done on the
macroeconomic level to reveal real costs rather than neglect “hidden” costs, such as avoided peak-load
management.
PV-hybrid systems should and could be promoted as feasible stand-alone solutions for remote settlement
areas combined with public interests (schools, hospitals) and/or commercial or industrial company demand
to “soften” load curves.
EE and RE awareness programs should be initiated jointly with regional ESCO branches; initiators could
serve as best-practice examples.
Specific rewards for pilot initiators should be considered to accelerate the deployment of small-scale RE
systems for self-supply.
By comparison with internationally proven EE/RE site planning and operation experience, positive
employment aspects could be identified for respective KSA engineers and consultants.
Additional EE improvement is technically feasible if the public sector and power generation
companies create incentive programs, promote a legal environment and house-management
awareness, and enforce implementation of the identified EE measures among Saudi households.
The achievable savings in residential metering are shown in Table 7-22.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-22: Potential EE Savings in the Residential Sector
Load sector
AC
HH lighting
Cooking
Decentralized desalination
•
•
HH demand analyzed
30% by electric supply
20%–30% from total HH
demand
Specific consumption
technology analyzed
Possible savings derived,
%
Overaged
Overaged
70% by LPG
Not yet existing widely
30–35
15–20
10–15
30–40
The identified energy-saving potentials in industrial sectors mandate having an overall EE strategy in
KSA that has to be supported by adequate regulatory measures to be successful.
− Closing the gap of available demand data for Saudi Arabia’s industry would significantly improve
the analysis and facilitate the planning of policies to promote EE.
− EE standards should become obligatory to accelerate adaption of EE measures.
− Energy audits have already proven that the analyzed EE potential is realistic; replication is
necessary to achieve savings.
− First EE projects should be presented publicly to function as role models for other Saudi
companies.
− Implementation of an EnMS that monitors energy consumption and improves efficiency
− It is highly recommended that sustainability programs be established and energy management
and sustainability teams developed to manage the energy operations in major industrial sectors
(e.g., power generation, cement, petrochemicals, refineries).
Integration of RE applications in the power generation system:
− The regulatory framework should be adapted to ensure understanding and viability of RE
projects.
− Comparison of electricity generation technologies should be done on the macroeconomic level
to reveal real costs rather than neglect “hidden” costs such as subsidies, and consider follow-up
costs from necessary technical improvements to grid extension and fast-starting reserve
capacities for balancing of bigger RE investments.
− PV-hybrid systems should and could be promoted as feasible stand-alone solutions for remote
areas in KSA or for large or isolated companies. The existing goals for large-scale RE plants
should be replenished with targets for small-scale applications, such as hybrid systems or RE for
self-supply.
− RE awareness and training programs should be initiated; initiators could serve as best-practice
examples.
− Rewards and incentive programs for initiators should be considered to accelerate the
deployment of small-scale RE systems for self-supply.
7.10 Business Opportunities (BO)
BOs Resulting from Increased Energy and Production Efficiency
As a primary result for commercial service clients, the implemented EE measures may initiate increased
production efficiency with a decreased volume of primary or secondary fuel and, consequently, lower
operating expenditure cost. This would lead to an increased payment schedule and, consequently, to a
reduced payback time for the considered EE cases, provided that all additional products could be sold.
Alternatively, a higher production volume by the same amount of consumed energy may lead to an
increased company turnover (and business result), depending on the existing national or international
market for the generated additional products.
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Economically, the considered commercial enterprises may achieve a better market position and
operational performance when implementing EE measures efficiently.
For public clients (e.g., hospitals), the increased service efficiency may allow improved service in the
form of an increased number of treated patients or number of possible diagnostic actions per month
and year.
Larger commercial and industrial clients in KSA having an annual energy consumption >10 MWh use
economically interesting conditions for their own electricity generation on the basis of diesel or LPG
fuel, applying existing legislative gaps for generation safety (and quality), and dismissing local emission
control.
BOs Resulting from Increased Number of Jobs
As a secondary result of the implemented EE measures, an increased number of jobs could be achieved
in the design, planning, implementation, and operation areas for new EE equipment and for operating
the installed RE equipment, leading to an increased company diversity, depending on the existing
business structure and production technology.
The ISO 50001 energy management standard recommends the installation of an energy engineer on
staff for companies with a consumption volume >10 MWh/y. Most of the investigated, smaller,
commercial EA clients had a consumption level between 1 and 10 MWh; therefore, a KSA-adapted
standard is proposed for the installation of an energy manager for companies already at >1 MWh/y.
This RE measure could create around 5,000 additional engineering jobs, when considering about 50
percent of the Saudi commercial-industrial service companies would be applicable for such a new
standard.
A rather small RE proportion was realized in KSA for 2012 (<1 percent of total generation capacity,
mainly for isolated PV field-test installations). On the basis of K. A. CARE figures for 2032, projections for
2012–2040 are that RE shares in KSA will increase from nearly 0.5 percent up to 25 percent, resulting in
a 1 percent increase per year to a total capacity of 96.4 GW (Table 7-23) of the expected RE generation
capacity in 2040.
Typical operational service-staff benchmarks for this type of BO impact in western European countries
were reported to be one staffperson per 1 MW installed and operated RE unit generation capacity (for
wind power) and about one staffperson per 2 MW installed and operated peak capacity for bigger PV
farms.
For CSP, installations do not exist that have reliable LT OP experience records. Data were collected from
Spanish medium-time CSP operators to calculate a 1.5-person staff as average operational effort.
In accordance with the current status of population development in KSA, and using roughly the same
employment rates as in western Europe (i.e., 35–38 percent of the population), this would give a RE
employment share of about 0.8 percent, which could be doubled through requested, specific, selfproduction of spare parts and/or productive operations and maintenance and workshop services, as
shown in Table 7-24.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
Table 7-23: System Power Capacity and Electricity Production for KSA
Corresponding to the expected generation capacity and operation conditions to 2040 in KSA and
applying the results from the KSA power system projections, the following employment figures could be
achieved in 2040:
Table 7-24: Employment Figures that Could be Achieved by 2040
Type of RE source
Operation staff
Maintenance staff
Workshop staff
13,800
24,500
57,800
8,000
12,000
20,000
15,000
12,000
15,000
35,000
20,000
10,000
Wind power
PV-Solar
CSP
Biomass/biogas
In total, the estimated employment impact for qualified engineers is approximately 150,000 to 163,000
OP specialists and 80,000 employees in specific KSA workshops, starting with spare parts distribution,
unit overhauling, and reproduction of key equipment, which must work in extreme Saudi weather
conditions.
Interim Complex BO Conclusions
Previous studies have shown that the rate of annual energy waste in Saudi Arabia is about 45 percent,
which is an average that threatens the country’s economic life essentially and future generations,
unless it is restored by all means possible to achieve optimal guidance of soundly adapted electric and
primary energy consumption per sector.
The “national campaign for electric consumption guidance” was initiated by the Ministry of Water and
Electricity in Riyadh to elicit the response of every citizen, company, and resident to the guidance, and
for them to contribute by following the correct behaviors when using the electrical power.
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Within the data analysis and the elaboration of the study, several possibilities to strengthen the
awareness of possible energy savings were detected, especially for the group of medium-sized
commercial-industrial service clients, through the following:
•
•
•
•
Improved legislation to support EE in the commercial-industrial setting and for the building
construction sector, plus information, promotion, and enhancement of a wide use of RE sources at
the decentralized level
Enhanced national promotion of identification and implementation of EE measures, using an
improved and diversified national EE regulation, including terms of the ISO 50001 standard in
compliance with existing national EE legislation and labeling in KSA
Prioritized measures for installation and regular monitoring of energy labeling of products,
establishing energy engineers in companies and utilities having an energy consumption >10 MWh/y,
and reporting a larger specific deficit in EE benchmark comparison
Strengthened implementation of EE measures on the basis of an improved national EE regulation
and using national/regional ESCO utilities to be established as soon as possible after having
implemented a more self-regulated legislation in the construction sector and controlling the
respective SASO regulation for EE labeling (Table 7-25).
Table 7-25: EE and RE Project Opportunities in KSA 2040
Scenario sector
Units
Final energy consumption
Electricity consumption
EE savings accumulated
RE integration
Cumulated RE inputs
Accumulated CO2 reductions
Mtoe
TWh
TWh
GW installed
TWh
Billion tons CO2
Base case,
2009
BAU, 2040
99,6
196
–
–
–
425.5
849.8
–
–
(212.2)
Project case, 2040
Conservative Optimistic
781.8
2,050
96.4
569.6
2,200
702.7
4,413
96.4
490.5
3,460
In addition, possible cumulated oil exports of 157.4 Mtoe could be realized by 2040 in KSA because of
implemented energy-savings measures and respective RE implementation equal to, at a minimum,
US$17 billion.
Evaluation criteria, shown in Figure 7-6, may play a certain role for successful project preparation,
implementation, and monitoring of results; they should be analyzed carefully before implementation.
They represent the general, internationally proven project background and may have to be
accomplished by specific regional legislation and regulation.
Depending on the national energy system action, planning EE/RE investments should be considered
more complex for long-term sustainability in KSA and be supported and prioritized against simple
system-expansion planning, especially where stabilizing the regional energy infrastructure.
Starting from a technical installation frame capacity of about 10 MW, a careful grid analysis has to be
conducted to guarantee a sound feed-in of all generated RE energy via the existing grid structure to
balance the regional, inductive load regime and to avoid unnecessary grid losses.
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CHAPTER 7: Study Findings and Conclusions, Recommendations, and Business Opportunities
• CBA analysis positive
• Project local feasibility approved
• Financial FS confirmed after monitoring
EE/RE Project economy
EE/RE project
EE/RE Project ecology
EE/RE Project OP safety
• environmental construction impact
• emission reduction control
• check of sustainability of savings (CDM)
• counter-check of existing tariffs and cost
• establishment of a sound RE feed-in practice
• cross-check of adoption of project size against
existing grid capacity
Figure 7-6: Concluded Complex Findings by EU Sustainability Criteria
From a technical installation capacity size of about 100 MW, a careful grid and load-flow analysis must
be made for planned RE locations to be able to balance the effort for a potential grid enhancement
between the production/generation and consumption for interested system parties.
The cumulated savings from this task have been achieved following the strengths, weaknesses,
opportunities, and threats methodology procedure.
List of Extra References Used in this Subreport
No
1
2
3
4
5
6
7
7-34
Title
Origin/Year
Study status report on RE
investments in Germany
EBRD SEEF status report on EE
investments
BMU-German Ministry of
Environment, Berlin, 2012.
EBRD London, 2011
www.ebrdseff.com
AREE monitoring report for a pilot LE CEC design Sweden, 2010; followresidential building (310 m2) in Jordan up to a MEDA project from 2008
Comment
Regularly updated
Regularly updated
Monitoring was done by a 1year metering with inside and
outside temperature analysis
ISI monitoring report for a LE building ISI Fraunhofer Institute,
50% from total savings
in South Korea
through improved building HR
2011
envelope
Sheffield University, UK, Faculty of Comparative case study for a
H.M. Taleb et al. Concept for
Architecture:
new 6-apartment MS house in
sustainable residential buildings in
KSA
Applied Energy, 2001; 88: pp 383– Jeddah (in total, 32% savings
possible)
399
HVAC operations manual, edition for
SME application
Gasson, A. Tariff policy in KSA—a
global perspective
Recknagel-Sprenger HVAC data
handbook, 2009 edition
Saudi Water and power forum,
Jeddah No-2008
1 °K target temp (−) may
deliver 7% savings
Hints for a savings-oriented
tariff policy
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