VTT-R-06411-09
Yearly average daily consumption (MWh/h)
RESEARCH REPORT
5000
4500
Heating
4000
Others
3500
AC & Heating
3000
Dish w asher
2500
Washing and dry
2000
Computer
Audiovisual
1500
Cold
1000
Lighting
500
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Week day
Saturday
Sunday
Distributed resources at customers’
premises
Authors:
Corentin Evens, Seppo Kärkkäinen, Hannu Pihala
Confidentiality:
Restricted
RESEARCH REPORT VTT-R-06411-09
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Report’s title
Distributed resources at customers’ premises
Customer, contact person, address
TEKES
Jarkko Piirto
PL 69, 00101 Helsinki
Order reference
TEKES
Juha Linden
PL 69, 00101 Helsinki
Project name
ENETE
1573/31/08
431/31/08
40298/08
40137/08
Project number/Short name
INCA
31305
27901
Author(s)
Pages
Corentin Evens, Seppo Kärkkäinen, Hannu Pihala
60/
Keywords
Report identification code
Residential flexibility, load curves, scenarios
VTT-R-06411-09
Summary
This report is part of the first stage of the ENETE and INCA programs. It presents first the
various distributed energy resources that could be found at the residential level including generation, storage as well as the normal consumption.
It follows with presenting an attempt at decomposing residential load curves into the different
end-uses for 2006. It uses this decomposition to estimate a global potential for residential
flexibility at the national level.
The end of the report builds two scenarios for the year 2020. The goal is to estimate how the
potential calculated for 2006 could evolve in a situation where the consumers and the government are not overly concerned by environmental issues on one hand and, on the other hand, a
scenario that could reach the targets set by the European Union for 2020 regarding green
house gases emissions, energy efficiency and renewable energy uses.
Confidentiality
Restricted
Espoo 12.2.2010
Written by
Reviewed by
Accepted by
Corentin Evens,
Research Scientist
Hannu Pihala,
Senior Research Scientist
Seppo Hänninen,
Technology Manager
VTT’s contact address
Corentin Evens, Biologinkuja 7, 02044 VTT
Distribution (customer and VTT)
ENETE consortium, INCA consortium
The use of the name of the VTT Technical Research Centre of Finland (VTT) in advertising or publication in part of
this report is only permissible with written authorisation from the VTT Technical Research Centre of Finland.
RESEARCH REPORT VTT-R-06411-09
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Preface
This report has been written by C. Evens, S. Kärkkäinen and H. Pihala for VTT in the context
of the ENETE and the INCA projects, funded for the biggest part by TEKES. It comes in the
following of work done in the ADDRESS project for the EU Commission.
The purpose of this report is to present the situation regarding flexibility characterization at
the residential level as well as scenarios regarding its evolution for the year 2020.
Espoo 12.2.2010
Corentin Evens, Seppo Kärkkäinen and Hannu Pihala
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Contents
Preface ........................................................................................................................3
1 Introduction.............................................................................................................6
2 Potential distributed energy resources of small customers.....................................7
2.1 DG and RES at consumers’ premises ............................................................7
2.1.1 The characteristics of generation systems ...........................................7
2.1.2 Capabilities for active use of DG and ancillary services.......................9
2.2 Status of energy storages at customer level ...................................................9
2.2.1 Characteristics of storages...................................................................9
2.2.2 Plug-in Hybrid Electric Vehicles as energy storage............................10
2.3 Consumers’ loads and their flexibility (DR) ...................................................11
3 2009 Background .................................................................................................14
4 Assumptions about the evolution of the key driving forces and forecast of macrovariables ...............................................................................................................27
5 Projections on changes in electrical end uses......................................................31
6 Load profiles in 2020 ............................................................................................34
7 Projections on changes between consumer classes ............................................36
7.1 New residential buildings ..............................................................................36
7.2 Change of the heating systems in oil-heated houses to heat pump heating .37
7.3 Penetration of heat pumps into existing electrically heated houses ..............39
7.4 Penetration of plug in electric and hybrid vehicles ........................................40
7.4.1 Charging infrastructure.......................................................................40
7.4.2 Influence of EVs on the electricity consumption in Finland ................41
8 Flexibility characterization.....................................................................................44
9 Scenario narratives...............................................................................................46
9.1 Baseline scenario..........................................................................................49
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9.2 Target scenario .............................................................................................50
9.3 Scenario comparison ....................................................................................51
10 Conclusions ..........................................................................................................52
References ................................................................................................................53
Appendix: DR potential of diesel generators at large customers
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1
Introduction
The report discusses on the distributed energy resources (DER) at customer level; main emphasis is at small customers, but also the potential of existing diesel generators at large customers is analysed in the Appendix. DER includes distributed generation (DG), energy storages and flexible loads.
The chapter 2 gives the general characteristics of DER at customers. The chapters after that
are specifically directed to the analysis of the flexibility potential of residential customers.
It should be noted that starting at the chapter 3, we do not consider service sector buildings or
summer houses.
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2
Potential distributed energy resources of small customers
This chapter investigates the capabilities and potentials in terms of flexibility and service provision of distributed energy resources (DER) including loads, DG, renewable energy sources
(RES) and storage installed at consumers’ premises. DER is the resource used by aggregators
in getting active demand into market.
2.1
DG and RES at consumers’ premises
For the purpose of this report, we consider technologies that can be installed at consumers’
premises and connected to low-voltage networks.
These technologies include:
electricity production technologies
electric energy storages
heat/cool storages and solar heat connected to heat storages
plug in vehicles at consumers as electricity users, generators and storage
2.1.1
The characteristics of generation systems
We give here the main characteristics of several production technologies:
Internal combustion engines (see the annex about distributed Diesel generators):
o They are based on a mature technology, reliable, with contained capital costs
and good flexibility of use, but with high maintenance costs, high noise and
high emissions (NOx and CO).
o Electrical efficiency varies between 20 and 50% depending on the size and operating point. Total efficiency is around 70-85%. The specific cost is about a
thousand of euros/kWe for sizes >10 kW, increases for small sizes, around values of 5 - 6000 €/kWe for a few kWe systems.
Micro gas turbines:
o These new systems are characterized by a variable and high-speed turbine rotation (50,000 ÷ 120,000 rpm).
o Electrical efficiency are 15-25%, total efficiency ranging from 70 to 90%, the
specific cost is comparable to those of internal combustion engines. The dimensions and weights are relatively low as noise and emissions.
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Stirling engines:
o They operate in a gas closed-cycle, with external combustion and use various
types of fuels, with low noise. The gaseous emissions depend on the fuel used.
o These engines have very high capital specific costs (about 3000 €/kWe), but
are characterized by reduced planned maintenance. Start-up times are relevant
enough, electric performance is around 10-25%, but these systems have high
thermal efficiency (total efficiency often exceeding 90%).
Fuel cells:
o They are characterized by good availability of exploitable heat for cogeneration, good scalability with high efficiency under partial loads, reduced pollutant emissions, no moving mechanical parts and reduced noise.
o Electrical efficiency range between 30 and 50%, total efficiency between 70
and 85%. The specific costs of the cells are about 4-5000 €/kW,
Biogas/Biomass:
o Bio-fuel production and transportation costs represent a relevant barrier for the
dissemination for this type of generation. The size of the principal current Biogas/biomass power plants are, moreover, not so suitable for small application.
Photovoltaic:
o It is based on a clean technology and it is, moreover, very well suited to providing access to energy in rural areas, providing also economic opportunities.
o Average current capital costs for PV plants are 5.000 – 7.000 €/kW, operation
yearly operation costs are around 60-80 €/kW (1-1.5% capital costs).
Wind:
o It is clean and fuel-free.
o Current average total installing costs are around 6.000 €/kW with 1.100 €/year
for operation and maintenance costs.
Typically household cogeneration system requires high availability, often with periods of continuous operation, in order to be able to be considered useful. Factors such as malfunctions
involving unforeseen maintenance costs reduce the advantages of such systems. Thus, while
the CHP systems with internal combustion engines meet household’s needs and are already
widely available on the market, with benefits and costs well-established, the microturbine systems and Stirling engines need further studies in order to make them more suitable for market’s requirements. The fuel cell systems are, however, in large part, still at the prototype stage and have yet to complete testing on experimental facilities. On the other hand, respect to
their potential in terms of efficiency, noise and low emissions, the Stirling engine systems and
Fuel Cell are considered the solutions of the future for domestic users.
The costs of these systems are indicative of their current trading condition, but the forecasts
for the next five years give a significant reduction due to their entry to the mass markets.
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As far as concern the generation systems from renewable sources (PV and Small Wind) it is
evident that they are not easily controllable; their availability and flexibility is closely linked
to the capability of coupling with storage systems.
2.1.2
Capabilities for active use of DG and ancillary services
Current technologies do not allow generators from renewable sources like sun or wind to deliver ancillary services. However, in the case of power electronic interface to the grid, a fourquadrant inverter could be adopted to contribute to voltage regulation and power quality in the
distribution system.
Due to the strong stochastic nature of this type of renewable energy, integrating active demand functions is possible only in the cases in which energy storage is included.
It is possible for CHP systems to integrate active demand functions; however this is restricted
due to several issues.
To obtain a so defined high efficient CHP system, the use of the produced heat is crucial. Also, generally, micro and small scale CHP units show weak partial load efficiency. Both facts
lead to base load role for CHP with between 4000 and 7000 operational hours per year. This
results into a low level of active demand functions.
Compatibly with thermal production process constraints and with load reduction capability
services can range from peak shaving to tertiary reserve and voltage regulation, up to support
in islanded conditions.
Heat and perhaps electricity storage becomes of higher importance for active demand function
integration.
2.2
Status of energy storages at customer level
Energy storages have a key role in an efficient distributed energy management. Most of the
problems in power quality, distribution reliability and peak power management can be solved
with energy storages. They would allow a larger share of uncontrollable energy sources (wind
and solar powers) and give new possibilities for demand side management, and for customer
level energy cost control.
2.2.1
Characteristics of storages
Cost effective, smart energy storages give potential for building energy management especially when they are used in combined heat and power (CHP) production systems such as fuel
cells and micro turbines. Energy storages also give the possibility to manage uncontrollable
power production in renewable energy production systems such as photovoltaic and wind
power systems. Uninterruptible power delivery can be essential even in single family houses
for example when they are used as a home office with computer systems or with critical medical equipments, which could become more common in the near future.
Energy storage systems in residential applications include the storage systems that provide
electric power output. The power can be stored in the form of electricity for storages such as
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capacitors or supercapacitors, mechanical power for flywheels or electrochemical for batteries
and flow batteries.
Super (ultra) capacitors and flywheels can provide fast power response needed for distribution
line stability and power quality (reactive power and voltage control, fault current limitation)
support.
Flow batteries like vanadium redox batteries can fulfill variable power and energy demands.
Batteries, flywheels and capacitors are suitable for energy management, peak shaving and for
mobile power applications.
Thermal energy storages are used in heating and cooling systems. Thermal energy can be stored as sensible heat, latent heat and chemical energy. They can also provide ancillary type reserve services for local and district thermal energy production systems. Advanced thermal energy systems for heating and cooling provide possibilities to integrate active demand functions.
The use of energy storages is pushed by an increased demand for energy efficiency, for a reduction of CO2 and other emissions and for an increased exploitation of renewable resources
such as solar and wind powers.
A comparison of the storage technologies shows that they all are different in terms of network
applications and energy storage scale. In order to achieve optimum results, the specifications
of the storage device have to be studied accurately, before the final storage type selection.
Most of the technologies presented here still require intensive research and development in
order to become available for small scale implementation.
The current storage costs are still high. The electric energy lost in energy storage drives up the
overall costs together with the required capital investment for the energy storage system. These costs will tend to decline with increasing degree of market acceptance.
2.2.2
Plug-in Hybrid Electric Vehicles as energy storage
The future perspectives of PHVEs are directly linked to batteries developments. Specifically:
Lead-acid batteries are characterised by very low cost and high weight per stored kWh;
their technology is very mature, but their poor technical performance makes them progressively obsolete for the application onboard vehicles;
Nickel-metal hydride (NiMH) batteries have far better performances than lead-acid ones,
and considerable higher cost. Today they are considered more cost-effective than leadacid batteries but, when compared to lithium batteries, they are considered to be losing in
the medium term, even if today they are more mature;
Lithium-based batteries may be classified into several different subtypes, having in any
case higher specific energy and power than NiMH, with comparable costs. However, in
sizes such as needed onboard vehicles the technology is not as mature as for the NiMH
batteries;
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Na-NiCl batteries are hot batteries operating at about 230°C. They are both mature and
cost-effective for fleet operation. However, for private vehicles, they are penalized by the
need of continuously absorbing power from the mains to be kept hot.
If all the above considerations are simultaneously taken into account, the best candidate for
near future battery electric vehicles is the lithium batteries.
A distributed storage system based on PHEVs would be available when their share is increasing. Such storages could participate to active demand strategies and contribute to the optimization of production and utilization curves.
2.3
Consumers’ loads and their flexibility (DR)
A first broad classification of loads can be made according to their capability for being shifted
or not. Shiftable loads are those that can be consumed at any point in time, whose total consumption of energy is independent from the period at which they do it but they have to be
consumed completely for achieving their function. An example of this kind of load is a washing machine. This flexibility can be used for planning their activation in low prices periods or
to avoid peak consumption periods.
Curtailable loads are those loads for which, once interrupted, the energy that they were going
to consume is saved and it can not be consumed at a later point in time. An example of this
kind of load is a light. This flexibility can be used to reduce consumption in peak periods but
it may have a direct relationship with the comfort of the user. More specific control types are
described in the following paragraphs. The curtailment of loads should be considered carefully when considering energy efficiency issues. Loads that can be curtailed with no loss of
comfort would easily be made flexible, but under energy efficiency concerns, that load should
probably be turned off regardless of the flexibility needs.
Current appliances have different types of functioning conditions with different degrees of
energy efficiency that give different consumption patterns which may be used to provide flexibility of use. Dishwashers are examples of this class of flexibility by incorporating different
types of operations such as normal or ecological.
Taking into account the characteristics of appliances, the following control actions have been
identified:
Start/stop completely the execution of an appliance with or without scheduling it to another time period.
Modify the consumption pattern of the appliance by activating it in a higher efficiency
consumption pattern.
Interrupt the appliance work at intermediate stages when the interruption does not affect a
later continuation.
Interrupt the consumption of appliances in a stand by operation.
Modify the settings of comfort control devices of appliances in such a way that without
affecting much the comfort a reduction in consumption is achieved.
Use the thermal energy storing capabilities of buildings or specific storages.
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The effect of the control actions together with the energy reduction that they achieve will have
to be taken in consideration for the classification of the equipments from their flexibility point
of view and for the design of the algorithms that upon the reception of an external system decide on the more beneficial action to apply.
Therefore, the energy impact of the action taken can be classified according to the following
criteria:
The action reduces the consumption by a certain percentage and has no effect when the
control action finished. For example, switching the lights off or lowering their consumption.
The action reduces the consumption by a certain percentage but it has a pay-back that depends on the duration of the action. For example a fridge or an air conditioning system. In
this category, a difference can be made if the pay-back energy is either higher or lower
than the energy saved during the control period.
The types of loads that are considered in this report are the ones corresponding to the residential sector and to the small commercial sector and have been identified as the following:
White Goods
o Washing machines
o Dish Washers
o Dryers
o Ovens
o Cookers
o Fridges/Freezers
Air conditioners
Water heater
Heating systems
Consumer Electronics: PCs, TVs, Music systems, etc.
Lighting
Electric vehicles
Others like agricultural loads, saunas etc.
From these, the most promising load types to be integrated into Active Demand activities
are:
Loads with thermal inertia provide good characteristics for load curtailment or interruption, these loads include: Air Conditioning Systems, Space Heating and Water Heating. They however required a pay-back period or a previous charging.
Within the white goods classification there are devices where load shifting or interruption
can be applied without affecting too much the user’s comfort and behavior, these loads include: Washing Machines, Dryers and Dishwashers.
Electric Vehicles could be also good candidates for Active Demand, even if the
ADDRESS scope limits their use as loads.
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Other load types such as fridges and lighting offer less capability to be controlled from a
flexibility point of view although their use for specific control applications (short duration,
fast responding and high price applications) could be an option.
In case of agricultural loads, their management could be useful in regions where they contribute significantly to electricity consumption.
The rest of the loads are not considered good candidates for participating in active demand for
different reasons. There are loads which are not well suited for their control due to the discomfort on the users that the control actions carry on them, or because the control actions are
very limited in the sense that their function is greatly affected by them, examples of these are
cookers, ovens, and electronic appliances.
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3
2009 Background
Figure 1 shows the development of electricity consumption in Finland from 1998 to 2008. As
can be seen the share of the industry is high in Finland and the changes in industrial consumption have a clear effect on the total consumption (strike in pulp and paper industry in 2005 and
recession in 2008).
Losses
Service and building
TWh
Housing and agriculture
100
Industry
90
80
70
60
50
40
30
20
10
0
1998
2000
2002
2004
2006
2008
Figure 1. Electricity consumption in Finland 1998-2008
MW
20 000
18 000
16 000
14 000
12 000
10 000
8 000
6 000
4 000
2 000
1970
1975
1980
1985
1990
1995
2000
Figure 2. Peak load development up to 2008 in Finland
2005
0
2010
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Figure 2 shows the corresponding development in peak load in Finland from the early 1970’s
to 2008.
In recent studies, the year 2006 has been used as a basic reference year because those studies
started in 2007 or 2008 referring to the year 2006. We also use the year 2006 as starting point
for the reason that it is the one for which we have most data available.
Table 1. Breakdown of electricity consumption into different consumption segments in 2006
Electricity
consumption GWh
% of net
consumption
675
0.8 %
Electric Heating
Residential buildings
Other
9 119
8 156
963
10.5 %
9.4 %
1.1 %
Industry
Pulp and paper
Basic metal
Chemicals
Machinery, electrical equipment
Wood and wood products
Food, beverages, tobacco
Other
47 680
26 439
5 588
4 871
1 907
1 643
1 470
4 728
54.9 %
30.5 %
6.4 %
5.6 %
2.2 %
1.9 %
1.7 %
5.4
Households
10 564
12.2 %
Real estate
1 895
2.2 %
Holiday residences
525
0.6 %
Agriculture
900
1.0 %
Construction
270
0.3 %
Services and public sector
15 152
17.5 %
Net consumption
Losses
Total consumption (incl. losses)
86 780
3 244
90 024
100.0 %
Sector
Transport
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Table 1 shows the breakdown of the consumption into different segments according to the
official statistics. The residential sector corresponds to about one quarter of the total consumption and service and public sectors to about 17.5 %. In the following, we will concentrate on the residential sector.
Heating and cooling consume at least 40 % of all primary energy within the EU. Heating is an
important part of life in cold regions. About 20 % of energy used in Finland is for heating
buildings and about a third part of that is used for heating small residential houses. Electric
heating is also the main source of flexibility in the residential sector in Finland (through demand interruptions), and therefore the heating is analyzed in details in this report.
Figure 3 shows the development of energy sources for heating between 1975 and 2006. In the
early 1970’s there were mainly three heat sources, whereof oil was the most used. District
heating was common only in larger cities, while wood heating was deployed in rural areas
where wood was available free of charge from the house owner’s forests. The oil crisis
changed all that, oil use decreased rapidly hand in hand with the overall demand for heating
energy, due to increased energy efficiency. The demand for heating has risen steadily since
the early eighties, with sharp peaks at 1985-1988 due to a spell of cold winters. District heating has today the decidedly largest share of the heating market. Electricity use has grown,
bringing it on the same level with oil and wood.
70
60
50
TWh
40
30
20
10
0
1975
1980
Biofuels
1985
Oil
Peat and fossils
1990
District heat
1995
Electric heating
2000
2005
Heat pumps
Figure 3. Energy sources for heating residential, commercial and public buildings in Finland
between 1975 and 2006. Use of electricity for heating has increased considerably. Heat
pumps (ground source heat etc.) have a small share, but the growth rate is high. Electricity
used for heat pumps is not included here in the statistics of heat pumps, but are presumably
more or less part of electric heating [7]
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Table 2 shows the number of households in three main categories of the residential sector [1].
A small number of apartments (1 – 2 %) have also electric heating but that is not considered
here.
Table 2. Number of households of each type in Finland in 2006 [1]
Year
Number of households
Detached houses
Terraced houses
Apartments
2006, total
996,263
340,979
1,065,423
2006, w/ electric heating
470,000
130,000
0
2006, w/o electric heating
526,263
210,979
1,065,423
At that time there were in Finland 260.000 detached houses that use oil for heating and about
470 000 detached houses that use purchased electricity for heating. The total number of detached houses is almost 1.1 million in Finland at the end of 2006, of which just about 1 million are permanently inhabited as can be seen from the Table 2 above.
The total floor area of the Finnish detached house stock is around 140 million m2, comprising
1.1 million houses, and is estimated to be 160 million m2 in year 2020, equaling to approximately 1.2 million permanent housing units. The main heating system in detached houses of
Finland is electricity (see Figure 4). Comparing the chosen solutions for new houses in 2005
and 2007 (Figure 5) to the existing shares in 2005, several trends can be seen. Electric heating
is still the number one, although some of its momentum seems to have been lost. Use of oil is
clearly decreasing, with almost no new houses selecting oil as their main heating system in
2007. Surprisingly, biomass is also recessing, which may be a result of price hikes of wood in
recent years. Heat pumps are cornering the market really fast, having shares of 30 % of the
yearly market already in 2007. Air-to-air heat pumps are usually not used as main heat source,
which is why they aren’t a separate entity in Figure 4. District heating is gaining markets, increasing its total share slowly but steadily.
Electricity
Ground source heat
Wood, peat
District heating
Oil
Other
0%
20 %
40 %
60 %
80 %
100 %
Figure 4. Heating systems of small Finnish residential houses [8]
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Electricity
2007
Ground source heat
Biomass
District heating
Oil
2005
Exhaust air heat pump
0%
20 %
40 %
60 %
80 %
100 %
Figure 5. Main heating systems of new detached houses in 2005 and in 2007. Pellets comprised 4 %-points of biomass share in 2007 [9]
The electric heating in Finland can be divided into four main categories which have very different load profiles and electricity consumptions, namely:
-
Direct electric heating which is based on room radiators,
-
partial storage heating where heat storage capacity exist in building constructions or in
artificial storages,
-
full storage heating where large hot water storage can store all heating energy needed during night time and
-
heat pump heating mainly either as a ground source heat pumps or air-to-air heat pumps
(in addition to other electric heating); also exhaust air heat pumps in new houses are increasing their market share.
In most cases, domestic hot water is produced during night time, at least in storage based systems, in order to take advantage of time of use tariffs.
The shares of different types of electric heating are not known exactly. However the share of
heat pumps is increasing rapidly. Finland is quite successful in implementing heat pumps on
an European level. There are approximately 150.000 heat pumps in Finland of which about
100.000 are air-source heat pumps and about 40.000 are ground source heat pumps. The
amount of heat pumps increased in Finland by 40 % in 2007. Annual sales are approaching
(and surpassed in 2008) 50 000 heat pumps. Heat pumps gathered 2.8 TWh of “free” heat in
2007 (see Figure 6).
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6000
5000
GWh
4000
3000
2000
1000
Electricity
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
0
Ground or air source heat
Figure 6. Heating produced by heat pumps in Finland 1976-2007. The useful energy (heating) from heat pumps is more than double the electricity used as input. Most energy production is from ground source heat pumps, although air heat pumps are more numerous
On the basis of the above development, we have very roughly approximated the numbers of
different types of electrically heated detached and terraced houses in 2006. In the Table 3, a
rough estimation of the household numbers divided by the type of housing and the heating
system is done. Those values are approximations and will be used in the following analyzes
and in the construction of scenarios.
Table 3. Estimation of the number of households divided per categories and heating systems
in 2006
Non-electric
heating (oil,
biomass, district
heating)
Heat pumps
Direct
electric
heating
Air-Air
AirWater
Ground
source
Partial storage electric
heating
Full storage electric heating
Detached
houses
527.000
120.000
75.000
5.000
20.000
240.000
30.000
Terraced
houses
211.000
40.000
0
0
0
80.000
10.000
1.066.000
0
0
0
0
0
0
Apartments
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The background information on the end-uses in residential sector used in this report are
mainly load curves for different consumers groups (gathered by VTT), a study made by Adato
[1] in 2008 about the electricity end-uses, in terms of energy, in the residential sector and a
measurement campaign realised in Denmark about electricity end-uses in real-time [3]. In this
more detailed study [1], the households’ consumption is a little bit lower than in Table 1:
households 10.992 GWh and electric heating 6701 GWh. The main reason for the difference
is the mild winter in 2006 and some uncertainties in electricity consumption in holiday residences and in other non permanent housings.
In the following of this report, we will not consider the consumption at holiday residences.
The reason for this is that we do not have data concerning their load curves, but also that their
consumption is very dependant on their location, their level of modernization, the period of
the year, the weather and other factors that make it very complicated to pull out generalizations about them.
The Adato report [1] divides the Finnish residential sector into three categories and their estimated cumulated consumption for each end-use (see Table 4). In addition, the number of
households in each category is given in Table 3.
The corresponding average consumption per end-use in different types of houses is given in
Table 5.
Similar data for the service sector was not available to us for 2006. The latest ones we had
were for 1998. That is why the service sector will not be included in the end of this report. We
should however keep in mind that there is also there a source of potential flexibility. The 1998
data showed that the consumption in service buildings could be divided as ([10]):
-
907 GWh for cooling and ventilation,
-
404 GWh for heating of space and water,
-
733 GWh for lighting,
-
593 GWh for computing, copying and priting and
-
1352 GWh of unidentified consumption
RESEARCH REPORT VTT-R-06411-09
21 (60)
APPARTMENTS
ALL
GWh
Indoor lighting
Cold appliances
Entertainment appliances
Electric sauna
Cooking appliances
Computers and related
Heating and AC
Washing machines and driers
Floor heating
Dish washers
Car heating
Outdoor lighting
Others
Household total
Electric heating, hot water
TOTAL
469
490
283
91
245
168
52
110
83
54
0
0
202
2247
0
2247
GWh
20,9 %
21,8 %
12,6 %
4,0 %
10,9 %
7,5 %
2,3 %
4,9 %
3,7 %
2,4 %
0,0 %
0,0 %
9,0 %
100 %
0%
100 %
219
186
117
148
98
57
54
52
96
36
24
12
156
1255
865
2120
10,3 %
8,8 %
5,5 %
7,0 %
4,6 %
2,7 %
2,5 %
2,5 %
4,5 %
1,7 %
1,1 %
0,6 %
7,4 %
59,2 %
40,8 %
100 %
TERRACED HOUSES
No electric heating Electric heating
GWh
GWh
145
121
78
92
64
38
33
33
43
24
11
8
101
791
0
791
18,3 %
15,3 %
9,9 %
11,6 %
8,1 %
4,8 %
4,2 %
4,2 %
5,4 %
3,0 %
1,4 %
1,0 %
12,8 %
100 %
0%
100 %
Table 4. Cumulated consumption by appliances for the different types of housing
74
66
39
55
35
18
21
20
53
12
13
4
55
465
865
1330
5,6 %
5,0 %
2,9 %
4,1 %
2,6 %
1,4 %
1,6 %
1,5 %
4,0 %
0,9 %
1,0 %
0,3 %
4,1 %
35,0 %
65,0 %
100 %
ALL
GWh
1738
785
434
613
309
182
515
229
278
171
191
73
2201
7719
5435
13154
13,2 %
6,0 %
3,3 %
4,7 %
2,3 %
1,4 %
3,9 %
1,7 %
2,1 %
1,3 %
1,5 %
0,6 %
16,7 %
58,7 %
41,3 %
100 %
DETACHED HOUSES
No electric heating Electric heating
GWh
GWh
977
450
244
310
169
106
405
124
80
94
95
44
1977
5075
0
5075
19,3 %
8,9 %
4,8 %
6,1 %
3,3 %
2,1 %
8,0 %
2,4 %
1,6 %
1,9 %
1,9 %
0,9 %
39,0 %
100 %
0%
100 %
761
335
190
303
141
77
110
105
198
77
96
29
224
2646
5435
8081
9,4 %
4,1 %
2,4 %
3,7 %
1,7 %
1,0 %
1,4 %
1,3 %
2,5 %
1,0 %
1,2 %
0,4 %
2,8 %
32,7 %
67,3 %
100 %
RESEARCH REPORT VTT-R-06411-09
22 (60)
APPARTMENTS
ALL
kWh
kWh
Indoor lighting
Cold appliances
Entertainment appliances
Electric sauna
Cooking appliances
Computers and related
Heating and AC
Washing machines and driers
Floor heating
Dish washers
Car heating
Outdoor lighting
Others
Household total
Electric heating, hot water
TOTAL
440
460
266
85
230
158
49
103
78
51
0
0
190
2109
0
2109
20,9 %
21,8 %
12,6 %
4,0 %
10,9 %
7,5 %
2,3 %
4,9 %
3,7 %
2,4 %
0,0 %
0,0 %
9,0 %
100 %
0%
100 %
642
545
343
434
287
167
158
153
282
106
70
35
458
3681
2537
6217
10,3 %
8,8 %
5,5 %
7,0 %
4,6 %
2,7 %
2,5 %
2,5 %
4,5 %
1,7 %
1,1 %
0,6 %
7,4 %
59,2 %
40,8 %
100 %
TERRACED HOUSES
No electric heating Electric heating
kWh
kWh
687
574
370
436
303
180
156
156
204
114
52
38
479
3749
0
3749
18,3 %
15,3 %
9,9 %
11,6 %
8,1 %
4,8 %
4,2 %
4,2 %
5,4 %
3,0 %
1,4 %
1,0 %
12,8 %
100 %
0%
100 %
569
508
300
423
269
138
162
154
408
92
100
31
423
3577
6654
10231
5,6 %
5,0 %
2,9 %
4,1 %
2,6 %
1,4 %
1,6 %
1,5 %
4,0 %
0,9 %
1,0 %
0,3 %
4,1 %
35,0 %
65,0 %
100 %
ALL
kWh
1745
788
436
615
310
183
517
230
279
172
192
73
2209
7748
5455
13203
DETACHED HOUSES
No electric heating Electric heating
kWh
kWh
13,2 %
6,0 %
3,3 %
4,7 %
2,3 %
1,4 %
3,9 %
1,7 %
2,1 %
1,3 %
1,5 %
0,6 %
16,7 %
58,7 %
41,3 %
100 %
Table 5. Consumption by appliance for the different types of housing brought back to an average per household
1856
855
464
589
321
201
770
236
152
179
181
84
3757
9643
0
9643
19,3 %
8,9 %
4,8 %
6,1 %
3,3 %
2,1 %
8,0 %
2,4 %
1,6 %
1,9 %
1,9 %
0,9 %
39,0 %
100 %
0%
100 %
1619
713
404
645
300
164
234
223
421
164
204
62
477
5630
11564
17194
9,4 %
4,1 %
2,4 %
3,7 %
1,7 %
1,0 %
1,4 %
1,3 %
2,5 %
1,0 %
1,2 %
0,4 %
2,8 %
32,7 %
67,3 %
100 %
RESEARCH REPORT VTT-R-06411-09
23 (60)
On the basis of the typical load curves of different types of residential customers and the
Eureco report [3], we have divided the yearly average load curves for different residential
consumers into different end uses (Figure 7). This load curve and others presented in this section are an average value for a group of consumers; there are not the load curves for a single
household.
However, the average yearly load curve is not representative when estimating flexibility potential in peak load situation, which occur in winter time in Finland. Therefore the next step is
to consider an average winter day: we have taken here the third and forth weeks of January,
and adjusted this division to correspond to winter time. The assumptions we take here are that
only lighting and heating applications are responsible for the change. The results of this new
division can be seen in Figure 8.
In Nordic countries, as can be seen in the tables above, the most important source of flexibility is electric heating. In order to extract the heating consumption from the available load curves, we consider that the electrical consumption is otherwise similar to consumption in nonelectrically heated consumers. Thus the heating loads are obtained by simple difference
(Figure 9). The corresponding total load curves are presented in the Figure 10. It should be
noted here that load curves related to heat pump heating is based on quite a limited number of
measurements and the types of heat pumps are not known. We take the guess that there is
probably a mix of ground and air based heat pumps.
Figure 7. Estimation of the yearly average load curve composition for a detached household
with no electric heating and for a single apartment
RESEARCH REPORT VTT-R-06411-09
24 (60)
Figure 8. Estimation of the winter (January 14th) average load curve composition for a detached household with no electric heating and for a single apartment
18000
Direct heating
Consumption (Wh/h)
16000
Full storage
14000
Partial storage
12000
Heat pumps
10000
8000
6000
4000
2000
0
1
3
5
7
9
11
13
15
Time of day (h)
17
19
21
23
Figure 9. The different heating types and their estimated typical load curves per average household during a Winter (January) week day.
RESEARCH REPORT VTT-R-06411-09
25 (60)
Figure 10. The different heating types added to the detached household load curve (in watts
per average household)
It has to be noted that heating loads are heavily dependent on the outdoor temperature with
specific temperature coefficients of each heating type. The load curves of the above figures
are based on the typical winter day in January with an average outdoor temperature of -8.7ºC,
which is the average weighted outdoor temperature of whole Finland in January.
RESEARCH REPORT VTT-R-06411-09
26 (60)
From these typical load curves for different consumer types, we can build a global accumulated load curve for residential consumers in Finland (Figure 11). The total consumption is
also divided between the different housing types and between the different identified enduses.
Apartment
Yearly average daily consumption (MWh/h)
5000
Terraced, full storage
4500
Terraced, partial storage
4000
Terraced, heat pump, GW:
3500
Terraced, heat pump, AW:
3000
Terraced, heat pump, AA:
Terraced, direct heating
2500
Terraced, no electric heating
2000
Detached, Full storage
1500
Detached, Partial storage
1000
Detached, Heat pump, GS:
500
Detached, Heat pump, AW:
Detached, Heat pump, AA:
0
Yearly average daily consumption (MWh/h)
1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
Week day
Saturday
Sunday
Detached, direct heating
Detached, no electric heating
5000
4500
Heating
4000
Others
3500
AC & Heating
3000
Dish w asher
2500
Washing and dry
2000
Computer
Audiovisual
1500
Cold
1000
Lighting
500
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Week day
Saturday
Sunday
Figure 11. Aggregated winter load curves for the residential sector divided by housing type
and end-uses.
RESEARCH REPORT VTT-R-06411-09
27 (60)
4
Assumptions about the evolution of the key driving forces
and forecast of macro-variables
The main driver or one of the main drivers, for future demand trends is the technological development. Electricity is used to get tasks like refrigeration, heating, lighting, etc. The manufacturers are improving processes and designs constantly, usually resulting in the same tasks
being done with less energy and electricity. One of the technological changes is the arrival
and the increase penetration of plug-in electric and hybrid vehicles.
Concerning the technology changes in end-uses the two main scenarios are discussed here:
-
Business As Usual (BAU): No considerable change in behavior. The consumers replace
their appliances for new ones without a special concern for their electrical consumption.
-
Best Available Technology (BAT): When replacing their appliances, the consumers opt
for the best technology available on the market in order to reduce their consumption.
These scenarios are discussed in more details in chapter 4.
The energy and environmental policy have an essential effect on the energy consumption. We
discuss here two main frames of policies for the purpose of building scenarios:
-
A baseline scenario based mainly on the present policy and an annual economic growth of
2 %.
-
A target scenario with a forced policy to fulfill the EU requirements for 2020. The estimated effect of these policies is the annual decrease of 0.8 % in GNP compared to the
baseline scenario.
The trends are not just technology based. Some small changes in electricity use and human
behavior can already today be noticed compared to the 20th century: stores have longer opening hours and are often open on Sundays as well, people are awake later in the evenings, and
the use of warm water boilers is more irregular. Also home working is increasing, thus decreasing the needs for transportation and increasing the electricity consumption of homes.
Some changes concern the time when something is done, while others concern how much
electricity is used.
The impact of tariffs on loads for example will grow with the penetration of smart and real
time metering and, by conjunction, dynamic tariffs. Loads will flatten out to a certain degree
in the future, but it is difficult to predict in what manner.
Rising fuel and electricity prices as well as an increased environmental awareness has brought
heating of houses and buildings to the fore once again. Several trends concerning heating can
be observed:
-
Change of heating source in existing buildings,
-
Climate change will diminish the need for heating, one estimate being with 12 % by 2030
RESEARCH REPORT VTT-R-06411-09
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-
Changes in preferences of heat source to new buildings,
-
Installation of auxiliary heating equipment like air-source heat pumps and solar heat panels to existing buildings,
-
Increased heat recovery (e.g. from exhaust air and from food market refrigeration apparatus),
-
New buildings are built to be more energy efficient because of better insulation and
-
The concepts of low-energy and passive houses are developed.
The Finnish Ministry of Employment and the Economy published a National Climate and Energy Strategy in November 2008 [4]. The total consumption in a baseline and vision/target
scenarios are presented in Figure 12 and in Table 7. The consumption in the target scenario is
expected to be significantly lower than in the baseline-scenario. Electricity consumption even
stays below 100 TWh and slowly starts to decrease sometime after 2020.
The basic assumption in the baseline scenario on the economic growth is the annual increase
of about 2 % resulting in the GNP in 2020 that is 140 % higher than in 2005.
The target scenario is based on the strategy where all the EU requirements for 2020 are fulfilled. This means the enforced policy to support the use of renewable, increase the energy
efficiency and decrease the greenhouse gases. These effects on the economy are shown in the
Table 6. The GNP is annually about 0.8 % less than in the baseline scenario.
Table 6. Effect of the target scenario on the economy. Changes compared to the baseline scenario
Greenhouse gas
target to the CO2
trading sector
Target for
renewable
Target for
energy saving
Total effect on
the GNP
Households consumption
- 1.0
0
- 0.1
- 1.1
Investments
- 0.2
0
+ 0.3
+ 0,1
Export
+ 0.4
- 0.3
- 0.5
- 0.4
Import
+ 0.3
0
+ 0.2
+ 0.5
Total effect on the
GNP
- 0.5
- 0.2
- 0.1
- 0.8
RESEARCH REPORT VTT-R-06411-09
29 (60)
140
TWh
120
100
80
60
40
20
0
1960
1970
1980
1990
2000
2010
2020
2030
2040
2050
Industry and Construction
Housing
Electric Heating
Services and Public Sector
Traffic and Agriculture
Losses
Baseline
Target scenario
Vision scenario
Industry forecast
Figure 12. Electricity consumption in Finnish scenarios [4]
Table 7. Finnish electricity consumption scenarios [4]
TWh
2006
2020
2007
Baseline
2030
Target
Baseline
2040
2050
Vision Baseline Vision Baseline
Industry and building
48.0
48.5
56
56
58
61
63
Households
13.0
12.5
15
13
16
17
18
Electric heating
9.1
9.0
10
8
9
9
8
Services
15.2
15.2
18
16
19
20
20
Other
1.6
1.8
2
2
2
2
3
Losses
3.2
3.3
4
4
4
4
4
Total
90.0
90.3
103
99
108
95
113
90
116
Vision
80
The Confederation of Finnish Industries and the Finnish Energy Industry have published their
expectations on the electricity demand development in Finland [5]. According to the report
the consumption will be 107 TWh in 2020 and about 115 TWh in 2030. The increase is expected to be the highest within metal industry and the service sector. The report assumes the
electricity use to become more efficient but this is compensated by the economic growth and
increased use of products and services.
RESEARCH REPORT VTT-R-06411-09
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Electrical heating is presumably increasing as well, but less than what the organizations predicted in 2004.
There are large differences in the views on how the demand will develop in the future. In this
report, the scenarios of Finnish Ministry of Employment and the Economy are the sources we
used primarily.
If the economic indicators are compared with the baseline and target scenarios, it can be said
that the target scenario can be taken as the realistic scenario where the growth of GNP is
lower than in the baseline scenario. If these are combined with the technology scenarios it can
be said that the target scenario corresponds roughly to the BAT scenario where energy savings related to lighting and white goods are achieved. Correspondingly the baseline scenario
is related to BAU scenario.
In the following two basic scenarios are discussed:
-
baseline scenario which corresponds mainly to BAU technology scenario and
-
target scenario corresponding mainly to BAT technology scenario.
RESEARCH REPORT VTT-R-06411-09
31 (60)
5
Projections on changes in electrical end uses
The Table 8 shows the estimated evolution for the electricity consumption per appliance category by 2020 according to the Adato projections [1]. That table doesn't include electric heating, which will have to be considered separately.
As described above, the two scenarios studied are defined here:
-
Business As Usual (BAU): No considerable change in behaviour. The consumers replace
their appliances for new ones without a special concern for their electrical consumption.
-
Best Available Technology (BAT): When replacing their appliances, the consumers opt
for the best technology available on the market in order to reduce their consumption.
In both scenarios, an overall load reduction is evident. It is due mainly to improvements in
cold appliances; computers and lighting technologies (see Table 8). On the other hand, the
entertainment appliances would lead to a consumption increase. This is not due to the technologies, but to an estimated increasing number of those appliances in the households.
Table 8. Projections made by Adato [1] according to the scenarios Business As Usual and
Best Available Technology
2006 2020 (BAU) 2020 (BAT)
GWh
GWh
GWh
Cold appliances
1 627
1 227
767
Cooking appliances
653
693
577
Dish washers
261
290
268
Washing machines and driers
392
423
347
Entertainment appliances
834
1 076
860
Computers and related
408
240
87
Electric sauna
852
971
971
Heating and AC
669
809
566
Floor heating
206
227
227
Car heating
218
225
225
Indoor lighting
2 427
2 002
845
Outdoor lighting
89
99
22
Others
2 572
2 650
2 650
TOTAL
11 207
10 931
8 412
From the projections on changes in electrical end uses as well as estimates on the number of
households of each type, we deduce the average consumption for each appliance per household (Table 9 and Table 10). The principal assumption taken here is that the relative (in %)
changes in consumption for each appliance is the same regardless of the household type.
RESEARCH REPORT VTT-R-06411-09
32 (60)
APPARTMENTS
ALL
kWh
Indoor lighting
Cold appliances
Entertainment appliances
Electric sauna
Cooking appliances
Computers and related
Heating and AC
Washing machines and driers
Floor heating
Dish washers
Car heating
Outdoor lighting
Others
Household total
Electric heating, hot water
TOTAL
kWh
TERRACED HOUSES
No electric heating Electric heating
kWh
kWh
ALL
kWh
DETACHED HOUSES
No electric heating
Electric heating
kWh
kWh
330
316
312
89
222
84
54
101
78
51
0
0
178
18,2 %
17,4 %
17,2 %
4,9 %
12,2 %
4,7 %
3,0 %
5,6 %
4,3 %
2,8 %
0,0 %
0,0 %
9,8 %
482
374
403
450
277
89
174
150
282
107
66
36
429
8,6 %
6,7 %
7,2 %
8,0 %
4,9 %
1,6 %
3,1 %
2,7 %
5,0 %
1,9 %
1,2 %
0,6 %
7,6 %
516
393
434
452
293
96
172
154
204
115
49
38
449
15,3 %
11,7 %
12,9 %
13,4 %
8,7 %
2,9 %
5,1 %
4,6 %
6,1 %
3,4 %
1,5 %
1,1 %
13,3 %
427
348
352
439
260
74
178
151
409
93
94
31
397
4,6 %
3,7 %
3,8 %
4,7 %
2,8 %
0,8 %
1,9 %
1,6 %
4,4 %
1,0 %
1,0 %
0,3 %
4,3 %
1309
541
511
638
299
98
569
226
280
174
180
74
2071
11,0 %
4,5 %
4,3 %
5,3 %
2,5 %
0,8 %
4,8 %
1,9 %
2,3 %
1,5 %
1,5 %
0,6 %
17,4 %
1393
587
544
611
310
108
847
231
152
181
170
85
3521
15,9 %
6,7 %
6,2 %
7,0 %
3,5 %
1,2 %
9,7 %
2,6 %
1,7 %
2,1 %
1,9 %
1,0 %
40,3 %
1215
489
474
668
290
88
257
219
422
166
192
62
447
7,8 %
3,2 %
3,1 %
4,3 %
1,9 %
0,6 %
1,7 %
1,4 %
2,7 %
1,1 %
1,2 %
0,4 %
2,9 %
1815
100 %
3319
59,0 %
3366
100 %
3253
35,0 %
6969
58,4 %
8739
100 %
4990
32,2 %
0
0%
2308
41,0 %
0
0%
6053
65,0 %
4963
41,6 %
0
0%
10520
67,8 %
1815
100 %
5627
100 %
3366
100 %
9306
100 %
11932
100 %
8739
100 %
15510
100 %
Table 9. Consumption by appliance for the different types of housing brought back to an average per household in the BAU scenario by 2020
RESEARCH REPORT VTT-R-06411-09
33 (60)
APPARTMENTS
ALL
kWh
Indoor lighting
Cold appliances
Entertainment appliances
Electric sauna
Cooking appliances
Computers and related
Heating and AC
Washing machines and driers
Floor heating
Dish washers
Car heating
Outdoor lighting
Others
Household total
Electric heating, hot water
TOTAL
kWh
139
197
249
89
185
31
38
83
78
47
0
0
178
10,6 %
15,0 %
19,0 %
6,7 %
14,1 %
2,3 %
2,9 %
6,3 %
5,9 %
3,6 %
0,0 %
0,0 %
13,5 %
203
234
322
450
231
32
122
123
282
99
66
8
429
1314
100 %
0
0%
1314
100 %
TERRACED HOUSES
No electric heating Electric heating
kWh
kWh
4,1 %
4,8 %
6,6 %
9,2 %
4,7 %
0,7 %
2,5 %
2,5 %
5,7 %
2,0 %
1,3 %
0,2 %
8,7 %
218
246
347
452
244
35
120
126
204
106
49
9
449
8,4 %
9,4 %
13,3 %
17,4 %
9,4 %
1,3 %
4,6 %
4,8 %
7,8 %
4,1 %
1,9 %
0,3 %
17,2 %
180
218
281
439
216
27
124
124
409
86
94
7
397
2601
53,0 %
2605
100 %
2308
47,0 %
0
0%
4909
100 %
2605
100 %
ALL
kWh
2,1 %
2,5 %
3,3 %
5,1 %
2,5 %
0,3 %
1,4 %
1,4 %
4,7 %
1,0 %
1,1 %
0,1 %
4,6 %
553
338
409
638
249
35
398
185
280
160
180
16
2071
2602
30,1 %
6053
69,9 %
8655
100 %
DETACHED HOUSES
No electric heating
Electric heating
kWh
kWh
5,3 %
3,2 %
3,9 %
6,1 %
2,4 %
0,3 %
3,8 %
1,8 %
2,7 %
1,5 %
1,7 %
0,2 %
19,8 %
588
367
435
611
258
39
592
190
152
167
170
19
3521
8,3 %
5,2 %
6,1 %
8,6 %
3,6 %
0,5 %
8,3 %
2,7 %
2,1 %
2,3 %
2,4 %
0,3 %
49,5 %
513
306
379
668
241
32
180
180
422
153
192
14
447
3,6 %
2,1 %
2,7 %
4,7 %
1,7 %
0,2 %
1,3 %
1,3 %
3,0 %
1,1 %
1,3 %
0,1 %
3,1 %
5512
52,6 %
7109
100 %
3727
26,2 %
4963
47,4 %
0
0%
10520
73,8 %
10475
100 %
7109
100 %
14247
100 %
Table 10. Consumption by appliance for the different types of housing brought back to an average per household in the BAT scenario by 2020
RESEARCH REPORT VTT-R-06411-09
34 (60)
6
Load profiles in 2020
From the estimated division of the detached house without electric heating and the results of
the Adato report, we can estimate the evolution of the load curve according to the degree of
penetration of more efficient end uses (Figure 13). It can be seen that in the BAT scenario a
considerable decrease in load level and peak load is achieved. The main part of this load reduction is due to improved lighting systems and cold appliances.
1400
2006
1200
BAU, 2020
BAT, 2020
Consumption (W)
1000
Detached house
800
600
Apartm ent
400
200
0
1 2 3
4 5
6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of day (h)
Figure 13. Estimates of load curves for an apartment and a detached house according to the
BAU and BAT scenario
To simulate an increase in the penetration of teleworking (seeFigure 14), starting we have
started from the basic load curve and considered the following modifications:
-
Increase of 10% of lighting consumption and of 50% for the computer consumption during the working hour.
-
Even repartition of the consumption of white goods during the day and of 50% of the nonattributed loads during the working hours.
RESEARCH REPORT VTT-R-06411-09
35 (60)
1400
Detached house
Detached house, telew orking
1000
Appartment
Appartment, telew orking
Consumption (W)
1200
800
600
400
200
0
1 2 3
4 5
6 7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of day (h)
Figure 14. Estimates of load curves for an apartment and a detached house with an increase
in the share of teleworking
RESEARCH REPORT VTT-R-06411-09
36 (60)
7
Projections on changes between consumer classes
By 2020, we can expect some new houses to be built and changes in the general category to
which households belong. The following changes are discussed in the following:
-
effects of new residential buildings built until the year 2020,
-
change of the heating systems in oil-heated houses to heat pump heating,
-
penetration of heat pumps into existing electrically heated houses and
-
penetration of plug in electric and hybrid vehicles
The scenarios that we will build consider different degree of changes in these aspects, along
with the changes in energy efficiency and environmental policy discussed in the previous
chapter.
7.1
New residential buildings
In the reference [2] some scenarios on the effects of new houses have been developed. There
are roughly 15.000 new detached houses being built each year. Assuming new small residential houses to be built yearly at the same rate as in 2007 (15.363 houses per year), there will be
200.000 new houses in Finland by 2020. Electricity demand of each house varies according
the type of heating selected. In the Table 11, the energy demand of both standard and standard
low energy detached houses are compared. These are assumed to have a living area of 131 m2,
corresponding to an air space of 327 m3, and two adults and two children as residents. Motiva
calculation tool for the heating demand of new houses [6] is used to calculate electricity and
other heating demands for the two house types.
Table 11. Annual final energy demand with different types of heating in two standard small
residential house types based on the use of Motiva’s calculating tool. The final energy for
heat pump houses consists of the use of electricity, but not the “free” heat from the
ground/air
Oil
Electricity
Ground heat
pump (bore
hole)
Final heating energy
24882
20458
7717
21576
10172
Other electricity demand
7128
6204
6816
6948
7692
Total electricity demand
7128
26662
14533
6948
17864
Final heating energy
15721
12278
4808
12893
5476
8136
6156
6816
6816
8568
8136
19434
11624
6816
14044
Standard type house Energy demand kWh/year
Standard small
residential house
Standard low energy
Other electricity demand
small residential house
Total electricity demand
District
heating
Air-heat
pump
RESEARCH REPORT VTT-R-06411-09
37 (60)
The increase in electricity demand due to the new houses will vary between 1.4 TWh and 5.3
TWh depending on the type of the house and the type of the heating system (see Figure 6 15).
Oil or district heating would have the least effect on electricity demand, whereas direct electric heating the largest. Heat pumps would reduce the use of electricity by 9-12 MWh compared to direct electric heating in a standard house, and by 5-8 MWh in a low energy house.
Of course, the smaller the saving, the less annual gain is available to pay off the investment
with.
Electricity demand
/GWh/a
6000
Oil (SH)
Electric (SH)
5000
Ground heat (pore hole+
heat pump) (SH)
District (SH)
4000
Air-heat pump (SH)
3000
Oil (LEH)
2000
Electric (LEH)
Ground heat (pore hole+
heat pump) (LEH)
District (LEH)
1000
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
0
Air-heat pump (LEH)
year
Figure 15. Change of electricity demand in Finland from 2007 to 2020 due to new small residential houses according to different heating types selected. All new houses are expected to
be standard (+) or low energy (o) houses. Annual growth rate of 15 363 is assumed (matching 2007)
The mix of different types of houses and heating systems depend on economic, technical and
regulatory drives. In section 6.8 the assumption of different scenarios are described.
7.2
Change of the heating systems in oil-heated houses to heat
pump heating
In existing houses the changes from oil based and electricity based heating systems to heat
pump based systems are analysed in [2]. The results of the different cases are summarized in
Table 6 13. There are 260.000 oil heated houses at the moment. If it assumed that 200.000 oil
heated houses in Finland were changed, whereof:
-
100.000 to ground source heat pumps,
-
50 000 to have air-air heat pumps as supplement, and
- 50 000 to air-water heat pumps,
the electricity consumption would increase by 2.2 TWh.
RESEARCH REPORT VTT-R-06411-09
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The effect of the 200.000 new heat pumps in the previously oil-heated houses on the Finnish
system peak load week is simulated in Figure 6 16. The Finnish peak load week from 2006 is
used as a basis. A quite cold week is selected from the VTT House Model results to represent
the additional heat pump loads, as system peak load and cold weather go hand in hand. The
simulation shows that the power system peak increases with 1100 MW due to the new heat
pumps.
Table 12. Converting old oil heated (Base case 1) or direct electric heated (Base case 2)
houses to different types of heat pump houses. GSHP=Ground source heat pump, AAHP=
Air-to-air heat pump, AWHP= Air-to-water heat pump
Base case 1 Base case 2
Heating type
Case 1a
Case 1b
Case 1c
Case 2b
Oil
Dir. electr.
-
-
GSHP
AAHP
AWHP
AAHP
GSHP
Electricity (MWh/a)
0,0
33,2
10,7
5,9
16,9
25,4
10,7
Peak (kW)
0,0
14,2
7,3
1,9
12,3
15,1
7,3
-
2346
1455
3030
1368
1682
1455
Heat pump
MW
Peak hours (h/a)
Oil
Case 2a
Dir. electr.
18000
10,0
16000
5,0
14000
0,0
12000
-5,0
10000
-10,0
8000
-15,0
6000
-20,0
4000
-25,0
2000
-30,0
0
0:00
Outside temperature used for the heat pumps C
Case
-35,0
12:00
0:00
12:00
0:00
12:00
0:00
12:00
0:00
12:00
0:00
12:00
0:00
12:00
16.1.-22.1.2006
FI
200 000 heat pumps
Outside temperature for heat pump load
Figure 16. Electrical demand assuming 200 000 oil heated houses in Finland are changed to
heat pumps, it will have an increasing effect, even as high as 1100 MW, on the system peak
load
RESEARCH REPORT VTT-R-06411-09
39 (60)
7.3
Penetration of heat pumps into existing electrically heated
houses
We have assumed that the majority of air heat pumps installed in Finland are in detached electrically heated houses, although detached houses with other fuels, semi-detached houses and
service sector buildings have their share of air heat pumps. However, it is fair to have a case
studying new heat pumps in 200 000 electrically heated houses, whereof:
-
50.000 would be changed to ground source heat pumps from partial storage electrically
heated houses and
-
150.000 would get air-air-heat pumps as supplement (50.000 with partially storage and
100.000 directly electrically heated).
This case in turn would bring electricity savings of 2.3 TWh assuming the heating energy
consumptions according to the Table 12. Combining case 1 and case 2 results in a net increase
of 0.1 TWh1. The effect of 200 000 new heat pumps in electric heating houses on the Finnish
system peak load week is simulated in Figure 17. Again, a Finnish winter week from 2006 is
used as basis, and the same methodology is used for heat pump loads as in case 1. The result:
the peak decreases by 400 MW thanks to new heat pumps in direct electric heated houses.
But, combining case 1 and 2 still leaves a net increase of 70 MW for the system peak load.
1
These simulations are based on the assumption that the electricity consumption in electrically heated houses is
33.2 MWh/a, which is much higher that the average consumption as seen in Table 5. Therefore the load profile
simulations in the later scenario simulations are made assuming that the electricity consumption is 22 MWh/a in
the houses where heat pumps are installed. The resulting electricity consumption after heat pump installations
are adjusted correspondingly.
RESEARCH REPORT VTT-R-06411-09
MW
40 (60)
16000
16000
14000
14000
12000
12000
10000
10000
8000
8000
6000
6000
4000
4000
2000
2000
0
0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00
Case 2: new load
Saved electricity
0
As comparison, heat pumps replacing oil heating (Case 1)
Figure 17. Electrical demand totalized 200.000 direct electric heated houses in Finland get
heat pumps as supplements or as new heating systems, it will decrease the system peak load
with 400 MW
7.4
Penetration of plug in electric and hybrid vehicles
7.4.1
Charging infrastructure
There are two limits to electric vehicle charging. The first is set by the battery properties, i.e.
how much charging power it can withstand without detrimental effects. The second is due to
the charging infrastructure. Most household wirings have rather limited amperage and this
will limit the charging power even if the batteries could withstand larger currents. However,
In the Nordic countries many parking spaces have electric outlets for the cars to enable prewarming of the engine before use. These could also be used to charge electric vehicles.
Fast charging a vehicle would require around 100 kW and this is not feasible in residential
buildings. Dedicated charging stations, which could first be implemented in current gasoline
stations, would be required and they would need to tap into distribution substations. A few
thousand outlets in Finland would increase the power production capacity demand with at
most a few hundred megawatts, if they are all assumed to be active at the same time.
Charging opportunity at parking areas near workplaces could be used as well. Power outlets in
these locations could be useful to people living in apartment houses without a charge possibility and they could enable larger share of electricity use in PHEVs with small batteries. The
billing in this case would become an issue. Monetary value of the required annual electricity
would not be large, but still large enough to be meaningful. It will be a matter of identifying
the car or the user and measuring the total charge. The developments in automatic meter reading (AMR) and two-way communications will no doubt lead to the introduction of workable
solutions for this.
RESEARCH REPORT VTT-R-06411-09
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7.4.2
Influence of EVs on the electricity consumption in Finland
The best way to describe how EVs will influence the power system in Finland is probably to
use a case study. We take several assumptions in this case study:
-
Slow charging, max 12A, 220V, is assumed for all EVs.
-
EVs are assumed to be without heating or air-conditioning. Heating and/or airconditioning would use approximately an extra 10% according to preliminary calculations.
-
The share of different EVs is assumed to be as shown in Table 13.
Table 13. Estimation of electric vehicles’ specific electricity consumption, average annual
mileage and annual electricity consumption. In addition, a very raw estimate of the share of
different types of EVs to be found on the roads in 15-25 years
Electricity
consumption
kWh/km
Trip km/a
on
electricity
Annual
consumption
MWh/a
Share of electric
vehicles
Full electric vehicles
• FEV 0,25
• FEV 0,17
0,25
0,17
17 400
17 500
4,34
2,97
5%
15 %
Plug-in hybrid vehicles
•
PHEV 0,25
•
PHEV 0,17
0,25
0,17
14 100
14 000
3,53
2,38
20 %
60 %
The charging load profiles for workdays, Saturdays and Sundays, and for the different types
of EVs are shown in Figure 18. The load profiles are averages taking into account that some
cars are already fully loaded while some are not connected to the grid at the time. Charging
concentrates to afternoons and evenings when people arrive from work, shopping and leisure.
This is due to the assumption that most people will charge only at home.
As an example the case of one million electric vehicles is studied. One million personal vehicles are about a half of the active car fleet in Finland. If they were converted to run mostly on
electricity, the electricity consumption in the country would rise by 2.8 TWh, which amounts
roughly to 3 % of electricity consumption.
Charging in the simulation is done right away, without any price or demand response. The
effect of charging of EVs on the system load can be seen in Figure 19.
RESEARCH REPORT VTT-R-06411-09
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1400
1200
1000
MW
800
600
400
200
0
0:00
12:00
0:00
EV 0.17
12:00
PHEV 0.17
0:00
EV 0.25
12:00
PHEV 0.25
Figure 18. Daily charging profile for a million electric vehicles of different types assuming
charging begins as soon as cars are plugged in. 0.17 refers to a consumption of 0.17 kWh/km
of EV sedan and 0.25 kWh/km for a SUV type of EV. PHEVs use less electricity as they run
partly on gasoline
18000
16000
14000
MWh
12000
10000
8000
6000
4000
2000
0
0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00
16.1.-22.1.2006 (Monday - Sunday)
FI
Electric vehicles
Figure 19. The effect of 1 million EVs on the system peak load in Finland. The time of the
peak load moves from morning (8:00-9:00) to evening (17:00-18:00), and rises by 700 MW
RESEARCH REPORT VTT-R-06411-09
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In the scenarios described in chapter 7, the number of electric vehicles is assumed to be
clearly less than one million in the defined two scenarios. In the scenarios EVs are considered
as an independent load. A household with an electric car wouldn't change its class, but it
would simply add the load related to the EV to its normal profile.
RESEARCH REPORT VTT-R-06411-09
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8
Flexibility characterization
Generally speaking, in the Nordic countries, the largest source of flexibility will be in electric
heating, and that will increase with a number of new electrical heating systems in houses.
On the basis of the Figure 11, it is possible to roughly estimate the potential manageable load
for an average Finnish winter day in the residential sector in 2006.
The loads are sorted in four categories:
Easily manageable: Heating and white goods. They can be controlled with little or no
comfort loss for 1 - 2 hours by applying smart technologies.
-
Hardly manageable: Cold appliances and computers. Some of those loads could be used to
achieve specific goals such as short-period interruptions.
-
Fixed: Lighting and audiovisual. They can not be managed without an important loss of
comfort.
-
Undetermined: Includes the undetermined end-uses. A part of it may be manageable (additional heating apparatus, battery based appliances for example), but not all of it (basic
cooking appliances).
Yearly average daily consumption (MWh/h)
-
5000
4500
4000
3500
Undetermined
3000
Fixed
2500
Hardly manageable
2000
Easily manageable
1500
1000
500
0
1 4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Week day
Saturday
Sunday
Figure 20. Estimated potentiality in the residential sector during an average winter week
Figure 20 shows a large manageable potential in Finland. It is nearly entirely due to electric
heating. We can also notice that that potential is higher in the night than in the day, due to
night tariffs and its utilization in partial or full storage heating systems.
In the next section the development of manageable load in scenarios in 2020 are estimated
using the similar assumptions for flexibility as above.
RESEARCH REPORT VTT-R-06411-09
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The figures developed so far were representing a Finnish winter week (in January) with an
average temperature of -8,7ºC. It would be however interesting to study the situation on a
colder winter day in order to has an idea of the temperature dependency in that case. The
Figure 21 shows an estimate of the situation during the week of January 14th 2006 with the
outdoor temperature being in one case -25ºC and in the other case the average measured temperature for January 2006, -8,7ºC. We have taken the assumption here that the temperature
has an effect only on the heating loads. This is the reason why the variation is almost only in
the easily manageable share of the loads. However, the winter season changes the lighting and
heating usage compared to the yearly average situation. We haven't considered the possibility
that dark and cold season would increase the use of indoor entertainment applications or the
volume of laundry to be washed.
Average winter daily consumption (MWh/h)
6000
5000
4000
3000
2000
1000
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Week day
Easily manageable, low temp
Hardly manageable, low temp
Fixed, low temp
Undetermined, low temp
Saturday
Sunday
Easily manageable, avg temp
Hardly manageable, avg temp
Fixed, avg temp
Undetermined, avg temp
Figure 21. Estimated potentiality in residential sector during a winter week depending on the
temperature. The January 2006 average temperature was -8,7ºC and the low temperature
chosen is –25ºC.
RESEARCH REPORT VTT-R-06411-09
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9
Scenario narratives
For the horizon 2020, we have decided to consider two scenarios representing what we see as
extreme, but plausible, situations. The first one is rather pessimistic regarding energy efficiency (baseline scenario) while the other one is a lot more optimistic or realistic corresponding the fulfilling of EU targets in 2020 (target scenario). The Table 14 lists the basic assumptions we have made for each scenario.
Both scenarios consider the apparition of 200.000 new houses and the switching of 200.000
previously oil heated houses to air pumps. Heating systems of new houses in both scenarios
are assumed to be: 0% oil, 10% district heating, 40% electric (half direct and half partially
accumulating), 50% heat pumps (40% air based and 10% ground based)
Table 14. Summary of the characteristics of the two studied scenarios
Energy efficiency
New houses2
Electric vehicles
2
Baseline scenario
90 % BAU / 10 % BAT
90 % standard / 10 % low-energy
200.000
Target scenario
10% BAU / 90 % BAT
60 % standard / 40 % low-energy
500.000
It is assumed that low-energy new houses all fall in the BAT category regarding energy efficiency
RESEARCH REPORT VTT-R-06411-09
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Table 15 shows some assumptions taken in building the scenarios.
Basic results of the scenarios are presented in Figure 22 and Figure 23.
RESEARCH REPORT VTT-R-06411-09
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Table 15. Assumptions related to the driving forces in selected scenarios
Technology
Variables
Driving force
Scenario
Changes in efficiency
“Smart”
New Technology
Baseline
Little change or
natural evolution
No
No
Target
Much higher than
natural evolution
may be
Yes
Technological
Economy
Variables
Driving Force
Scenario
Gross domestic product
Baseline
2%
Target
1.2 %
Unemployment
Economical
Same as in baseline
Attitudes
Variables
Driving Force
Level
New energy efficient buildings
incentives
E-working
Baseline
Less probably
No
Less
Target
More probably
High probably
more
Attitudes
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9.1
Baseline scenario
Apartment:
Yearly average daily consumption (MWh/h)
5500
Terraced, f ull storage:
Terraced, partial storage:
4500
Terraced, heat pump, GW:
Terraced, heat pump, AW:
3500
Terraced, heat pump, AA:
Terraced, direct heating:
2500
Terraced, no electric heating:
Detached, Full storage:
1500
Detached, Partial storage:
Detached, Heat pump, GS:
500
Detached, Heat pump, AW:
Detached, Heat pump, AA:
-500 1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
Week day
Saturday
Sunday
Detached, direct heating:
Yearly average daily consumption (MWh/h)
Detached, no electric heating:
6000
Electric vehicles
5000
Heating
Undetermined
4000
AC & Heating
Dish w asher
3000
Washing and dry
Computer
2000
Audiovisual
Cold
1000
Lighting
0
Yearly average daily consumption (MWh/h)
1
7
13
19
Week day
25
31
37
43
Saturday
49
55
61
67
Sunday
6000
5000
4000
Fixed
Undetermined
3000
Hardly manageable
Easily manageable
2000
1000
0
1
7
13
19
Week day
25
31
37
43
Saturday
49
55
61
67
Sunday
Figure 22. Aggregated load curves in 2020 in baseline scenario in average winter day (temperature of -8,7ºC)
RESEARCH REPORT VTT-R-06411-09
50 (60)
9.2
Target scenario
Apartment:
Yearly average daily consumption (MWh/h)
5500
Terraced, f ull storage:
Terraced, partial storage:
4500
Terraced, heat pump, GW:
Terraced, heat pump, AW:
3500
Terraced, heat pump, AA:
Terraced, direct heating:
2500
Terraced, no electric heating:
Detached, Full storage:
1500
Detached, Partial storage:
Detached, Heat pump, GS:
500
Detached, Heat pump, AW:
Detached, Heat pump, AA:
-500 1
5
9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69
Week day
Saturday
Sunday
Detached, direct heating:
Yearly average daily consumption (MWh/h)
Detached, no electric heating:
6000
Electric vehicles
5000
Heating
Undetermined
4000
AC & Heating
Dish w asher
3000
Washing and dry
Computer
2000
Audiovisual
Cold
1000
Lighting
0
Yearly average daily consumption (MWh/h)
1
7
13
19
Week day
25
31
37
43
Saturday
49
55
61
67
Sunday
6000
5000
4000
Fixed
Undetermined
3000
Hardly manageable
Easily manageable
2000
1000
0
1
7
13
19
Week day
25
31
37
43
Saturday
49
55
61
67
Sunday
Figure 23. Aggregated load curves in 2020 in target scenario in average winter day (temperature of -8,7ºC)
RESEARCH REPORT VTT-R-06411-09
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9.3
Scenario comparison
6000
5000
4000
3000
2000
1000
0
1
4
7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70
Week day
Saturday
Sunday
2006, total
Baseline, total
Target, total
2006, manageable
Baseline, manageable
Target, manageable
Figure 24. Comparisons of baseline and target scenarios in 2020 with the 2006 situation
Figure 24 shows the main differences between scenarios and the comparison with the 2006
situation.
The loads considered manageable in the scenarios are mainly constituted by heating systems
and electric vehicles. White goods such as dish washers and clothes washers and dryers are
also included, but it can be seen that their share of the total load curves is rather small.
RESEARCH REPORT VTT-R-06411-09
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10
Conclusions
The total consumption and manageable loads in residential sector will increase in both scenarios compared to the year 2006. The consumption for the existing applications will decrease
due to improvement in energy efficiency. However, increases in the numbers of households as
well as the switching to electricity from other fuels for heating and the apparition of electric
vehicles will push the total consumption up.
The share of heating loads in winter days is dominating and it gives possibilities to use the
flexibility. The figures in this report are based on the average winter day temperatures. The
share of heating loads can be even much higher, if the outdoor temperature is low. Peak loads
in Finland are typically winter days with a very low outdoor temperature. We can see here
that the flexibility potential is largest at such times for residential consumers. This is due to
the fact that a large part of the winter residential load is related to heating devices. Those
peak-load times are also the most susceptible to produce a need for load reduction. It should
be noted however that load flexibility in cold winter days can compensate for intra-day variations, but will not reduce the total daily consumption.
There are also needs for flexibility outside the peak load periods especially when the share of
uncontrollable DG (mainly wind and CHP in Finland) is increasing. In summer time heating
loads are not used, with the exception of hot water productions. Therefore other end-uses
could also become interesting for flexibility purposes. In the future scenarios, electric vehicles
seem to be particularly promising to obtain flexibility in summer time.
The potential derived in this report is theoretical. The control technologies are not as of now
developed enough, or cheap enough, to make use of the flexibility from each appliance.
RESEARCH REPORT VTT-R-06411-09
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References
[1]
Kotitalouksien sähkönkäyttö 2006, Adato, 02/10/2008
[2]
Göran Koreneff, Maija Ruska, Juha Kiviluoma, Jari Shemeikka, Bettina Lemström,
Raili Alanen & Tiina Koljonen, Future development trends in electricity demand. VTT
Reserch notes 2470, 2009, 79 p.
[3]
Eureco, Final Report, End-use metering campaign in 400 households of the European
Community, 01/2002
[4]
TEM 2008. Pitkän aikavälin ilmasto- ja energiastrategia (Long-term climate and energy
strategy). Valtioneuvoston selonteko eduskunnalle 6. päivänä marraskuuta 2008. Ministry of Employment and the Economy. Available: www.tem.fi
[5]
Electricity demand in Finland in 2020 and 2030. The Confederation of Finnish Industries EK and the Finnish Energy Industries. November 2007. Available: www.energia.fi
[6]
Motiva calculation tool for comparison of heating systems in standardized detached
houses. Available:
http://lammitysjarjestelmat.hosting.ambientia.fi/tyyppitalovertailu.php
[7]
Statistics Finland http://www.stat.fi/
[8]
Finnish Oil- and Gas Federation
[9]
www.suomirakentaa.fi
[10] Pohjolainen, J. 2000. Palvelujen energiatilastoinnin kehittäminen. Tilastokeskus,
katsauksia 2000/4. Helsinki: Statistics Finland.
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Appendix: DR potential of diesel generators at large customers
1
General
A standby generator is a back-up electrical system that usually operates automatically. Within
seconds of an outage in the grid an automatic transfer switch senses the voltage loss, commands the generator to start and then transfers the electrical load to the generator. The standby
generator begins supplying power to the circuits. After grid power returns, the automatic
transfer switch transfers the electrical load back to the grid and signals the standby generator
to shut off. It then returns to standby mode where it awaits the next outage. To ensure a proper
response to an outage, a standby generator has to be tested at certain intervals like every
month. Most units run on diesel, natural gas or liquid propane gas.
Diesel powered generator sets are typical choice for standby and emergency power systems,
worldwide. They are able to start and supply load in less than 10 seconds, and rated load in a
single step. Diesel generators are also well suited to utility peaking plants, Distributed Generation (DG) facilities, peak shaving (or peak lopping), and power management at large commercial or industrial sites. Units can be permanently installed or mobile power systems. Typical installation sites are hospitals, factories, airports, office buildings, hotels, data centres,
telecommunication sites etc.
Industries, greenhouses and utilities have also cogeneration systems that produce multiple
types of energy from a single source of fuel. For example, cogeneration systems using natural
gas engines can generate electricity and thermal energy for heating or cooling purposes, with
a savings of up to 35% on total energy expenditures.
The single unit size of diesel generators for back-up installation typically varies from a few
kilowatts up to 2,5 MW. In large installations these big MW-units are combined and total installation power in one site can be 10 MW or even more.
Normally standby generation units are designed to supply all critical loads in the site. For example, in hospitals the system can be designed to supply even 40 percent of the hospital’s total needs, including all of its critical loads, such as the intensive care unit, operating rooms
and computers.
For standby sets which are islanded, the emergency load is often only about 1/4 of the set's
standby rating. This apparent over size is required in order to be able to meet starting loads
and to reduce the starting voltage drop.
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2
Parallel operation with the mains [11]
Parallel operation is necessary to load the generator set up to the rated power. In islanded operation only a partial loading is possible (typically less than half of rated power). Therefore
parallel operation is preferred when the generator set is used for demand response or peak
shaving.
Reliable protection of generators in parallel operation with the mains, realised by very quick
decoupling, is very important in case of mains failure or when voltage or frequency deviate
from normal values. Mains auto reclosing are very dangerous for synchronous generators. The
mains voltage returning after 300 ms can hit the generator in asynchronous mode and induce
very high currents in the generator windings. The same very fast de-coupling is also necessary
in case of transient mains failures. In mains failure the voltage jumps to another value and the
phase position changes. This procedure is named phase or vector surge. Vector surge monitor
detects those surges and trips the generators mains circuit breaker instantaneously [12].
Generally there are two different situations:
-
Only mains parallel operation and no single operation. In this application the protection
relay protects the generator by tripping the generator circuit breaker in case of mains failure.
-
Mains parallel operation and single operation. For this application the protection relay
trips the mains circuit breaker. Here it is insured that the generator is not blocked when it
is required as emergency set.
Many distribution network operators have their own special requirements for the protection of
generators in parallel operation with the mains [3].
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3
Potential of back-up standby generators for demand response in Finland
In a study [14] published in 1990, the possibilities and significance of diesel standby generators for production of peak electricity in Finland, it was found that there were about 3800 installed diesel generators. The total output of units in a single site varied from 30 – 4000 kW.
The largest unit output of a single diesel generator was about 1000 kW. The total installed
output in Finland was estimated to be about 450 MW. The technically feasible standby power
capacity (unit size over 160 kW) for peak shaving was estimated to be at least 132 MW. The
effective use of standby generators for peak clipping required typically parallel operation capability with the mains. At that time it was not common to acquire and use standby generators
parallel with the mains.
In a more recent study [15] concerning mainly demand response potential of flexible loads in
big industrial customers installed diesel standby power was also considered. It was found that
in those ten interviewed industrial sites the total installed back-up power was about 30 MW.
The number of diesel sets was 33. In the inquiry the possibility to use back-up generators for
production of electricity in other situation than grid failures was also taken into account. The
result was that generators should be fully automatic in order to avoid additional cost of using
staff. In this case the compensation of using standby diesels for power production should be
between 200 and 250 €/MWh (diesel fuel price about 0,5 €/litre).
Fingrid, the Transmission System Operator in Finland, will have in the near future a need to
increase fast disturbance reserve capacity (activated manually in 15 minutes) because of increasing intermittent renewable generation (wind power) and because of the coming commissioning of the fifth nuclear power plant. For this reserve capacity, Fingrid uses its own resources and also purchases reserve maintenance from other resource owners.
Because of this increasing future reserve capacity need, in the beginning of 2009 a confidential study [16] from the potential of existing big standby generators possible for fast disturbance reserve was performed by VTT. Only those sites where the installation power of
standby power exceeded one MVA were under consideration. This one MVA power could
consist of many units in the same site.
The number and power of existing big standby generators was determined by interviewing
manufacturers of diesel aggregates and their clients. Also the reference data of import agents
was examined.
As one result of this study, some very large diesel generator units (power rating from 10 MW
to 20 MW) and some gas turbine units (power rating of 5 MW) were found to have a potential
as fast disturbance reserve capacity. The total capacity of these big units was about 45 MW.
In this study the number of sites where the installed diesel standby power (diesel size smaller
than 2,5 MVA) exceeded one MVA was found to be 25. The total number of diesel units in
these sites was 78 and the total installed power was about 68 MW. Most of these generators
are able to operate in parallel with the mains and therefore they can be loaded to rated power.
Those generators which are not yet running parallel could be converted to parallel running
and then they could supply power into the grid also on monthly load tests. The investment
cost for this kind conversion was estimated to be about 30 000 € / generator.
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The above estimated standby power potential does not cover all potential customers who own
big diesel generators. For example the generators of Finnish defence forces and telecommunication sites are not included in the above mentioned potential. Also data centres have big
standby power installations and only some are included in the above figures.
It is estimated that data centres use electricity about 0,8 TWh/a [17]. If we assume that these
centres use the same power all the time during the whole year we get an average power of 92
MW. Roughly estimated, this is also the total standby power which should be available in the
case of mains failure. In the future standby power in data centres will increase rapidly because
of the planned new big centres which each consume over 10 MW of power. The biggest new
data centre owned by Google will be constructed in Hamina. There the average power is estimated to be about 30 MW.
Much standby power is installed in telecommunication sites. It is estimated that these sites use
electricity annually about 1,3 TWh [17]. This electricity use of a constant power during the
whole year would mean an average consumption of about 150 MW. In the future this consumption will possibly double to about 2,6 TWh and 300 MW by 2015 because of new advanced mobile networks. Again, roughly estimated, it means that if all new consumption has
back-up power the required standby power will be about 300 MW.
Both data centres and telecommunication sites are very important for society. Therefore the
use of standby power in these sites for other purposes like fast disturbance reserve is from the
reliability point of view very challenging. Table A.1 summarizes the standby power concerning mainly big generator sets, in telecommunication exists also smaller generator sets.
Table A.1. Standby power (MW) of big generator sets and estimation for year 2015.
Standby
power
(MW)
Year 2009
Year 2015
Big separate
units
(>5 MW)
45
-
Other sites with
big units
Telecommunication
Data centres
Total
60
-
150
300
92
150
347
450
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4
DR-pilot in Finland
Suomen ElFi Oy and Energiakolmio Oy started in the beginning of 2009 a pilot project called
tasepooli. Its purpose was to aggregate demand response into the electricity market [18]. This
demand response was brought into spot market and as reserve capacity for Fingrid. This pilot
ended at the end of April 2009. In tasepooli diesel generators were also aggregated for demand response. The total power of diesel generators was about 10 MW.
One customer who participated on tasepooli and owns diesel generators was Digita Oy [18].
Digita Oy is the leading Finnish distributor of radio and television services, and an important
developer of data communication networks and network infrastructure. Digita is responsible
for national transmission and broadcasting networks as well as for the radio and television
stations. The broadcasting network covering the whole country comprises 36 major stations,
101 sub-stations and dozens of transmission link stations. Digita’s Transmission Control Centre maintains and controls the functioning of the broadcasting network and the technical quality of all broadcasts 24 hours a day.
The diesel back-up generators in telecommunication sites can be remotely started from
Digita’s Transmission Control Centre in the case when the aggregator sends the call. Those
generators whose power rate was big enough had been chosen for use in this pilot. All generator sets operate islanded and are able to supply all electricity for telecommunication stations.
Diesel generators are also operated remotely before thunderstorms in order to avoid possible
voltage transients from the grid. Tests of generators are remotely run from the Control Centre
in wintertime demand response use could replace these test runs.
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5
Examples from Great Britain
National Grid of GB [19] procures a range of balancing services from large electricity consumers that are able to interrupt their load, or run backup generators, to help balance the electricity system in Great Britain. These consumers are generally known as “Demand-side providers” and are typically industrial or commercial sites of a few megawatts or more.
5.1
Frequency Response
National Grid procures frequency response services, to keep the electricity system frequency
close to 50Hz on a second by second basis, by automatically altering the production or consumption of electricity in real time. A typical demand side provider of frequency response
services would have electricity load that could be shed instantaneously and automatically in
the event of a significant variation in system frequency. Trigger levels are set to statistically
manage how many times per year this is likely to happen.
5.2
Fast Reserve
National Grid procures fast reserve to meet large, rapid rates of change of demand for which
conventional power stations are too slow to respond. A typical demand side provider of fast
reserve would be very large (e.g. tens of megawatts) and, upon receipt of an electronic instruction from National Grid, would be able to start backup generation and/or reduce demand
very quickly (e.g. within a couple of minutes) and run for a short period.
5.3
Short Term Operating Reserve (STOR)
National Grid procures STOR during defined times of the day, in order to have reserves available to cater for general variations in demand and generation failures. A typical demand side
provider of STOR would, upon receipt of an electronic instruction from National Grid, be
able to start back up generation and/or reduce electricity demand within timescales of up to
four hours, and be able to run for a couple of hours.
Major technical requirements for a STOR provider:
-
Offer a minimum of 3MW or more of generation or steady demand reduction (this can be
from more than one site);
-
Deliver full MW within 240 minutes or less from receiving instructions from National
Grid;
-
Provide full MW for at least 2 hours when instructed;
-
Have a Recovery Period after provision of Reserve of not more than 1200 minutes (20
hours);
-
Be able to provide STOR at least 3 times a week.
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5.4
Constraint Management
National Grid procures Constraint Management Services to alleviate localised power flow
constraints on the high voltage transmission network, for example during a planned network
maintenance activity. A typical demand side provider would be able to, on a pre-planned basis, shutdown its demand or run backup generation continuously for a sustained period, e.g. a
number of days. Occasionally the need for the service would only be for defined periods during the daytime.
5.5
Example of Demand-side provider
One example of Demand-side providers is Wessex Water [20]. It is one of ten water and sewerage companies in England and Wales, covering Somerset, Dorset, Wiltshire and parts of
Avon. Wessex Water has about 550 emergency standby diesel engines whose total power capacity is about 110 MW. Their primary function is to power essential services such as sewage
and water supply works during power failures which happen on average a few hours each
year. Of this number, about 33 units (power capacity about 18 MW) are also used in a number
of non-emergency ways commercially which are called collectively Load Management, and
which includes routinely feeding power into the Local Distribution System and ultimately the
National Grid. These generators currently have a 4 minute start up and paralleling capability
automatically from the control room. These units are quite small, ranging from 260 to 800
kW, and are used by the National Grid on a regular call-off basis to supplement its arrangements with power station owners. Wessex Water sets are sized initially on the standby rating
for emergency use, but are run on Load Management at the Continuous rating level which is
about 80% of the standby rating. More diesels could be utilized and connected to the Load
Management System with proper incentives.
According to EA Technology nationally there are up to 20 GW of emergency diesels. With
the right financial incentives and explanations of the benefits large numbers of these could be
brought into the Reserve Service type of scheme. Over 20 years this practice and associated
technology will probably become standard.
RESEARCH REPORT VTT-R-06411-09
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References
[11] Varavoimalaitokset, ST -käsikirja Nro 31, Sähkötieto ry, Espoo 2000, 118 s.
[12] XG2 – Generator-/Mains Monitor. Manual XG2, November 1997. Woodward 19942008. p. 20. http://search.woodward.com/PDF/IC/DOK-TD-XG2-E.pdf
[13] Ohjeet sähköä tuottavan laitteiston liittämiseksi Helen Sähköverkko Oy:n sähkönjakeluverkkoon. 6.08/06. 10 s.
[14] Pihala H. & muut, 1991, Pienten varavoimakoneiden käyttö sähkön huipputehon leikkauksessa, VTT tutkimuksia 732, Espoo 1991. 135 s. + liitteet 3 s.
[15] Pihala H., Farin J., Kärkkäinen S., Sähkön kysyntäjouston potentiaalikartoitus teollisuudessa. PRO3/P3017/05. VTT, 31.08.2005.
[16] Pihala H., Varavoimakoneet ja Fingridin nopea häiriöreservi, VTT-R-03218-09, tutkimusraportti 8.6.2009, luottamuksellinen.
[17] Mobiiliverkoista tulee isoja sähkörohmuja, Tekniikka&Talous 25.9.2009, s.10
[18] Kysyntäjoustoa yhdistämällä tehokkaasti markkinoille. Digitan varavoima mukana
markkinoilla. Energiainfo 1/09, Energiakolmion sidosryhmälehti. s. 10-11.
[19] http:// www.nationalgrid.com/uk/Electricity/Balancing/demandside/servicedescriptions/
[20] http:// www.claverton-energy.com/National Grid’s use of Emergency Diesel Standby
Generator’s in Dealing with Grid Intermittency and Variability Potential Contribution
in Assisting Renewables