An Analytic Structure for Sustainable Energy in Competitive Electricity Markets
by
Arnob K. Bhattacharyya
Submitted to the Department of Electrical Engineering and Computer Science
in Partial Fulfillment of the Requirements for the Degrees of
Bachelor of Science in Electrical Engineering and Computer Science
and Master of Engineering in Electrical Engineering and Computer Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 9, 2003
V Massachusetts Institute of Technology 2003. All rights reserved.
OF TECHNOLOGY
0- .
JUL 3 0 2003
LIBRARIES
Author
Department of Electrical Engineering and omputer Science
D n ay 9, 2003
Certified by
Stephen R. Connors
Coordinator - Multidisciplinary Research
Director, Analysis-Group for Regional Electricity Alternatives
Thesis-j.ppervisor
Accepted by
Arthur C. Smith
Chairman, Department Committee on Graduate Theses
B'K.R
__j1
An Analytic Structure for Sustainable Energy in Competitive Electricity
Markets
by
Amob Bhattacharyya
Submitted to the Department of Electrical Engineering and Computer Science on May 9 th 2003 in
partial fulfillment of the requirements for the degrees of Bachelor of Science in Electrical
Engineering and Computer Science and Master of Engineering in Electrical Engineering and
Computer Science
ABSTRACT
Vast resource requirements and copious production of emissions and their associated social and
environmental concerns have made the electric utility sector a hot topic of several debates. Some
of the core issues include increased competition, increased population growth accompanied by
increases in demand, and global climate change. Although this is a global problem, we address the
issues presented on a more regional level by looking at the Scandinavian utility sector. In this
thesis, PROSYM, a chronological electric power production costing simulation computer software
package, is tested as a possible model to be used for a sustainability project. It is determined that
PROSYM's hydro logic is inept and suggestions are made as to how to improve the model to
handle a system with a large amount of hydro generation. With these fixes in place, we then shift
focus to the benefits and costs of adding alternative energy sources to the system. Multiple
simulations are run to test the feasibility of adding wind to the Scandinavian system. Emissions
and transmission line capacities are analyzed to measure the effect of this added generation.
Having looked at both a model and an alternative energy source, detailed suggestions are made as
to the requirements for a sustainable production model. Model, data, and scenario requirements
are discussed thoroughly in an effort to attract multiple stakeholders and to engage them in a
debate over the critical issues and the future direction for a planning project in Scandinavia.
Thesis Supervisor: Stephen R. Connors
Title: Coordinator - Multidisciplinary Research
Director, Analysis Group for Regional Electricity Alternatives
3
4
Acknowlegdements
I would like to thank my advisor and mentor Stephen R. Connors for his unrelenting support. He
provided the guidance and inspiration I needed to complete the work, by constantly taking time out
of his busy schedule to design work arounds for problems encountered. Furthermore, the input
from Audun Botterud and Kaare Gether helped to develop the thesis making it more
comprehensive. Audun worked on several projects for more than the span on a year, yet he always
found time to brainstorm with me. Thanks!!
I would also like to thank Prasad Ramanan for several late night discussions that made me look at
problems in a different light. His guidance helped me to sail through my graduate studies. It has
truly been a privilege to work with him throughout my MIT career. I wish you the best in your
career and your life.
Lastly, to my family who has been always supportive and understanding, I couldn't have done it
without you. I owe all my success to your love.
5
6
Table Of Contents
INTRODUCTION
2 OVERVIEW OF MODELLING AND DATA ISSUES
2.1 The PROSYM and EMPS Models
2.1.1 PROSYM
2.1.2 EMPS
2.1.3 PROSYM vs. EMPS
9
11
11
11
11
12
2.1.4 Current Work
13
2.2 Geographic Division
2.3 Load Division
2.3.1 Norway
2.3.2 Sweden
13
14
14
15
2.3.3 Denmark
2.4 Transmission Lines
2.5 1Hydro Production
2.6 Heat Rates for Thermal Plants
2.7 Modeling Germany, Finland, Poland, and Russia
2.8 Profiles for Nuclear and CIP Plants
16
17
18
19
21
22
3 1IYDRO SCIIEDULING AND TIlE PROSYM MODEL
3.1 Hydro Scheduling Methodology
25
25
25
25
26
3.1.1 Hydro Scheduling Issues
3.1.2 External Hydro Scheduling
3.1.3 Scenario Selection and Data Preparation
3.21 lydro Scheduling Results
3.2.1 Varying Hydro/Flat Thermal Results
27
27
3.2.2 Flat Ilydro/Varying Thermal Results
29
3.2.3 Varying I lydro/Thermal Results
3.3 Improving the Hydro Scheduling Methodology
30
33
3.3.1 Representative Weeks
3.3.2 Results of Ilydro Scheduling Methodology for Representative Weeks
3.3.3 Factor Adjustment for Run-of-River Content
3.3.4 Results of Factor Adjustment for Run-of-River Content
3.4 Pitfalls and Alternatives
33
35
38
40
42
3.4.1 Pitfalls of Hydro Scheduling Methodology
42
3.4.2 Alternatives
43
4 INTEGRATING WIND INTO TIE ANALYSIS OF TIE SCANDINAVIAN SYSTEM
4.1 Wind in Norway
45
45
45
48
49
49
4.1.1 Load Characteristics in Norway
4.1.2 Wind Characteristics in Norway
4.2 Converting Wind to Power
4.2.1 Converting Data to I lub Height
50
50
51
52
52
4.2.2 Analyzing Wind in Norway
4.2.2.1 NorwayS
4.2.2.2 NorwayW
4.2.2.3 NorwayM
4.2.2.4 NorwayN
7
4.2.3 Estimating the Power Curve
4.2.4 Total Power
4.2.5 Power Surface Curves
4.2.6 Load Surface Curves
54
57
58
60
4.3 PROSYM Simulations
60
4.3.1 Transmission Lines
61
4.3.2 Transmission Area Prices
63
4.3.3 CHP Generation
65
4.3.4 Hydro Scheduling
65
4.4 Transmission Line Solution
66
4.5 Issues with Wind
66
4.6 Conclusions
67
5 MODEL AND DATA REQUIREMENTS FOR A PLANNING PROJECT IN SUSTAINABLE
ENERGY
69
5.1 The Optimal Model
69
5.1.1 System Boundary & Time Resolution
69
5.1.2 Economics
70
5.1.3 Wind
70
5.1.4 H ydro
70
5.1.5 Thermal
71
5.1.6 Hydro/Thermal Coordination
72
5.1.7 Price-Flexible Load
5.1.8 Cost/Bid-based Dispatch
72
74
74
74
74
75
75
75
76
78
78
78
79
79
80
5.2 Data
5.2.1 Load Data
5.2.2 Hydro Data
5.2.3 Wind Data
5.2.4 Heat Data
5.2.5 Plant Data
5.2.6 Database Organization
5.2.7 Database Security
5.3 Scenario Selection
5.3.1 Supply Side Technology Options
5.3.2 Demand Side Technology Options
5.3.3 Bidding Strategies
5.3.4 Emission Trading
5.4 Multi-Attribute Trade-Off Analysis
5.5 Conclusions
81
82
83
85
CONCLUSION
REFERENCES
8
INTRODUCTION
Environmental emissions, increasing demand for power, and vulnerability of electric power
networks have increased concerns about the sustainability of the electric sector. These issues not
only play an important role in the choice of future options for system development, but in current
operation as well. It is important that we understand that the issues mentioned above are not
disjointed. A solution that decreases environmental emissions at the risk of higher vulnerability of
the power network is not robust. Any proposed solution will be complex, will have to be
implemented with finite resources, and will have some degree of impact on society as a whole.
Decision makers and planners in the electricity sector are searching for an elegant solution to the
sustainability problem by addressing the issues mentioned above. There are an ample number of
suggestions for solving this problem. Technological advances in power generation, power storage,
end-use efficiency, and load management have provided us with both demand side and supply side
solutions. The key is to accurately model these new technologies and to use them effectively to
benefit the system in the present and the future. With the emergence of policy measures to control
emissions and increased competition, these new technologies must be flexible. They must adapt to
constraints that change rapidly and unpredictably.
Thus, any solution to this problem must balance the social, economic, and environmental
dimensions of sustainable development. They must contribute to the management of risk and the
improvement of flexibility, in order to avoid serious disruption of the energy system [I]. However,
social concerns and regulatory policies which express themselves to govern system operation can
change quickly.
Changes in technology and public concern are reflected by changes in the
regulatory and decision-making process, and policy options have proliferated, including emissions
trading and taxes and various structures for increased competition. It is left up to the decision
makers to deal with the conflicts of interest which exist between technological appropriateness,
social acceptance, economic soundness and responsibility towards future generations, with the goal
of social and economic development which achieves or maintains a high standard of living and
improves quality of life [2].
9
As the above discussion has outlined, there is a need for research that will address the problems
related to sustainability and the electric utility sector on the technical, systems, and decisionmaking levels.
There are several goals for this project. The idea is to involve a wide range of participants from the
electricity sector, utility sector, environmentalists, and customers and to engage them in a debate of
the current issues. It is important to identify and implement a wider range of measures that will
serve as accurate indicators of sustainability.
Improved modeling tools that simulate electric
system operation under competition, and include transmission and distribution effects are essential
to a project in sustainability. Analyzing technology options that are considered 'next generation' is
of importance as this will give us insight into the limits of the current system.
These are some of the end goals for this project.
We need to enumerate some of the more
immediate goals that will help us reach these end goals. The most important of these goals is the
gathering of data that describes the Scandinavian electricity market accurately and effectively. The
next is to gather a group of stakeholders who can enlighten us as to the most critical issues and
those that can be addressed at a later time. Using this information a set of scenarios must be
developed that analyzes a wide array of both supply and demand side options. Finally, a model
must be chosen to test these scenarios and to further identify and refine the goals for this planning
project.
We have attempted to address some of the goals mentioned above in this paper. The first major
issue that we tackled was the selection and examination of a possible model for the project. Using
a minimal data set, we tested the model to determine its feasibility for the project. After analyzing
the model, we present possible improvements to enhance the functionality of the model in Chapter
2. While testing the model, meetings were held with possible stakeholders to determine particular
areas of interest. One area of interest was chosen and researched quite thoroughly and is presented
in Chapter 3. Chapter 4 integrates both the previous chapters and presents the requirements for a
model that is to be used in this planning project. The chapter also discusses possible scenarios that
should be explored and tested once a model has been chosen. Finally, we conclude and reiterate
our findings.
10
2 OVERVIEW OF MODELLING AND DATA ISSUES
2.1 The PROSYM and EMPS Models
2.1.1 PROSYM
The electric power system in Scandinavia was simulated using the PROSYM model. PROSYM is
a chronological model that represents the operation of several individual generating units which
serve customer electricity demand on an hourly basis. As a general matter, the units with lower
operating costs have priority in the dispatch over higher cost units, so that the total cost of
operating the system is minimized. The model also recognizes generator operating constraints such
as minimum down time and maximum ramp rates as well as transmission constraints between each
of the individual "transmission areas" in the study region. In PROSYM, the electric industry is
divided into a number of interlinked transmission areas, which correspond to the utilities'
transmission capabilities and geographic boundaries; there are eight transmission areas in the three
country region used in this analysis [3].
2.1.2 EMPS
EMPS has been developed for optimization and simulation of hydro-thermal power systems with a
considerable share of hydropower. It takes into account transmission constraints and hydrological
differences between major areas or regional subsystems. The EMPS model optimizes the
utilization of storage capacity (hydropower, gas, emission quotas) in relation to demand and
alternative sources of supply. The model has been developed for studying the operation of the
Nordic and European power systems, and may also include gas markets and emissions quotas. The
model is well suited for simulating the utilization of national or international energy resources, the
interaction between hydropower and thermal-based generation, and e.g. the interaction between a
gas- and an electricity market. Several hundred production units may be represented in a number of
subsystems which may e.g. represent parallel markets for electricity and gas. Among available
results are simulated market prices, generation, consumption and trade as well as emissions and
economic results. At present, the model is frequently used for forecasting spot price development
[4].
11
2.1.3 PROSYM vs. EMPS
Time resolution: PROSYM has an hourly while the EMPS model has a weekly time resolution. It
is therefore possible to add more detailed operational constraints with the Prosym model.
Thermal description: PROSYM allows the modeling of thermal plants in a very detailed fashion,
using max/min capacity, heat rates, ramp rates, start up/shut down costs, and variable operating
costs. The EMPS model represents the thermal generation simply with marginal costs and
available capacity.
Hydro description: The EMPS model has a detailed description of the optimization of hydropower
generation including all hydropower plants in the system. The network of waterways is also
represented in the model. The Prosym model has no long-term optimization of hydropower, but
dispatches the available hydro energy throughout the week, using internal hydro logic. Each single
hydro unit can in theory be represented, but waterways and run of water between plants are not
represented in the model. By using the EMPS model for long-term and Prosym for short-term
dispatch of resources we should, at least in theory, end up with a good hydro description.
Transmission: The EMPS model divides Scandinavia into 15-20 areas and normally applies simple
constraints on the weekly energy transport between the areas. It is possible to add an additional DC
loadflow module to represent the physical flow with more detail. PROSYM can handle up to 10
areas with hourly constraints on the transport capacity between areas.
Market design: Prosym can simulate a bid-based market, with possible strategic behavior from
participants with market power. The EMPS model does not have this option and aims at
minimizing total cost by optimal use of the hydro reservoirs. In Prosym we can also represent
ancillary services like reserve requirements. This may not be important in a system with more than
50 % hydropower.
Outages: In Prosym outages can be drawn from a probability distribution. In the EMPS model the
outages are only represented as deterministic reductions in available capacity for the various plants.
12
2.1.4 Current Work
The most crucial element of our work is to compile all necessary data, which include demand data,
heat rates for thermal plants, weekly energy values, and fuel ratios for CHP plants that use two or
more fuels. The next step is to determine scenarios to test in the short- and long-term. This is
important as there are several issues that need to be considered and they must be compiled in an
intelligible manner that will allow easy analysis of the results. Next, simulations spanning 1-year
to 30-years into the future must be run and results must be collected and analyzed. Running these
simulations allow us to learn the intricacies of the model, as well as its constraints. Then a
different set of scenarios must be run and the results of these scenarios must be compared with
previously collected results. This process is repeated until all alternatives have been exhausted.
2.2 Geographic Division
To proceed in our study of the Scandinavian area using the PROSYM model, the first step was to
reduce the aggregate nature of the data. To accomplish this, we began by dividing each country in
our model into distinct transmission areas. Thus, Norway was divided into four sub-areas, Sweden
into two sub-areas and Denmark into two sub-areas. In the EMPS model, Norway consisted of
twelve distinct areas. We tried to choose sub-areas so that the main bottlenecks in the system are
still represented. This led us to reduce the twelve sub-areas into four as shown below.
Glomma
Ostland
Sorost
Hallingdal
Telemark
Sorland
Vestsyd
Vestmidt
Norgemidt
Helgeland
Troms
Norway South
Norway West
Norway Mid
Norway North
Finnmark
Table 2-1: Table of EMPS and PROSYM transmission areas.
13
Sweden and Denmark were divided into two sub-areas, Sweden North and South and Denmark
East and West. These divisions were the same as those in the EMPS model for Sweden and
Denmark. Once these divisions had been determined, the next step was to divide the aggregate
load amongst the newly created areas.
2.3 Load Division
2.3.1 Norway
From the EMPS model, we acquired data for each of the twelve areas in Norway for 1999. We
aggregated the data for the twelve sub-areas into the PROSYM areas that were created. The data
from the EMPS model was quite detailed, as it divided the demand for each of the twelve areas in
Norway into three parts, an industrial, residential, and price-flexible part, but has an hourly time
resolution. As we aggregated the data into the four PROSYM areas, we were able to maintain the
same level of detail as the EMPS model. This allowed us to extract ratios for the industrial,
residential, and price-flexible parts of the load for each of the four PROSYM areas. The sum of
these three parts for each area was the fraction of the total load that was assigned to that area. We
combined the residential and price-flexible ratios into one; however the industrial was not easily
combinable. Given that most of the industry in Norway is heavy industry which is running all the
time and requires an almost constant amount of electricity, the total industrial load was subtracted
from the aggregated Norwegian load. The Norwegian load, minus the industry, was then divided
into the four PROSYM areas using the combined residential and price-flexible ratios. Once this
was complete, the fraction of the total industrial load for each area was added back to create four
separate demand profiles for the four PROSYM areas.
The figures below shows the total Norwegian load broken up into four separate areas for Jan.
2001.
14
2 0 th,
Norway South
Norway West
3500 -- 3000
-
- - ---
- ----
-
- - - -,_
12000
-
-
_-
--
-_-
_--
--
10000-
2500-
8000
2000
6000
1500-
-
---
-
-
-
-
4000 --
1000-
2000
5001
3
5
7
9
11 13 15 17 19 21 23
1
3
5
7
9 11
Hours
Hours
Norway North
2500
-- - - - -
-
- -
13 15 17 19 21 23
Norway Mid
-
-
-- -
3500
- -
---
-A
3000
2000
2500
1500
--
-
-
_
-
2000------
m
1000-
1500
1000
500
500 --0
1
3
5
7
9
11
13
15
17
19
21
23
1
Hours
3
5
7
9
11
13
15
17
19
21
23
Hours
Figure 2-1: Variation in demand over a period of 24 hours in Norway.
As can be seen from above, the demand follows the same pattern in all four areas. This is because
they are all based on the same aggregate hourly load data for Norway for 2001. Demand in all
areas has a bi-modal profile. The first peak occurs at approximately noon, after which demand
decreases slightly. We see that demand then rises to a second higher peak at approximately 5 in
the evening, when people get home and start preparing dinner. From the graphs above, we see the
Norway South has the largest load compared to the other areas as it is compromised of six subareas with a denser population.
2.3.2 Sweden
For Sweden, we also acquired data from the EMPS model. Since Sweden is modeled as North and
South in the EMPS model, we directly determined the fraction of the total load for each area. With
these ratios, we divided the 2001 data into North and South. Below is the graph of demand in the
two areas of Sweden for January 2 0 th 2001.
15
Sweden South
Sweden North
4500
40003500300025002000------1500 - 100 0 - -- - - - - -_--500
0
1 3 5 7
_---
4000
-
2000
0
__
1
11 13 15 17 19 21 23
9
-
---
20000 -18000
16000-.
14000
12000
10000
8000
6000
5000 -
3
5
7
9
11 13 15 17 19 21 23
Hours
Hours
Figure 2-2: Variation in demand over a period of 24 hours in Sweden.
As can be seen from the graphs above, both Sweden North and Sweden South follow the same
pattern. Similar to Norway, Sweden's demand profile is bimodal. Demand reaches its first
maximum at approximately noon, decreases a bit for the following 3 hours and then reaches a new
higher maximum at approximately 5 in the evening. It is obvious that Sweden South has a much
higher load than Sweden North.
One point to take note of for both Norway and Sweden is that these demand profiles were
artificially created from real data. That is, we have applied our own ratios to separate the load into
separate parts to create different demand profiles. These ratios were extracted from EMPS input
data, but may not be entirely accurate.
2.3.3 Denmark
For Denmark, we acquired real historical data for East and West from Nord Pool. This means that
we did not apply any ratios to the total demand to break them into their respective parts. The data
in the figure below is therefore real data, and that explains why they have slightly different shapes,
as opposed to the sub-areas in Norway and Sweden. Below is the graph of demand in the two areas
of Denmark for January
2 0 th,2001.
16
Denmark West
Denmark East
- -
2500-
- -
- -
-
-
3500---
------
3000
2000-
2500-
1500
-
2000
1000
-
-
-
--
S1500-
_
1000
500-
500
0
,
1
0
3
5
7
9
1
11 13 15 17 19 21 23
3
5
7
9
11 13 15 17 19 21 23
Hours
Hours
Figure 2-3: Variation in demand over a period of 24 hours is Denmark.
Once again we see that the demand in Denmark East and West follow the same pattern. Similar to
Norway and Sweden, Denmark's demand profile is bimodal. The first peak occurs at noon and the
second higher peak occurs at approximately 6 in the evening. We also notice that the demand
profile in Denmark is not as smooth as the other two countries. This can be attributed to the fact
that this is real data. Also, Denmark is different from the other two countries as its load is almost
evenly shared between East and West.
2.4 Transmission Lines
The transmission lines were modeled with the help of the EMPS model. The model had a list of all
lines between the various areas, their MW capacity, loss rate, and duplex characteristics. Since we
reduced the number of Norwegian areas from twelve in the EMPS model to four in the PROSYM
model, some transmission lines needed to be aggregated. For example, Norway South has one
transmission line that is an aggregate of all the transmission lines to the six individual sub-areas
that it is composed of. This single transmission line is equivalent in capacity, loss rate, and duplex
characteristics to all the transmission lines entering and leaving the six sub-areas comprising
Norway South. Transmission lines were created in a similar fashion for the rest of Norway. Since
Sweden and Denmark were represented the same way as the EMPS model, no aggregation was
needed for lines leaving those two countries. Below is a table of the all the links in the PROSYM
model.
17
FROM
TO
Norway South
Norway North
Norway Mid
Norway South
Denmark East
Norway South
Norway North
Norway Mid
Sweden North
Denmark West
Denmark West
Sweden North
Sweden North
Sweden South
Sweden South
Norway West
Norway Mid
Norway South
Sweden South
Sweden South
CAPACITY (MW)
1040
785
500
1850
1190
8700
900
300
6500
560
CAPACITY BACK (MW)
LOSS (%)
1040
785
500
1600
1140
8700
900
300
6500
560
3
.1
.1
.1
2
.1
.1
.1
3
3
Table 2-2: Transmission lines with associated loss rates between the transmission areas
2.5 Hydro Production
For hydropower we had access to weekly inflow statistics at Sintef Energy Research (SEfAS) for
both Sweden and Norway. The hydrological inflow series usually run from 1930 until 1990, so we
had sufficient statistical material for hydropower. In the EMPS model these series are used for
long-term stochastic optimisation of the storable hydropower generation. The time resolution in
the EMPS model is one week, and the optimisation usually takes place over a time period of I to 5
years. We used the EMPS model for long-term hydropower scheduling purposes in our trade-off
analysis, but we intended to use PROSYM for more detailed modelling with hourly time
resolution. We therefore needed to prepare the weekly results for hydropower generation from the
EMPS model so that they could be transferred to the PROSYM model.
PROSYM requires a
weekly or monthly available hydropower production as input, and allocates the energy between the
hours of the week, using specific logic for hydropower. The level of detail and complexity applied
in this logic is flexible.
The simplest and fastest way is to let the available hydropower be
scheduled in order to meet the demand as much as possible during high demand hours in the
system, and therefore level out the demand met by thermal units. The model can also optimize the
hydropower generation through iterations based on an assessment of the marginal value of water in
the reservoirs.
In the last case water values from the EMPS model can possibly be used as initial
marginal values of the water in PROSYM.
The PROSYM model treats the weekly/monthly hydro generation as a deterministic variable. We
will therefore need to create a set of scenarios to incorporate the uncertainty in hydro inflow into
18
our model. Figure 2-4 illustrates how this can be done, by reducing the simulated values of weekly
hydro generation and possibly also water values from EMPS, into for instance three scenarios for
the availability of hydro energy in the power system. The methodology described here is directly
applicable for the storable part of the hydropower generation. The non-storable part of the inflow
can also be modelled in the same way, but with no flexibility in how to dispatch the energy over
the week. We therefore represent the run of river fraction of the hydropower as minimum weekly
capacities in PROSYM. Alternatively, run-of-river generation can also simply be modelled as a
reduction in the demand.
WV 1
W~d
WV?
WVI
,
WV
WV59
WV60
4
HGI
HG 2
HG,
HG,
HG59
HG60
Figure 2-4: The transfer of weekly hydropower data from the EMPS model to the PROSYM model:
a) Extraction of simulated weekly results for hydro generation (HGj) and possibly also water values
(WVi) from the EMPS model. b) Reduction to three (or more) hydro scenarios (dry-d, normal-n,
wet-w). c) Input of hydro scenarios to the PROSYM model.
Unfortunately, we have not been able to obtain plausible results with the built-in hydropower logic
in PROSYM. Therefore, we have developed a logic that will treat the hydro power outside of the
PROSYM model, as further described in Chapter 2.
2.6 Heat Rates for Thermal Plants
The heat rates for thermal plants were acquired from data in a previous study. PROSYM provides
us three ways of modeling heat rates. The first is an incremental method for which we specify a
19
few levels of production and the associated incremental heat rate to run at those production levels.
The second is an average method for which we specify a level of production and an associated
average heat rate for that level.
And lastly, a coefficient option which allows us to specify
coefficients to create a heat rate curve. As we did not have detailed information available, we
decided to use a flat average heat rate curve. This means that the efficiency of the plant remained
constant at all levels of production. As production is increased, the cost associated with production
is directly related to the cost of fuel. The heat rate determines the initial marginal cost, but has no
bearing on the cost as production is increased from the minimum to the maximum level.
As more
data becomes available we may switch to other options of modeling heat rates.
Sweden's nuclear and coal plants were assigned values to create a flat average heat rate curve.
Since there was no readily available data on heat rates for nuclear and coal plants, an average heat
rate for similar nuclear and coal plants was assigned to Sweden's plants. For Sweden's CHP
plants, average heat rates between 8000 GJ/GWh and 10000 GJ/GWh were assigned.
data becomes available these numbers will become more precise.
As more
For Denmark, data was
extracted from the EMPS model for all CHP plants leading to average heat rates ranging from
8,000 GJ/GWh to 65,000 GJ/GWh.
Below is a table of the various thermal plants in Sweden and Denmark.
Plant Type
Sweden
Capacity (MW)
Plant Type
Location
Denmark
Capacity (MW)
Location
Nuclear
9500
South
CHP
400
West
CHP
635
South
CHP
616
West
CHP
642
South
CHP
700
West
CHP
CHP
CHP
641
300
170
South
South
South
CHP
CHP
CHP
681
700
633
West
West
West
Com. Turbine
Com. Turbine
CHP
180
415
151
South
South
North
CHP
CHP
CHP
CHP
CHP
CHP
1374
250
522
1382
500
249
West
East
East
East
East
East
CHP
166
East
CHP
700
East
CHP
466
East
Table 2-3: Thermal plants in Sweden and Denmark.
20
2.7 Modeling Germany, Finland, Poland, and Russia
Although we are modeling the Scandinavian electricity market, we must take into account that a
portion of electricity that is used in Scandinavia is purchased from surrounding areas such as
Germany, Finland, Poland, and Russia. In the same respect, when there is overproduction in
Scandinavia electricity is sold to these areas as well. It is important to account for these areas as
the prices and dispatch of the system is very dependent on the conditions in the neighboring areas.
The imports serve as another source of electricity, while the exports increase the actual demand
that the system faces in times of surplus generation. However, accounting for these areas does not
mean including them as generation sources in our model. Including them as generation sources
would increase the complexity of the model and would make our results harder to evaluate. It
would also increase the input data requirement considerably. Thus, we decided to represent these
areas with purchase/sales contracts. A purchase contract allows an area to purchase electricity if
the contract price of the electricity is lower than the marginal cost of increasing production of the
next unit. A sales contract allows an area to sell electricity at a specified contract price, if there is a
surplus in that area. The amount of electricity that can be purchased/sold was determined by the
physical capacities of the links connecting Norway, Sweden, and Denmark with Germany, Finland,
Poland and Russia. For example, there is a link of capacity 600 MW between Sweden South and
Germany. This link was converted into an equivalent purchase and sale contract with a maximum
capacity of 600 MW. Thus, Sweden can purchase a maximum of 600 MW at any given time and
sell a maximum of 600 MW at any given time. These purchase/sales have a price associated with
them. The prices that were assigned to these contracts were based on a price profile of low offpeak prices (7pm to 7am and weekends) and higher peak prices (7am to 7pm). One thing that
these contracts do not take into account is emissions. When sales are made from Scandinavia to
other areas, then emissions can be calculated, but when electricity is purchased from surrounding
areas the emissions are not taken into account. An appropriate solution to modeling emissions for
purchases must be devised.
Below is a table of all the transactions that occur between transmission areas in Scandinavia and
neighboring areas.
21
Transmission Area
Transaction
Capacity (MW)
Country
Sweden North
Sweden North
Sweden South
Sweden South
Sweden South
Sweden South
Sweden South
Sweden South
Denmark West
Denmark West
Denmark East
Denmark East
Purchase
Sales
Purchase
Purchase
Purchase
Sales
Sales
Sales
Purchase
Sales
Purchase
Sales
900
500
600
600
550
600
600
550
900
1300
600
600
Finland
Finland
Germany
Poland
Finland
Germany
Poland
Finland
Germany
Germany
Germany
Germany
Table 2-4: Purchase/Sales contracts between Scandinavia and surrounding areas
2.8 Profiles for Nuclear and CHP Plants
In order to add more detail to our model, we created weekly profiles for the nuclear plant in
Sweden and the CHP plants in Denmark. The idea for having such profiles was to mimic reality.
In real life the nuclear and CHP plants display a seasonal pattern, with more production in the
winter months and lower production in the summer months.
The figure below illustrates the heat-power characteristics of a typical CHP plant.
F
IsofuelC
urves
0-
E
-10
D-
)% Fuel
PR ...................................... ....................... ............. . ...... .. Minimum
Fuel
Ma ximum Heat
Extraction
Heat
Figure 2-5: Heat characteristics of typical CHP plant.
22
When this unit generates maximum power, heat production is the waste heat from the unit (Point
A).
As power generation decreases, heat production capacity increases and more heat can be
extracted for the steam turbine. However, when power generation decreases further below PB, heat
production capacity decreases. Along the curve AB, the amount of fuel burned is constant, power
generation decreases and heat extraction increases. Along BC, the heat extraction valve is set to its
maximum opening while the amount of fuel burned decreases [5].
For CHP plants, there are two possible profiles that are distinctly different. A CHP plants may
have follow either electricity demand or heat demand which are two different profiles. A CHP
plant may primarily produce electricity, in which case heat would be a byproduct or a CHP plant
may primarily produce heat, in which case electricity would be a byproduct. Lack of data has
prevented us from making that distinction for the CHP units in Sweden and Denmark. All CHP
units are have been assigned a fixed heat profile which in reality is not the case.
23
24
3 HYDRO SCHEDULING AND THE PROSYM MODEL
3.1 Hydro Scheduling Methodology
3.1.1 Hydro Scheduling Issues
After running several different scenarios, it became apparent that PROSYM did not dispatch hydro
in a logical manner. PROSYM was instructed to use all available hydro in the transmission areas
to level the system load. Although the load was being leveled, it became apparent that only one
hydro power plant was being dispatched to level the load. One possible explanation into this
behavior is the fact that hydro is such a large part of the overall system in Scandinavia,
approximately 60% of total generation. In fact, it seems as if the largest hydro power area was able
to single-handedly level the load in the entire system. Therefore only the aggregate hydro power
plant represented in the model for Sweden North had a varying production level across the hours of
the day. All other hydro power plants produced a constant amount of energy for every hour of the
week. Several attempts were made to correct this situation, including restricting hydro generation
in each transmission area to level the load in that particular area and the use of hydro banking to
reduce production when prices are low and increase production when prices were high. However,
a feasible solution could not be found.
After much deliberation it was decided that hydro
scheduling should be done outside the model.
3.1.2 External Hydro Scheduling
There were several issues that had to be considered before hydro scheduling could be done outside
the model. By scheduling hydro outside the model, one would have more power & flexibility in
how the hydro resource is utilized throughout the day. Also, by removing hydro from the model,
real market situations could be mimicked more accurately. These were among the benefits from
removing hydro scheduling from the PROSYM model. There were several pitfalls as well, the
most important being how to deal with pricing in areas that were solely comprised of electricity
generation derived from hydro power. Another issue that required consideration was transmission
line constraints.
Questions arose as to whether or not transmission line capacities had to be
adjusted in response to hydro scheduling being done outside the model. The positive/negative
25
aspects of hydro scheduling outside of the model became more apparent after running several
simulations.
3.1.3 Scenario Selection and Data Preparation
The next step was to decide what scenarios to run, more specifically how to schedule the hydro.
Obviously there were an infinite number of ways to schedule the hydro, so the first thing was to
clearly define the boundary cases. The first boundary case attempts to schedule hydro in a manner
that leads to a completely flat thermal production curve and a varying hydro production curve. The
other extreme boundary case attempts to schedule hydro in a manner that leads to a completely flat
hydro production curve and a varying thermal production curve. The next logical case is one that
falls in between these two boundary cases with both varying thermal and hydro production curves.
The key to picking this case is to schedule hydro in a manner that both mimics reality, given the
technical constraints in the system and which leads to PROSYM output that is reasonable as well.
Before scheduling the hydro, a method for coordination among the various hydro power plants was
developed. Since there are six aggregated hydro power plants, one in each area, there are several
ways of dispatching them. One way of scheduling hydro is to start with the largest hydro power
plant. Once it has produced its maximum capacity, the next plant is dispatched. This process
continues until the load is met or all available energy from hydro power is exhausted. Another
strategy is one where the hydro power is scheduled in such a manner that it first levels the load in
its respective area and extra available energy is exported to other areas. The strategy chosen for
the analysis below was one in which each hydro power plant produces a fraction of the total hydro
production. More specifically, the total available hydro power (which includes the run-of-river
content) was calculated from which a fraction was extracted indicating the amount of energy (as a
percentage) that each hydro power plant is responsible for. Once the part of the system load that is
to be satisfied using hydropower is calculated, the fractions determine how much each power plant
will produce in each hour of the week. In this way, every hydro power plant is responsible for a
fraction of the load for every hour.
Using the above described methodology, hydro power production was determined for each of the
areas containing hydro power plants. Then, for each of the areas a new load was calculated by
26
subtracting the hydro power production from the original load. This led to new loads that were
both positive and negative. For Norway, which has no other means of electricity production, a
positive load in a transmission area indicates an area that must import to meet the load and a
negative load indicates a transmission area will export. These new loads were entered into the
model and simulations were run. For the first run of hydro scheduling, week 8 was used as it is a
regular winter week. Before deciding on a particular methodology, simulations will be run on
several hand-picked weeks to assure accuracy of the chosen method.
3.2 Hydro Scheduling Results
3.2.1 Varying Hydro/Flat Thermal Results
For the first scenario that was run, the results were adequate. From the graphs shown below, we
see that hydro varies throughout the week and that thermal production is constant.
---
60000
-
--
-
50000-
30000
20000
10000
0
1
12 23 34 45
56
67 78
89 100 111 122 133 144 155 166
Hours
m Hydro mThermal
Figure 3-1: The total load divided into the parts met by hydro and thermal generation.
In order to gauge the accuracy of our hydro scheduling methodology, imports/exports from
PROSYM are compared with actual imports/exports for that week in Norway. From the figures
below, it is apparent that Norway imports during off-peak hours and exports during peak-hours
which is consistent with what occurs in reality. One point to take note of is that the difference
between peak imports and peak exports that PROSYM produces is approximately 4000, where as
in reality the difference is only 3000.
27
Imports(+)IExports(-)
3000 4000
2000
1000
- 1/11%
1fr
0
-1000
3 25
7V49 Y173
7 109 21
85
145 157
A0
-2000
-3000Hours
Figure 3-2: Imports/exports for Norway in week 8 using the flat thermal and varying hydro scheme. The figure
on the left is the estimated imports/exports from the PROSYM model and the figure on the right is the actual
exchange for Norway.
Another issue that arose is the fact that scheduling hydro in this manner is not realistic, as no hydro
power is banked for future use. All available hydro power is dispatched and thermal production is
flat across all hours of the week. This issue becomes more apparent as we focus on the prices in the
transmission areas. In the figure below, prices in Norway North drop down.
15 ------- -- -- -10
5 --
--- -
----
-
-
-
-
- - - - --- -
L
-
- -
-
--
-
20 ---- -- ---
-
-
- - ---
-
-
---
-
0
1
25
49
97
73
121
145
Hour
marginal cost) -NorwayM(Running marginal cost)
-NorwayN(Running
NorwayS(Running marginal cost)
marginal cost)
-NorwayW(Running
SwedenN(Running marginal cost) - SwedenS(Running marginal cost)
marginal cost)
- DenmarkW(Running marginal cost) -DenmarkE(Running
Figure 3-3: Prices in the transmission areas for week 8 estimated by PROSYM using the flat thermal
and varying hydro scheme.
This is due to the fact that there is too much hydropower available in that area and all of it cannot
be exported due to transmission constraints. Therefore since the power cannot be saved or stored,
it simply goes to waste.
In reality, system operators have knowledge of transmission line
constraints and will schedule hydro in a manner that would not violate these constraints. They
might choose to bank the hydro for later use when prices are high. However this scheduling
28
methodology lacks the flexibility to allow hydro banking, as hydro power production is
predetermined.
3.2.2 Flat Hydro/Varying Thermal Results
The next scenario that was run gave results that were not entirely accurate. From the graphs shown
below, we see that in this scenario, hydro production is a constant throughout the week and thermal
production varies from hour to hour.
60000
50000
40000
30000
20000
10000
0
1
13 25 37 49 61
85 97 109 121 133 145 157
73
Hours
mThermal U Hydro
Figure 3-4: The total load divided into the parts met by hydro and thermal generation.
Once again, looking at the imports/exports from PROSYM (below) and comparing them to actual
imports/exports for that week, the PROSYM output indicates that Norway imports during peak
hours and exports during off-peak hours, which is opposite of what actually occurs in reality.
Another important point to take note of in this scenario is that in a system which is dominated by
hydro power, it is illogical to have a flat hydro production curve. Especially in the Scandinavian
case, where hydro power accounts for more than half of the energy production in the area, a
varying thermal production along with a constant hydro production is unable to meet the load.
29
Inpods(+Y/Bqorts(-)
Imports(+yExports(-)
4000
3000
2000--1000
-201
-2000 - -3000-4000AOM
MW
The figure
Figure 3-5: Imports/exports for Norway in week 8 using the varying thermal and flat hydro scheme.
the right is the actual
on the left is the estimated imports/exports from the PROSYM model and the figure on
exchange for Norway.
3.2.3 Varying Hydro/Thermal Results
The last scenario that was run gave results that were closer to reality. This last scenario was partly
derived from the first case where thermal production was flat and hydro production varied
throughout the day. Since the imports/exports data for that scenario was closest to reality, the
loads were adjusted using the formula below, to produce an import/export graph that was more
similar to the actual.
ltnew =
1+
1,
-
()
C
where
itnew
= new varying hydro area load
= average hydro area load over week
= hydro area load with complete load leveling
C1
= load leveling factor (= 0.5)
The purpose of using the formula was to compress the graph so that that difference between peak
by
imports and peak exports is decreased to a level that mimics reality more closely. This is done
and
calculating the average load over the week and multiplying the difference between the average
hydro
the actual by a load-leveling factor. This methodology gives us varying thermal and
have
production curves as shown below. The two curves follow the same pattern as they both
30
higher production during peak hours and lower during off-peak hours. Comparing PROSYM
import/export data to the actual data from the graphs below, we see that this new method has
decreased the difference between peak imports and exports to approximately 3000.
Imports(+)/Exports(-)
4000
2000 -
-
-
_--
3000
1500
2000
1000
-
500
-1000
0
13 25 37 49 61
-500
73 85
7 10
21 13 145157
-0
..
..
Acofo-
49515
-1000
Hours
Figure 3-6: Imports/exports for Norway in week 8 using the varying thermal and varying hydro scheme. The
figure on the
left is the estimated imports/exports from the PROSYM model and the figure on the right is the
actual exchange for Norway.
This analysis assumes that hydro producers are behaving ideally by trying to level the load
completely.
However, there are several reasons as to why hydro producers may not act
idealistically. For example, transmission limits may impose constraints on the manner in which
hydro producers schedule production. Uncertainty in load may also lead to non-idealistic behavior
as load estimates are essentially predictions as to how much power will be needed. Lastly, hydro
producers may purposely act in a non-idealistic fashion by decreasing production leading to higher
prices. Production is then increased at these high prices in order to maximize profits.
Looking at graphs for other areas, PROSYM import/export data for Sweden seems to be a
translated version of the actual import/export graphs. The peaks and shape of the graph are very
similar, however the PROSYM output indicates that Sweden is mainly an importer, where as in
actuality Sweden both imports and exports.
There are two possible explanations for the
discrepancy in this case. First, Sweden's electricity infrastructure is composed of both thermal and
hydro generation.
The load-leveling factor analysis used does not take this into account.
Secondly, the run-of-river content in Sweden South is a large percentage of the actual energy
production from hydro. The analysis used does not differentiate between run-of-river content and
actual energy production. Future iterations of the load-leveling analysis will take these issues into
account.
31
Imports(+)/Exports(-)
Imports(+)/Exports(-)
2500
2000
-
1500
1000
---
500
1000
0
500-
-
-- ---
A
-500
0
13 25 37 49 61
-500
-
73 85 97 109 121 133 145 157
-
Hours
I1
Hours
-15WJ
Figure 3-7: Imports/exports for Sweden in week 8 using the varying thermal and varying hydro scheme. The
figure on the left is the estimated imports/exports from the PROSYM model and the figure on the right is the
actual exchange for Sweden.
Finally, examining the PROSYM import/export graphs for Denmark, there seems to be a regular
pattern with high exports during off-peak hours and low exports during peak hours. Comparing
the PROSYM data with actual imports/exports for Denmark, it is consistent in the fact that both
describe Denmark as an exporter. However, the actual data does not have the same regular pattern
as the PROSYM output. In the case of Denmark, the load-leveling methodology has no direct
effect as Denmark's generation does not include hydro. However, this method may be indirectly
affecting the PROSYM results for Denmark. But, as this load-leveling methodology is improved
for Norway and Sweden, it may adjust the results for Denmark as well. The large fraction wind
power might give "irregularities" in the actual Danish data.
0 0
Imports(+)/Exports(-)
Import(+)1EkSport(-)
500-
0 -
1
-500-
...
13 25 37 49 61
-.
-.. ..
- ..11"11.1111..11.
........
-7fm
1
73 85 97 109 121 133 145 157
-f--l
1 ,b
1000
-1500
-1500-
-2000
-2500
.
-
----
Hours
Hours
2--2000
-
-
Figure 3-8: Imports/exports for Denmark in week 8 using the varying thermal and varying hydro scheme. The
figure on the left is the estimated imports/exports from the PROSYM model and the figure on the right is the
actual exchange for Denmark.
Validity of these results can be further verified by examining the prices in the various transmission
areas. As can be seen from the graph below, prices are high during peak hours and lower during
off-peak hours. The prices follow a very similar pattern throughout the week, with some variation
32
in the weekend. Unlike before, where the price in Norway North fell to $3, there are no such price
drops or spikes. This demonstrates that transmission constraints were not violated.
20
18
16
14
12
.
.0
------
- ----
- -
--
---
---
---
10
8
6
4
2
0
1
25
49
97
73
145
121
Hour
marginal cost)
marginal cost) -NorwayM(Running
-NorwayN(Running
cost)
marginal
NorwayS(Running
cost)
marginal
-NorwayW(Running
cost)
marginal
SwedenN(Running marginal cost) - SwedenS(Running
- DenmarkW(Running marginal cost) - DenmarkE(Running marginal cost)
Figure 3-9: Prices in the transmission areas for week 8 estimated by PROSYM using the flat thermal and
varying hydro scheme.
Overall the results acquired from this method closely mimic reality.
transmission constraints are violated and prices are reasonable.
Using this method no
The PROSYM import/export
profile for Norway matches the actual data very closely. For Sweden and Denmark, the profiles
have the correct shape and accurately describe the region as an importer/exporter, but further work
is required in order to account for areas with thermal production. Also, the load-leveling method
must be improved in areas for which the run-of-river content is a large part of the hydro
production.
3.3 Improving the Hydro Scheduling Methodology
3.3.1 Representative Weeks
In order to continue with this analysis, a representative set of weeks from the year must be chosen
in order to test the results. Although this analysis can be done for all weeks of the year, a good
sample set of weeks will suffice. In order to choose these weeks, the load as well as the inflow
data for Scandinavia was analyzed. Looking at the graph below, the load in week 6 is the highest
33
I
a
throughout the year. This was probably one of the coldest weeks in Scandinavia and would be
good week to use in testing our hydro scheduling methodology.
Load per week
-
8000 --
-
-~~~---
----
7000
---
-------
---
--
300000
250000
6000 200000
5000-
150000 C
4000
00
3000-
100000
2000
50000
1000
0
0F
Figure 3-10: The aggregate load for Norway in 2001.
Week 30 was the next logical choice as it had had the lowest load throughout the year. Week 30
was a summer week with low load coupled with a high run-of-river content and moderate reservoir
inflow in both Norway and Sweden, as shown by the figure below.
7000
-----
-
6000 ---
5000
I
I
4000
U
II
I
ii
I
I.
I
_________________
II
_________________
3000
2000-
0Week no.
I Load
ROR
Res inflow
levels for 2001.
Figure 3-11: The aggregate load for Norway along with ROR content and reservoir inflow
34
Week 24 was another logical choice as it represented a late spring week with high reservoir inflow
and run-of-river production coupled with moderate temperatures in both Norway and Sweden.
4000
-
---
3500-
3000-
2500-
I
2000-
-
150o
-
-
-
1000-
500o
0
Week no.
* Load B ROR
U Res iflow
Figure 3-12: The aggregate load for Sweden along with ROR content and reservoir inflow levels for 2001.
Thus far, weeks 6, 8, 24, and 30 were chosen. To represent a typical fall and winter week, weeks
41 and 48 were chosen, respectively. Week 41 was a typical fall week characterized by moderate
load, run-of-river content, and reservoir inflow. Week 48 was a winter week characterized by high
load coupled with low run-of-river content and low reservoir inflow for both Norway and Sweden.
Using the weeks selected here, the hydro scheduling methodology will be fine tuned and tested for
Norway, Sweden, and Denmark.
3.3.2 Results of Hydro Scheduling Methodology for Representative Weeks
Once the weeks were selected, the corresponding data had to be prepared for the analysis. Using
the formula presented above, new loads were created for each of the areas for the weeks selected.
These new loads were entered into PROSYM and consequently PROSYM produced import/export
graphs for each of the areas. Comparing these graphs with actual exchange data shows that the
factor method works well, but improvements are required. An issue that was encountered when
preparing the data was the fact that during several of the weeks chosen, the run-of-river content
35
was often a large percentage the energy production for an area. In this case, to avoid losing the
energy from the run-of-river content, much flexibility was lost in how the hydro was scheduled. In
particular, Sweden South had a hydro production curve that was approximately flat for week 24
and week 30 as the run-of-river content was almost 100% of the production for that area. In this
case, all hydro production came strictly from the run-of-river content and therefore was not
scheduled in a manner that took into account peak/off-peak periods.
The results for week 6 are quite similar to those of week 8 presented previously. PROSYM
produces an import/export graph for Sweden which is a translated version of the actual
import/exports for that area.
2500-2000_
4000---3000 2000 -
1500
I
1000 -
0
1000 -
2
5-9
-
--
4 -
5-
-,
-
1000
500-
0
-500-11529
-1000
-2000 - -
-1500
-2000
-
-3000
Hours
-
Norway Actual -
Hours
Norway PROSYM
-
Sweden Actual -
Sweden PROSYM
Figure 3-13: Actual and estimated imports/exports for both Norway and Sweden for week 6.
36
For week 24 and week 30, the import/export graphs created by PROSYM are all translated
versions of the actual import/exports. This translation can be attributed to the high run-of-river
content during these weeks. A correction for this will be discussed later in this chapter.
4000 - - -
--
-
-
-
-
4000
3000
3000 -
2000
2000-
1000
2
1000 -
2
0
-1oo
2
1 8
3
0 . . ... ... . .. .. .. .. . . ......
......
.....
991 312 141 155
-1000-
-2000-
1
15 29 43
57 71
85
-
-2000 --
-3000
Hours
-
Norway Actual -
5
714
99113
Hours
Norway PROSYM
Sweden Actual
-
-
Sweden PROSYM
Figure 3-14: Actual and estimated imports/exports for both Norway and Sweden for week 24.
5000
99 113 97 141
-
-1000 -
3000_
-1500
20002
15 29 43 57 71 85
-500
4000.
-2000 -
1000 -
S -2500
0
A--
---
-3000 -
-1000
15
-2000
-----
29 43
71
5
85 99
-----
--
13 127 141 155
-3500
---__
-4000
-
- -
-
Norway Actual -
-
-
--
-
Hours
Hours
Norway PROSYM
-
Sweden Actual -
Sweden PROSYM
Figure 3-15: Actual and estimated imports/exports for both Norway and Sweden for week 30.
37
I
Since the run-of-river content is still a large percentage of the energy production in week 41, the
same translation phenomenon can be seen quite clearly. For week 48, the translation is less
pronounced as much of the snow has melted leading to a smaller run-of-river contribution.
-500 2000-
-1000
-1500-
1000-
2
10 -
n
-2000
-2500
-3000
-
-
- -
-3500
-2000
-----
-3000
-4000 --4500 --
----
-
-
-
-
-
Hours
Hours
-
Norway Actual -
Sweden Actual -
-
Norway PROSYM
Sweden PROSYM
Figure 3-16: Actual and estimated imports/exports for both Norway and Sweden for week 41.
2500-
2000-
2000
1000-
1500-
0-1000-
2
15 "T
43
7
85
11
12
17
1000
500
-2000-
0-
-3000-
-500
-4000-5000- I
. ........ ...... " -nnfi
...................
.....
a.. . . .,. . .
S13 25 37 49 61
73 85 97 109 12113314
157
-1000
Hours
-
Norway Actual -
Hours
Norway PROSYM
-
Sweden Actual -
Sweden PROSYM
Figure 3-17: Actual and estimated imports/exports for both Norway and Sweden for week 48.
3.3.3 Factor Adjustment for Run-of-River Content
As presented above, when the run-of-river content is low, the hydro scheduling methodology
works quite well. However, as the run-of-river content increases, the import/export graphs created
by PROSYM maintain the same shape as the actual exchange data, but a translation takes place.
are
Looking at the graphs for week 24 above, it is apparent that the PROSYM import/export graphs
a translated version of the actual exchange data. To determine the root of this behavior, the run-ofthe
river content was analyzed for the six weeks in question. From the table below, we see that
run-of river is a small percentage of the energy production in the winter weeks except for Norway
is a
Mid and Sweden South. Proceeding through the year, we see that the run-of-river content
as
much larger percentage of the energy production in the various areas. This is accurate because
38
the temperature rises, snow on the mountains melt and drain into the rivers. As the contribution
from the run-of-river gets larger, the hydro scheduling methodology loses flexibility as the run-ofriver content must be utilized or else it is wasted. This is a dilemma faced by hydro producers in
reality because there is no control over the timing and the amount of the run-of-river content.
Since it must be used and there is no control over how much comes and when, hydro producers
lose much freedom in scheduling the hydro the way they wish to. Nonetheless, as can be seen
from the import/export data, the hydro producers do deal with this issue in some respect.
Therefore, the hydro scheduling methodology presented must account for this run-of-river situation
appropriately.
NorwayN
NorwayM
NorwayW
NorwayS
SwedenN
SwedenS
Week 6
Week 8
Week 24
Week 30
Week 41
Week 48
1%
19%
4%
7%
2%
21%
2%
19%
4%
6%
5%
13%
56%
68%
61%
75%
66%
100%
66%
82%
84%
73%
90%
99%
33%
54%
63%
70%
86%
95%
5%
26%
10%
17%
24%
89%
Table 3-1: The percentage of hydro that is run-of-river for the six hydro areas in our analysis.
In order to correct the hydro scheduling methodology, the factor method must somehow take into
account the run-of-river content when producing the new loads. Given that the shape of the graph
is correct, one must determine how to translate the graph appropriately such that it more closely
mimics what occurs in reality. One such method for doing this is to adjust the new loads that are
calculated by subtracting a fixed amount from them. This would essentially just translate the curve
downward. The next step is calculation of this factor. Looking at the graphs closely we see that in
most cases the import/export graphs produced by PROSYM are approximately 1000 above the
actual import/export graphs. One possible method for adjusting the loads is to simply subtract
1000 from the load in each hour of the day. However, this method does not take into account the
percentage of hydro energy that comes from the run-of-river content. A better method, one that
involves the run-of-river content, is one where the amount subtracted from the new load is the
percentage of hydro energy that consists of run-of-river content multiplied by 1000, as shown in
the figure below.
39
itnew
_I+= (
I
Ci-
(2)
RO
e000
where
new varying hydro area load
Anew
=
average hydro area load over week
hydro area load with complete load leveling
C1
=
R
load leveling factor (= 0.5)
=run - of - river percentage
In order to use the formula above, we must aggregate the run-of-river percentages for each week
for all of Norway and Sweden. Doing this gives us the run-of river percentages presented below.
Norway
Sweden
Week 6
6%
3%
Week 8
6%
6%
Week 24
68%
71%
Week 30
76%
91%
Week 41
61%
87%
Week 48
14%
30%
Table 3-2: The percentage of hydro that is run-of-river for Norway and Sweden.
3.3.4 Results of Factor Adjustment for Run-of-River Content
Using these percentages and the correct formula presented above, new loads were created and
simulations were run once again. As shown below, the improved formula has taken into account
the run-of-river content and PROSYM has produced import/export graphs that mimic reality. Only
weeks with run-of-river percentages above 50% are presented below.
40
a) Week 24
-
4000 ----------------
3000
2000
1500 1000-
1000
S
500-
0-
-1000
-..
-
-
-1500
-~
1
- -----
-1000 -
------------------------
7
3- 25 37-4 9- 61- 73
-500 ---
-2000 -1
-3000
-
- --
-
-
-- -
-
3000 2500
--------
Hours
Hours
Norway Actual -Norway
-- Sweden Actual -Sweden
PROSYM
PROSYM
b) Week 30
3
2
0
---
---------
4000 -----
35003000 ----2500
2000
1500 1000
500 0
-500
-1000
-1500
... .,.
........
0 - " -. .0 .. - --.---
.0 ....
97-10912-133-145457
-500 113- 25--37--49--61-73
-1000 -
2
-1500
-2000
-2500
-3000
-----
-
-3500 -4000
---
--
-
-
---
--
--
--
-
-
-
-
-
-4500
-
---
Norway Actual
-
-
Hours
Hours
-
-
Norway PROSYM
-
Sweden Actual -
Sweden PROSYM
c) Week 41
3000 -
2000 -
0-500 .-- 13-25-37--49-61
----
-
1000
109-421-1133-145-1-57
-1500
0q
723-85-97
-1000 ----
;25 R
49'j;
73j85 P7 11121 13
45 157
2
_--
-2000
-2500
-3000
-1000
-3500
-2000
-4000
-4500
-3000
-
---
--
Norway Actual -Norway
- -
----
Hours
Hours
PROSYM
--
Sweden Actual -
Sweden PROSYM
Figure 3-18: a) Actual and estimated imports/exports for both Norway and Sweden for week 24. b) Actual and
estimated imports/exports for both Norway and Sweden for week 30. c) Actual and estimated imports/exports
for both Norway and Sweden for week 41.
41
3.4 Pitfalls and Alternatives
3.4.1 Pitfalls of Hydro Scheduling Methodology
As was presented above, the hydro scheduling methodology does a good job of estimating reality.
However, there remain two pitfalls of this methodology. First, as was mentioned earlier, the hydro
scheduling methodology directly effects areas that have hydro power, but has an indirect effect on
areas without it, mainly Denmark.
We were hoping that improving the hydro scheduling
methodology for Norway and Sweden would positively impact Denmark and bring the PROSYM
output for Denmark closer to reality. However, after much analysis, we were unable to improve
the PROSYM import/export graphs for Denmark. One major reason for this is the fact that there is
much wind in Denmark as mentioned earlier. This wind must be used to generate electricity or
else goes to waste. Because of the timing and variability of the wind, it may be prudent to remove
wind from the PROSYM model, and dispatch it as we did the hydro. This may improve the results
from PROSYM. The second issue to be considered is that this hydro scheduling methodology
presented here does not take into account areas that have a mix of hydro and thermal generation.
Although, it hasn't proven to be much of an issue, one might want to distinguish between the part
of load that is to be met by hydro and that which is to be met by thermal. This hydro/thermal issue
can be addressed in the same manner as the issue of the run-of-river content. In that case, the
hydro scheduling methodology could be further improved with a factor that represents generation
from thermal units. This is a possible area for future work.
This entire analysis presented here has been based solely on 2001 data. Although the hydro
scheduling methodology works well for this year, does not necessarily imply that it will work
flawlessly for another year.
This methodology is not complex enough to account for
unprecedented weather conditions, political unrest, and various other shocks to the electricity
sector. There are certainly areas for improvement. First, the factor of .5 that has been used may
need readjusting. A better factor can be calculated by using more data to repeat the analysis
presented here. This is true of the fix for the run-of-river content as well. No two years have the
same weather patterns, and thus the run-of-river content may need to be adjusted for a dry year,
wet year, and an average weather year.
The hydro scheduling methodology presented here
42
suggests an acceptable alternative for hydro dispatch outside the PROSYM model, but a more
involved solution may be required for a thorough study in the area of electricity planning.
3.4.2 Alternatives
There are alternatives available for short/long-term hydro scheduling that are currently being
explored in decentralized markets. One such short-term hydro scheduling scheme is presented in
[6].
In this paper, the algorithm presented uses a detailed model of the interconnected hydro
system to determine the half-hourly operating schedule based on allocated water releases with the
objective of maximizing overall returns from the market. The plan is revised continuously and
updated every five minutes as actual generation requirements and inflows change. This allows
continuous real-time optimization which is necessary for continuously changing spot prices and
inflows into reservoirs. There are several long-term hydro scheduling models available as well.
[7] presents a new efficient algorithm to solve the long-term hydro-scheduling problem.
The
algorithm is based on using the short-term memory of the Tabu search approach to solve the
nonlinear optimization problem in continuous variables of the LTHSP.
43
44
4 INTEGRATING WIND INTO THE ANALYSIS OF THE SCANDINAVIAN
SYSTEM
4.1 Wind in Norway
4.1.1 Load Characteristics in Norway
Now that we have coped with the problem of hydro in PROSYM, it is time to look at other sources
of renewable generation, namely wind. In this day and age, windpower is everywhere. Most of
the larger wind farms can be found in Europe, as the government has forced the European
countries to find sources of generation that will not only meet future demand, but will do so in an
environmentally safe way [8]. One of the newest wind farms is that off the coast of Denmark
called Horns Rev. It is a project funded by Vestas which setup seventy 2MW turbines with a
distance of approximately half a kilometer between them poised to produce 160 million kWh every
year - almost enough to cover the electricity consumption of 130,00 households [9]. [10] states
that over 4,000 MW of wind power may be installed offshore in Denmark over the coming 30
years. From an environmental point of view wind farms have very few drawbacks. Studies have
shown that wind farms at sea have no significant influence on bird life. In terms of human
considerations, the wind farms at Horns Rev are located so far from the coast (at distances ranging
from 10 to 18 km) that the visual impact of the farm is expected to be minimal or non existent when
viewed from the shore, depending on weather conditions.
A calculation of energy use in
manufacturing, deployment and maintenance of an offshore wind farm shows that the energy thus
consumed is less than 2.5 percent of the energy produced by the farm, thus making wind energy
one of the cleanest generating technologies available.
It is obvious that Denmark has had much success in combining these wind farms with its existing
CHP plants. However, combining wind farms with another non-dispatchable energy source, such
as hydro, is not a trivial task. This is the case for Norway where almost 100% of the energy
production comes from hydro.
In order to meet future demand, Norway must look to other
renewable generation sources. We begin our assessment of adding wind to Norway's current
power system by examining the characteristics of electricity demand for the past four years.
45
b) 1999
a) 1998
29
29
77
77
125
125
173
173
221
221
269
269
317
317
365
365
0
5000
10000
0
VN
-
15000
20000
0
25000
5000
10000
15000
TI.-
(j
(N
25000
20000
d)2001
c) 2000
30
30
78
78
126
126
174
174
222
222
270
270
318
318
(Nj
0
r-
--
(r
N
5000
C(4
T-N
(N
0
10000 15000 20000 25000 30000
5000
10000
15000
20000
25000
Norway in 2001.
Figure 4-1: a) Load in Norway in 1998 b) Load in Norway in 1999 c) Load in Norway in 2000 d) Load in
From the figures above, we can see that Norway is a winter peaking season. High electricity
demand occurs between the hours of 7am to about 10pm, with the highest occurring between 9am
and about 3pm and again between 5pm and 9pm as people return home from work. Over the
years, we see that electricity demand during the winter season has increased considerably. This
corresponds to the red bands in the graphs, which are getting darker and darker and are also ending
later in the year and beginning earlier in the year. This phenomenon can be due to the fact that it is
getting colder in Norway. The summer season, represented by the blue bands, has not changed
much over the year, maintaining the same shape and duration. Overall, load has grown slowly
over the years, with higher growth in the winter season. This can be seen more clearly in the figure
below.
46
4000
--
3500
3000
--
2500
2000
2000
1998
1999
2000
1500
-_ 2001
1000
500
1
5
9 13 17 21 25 29 33 37 41 45 49 53
Weeks
Figure 4-2: Load in Norway for four consecutive years.
47
4.1.2 Wind Characteristics in Norway
to wind
Now that we have looked at some of the load characteristics of Norway, we turn our focus
characteristics of the country. We begin by looking at a wind topography chart for Norway shown
below.
rm/s
>.0
5.0-6.0
4.5-5.0
3.5-4.5
<3.5
VV/m
2
m/s
>250
:50-250
&5-7.5
100-150
5.5-6.5
50-100
4.5-5.5
<50
>7.5
<4_5
2
W/rn
>500
300-500
200-300
100-200
<100
rm/s
>8.5
7.0-8.5
6.0-7.0
5.0-6.0
<5.0
W/m2
>700
400-700
250-400
150-250
<150
rm/s
>9.0
8.0-9.0
7.0-8.0
5.5-7.0
<5.5
2
W/m
>800
600-800
400-600
200-400
<200
m/s
W/m
2
>1800
> 1.5
10.0-I1.5 1200-1800
8.5-10.0 700-1200
400-700
7.0-8.5
<7.0
<400
T5
<5
Figure 4-3: Wind topography chart for Norway and Sweden.
As can be seen from the figure above, Norway has pretty strong winds throughout the entire
country. The strongest winds are obviously along the coast line and they decrease as we move
a
more inland. A wind park must satisfy several demands such as average wind speeds of 9 m/s,
distance of 500 meters between a wind turbine and nearby buildings, and easy access to existing
This
transmission lines. The winds along the coast have an average speed of 7.5 m/s or greater.
would be a good place to build wind farms as one can expect a strong continuous wind coming off
the ocean. Also, the coastline would be an ideal location because there would be fewer buildings
and
and other structures that would impede the wind. They would be far enough from homes
48
buildings having little or no visual impact on Norwegians living along the coast. Overall the coast
of Norway would be an excellent location for new wind parks.
To continue evaluating the coast as a site for new wind parks, we analyze wind data from ten
different locations along the coast. This analysis may lead to the discovery of trends and patterns
in the wind that can be exploited in order to produce more energy. We acquired hourly wind data
for ten locations along the coast.
The measurement of wind speeds was done using a cup
anemometer. This anemometer measured the wind speed for every second of a ten minute period.
Those measurements were then averaged and one wind speed was produced for the first ten
minutes of the hour. This process was repeated for the six ten minute intervals in an hour. These
six values were taken and then averaged to produce the average wind speed for each hour of the
day. There was data missing for two days of the year. To fix this, we estimated the average wind
speed for those two days. Even if our estimation is incorrect, it will have little impact on our
analysis.
4.2 Converting Wind to Power
4.2.1 Converting Data to Hub Height
To begin our analysis of the wind at these ten locations, the first step was to convert the wind speed
to hub height. The anemometer measured the wind speed at 10 meters, where as the height of the
wind turbine is 80 meters. The fact that the wind profile is twisted towards a lower speed as we
move closer to ground level is called wind shear. To account for this wind shear phenomenon, the
wind speeds at 10 meters had to be scaled up to hub height. This was done by using the formula
shown below.
49
vnew =V
ln(z /z 0 )/In(zref / z0 )
()
where
Vnew = wind speed at height z above ground level.
vref = reference speed, i.e. a wind speed we already
know at height zref
z = height above ground level for the desired velocity, v.
z = roughness length in the current wind direction.
z
= reference height, i.e. the height where we know the exact wind speed v
ref
ref
The roughness class that was used was that of a completely open terrain with a smooth surface.
This roughness class has a roughness length of .0024 meters which was used in the calculations
above. Using this roughness length and the formula presented above, the data was scaled up to a
height of 80 meters. We then proceeded to analyze the characteristics of the wind in ten different
positions along the coast.
4.2.2 Analyzing Wind in Norway
4.2.2.1 NorwayS
In order to proceed with our analysis, we averaged the hourly data for the twelve months of the
year. The next step was to group the different wind measurement locations into the PROSYM
areas that were discussed in Chapter 1.
The figure below shows the wind speeds recorded in
NorwayS. As can be seen below, the highest winds seem to occur in the winter season. Winds
speeds tend to drop off as we enter the spring season. During the winter season, it is generally
windy throughout the day in this location with some strong gusts in the middle and end of the day.
Another phenomenon that can be seen below is the increasing wind speeds during the evening and
nighttime hours of the summer.
50
10
1
Month
0
4
2
I l ours
12
10
8
6
Figure 4-4: Wind characteristics of one location in NorwayS.
4.2.2.2 NorwayW
The next area that we focused on was NorwayW. It is quite similar to the NorwayS case, in that
the highest wind speeds are recorded during the winter season. As can be seen below, for the first
location (a), we see an increase in the wind speed during the evening and nighttime hours of the
summer season. However, the second location (b) is clearly a much better site for a wind park as
the wind is consistently strong throughout the year.
b) Location 2
a) Location 1
9
8
7
8
5
14
10
4
a
0
0
1
2
3
4
5
6
7
8
IHours
Month
Hours
Mo
0
9
2
4
6
8
Figure 4-5: Wind characteristics of two different locations in NorwayW.
51
10
12
14
16
4.2.2.3 NorwayM
We had measurements from two different
The third area that we looked at was NorwayM.
locations. The first location (a) had high wind speeds in the winter season, with a constant wind
speed between 4 and 6 m/s in the spring and summer season. The second location (b) had much
stronger winds throughout the year, with the highest occurring in the winter season. During the
summer season the wind dropped down a bit, but remained relatively strong. The second location
would be an ideal spot for a wind park site.
b) Location 2
a) Location 1
16
142
12
10-.
74.-
Cf4
4-
4,-
2.
Hours
Month
0
1
2
3
5
4
6
7
8
9
Mont
0
10
2
4
6
8
10
12
14
16
U
Figure 4-6: Wind characteristics of two diff erent locations in NorwayM.
4.2.2.4 NorwayN
For the last PROSYM area, we had wind data for five different locations. As was true of all the
other areas, NorwayN had higher wind speeds during the winter season. For several of the
locations, we see the wind drop off a bit at the end of the winter season, only to rise again a month
later. Four of the five locations consistently have high winds of about 8 to 10 m/s throughout the
year. During the summer time, wind speeds in all the locations drop off a bit yet remain strong. If
one had to choose a the site for a wind park, I would choose location (a). The winds in location (a)
are consistently strong with wind gusts of up to 15 m/s in the winter season which is higher than all
the other locations. During the summer season, we see that the wind is relatively constant and
strong with no major drops in wind speeds, making this a good site for a wind park.
52
b) Location 2
a) Location 1
1414
10
M-
1
ui
o
10
0
4,
oo
Q0
12
o 0'
-
COillM
Vl
0
2
10
8
6
4
I lours
Month
iHus
Month
12
14
0
16
2
4
6
8
10
12
14
d) Location 4
c) Location 3
14
10
10
8
4
4-
0.
Hours
Month
0
2
4
6
8
10
12
0
14
53
2
4
6
8
10
12
e) Location 5
14
I--
1
T:;l
T'r
Month
0
2
4
Hours
8
6
10
12
14
Figure 4-7: Wind characteristics of five different locations in NorwayN.
4.2.3 Estimating the Power Curve
Once we decided on the ideal locations for wind parks in the four different PROSYM areas, the
next step was to determine the type and number of wind turbines to install in each of these
locations. We chose to install Vestas V80 - 2.0MW onshore wind turbine because these wind
turbines were used in the recently completely Horns Rev Project completed off the shore of
Denmark. Once we had decided on the wind turbine, we had to obtain the power curve for this
wind turbine. The power curve told us the MW output of the wind turbine at different wind
speeds, as shown below.
54
V-80-2.0 FMApaiwer curves
1
i
AF(
X-41
I
,OeI
0
21120
1
1
.Au1f
Ifi I Id
S101i
0.--....
11I1(k
1
I IT 4IA"O
W St dIKI1i
111
Figure 4-8: Actual power curve of a 2MW Vestas wind turbine.
The next step was to take the wind data for the areas selected above and use the power curve above
to convert the wind speeds to power values. Given that we have over 8000 hours of data for each
of the areas, it would have been easy to convert it to power values if we had the equation for the
curve above. However, Vestas failed to provide the equation for the power curve and so we had to
use regression analysis to estimate an equation for the curve above. The figure below shows the
results of the regression analysis.
Looking at the R value for the analysis, we see that the
estimated equation is over 99% accurate. We had to use an equation of the 6t order to ensure this
accuracy.
55
4 + 25.241x
5
6
250&y= -0.001x + 0.0741x - 2.0824x
112.64
206.56x
3
-
115.37x
2
+
R2 = 0.
2000 _
1500
1000
0
CL
500
0
5
--
-500
25
20
-
15
10
30
Wind (m/s)
Data -
*
Figure 4-9: Estimate of the actual power curve in Excel.
Using this equation, the wind speeds were converted to power measurements. Once we had
calculated power measurements, we had to determine how many wind turbines we would have in
each area. Norway plans to have 3 TWh of wind generation by 2010. We decided to use this
number to calculate the number of wind turbines. In the figure below, we see the wind expansion
plans for the biggest Norwegian power producer, Statkraft. They want to build a total of 730 MW
of generation at the locations shown, which is to produce 2 TWh of electricity a year.
Fremtidige vindpark
'7
Vadso 40 MW
BerlevAg 40MW
Lebesby 35 MW
Nordkapp 50 MW
Hammerfest 110 MW
Hitra 56 MW
Smela 150 MW
TOTALT
Frana 130 MW
ca.
Tilsvarer en
730 MW
Arlig
7energiproduksjon p6
arlig
stromforbruk for
100 000 husstander
ca. 2 TWh =
Austevoll 40 MW
BomIo 40 MW
Eigersund 40 MW
Figure 4-10: Statkraft's plans for wind turbines in Norway.
56
To get an idea of the maximum load hours, we divide the 2TWh by the 730 MW of generation and
we arrive at approximately 2740 maximum load hours. We repeated this calculation using 3 TWH
and 3000 maximum load hours because our wind measurements were further north where the wind
is better. This led us to 1000 MW of installed capacity. Since we used the Vestas 2 MW machine,
we would need to install 500 wind turbines. Given the wind characteristics discussed above, we
decided to install 150 wind turbines in each of NorwayN, NorwayM, and NorwayW, while only
installing 50 in NorwayS.
4.2.4 Total Power
Using the wind turbine numbers, we calculated the total power output for each of the areas. These
power numbers assumed that the wind turbines were always running and that the wind was always
blowing in the right direction. Since this is not always the case, we adjusted the power output
numbers slightly to reflect periods of downtime. Over the course of a year, wind turbines are out
of service for approximately 2 weeks which is about 3%. Although these wind turbines can rotate
to find the optimal wind direction, there is some power lost and the rotation may not result in the
most optimal position. Therefore, we reduced the total power output numbers for each of the areas
by 5%.
We then aggregated the hourly power output numbers into weekly numbers. Below is a graph
showing the load, hydro production estimated by the EMPS model, and the estimated wind for
Norway in 2001.
57
Weekly 2001 load, simulated hydro generation (EMPS), and estimated wind for Norway
-
3500
3000-
2500-
2000U
0 Hydro
MWind
1500
1000
500
NON
)
N"
N
1A
tA
)
~
Week no.
Figure 4-11: Simulated hydro generation (EMPS), weekly load for 2001 and estimated wind for Norway.
As can be seen from the figure above, the wind is a very small part of the generation in Norway.
In 2001, hydro production led to approximately 125 TWh of electricity. The wind plants that were
added generated approximately 4TWh of electricity. This is a meager 3.2% of the generation in
Norway. Another point to make note of is that the generation from wind is high when the load is
high and then falls when the load is low. Although the goal was to produce 3TWh of generation,
we see that we underestimated the maximum load hours. This just shows that the winds were
stronger than we had expected.
4.2.5 Power Surface Curves
To analyze more closely the power generated in each of the areas, we plotted the aggregate power
from wind for each hour of the 12 months in the year. As can be seen from the figure below, the
shape of the curve for each of the areas is closely related to the wind graph for that area presented
earlier. Since NorwayS is only given 50 wind turbines, its output is considerably lower than the
other areas.
58
b) NorwayM
a) NorwayN
6
4
4-
0
0
Month
5
4
3
2
1
0
''D
Hours
6
7
Mon0
0
9
8
E 1-7
'2 3
4'
58ours
2
3
4
5
6
7
8
9
2
3
4
5
6
7
8
9
1
1
1
d) NorwayS
c) NorwayW
99
88
8~
7-
7"
U-4
5.-
1
0
MontIh*
0
1
2
3
4
5
6
7
8
9
0
1
Figure 4-12: a) Power surface curve for NorwayN b) Power surface curve for NorwayM c) Power
surface curve for NorwayW d) Power surface curve for NorwayS
59
4.2.6 Load Surface Curves
After looking at the power surface curves, we analyzed the load surface curves in Norway for 2000
and 2001. From the figures below, we see that the wind has approximately the same seasonal
pattern as the load. It is high in the winter season and low in the summer season. However, the
wind does not follow the diurnal pattern of the demand. The load rises during the peak hours of
7am to 9pm, but the wind is more unpredictable.
b)2001
a) 2000
0
100
200
300
400
500
0
600
Figure 4-13: a) Load surface curve for Norway in 2000
10000
b) Load surface
0
300
400
500
600
700
curve for Norway in 2001.
4.3 PROSYM Simulations
The next step of our wind analysis was to add the estimated wind generation and re-mun our
PROSYM simulations. However, we were not particularly interested in looking at import/export
graphs. We focused more on transmission line usage and thermal production. Transmission line
usage was an important aspect as we needed ensure that there was enough transmission line
capacity to transfer electricity from one area to another with this added generation.
We also
examined thermal production to see which areas had reduced its production which meant a
decrease in emissions.
60
In order to run the simulations, we had to adjust the loads to take into account the energy from
wind. This was done using the following formula.
-
R 01000
(2)
where
new varying hydro area load
/ average hydro area load over week
hydro area load with complete load leveling
/t'l
=
C/I
R
= load leveling factor (= 0.5)
run - of - river percentage
w
= wind power
As can be seen from the formula, the hydro area load with complete load leveling for each hour
was reduced by the amount of wind energy for that hour. This wind had to be deducted from the
load before doing the hydro scheduling because wind is non-dispatchable. Using these new loads,
we proceeded to run simulations for the previously selected weeks.
4.3.1 Transmission Lines
From the table below, we see that the link utilization between NorwayN and NorwayM, as well as,
NorwayM and NorwayS is approximately 100%. This means that even before adding the wind to
our analysis, these links were transferring their maximum capacity for all 168 hours in the week.
However, when we look at the transmission area prices, we do not observe a drop in price. This
means that although the link is transferring its maximum capacity in all hours of the week, all
available energy is transported from one area to another.
61
From
To
Week 6
Week 8
Week 24
Week 30
Week 41
Week 48
NorwayS
DenmarkW
-
-
-
22.1%
-
-
DenmarkW
NorwayS
61.4%
45.9%
23.4%
-
23.5%
33.7%
NorwayN
SwedenN
NorwayM
SwedenN
NorwayN
SwedenN
35.9%
-
65.1%
2.8%
-
93.4%
-
51.6%
-
30.9%
-
50.3%
0.5%
1.1%
SwedenN
NorwayM
37.6%
36.6%
100.0%
98.6%
99.8%
6.0%
NorwayS
SwedenS
5.2%
15.2%
-
-
-
30.2%
SwedenS
NorwayS
5.5%
0.5%
40.4%
14.6%
22.2%
-
NorwayS
NorwayW
-
-
.6%
-
-
-
NorwayW
NorwayN
NorwayS
NorwayM
31.5%
99.2%
28.4%
92.2%
1.1%
42.7%
6.8%
5.7%
6.8%
40.0%
23.6%
78.2%
NorwayM
NorwayN
-
-
-
0.1%
-
-
NorwayM
NorwayS
100.0%
100.0%
94.0%
31.4%
100.0%
100.0%
NorwayS
NorwayM
-
-
-
0.2%
-
-
Table 4-1: Link utilization rates for all the links between Norway and surrounding areas before adding wind.
After adding the wind, we see that the link between NorwayM and NorwayS still has a link
utilization of 100%. In the case of NorwayN and NorwayM, we see that the link utilization has
dropped a bit in all of the weeks. This is due to the fact that with the added wind power for
NorwayM, it no longer needs to import as much electricity from NorwayN. We also see that there
is a drop in link utilization between SwedenN and NorwayM. This reflects the fact that the added
wind power has decreased the load in NorwayM requiring that area to import less. This in turn
leads NorwayN with more electricity to export. However, NorwayS which did not benefit as much
from the added wind power, still needs NorwayM to export electricity. Examining more closely,
we see that the link utilization between NorwayN and SwedenN has increased considerably. This
shows that the electricity that was previously going to NorwayM is now being exported to
SwedenN.
62
From
To
Week 6
Week 8
Week 24
Week 30
Week 41
Week 48
NorwayS
DenmarkW
-
-
-
39.5%
3.5%
-
DenmarkW
NorwayS
60.3%
100.0%
21.2%
-
14.5%
41.4%
NorwayN
SwedenN
NorwayM
SwedenN
NorwayS
SwedenN
NorwayN
SwedenN
NorwayM
SwedenS
64.2%
8.2%
6.1%
17.0%
99.2%
89.0%
65%
56.6%
99.8%
-
38.0%
95.6%
-
1.4%
9.5%
66.2%
-
84.2%
35.2%
50.0%
SwedenS
NorwayS
2.3%
4.3%
30.2%
12.4%
16.2%
-
NorwayS
NorwayW
-
-
.2
-
-
-
NorwayW
NorwayN
NorwayS
NorwayM
34.0%
95.0%
28.4%
62.0%
2.3%
23.1%
8.0%
7.1%
9.1%
35.6%
26.4%
72.4%
NorwayM
NorwayN
-
-
-
0.1
-
-
90.6%
100.0%
-
-
NorwayM
NorwayS
100.0%
38.6%
100.0%
56.7%
NorwayS
NorwayM
-
24.8%
-
-
Table 4-2: Link utilization rates for all the links between Norway and surrounding areas after adding wind.
4.3.2 Transmission Area Prices
After having examined some of the link utilization factors, we looked at the transmission area
prices for the six weeks that we had chosen previously. From the figures below, we see that in
NorwayN the price drops down to the dump power price. This means that NorwayN had more
electricity than it could transfer. Although the link utilization is below 100%, the drop in price still
occurred because there were specific hours in which the amount of electricity that needed to be
exported exceeded the capacity of the link. The link utilization tells us how often the link is
transferring at maximum capacity. So if we looked at the link utilization for the hours in which the
price drops down, we would see the link utilization at 100% with extra electricity to be exported.
The graphs below show only the weeks in which the price drops down to the dump power price.
We see that the price drops down to the dump power price in the winter weeks, but remain stable
during the summer weeks. A possible explanation for this behavior is the fact that in the winter
season production from hydro is a lot higher than in the summer. Therefore, the transmission lines
are more heavily taxed during the winter season. The wind follows the same seasonal pattern with
stronger winds in the winter.
Thus, adding wind to the system further places stress on the
transmission lines leading to electricity that cannot physically be transported. This forces the price
to drop down to the dump power price.
63
a) Week 6
201816
14
12
10
18L
--
-- --
---
-----
--
-- --
- ------------------- --
--
----
--
--
- - - -- - -
---
6
4
I
L
2
0
73 Hour
49
25
1
145
121
97
b) Week 8
20
18
1614I0
12
10
2-
-I
-
-
-
-------------
----
--
--
-
-
-
-
I
-
-
-
0- I
73 Hour
49
25
1
145
121
97
c) Week 48
20
1816
--
14-
- - - --------------
--
---
-- - - - - -
--
-
- --
-
12C0
108-
64-
-
-
20-
-
--
---
-------
---
I
I
1
------
------
-------
----
25
49
73 Hour 97
121
145
--- Norway M Ajusted marginal cost
N~justed marginal cost)
sted marginal cs
NorwaySA
-- NorwA djusted marginal cot
- Sweden(dusted marginal cos
-- Swedler jdusted marginal cost)
E Ajusted marginal cost)
(djusted marginal cost) -Denmark
-Denmark
-Norway
Figure 4-15: a) Transmission area prices for week 6. b) Transmission area prices for week 8. c) Transmission
area prices for week 48.
64
4.3.3 CHP Generation
Adding wind to the system also has an effect on thermal production. The added generation leads
to lower production from CHP plants as can be seen from the figure below.
CHP Generation
10000
90 0 0 --
-- - -
--
---
- --
8000
-- ---- --- --
7000
-
6000 - -
3: 5000
-
7 - - - - - --- - -- -- -
-
4000 -- - --3000
2000
----
---
-----
- -- --
---
-------
1000
- - - - - --
--- - - -- - --- - --- - ---
----
--
---- ----
- -----
L - -
------
----- -------
- -
-- ----
-------
0
1
25
49
73 Hour 97
CHP(Before)
121
145
CHP(After)
Figure 4-16: CHP generation in Denmark before and after adding wind to the system.
Lower thermal production means a decrease in emissions of noxious gases from these plants. If we
had more detailed data on CO 2 and SO 2 emissions for thermal plants, we could have calculated the
precise amount by which these gases would decrease due to the added generation from wind.
Overall, we see that electricity generation from wind will benefit the environment.
Actual
reduction percentages may be calculated at a future time.
4.3.4 Hydro Scheduling
There are some pitfalls to the analysis above. After adding the wind generation to the system, we
did not change the hydro production. In reality, this would not be the case. Hydro producers
would adjust their production to account for the wind generation. They might choose to lower
their production when the wind is high and increase their production when the wind is low in order
to maximize their profits. For example, they might bank some hydro during the winter and then
use it to increase their production in the summer months. Not only would this decrease thermal
production, in turn decreasing emissions, but would also allow the hydro producers to make a
higher profit. Also, the problems with the transmission lines reaching their maximum capacity
65
may be averted. None the less it is an important issue that needs to be tackled as some of the
transmission lines in Norway are nearing maximum capacity without even adding the wind energy.
Overall, it was near impossible to model the reaction of the hydro producers to the added wind
energy.
4.4 Transmission Line Solution
A possible solution to the transmission line problem can be found in [11]. This paper analyzes an
algorithm that may be used as a short-term solution while transmission lines are being constructed.
The algorithm has four main parts. The first part entails estimating the wind energy Nw produced
by the wind turbine, taking into account the existing wind speed, the ambient density and the
selected wind turbines power curve. The next step is to compare the energy production of the wind
and the hydro with the consumer energy demand, Nd. If any energy surplus occurs (Nw > Nd), the
energy is stored by a battery system and the process is repeated for another time step. If there is
an energy deficit (Nd - Nw> 0 ), the battery is discharged to meet the difference. If the battery is
near empty and unable to meet the deficit, the battery size is increased and power is drawn from
batteries in neighboring areas.
4.5 Issues with Wind
Wind is an important addition to the power system in Norway. However, this addition comes with
both positive and negative aspects. A few of the positive aspects include reduction in emissions
due to a reduction in thermal production and another source of renewable energy that can be used
to meet consumer demand.
consequences.
However, adding wind to a system may have some negative
Simulations show that adding a wind farm provides little service in terms of
capacity [12]. If the wind/hydro combination must provide the same level of service as a totally
hydro scenario, the wind farms require large additional hydro backup capacity. For example, if
there is an increase in demand of 56MW, then simulations show that 98 MW of windpower would
be needed plus 48 MW of additional hydro-capacity. Similar results are valid for large networks as
well. This problem arises because of our inability to forecast the wind accurately. There are two
basic methods to forecast the energy output from wind farms. The first uses a persistence model
which looks at prior intervals to estimate the wind energy for a few hours in the future. The second
66
uses a meteorological model which use wind speeds to forecast wind output for the next few days
[13]. Although these models have improved over the year, they are still not 100% accurate when it
comes to forecasting the wind. For that reason, extra capacity must be reserved (at a cost) so that
demand is met if the wind is below forecast.
A similar problem occurs in wind and thermal
system, such as Denmark. With a large amount of non-dispatchable power (wind), the system may
become unbalanced as the production does not follow the load. There are three distinct scenarios
that might occur: the wind is rising later than forecast (deficit), the wind is rising earlier than
forecast (surplus), and the wind rises as forecast. In the case of a deficit, production at local CHP
plants can be increased.
However, these plants must be kept on reserve status. In the case of a
surplus, the excess power can be exported provided that capacity is available on the
interconnections to Norway, Sweden, and Germany.
However, if there is too much power to
export then the system may become unstable. To balance the system, Denmark is experimenting
with four scenarios: closing down wind turbines, closing down local CHP plants, introducing
flexible loads, and installing heat pumps [14].
Overall, we see that adding wind to a hydro or
thermal system has both advantages and disadvantages. It is important to look at all sides of this
issue before charging ahead and building wind power plants.
4.6 Conclusions
From the discussion above, we see that there are both positive and negative consequences from
building wind farms.
The positive include a reduction in emissions from thermal plants and
another source of energy that can be used to meet future demand.
However, these benefits do
come at a cost. When building wind farms, other issues must simultaneously be considered, such
as transmission line capacity and backup systems. Since wind may fall below forecast, we must
pay an additional price to build backup systems. Similarly, wind may be above forecast, in which
case we must build more transmission lines to transport the electricity. The whole argument boils
down to whether or not the benefits of building wind farms outweigh the costs.
67
68
5 MODEL AND DATA REQUIREMENTS FOR A PLANNING PROJECT IN
SUSTAINABLE ENERGY
5.1 The Optimal Model
Analysis of the PROSYM model has given us an idea of what some of the requirements are for a
model that is to be used in a project focusing on sustainable energy. Although the PROSYM
model is used by several electric companies throughout the US, it is not an adequate model for a
power planning study. There are several areas in which a model must be proficient in order to be
used in such a study. Each of these areas is described in detail below. We begin with nondispatchable energy sources such as wind, continue with a semi non-dispatchable energy source hydro, and finally discuss dispatchable energy sources such as nuclear and thermal production.
We also discuss cost-based dispatch and bid-based dispatch.
5.1.1 System Boundary & Time Resolution
Before dealing with the dispatch of energy, we must first consider the issues of system boundary
and time resolution.
The model should be complex enough such that the whole of the
Scandinavian market can be represented. It is also important that the model allow us to represent
neighboring areas with which exchange occurs through buy/sell contracts or some similar
mechanism. This is crucial as the Scandinavian market is not self-contained. It is important that
exchange with these neighboring areas be represented elegantly, as including other countries in
their entirety in our analysis would cause us to lose focus on our initial goals and make the system
unnecessarily large.
Another important aspect of the model is the time resolution. In order to study the response of the
system to random spikes in demand, to shortages in generation, and over-capacity of transmission
lines we need to observe the system from hour to hour. Hourly time resolution also allows us to
analyze the response of the system to scheduled and unscheduled shutdowns.
Overall, it is
important that the model provide the detail that comes with an hourly time resolution so that we
may study the behavior of the system in an accurate and precise manner. The model must also be
able to forecast demand for periods in the future. Low load and high load scenarios must be tested
69
in this study, and it is important that the model be furnished with internal algorithms to create these
load scenarios. The model must also have some notion of investments.
5.1.2 Economics
Given that this is a planning study that will look 25 to 50 years in the future, the model must be
able to calculate the costs involved with repairing plants, building new transmission lines, and most
importantly with building new plants. It is important that the model have the economics of such
actions built in so that one may analyze the effect these investments have on electricity prices.
These built in economics will allow us to see how soon investments in infrastructure begin to pay
off. This will be an important selling point for several of the sponsors involved in the planning
project.
5.1.3 Wind
When dealing with wind, the model must reduce the hourly demand by the amount of electricity
available from wind power. If the demand is low, and the wind is blowing harder than usual, there
may be extra electricity available. In this case, the model must export the electricity to surrounding
areas. The goal of the model should be to export the extra electricity to areas with thermal
generation. This way, the extra electricity from wind can be used to reduce the production of
thermal plants, leading to lower emissions among other benefits. If wind is to become a larger part
of the system, the model must somehow represent the uncertainty in the wind and the need for
additional operating reserves in the system. This may be beyond the scope of this planning study.
Other renewable energy sources should be treated in a similar fashion.
5.1.4 Hydro
The next area that the model needs to be proficient in is semi non-dispatchable energy sources,
such as hydro.
Hydro is semi non-dispatchable because the run-of-river content cannot be
controlled, where as hydro in the reservoirs can be controlled to increase production when needed.
In Scandinavia, hydro accounts for over 60% of the electricity generation, therefore it is important
that the model schedule and dispatch hydro in a manner that closely reflects reality. Given hourly
demand for each area and a target electricity generation number, the model must ensure that the
hydro is dispatched in a manner such that the demand is met, transmission constraints are not
70
violated, and that any extra hydro energy is used to level out peaks in the system. If transmission
lines are at capacity, the model should make an attempt to transfer the electricity through other
underutilized lines. The model should also observe transmission area prices and should expend
reserves in order to reduce the price if it is too high.
Likewise, if reserves are low and extra
available capacity, then the model should bank hydro for use in future times. During the summer
months, when there is a large run-of-river content, the model must ensure that the run-of-river
content is used, that any extra electricity generated from hydro is exported to thermal production
areas, and that transmission constraints are not violated.
5.1.5 Thermal
Nuclear and thermal generation is the last step in the dispatch process. The model must first create
a list of all thermal plants and rank them by cost and emissions. The next step is to begin at the top
of the list with the plant that has the lowest cost and emissions and work its way down until all
remaining demand is met (merit order dispatch). For the model to be able to do this, it must have
some notion of startup and shutdown times, ramp rates, heat rates, days required for maintenance,
and fuel used. Without this specific information, the model is unable to calculate accurately the
cost and emissions required to create the dispatch list.
Another area that the model must
particularly be adept is CHP as this is a major component of Denmark's generation. A Combined
Heat and Power (CHP) Plant is a term used to refer to an installation where there is simultaneous
generation of energy to perform a task (usually electricity) and usable heat in a single process.
CHP technology represents an attempt to use the 'waste' heat in a constructive fashion. Rather than
being dissipated into the atmosphere it may be used to provide heating for a nearby community, or
to operate some part of the process that converts the energy. To deal with CHP, the model must
either convert the CHP plants to a thermal plant with reduced fuel use due to the additional heat
production, or reduce the load by the amount of electricity generation from the CHP plant. The
CHP plant must be converted to a thermal plant with reduced fuel use to avoid double counting of
emissions.
71
5.1.6 Hydro/Thermal Coordination
An important aspect of the model is its ability to coordinate hydro and thermal scheduling to some
extent. When scheduling the hydro, the model should reserve a small fraction to smooth out spikes
in demand. This prevents the thermal plants from having to drastically increase their generation
from hour to hour, and also keeps the transmission area prices relatively stable. The model should
schedule the hydro, then schedule thermal production, and then return to the hydro and adjust
production in order to correct for demand spikes and other abnormalities in demand. Therefore, it
is important that this logic be built into the model. The optimal model would do an integrated
optimization/scheduling of hydro and thermal plants.
5.1.7 Price-Flexible Load
Another option that the model must allow is price-flexible load. This is load that responds to
current transmission prices. Therefore, if the price is too high the plant is turned off or production
is lowered. If the price is low, then production is increased. It is important that the model allow
for this as we see this happening more often in the real system.
72
The scheduling and dispatch issues discussed above lead us to the diagram shown below.
Start
Repeat for all
hydro
Create load target
based on specified
area load
Yes
Schedule run-ofriver component,
adjust load and
remaining energy
Yes
All energy
scheduled?
More stations?
No
No
Find highest load
target
Dne
]
Store results of
hydro
Increment
generation, adjust
load, and perform
energy adjusting
Schedule
thermal
Readjustment
of hydro
No
Done
Yes
Adjust hydro
to reduce
thermal
generation
Figure 5-1: Flowchart demonstrating the logical process of scheduling hydro and thermal.
73
5.1.8 Cost/Bid-based Dispatch
The model to be used must also allow two distinct types of dispatch: cost-based and bid-based.
Cost-based dispatch is when plants are dispatched in the order of increasing costs until all demand
is met. In bid-based dispatch, plants submit their bids to an Independent System Operator (ISO)
who determines a market clearing price. Plants submit bids several times until a clearing price is
chosen such that load is met. If the price the plant submits is below the clearing price, that plant is
paid the clearing price. Any plant above the clearing price does not generate any electricity. Costbased dispatch equals bid-based if the participants behave properly and bid their marginal costs
corrected for start-up and shut-down costs. Therefore, it is important that a model allow bid-based
dispatch allowing one to study the effects of various bidding strategies. These strategies would
have long reaching effects on the bids of other plants. The model should provide the user with bids
for each energy block, as well as the market clearing price. The model should also allow for user
defined bids and also be able to calculate bids based on heat rates, startup and shutdown costs, and
ramp rates.
5.2 Data
5.2.1 Load Data
Now that we have analyzed some of the model requirements, we move forward and discuss some
of the data issues. This planning study requires a host of data that must be organized and validated.
The first essential piece of data is the load profiles for each of the countries in Scandinavia.
Demand data is available as far back as 1930. This data must be pulled together and aggregated in
some manner. For example, the load profiles used in Chapters 1 & 2 are an average of the past 60
years of load data. We must use all the data available to create three distinct load profiles: a low
load profile, an average load profile, and high load profile. Load data must be on an hourly time
frame.
5.2.2 Hydro Data
The next piece of data required is hydro data. For hydro, we need data describing the reservoir
levels, inflow into these reservoirs, run-of-river content, and hydro production levels for as far back
74
as possible. Once again, this data must be aggregated in some fashion to create three distinct hydro
production profiles: a dry-year profile, an average year profile, and a wet-year profile. It would be
helpful to have hourly data in this case, but weekly data should be sufficient.
Numbers on
upper/lower limits for reservoir levels would also be helpful.
5.2.3 Wind Data
For wind data, we will need data from existing wind farms as well as locations where new wind
farms may be built. We will need wind speeds, the heights of the measurement, as well as the
location of these measurements (roughness factors) so that we may calculate the wind speeds at
hub height. The available data will be aggregated to create three distinct wind profiles: a low wind,
an average wind, and a high wind profile. The time granularity for this wind data must be at the
hourly level.
5.2.4 Heat Data
Another crucial piece of information will be data on heat profiles. This data will be used mainly
for areas with CHP plants. It is important to have this data as the heat profile and the load profile
do not follow each other. Specifically, the heat profile will have a higher profile in the morning
and evening hours, while having a low profile in the daytime and afternoon hours. This is opposite
of the load profile which is high during peak hours (7am to 9pm) and low the rest of the time.
Using the heat profiles, we must create three distinct heat profiles: a cold-year profile, an average
year profile, and a warm-year profile. As was mentioned earlier, one way to handle CHP is to
deduct from the load the amount of heat produced.
This is where the heat profile will play a
crucial role.
5.2.5 Plant Data
Once we have created these profiles, we must create an accurate description of the electricity
system in Scandinavia. To do this, we will need a description of all the plants in the system. A
complete and accurate description would include the location of the plant, the type of plant, and the
energy output of the plant. The location of the plant will not only include the physical location, but
will also contain the name of the plant itself. These two pieces of information will help to
distinguish one plant from another. The plant type will indicate the type of generation available at
75
the plant. The energy output will give us an idea of the size of the plant. Along with the energy
output come other crucial pieces of data such as heat rates, startup and shutdown costs, ramp rates,
fuel use, emissions, maintenance costs, and variable and fixed operating costs. Heat rates (plant
efficiency) and ramp rates (fraction of capacity by which a plant can increase its load per minute)
are important in case we need to increase the production of the plant. These rates will help us to
calculate how quickly we can increase the production, and more importantly at what cost. The
startup and shutdown costs are important as they describe the fuel and attrition costs to startup and
shutdown a plant. In terms of cost, maintenance, variable, and fix operating costs tells us how
much money is being spent to keep this plant running. This information is important because we
might choose to shutdown plants that are too expensive to run and find alternative generation
sources. There are two other pieces of information that are important. Data on fuel use for each of
the plants will be required as well as fuel prices. This will be important it calculating the costs of
running the plant, but more importantly in determining the type and amount of emissions.
Emissions data is dependent not only on the fuels used, but also on the type of plant. Therefore,
we will need detailed emissions data for each plant in the study. The last piece of data that is of
importance is the transmission line network.
We will need to know the capacity of all the
transmission lines, their loss rates, as well as other link characteristics.
5.2.6 Database Organization
This vast array of data must be organized in a database with several tables. To make sure that each
record (each entry) in these tables has a unique value, we must designate a database field to contain
a value that is unique across all of the records in that table. For example, one may choose an
existing field in the database which is guaranteed to be unique -- a social security number would
work for a U.S. citizen and an ISBN would work for a book. In many cases, the best idea is to add
a new field to the table to serve as the unique identifier. And in some cases, you may use a
combination of two or more fields that, while not individually unique, are guaranteed to be unique
when used together. Regardless of the method used, what we have created is called a database key.
Database keys are essential to good database design. In most cases, each table in the database
should have its own key field. Any arbitrarily-created key field for a database table is typically
called a primary key.
76
Country Table
Database
Country Name
-Pt-
9
Total Available Capacity
Transmission Table (3)
Generation 7able (3)
Transmission Line Name
Generation Tvye
*
Capacity
"
*
Losses
*
Wheeling Charges
*
*
Minimum Capacity
Maximum Capacity
Percentage of Total
Plant Table (# of distinct
generation tyes)
Plant Name
"
Minimum Capacity
*
Maximum Capacity
*
Heat Rates
*
"
Ramp Rates
VOM and Fixed Costs
*
Fuels Used
*
Startup & Shutdown
Costs
Maintenance Costs
*
Figure 5-2: Suggestion for a database to store study data.
The figure above is a proposed structure for a database containing our data. We see that there will
be four distinct tables. The first table will be the Country Table with the country name as the
primary key. Other data stored in this table will include total available capacity. For each record
in the country table (3 for the three Scandinavian countries) there are two additional tables, the
Transmission Table and the Generation Table. The Transmission Table has as its primary key the
transmission line name and stores line information such as capacity, losses, and wheeling charges.
The Generation Table has as its primary key the generation type and stores minimum and
maximum capacities for each, as well as the percentage of total capacity for each of the generation
types. For each generation type, there will be a Plant Table which stores information about each of
77
the physical plants. The primary key for this table is the plant name and the table stores the
information shown above. There will be as many distinct tables as there are generation sources.
Using the structure above, we will be able to manage and store the data accurately.
5.2.7 Database Security
Now that we have a formal structure in which to store data, we must ensure that the data we store
is accurate.
Often times, data is acquired from sources that are not reliable. Therefore, it is
important that the same data be acquired from several different sources in order to ensure
correctness. This process must be done before data is entered into the database and periodically
throughout the project. If it is found that two sources of data do not agree, then another source
must be found. If this is not a possibility, then some averaging must take place to produce one
coherent data set. In terms of access to the database, everyone is granted 'read' access so that they
may view the data. However, only a select few should have 'modify' access to add or correct data
in the database. This ensures the integrity of the data in the database. A copy of the database
should be stored so that in case of failure, one can revert back to the last saved state. Overall, the
issues raised here will ensure that the integrity of the project.
5.3 Scenario Selection
Now that we have looked at some of the model and data issues, we turn our focus to some of the
scenarios that can be explored in this project. There are several different ideas that can be tested
such as demand-side technology options, supply-side technology options, various bidding
strategies, as well as emissions trading. Each of these ideas is described in detail below.
5.3.1 Supply Side Technology Options
Although there are several technological options available, the focus should be on natural gas
power plants, wind farms, and other renewable energy sources. It is important to get acquainted
with some of the ongoing issues that exist in the Scandinavian power market and then suggest
technology options that are acceptable to all the countries in the region. For example, Denmark is
quite strongly opposed to the use of nuclear power plants. Therefore it would be senseless to
consider an architecture that adds more nuclear plants to the existing structure, as it would not be a
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viable alternative for Denmark. Before considering different technology options, we must first
determine a baseline scenario.
In the case of the Scandinavian power market, an appropriate
baseline scenario would be the current system architecture. Using this as the baseline, changes can
be made to the system structure. For example, new technological options will be constructed
containing more wind and biomass generation plants. Another option will contain more efficient
thermal plants. This will be done by retiring old coal plants, and replacing them with gas power
plants which produce more energy with fewer emissions. The goal is to introduce more renewable
energy sources into the Scandinavian market which will not only meet future demand, but will also
reduce emissions. The project should also try and incorporate work being done on alternative fuels
such as hydrogen. The ultimate goal is to produce an optimal mix of technologies which is costeffective to implement and addresses several of the issues presented.
5.3.2 Demand Side Technology Options
On the demand side, focus should be placed on energy efficiency issues. There are methods that
consumers of energy can employ to reduce their overall consumption. Energy efficient lighting,
heat pumps, and distributed small-scale cogeneration plants for production of electricity plus heat
and/or steam are example of such methods.
There are a variety of options available for the
development and adoption of new energy-efficient technologies. Regardless of the option chosen,
the efforts must pay attention to the needs of users, deployment issues, and communication
amongst all pertinent market actors, including manufacturers, users, distributors, energy utilities,
and governments. One option that should be looked at closely will be improving district heating
systems by expanding the use of combined heat and power plants. These plants produce two in
demand products for the price of one. We should try and exploit this technology to benefit us in
the long-run.
5.3.3 Bidding Strategies
In addition to studying various technological options for the Scandinavian markets, it is important
to study the effect of different bidding strategies on different technology options. There are
different bidding strategies that companies employ during peak and off-peak consumption hours.
Under electricity market environment, profits of generation companies depend, to a large extent, on
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their bidding strategies. As a result, how to develop the optimal bidding strategy has become a
major concern of generation companies.
Analyzing these different strategies on the new architectures will determine whether these
strategies have a positive or negative impact on the architecture. Specifically, one must analyze the
market clearing prices and the amount of emissions that arise from different strategies.
Furthermore, trying to determine an optimal strategy that maintains low electricity prices, keeps
the companies profitable, and does not increase emissions will be an important result of this
planning project.
[15] presents a method for building such an optimal bidding strategy for
generation companies in those power markets in which the uniform market clearing price and
stepwise bidding protocol are used. Rivals' bidding prices are represented as stochastic variables
of normal distributions.
A stochastic optimization model for developing the optimal bidding
strategy is established, and solved by the Monte Carlo method.
5.3.4 Emission Trading
Emission trading is another area that is of great importance. Since the Scandinavian countries are
one of the lowest emission producing markets in the world, they can further reduce energy prices
by collecting revenue from emission trading. As not all countries will be able to meet their Kyoto
Treaty requirements immediately, there will be a market for emission trading. The Scandinavian
markets can use this mechanism as a temporary source of revenue as they transform their old
architecture to a more efficient one. The short-term electricity market with a bid-based dispatch
provides the foundation for building a system that includes tradeable transmission "rights" in the
form of transmission congestion contracts. Coordination is unavoidable, and spot market locational
prices define the opportunity costs of transmission that would determine the market value of the
transmission rights without requiring physical trading and without restricting the actual use of the
system.
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5.4 Multi-Attribute Trade-Off Analysis
Multi-attribute trade-off analysis will be employed to measure the benefits and disadvantages of
various technological solutions in the Scandinavian markets as described above. As can be seen
from the figure below, the first step of such an analysis is to determine the important issues. Then
a set of attributes is developed which measure performance relative to those issues. In this project,
the two important issues that will be focused on are cost and emissions.
After these attributes have been decided, we proceed to the second step. A set of alternatives that
include various technological options must be developed. To avoid the typical bias toward supply
side solutions, the scenarios will include technological options that emphasize both demand side
and supply side technologies.
However, before proceeding we must make assumptions about
common uncertainties such as electricity demand, fuel costs and availability, and the performance
of new technologies. These uncertainties are bundled into a set of futures that are used to carry out
sensitivity analysis for the various technological alternatives. Each future and each technology
option form a scenario. Using a model of our choice, we can analyze the different scenarios from
both a cost and environmental standpoint.
Analyzing the results from the different scenarios will force us to find better technological
strategies than the ones initially selected. At this time, the choice of attributes may also be revised.
This is an iterative process that will be repeated a number of times until consensus is reached as to
what should be included in the analysis.
In the last step of this analysis, we will have a number of solutions that satisfy the constraints for
the given system attributes. This is one of the benefits of using multi-attribute trade-off analysis.
There is not one optimal solution; rather there is a basket of solutions to choose from. Other
strategies should be given attention, as understanding these strategies may prevent poor
investments [16].
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trade-off analysis in a multi-stakeholder policy debate.
5.5 Conclusions
We have looked at some of the modeling issues required for a planning project in sustainable
energy. The discussion above has presented some of the requirements for a model that is to be
used. We have also described some of the essential pieces of data that will be required. This data
needs to be stored in an organized manner and we have suggested a database structure that
achieves that goal. Finally, we suggested some of the possible scenarios that can be tested using
the model and the data. Scenarios include both demand and supply-side technologies. Overall,
this chapter presents a good start point for moving forward with this project. The suggestions in
this chapter will be useful in leading us into the next phase of this project.
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CONCLUSION
This project has laid a strong foundation for future research initiatives in the area of sustainable
energy. We began by trying to construct a working replica of the Scandinavian electricity system
that was detailed enough to allow us to test various strategies, but not too detailed that we would
get lost in the complexities of the system. With this accomplished, we proceeded to address some
of the data issues. We arranged the data in a manner that reflected the artificial divisions that we
imposed on Norway and Sweden. Denmark did not require any data massaging as we had actual
data for Denmark East and West. We then addressed the hydro data issue which required pulling
data from SINTEF and using the EMPS model to schedule hydro on a weekly time resolution, in
an effort to produce a weekly profile that could be used as input to the PROSYM model. Lastly,
we estimated heat rates (plant efficiencies) and included generation from surrounding areas so that
we had a complete picture of the electricity system in Scandinavia.
The next step was to run simulations with the PROSYM model and to analyze the results. As we
ran several different scenarios, we found that the hydro scheduling methodology of PROSYM did
not produce results that were in line with actual system results. We tried to develop a work around
for this problem and the best solution developed was one where the hydro scheduling was done
outside the model. External hydro scheduling can be done in several ways, so we determined a
method that would produce accurate results.
The import/export results from PROSYM were
compared against the actual import/exports of the country. Once a suitable method was found, we
repeated the analysis for several other representative weeks in the year. We found that the hydro
scheduling methodology needed to be improved to account for the run-of-river content. With this
fix in place, results improved for Norway and Sweden, but not for Denmark. We found that the
hydro scheduling methodology was quite rudimentary to be used for a planning project, but it
furnished decently accurate results.
After addressing some of the modeling issues, we moved on to look at wind as a viable generation
alternative. Wind speeds measured in nine different locations throughout Norway were thoroughly
compared with the load characteristics of Norway. The wind speeds in each of these areas were
scaled to hub height and a location that had the strongest and most consistent wind speeds was
83
chosen for each of the four areas in Norway. These wind speeds were then converted to power
using the power curve for a Vestas 2MW wind turbine and the resulting power was added to the
system.
Scenarios were then repeated to check for the effect of this added generation on the
system. Early results show that this added generation would have some detrimental effects on the
already over-taxed transmission system. There were however emissions benefits from adding
wind to the system.
The results are not complete as the response of hydro producers was
impossible to model.
Finally, we wrapped up the entire discussion by providing a glimpse into some the data and model
requirements for a project of this magnitude. We suggested a simple database structure that would
store the necessary data in a coherent and easily accessible manner. We also listed some of the
crucial pieces of data that is required to continue with this planning project. We also touched on
some of the areas of proficiency for a model used for a project in Scandinavia which is
characteristically different than other areas because of its enormous hydro resource. Once the data
is compiled and the model chosen, scenarios must be developed and the multi-attribute trade-off
analysis must be done.
With this information in hand, the next step is to involve stakeholders in a debate of the important
issues affecting the Scandinavian system. The research presented here serves as a guideline for
this debate as it enumerates the several different areas that must simultaneously be considered.
This study has provided a richer understanding of the unique characteristics of the Scandinavian
utility system.
Some of the analysis and conclusions presented in this report are based upon
forecasts and other data that contain some uncertainties. However, the emphasis in this project is
to combine the modeling of the Scandinavian power system with decision analysis tools to produce
preliminary results that can be analyzed by stakeholders at various levels. Public interest should
focus on the primary measures of cost and emissions which this study has analyzed to create a
coordinated mix of certain options that seem reasonable. A balanced program of nuclear life
extensions, end-use efficiency programs, and increase generation from sustainable energy sources
appear to offer cost effective benefits. The next step is to choose the appropriate model as outlined
in Chapter 4 and to begin the enormous task of compiling data describing the entire Scandinavian
electricity system.
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