1
Initial Studies of Power Plant Construction
under a Deregulated West African Power Pool
using System Dynamics
M. Gebremicael and K. Tomsovic
Abstract— Our research group has been developing models to
understand the long-term interactions between investment and
performance in the electric power system. In this paper, we discuss
some of our initial results in applying these tools to understand the
expected pattern of investment in the proposed West African Power
Pool (WAPP). The main result shows that the interconnection
between countries has a clear impact on the local system prices and
investments in new construction but there will still be large
regional variations in prices and new construction.
Index Terms— Deregulation, investor behavior, market models,
power system planning, system dynamics.
B
sophisticated and high tech economy of California, can the
developing world be expected to fare any better?
Our research group has been developing models to
understand the long-term interactions between investment and
performance in the electric power system. Specifically, we
have contributed by developing modeling and analysis tools
of:
•
•
I. INTRODUCTION
EGINNING in the early 1990s, much of the industrialized
world began experimenting with deregulation of the
electricity sector, primarily in an effort to reduce costs. The
results have often been more than disappointing, catching
even the biggest proponents by surprise. For example in
California, the electricity markets opened for business in
1998. Roughly two years later in the summer of 2000,
California was experiencing rotating outages and price spikes
at multiples of a 100 or more. This crisis was only expected to
worsen but just as suddenly conditions stabilized and neither
chronic outages nor price spikes reappeared in the summer of
2001. One key to the stabilization of the market was that many
new power plants came on line. In one sense, the markets
worked by creating new investments, but the unfortunate
result was a glut in supply. Some have claimed this instability
is a fundamental artifact of poorly designed wholesale
electricity markets [1] while others simply believe greater
public investment can “break the cycle of boom and bust” [2].
Despite such failures and lack of understanding of the
underlying causes, governments throughout the developing
world have also begun experimenting with deregulation of the
electricity sector. In these countries, while the primary
motivation is to encourage private sector investment, the
market designs are similar. If a crisis could arise in the
The work reported in this paper has been supported in part by the National
Science Foundation (NSF) and the Office of Naval Research under NSF grant
ECS-0224810 and in part by NSF under ECS-0424461.
M. Gebremicael is with the School of EECS, Washington State University,
Pullman, WA 99164, USA (e-mail: mengs_merhai@wsu.edu).
K. Tomsovic is with the School of EECS, Washington State University,
Pullman, WA 99164, USA (e-mail: kevin_tomsovic@wsu.edu).
•
pricing regimes [3], market transparency [4], and bidding
activity under transmission constrained systems [5], in
order to understand some of the market forces on
suppliers,
investor behavior, in order to understand the sluggish
behavior of investors whose construction of new power
plants lags behind the growth in demand [6],
transmission network planning and its impact on in
investment decisions in different supply options [7].
In this paper, we discuss some of our initial results in
applying these tools to understand the expected pattern of
investment in the proposed West African Power Pool
(WAPP). The main result shows that the interconnection
between countries has a clear impact on the local system
prices and investments in new construction but there will still
be large regional variations in prices and new construction.
II. BACKGROUND
A. System Dynamic Studies for Power System Planning
Most planning models in the electric industry are static with
the primary focus of the studies centered on studying possible
future scenarios. These models fail to represent the dynamics
of the planning process under market conditions. The models
we are constructing incorporate these dynamics using
concepts from the field of system dynamics, a simulation
method pioneered by Forrester [8] and popularized in texts by
Ford [9] and Sterman [10]. System dynamics can be defined
as [11]
branch of control theory which deals with socio-economic
systems and that branch of management science which
deals with problems of controllability.
Despite the connection to control theory, system dynamics
studies, designed to gain insight into trends developing over
2
difficult to forecast due to underserved load. The following
describes the status of this model.
Methodology and Data
The general building blocks of the model to perform the
dynamic system of competitive electric market require:
•
Fig. 1 An example of modeling using exponential growth system dynamics
approach
years or decades, differ greatly from the detailed modeling
Fig. 2 An example of modeling using an engineering approach
(implemented in Simulink)
approaches of engineering planning studies. The emphasis in
system dynamics is on information feedback and icon-based
modeling with a clear portrayal of the “stocks” and flows. A
stock is essentially an accumulator or integrator. Models are
built up from these stocks and flows forming coupled sets of
first-order differential equations. Fig. 1 shows the Stella®
model of a simple first order system representing peak
demand with exponential growth. The key to these models is
including all the relevant influences and verifying with
historical data.
Precise understanding of the future power system
performance must include analysis of the transmission system.
However, long-term investment models cannot easily
incorporate detailed power system operations models. For
example, even for daily operations, where specific details of
the interconnections are known, most power exchanges use a
simplified linearized transmission model to avoid
computational problems. For the broader analysis in this work,
even greater approximations are needed.
The traditional engineering approach to modeling requires
an explicit mathematical description of the relations among
the system variables. Fig. 2 shows the Simulink® model of the
same exponential growth system shown in Fig. 1. Both of the
system dynamics and engineering approaches may struggle
with equality and inequality constraints but the engineering
approach has an advantage due to the explicit representation.
In this work, the power flow equations and the transfers across
regions are the primary constraints of concern. For more
details on the model development, the reader is referred to [7,
12-13].
III. NUMERICAL STUDY OF WEST AFRICAN POWER POOL
A. Modelling of West Africa Power Pool
Our original work focused on the Western US. That system
is rather developed and there is little difficulty in arriving at
salient parameters for the model. For developing countries,
this problem is not trivial as even the expected demand is
Initialization of parameters for each area. Specifically:
Combined cycle (CC) plants total levelized cost,
construction permit shelf life,
the goal for permits by developers,
initial peak annual demand,
demand annual growth rate,
investor weight given to CCs in the construction
pipeline,
o
generating capacity from all units,
o
variable cost of different units,
o
natural gas prices,
o
transmission network topology and line parameters,
and
o
peak demand for each area over the study period.
• Creation of a typical 24 hour demand curve for each
month of the year.
• Computation of the price and power generated for each
demand hour of a given day that represents an entire
month and which can be used to determine the forecasted
profit for each generator.
o
o
o
o
o
o
These inputs then feed into a market simulator that
determines expected prices for typical days during each month
of the year. From these simulated prices and forecasting the
trend, the nature of investments in the generator market is
modeled. Here, we focus only on CC plants. Generally, we
assume rational investor behavior but discount generators in
the process of being built. This represents typical market
activity as competitors rush to the market expecting to take
advantage of favorable conditions before prices drop. Fig. 3
shows a very high level view of the developed model. The
interconnections between countries are shown in Fig. 4. The
simulation begins at the point when there is enough supply to
meet the demand. For this purpose, the Table I shows the data
made to run the 14 WAPP model.
Area
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
TABLE I
WAPP SUPPLY AND PEAK DEMAND DATA
Country
Supply
Demand
Adjusted
(MW)
(MW)
Demand (MW)
Benin
91.1
98
78
Burkina Faso
124.1
102.1
--Cote D’Ivoire
708
1203
--Gambia
22.8
21
16
Ghana
1622
1281
--Guinea
191.7
241.98
161
Guinea Bissau
8.3
21.4
5.05
Liberia
7.2
44.2
4.42
Mali
231.4
126.4
--Niger
57.5
69.1
39.1
Nigeria
3959.4
4100
3200
Senegal
382.2
284.39
--Sierra Leone
95.57
23.6
--Togo
100
116
31.6
3
Demands Hourly
Shape Factors
Monthly Shape
Factor Subsystem
-C-
y Demand Factor
Hourly demand f actor
Demand A1
Monthly demand f actor
WAPP_14area_main
Demand A2
P_dem1
Read data Subsystem
Demand A3
P_dem2
em
Demand A4
P_dem3
Demand A5
boom1.mat
P_dem4
From File
Demand A6
P_dem5
Scope
Demand A7
P_dem6
P_dem7
P_demands
boom1.mat
em
P_dem8
Demand A9
pricecap
S_pricesnew
pricecap1
P_dem9
CCS on line
Prices
S function
Demand A8
Demand A10
MATLAB
Function
Plot for price, gen type,
gen area, and average price
P_dem10
Peak Demands
P_dem11
P_dem12
To File1
Prices
Present CCs online
em
Demand A11
Investor behavour
Demand A12
Scope1
P_dem13
P_dem14
Demand A13
Demand A14
Demand
Subsystem1
boom1.mat
Area_2
From File1
To Workspace
Two area complet model
for the purpose of
study
Fig. 3 High level Simulink model of the long term pricing and investment behavior
Fig. 4. Interconnections between countries of the WAPP
4
For the purpose of our simulations, the available data required
some modification. The primary concern is the peak demand
as this must be adjusted below available supply.
New CCs On-Line for Nigeria
1.4
1.2
New CCs On-Line for Togo
0.9
0.8
New CCs Online (MW)
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
Time (month)
20
25
1
New CCs Online (MW)
B. Construction of New Units
For a 30-month simulation of this market, countries with
shortage and relatively high prices show new CCs being built.
For example in Togo (Fig. 5), results show 0.75 MW new CCs
to come some time during the 18th month and construction
continues almost constant through the 27th month. Over the 11
month period, there are about 8.25 MW of new CCs brought
on-line. Some of the countries, such as Sierra Leone, and Mali
show no new CCs, this can be explained from the data given
in Table 1. The two countries have adequate supply through
out the simulation period in order to keep the price of
electricity below the cost of the construction of new CCs. Mali
also experiences a very low electricity price as shown in the
next section due to its very low variable O&M cost of
generators.
Niger (Fig. 6), among others, sees new CCs come on line
relatively early since the peak demand nearly equals the
available supply. Other countries show a much greater delay,
e.g., Nigeria (Fig. 7). Note, the initial value for the CCs under
0.8
0.6
0.4
0.2
0
0
5
10
15
Time (month)
20
25
30
Fig. 7. Monthly rate of CCs connecting to the system in Nigeria
construction and permit blocks is set to be zero and so there is
at least a 12 month delay in building.
C. Electricity Prices
For the same 30 month simulation of the market, Fig. 8-10
show prices in selected countries. The price of electricity for
some countries is extremely expensive. This stems primarily
from the relatively high cost of gas and higher O&M cost.
Moreover, the transfer capacities between countries are not
sufficient to alleviate the price differences. This is shown
clearly in Fig. 8.
Fig 4 shows the highest price was experienced in Senegal.
Based on the available data, Senegal has variable O&M cost
for hydro generation of $22.86, while Mali on the other hand
has $0.29 for thermal and $0.76 for hydro. This explains the
consistently low price of electricity in Mali. We cannot vouch
for the validity of the cost data but they do show the effects of
these costs on an interconnected system. Finally, Fig. 5 shows
the price of electricity for Nigeria, which is moderate.
IV. CONCLUSIONS
30
This paper has shown a relatively simple study of the
construction patterns and prices for a 30 month study of the
WAPP. The developed model faces several challenges in
developing more meaningful results. Namely:
Fig. 5. Monthly rate of CCs connecting to the system in Togo
New CCs On-Line for Niger
1.4
1.2
Prices for each area
New CCs Online (MW)
1
140
a1
a2
a3
a5
a14
120
0.8
price($/mwh
100
0.6
80
0.4
60
0.2
0
40
20
0
5
10
15
Time (month)
20
25
Fig. 6. Monthly rate of CCs connecting to the system in Niger
30
0
0
5
10
15
20
25
hou
Fig 8. Electricity prices for Benin (a1), Burkina Faso (a2), Cote D’Ivoire (a3),
Ghana (a5), and Togo (a14).
5
Prices for each area
[8]
500
a9
a12
450
[9]
[10]
[11]
[12]
400
350
price($/mwh)
300
250
200
150
[13]
100
50
0
0
5
10
15
20
25
hour
in Electric Power Systems” submitted to Journal of Critical
Infrastructures.
J. W. Forrester, Industrial Dynamics, Pegasus Communications,
Waltham, MA.
A. Ford, Modeling the Environment, Island Press.
J. Sterman, Business Dynamics, Irwin McGraw-Hill, 2000.
G. Coyle, Management System Dynamics, John Wiley, 1977.
A. Dimitrovski, M. Gebremicael, K. Tomsovic, A. Ford and K.
Vogstad, “Comprehensive Long Term Modeling of the Dynamics of
Investment and Growth in Electric Power Systems,” 2004 EPNES
Workshop, Mayaguez, Puerto Rico, July 13-14 2004.
A. Bose, K. Casavant, A. Dimitrovski, A. Ford, K. Tomsovic and L.
Lutzenhiser, “Modeling the Interaction Between the Technical, Social,
Economic and Environmental Components of Large Scale Electric
Power Systems,” 2003 EPNES Workshop, Orlando, FL, Oct. 23-24
2003.
VI. BIOGRAPHIES
Fig 9. Electricity prices for Mali (a9) and Senegal (a12).
M. Gebremicael received the BS in Electrical Engineering from Washington
State University in 2003. He is currently an MS student in Electrical
Engineering at WSU.
Prices for each area
60
a11
Kevin Tomsovic received the BS from Michigan Tech. University, Houghton,
in 1982, and the MS and Ph.D. degrees from University of Washington,
Seattle, in 1984 and 1987, respectively, all in Electrical Engineering. He is
currently Program Director in the ECS Division of the Engineering
Directorate at National Science Foundation and a Professor in the School of
EECS at Washington State University. Visiting university positions have
included Boston University, National Cheng Kung University, National Sun
Yat-Sen University and the Royal Institute of Technology in Stockholm. He
held the Advanced Technology for Electrical Energy Chair at Kumamoto
University in Japan from 1999-2000.
50
price($/mwh)
40
30
20
10
0
0
5
10
15
20
25
hour
Fig 10. Electricity prices for Nigeria.
•
•
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obtaining more realistic data on costs by country,
developing a model of investment appropriate for West
Africa,
researching issues associated with under served demand,
and
incorporating other demand side models into the analysis.
V. REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
A. Ford, “Boom & Bust in Power Plant Construction: Lessons from the
California Electricity Crisis”, to appear in a special issue of the Journal
of Industry, Competition and Trade.
S. David Freeman, Chairman of the Board of the Power Authority,
interviewed in the Contra Costa Times, August 13, 2001.
S. Vucetic, K. Tomsovic and Z. Obradovic, “Discovering Price-Load
Relationships in California's Electricity Market,” IEEE Transactions on
Power Systems, Vol. 16, No. 2, May 2001, pp. 280-286.
L. Xu, K. Tomsovic and A. Bose, “Topology Error Identification using
a Two-State DC State Estimator,” submitted to IEEE Transactions on
Power Systems.
T. Peng and K. Tomsovic, “Congestion Influence on Bidding Strategies
in an Electricity Market,” IEEE Transactions on Power Systems, Vol.
18, No. 3, August 2003, pp. 1054-1061.
A. Ford, “Simulation Scenarios for the Western Electricity Market: A
Discussion Paper for the CEC Workshop on Alternative Market
Structures
for
California,”
Nov
2001,
online
at
http://www.wsu.edu/~forda.
A. Dimitrovski, K. Tomsovic, and A. Ford, “Comprehensive Long
Term Modeling of the Dynamics of Investment and Network Planning