A COMPARATIVE STUDY OF MARKET BEHAVIORS IN A FUTURE SOUTH
AFRICAN ELECTRICITY MARKET
J. Yan* J. Sousa**, and J. Lagarto**
* Electrical Engineering Department, University of Cape Town, Private bag, Rondebosch 7701 South Africa
(phone: +27-21-650-2790; fax: +27-21-650-3465; e-mail: jun.yan@uct.ac.za).
** Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro, 1 1950-062 Lisboa
PORTUGAL E-mail: jsousa@deea.isel.ipl.pt,& jlagarto@deea.isel.ipl.pt
Abstract : This paper presents a comparative study on market behaviors in a propsed South African electricity market using
two market simulation software, in order to evaluate different market structures and competition models. The results show
that the design of market structure is essential to ensure proper competition. Market structure plays an important role on the
determination of market clearing price and production. The more concentrated the market will result in higher market price.
Overall profits in the market also vary with the change of market structure and competition models.
Keywords: Electricity markets, game theory, market strategies, market structure, power system economics, conjectural
variations.
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a power exchange or bilateral markets or both, and risks
managed through derivative financial markets [2].
1. INTRODUCTION
Competition has been introduced based on the assumption that
it will bring benefits to the end consumers. The most critical
issue in the restructuring power system is the gaming behavior
of the generators [3].
Since late 1990s, privatization and deregulation in the South
African Electricity supply industry (ESI) have gradually
started. Due to the issues arose in the international markets,
such as the case of UK liberalization, although the capacity
divestments were substantial since the early 1990s, the level
of market power was underestimated [1]. Therefore, the
deregulation process is now at the stage of intensive research
and planning.
South Africa is currently experiencing high increase of
electricity demand, which requires significant capacity
expansion. Eskom is the major electricity supplier in South
African, with over 90 percent share of the electricity supply
market. The future capacity expansion program relies on the
participation from Eskom.
Two broad electricity market models have emerged over the
past two decades. The first is competition for access to the
electricity market. The private sector is encouraged to invest
in new power-generation capacity through tenders or auctions.
The second electricity market model involves competition
between electricity generators and suppliers to dispatch and
sell electricity to consumers. Competition is managed through
However, these are based on the assumption that there is
significant participation from the potential Independent Power
Producers (IPPs) to encourage competition through bidding
process. Otherwise, it does not effectively change the market
shares between Eskom and IPPs.
IPPs also face number of significant challenges, such as grid
access, and financial viability of buyers. Targeting 70 percent
of the future power generation capacity, Eskom’s capacity
expansion planning has a significant impact on the generation
sector.
In fact, Eskom’s plan to integrate the private sector firms,
including the IPPs and the black economic empowerment
(BEE) firms, may potentially strengthen Eskom’s market
share.
A more feasible and appropriate option to achieve fair
competition is to group the existing power stations under
Eskom into clusters. In this paper we use two market
simulators to simulate the gaming behavior of supply side
players in the Eskom Power Pool (South Africa).
The comparison of different market models, such as perfect
competition, monopoly and cluster, is carried out using
SiMEC – a game theory market simulator developed by the
research and development team of the Power Systems Unit at
ISEL, based on a conjectural variations model [4, 5]. The
comparison of perfect competition and Nash-Cournot
(quantity game) is conducted using Plexos – a commercially
available power market simulator.
2. MODELLING THE ESKOM POWER POOL
In this paper, we simulate a case study of the Eskom Power
Pool, when the main model is to pair Eskom’s power stations
to form clusters in its generation sector. The purpose is to
show the importance of the market structure design of the
South African ESI.
The clusters for the market structure adopted (named Clusters
I) group technologies and installed capacity as follows:
The purpose of having the second comparison is to show that,
even within a specific market structure, different competition
models can influence the market results. The perfect
competition model can also be considered as economic
dispatch.
The key market indicators used to analyze market behaviours
of IPPs include the following:
 Market price
 IPP’s power production
 IPP’s total revenue and total profits
TABLE 1:MARKET STRUCTURE OF CLUSTERS I
Cluster
Technology
A.1
C.1
D.1
E.1
F.1
G.1
N.1
Total
Hydro, Gas turbine
Coal
Coal
Coal
Coal
Coal
Nuclear
Capacity
(MW)
2114
5670
7293
5458
7350
6300
1840
36025
3. SIMULATION RESULTS
3.1 Comparison of different market structures
The simulations were carried out using a defined competitive
regime for the three different market structures – Monopoly,
Clusters I and Clusters II – which are then compared to the
perfect competition outcome obtained by the simulation of the
Eskom Power Pool, using the SiMEC simulator, with a
conjectural variations of -1.
In order to study the impact of different market structures, we
have also considered another arrangement for the Eskom
Power Pool.
The time span of the simulation was one month using data of
consumption, interconnections load flows and estimation of
hydro power generation of July 2003.
This structure considers less supply side agents, which leads
to more installed capacity for each cluster. The clusters for
market structure (named Clusters II) are established as
follows:
For clarity purposes only the results corresponding to one
week of the simulation were plotted, as the results are
somehow similar for the rest of the month as they are basically
driven by the fluctuations of the demand.
TABLE 2: MARKET STRUCTURE OF CLUSTERS II
Cluster
Technology
A.2
B.2
Coal
Gas turbine, Coal
C.2
D.2
Total
Coal
Nuclear, Hydro
Capacity
(MW)
10840
9972
Figure 1 shows market prices for one week obtained by the
simulations in Monopoly, Cluster I, Clusters II and perfect
competition.
11373
3840
36025
For the first comparison, we compare the most representative
market structures – Monopoly, Clusters I and Clusters II –
which are compared to the outcome that would be achieved
under the paradigm of perfect competition, which is also
simulated as a reference case.
For the second comparison, we compare the perfect
competition model and the Nash-Cournot model in a specific
market structure. We choose clusters I as shown in table 1.
A proper market structure design should be always
accompanied with an entity to monitor and investigate gaming
behaviours.
M o n o p o ly
Clu s ters I
Clu s ters II
Perfect Co mp etitio n
Figure. 1: Market prices for different market structures.
It can be seen, as expected, that the market prices in perfect
competition are the lowest among all the structures simulated.
On the opposite side, the highest prices correspond to the
monopolistic situation, where all the power plants are owned
by a single company.
It can be seen that in the case of monopoly a reduction in 15%
of power production leads to a rise in the average price of
220%, compared to the perfect competitive outcome.
Between these two popular situations, the prices for Clusters I
and Clusters II market structures are intermediate, being the
prices of the Clusters I lower than prices of the Clusters II.
These differences in market prices among monopoly, Clusters
I and Clusters II put in evidence the impact of the market
structure once we can see that for the same degree of
competition the lower prices correspond to market structures
with a bigger number of supply side players.
For Clusters I there is an increase in price of 54% with a
decrease in power production of 4%, whereas Clusters II,
which corresponds to a situation of a more concentrated
market, induces an increase of 78% in price and a decrease of
7% in power production, compared to the perfect competitive
outcome.
It is also interesting to observe that the differences in prices
are higher in off-peak hours for the monopolistic situation.
This occurs because in off-peak hours the reserve margin is
higher which induce more competition when different players
are presented in the market. However, in the case of monopoly
a single player faces all the demand which gives all the power
to set prices in order to maximize profits without the fear to
lose market shares for other competitors.
The hourly market price is closely related to the market
quantities that are shown in figure 2, as expected.
The observed increase in price is due to the low elasticity of
the demand curve in the wholesale market, that is, to a high
change in price, electricity demand responds with a small
variation of quantity.
From Table 4, we can see that the market structure which has
the highest incomes and the lowest costs is the monopoly.
In fact, the incomes are 85% above the incomes in perfect
competition and the costs are 22% below, thus reflecting a
90% higher profit than in perfect competition.
For Clusters I and Clusters II we observe an intermediate
situation.
TABLE 4:RELATIONSHIP BETWEEN MARKET PROFITS, COSTS AND INCOMES
FOR THE DIFFERENT MARKET STRUCTURES: MONOPOLY, CLUSTERS I,
CLUSTERS II AND PERFECT COMPETITION
Market structure
Profits
Costs
Income
Monopoly
Clusters I
Clusters II
1.90
0.78
1.85
1.49
1.68
0.94
0.90
1.47
1.64
Perfect Competition
1.00
1.00
1.00
3.2 Comparison of competition models
M on opoly
Clus ters I
Clus ters II
Perfect Co mpetitio n
Figure 2: Market quantities for different market structures.
In Table 3 we present the relationship between the different
market structures concerning average price and total quantity
of the month studied having perfect competition has the
reference case which is normalized to 1.00.
TABLE 3:RELATIONSHIP BETWEEN MARKET QUANTITIES AND PRICES FOR THE
DIFFERENT MARKET STRUCTURES (MONOPOLY, CLUSTERS I, CLUSTERS II)
AND PERFECT COMPETITION
Market structure
Av. Price
Quantity
Monopoly
2.20
0.85
Clusters I
1.54
0.96
Clusters II
Perfect Competition
1.78
1.00
0.93
1.00
In this section, we use Plexos to simulate and compare the
results from two competition models, perfect competition and
Nash-Cournot. As the Nash-Cournot competition requires a
time series in medium term, the time span of the simulation
was four month during May up to August 2003.
IPPs’ generation and net profits are presented during the
above four month period. The market clearing price is
presented in a typical week.
The cluster D includes two generators that have the highest
bid from their units among the base load clusters. It meets our
expectation that the less expensive IPPs will withhold capacity
to force the more expensive IPPs to supply the load. The load
will have to pay higher market clearing price for its
consumption.
250
150
100
Millions
Market price (Rand/MWh)
200
1600
1400
50
0
1
9
17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161
NC
PC
Figure. 3: Market prices for different competition models.
As shown in figure 3, market price is higher in Nash-Cournot
model than the one in perfect competition model. The major
difference in the assumption of Nash-Cournot between the
conventional literature and this paper is that, there is no
general capacity withhold in the market. According to the
Classical Nash-Cournot competition model, total generation
may decrease due to capacity withhold. It means that there
will be a mismatch between supply and demand. However, in
the real power system, we can not simply ignore the basic
requirement of covering the load demand. Therefore, demand
is satisfied in our simulations during all stages.
Millions
In our simulations, the capacity withhold is reflected in
capacity withdraw by certain IPPs. Then, the reduction of
power output from these IPPs will be covered by the increased
output from other IPPs.
20
Net profit (Rand)
1200
1000
800
600
400
200
0
Cluster A
Cluster C
Cluster D
Cluster E
NC
Cluster F
Cluster G
Cluster
Nulear
PC
Figure. 5: Net profits of IPPs for different competition models.
As shown in figure 5, all IPPs’ profits increased in the NashCournot competition, including the IPPs that produced less
power. When IPPs choose the quantity strategy, they look for
the equilibrium, at which the overall profit for all generators is
maximized. And the players’ gaming behaviors results in a
significant increase of their profits.
As expected, all IPPs have obtained more profits from the
higher increase of market price. For the IPPs that reduced
their productions, the increase of market price is more than the
decrease of their outputs.
18
16
4 CONCLUSIONS
Generation (MWh)
14
12
10
8
6
4
2
0
Cluster A
Cluster C
Cluster D
Cluster E
NC
Cluster F
Cluster G
Cluster
Nulear
PC
Figure. 4: Generations of clusters for different competition models.
Figure 4 shows that most IPPs, Clusters C, E, F and G with
base load plants, withheld capacity in Nash-Cournot. The
reduction of power supply from these IPPs were covered by
the increase of output from cluster D and peak cluster A. The
nuclear cluster did not change its output, as expected to be
always available.
In this paper we forecast of the behaviour of the South African
Electricity Market. The study is based on numerical
simulation of an oligopolistic game theory model, which
represents the strategic interaction of the players presented in
the market.
The simulations were carried out in two sections. One was
focused on different market structures ranging from monopoly
to perfect competition, using the SiMEC simulator which has
de flexibility of adjusting the degree of competition of the
market players. The other was focused on the competition
within a specific IPP model, using the Plexos simulator in its
Nash-Cournot model.
The results achieved show that the market structure affects the
market outcome, such as market clearing price and power
production. The more concentrated the market the higher the
price increase and the lower the power production. In
particular, the clusters considered by Eskom in 2003 (Cluster I
of the paper), induce an increase in price of 54% with a
decrease in power production of 4%, in comparison with the
perfect competition outcome.
Differences in prices are higher in off-peak hours for the
monopolistic situation. This is due to the fact that off-peak
hours present higher reserve margin which induce more
competition when different players are presented in the
market. In monopoly, a single player faces all the demand and
has the power to set prices in order to maximize profits,
without the fear to lose market share.
Overall market incomes, costs and profits also vary with
market structure. Incomes are higher, costs are lower and thus
profits are higher when the market structure holds less supply
side players.
The production pattern differs in the Nash Cournot
competition. The results show that the clusters with more
economical generators will withdraw certain amount of
capacity supply and force the more expensive generators to be
dispatched much more compared to the perfect competition
model. As a result, almost all clusters have obtained more net
profits through such gaming behaviors. The profit in Nash
Cournot competition is always higher than the ones in perfect
competition.
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C. Day and D. Bunn, “Generation asset divestment in the England and
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1999.
[2]
A. Eberhard, Electricity market structure matters, Business Day in South
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[3]
D. Kamerschen, P. Klein and D..Porter, “Market structure in the US
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[4]
J. Sousa, “Integration of liberalized electricity energy markets with
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[5]
J. Sousa, “Impact of Regional Electricity Markets Integration on Pool
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