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 771 South Africa (phone: +27-21-65-279; fax: +27-21-65-3465; e-mail: jun.yan@uct.ac.za). ** Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro, 1 195-62 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. 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 199s, 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 199s, 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 9 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 a power exchange or bilateral markets or both, and risks managed through derivative financial markets [2]. 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 7 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: TABLE 1:MARKET STRUCTURE OF CLUSTERS I Cluster Technology Capacity (MW) A.1 Hydro, Gas turbine 2114 C.1 Coal 567 D.1 Coal 7293 E.1 Coal 5458 F.1 Coal 735 G.1 Coal 63 N.1 Nuclear 184 Total 3625 In order to study the impact of different market structures, we have also considered another arrangement for the Eskom Power Pool. 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: TABLE 2: MARKET STRUCTURE OF CLUSTERS II Cluster Technology Capacity (MW) A.2 Coal 184 B.2 Gas turbine, Coal 9972 C.2 Coal 11373 D.2 Nuclear, Hydro 384 Total 3625 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 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. The time span of the simulation was one month using data of consumption, interconnections load flows and estimation of hydro power generation of July 23. 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. Figure 1 shows market prices for one week obtained by the simulations in Monopoly, Cluster I, Clusters II and perfect competition. 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. Monopoly Clusters I Clusters II Perfect Competition 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. 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. 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. 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 22%, compared to the perfect competitive outcome. 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. 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 9% 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 1.9.78 1.85 Clusters I 1.49.94 1.47 Clusters II 1.68.9 1.64 Perfect Competition 1. 1. 1. 3.2 Comparison of competition models Monopoly Clusters I Clusters II Perfect Competition 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.. 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 2.2.85 Clusters I 1.54.96 Clusters II 1.78.93 Perfect Competition 1. 1. 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 23. IPPs generation and net profits are presented during the above four month period. The market clearing price is presented in a typical week.
Market price (Rand/MWh) 25 2 15 1 5 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. Millions 16 14 12 1 9 17 25 33 41 49 57 65 73 81 89 97 15 113 121 129 137 145 153 161 NC PC Figure. 3: Market prices for different competition models. Net profit (Rand) 1 8 6 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. 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. Generation (MWh) Millions 2 18 16 14 12 1 8 6 4 2 Cluster A Cluster C Cluster D Cluster E Cluster F Cluster G Cluster Nulear 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. NC PC 4 2 Cluster A Cluster C Cluster D Cluster E Cluster F Cluster G Cluster Nulear Figure. 5: Net profits of IPPs for different competition models. As shown in figure 5, all IPPs profits increased in the Nash- Cournot 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. NC PC 4 CONCLUSIONS 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 23 (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. REFERENCES [1] C. Day and D. Bunn, Generation asset divestment in the England and Wales electricity market: a computational approach to analysing market power, Decision technology centre, London Business School, April 1999. [2] A. Eberhard, Electricity market structure matters, Business Day in South Africa, September 23, 24. [3] D. Kamerschen, P. Klein and D..Porter, Market structure in the US electricity industry: a long-term perspective, Energy Economics, no. 27, pp. 731-751, 25. [4] J. Sousa, Integration of liberalized electricity energy markets with applications to the IBELM, Ph.D. thesis, Universidade Nova de Lisboa, 25. [5] J. Sousa, Impact of Regional Electricity Markets Integration on Pool Prices: An Application to the Iberian Electricity Market, presented at the 25 Australasian Universities Power Engineering Conference, Australia.