Simulation of the Central European Market for Electrical Energy

Transcription

1 Simulation of the Central European Market for Electrical Energy T. Mirbach and H.-J. Haubrich RWTH Aachen University Institute of Power Systems and Power Economics Schinkelstr. 6, D Aachen, Germany Tel.: Fax : Tobias.Mirbach@iaew.rwth-aachen.de URL: Abstract The establishment of an efficient European Market for electrical energy is one of the main objectives of the pan-european liberalization. As a result, basic conditions for the congestion management have been determined, new methods for cross-border trading have been introduced 1 and the export fee was abolished in In addition, the substitution of the installed capacity of power plants due to obsolescence in the following decades as well as political decisions that effect the power market, e.g. the abandoning of nuclear power in Germany and the implementation of emission certificates, will influence the generation costs. Furthermore, there is an increasing concentration in the European Power Market which encourages trading strategies of companies which may influence the market price. Considering these circumstances, the paper presents the current status of a method which is under development to simulate the Central European Market for electrical energy by means of adequate models to take into account the technical constraints of power generation and transmission as well as the impact of trading strategies that influence the market price. Keywords: market simulation, fundamental model, cross-border trade, strategic behaviour, Benchmark-Analysis 1 Regulation (EC) No 1228/2003 of the European Parliament and of the Council of 26 June 2003 on conditions for access to the network for cross-border exchanges in electricity 2 European Commission, 10th Electricity Regulatory Forum of Florence, (status ) 1

2 1 Introduction Due to the liberalization of the European electricity market, power generation companies (PGC) are exposed to national and international competition. In addition, there are new conditions because of the changing situation of the power market which primarily influences the market price of electrical energy and with it the economic profitability of generating units. Fundamental models, which simulate the market for electrical energy, allow to investigate the future development of the market price. The advantage of fundamental models is the explicit consideration of structural changes in the power market, e.g. the increasing cross-border trade, the modified structure of the power generation mix as well as the consequences of the oligopolistic market structure. In the following chapter the considered system of the simulation model and its relevant components are defined. Based on the analyzed components, the results of a Benchmark-Analysis of the German Power Market are presented. Afterwards (chapter 3) an iterative three-stage method for the simulation of the Central European Market for electrical energy, which is being developed, is described. 2 Analysis Fundamental models assume that in principle the market price is based on the marginal costs of the associated power generation mix as a fundamental price component. In addition, a nonfundamental component the spread between the fundamental component and the market price which results from the imperfect market can be observed. To simulate the market using a fundamental model, it is essential to define the time horizon, the geographical focus, as well as the relevant components of the considered system, which are described as follows. Time horizon and geographical focus Fundamental models are adequate to investigate the mid-term and long-term development of the market price. Therefore, the considered time horizon for the market simulations amounts to one by several years. 2

3 Concerning the changing conditions for the congestion management and the abolished export fee, an increasing cross-border trade can be observed in Central Europe. Due to the intensified crossborder trade, congestion at the transnational interconnections, which were originally not dimensioned for cross-border trading, occurs. Hence, nearly the total cross-border capacity is allocated by auctions in Central Europe. The major share is auctioned explicitly, so the allocation of the transfer capacities and the trading at power exchanges is decoupled. Therefore, the cross-border capacities are not allocated optimally. Especially implicit auctions the combination of the allocation of cross-border capacity and the trading at power exchanges offer an optimized allocation of the transfer capacities. Implicit auctions has been already introduced at the border between Germany and Denmark as well as Italy and its neighboring countries. In addition, in the context of the Trilateral Market Coupling between the power exchanges APX, Belpex and Powernext, the cross-border capacities between Netherlands, Belgium and France are allocated by implicit auctions since November Fig 1a gives an overview of the currently implemented mechanisms for day-ahead auctions in Central Europe 4. (a) Day-Ahead-Auctions explicit implicit no congestion (b) Correlation of market prices correlation coefficient EEX-APX EEX-EXAA EEX- Powernext Fig. 1: Day-ahead auctions in Central Europe (a), correlation of the market prices of different power exchanges in Central Europe (b) 3 Belgian Power Exchange (Belpex), (status ) 4 European Transmission System Operators (ETSO), (status ) 3

4 A consequence of the increasing cross-border trading is the rising correlation of the national market prices in Central Europe (fig. 1b) 5. Hence, an isolated model of the German electricity market appears to be inadequate. Consequently, the power transfer between the neighboring countries and hence the cross-border transmission network has to be considered. Therefore, the investigated system consists of Germany, its neighboring countries, and in addition Italy, due to its influence on the congestion situation in Central Europe. System components The energy sector of every country is subdivided in supply and demand (fig. 2). The power supply is provided by PGC, which primarily generate the electric energy in thermal and hydro power plants. The demand is separated in wholesale of electrical energy and reserve. In addition, relevant influencing factors on power supply and demand have to be identified and considered. power supply power plants thermal hydro power demand wholesale ~ ~~ ~ ~~ PGC reserve influencing factors: - fuel prices - reliability of power plants - weather -... Fig. 2: Considered system for the simulation of the Central European Market for electrical energy The influencing factors can be classified in fundamental e.g. fuel prices, CO 2 -prices, reliability of power plants and non-fundamental factors, such as the strategic behavior of the market participants resulting from the imperfect competition (fig. 3). 5 E-Control, Marktbericht 2006, (status ) 4

5 strategic behavior season/ weather reliability of power plants CO 2 -prices market price fuel prices im-/export structure of power generation fundamental non-fundamental Fig. 3: Influencing factors on the market price The fundamental factors influence the power supply and demand directly. However, nonfundamental factors are market dependent. According to the market structure and the number of market participants, the non-fundamental component results from the trading strategies of the market participants, e.g. to compensate the investment costs and to generate revenues, and has to be analyzed in addition to the fundamental component. Benchmark-Analysis For the method to simulate the Central European Market for electrical energy, it is essential to investigate the different influencing factors. By means of a Benchmark-Analysis of the German Power Market, an ex-post comparison between the marginal generation costs the fundamental price component and the market price is performed. The application of a fundamental model for the power generation planning yields the historical marginal costs, assuming that the demand is covered by an optimal economic dispatch for the entire system. Hence, the power generation planning of the individual PGC is disregarded. In the power generation planning, a time-dependent power demand can be covered with the stand-by power plants under consideration of the technical restrictions of power generation, e.g. the minimum and maximum power output level, minimum up- and down-times as well as non-linear efficiency curves of thermal power plants. This leads to an optimization problem of minimizing the system generation costs for the power generation of thermal power plants as well as 5

6 the optimal use of the water quantity in hydro power plants. The input data consists of generation capacities of the hydro and thermal power plants, the power generation of the units in must-run operation, fuel prices, CO 2 -prices, reliability of power plants and the power demand for wholesale and reserve respectively. In addition, the cross-border power transfer can be optimized or be defined as input data. The result of the market simulation is an optimal schedule for the operation of the power plants covering the power demand. Furthermore, the hourly marginal costs for each market area, which represent the fundamental component of the market price, can be determined. Fig. 4 gives an overview of the fundamental model 6. Input data power plants (hydro and thermal) definition of must-run-units fuel- and CO 2 -prices power demand (wholesale and reserve) cross-border trade (optional): - transmission network for optimizing the power transfer - power transfer (defined) Power generation planning objective function minimization of variable generation costs constraints - power and reserve balance - technical restrictions of the power plants - transmission network constraints Output data optimized scheduled operation of the power plants marginal costs (hourly) and total costs for covering the power demand Fig. 4: Fundamental model for the power generation planning As preliminary investigation, the presented fundamental model was adopted for a Benchmark- Analysis of the German Power Market in 2004 and The optimal scheduled operation of the power plants is determined ex-post under consideration of the historical data described above. Subsequent to the market simulation, the hourly marginal costs for the German Power Market the fundamental price component are compared with the market prices of the European Energy Exchange (EEX), representative for the German Power Market, in 2004 and The difference between the market prices and marginal costs represents the non-fundamental price component. 6 Hartmann, T.; Blaesig, B.; Hinüber, G.; Haubrich, H.-J. Stochastic Optimization in Generation and Trading Planning, Operations Research Proceedings 2006, Selected Papers of the Annual International Conference of the German Operations Research Society (GOR) 6

7 The relevant input data for the market simulation is created under consideration of different sources of information that are based on publicly available data. The generation capacities of the hydro and thermal power plants as well as the technical data of the power plants are taken from the IAEW data base. The fuel prices are based on 7,8, which are adjusted for taxes and transportation costs depending on the type of fuel. The prices for emission certificates are provided by EEX. The hourly time series of the power demand is based on the vertical load of the four German transmission system operators (TSO) adjusted for the total energy consumption of Germany in 2004 and The data concerning the required reserve power are available from the German TSO. In the following, two different variants of the power generation planning concerning the variable power generation costs are considered. In addition, for the market simulation in 2005 the implementation of emission certificates has to be considered. variant A: variant B: power generation costs include fuel costs power generation costs including costs for fuel, operating and start-up Fig. 5 shows the results of the Benchmark-Analysis separately for 2004 and In 2004, the difference between the average market price and the marginal costs (MC) of the market simulation the non-fundamental price component for each hour of the year amounts to 2,9 /MWh in variant A, which represents about 10 % of the market price. Under consideration of the costs for operating and start-up in addition to the fuel costs (variant B) the difference decreases to 0,3 /MWh. Hence, the influence of the operating and start-up costs amounts to 2,6 /MWh in In 2005, disregarding the emission certificate costs (CO 2 -prices), the difference between the average market price and the marginal costs increases to 13,0 /MWh (variant A) and 9,5 /MWh (variant B) respectively. Under consideration of emission certificate prices the non-fundamental component decreases to 0,7 /MWh in variant A. In variant B the average marginal costs of the market simulation exceed the market price by 2,6 /MWh. 7 Bundesamt für Wirtschaft und Ausfuhrkontrolle (BAFA), (status ) 8 Statistik der Kohlenwirtschaft e.v., (status ) 7

8 /MWh market prices/marginal generation costs D =13,6= 2,9 = 0,3 D =13,6= 13,0 = 9, (disregarding CO 2 -prices) D =13,6= 0,7 = -2, (including CO 2 -prices) market prices (EEX) MC (variant A) MC (variant B) Fig. 5: Average market price / marginal generation costs (MC) in 2004 / 2005 Based on the average market price and marginal costs for each hour in 2004 and 2005, the results of the market simulation are analyzed in detail. In the following, the hourly average market price and marginal costs on weekdays are investigated. Fig. 6 shows the results of the analysis for each hour separately for 2004 and In addition, the two variants A and B (see above) as well as the influence of the implementation of emission certificates in 2005 are analyzed. In 2004, the average market price for the first six hours on weekdays (hours 1 to 6) is lower than the marginal costs of the market simulation in variant A and in variant B respectively under additional consideration of the operating and start-up costs. In contrast, the market price is higher compared with the marginal costs in the remaining hours on weekdays, especially during peak hours (hours 8 to 20). Thus, the nonfundamental component has a relevant share in the market price especially during peak hours. In addition, the results show that the operating and start-up costs are higher during peak hours compared with the remaining hours on weekdays, which can primarily be explained by the low hourly related start-up costs per MWh produced (i.e. ratio of total start-up costs per year and power generation per year) of base-load power plants compared with peak-load power plants. In 2005, neglecting the emission certificate costs (CO 2 -prices), the average market price for the hours 1 to 6 is on the same level as the marginal costs of the market simulation in variant A and B respectively. Analog to the results in 2004, the average market price exceeds the marginal costs for the 8

9 remaining hours (hours 7 to 24), especially during the peak hours. The maximal deviation results to 28 /MWh in variant A and 22 /MWh in variant B (hour 12). 80 /MWh market prices/marginal generation costs h 24 time market prices (EEX) MC (variant B) MC (variant A) market prices/marginal generation costs 80 /MWh (disregarding CO 2 -prices) h 24 time market prices (EEX) MC (variant B) MC (variant A) market prices/marginal generation costs 80 /MWh (including CO 2 -prices) h 24 time market prices (EEX) MC (variant B) MC (variant A) Fig. 6: Hourly average market price / marginal generation costs (MC) on weekdays in 2004 /

10 Under consideration of emission certificate prices in 2005, the marginal costs increase. Hence, the maximal non-fundamental component decreases to 18 /MWh in variant A respectively 12 /MWh in variant B (hour 12). Furthermore, during the hours 1 to 6 the average level of the marginal costs is higher by 12 /MWh (variant A) and 9 /MWh (variant B) compared with the market price. In conclusion, the results of the Benchmark-Analysis of the German Power Market show that the non-fundamental component has a relevant share in the market price especially during peak hours, and in consequence has to be considered in addition to the fundamental component for the simulation of the power market. 3 Method Based on the analysis, an iterative three-stage method consisting of power generation planning, strategy planning and cross-border matching for the simulation of the Central European Market for electrical energy is being developed (fig. 7). power generation planning power generation power power generation planning planning generation planning PGC power generation cost curve strategy planning national price Preis quantity PGC power supply curve market price, quantity matching cross - border country 1 country 2... country n Fig. 7: Three-stage method for the simulation of the Central European Market for electrical energy 10

11 A method for power generation planning using the fundamental model (chapter 2), which optimizes the scheduled operation of the power plants by minimizing generation costs, yields cost curves for power generation. The power generation cost curves are passed on to the subsequent strategy planning. The strategy planning optimizes the power supply curves under maximizing the producer surplus. The subsequent stage realizes the cross-border matching under consideration of the transmission network by maximizing the social welfare. The results of the three-stage method are the hourly market price and the quantity produced by each PGC. In the following, the strategy planning as well as the cross-border matching are described. Strategy planning Based on the hourly cost curves for power generation determined by the power generation planning for each PGC the price or the quantity bidden by the PGC can be varied strategically. By using adequate models, e.g. game theory models such as Bertrand (price competition) and Cournot (competition in quantities), the strategy planning optimizes the hourly power supply curves of the PGC under maximization of the producer surplus (1). max n t = 1 PP i ( MP MC ) Q for each PGC t PP,t PP, t (1) The producer surplus can be calculated by the hourly market price (MP), the marginal costs (MC) of the power plant (PP) and the quantity (Q) that is produced by the power plant. In addition, technical constraints such as the hourly energy and reserve balance as well as the minimum and maximum power output level of the power plants has to be considered. Cross-border matching Subsequent to the strategy planning the cross-border matching is performed. The input data consists of the cross-border capacities for the considered system and the aggregated power supply curves of all PGC as well as the power demand curves for each country or market area respectively (fig. 8). 11

12 cross-border capacity power supply and demand curves (aggregated), cross-border capacity Auction AuctionOffice I spez. Preis / ( MWh) F price ( /MWh) D ask Nachfrage MP mp market price, demand and supply (matched), cross-border power transfer bid Angebot energy(mwh) Energie ( mp: market price Fig. 8: Cross-border matching The cross-border matching is realized by maximizing the social welfare under consideration of the energy balance and the transmission network (2). The social welfare is represented by the area between the demand curve and the supply curve and can be calculated by means of the prices (P) and the quantities (Q) of the matched bids and asks for all market areas (MA). The results of the crossborder matching are the hourly market price and the quantity matched for each PGC. max n t = 1 m MA= 1 ( (P P ) d Q) ask,ma,t bid,ma, t (2) 4 Conclusion Due to the liberalization of the European energy sector, PGC are exposed to structural changes. The new conditions such as new methods for the congestion management, the implementation of emission certificates as well as the high replacement demand due to the abandoning of nuclear power plants in Germany and the age distribution of the power plants in Europe lead to an increasing crossborder trade and influence the power generation mix as well as the power generation costs. In addition, the increasing concentration in the European Power Market encourages trading strategies of PGC, e.g. to compensate the investment costs and to generate revenues, which influence the market price. Good 12

13 knowledge of the future development of the market price are essential especially for PCG concerning the evaluation of existing power plants, investment decisions for new capacity as well as for mid- and long-term trading. Considering these circumstances, a method is under development to simulate the Central European Market for electrical energy. As preliminary research, a Benchmark-Analysis of the German Power Market for 2004 and 2005 using a fundamental model, shows that the non-fundamental component has a relevant share in the market price especially during peak hours and has to be modeled. Based on this investigation a three-stage method consisting of power generation planning, strategy planning and cross-border matching is presented. Biography Tobias Mirbach was born in Viersen, Germany, on October 7, He studied Electrical Engineering at RWTH Aachen University where he graduated in 2005 (Dipl.-Ing.). Since 2005 he is member of the academic staff of the Institute of Power Systems and Power Economics at RWTH Aachen University. His present fields of interest include market simulation and the investigation of the development of the market price for electrical energy. Furthermore he has worked on research and industry projects in the field of congestion management and decision support for the extension of generating capacity. 13