Modelling the Electricity Market

Transcription

1 Modelling the Electricity Market European Summer School München, July 3rd, 2009 Dipl. Wi.-Ing. Serafin von Roon 11 Research Center for Energy Economy Am Blütenanger München

2 Agenda Overview over Electricity Markets Different Models of Pricing Approaches of Modelling Electricity Prices Results and Comparison of two different Approaches 2

3 Overview over Electricity Markets before Liberalisation and Unbundling Industry Electric Power Company Trade & Commerce Customer Private Households 3

4 Overview over Electricity Markets since Liberalisation and Unbundling Power Generation Load-Frequency Control OTC Trade Exchange (EEX) Power Network Sales & Distribution Industry Trade & Commerce Customer Household 4 EEX: Derivatives; Day-ahead; Intra-day

5 Pricing: European Energy Exchange (EEX) without a price (no alternative), demand must be covered successful unsuccessful 50 Supply Demand Price [a.u.] theory:{ profit Market Clearing Price theory: marginal costs of different power plants theory: costs of best alternative to cover the demand, e.g. individual contracts, self-generating ,001 20,002 30,003 Load [a.u.] FfE _00207

6 Pricing: Load Frequency Control Power successful unsuccessful 40 demand 30 Price [ /MW] marginal costs + calculated profit technical need 0 0 1,000 2,000 3,000 4,000 5,000 6,000 Cumulated Reserve Power [MW] FfE _

7 Approaches of Modelling Electricity Prices Approach Fundamental by Neural Networks Statistical Agent-based Essential Factors costs of generation of electricity technical conditions consumer load profile historic data of correlations of input and output modelling correlation function with least error historic data of the energy market individual optimisation agents improve their strategies based on experience FfE _

8 Fundamental Approach - Methodology based on the load profile that has to be covered by conventional power plants the merit order is a result of the power plant data base For each hour there we get a certain price according to the load needed to be provided 8

9 Fundamental Approach - Results Mon Tue Wed Thu Fri Sat Sun EEX Fundamental Approach 250 % 200 % EEX Fundamental Approach Price in per MWh % 100 % 50 % 0 FfE _ % FfE _ Jan Jan Jan Jan Jan Jan Jan-07 Day of the Year EEX Fundamental Approach average price in per MWh FfE _ The two graphs show the characteristic of a whole year and an exemplary week (normalised with the week mean price). The left graph shows that the fundamental approach mostly overestimates the real price for electricity, this is also proved by the data. The other graph illustrates that the characteristic on weekdays is achieved pretty well while the weekend price fluctuations can hardly be recognised.

10 Fundamental Approach - Weaknesses weaknesses of this approach: In this calcualtion the model is too static, changing costs for fuel or CO 2 -certificates are not regarded extreme weather conditions are not taken into account as the graph shows about 800 hours - which equals almost 10 % of the year s prices - cannot be reached by the model, especially high prices do not occur 10

11 Design of Synthetic Neural Networks background is the composition of the human brain (real neural network) neurons are stimulated by input signals; depending on processing functions in neurons output signals will result by repeating correlations of input and output signals (training) the connections to the neurons will increase (learning) synthetic neural networks consist of layers with synthetic neurons by entering input parameters and defining associated output parameters the network can be trained backpropagation algorithm (change of the connection weightings) Inputs Source: Westdeutscher Rundfunk, Köln, 2009 Input Layer Hidden Layer Output Layer Output 11 Connection Weightings

12 Implementation of a Synthetic Neural Network for Modelling Electricity Prices training network with influencing factors on the electricity price in 2007 input parameters in resolution of one hour consumer load wind load coal price gas price oil price price of CO 2 emission certificates temperature atmospheric fallout 12 input parameters with a resolution lower than one hour are approximated (e. g. coal with quarterly prices) modelling electricity prices for 2007 and 2008 by entering associated input parameters to the 2007-trained network

13 Results of Modelling Electricity Prices for 2007 and Simulated Electricity Price in 2007 Real Electricity Price in Simulated Electrcity Price in 2008 Real Electricity Price in Price in per MWh Price in per MWh Hour of the Year 2007 FfE _ comparison over a time period of 3 days: Hour of the Year 2008 FfE _ Energy Price in per MWh Time of Day 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 240 Simulated Price 200 Real Price FfE _ Jun Jun Jun Jun-08 Date some days match bad Energy Price in per MWh Time of Day 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 6 AM 12 PM 6 PM 12 AM 120 Simulated Price 100 Real Price FfE _ Jul Jul Jul Jul-08 Date some days match well

14 Statistical Approach Historic EEX-Prices Day-Ahead-Price in Day-Ahead-Price in FfE _ Jan Jan Jan Jan Jan Jan Dec-07 Date 800 Modelled Price Paths 600 EEX-Data Thedatabaseforthestatistical approach are the years The changes of the daily mean prices are evaluated and are the basis of the model. The model does not reproduce the course of 2008 s prices at all. Instead the price reach extreme values and are not realistic. Therefore this model is not very useful FfE _ Oct-07 1-Jan Mar May-08 7-Aug Oct Dec-08 Date

15 Agent-based computational Economics (ACE) supply side agent 1 agent 2 apply their strategies market 1 market 2 demand side time variable fixed demand or consumer agents agent.. market.. next time step agents offer products on different markets agents learn and adopt their strategies pricing according to supply and demand profit payout 15

16 Agent-based computational Economics (ACE) application to electricity markets: agents can be: utilities, renewable energy producers, governments/regulators, consumers, traders, grid operators, etc. according to the research question and abstraction level the agents for the models are chosen the markets can be: spot market, balancing market, CO 2 market advantages: realistic behaviour of individual agents can be simulated the effects of a wide range of influences on the pricing can be tested disadvantages: high complexity of the model, difficult to comprehend and implement parameters can be set in order to achieve desired results 16

17 Summary and Conclusion There a different markets for electricity with individual pricing In Germany the most important market is the Spot market at the European Energy Exchange. These prices are reference prices for other markets There a different approaches to model electricity markets The presented approaches have difficulties to reproduce the historic extreme prices Predicting future electricity prices not only mean prices but also the temporally price structure - is a challenge 17

18 Discussion Thank you for the attention! 18