Agent-based modelling of the UK short term electricity market: Effects of intermittent wind power

This work focuses on modelling the electricity trading and market mechanism currently in place in the UK, using an agent-based approach and a learning strategy for the agents to update their bidding rules. The ongoing consultations by the Department of Energy and Climate Change on the possible models for a capacity mechanism reflect the unavoidable shift towards low-carbon and more intermittent sources of generation. One of the issues of concern is the way the system operator adapts the balancing mechanism to run in a more efficient and economical way. Here we present an agent-based model comprising two interconnected parts: a representation of the power exchange and a model of the balancing mechanism along with the settlement system. In order to assess the influence of different types of generation on the system balancing prices, we model the generating units based on the size and type of fuel involved. The agent-based model incorporates the operating decisions and control mechanisms of the system operator, and the functions of various trading entities such as generators and suppliers participating within this market. Based on this model, we report investigations into the effect of high penetrations of distributed intermittent generation in influencing the energy balancing prices.

[1]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[2]  Michael J. North,et al.  Validation of an Agent-based Model of Deregulated Electric Power Markets , 2005 .

[3]  J. Contreras,et al.  A Cobweb Bidding Model for Competitive Electricity Markets , 2001, IEEE Power Engineering Review.

[4]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  S. Cincotti,et al.  Agent-based simulation of power exchange with heterogeneous production companies , 2005 .

[6]  Marco Raberto,et al.  Price dynamics and market power in an agent-based power exchange , 2005, SPIE International Symposium on Fluctuations and Noise.

[7]  Derek W. Bunn,et al.  Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market ☆ , 2001 .

[8]  Alvin E. Roth,et al.  Modelling Predicting How People Play Games: Reinforcement learning in experimental games with unique , 1998 .

[9]  R. Leombruni,et al.  Why are economists sceptical about agent-based simulations? , 2005 .

[10]  M. Ventosa,et al.  Optimal offering strategies for generation companies operating in electricity spot markets , 2004, IEEE Transactions on Power Systems.

[11]  Leigh Tesfatsion,et al.  Market power and efficiency in a computational electricity market with discriminatory double-auction pricing , 2001, IEEE Trans. Evol. Comput..

[12]  I. Erlich,et al.  Strategies for Wind Power Trading in Competitive Electricity Markets , 2008, IEEE Transactions on Energy Conversion.

[13]  Karsten Burges,et al.  Pros and cons of exposing renewables to electricity market risks--A comparison of the market integration approaches in Germany, Spain, and the UK , 2008 .

[14]  M. Saguan,et al.  Large-scale wind power in European electricity markets: Time for revisiting support schemes and market designs? , 2010 .

[15]  William W. Hogan.  Transmission Market Design , 2003 .

[16]  Derek W. Bunn,et al.  Model-Based Comparisons of Pool and Bilateral Markets for Electricity , 2000 .

[17]  L. Soder,et al.  Minimization of imbalance cost trading wind power on the short term power market , 2005, 2005 IEEE Russia Power Tech.

[18]  F. Schweppe.  Spot Pricing of Electricity , 1988 .

[19]  Anthony J. Bagnall,et al.  A multiagent model of the UK market in electricity generation , 2005, IEEE Transactions on Evolutionary Computation.

[20]  G. Strbac,et al.  Value of combining energy storage and wind in short-term energy and balancing markets , 2003 .

[21]  Leigh Tesfatsion,et al.  Testing the Reliability of FERC's Wholesale Power Market Platform: An Agent-Based Computational Economics Approach , 2004 .

[22]  T. Veselka,et al.  Multi-agent power market simulation using EMCAS , 2005, IEEE Power Engineering Society General Meeting, 2005.

[23]  L. Tesfatsion,et al.  Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework , 2007 .

[24]  Derek W. Bunn,et al.  Agent-based simulation-an application to the new electricity trading arrangements of England and Wales , 2001, IEEE Trans. Evol. Comput..

[25]  Pascal Perez,et al.  Complex Science for a Complex World: Exploring Human Ecosystems with Agents , 2010 .

[26]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[27]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[28]  A. David,et al.  Optimal bidding strategies and modeling of imperfect information among competitive generators , 2001 .

[29]  Daniel J. Veit,et al.  A Critical Survey of Agent-Based Wholesale Electricity Market Models , 2008 .

[30]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .