Agent-based modeling of wholesale electricity market

Abstract
The article investigates the free electricity market via agent-based modeling. The aim is to create a simple and quite plausible simulation model of the market, where suppliers and buyers, participating in the bilateral auction, learn to submit the most profitable bids. Whereas this kind of simulation models has been developed for the EU and USA markets by foreign researchers, for the Russian market they have not been made yet. The suggested theoretical agent-based model of interaction in the day-ahead market uses Erev and Roth learning algorithm and allows us to calculate and analyze equilibrium price, volume, social welfare and its distribution between buyers and sellers of electricity. The modeling demonstrates that it is possible to lower prices and redistribute social welfare in favor of buyers, provided that the learning agents can submit any bids.

Keywords
agent-based modeling, learning agents, wholesale electricity market, day-ahead market



References:

1. Hobbs B. F., Metzler C. B., Pang J. S. Strategic gaming analysis for electric power systems: an mpec approach. IEEE Transactions on Power Systems. 2000. Vol. 15. № 2. P. 638–645.
2. Lisin E. M., Strielkovski V., Grigor'eva A. N., Anisimova Ju. A. Sovremennye podhody k razrabotke modelej rynkov elektroenergii i issledovaniju vlijanija rynochnoj sily na konjunkturu energorynka [Modern approaches to modeling electricity markets and studying of the market forces’ influence on the situation on the energy market]. Vektor nauki TGU[Science vector TSU], 2013, no. 1 (23), p. 188–197. 3.
3. Makarov V. L., Bahtizin A. R. Novyj instrumentariy v obshestvennyh naukah – agentorientirovannye modeli: obshee opisanie i konkretnye primery [New tools in the social sciences – agent-based models: general description and specific examples]. Ekonomika i upravlenie [Economics and management], 2009, no. 12, p.13–25.
4. Bonabeau E. Agent-based modeling: Methods and techniques for simulating human systems. National Academy of Science, 2002.
5. Wooldridge M., Jennings N. Intelligent agents: Theory and practice. Knowledge Engineering Review. 1995. Vol. 10. № 2.
6. Li H., Tesfatsion L. The AMES wholesale power market test bed: a computational laboratory for research, teaching and training. Iowa State University, Ames, 2009.
7. Nicolaisen J., Smith M., Petrov V., Tesfatsion L. Concentration and capacity effects on electricity market power. Proceeding of the 2000 Congress on Evolutionary Computation, La Jolla. USA, 2000. P. 1041–1047.
8. Nicolaisen J., Petrov V., Tesfatsion L. Market power and efficiency in a computational electricity market with discriminatory double-auction pricing. IEEE Transactions on Evolutionary Computation. 2001. Vol. 5. № 5. P. 504–523.
9. Bower J., Bunn D.W. Model-based comparison of pool and bilateral markets for electricity. Energy Journal. 2000. Vol. 21. № 3. P. 1–21.
10. Bower J., Bunn D.W., Wattendrup C. A model-based analysis of strategic consolidation in the German electricity industry. Energy Policy. 2001. Vol. 29. № 12. P. 987–1005.
11. Weidlich A., Veit D. Bidding in interrelated day-ahead electricity markets: Insights from an agent-based simulation model. Proc. of the 29th IAEE International Conference. Potsdam, 2006.
12. Weidlich A., Veit D. A critical survey of agent-based wholesale electricity market models. Energy Economics. 2008. Vol. 30. № 4. P. 1728–1759.
13. Bower J., Bunn D. W. Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market. Journal of Economic Dynamics and Control. 2001. Vol. 25. № 3–4. P. 561–592.
14. Zhou Z., Chan W. K., Chow J. H. Agent-based simulation of electricity markets: a survey of tools. Artificial Intelligence Review. 2007. Vol. 28. № 4. P. 305–342.
15. Liu Z., Yan J., Shi Y., Zhu K., Pu G. Multi-agent based experimental analysis on bidding mechanism in electricity auction markets. International Journal of Electrical Power & Energy Systems. 2012. Vol. 43. № 1. P. 696–702.
16. Erev I., Roth A. E. Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. The American Economic Review. 1998. Vol. 88. № 4. P. 848–881.
17. Roth A. E., Erev I. Learning in extensive-form games: experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior. 1995. № 8. P. 164–212.