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Agent-based modeling of wholesale electricity market

Author

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  • Rashidova E.A.

    (Новосибирский государственный университет)

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.

Suggested Citation

  • Rashidova E.A., 2017. "Agent-based modeling of wholesale electricity market," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 70-85.
    • Handle: RePEc:nos:wjflnh:2017_1_06e
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    File URL: http://www.nsu.ru/rs/mw/link/Media:/59818/06.pdf
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    References listed on IDEAS

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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, University Library of Munich, Germany.
    3. Li, Hongyan & Tesfatsion, Leigh S., 2009. "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training," Staff General Research Papers Archive 13073, Iowa State University, Department of Economics.
    4. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    5. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    6. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
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    8. Nicolaisen, James & Smith, Matthew & Petrov, Valentin & Tesfatsion, Leigh, 2000. "Concentration and Capacity Effects on Electricity Market Power," Staff General Research Papers Archive 1847, Iowa State University, Department of Economics.
    9. Bower, John & Bunn, Derek W. & Wattendrup, Claus, 2001. "A model-based analysis of strategic consolidation in the German electricity industry," Energy Policy, Elsevier, vol. 29(12), pages 987-1005, October.
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    Citations

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    Cited by:

    1. Rietmann, Nele, 2019. "Elektromobilität: Was treibt Konsumenten zum Kauf?," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 36(3), pages 12-21.
    2. Deepak, K. & Pattanaik, M.S. & Ramanujan, R.V., 2019. "Figure of merit and improved performance of a hybrid thermomagnetic oscillator," Applied Energy, Elsevier, vol. 256(C).
    3. Gaivoronskaia, E. & Tsyplakov, A., 2018. "Using a Modified Erev-Roth Algorithm in an Agent-Based Electricity Market Model," Journal of the New Economic Association, New Economic Association, vol. 39(3), pages 55-83.

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    More about this item

    Keywords

    agent-based modeling; learning agents; wholesale electricity market; day-ahead market.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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