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How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach

Author

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  • Debin Fang

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

  • Qiyu Ren

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

  • Qian Yu

    (School of Economics, Wuhan University of Technology, Wuhan 430070, China)

Abstract

The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, making it difficult to analyze generators’ behaviors. With the difficulties to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, where in some cases the results are strictly bound to the initial estimations and the results are chaotic. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rules by employing an auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generators’ price-related bidding strategy. Nash equilibriums are derived depending on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators, which lead to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

Suggested Citation

  • Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:9-:d:192193
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    References listed on IDEAS

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    1. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
    2. Alp E. Atakan & Mehmet Ekmekci, 2014. "Auctions, Actions, and the Failure of Information Aggregation," American Economic Review, American Economic Association, vol. 104(7), pages 2014-2048, July.
    3. Vyacheslav Kalashnikov & Vladimir Bulavsky & Vitaliy Kalashnikov & Nataliya Kalashnykova, 2014. "Structure of demand and consistent conjectural variations equilibrium (CCVE) in a mixed oligopoly model," Annals of Operations Research, Springer, vol. 217(1), pages 281-297, June.
    4. Congjun Rao & Yong Zhao & Junjun Zheng & Cheng Wang & Zhongwen Chen, 2016. "An extended uniform-price auction mechanism of homogeneous divisible goods: supply optimisation and non-strategic bidding," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 4028-4042, July.
    5. William Samuelson, 2014. "Auctions: Advances in Theory and Practice," International Series in Operations Research & Management Science, in: Kalyan Chatterjee & William Samuelson (ed.), Game Theory and Business Applications, edition 2, chapter 0, pages 323-366, Springer.
    6. Yi Tang & Jing Ling & Tingting Ma & Ning Chen & Xiaofeng Liu & Bingtuan Gao, 2017. "A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity," Energies, MDPI, vol. 10(5), pages 1-16, May.
    7. Aliabadi, Danial Esmaeili & Kaya, Murat & Şahin, Güvenç, 2017. "An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms," Energy Policy, Elsevier, vol. 100(C), pages 191-205.
    8. K. Ruddell & A. B. Philpott & A. Downward, 2017. "Supply Function Equilibrium with Taxed Benefits," Operations Research, INFORMS, vol. 65(1), pages 1-18, February.
    9. González-Aparicio, I. & Monforti, F. & Volker, P. & Zucker, A. & Careri, F. & Huld, T. & Badger, J., 2017. "Simulating European wind power generation applying statistical downscaling to reanalysis data," Applied Energy, Elsevier, vol. 199(C), pages 155-168.
    10. Juan Aparicio & Juan Ferrando & Ana Meca & Julia Sancho, 2008. "Strategic bidding in continuous electricity auctions: an application to the Spanish electricity market," Annals of Operations Research, Springer, vol. 158(1), pages 229-241, February.
    11. Severin Borenstein & James B. Bushnell & Frank A. Wolak, 2002. "Measuring Market Inefficiencies in California's Restructured Wholesale Electricity Market," American Economic Review, American Economic Association, vol. 92(5), pages 1376-1405, December.
    12. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    13. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    14. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    15. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    16. Wedad Elmaghraby, 2005. "The Effect of Asymmetric Bidder Size on an Auction's Performance: Are More Bidders Always Better?," Management Science, INFORMS, vol. 51(12), pages 1763-1776, December.
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