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An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand

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  • Wang, Jianhui
  • Zhou, Zhi
  • Botterud, Audun

Abstract

In this paper we propose an evolutionary imperfect information game approach to analyzing bidding strategies in electricity markets with price-elastic demand. In previous research, opponent generation companies’ (GENCOs’) bidding strategies were assumed to be fixed or subject to a fixed probability distribution. In contrast, the adaptive and learning agents in the presented model can dynamically update their beliefs about opponents’ bidding strategies during the simulation. GENCOs are represented as different species in the coevolutionary algorithm to search the equilibrium. By modeling the evolutionary gaming behavior of GENCOs, the simulation can capture the dynamics of GENCOs’ strategy change. This is important for analyzing transitory behavior of agents in the market in addition to the long-run equilibrium state. Simulations show that due to the adaptive learning, the bidding evolution is different from the one in the traditional game.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:3459-3467
    DOI: 10.1016/j.energy.2011.03.050
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    References listed on IDEAS

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    Cited by:

    1. Shivaie, Mojtaba & Ameli, Mohammad T., 2015. "An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm," Renewable Energy, Elsevier, vol. 83(C), pages 881-896.
    2. Fang, Yujuan & Chen, Laijun & Mei, Shengwei & Wei, Wei & Huang, Shaowei & Liu, Feng, 2019. "Coal or electricity? An evolutionary game approach to investigate fuel choices of urban heat supply systems," Energy, Elsevier, vol. 181(C), pages 107-122.
    3. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
    4. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, Open Access Journal, vol. 12(1), pages 1-1, December.
    5. Bin Ye & Jingjing Jiang & Lixin Miao & Ji Li & Yang Peng, 2015. "Innovative Carbon Allowance Allocation Policy for the Shenzhen Emission Trading Scheme in China," Sustainability, MDPI, Open Access Journal, vol. 8(1), pages 1-1, December.
    6. repec:gam:jsusta:v:8:y:2015:i:1:p:3:d:61052 is not listed on IDEAS
    7. 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.
    8. Fang, Debin & Zhao, Chaoyang & Yu, Qian, 2018. "Government regulation of renewable energy generation and transmission in China’s electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 775-793.
    9. Motalleb, Mahdi & Annaswamy, Anuradha & Ghorbani, Reza, 2018. "A real-time demand response market through a repeated incomplete-information game," Energy, Elsevier, vol. 143(C), pages 424-438.
    10. Lu, Tianguang & Ai, Qian & Wang, Zhaoyu, 2018. "Interactive game vector: A stochastic operation-based pricing mechanism for smart energy systems with coupled-microgrids," Applied Energy, Elsevier, vol. 212(C), pages 1462-1475.
    11. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, Open Access Journal, vol. 9(9), pages 1-1, September.
    12. Shafie-khah, Miadreza & Parsa Moghaddam, Mohsen & Sheikh-El-Eslami, Mohamad Kazem & Rahmani-Andebili, Mehdi, 2012. "Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment," Energy, Elsevier, vol. 40(1), pages 139-150.
    13. Aviad Navon & Gefen Ben Yosef & Ram Machlev & Shmuel Shapira & Nilanjan Roy Chowdhury & Juri Belikov & Ariel Orda & Yoash Levron, 2020. "Applications of Game Theory to Design and Operation of Modern Power Systems: A Comprehensive Review," Energies, MDPI, Open Access Journal, vol. 13(15), pages 1-1, August.
    14. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    15. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    16. Huiru Zhao & Yuwei Wang & Mingrui Zhao & Qingkun Tan & Sen Guo, 2017. "Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing," Energies, MDPI, Open Access Journal, vol. 10(7), pages 1-1, July.
    17. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    18. Jalali, Mohammad Majid & Kazemi, Ahad, 2015. "Demand side management in a smart grid with multiple electricity suppliers," Energy, Elsevier, vol. 81(C), pages 766-776.

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