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Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market

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  • Yu, Liying
  • Wang, Peng
  • Chen, Zhe
  • Li, Dewen
  • Li, Ning
  • Cherkaoui, Rachid

Abstract

Finding Nash equilibrium in an imperfect market with active and strategic resources is key to the successful operation of the electricity market. A hierarchical Nash distributed reinforcement learning (HNDRL) framework is proposed to realize the interaction between the market participants and independent system operator (ISO). In the bidding stage, multi-agent Nash policy reinforcement learning is utilized to update the bidding strategies for market participants, i.e., generation company (GenCo) and distributed company (DisCo), which maximizes their own profit with imperfect information. The bidding action is updated by the probability iteration to deal with the continuous bidding strategy. And in the market clearing stage, the distributed dynamic-average consensus optimization algorithm is proposed at the ISO level to obtain the locational marginal price (LMP) and transaction quantities. With the participation of the demand-side in the electricity market, the proposed HNDRL framework can reach a Nash equilibrium with the optimal bidding strategy and handle global resource constraints in a distributed way. In addition, theoretical analysis is provided to ensure that the participants’ strategy exponentially converges to the Nash equilibrium for both convex and non-convex problems. Finally, the IEEE 30-bus system is employed to illustrate the efficiency. Compared with the existing method, the simulation results show that the proposed HNDRL framework has a faster convergence rate with the range of 2.667–3.333 times and can obtain higher profit with the range of 1.4286%–7.1429% at the Nash equilibrium point.

Suggested Citation

  • Yu, Liying & Wang, Peng & Chen, Zhe & Li, Dewen & Li, Ning & Cherkaoui, Rachid, 2023. "Finding Nash equilibrium based on reinforcement learning for bidding strategy and distributed algorithm for ISO in imperfect electricity market," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923010681
    DOI: 10.1016/j.apenergy.2023.121704
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    References listed on IDEAS

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    1. Tsaousoglou, Georgios & Ellinas, Petros & Varvarigos, Emmanouel, 2023. "Operating peer-to-peer electricity markets under uncertainty via learning-based, distributed optimal control," Applied Energy, Elsevier, vol. 343(C).
    2. Daron Acemoglu, Ali Kakhbod, and Asuman Ozdaglar, 2017. "Competition in Electricity Markets with Renewable Energy Sources," The Energy Journal, International Association for Energy Economics, vol. 0(KAPSARC S).
    3. Federico, Giulio & Rahman, David, 2003. "Bidding in an Electricity Pay-as-Bid Auction," Journal of Regulatory Economics, Springer, vol. 24(2), pages 175-211, September.
    4. Hong, Qiuyi & Meng, Fanlin & Liu, Jian & Bo, Rui, 2023. "A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets," Applied Energy, Elsevier, vol. 330(PA).
    5. 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.
    6. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    7. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    8. Jong-Shi Pang & Masao Fukushima, 2005. "Quasi-variational inequalities, generalized Nash equilibria, and multi-leader-follower games," Computational Management Science, Springer, vol. 2(1), pages 21-56, January.
    9. Shen, Jian-jian & Cheng, Chun-tian & Jia, Ze-bin & Zhang, Yang & Lv, Quan & Cai, Hua-xiang & Wang, Bang-can & Xie, Meng-fei, 2022. "Impacts, challenges and suggestions of the electricity market for hydro-dominated power systems in China," Renewable Energy, Elsevier, vol. 187(C), pages 743-759.
    10. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "A two-stage energy management for heat-electricity integrated energy system considering dynamic pricing of Stackelberg game and operation strategy optimization," Energy, Elsevier, vol. 244(PA).
    11. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
    12. Esmaeili Aliabadi, Danial & Kaya, Murat & Sahin, Guvenc, 2017. "Competition, risk and learning in electricity markets: An agent-based simulation study," Applied Energy, Elsevier, vol. 195(C), pages 1000-1011.
    13. Al-Agtash, Salem Y., 2010. "Supply curve bidding of electricity in constrained power networks," Energy, Elsevier, vol. 35(7), pages 2886-2892.
    14. Jin, Xiaolong & Wu, Qiuwei & Jia, Hongjie, 2020. "Local flexibility markets: Literature review on concepts, models and clearing methods," Applied Energy, Elsevier, vol. 261(C).
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