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A Bidding Strategy for Power Suppliers Based on Multi-Agent Reinforcement Learning in Carbon–Electricity–Coal Coupling Market

Author

Listed:
  • Zhiwei Liao

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Chengjin Li

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Xiang Zhang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Qiyun Hu

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Bowen Wang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

Abstract

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization.

Suggested Citation

  • Zhiwei Liao & Chengjin Li & Xiang Zhang & Qiyun Hu & Bowen Wang, 2025. "A Bidding Strategy for Power Suppliers Based on Multi-Agent Reinforcement Learning in Carbon–Electricity–Coal Coupling Market," Energies, MDPI, vol. 18(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2388-:d:1650634
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    References listed on IDEAS

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    1. Ren, Kezheng & Liu, Jun & Liu, Xinglei & Nie, Yongxin, 2023. "Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation," Applied Energy, Elsevier, vol. 336(C).
    2. Wang, Benke & Li, Chunhua & Ban, Yongshuang & Zhao, Zeming & Wang, Zengxu, 2024. "A two-tier bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism for coupled electricity and carbon markets," Applied Energy, Elsevier, vol. 368(C).
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