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

Zhiwei Liao (), Chengjin Li, Xiang Zhang, Qiyun Hu and Bowen Wang
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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

Energies, 2025, vol. 18, issue 9, 1-18

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.

Keywords: carbon trading market; multi-market-coupling environment; decision collaborative optimization; multi-agent deep deterministic policy gradient; optimal bidding strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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