电-碳-绿证耦合市场下多主体演化博弈竞价分析

Evolutionary Game Bidding Analysis for Multiple Entities in Coupled Electricity-Carbon-Green Certificate Markets

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摘要当前,碳排放交易市场和绿色证书交易市场已成为促进电力行业碳减排和激励新能源发电上网的重要机制,同时与电力市场形成了密切耦合的市场环境。在这种电-碳-绿证耦合市场环境下,如何分析各市场主体的竞价行为及市场综合运行效率是一个重要的研究课题。为此,该文提出了一种基于多智能体深度强化学习的电-碳-绿证耦合市场多主体演化博弈竞价模型。首先,根据各市场的特点构建电-碳-绿证耦合市场交易模型;其次,基于演化博弈理论建立电-碳-绿证耦合市场下多主体演化博弈竞价模型,并采用多智能体交叉熵-孪生延迟确定性策略算法对模型进行高效求解,以模拟多市场主体的策略学习与动态调整过程;最后,通过算例仿真深入分析了耦合市场因素对多主体竞价行为和均衡结果的影响,验证了所提模型的有效性及算法的优越性。 Currently,carbon emission trading markets and green certificate trading markets have emerged as crucial mechanisms to promote carbon emission reduction in the power sector and incentivize renewable energy integration.These markets are closely coupled with electricity markets,forming a complex multi-market environment.Under this coupled market environment,how to analyze the bidding behavior of various market entities and the comprehensive operational market efficiency is an important research topic.Accordingly,this paper proposes a multi-agent deep reinforcement learning-based evolutionary game bidding model for multiple entities in coupled markets.First,a transaction model for the electricity-carbon-green certificate coupled market is constructed,accounting for the characteristics of each market.Second,in order to simulate the strategy learning and dynamic adjustment process of multiple market entities,a multiple entities evolutionary game bidding model in the electricity-carbon-green certificate coupled market is established,and a multi-agent cross-entropy twin delayed deep deterministic policy gradient algorithm is adopted to efficiently solve the model.Finally,case studies are conducted to analyze the impact of coupled market factors on bidding strategies and equilibrium outcomes,validating the effectiveness of the proposed model and the superiority of the algorithm.

作者孙惠娟方欣周斌彭春华 SUN Huijuan;FANG Xin;ZHOU Bin;PENG Chunhua(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,Jiangxi Province,China)

机构地区华东交通大学电气与自动化工程学院

出处《电网技术》北大核心 2026年第1期198-209,I0099-I0103,共17页 Power System Technology

基金国家自然科学基金项目(52267007,52567008) 江西省自然科学基金项目(20242BAB26070)。

关键词电-碳-绿证耦合市场多主体深度强化学习演化博弈竞价分析 electricity-carbon-green coupled market multiple entities deep reinforcement learning evolutionary game bidding analysis

分类号 TM721 [电气工程—电力系统及自动化]