电力建设2026，Vol.47Issue(8)：14-25,12.DOI:10.12204/j.issn.1000-7229.2026.08.002
多智能体强化学习驱动的园区产消者P2P能源交易策略
Multi-Agent Reinforcement Learning-Driven P2P Energy Trading Strategy for Community Prosumers
摘要
Abstract
[Objective]To address the variations in resource allocation and energy consumption behavior among prosumers in peer-to-peer(P2P)community energy trading scenarios,as well as the limited adaptability of traditional model-based methods in uncertain environments,this paper proposes a multi-agent reinforcement learning method that features both scalability and privacy protection capabilities.[Methods]First,three representative types of heterogeneous prosumer models are constructed.Second,a community energy trading model based on the mid-market rate pricing mechanism is established,and a flexibility incentive mechanism is introduced.Finally,the energy trading decision-making problem of prosumers is formulated as a partially observable Markov decision process,and a soft actor-critic algorithm based on dynamic mean-field(DMF-SAC)approximation is proposed to solve the energy management strategies of prosumers.[Results]Simulation results demonstrate that the proposed method outperforms baseline methods in terms of convergence performance,computational overhead,and operating costs.It also effectively improves the local consumption of distributed energy and enhances peak-shaving and valley-filling capabilities.[Conclusions]The proposed method effectively improves the efficiency and economic benefits of collaborative optimization for heterogeneous prosumers while balancing privacy protection and system scalability,which holds significant value for energy trading and management in community-based markets.关键词
点对点(P2P)能源交易/能源管理/多智能体强化学习/隐私保护/动态均值场软策略-评价(DMF-SAC)算法Key words
peer-to-peer(P2P)energy trading/energy management/multi-agent reinforcement learning/privacy preservation/dynamic mean-field soft actor-critic(DMF-SAC)algorithm分类
信息技术与安全科学引用本文复制引用
陈桂力,陈丹红,文宏武,郑勇伟,麦亮,冯霞山,钟麒深,胡一鸣,郑杰辉..多智能体强化学习驱动的园区产消者P2P能源交易策略[J].电力建设,2026,47(8):14-25,12.基金项目
This work is supported by National Natural Science Foundation of China(No.52477097). 国家自然科学基金项目(52477097) （No.52477097）
