南方电网技术2025，Vol.19Issue(9)：82-93,12.DOI:10.13648/j.cnki.issn1674-0629.2025.09.008
基于多智能体深度强化学习的大容量电池储能电站功率分配策略
Power Allocation Strategy for Large-Capacity Battery Energy Storage Power Station Based on Multi-Agent Deep Reinforcement Learning
摘要
Abstract
The decision variables for power allocation of large-capacity battery energy storage power station are numerous,and their strategies need to consider multiple optimization objectives and the uncertainty of automatically adapting to the scenario.Therefore,this paper proposes a power allocation decision-making method for battery energy storage power stations based on multi-agent deep reinforcement learning(MADRL).Firstly,based on the structure and power allocation characteristics of large-capacity battery energy storage power stations,a power allocation decision framework based on MADRL is constructed.Each energy storage unit is equipped with a power allocation intelligent agent,and multiple agents form a cooperative relationship.Then,a multi-agent DRL model for power allocation is designed,considering the optimization objectives of active power loss,state of charge(SOC)consistency,and state of health loss of battery energy storage power stations.The deep deterministic policy gradient(DDPG)algorithm is used to decentralize the training of network parameters for each agent.After the algorithm converges,the charging and discharging power values of the energy storage subsystem are obtained.Finally,the effectiveness of the proposed method is verified by the example,which can effectively improve the SOC balance of the energy storage subsystem while reducing active power loss,state of health loss,and charging and discharging switching times.关键词
电池储能电站/功率分配/多智能体/深度强化学习/SOC一致性Key words
battery energy storage power station/power allocation/multi-agent/deep reinforcement learning/SOC consistency分类
信息技术与安全科学引用本文复制引用
彭寒梅,赵长桥,谭貌,陈颉,李辉..基于多智能体深度强化学习的大容量电池储能电站功率分配策略[J].南方电网技术,2025,19(9):82-93,12.基金项目
国家自然科学基金资助项目(51777179) （51777179）
湖南省自然科学基金资助项目(2023JJ50241). Supported by the National Natural Science Foundation of China(51777179) （2023JJ50241）
the Natural Science Foundation of Hunan Province(2023JJ50241). （2023JJ50241）
