东北电力技术2026，Vol.47Issue(9)：1-6,32,7.
基于多智能体深度强化学习的多微网低频减载策略
Strategy of Multi-Microgrids Under-Frequency Load Shedding Based on Multi-Agent Deep Reinforcement Learning
摘要
Abstract
To prevent a sharp frequency drop due to post-fault power shortage during multi-microgrids islanded operation,it proposes an under-frequency load shedding strategy based on multi-agent deep reinforcement learning.Firstly,it models each microgrid as an agent and formulates a Markov decision process(MDP)using the source-load-storage(SLS)data after islanding.It comprehensively considers the load frequency regulation effect,the importance of the load and the cost of low-frequency load shedding actions.Second-ly,the multi-agent system interacts with the environment to learn.The under-frequency load shedding strategy is trained offline and subsequently implemented online through real-time decision-making to execute load shedding actions.Finally,the effectiveness of the proposed strategy is validated using an improved IEEE 37 bus test system.The simulation results demonstrate that the proposed strategy can significantly shorten the decision response time,suppress frequency fluctuations,and accelerate the restoration of the system frequency to the normal operating range while minimizing the total cost of load shedding.关键词
功率缺额/低频减载/多智能体/深度强化学习Key words
power shortage/under-frequency load shedding/multi-agent/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
褚四虎,雷远华,罗焕昆,童威,封焯文..基于多智能体深度强化学习的多微网低频减载策略[J].东北电力技术,2026,47(9):1-6,32,7.基金项目
湖南省重点研发计划项目(2023SK2078) （2023SK2078）
