中国电力2025，Vol.58Issue(5)：11-20,32,11.DOI:10.11930/j.issn.1004-9649.202408092
基于多智能体深度策略梯度的离网型微电网双层优化调度
Two-layer Optimization Scheduling for Off-grid Microgrids Based on Multi-agent Deep Policy Gradient
摘要
Abstract
To address the voltage limit violations and bidirectional power flow problems arising from high-penetration integration of distributed renewable energy,this paper proposes a two-layer active-reactive power cooperative optimization method to achieve cooperative optimal dispatch of active and reactive power in off-grid microgrids,ensuring the secure and stable operation of the system while enhancing operational economy.The lower-level model optimizes slow-regulating discrete devices based on mixed-integer second-order cone programming,while the upper-level model optimizes fast-regulating continuous devices using a multi-agent deep policy gradient algorithm.The two-layer model coordinates both active and reactive power flows of the microgrid,enabling real-time monitoring of the microgrid's status and online decision-making for the optimization of device regulation,without reliance on precise power flow models or complex communication systems.Finally,the feasibility and effectiveness of the two-layer optimization model are validated in the improved IEEE 33-bus microgrid system.关键词
离网型微电网/多智能体/深度强化学习/混合整数线性规划/多时间尺度/有功无功协同优化Key words
off-grid microgrid/multi-agent/deep reinforcement learning/mixed integer linear programming/multi-time scale/active-reactive power cooperative optimization引用本文复制引用
樊会丛,段志国,陈志永,朱士加,刘航,李文霄,杨阳..基于多智能体深度策略梯度的离网型微电网双层优化调度[J].中国电力,2025,58(5):11-20,32,11.基金项目
国家电网有限公司科技项目(5400-202313823A-4-1-KJ). This work is supported by the Science And Technology Project of SGCC(No.5400-202313823A-4-1-KJ). （5400-202313823A-4-1-KJ）
