基于强化学习的灾后配电网应急抢修决策方法

Post-disaster emergency repair and reconfiguration plan of faulty distribution system based on reinforcement learning

灾后故障配电网的应急抢修及重构需求,面临着灾情的不确定性、多抢修队调配特性、抢修与重构耦合等多个挑战。本文基于强化学习构建了灾后多抢修队的抢修调配模型,设计了状态粗筛机制以固定强化学习状态及动作空间大小,搭建了配电网抢修恢复测试环境。在不同恢复模式、多个故障场景下的测试结果表明:强化学习在提出的多个评价指标下综合表现稳定,可以作为灾后动态抢修的可选方案。

The post-disaster emergency repair and reconfiguration plan of the faulty distribution system are confronted with the challenges from the uncertainty of the disaster, the deployment of multiple emergency repair teams and the coupling of emergency repair and reconfiguration. This paper builds a model for emergency repair deployment of multiple repair teams based on reinforcement learning. A state coarse screening mechanism is designed to fix the size of the state and action space of reinforcement learning. An environment based on OpenAI Gym is constructed for the repair and reconfiguration of the distribution system. The experimental results show that reinforcement learning has a stable comprehensive performance under the proposed multiple evaluation indicators, and it can be used as an optional solution for dynamic repair after a disaster.