电力系统优化控制中强化学习方法应用及挑战

Review on Critical Problems in Reinforcement Learning Methods Applied in Power System Optimization and Control Scenarios

强化学习(reinforcementlearning,RL)方法目前已应用于电力系统的多个领域,在电力系统优化与控制领域的一些应用展现出良好的结果。但在强化学习方法落地于实际电力系统应用的过程中依然存在一些关键性问题。该文首先概述强化学习基础理论与研究现状,随后提出强化学习理论落地于电力系统各领域优化与控制过程中存在的关键问题。最后探讨强化学习应用于电力系统优化与控制的研究展望。

Reinforcement learning(RL)method has been applied in some fields of power system.The applications in power system optimization and control scenarios show admirable results.However,there are still some critical problems in the process of applying reinforcement learning methods to real-world power system applications.This paper first summarizes the basic theory and the state-of-art progress of reinforcement learning.Then,some critical problems in applications of reinforcement learning in various optimization and control scenarios in power system are pointed out.Finally,some future directions of reinforcement learning applied to power system decision-making and control scenarios are discussed.