Research and Application of Safe Reinforcement Learning in Power System

Abstract

Agent exploration of reinforcement learning is a necessary way for reinforcement learning algorithms to obtain information. In order to obtain more exploratory information, some deep reinforcement learning algorithms even increase the exploration of agents. Reinforcement learning has been successfully applied in many intelligent control fields, however unlimited exploration may bring disastrous consequences to agents, there are still many concerns that need attention in the application of real world, one of which is the safety issue. The safe reinforcement learning approximately enforces the constraint conditions in each policy update, thus further improving the security and robustness of intelligent algorithm. Furthermore, according to the particularity of electric energy production, transmission and consumption, power system operation needs to meet the requirements of safety, stability and efficiency. This paper summarizes the theory and characteristics of safe reinforcement learning, and then discusses the application of safe reinforcement learning in power system. Finally, we propose a prospect for the challenging problems of safe reinforcement learning in power field.