Dispatch Strategy for Transmission Overload Based on Safe Reinforcement Learning

Abstract

Active power dispatch is one of the major operation tasks for power system that keeps the power generation and consumption in a real-time balance. Real-time decisions for active power dispatch can be seen as a modification on the Day-ahead OPF (DAOPF) and are constrained by power system safety rules. Consequently, active power dispatch focuses mainly on the perspective of operation safety which is influenced by renewable generation power fluctuation, load variation and maintenance. It is impossible to incorporate these stochastic factors in a DAOPF problem. To solve this problem, active power dispatch is formulated into a CMDP problem, where the target is to optimize the dispatch policy that maximize the reward without breaching the safety constraints. Risk of blackout arises from the violation of safety constraints and potential human and property damage would be enormous, which makes the problem different from common RL task. To handle this problem, IPO algorithm, which belongs to safe RL, is adopted. In IEEE 14-bus system, the proposed method is implemented to show the advantages from full aspects.