Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures

TL;DR

This paper explores the application of Deep Reinforcement Learning to solve the Optimal Power Flow problem, addressing challenges like divergence and degeneration, and proposing improvements for robustness and performance.

Contribution

It introduces a novel DRL-based approach for OPF, discusses its drawbacks, and proposes specific algorithms and reward functions to enhance solution stability and effectiveness.

Findings

Improved DRL algorithm reduces divergence in OPF solutions.

Proposed reward function addresses inherent DRL issues.

Demonstrates robustness of DRL in power system optimization.

Abstract

Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust and flexible enough to be applicable to problems of varying nature and domain. Presented work is evidence of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two classical algorithms have been presented to solve the OPF problem. The drawbacks of the vanilla DRL application are discussed, and an algorithm is suggested to improve the performance. Secondly, a reward function for the OPF problem is presented that enables the solution of inherent issues in DRL. Reasons for divergence and degeneration in DRL are discussed, and the correct strategy to deal with them with respect to OPF is presented.