A Distributed Control in Islanded DC Microgrid based on Multi-Agent Deep Reinforcement Learning

Abstract

This paper designs a novel distributed controller for the islanded DC microgrid. The proposed control method provides a data-driven multi-agent framework to solve the DC bus voltage regulation and current sharing. In order to accurately solve the control action, an online deep reinforcement learning (DRL) algorithm, called deep deterministic policy gradient (DDPG), is employed to secondary controllers in a DC microgrid. Based on the previous knowledge and current system state, DDPG algorithm generates the control action to compensate the voltage reference. In addition, the load reward function for each agent is designed to seek the optimal action of the system. Besides, the proposed control scheme is fully distributed, where each agent only exchange information with neighboring agents. Simulation results of a 4-DG DC microgrid demonstrate the effectiveness and satisfied performance of the proposed multi-agent DDPG-based control strategy.