Multi-Agent Deep Reinforcement Learning based Secondary Voltage Control of Inverter-Based AC Microgrids

In this paper, a new secondary voltage control system for islanded AC microgrids is introduced. This system utilizes a multi-agent framework to maintain voltage stability and ensure proportional distribution of current. An advanced off-policy deep reinforcement learning technique, specifically the twin delayed deep deterministic policy gradient (TD3), is integrated as the secondary controller within the AC microgrid configuration. A specialized reward function is developed to correct the discrepancies in current distribution and stabilize voltage levels, optimizing the performance of the TD3 algorithm. This control mechanism is structured in a distributed manner, allowing each agent to communicate and share vital control information. Simulation results on a three-distributed generators (DGs) AC inverter-based microgrid system validate the accuracy of this novel multi-agent TD3-based secondary voltage controller. These results demonstrate improved performance compared to those reported in previous studies.