Performance Comparison of Deep Reinforcement Learning Algorithms for Voltage Control of Buck Converter Feeding CPL in DC Microgrid

DC microgrids are emerging as a promising solution for easy and efficient energy distribution in various industrial and residential applications. However, they experience voltage perturbations due to the variability of renewable energy sources, load fluctuations, and instability problems caused by constant power loads (CPL). DC microgrids may encounter voltage instability without effective voltage regulation, potentially resulting in facility damage or even power outages. Therefore, it is essential to develop a controller that copes with requirements of robustness, steady-state, and dynamic performance. This study presents a comparative analysis of three deep reinforcement learning agents for voltage control of buck converter feeding CPL. The three agents we compare include double deep Q-network (DDQN), our improved multilayered proximal policy optimization (PPO), and twin delay deep deterministic policy gradient (TD3) agents.