Voltage Control for DC Microgrids: A Review and Comparative Evaluation of Deep Reinforcement Learning

Voltage stability in DC microgrids (DC MG) is crucial for ensuring reliable operation and component safety. This paper surveys voltage control techniques for DC MG, classifying them into model-based, model-free, and hybrid approaches. It analyzes their fundamental principles and evaluates their strengths and limitations. In addition to the survey, the study investigates the voltage control problem in a critical scenario involving a DC/DC buck converter with an input LC filter. Two model-free deep reinforcement learning (DRL) control strategies are proposed: twin-delayed deep deterministic policy gradient (TD3) and proximal policy optimization (PPO) agents. Bayesian optimization (BO) is employed to enhance the performance of the agents by tuning their critical hyperparameters. Simulation results demonstrate the effectiveness of the DRL-based approaches: compared to benchmark methods, BO-TD3 achieves the lowest error metrics, reducing root mean square error (RMSE) by up to 5.6%, and mean absolute percentage error (MAPE) by 7.8%. Lastly, the study outlines future research directions for DRL-based voltage control aimed at improving voltage stability in DC MG.