Dc Microgrid Voltage Regulation: A Q-Learning Reinforcement Approach for DC-DC Buck Converters

This paper presents a novel Q-learning-based adaptive PID control strategy for solar buck converters in DC microgrids, addressing the challenges of maintaining stable and efficient power conversion under varying environmental and load conditions. Traditional fixed-parameter PID controllers often struggle with these variations, leading to suboptimal performance. Our approach integrates a Q-learning agent into the control loop, dynamically adjusting PID parameters in real-time based on system state observations and performance feedback. Simulations using MATLAB/Simulink, comprising a solar PV array, buck converter, and variable load, demonstrate the effectiveness of the proposed method. The system's performance was evaluated under steady-state operation and dynamic load changes. Results show significant improvements in stability, efficiency, and responsiveness compared to traditional PID controllers, particularly in maintaining stable output voltage during rapid load fluctuations and varying irradiance. This research contributes to enhancing solar energy integration in modern power systems, advancing the development of more reliable DC microgrids. The adaptive nature of our approach offers potential applications in larger microgrid systems and hybrid energy configurations. Future work will explore multi-agent systems, deep reinforcement learning, and hardware implementation to further validate and extend this promising control strategy.