Improved Power Management of Standalone DC Microgrid With Hybrid Energy Storage System Using a Fixed Switching Frequency Model Predictive Control

Due to their high efficiency and simplicity, DC microgrids (MGs) are becoming increasingly popular. However, controlling DC MGs is a complex duty, where voltage control and power management are particularly challenging tasks. This paper presents a fixed switching frequency model predictive control (FSF-MPC) along with a modified rule-based power management algorithm (PMA) proposed for small-scale photovoltaic (PV) DC MG with hybrid energy storage system (HESS). State of charge (SoC) monitoring for both the battery and supercapacitors (SCs) ensures optimized power sharing and rapid charge-discharge capabilities. Based on reference powers generated by the PMA, the proposed control strategy determines the optimal duty cycle for each power converter, as well as regulating the DC-bus voltage. Simulation and experimental results are used to verify system efficacy by testing different operation modes. The FSF-MPC significantly enhances the system's performance compared to conventional control techniques, improving peak overshoot, settling time, and steady-state error.