A Novel Model Predictive Control-Based Energy Management for Stable and Efficient Operation of Renewable Integrated DC Microgrids

This paper presents a hierarchical model predictive control (HMPC)–based energy management system (EMS) for a DC microgrid integrating photovoltaic (PV) generation, wind energy conversion, battery energy storage, and dynamic DC loads. The proposed framework coordinates multi-source power flows while ensuring robust DC bus voltage regulation, efficient renewable energy utilization, and battery-friendly operation. A detailed MATLAB/Simulink model of the DC microgrid is developed and evaluated under realistic operating conditions, including variable solar irradiance, fluctuating wind speeds, bidirectional battery operation, and dynamic electric vehicle and industrial load demands. The performance of the proposed HMPC is quantitatively compared with an existing adaptive model predictive control (AMPC) strategy. Numerical results demonstrate that HMPC significantly improves DC bus voltage stability, reducing voltage deviation from ±1.67% under AMPC to below ±0.83% during renewable power variations and to ±0.083% under battery power fluctuations. Voltage standard deviation is reduced by up to 70–90%, indicating enhanced damping and faster convergence. Furthermore, HMPC achieves smoother battery power transitions and superior state-of-charge regulation, limiting SoC ripples to below ±0.005 and reducing electrochemical stress by approximately 60–70%. These improvements confirm stronger stability margins, improved disturbance rejection, and higher overall EMS efficiency. The results validate HMPC as a robust and scalable control strategy for reliable and stable operation of modern DC microgrids with high renewable penetration.