Optimized PI controller-based hybrid model for adaptive energy management in photovoltaic integrated electric vehicle charging microgrids

Abstract

The demand for charging stations (CSs) has increased due to the rapid expansion of electric vehicles (EVs), which has led to higher peak loads and grid instability. Microgrids (MGs) can benefit from the integration of renewable energy sources (RES), but their intermittent nature requires clever and flexible energy management techniques. For a DC microgrid that includes photovoltaic (PV) generation, fuel cells, battery storage, and EV charging infrastructure, this research proposes an optimized PI-based hybrid energy management system. To achieve reliable power balancing under changing EV demand and fluctuating RES availability, the hybrid controller combines a fuzzy logic controller (FLC) with a Dwarf Mongoose-based Red Panda Optimization (RPO) algorithm for optimal proportional integral (PI) tuning. MATLAB/Simulink is used to model and validate the proposed method. The results show that enhanced renewable use, efficient converter management, and dynamic tariff adjustment considerably reduce operating costs and increase stability. During off-peak hours, charging costs drops to a minimum of 0.034 USD/kWh. Weekday and weekend average costs drop to 0.086 and 0.088 USD/kWh, respectively, representing reductions of 45.26% and 56.11%. Power balance is improved, and greenhouse gas emissions are reduced by up to 55.75% when renewable energy is used. In comparison to traditional techniques, the proposed optimized PI-based hybrid controller also exhibits better voltage regulation and faster convergence. These results demonstrate the hybrid adaptive EMS improves power stability, cost effectiveness, and renewable integration for DC microgrids that facilitate EV charging.