Dynamic Energy Management in a Hybrid Microgrid Integrating PV, Wind, Fuel Cell and EV Battery Using Fuzzy Logic Control

This paper presents a dynamic energy management strategy for a community-scale campus hybrid microgrid integrating photovoltaic (PV) generation, aggregated wind power, a proton exchange membrane fuel cell, and battery energy storage to support electric vehicle (EV) charging infrastructure under variable environmental and load conditions. The system configuration is inspired by existing renewable energy installations and planned developments at the Federation University Mt Helen Campus, enabling realistic modeling of aggregated demand and coordinated multi-source operation. To enhance physical realism, power electronic conversion efficiencies and hierarchical control dynamics are incorporated, while the wind subsystem is represented using an aggregated generation model consistent with MW-scale operation. The proposed control architecture employs a Mamdani-type fuzzy logic controller (FLC) to coordinate distributed energy resources in real time based on solar irradiance, temperature, wind speed, load demand, and battery state of charge. A comprehensive MATLAB/Simulink model interfaces each source through converter-based power electronic stages, enabling adaptive power flow and stable system operation. Simulation results demonstrate uninterrupted load supply, reduced grid dependency, and effective bidirectional energy exchange. PV output varies between 18.76 and 95.12 kW, wind generation ranges from 1553.5 to 6493.84 kW, and the fuel cell provides a stable 1000 kW contribution, while the battery dynamically supports charging and discharging up to 203.07 kW. Power balance analysis confirms coordinated load sharing among all sources, with the grid supplying or absorbing power as required. Quantitative comparison with conventional PI-based dispatch demonstrates improved transient response, enhanced voltage regulation, smoother control effort, and improved power balance stability, with peak system efficiency reaching 97.83%, validating the proposed EMS as a robust and adaptive solution for EV-integrated renewable microgrids and next-generation smart energy systems.