Smart and Sustainable Energy Management of Hybrid PV/Wind Systems Using AI-Based Fuzzy Control

The rapid growth of renewable energy penetration has intensified the need for robust and intelligent energy management strategies capable of addressing the intermittency of solar and wind resources. This study presents a simplified fuzzy logic controller (FLC)–based energy management system (EMS) for a grid-connected PV/Wind hybrid system integrated with battery energy storage. A fuzzy logic–based Maximum Power Point Tracking (MPPT) scheme is developed for the photovoltaic subsystem to ensure efficient power extraction under variable irradiance and temperature conditions. MATLAB/Simulink-based investigations reveal that the proposed FLC-MPPT achieves a tracking efficiency of 98.6%, with a settling time reduced by 32% compared to conventional perturb-and-observe methods. The hybrid EMS effectively coordinates power flow among PV, wind, battery, and grid, reducing grid dependency by 41% and improving battery state-of-charge regulation within ±5% limits under dynamic load conditions. Battery cycling stress is reduced by 27%, enhancing storage lifespan. Furthermore, the integration of a Unified Power Flow Controller (UPFC) significantly improves system resilience under grid disturbances, achieving 38% voltage sag mitigation and 45% reduction in power oscillations during simulated fault conditions. The results confirm that the proposed simplified FLC-based EMS enhances energy utilization, system stability, and power quality in grid-linked hybrid renewable systems. This work directly supports UN SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) by enabling efficient, reliable integration of renewable energy sources into smart power systems.