Dagal, IdrissIbrahim, AL-WesabiHarrison, Ambe2025-03-092025-03-0920250045-79061879-0755https://doi.org/10.1016/j.compeleceng.2024.109991https://hdl.handle.net/20.500.12662/4714Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm Optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.eninfo:eu-repo/semantics/closedAccessGrey wolf optimization algorithmPartial shading conditionsEnergy storageBatteryArticle10.1016/j.compeleceng.2024.1099912-s2.0-85213962575Q1122WOS:001401534300001Q1