Optimization of a hybrid renewable energy system consisting of a of PV/ wind turbine/battery/fuel cell integration and component design

Amidst rapid technological advancements in the energy sector and increasing demands for sustainable practices, the transition to renewable energy sources is crucial for addressing environmental and economic challenges. This study optimizes a hybrid renewable energy system (HRES) incorporating photovoltaic panels, wind turbines, fuel cells, and battery storage in Libya's Darnah and Alkhums regions. By employing advanced optimization techniques, including Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Ant Colony Optimization (ACO), and Genetic Algorithms (GA), this research enhances system performance and efficiency. The Alkhums region achieved the lowest cost of energy (COE) at $1.86 using PSO, while Darnah recorded a COE of $1.875 via WOA. Additionally, Darnah demonstrated superior Renewable Energy Fraction (REF) utilization at 97.95% with ACO. This study highlights the effectiveness of integrating hydrogen technology within HRES, offering a viable pathway for sustainable energy solutions in Libya and similar regions, thereby supporting global sustainable development and climate policies.