Optimization of photovoltaics/wind turbine/fuel cell hybrid power system design for different regions in Libya

Abstract

Given the rapid technological advancements in the energy sector and the growing imperative for sustainable energy practices, there is a global focus on fostering the hydrogen economy and developing efficient energy management strategies through the utilization of green hydrogen. This study was conducted in Libya using Photovoltaics/Wind/Fuel Cell/Battery optimized by assessing the Whale Optimization Algorithm (WOA) and Ant Colony Optimization (ACO) for optimizing renewable energy systems in three Libyan regions: Almagrun, Sabha, and Alkufra. Key metrics include Loss of Power Supply Probability (LPSP), Levelized Cost of Energy (LCOE), Hybrid System Net Present Cost (HSNPC), Cost of Energy (COE), and Renewable Energy Fraction (RE) percentage. Results indicate Ant Colony Optimization improves system reliability and RE integration with lower Loss of Power Supply Probability and higher Renewable Energy Fraction percentages but incurs higher costs. Whale Optimization Algorithm, on the other hand, offers lower costs but compromises on reliability and Renewable Energy integration. Almagrun achieved the lowest Cost of Energy at $1.875 using Whale Optimization Algorithm, while Ant Colony Optimization delivered a superior Renewable Energy Fraction of 97.95%. This study is novel in its comparative analysis of Whale Optimization Algorithm and Ant Colony Optimization for hybrid energy systems, offering valuable insights into optimizing renewable energy integration in the context of Libyan regions. The findings suggest that the choice of optimization algorithm should be aligned with regional priorities—whether cost minimization or enhanced renewable energy integration—providing guidance for policymakers in the pursuit of sustainable energy development and climate mitigation strategies.