Optimization Techniques for Hybrid Renewable Energy Systems

Abstract

This literature review provides a comprehensive analysis of optimization techniques applied to hybrid renewable energy systems (HRES) across diverse geographical locations and applications. The review examines multiple optimization methodologies including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Mixed Integer Linear Programming (MILP), hybrid algorithms, and novel bio-inspired metaheuristic approaches. Performance metrics analyzed include Cost of Energy (COE), Net Present Cost (NPC), Life Cycle Cost (LCC), Loss of Power Supply Probability (LPSP), Renewable Fraction (RF), and CO₂ emissions reduction. The review examined relevant studies published between 2014 and 2025, identified through searches of major academic databases using predefined keywords related to HRES optimization, system sizing, energy management, metaheuristic optimization, and technical, economic, reliability, and environmental performance. The reviewed studies encompass various system configurations incorporating photovoltaic panels, wind turbines, battery storage, diesel generators, small hydro power, fuel cells, tidal energy, and hydrogen storage systems. Findings indicate that while traditional GA and PSO methods remain widely used, MILP and hybrid algorithms demonstrate superior performance in achieving optimal solutions with reduced computational time. Simulation-based tools such as HOMER software continue to be valuable for system design and optimization. However, significant research gaps persist, including limited evaluation of system reliability, incomplete consideration of all renewable sources, and insufficient comparative analysis between different optimization techniques. The integration of artificial intelligence and novel bio-inspired algorithms presents promising opportunities for advancing HRES optimization, particularly for microgrid applications and systems operating under dynamic weather conditions.