Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy Systems

Abstract

The optimal sizing of hybrid renewable energy systems is a critical challenge in achieving sustainable and cost-effective energy solutions. This study explores the application of meta-heuristic algorithms to address this complex optimization problem. Meta-heuristic algorithms, inspired by natural and social phenomena, offer efficient and versatile approaches for determining the optimal combination of renewable energy sources and storage technologies. Genetic Algorithms mimic the process of natural selection, Particle Swarm Optimization replicates the social behavior of particles, and Ant Colony Optimization draws inspiration from ant foraging patterns. Simulated Annealing and Tabu Search provide mechanisms for escaping local optima, while Harmony Search reflects the improvisation process of musicians. This research investigates the adaptability and effectiveness of these meta-heuristic algorithms in optimizing the sizing of solar panels, wind turbines, and energy storage components within hybrid renewable energy systems.