A Comparative Analysis of Meta-Heuristic Algorithms for Optimal Configuration of Hybrid Renewable Energy Systems for Remote Villages

Abstract

In the search for sustainable and reliable energy solutions, the deployment of hybrid renewable energy systems (HRES) has developed as a promising approach mainly for powering remote villages that lack access to centralized grids. The optimal configuration of these systems leads to a complex optimization problem through demanding the application of meta-heuristic algorithms to efficiently direct the massive solution space and recognize the most cost-effective and reliable setup. Numerous meta-heuristic algorithms have been engaged for this purpose. Through a comparative analysis of various meta-heuristic algorithms, particle swarm optimization helps in obtaining improved solutions. Particle swarm optimization (PSO) occurs as a powerful and effective optimization technique in addressing the complex task of determining optimal configurations for hybrid renewable energy systems positioned in remote villages.