Comparative analysis and optimizing of PV-wind-battery microgrid based on various metaheuristic algorithms

Developing efficient and inexpensive hybrid microgrids for distant regions presents considerable problems stemming from the intermittent characteristics of renewable energy sources, intricate system integration, and the necessity for a dependable and affordable energy supply. This paper presents a simulation model for a hybrid microgrid that integrates photovoltaic (PV) and wind energy with battery storage, focusing on optimizing system efficiency, dependability, and reducing energy costs. Innovative optimization methods, including the Political Optimization Algorithm (POA), Artificial Electric Field Algorithm (AEFA), Particle Swarm Optimization (PSO), Shuffled Frog Leaping Algorithm (SFLA), and a hybrid PSO-SFLA algorithm, are utilized to ascertain the ideal microgrid architecture. The model employs real-time meteorological data from Zawiet El-Awama village in Matruh, Egypt, representing the inaugural implementation of such a system in this isolated area. A thorough statistical study assesses the efficacy of these optimization strategies, utilizing MATLAB software for simulation and validation of the results. The hybrid PSO-SFLA algorithm exhibits superior performance relative to existing approaches, providing improved convergence speed, solution accuracy, and cost-effectiveness, hence presenting a potential strategy for sustainable microgrid design in remote regions.