Optimizing Solar-Powered Electrolysis Systems for Green Hydrogen Production Using Rat Swarm Optimization and Energy Storage Solutions

This study investigates the performance of a solar-powered electrolysis system for green hydrogen production, utilizing the Rat Swarm Optimization (RSO) algorithm to optimize Maximum Power Point Tracking (MPPT). Two cases are examined: one where the system operates solely on solar energy and another where energy storage is integrated to ensure continuous hydrogen production. MATLAB simulations were used to model both systems under varying environmental conditions. The results show that integrating energy storage significantly enhances system stability and efficiency, mitigating the intermittency of solar energy. In the solar-only scenario, hydrogen production fluctuated directly with solar irradiance, with reduced or no production during low sunlight periods or at night. In contrast, when energy storage was incorporated, the system demonstrated a steady hydrogen production rate by storing excess energy during peak sunlight hours and utilizing it during low solar power periods, ensuring continuous operation of the electrolyzer. Furthermore, the study highlights the advantages of using RSO for MPPT. The RSO algorithm demonstrated faster convergence, higher tracking accuracy, and improved stability compared to traditional algorithms like M&O. These improvements led to enhanced overall system performance, making solar-powered electrolysis systems for green hydrogen production more efficient, reliable, and suitable for practical, real-world applications.