Enhancing Hydrogen Production in Hybrid Standalone Microgrids

Dynamic energy management plays a pivotal role in optimizing hydrogen production and ensuring power quality in hybrid standalone microgrids. This study investigates energy management in systems comprising photovoltaic panels, wind turbines, batteries, fuel cells, and electrolyzers, using the Mayfly Optimization Algorithm (MOA) for converter control. Boost converters are employed as maximum power point tracking devices for photovoltaic and wind systems, with all controllers integrated using MOA. Comparative analyses with Particle Swarm Optimization, Grey Wolf Optimization, and Genetic Algorithm reveal that MOA achieves superior performance in reducing operational costs and enhancing hydrogen production efficiency. Hardware-in-the-Loop simulations, conducted using two OPAL-RT devices, validate the proposed control methodology under real-time conditions. The approach demonstrates improved response times and maintains voltage stability under variable wind speeds, solar irradiance, and load conditions. By addressing sluggish hydrogen production dynamics, the study provides a cost-effective alternative to battery banks for high-power microgrid applications. This research contributes to advancing dynamic energy management in hybrid microgrids, supporting the integration of renewable energy into resilient and efficient systems.