Performance evaluation of reinforcement learning for hydrogen integration in renewable microgrids

The transition towards sustainable energy solutions necessitates robust and autonomous management of intermittent renewable resources coupled with effective energy storage. This paper introduces an integrated hydrogen energy management framework specifically designed for isolated renewable energy systems, leveraging reinforcement learning (RL) to simultaneously optimize operational and structural resilience. The proposed system dynamically coordinates photovoltaic generation with electrolyser-driven hydrogen storage and fuel cell power generation, effectively balancing intermittent renewable supply against varying demand profiles. Through extensive scenario testing—including standard conditions, prolonged low-generation events, and periods of energy surplus—the model rigorously assesses resilience and reliability. Comparative analysis with a deterministic optimization benchmark reveals that the RL-based control approach achieves superior operational performance, notably reducing Loss of Load Duration (LOLD) occurrences and enhancing cyclical storage balance. Furthermore, detailed simulations identify critical storage capacity thresholds necessary to achieve targeted reliability levels ranging from 60% to 95%, elucidating clear guidelines for infrastructure scaling. The study highlights the adaptability and performance benefits of RL-driven management, achieving reliability as high as 95% under optimal storage conditions. These findings deliver valuable insights into storage-sizing strategies, resilient operational planning, and reinforce the viability of integrated hydrogen solutions in fostering autonomous, sustainable energy infrastructures.