Energy Trading Optimization for a Hydrogen-Powered Railway Microgrid Using Deep Reinforcement Learning

Managing energy in complex hydrogen-powered railway microgrids, which integrate renewable energy sources and energy storage systems, and an electrolyzer for hydrogen production, presents significant challenges, especially given their connection to the national grid and fluctuating electricity prices. This paper presents a novel approach for optimizing energy trading in hydrogen-powered railway microgrids. The method used is Deep Reinforcement Learning (DRL) modeling technique. The suggested DRL framework conceptualizes real-world constraints, such as uncertainties in weather forecasts, fluctuations in electricity pricing, and train schedules. The simulation findings indicate substantial reductions in operating expenses and enhanced integration of renewable energy resources, making the system more sustainable and economically viable.