Robust real-time energy management for a hydrogen refueling station using generative adversarial imitation learning

As the demand for hydrogen fuel increases with the rise of fuel-cell electric vehicles (FCEVs), the energy management of hydrogen refueling stations (HRSs) is crucial for operational efficiency and environmental sustainability. This study addresses this gap by proposing a novel energy management model for optimal real-time energy scheduling of on-grid HRSs using generative adversarial imitation learning (GAIL). The proposed algorithm mimics expert demonstrations to enhance decision-making and is evaluated across a wide range of scenarios, showing that GAIL significantly improves system profitability by up to 29% compared to traditional methods.