Unified control architecture for resilient hydrogen mobility with heterogeneous storage under realistic market logistics delays

The growing adoption of hydrogen-powered transport demands scalable and robust control strategies for hydrogen refueling stations (HRSs) operating under uncertainty in supply, demand, and market conditions. This study presents a delay-aware predictive control framework for renewable-integrated HRSs equipped with heterogeneous hydrogen storage systems and dual interaction with electricity and hydrogen markets. The station architecture enables simultaneous refueling across multiple fuel cell electric vehicle (FCEV) classes, each served by a dedicated high-pressure storage unit, while auxiliary tanks function as buffers and market reserves. Inter-storage coordination is managed using receding horizon control across layered decision stages, allowing flexible hydrogen routing under dynamic operating conditions. A central challenge is hydrogen delivery delays, which introduce a temporal gap between procurement actions and actual availability. The proposed formulation incorporates these delays within the control horizon, classified as deterministic (fixed lead times), stochastic (modeled via discrete uncertainty sets), and logistics-based (dependent on route planning, fleet capacity, and congestion, captured through time-dependent concave functions). A mode-switching mechanism allows the operator to activate one of four control strategies: deterministic MPC (DMPC), scenario-based stochastic MPC (SMPC), convex relaxed MPC (RMPC), and scaled risk-averse SMPC (SRA-SMPC) with conditional value-at-risk and chance constraints. Convex relaxation techniques are applied to address combinatorial complexity from binary variables, nonlinear tank dynamics, and inter-market constraints, ensuring real-time tractability while preserving constraint feasibility and economic performance. Numerical simulations confirm the framework’s effectiveness in coordinating storage operations, meeting demand, and reducing costs under uncertainty, with substantial computational benefits compared to conventional approaches.