Beyond Reactive Operation: Sensor-Informed Model Predictive Control for Renewable Hydrogen Storage

Abstract

Renewable hydrogen storage can absorb surplus wind and solar generation, reduce curtailment, and provide a flexible energy carrier. Its value depends not only on the electrolyzer, storage tank, and fuel cell, but also on the operational strategy used to coordinate these components. This study develops a sensor-informed model predictive control (MPC) framework for renewable hydrogen storage and evaluates its performance against a no-hydrogen configuration and a reactive hydrogen storage controller under identical operating conditions, component parameters, and evaluation metrics. The MPC uses receding horizon optimization with measured hydrogen storage and safety states, together with short-term forecasts of renewable generation, load, electricity price, and hydrogen service demand. Using synthetic but physically motivated time-series profiles, the results show that hydrogen hardware under reactive rule-based operation eliminates renewable curtailment and reduces operating cost; however, it frequently depletes the tank to the reserve boundary and fails to reliably meet subsequent hydrogen demand. In contrast, the sensor-informed MPC achieves full hydrogen accessibility, eliminates curtailment, reduces total operating cost to 71.2% of the no-hydrogen baseline, and lowers cost by 46.3% relative to the rule-based controller. The MPC imports more grid electricity than the rule-based case, showing a clear trade-off between hydrogen service reliability and grid dependence. These results indicate that reliable renewable-hydrogen operation depends not only on storage hardware, but also on predictive supervisory control that coordinates renewable use, hydrogen service, reserve security, and grid interaction.