State-of-health–aware scheduling for balanced load sharing in wind electrolyzer systems

The scheduling of renewable-powered electrolyzer systems critically affects their efficiency and lifetime, yet most existing strategies neglect balancing degradation under fluctuating power conditions. This study presents a state-of-health (SoH)-aware scheduling framework for electrolyzer systems powered by variable renewable energy. The framework aims to maximize hydrogen production while minimizing degradation by applying penalties for key stressors, including cold and hot starts, power fluctuations, and equivalent operating hours. The framework employs a mixed-integer linear optimization model to integrate electrolyzer performance, operational constraints, and degradation metrics, thereby determining the optimal dispatch of multiple stacks. We demonstrate that including the system health explicitly in the scheduling problem preserves the electrolyzer stacks while maintaining nearly identical hydrogen output. Our results show that SoH-aware scheduling reduces ramping events by up to 89% and power fluctuations by up to 77%, while lowering the annual hydrogen yield by only 0.03% compared to an optimization-based schedule that ignores degradation. Wind power curtailment remains very low at only 2.33% using tuned degradation-penalty weights, confirming efficient use of available renewable energy. Thus, the proposed SoH-aware scheduling framework balances stack utilization, limits premature aging, and extends system lifetime.