Exploring flexibility of hydrogen energy storage in power system via multi-time-resolution uncertainty

Hydrogen energy storage systems (HESS) hold significant potential for facilitating the large-scale integration of renewable power. However, existing operation models for water electrolysis devices often oversimplify the start-up process and lack the ability to capture multi-timescale power fluctuation, limiting their effectiveness in providing rapid flexibility. To address these limitations, this paper establishes an operation model for water electrolyzer that incorporates multiple start-up process, ensuring applicability across various electrolysis techniques. Furthermore, a multiple time-resolution uncertainty model is introduced to capture the fluctuation of wind power. Based on them, an adaptive robust unit commitment (UC) model is developed, incorporating the fast switching of electrolyzers during re-dispatch. The proposed optimization problem is efficiently solved using a nested column-and-constraint generation (NC&CG) algorithm. Case studies conducted on the modified IEEE-RTS 79 system validate the effectiveness and feasibility of the proposed method. The results demonstrate that the model considering multiple start-up process reduces total costs by 19.2% and wind curtailment by 92.7% compared to conventional approaches. When integrated with the multi-resolution uncertainty framework, an additional 68.9% reduction in wind curtailment is achieved, leading to an optimal total cost. Sensitivity analysis highlights the influence of electrolyzer technical characteristics. For instance, increasing the ramping rate from 15%/min to 20%/min enhances the tracking of rapid wind power fluctuation. Moreover, PEM, with its wider operation range and faster response, consistently outperform AEC across different wind volatility scenarios. This study underscores the necessity of high-fidelity modeling and multiple time-resolution analysis for unlocking the flexibility, particularly the fast-ramping potential, of HESS in renewable energy integration. The findings provide theoretical insights for the design and operation of HESS, supporting their large-scale deployment in future power systems