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| A capacity planning method for hydrogen energy storage incorporating extreme scenarios and conditional value at risk |
| DOI：10.19783/j.cnki.pspc.260023 |
| Key Words:new power system extreme scenarios hydrogen energy storage risk assessment energy storage capacity planning |
| Author Name | Affiliation | | LI Zhiwei | 1. State Key Laboratory of Alternate Electrical Power System (North China Electric Power University), Baoding 071003, China
2. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid, Baoding 071003, China | | DU Xiaoying | 1. State Key Laboratory of Alternate Electrical Power System (North China Electric Power University), Baoding 071003, China
2. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid, Baoding 071003, China | | WANG Jiakai | 1. State Key Laboratory of Alternate Electrical Power System (North China Electric Power University), Baoding 071003, China
2. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid, Baoding 071003, China | | ZHAO Shuqiang | 1. State Key Laboratory of Alternate Electrical Power System (North China Electric Power University), Baoding 071003, China
2. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid, Baoding 071003, China |
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| Abstract:In recent years, the increasing frequency of extreme weather events has significantly exacerbated the operational risks of power systems. To enhance the resilience and continuous power supply capability of power systems under extreme weather conditions, this paper proposes a hydrogen energy storage planning method that incorporates extreme scenarios and tail-risk assessment. First, the correlations between renewable energy outputs and various meteorological factors are analyzed, and representative typical and extreme renewable energy output scenarios are generated using a K-based multidimensional time series clustering algorithm. Second, considering the dynamic efficiency characteristics of proton exchange membrane (PEM) electrolyzers, a rotation-based operating strategy for electrolyzer arrays is developed, and a comprehensive mathematical model of the hydrogen energy storage system is established. Then, a tail-risk assessment and quantification approach based on conditional value at risk (CVaR) is introduced. Taking the minimization of the total system cost as the optimization objective, a hydrogen energy storage capacity planning model that simultaneously incorporates both typical and extreme scenarios is formulated. Finally, numerical case studies demonstrate that the proposed model can effectively optimize the system configuration scheme and ensure the safe and stable operation of new-type power systems under extreme scenarios. |
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