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Performance analysis of wind-hydrogen energy storage system using composite objective optimization proactive scheduling strategy coordinated with wind power prediction

Author

Listed:
  • Liu, Xinyi
  • Wang, Zitao
  • Xu, Shuai
  • Miao, Yihe
  • Xu, Jialing
  • Liu, Shanke
  • Yu, Lijun

Abstract

The large-scale deployment of wind energy encounters challenges like randomness, intermittency and fluctuation. Integrating energy storage systems and effective scheduling strategy can mitigate these issues. This paper proposes a composite objective optimization proactive scheduling strategy (COOPSS) integrated with ultra-short-term wind power prediction (WPP) to enhance the performance of the wind-hydrogen energy storage system (W-HESS). The COOPSS model includes four key components: a WPP module, a scheduling strategy, objective functions and parameter optimization. WPP provides projected output, while discrepancies between actual and planned outputs are addressed through a dynamic scheduling strategy to regulate the charge and discharge of the Hydrogen Energy Storage System (HESS). A composite objective function quantifies output accuracy, system fluctuation, and equipment health, with parameter optimization algorithms (Dynamic Information-driven Bayesian Optimization and Sparrow Search Algorithm) refining scheduling parameters. This approach reduces the deviation from the ideal state SoC by 59.4 % compared to real-time scheduling strategies and enhancing fluctuation suppression by 6 % (0.90 MW vs. 0.85 MW). Moreover, compared to wind power without HESS, COOPSS improves power accuracy by 13.9 % (19.76 MW vs. 17.02 MW). Furthermore, WPP accuracy is shown to influence optimization direction, with higher accuracy improving long-term scheduling and state management. COOPSS effectively reduces energy waste and enhances grid stability.

Suggested Citation

  • Liu, Xinyi & Wang, Zitao & Xu, Shuai & Miao, Yihe & Xu, Jialing & Liu, Shanke & Yu, Lijun, 2025. "Performance analysis of wind-hydrogen energy storage system using composite objective optimization proactive scheduling strategy coordinated with wind power prediction," Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225010588
    DOI: 10.1016/j.energy.2025.135416
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    References listed on IDEAS

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    1. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
    2. Gao, Dan & Jiang, Dongfang & Liu, Pei & Li, Zheng & Hu, Sangao & Xu, Hong, 2014. "An integrated energy storage system based on hydrogen storage: Process configuration and case studies with wind power," Energy, Elsevier, vol. 66(C), pages 332-341.
    3. Ding, Yunfei & Chen, Zijun & Zhang, Hongwei & Wang, Xin & Guo, Ying, 2022. "A short-term wind power prediction model based on CEEMD and WOA-KELM," Renewable Energy, Elsevier, vol. 189(C), pages 188-198.
    4. Usman, Muhammad R., 2022. "Hydrogen storage methods: Review and current status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    6. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    7. Liu, Hongyi & Han, Hua & Sun, Yao & Shi, Guangze & Su, Mei & Liu, Zhangjie & Wang, Hongfei & Deng, Xiaofei, 2022. "Short-term wind power interval prediction method using VMD-RFG and Att-GRU," Energy, Elsevier, vol. 251(C).
    8. Wang, Gang & Jia, Ru & Liu, Jinhai & Zhang, Huaguang, 2020. "A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning," Renewable Energy, Elsevier, vol. 145(C), pages 2426-2434.
    9. Kafetzis, A. & Ziogou, C. & Panopoulos, K.D. & Papadopoulou, S. & Seferlis, P. & Voutetakis, S., 2020. "Energy management strategies based on hybrid automata for islanded microgrids with renewable sources, batteries and hydrogen," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    10. Fang, Xiaolun & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Multiple time-scale energy management strategy for a hydrogen-based multi-energy microgrid," Applied Energy, Elsevier, vol. 328(C).
    11. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    12. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    13. Ren, Fukang & Lin, Xiaozhen & Wei, Ziqing & Zhai, Xiaoqiang & Yang, Jianrong, 2022. "A novel planning method for design and dispatch of hybrid energy systems," Applied Energy, Elsevier, vol. 321(C).
    14. Farouk Odeim & Jürgen Roes & Angelika Heinzel, 2015. "Power Management Optimization of an Experimental Fuel Cell/Battery/Supercapacitor Hybrid System," Energies, MDPI, vol. 8(7), pages 1-26, June.
    15. Yang, Mao & Wang, Da & Zhang, Wei, 2023. "A short-term wind power prediction method based on dynamic and static feature fusion mining," Energy, Elsevier, vol. 280(C).
    16. Qi, Ning & Huang, Kaidi & Fan, Zhiyuan & Xu, Bolun, 2025. "Long-term energy management for microgrid with hybrid hydrogen-battery energy storage: A prediction-free coordinated optimization framework," Applied Energy, Elsevier, vol. 377(PB).
    17. K/bidi, Fabrice & Damour, Cedric & Grondin, Dominique & Hilairet, Mickaël & Benne, Michel, 2022. "Multistage power and energy management strategy for hybrid microgrid with photovoltaic production and hydrogen storage," Applied Energy, Elsevier, vol. 323(C).
    18. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    19. Hassan, I.A. & Ramadan, Haitham S. & Saleh, Mohamed A. & Hissel, Daniel, 2021. "Hydrogen storage technologies for stationary and mobile applications: Review, analysis and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
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    1. Cui, Feifei & An, Dou & Xi, Huan & Ren, Zhigang, 2025. "Collaborative scheduling optimization of hydrogen-enhanced integrated energy system via goal-conditioned hierarchical reinforcement learning," Energy, Elsevier, vol. 338(C).

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