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Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics

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  • Liang, Tao
  • Chen, Mengjing
  • Tan, Jianxin
  • Jing, Yanwei
  • Lv, Liangnian
  • Yang, Wenbo

Abstract

This paper proposes a multi-electrolyzer switching scheduling strategy based on the Pelican Optimization Algorithm (POA) to enhance the efficiency of large-scale wind power hydrogen production systems. To validate the effectiveness of the proposed strategy, we analyzed wind power data from three typical days in northern Hebei, China, with a 2.5 MW wind turbine output. We designed three strategies for comparative analysis: a simple start-stop strategy, a rule-based multi-electrolyzer switching strategy, and a POA-based multi-electrolyzer switching strategy. The study results demonstrate that the POA-based strategy exhibits higher hydrogen production efficiency and system stability under various wind conditions. Particularly, in extreme wind scenarios, this strategy significantly reduces the start-stop cycles of electrolyzers, thereby lowering operational costs and improving overall system performance. The main contribution of this study lies in the novel optimization algorithm and its validation through real-world data, demonstrating its effectiveness in large-scale wind power hydrogen production systems. Our findings provide new insights for enhancing the utilization of renewable energy and the economics of hydrogen production systems.

Suggested Citation

  • Liang, Tao & Chen, Mengjing & Tan, Jianxin & Jing, Yanwei & Lv, Liangnian & Yang, Wenbo, 2024. "Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics," Renewable Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:renene:v:232:y:2024:i:c:s096014812401190x
    DOI: 10.1016/j.renene.2024.121122
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    References listed on IDEAS

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    1. SeyedGarmroudi, SeyedDavoud & Kayakutlu, Gulgun & Kayalica, M. Ozgur & Çolak, Üner, 2024. "Improved Pelican optimization algorithm for solving load dispatch problems," Energy, Elsevier, vol. 289(C).
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    3. El-Sattar, Hoda Abd & Hassan, Mohamed H. & Vera, David & Jurado, Francisco & Kamel, Salah, 2024. "Maximizing hybrid microgrid system performance: A comparative analysis and optimization using a gradient pelican algorithm," Renewable Energy, Elsevier, vol. 227(C).
    4. Yang, Bo & Liang, Boxiao & Qian, Yucun & Zheng, Ruyi & Su, Shi & Guo, Zhengxun & Jiang, Lin, 2024. "Parameter identification of PEMFC via feedforward neural network-pelican optimization algorithm," Applied Energy, Elsevier, vol. 361(C).
    5. Frank Gambou & Damien Guilbert & Michel Zasadzinski & Hugues Rafaralahy, 2022. "A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity," Energies, MDPI, vol. 15(9), pages 1-20, May.
    6. Buttler, Alexander & Spliethoff, Hartmut, 2018. "Current status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2440-2454.
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    1. Muhammad Anas Maqbool & Md Jahir Rizvi & Yeaw Chu Lee & Pablo Borja Rosales, 2025. "Technical Feasibility Analysis of Green Energy Storage Options and Hornsea Wind Farms," Energies, MDPI, vol. 18(9), pages 1-34, April.

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