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Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

Author

Listed:
  • Vincent Henkel

    (Institute of Automation Technology, Helmut-Schmidt-University, 22043 Hamburg, Germany)

  • Lukas Peter Wagner

    (Institute of Automation Technology, Helmut-Schmidt-University, 22043 Hamburg, Germany)

  • Felix Gehlhoff

    (Institute of Automation Technology, Helmut-Schmidt-University, 22043 Hamburg, Germany)

  • Alexander Fay

    (Chair of Automation, Ruhr University, 44801 Bochum, Germany)

Abstract

The integration of renewable energy sources into an energy grid introduces volatility, challenging grid stability and reliability. To address these challenges, this work proposes a two-stage optimization approach for the operation of electrolyzers used in green hydrogen production. This method combines site-wide and real-time optimization to manage a fluctuating energy supply effectively. By leveraging the dual use of an existing optimization model, it is applied for both site-wide and real-time optimization, enhancing the consistency and efficiency of the control strategy. Site-wide optimization generates long-term operational plans based on long-term forecasts, while real-time optimization adjusts these plans in response to immediate fluctuations in energy availability. This approach is validated through a case study showing that real-time optimization can accommodate renewable energy forecast deviations of up to 15%, resulting in hydrogen production 6.5% higher than initially planned during periods of increased energy availability. This framework not only optimizes electrolyzer operations but can also be applied to other flexible energy resources, supporting sustainable and economically viable energy management.

Suggested Citation

  • Vincent Henkel & Lukas Peter Wagner & Felix Gehlhoff & Alexander Fay, 2024. "Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers," Energies, MDPI, vol. 17(17), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4396-:d:1469836
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    References listed on IDEAS

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    1. Adrian Odenweller & Falko Ueckerdt & Gregory F. Nemet & Miha Jensterle & Gunnar Luderer, 2022. "Probabilistic feasibility space of scaling up green hydrogen supply," Nature Energy, Nature, vol. 7(9), pages 854-865, September.
    2. Flamm, Benjamin & Peter, Christian & Büchi, Felix N. & Lygeros, John, 2021. "Electrolyzer modeling and real-time control for optimized production of hydrogen gas," Applied Energy, Elsevier, vol. 281(C).
    3. Zhao, Pengcheng & Wang, Jingang & Xia, Haiting & He, Wei, 2024. "A novel industrial magnetically enhanced hydrogen production electrolyzer and effect of magnetic field configuration," Applied Energy, Elsevier, vol. 367(C).
    4. Martin Lindner & Benedikt Grosch & Ghada Elserafi & Bastian Dietrich & Matthias Weigold, 2023. "Holistic Approach for an Energy-Flexible Operation of a Machine Tool with Cooling Supply," Energies, MDPI, vol. 16(9), pages 1-25, May.
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    Cited by:

    1. Syed, Ahmad & Guo, Xiaoqiang & Wang, Ning & sun, Le & Zhang, Shiqi & Hua, Changchun & Tayab, Abu, 2026. "Next-generation control for electrolyzers: a review of GPT-based AI frameworks in renewable hydrogen systems," Applied Energy, Elsevier, vol. 406(C).
    2. Kilthau, Maximilian & Henkel, Vincent & Wagner, Lukas Peter & Gehlhoff, Felix & Fay, Alexander, 2025. "A decentralized optimization approach for scalable agent-based energy dispatch and congestion management," Applied Energy, Elsevier, vol. 377(PC).

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