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Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties

Author

Listed:
  • Zheng, Yi
  • Wang, Jiawei
  • You, Shi
  • Li, Ximei
  • Bindner, Henrik W.
  • Münster, Marie

Abstract

Hydrogen is believed as a promising energy carrier that contributes to deep decarbonization, especially for the sectors hard to be directly electrified. A grid-connected wind/hydrogen system is a typical configuration for hydrogen production. For such a system, a critical barrier lies in the poor cost-competitiveness of the produced hydrogen. Researchers have found that flexible operation of a wind/hydrogen system is possible thanks to the excellent dynamic properties of electrolysis. This finding implies the system owner can strategically participate in day-ahead power markets to reduce the hydrogen production cost. However, the uncertainties from imperfect prediction of the fluctuating market price and wind power reduce the effectiveness of the offering strategy in the market. In this paper, we proposed a decision-making framework, which is based on data-driven robust chance constrained programming (DRCCP). This framework also includes multi-layer perception neural network (MLPNN) for wind power and spot electricity price prediction. Such a DRCCP-based decision framework (DDF) is then applied to make the day-ahead decision for a wind/hydrogen system. It can effectively handle the uncertainties, manage the risks and reduce the operation cost. The results show that, for the daily operation in the selected 30 days, offering strategy based on the framework reduces the overall operation cost by 24.36%, compared to the strategy based on imperfect prediction. Besides, we elaborate the parameter selections of the DRCCP to reveal the best parameter combination to obtain better optimization performance. The efficacy of the DRCCP method is also highlighted by the comparison with the chance-constrained programming method.

Suggested Citation

  • Zheng, Yi & Wang, Jiawei & You, Shi & Li, Ximei & Bindner, Henrik W. & Münster, Marie, 2023. "Data-driven scheme for optimal day-ahead operation of a wind/hydrogen system under multiple uncertainties," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922014581
    DOI: 10.1016/j.apenergy.2022.120201
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    References listed on IDEAS

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    Cited by:

    1. Stefanos Tampakakis & Dimitrios Zafirakis, 2023. "On the Value of Emerging, Day-Ahead Market Related Wind-Storage Narratives in Greece: An Early Empirical Analysis," Energies, MDPI, vol. 16(8), pages 1-19, April.
    2. Abadie, Luis Mª & Chamorro, José M., 2023. "Investment in wind-based hydrogen production under economic and physical uncertainties," Applied Energy, Elsevier, vol. 337(C).
    3. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).

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