Data-driven Scheme for Optimal Day-ahead Operation of a Wind/hydrogen System Under Multiple Uncertainties


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.

Funding source: The authors would like to acknowledge financial support from ‘‘Synergies Utilizing renewable Power Regionally by means of Power To Gas project’’, which received funding in the framework of the joint programming initiative ERA-Net Smart Energy Systems’ focus initiative Integrated, Regional Energy Systems, with support from the European Union’s Horizon 2020 research and innovation program under grant agreement No 775970. This work is also funded by a research grant (37177) from VILLUM FONDEN, Denmark.
Related subjects: Applications & Pathways

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