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Numerical assessment and optimization of photovoltaic-based hydrogen-oxygen Co-production energy system: A machine learning and multi-objective strategy

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  • Wang, Ningbo
  • Guo, Yanhua
  • Liu, Lu
  • Shao, Shuangquan

Abstract

Green hydrogen production technology based on photovoltaic (PV), battery energy storage system (BESS) and proton exchange membrane (PEM) water electrolysis plays a crucial part in the transition process of energy to zero carbon technology. But few studies have focused on achieving all-day continuous hydrogen production, which is the key to large-scale hydrogen utilization. In this study, we developed an off-grid PV-BESS-PEM system for hydrogen and oxygen production. Firstly, the PV, BESS and PEM water electrolysis components were modeled and model validation was performed. Secondly, an energy management strategy (EMS) is proposed to achieve the goal of continuous and stable hydrogen production. Then, a surrogate model between design variables and optimization objectives is established based on machine learning methods, and a multi-objective optimization algorithm is used to drive the optimization process. Finally, a case study of the PV-BESS-PEM system is presented based on solar irradiation data in Lhasa, where there is sufficient solar resources and oxygen demand. The results demonstrate that the EMS can satisfy the purpose of all-day continuous and stable hydrogen production. The number of cells in the electrolyzer and the water temperature have a significant effect on the hydrogen production and the electrolysis efficiency. And the optimal system has increased the hydrogen production by 16.80 % and the electrolysis efficiency of PEM by 12.08 %, respectively. Herein, the methodological framework is expected to provide theoretical guidance for large-scale water electrolysis hydrogen production applications based on solar energy.

Suggested Citation

  • Wang, Ningbo & Guo, Yanhua & Liu, Lu & Shao, Shuangquan, 2024. "Numerical assessment and optimization of photovoltaic-based hydrogen-oxygen Co-production energy system: A machine learning and multi-objective strategy," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005482
    DOI: 10.1016/j.renene.2024.120483
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    References listed on IDEAS

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