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Low-Carbon Transition Pathway Planning of Regional Power Systems with Electricity-Hydrogen Synergy

Author

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  • Liang Ran

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Yaling Mao

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Tiejiang Yuan

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

  • Guofeng Li

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

Hydrogen energy leads us in an important direction in the development of clean energy, and the comprehensive utilization of hydrogen energy is crucial for the low-carbon transformation of the power sector. In this paper, the demand for hydrogen energy in various fields is predicted based on the support vector regression algorithm, which can be converted into an equivalent electrical load when it is all produced from water electrolysis. Then, the investment costs of power generators and hydrogen energy equipment are forecast considering uncertainty. Furthermore, a planning model is established with the forecast data, initial installed capacity and targets for carbon emission reduction as inputs, and the installed capacity as well as share of various power supply and annual carbon emissions as outputs. Taking Gansu Province of China as an example, the changes of power supply structure and carbon emissions under different scenarios are analysed. It can be found that hydrogen production through water electrolysis powered by renewable energy can reduce carbon emissions but will increase the demand for renewable energy generators. Appropriate planning of hydrogen storage can reduce the overall investment cost and promote a low carbon transition of the power system.

Suggested Citation

  • Liang Ran & Yaling Mao & Tiejiang Yuan & Guofeng Li, 2022. "Low-Carbon Transition Pathway Planning of Regional Power Systems with Electricity-Hydrogen Synergy," Energies, MDPI, vol. 15(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8764-:d:979700
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    References listed on IDEAS

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    Cited by:

    1. Cheng, Guishi & Luo, Ercheng & Zhao, Ying & Yang, Yihao & Chen, Binbin & Cai, Youcheng & Wang, Xiaoqiang & Dong, Changqing, 2023. "Analysis and prediction of green hydrogen production potential by photovoltaic-powered water electrolysis using machine learning in China," Energy, Elsevier, vol. 284(C).
    2. Rusin, Andrzej & Wojaczek, Adam, 2023. "Changes in the structure of the Polish energy mix in the transition period to ensure the safety and reliability of energy supplies," Energy, Elsevier, vol. 282(C).

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