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Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach

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  • Yu, Shiwei
  • Zhou, Shuangshuang
  • Zheng, Shuhong
  • Li, Zhenxi
  • Liu, Lancui

Abstract

To achieve China’s 2030 goals for renewable energy development, this paper proposes a constrained fuzzy multi-objective optimization (FMO) model to obtain the optimal generation mix of four kinds of renewable power: hydropower, wind power, photovoltaic (PV) power, and biomass power. The optimization results show that a) the optimal mix of accumulated installed capacity of renewable energy in China from 2017 to 2022 is hydropower > wind > PV > biomass, while that from 2023 to 2030 is PV > hydropower > wind > biomass; b) the accumulated installed capacity of PV has the fastest growth between 2017 and 2030, and PV power will replace hydropower as the largest renewable power source in 2023. However, hydropower remains the largest renewable power through 2030 in terms of on-grid power generation; c) these four kinds of renewable power generation can eliminate 34.9–37.5 billion tonnes of CO2 emissions by replacing coal-fired power generation, with hydropower contributing the most to the reduction; and d) Investment cost per kilowatt or the rate of power curtailment is not sensitive factor for optimal cumulative installed capacity. The results mean that China should promote PV power generation in a rapid but systematic manner while continuing to develop hydropower.

Suggested Citation

  • Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.
  • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:1086-1098
    DOI: 10.1016/j.renene.2019.03.011
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    References listed on IDEAS

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    Cited by:

    1. Unni, Arjun C. & Ongsakul, Weerakorn & Madhu M., Nimal, 2020. "Fuzzy-based novel risk and reward definition applied for optimal generation-mix estimation," Renewable Energy, Elsevier, vol. 148(C), pages 665-673.

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