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The grid parity analysis of onshore wind power in China: A system cost perspective

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  • Chen, Hao
  • Gao, Xin-Ya
  • Liu, Jian-Yu
  • Zhang, Qian
  • Yu, Shiwei
  • Kang, Jia-Ning
  • Yan, Rui
  • Wei, Yi-Ming

Abstract

The grid parity of wind generation has drawn increasing attention owing to the serious subsidy funding shortages in China, but scientific evidences for the grid parity feasibility are still not sufficient, because most studies have neglected the additional balancing cost and grid-connection cost caused by the wind generation. We hence develop an integrated methodology to analyze the grid parity of onshore wind generation from a system cost perspective, coupling a system generation cost model, a grid parity index model and a learning curve model. Key findings are summarized as follows: (1) The average system cost of wind generation declined from 0.84 yuan/kWh in 2006 to 0.57 yuan/kWh in 2017. Guangdong has the highest system cost, while Xinjiang costs the least. (2) The traditional LCOE approach underestimates the wind generation cost by about 15%, resulting in biased conclusions regarding the grid parity. (3) All the provincial grid parity indexes have values more than 1, indicating that the current grid parity of wind generation is impractical. (4) The national average grid parity time of wind generation are forecasted to be 2021, 2023 and 2026 when the on-grid coal generation prices are 0.50 yuan/kWh, 0.45 yuan/kWh and 0.40 yuan/kWh, respectively.

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  • Chen, Hao & Gao, Xin-Ya & Liu, Jian-Yu & Zhang, Qian & Yu, Shiwei & Kang, Jia-Ning & Yan, Rui & Wei, Yi-Ming, 2020. "The grid parity analysis of onshore wind power in China: A system cost perspective," Renewable Energy, Elsevier, vol. 148(C), pages 22-30.
    • Handle: RePEc:eee:renene:v:148:y:2020:i:c:p:22-30
    DOI: 10.1016/j.renene.2019.11.161
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    6. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    7. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying endogenous learning models in energy system optimization," Papers 2106.06373, arXiv.org.
    8. Li Ma & Die Xu, 2021. "Toward Renewable Energy in China: Revisiting Driving Factors of Chinese Wind Power Generation Development and Spatial Distribution," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    9. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
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    12. Jabir Ali Ouassou & Julian Straus & Marte Fodstad & Gunhild Reigstad & Ove Wolfgang, 2021. "Applying Endogenous Learning Models in Energy System Optimization," Energies, MDPI, vol. 14(16), pages 1-21, August.
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