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Potential impact of global stilling on wind energy production in China

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  • Liu, Fa
  • Wang, Xunming
  • Sun, Fubao
  • Kleidon, Axel

Abstract

The rapid increase in world's installed wind energy capacity may have masked the power loss caused by declining global surface wind speed (termed ‘global stilling’), particularly for China with huge wind energy investments. Here, we estimated the potential impact of global stilling on wind energy production in China, using data from 1226 meteorological stations between 1971 and 2015. We show that surface wind speeds have on average declined at a rate of 5.5% decade−1, while the corresponding wind power density dropped by 24.5% decade−1. Although the decline in wind speeds has slowed after 1991, the decline in wind power density did not show this slowdown. This was attributed to the absence of slowdown in the declining trend of strong winds. Compared with the wind speed levels in the 2000s, the mean capacity factor would have dropped from 20.7% in 2001 to 14.9% in 2015, with the largest absolute change in the “Three North” regions. Furthermore, wind electricity generation would have declined by 40 TWh/a using the installed capacity of 2015, representing a decrease of 16%. The correlation analysis indicates that the decreased wind speed is most likely caused by changes in the large-scale atmospheric circulation rather than increased surface roughness.

Suggested Citation

  • Liu, Fa & Wang, Xunming & Sun, Fubao & Kleidon, Axel, 2023. "Potential impact of global stilling on wind energy production in China," Energy, Elsevier, vol. 263(PB).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222026135
    DOI: 10.1016/j.energy.2022.125727
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    1. Gruber, Katharina & Regner, Peter & Wehrle, Sebastian & Zeyringer, Marianne & Schmidt, Johannes, 2022. "Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas," Energy, Elsevier, vol. 238(PA).
    2. Zhang, Dahai & Wang, Jiaqi & Lin, Yonggang & Si, Yulin & Huang, Can & Yang, Jing & Huang, Bin & Li, Wei, 2017. "Present situation and future prospect of renewable energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 865-871.
    3. Zhenzhong Zeng & Alan D. Ziegler & Timothy Searchinger & Long Yang & Anping Chen & Kunlu Ju & Shilong Piao & Laurent Z. X. Li & Philippe Ciais & Deliang Chen & Junguo Liu & Cesar Azorin-Molina & Adria, 2019. "A reversal in global terrestrial stilling and its implications for wind energy production," Nature Climate Change, Nature, vol. 9(12), pages 979-985, December.
    4. Lo Brano, Valerio & Orioli, Aldo & Ciulla, Giuseppina & Culotta, Simona, 2011. "Quality of wind speed fitting distributions for the urban area of Palermo, Italy," Renewable Energy, Elsevier, vol. 36(3), pages 1026-1039.
    5. Wu, Jie & Wang, Jianzhou & Chi, Dezhong, 2013. "Wind energy potential assessment for the site of Inner Mongolia in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 215-228.
    6. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    7. Ahmadpour, Ali & Mokaramian, Elham & Anderson, Simon, 2021. "The effects of the renewable energies penetration on the surplus welfare under energy policy," Renewable Energy, Elsevier, vol. 164(C), pages 1171-1182.
    8. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    9. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    10. Isabelle Tobin & Robert Vautard & Irena Balog & François-Marie Bréon & Sonia Jerez & Paolo Ruti & Françoise Thais & Mathieu Vrac & Pascal Yiou, 2015. "Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections," Climatic Change, Springer, vol. 128(1), pages 99-112, January.
    11. Tian, Qun & Huang, Gang & Hu, Kaiming & Niyogi, Dev, 2019. "Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016," Energy, Elsevier, vol. 167(C), pages 1224-1235.
    12. Pishgar-Komleh, S.H. & Keyhani, A. & Sefeedpari, P., 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 313-322.
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