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Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations

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  • Wang, Qiang
  • Luo, Kun
  • Yuan, Renyu
  • Zhang, Sanxia
  • Fan, Jianren

Abstract

To ameliorate the efficiency of wind farms, except assessing the wake effect between the wind turbines, the wake interference between the wind farms must be considered. Based on the Weather Research and Forecasting (WRF) model, the overall performance and power output characteristics, and wake interference effects between the adjacent wind farms in Hami region of Xinjiang province under real terrain and atmospheric conditions were investigated. The wind turbine drag parameterization (WTDP) scheme was elaborated. The results show that the wake of the whole field generally recovers at downstream 16.5 km under prevailing wind direction and annual average wind speed, and the frequency of power output around the rated power is up to 30%. Moreover, the disturbance induced by the wake effect of a large-scale wind farm on its downstream adjacent farm was quantitatively evaluated. Due to the impact of the upstream farm, the wake distance of the downstream wind farm is doubled. The influence on the power output presented a regularity of day-night alternation, with a higher frequency of great loss at night, dawn and evening. The average relative loss ratio reached 5.8%. This study is expected to provide a theoretical basis and engineering guidance for micrositing of wind farms.

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  • Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:1168-1180
    DOI: 10.1016/j.energy.2018.10.111
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    7. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(C).
    8. M. K. Islam & N. M. S. Hassan & M. G. Rasul & Kianoush Emami & Ashfaque Ahmed Chowdhury, 2023. "Forecasting of Solar and Wind Resources for Power Generation," Energies, MDPI, vol. 16(17), pages 1-23, August.
    9. Syed, Abdul Haseeb & Javed, Adeel & Asim Feroz, Raja M. & Calhoun, Ronald, 2020. "Partial repowering analysis of a wind farm by turbine hub height variation to mitigate neighboring wind farm wake interference using mesoscale simulations," Applied Energy, Elsevier, vol. 268(C).
    10. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    11. Gyatso, Ngawang & Li, Ye & Gao, Zhiteng & Wang, Qiang & Li, Shoutu & Yin, Qiang & Chen, Junbo & Jin, Peng & Liu, Zhengshu & Ma, Zengyi & Chen, Xuefeng & Feng, Jiajia & Dorje,, 2023. "Wind power performance assessment at high plateau region: A case study of the wind farm field test on the Qinghai-Tibet plateau," Applied Energy, Elsevier, vol. 336(C).
    12. Zhaobin Li & Xiaohao Liu & Xiaolei Yang, 2022. "Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes," Energies, MDPI, vol. 15(18), pages 1-28, September.

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