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Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer

Author

Listed:
  • Na, Ji Sung
  • Koo, Eunmo
  • Muñoz-Esparza, Domingo
  • Jin, Emilia Kyung
  • Linn, Rodman
  • Lee, Joon Sang

Abstract

High-resolution large-eddy simulation of the flow over a large wind farm (64 wind turbines) is performed using the HIGRAD/FIRETEC-WindBlade model, which is a high-performance computing wind turbine–atmosphere interaction model that uses the Lagrangian actuator line method to represent rotating turbine blades. These high-resolution large-eddy simulation results are used to parameterize the thrust and power coefficients that contain information about turbine interference effects within the wind farm. Those coefficients are then incorporated into the WRF (Weather Research and Forecasting) model in order to evaluate interference effects in larger-scale models. In the high-resolution WindBlade wind farm simulation, insufficient distance between turbines creates the interference between turbines, including significant vertical variations in momentum and turbulent intensity. The characteristics of the wake are further investigated by analyzing the distribution of the vorticity and turbulent intensity. Quadrant analysis in the turbine and post-turbine areas reveals that the ejection motion induced by the presence of the wind turbines is dominant compared to that in the other quadrants, indicating that the sweep motion is increased at the location where strong wake recovery occurs. Regional-scale WRF simulations reveal that although the turbulent mixing induced by the wind farm is partly diffused to the upper region, there is no significant change in the boundary layer depth. The velocity deficit does not appear to be very sensitive to the local distribution of turbine coefficients. However, differences of about 5% on parameterized turbulent kinetic energy were found depending on the turbine coefficient distribution. Therefore, turbine coefficients that consider interference in the wind farm should be used in wind farm parameterization for larger-scale models to better describe sub-grid scale turbulent processes.

Suggested Citation

  • Na, Ji Sung & Koo, Eunmo & Muñoz-Esparza, Domingo & Jin, Emilia Kyung & Linn, Rodman & Lee, Joon Sang, 2016. "Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer," Energy, Elsevier, vol. 95(C), pages 79-90.
  • Handle: RePEc:eee:energy:v:95:y:2016:i:c:p:79-90
    DOI: 10.1016/j.energy.2015.11.040
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    Citations

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    Cited by:

    1. 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.
    2. Buen Zhang & Shyuan Cheng & Fanghan Lu & Yuan Zheng & Leonardo P. Chamorro, 2020. "Impact of Topographic Steps in the Wake and Power of a Wind Turbine: Part A—Statistics," Energies, MDPI, vol. 13(23), pages 1-14, December.
    3. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    4. Na, Ji Sung & Koo, Eunmo & Ko, Seung Chul & Linn, Rodman & Muñoz-Esparza, Domingo & Jin, Emilia Kyung & Lee, Joon Sang, 2019. "Stochastic characteristics for the vortical structure of a 5-MW wind turbine wake," Renewable Energy, Elsevier, vol. 133(C), pages 1220-1230.
    5. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    6. Cuevas-Figueroa, Gabriel & Stansby, Peter K. & Stallard, Timothy, 2022. "Accuracy of WRF for prediction of operational wind farm data and assessment of influence of upwind farms on power production," Energy, Elsevier, vol. 254(PB).
    7. Zhe Ma & Liping Lei & Earl Dowell & Pan Zeng, 2020. "An Experimental Study on the Actuator Line Method with Anisotropic Regularization Kernel," Energies, MDPI, vol. 13(4), pages 1-19, February.
    8. 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).

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