IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p8051-d1299759.html
   My bibliography  Save this article

Upward Shift of Wind Turbine Wakes in Large Wind Farms

Author

Listed:
  • Zewei Wang

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaolei Yang

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

A detailed description of wake characteristics is essential for optimizing wind farm performance. Compared with the wake of a stand-alone wind turbine, less attention has been paid to wind turbine wakes in large wind farms. In this work, we investigate the vertical position of wakes for wind turbines in large wind farms with different streamwise turbine spacings and ground roughness lengths using large-eddy simulation with an actuator disk model. The simulation results reveal an upward shift of the wake center (defined as the position with the maximum velocity deficit) for the wind turbine deeply arrayed in the wind farm. Larger upward shifts of the wake center are observed for wind turbines in further downstream rows and wind turbines installed on the ground with higher roughness, for which the wake expands at a higher rate. It is conjectured that the upward shift of the wake center is caused by the upward shift of the turbulence-dominated momentum entrainment region and the constraint of ground on wake expansion. An analytical wake model incorporating the upward-shifting wake center was developed. In the proposed model, different expansion rates are employed for the lower and upper wake regions. The upward shift of the wake center is directly taken into account using the large-eddy simulation results. The comparison with the large-eddy simulation results demonstrates the importance of accounting for the upward shift of the wake center in analytical wake models.

Suggested Citation

  • Zewei Wang & Xiaolei Yang, 2023. "Upward Shift of Wind Turbine Wakes in Large Wind Farms," Energies, MDPI, vol. 16(24), pages 1-15, December.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8051-:d:1299759
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/8051/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/24/8051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu-Ting Wu & Fernando Porté-Agel, 2012. "Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study," Energies, MDPI, vol. 5(12), pages 1-23, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    2. Ziyu Zhang & Peng Huang & Haocheng Sun, 2020. "A Novel Analytical Wake Model with a Cosine-Shaped Velocity Deficit," Energies, MDPI, vol. 13(13), pages 1-20, June.
    3. Zhang, Shaohai & Duan, Huanfeng & Lu, Lin & He, Ruiyang & Gao, Xiaoxia & Zhu, Songye, 2024. "Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy," Energy, Elsevier, vol. 294(C).
    4. Ahmadi, Mohammad H.B., 2019. "Influence of upstream turbulence on the wake characteristics of a tidal stream turbine," Renewable Energy, Elsevier, vol. 132(C), pages 989-997.
    5. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    6. Razi, P. & Ramaprabhu, P. & Tarey, P. & Muglia, M. & Vermillion, C., 2022. "A low-order wake interaction modeling framework for the performance of ocean current turbines under turbulent conditions," Renewable Energy, Elsevier, vol. 200(C), pages 1602-1617.
    7. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    8. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    9. Sina Shamsoddin & Fernando Porté-Agel, 2014. "Large Eddy Simulation of Vertical Axis Wind Turbine Wakes," Energies, MDPI, vol. 7(2), pages 1-23, February.
    10. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    11. Dar, Arslan Salim & Armengol Barcos, Guillem & Porté-Agel, Fernando, 2022. "An experimental investigation of a roof-mounted horizontal-axis wind turbine in an idealized urban environment," Renewable Energy, Elsevier, vol. 193(C), pages 1049-1061.
    12. Souaiby, Marwa & Porté-Agel, Fernando, 2024. "An improved analytical framework for flow prediction inside and downstream of wind farms," Renewable Energy, Elsevier, vol. 225(C).
    13. Antonini, Enrico G.A. & Caldeira, Ken, 2021. "Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms," Applied Energy, Elsevier, vol. 281(C).
    14. Cortina, G. & Calaf, M., 2017. "Turbulence upstream of wind turbines: A large-eddy simulation approach to investigate the use of wind lidars," Renewable Energy, Elsevier, vol. 105(C), pages 354-365.
    15. Pryor, Sara C. & Barthelmie, Rebecca J., 2024. "Wind shadows impact planning of large offshore wind farms," Applied Energy, Elsevier, vol. 359(C).
    16. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    17. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    18. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
    19. Deepu Dilip & Fernando Porté-Agel, 2017. "Wind Turbine Wake Mitigation through Blade Pitch Offset," Energies, MDPI, vol. 10(6), pages 1-17, May.
    20. Victor P. Stein & Hans-Jakob Kaltenbach, 2022. "Validation of a Large-Eddy Simulation Approach for Prediction of the Ground Roughness Influence on Wind Turbine Wakes," Energies, MDPI, vol. 15(7), pages 1-25, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8051-:d:1299759. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.