IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p488-d1322095.html
   My bibliography  Save this article

The Atmospheric Stability Dependence of Far Wakes on the Power Output of Downstream Wind Farms

Author

Listed:
  • Richard J. Foreman

    (Renewables, UL International GmbH, Kasinoplatz 3, 26122 Oldenburg, Germany)

  • Beatriz Cañadillas

    (Institute of Flight Guidance, Technical University of Braunschweig, 38106 Braunschweig, Germany)

  • Nick Robinson

    (UL International, Richmond, BC V6V 2V4, Canada)

Abstract

Stability-dependent far-field offshore wind-farm wakes are detected in Supervisory Control and Data Acquisition (SCADA) wind power records from wind farms located in the North Sea. The results are used to assess the strengths and weaknesses of the Openwind engineering model, which in turn enables understanding of the wake signal captured by the SCADA data. Two experimental model set-ups are evaluated, the current standard set-up considering a neutral atmosphere and extended for stable conditions, and the other using a new atmospheric stability implementation called the far-wake atmospheric stability model (ASM) previously reported in Energies . The ASM approach enables the identification within wind power records of wakes of length at least 30 km depending on the atmospheric stability. The ASM approach would be useful for assessing which neighboring wind farms are likely to affect the wind turbine power output and to what extent the power output is affected by stability.

Suggested Citation

  • Richard J. Foreman & Beatriz Cañadillas & Nick Robinson, 2024. "The Atmospheric Stability Dependence of Far Wakes on the Power Output of Downstream Wind Farms," Energies, MDPI, vol. 17(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:488-:d:1322095
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/488/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/488/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    2. Beatriz Cañadillas & Richard Foreman & Gerald Steinfeld & Nick Robinson, 2023. "Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations," Energies, MDPI, vol. 16(7), pages 1-24, March.
    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. Sarlak, H. & Meneveau, C. & Sørensen, J.N., 2015. "Role of subgrid-scale modeling in large eddy simulation of wind turbine wake interactions," Renewable Energy, Elsevier, vol. 77(C), pages 386-399.
    3. Nicolas Tobin & Ali M. Hamed & Leonardo P. Chamorro, 2015. "An Experimental Study on the Effects ofWinglets on the Wake and Performance of a ModelWind Turbine," Energies, MDPI, vol. 8(10), pages 1-18, October.
    4. Anagnostopoulos, Sokratis J. & Bauer, Jens & Clare, Mariana C.A. & Piggott, Matthew D., 2023. "Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models," Renewable Energy, Elsevier, vol. 218(C).
    5. Li, Siyi & Zhang, Mingrui & Piggott, Matthew D., 2023. "End-to-end wind turbine wake modelling with deep graph representation learning," Applied Energy, Elsevier, vol. 339(C).
    6. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    7. Siyu Tao & Andrés Feijóo & Jiemin Zhou & Gang Zheng, 2020. "Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout," Energies, MDPI, vol. 13(3), pages 1-16, January.
    8. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    9. Lam, H.F. & Peng, H.Y., 2016. "Study of wake characteristics of a vertical axis wind turbine by two- and three-dimensional computational fluid dynamics simulations," Renewable Energy, Elsevier, vol. 90(C), pages 386-398.
    10. 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).
    11. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
    12. Jaime Liew & Kirby S. Heck & Michael F. Howland, 2024. "Unified momentum model for rotor aerodynamics across operating regimes," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    13. Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(C).
    14. 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.
    15. 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.
    16. Guillem Armengol Barcos & Fernando Porté-Agel, 2023. "Enhancing Wind Farm Performance through Axial Induction and Tilt Control: Insights from Wind Tunnel Experiments," Energies, MDPI, vol. 17(1), pages 1-20, December.
    17. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    18. Guoqing Huang & Yao Chen & Ke Li & Jiangke Luo & Sai Zhang & Mingming Lv, 2024. "A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm," Energies, MDPI, vol. 17(13), pages 1-22, July.
    19. Xu Ning & Decheng Wan, 2019. "LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics," Sustainability, MDPI, vol. 11(24), pages 1-26, December.
    20. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).

    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:17:y:2024:i:2:p:488-:d:1322095. 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.