IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i7p2949-d1105520.html
   My bibliography  Save this article

Cumulative Interactions between the Global Blockage and Wake Effects as Observed by an Engineering Model and Large-Eddy Simulations

Author

Listed:
  • Beatriz Cañadillas

    (Renewables, UL International GmbH, 26122 Oldenburg, Germany
    Institute of Flight Guidance, Technical University of Braunschweig, 38106 Braunschweig, Germany)

  • Richard Foreman

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

  • Gerald Steinfeld

    (ForWind, Institute of Physics, Carl von Ossietzky University Oldenburg, 26129 Oldenburg, Germany)

  • Nick Robinson

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

Abstract

By taking into account the turbine type, terrain, wind climate and layout, the effects of wind turbine wakes and other losses, engineering models enable the rapid estimation of energy yields for prospective and existing wind farms. We extend the capability of engineering models, such as the existing deep-array wake model, to account for additional losses that may arise due to the presence of clusters of wind farms, such as the global blockage effect and large-scale wake effects, which become more significant with increasing thermal stratification. The extended strategies include an enhanced wind-farm-roughness approach which assumes an infinite wind farm, and recent developments account for the upstream flow blockage. To test the plausibility of such models in capturing the additional blockage and wake losses in real wind farm clusters, the extended strategies are compared with large-eddy simulations of the flow through a cluster of three wind farms located in the German sector of the North Sea, as well as real measurements of wind power within these wind farms. Large-eddy simulations and wind farm measurements together suggest that the extensions of the Openwind model help capture the different flow features arising from flow blockage and cluster effects, but further model refinement is needed to account for higher-order effects, such as the effect of the boundary-layer height, which is not currently included in standard engineering models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:2949-:d:1105520
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/7/2949/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/7/2949/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Asmuth, Henrik & Navarro Diaz, Gonzalo P. & Madsen, Helge Aagaard & Branlard, Emmanuel & Meyer Forsting, Alexander R. & Nilsson, Karl & Jonkman, Jason & Ivanell, Stefan, 2022. "Wind turbine response in waked inflow: A modelling benchmark against full-scale measurements," Renewable Energy, Elsevier, vol. 191(C), pages 868-887.
    2. James Bleeg & Mark Purcell & Renzo Ruisi & Elizabeth Traiger, 2018. "Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production," Energies, MDPI, vol. 11(6), pages 1-20, June.
    3. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Rebecca J. Barthelmie & Gunner C. Larsen & Sara C. Pryor, 2023. "Modeling Annual Electricity Production and Levelized Cost of Energy from the US East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 16(12), pages 1-29, June.

    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. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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).
    9. 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.
    10. 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).
    11. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    12. 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.
    13. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    14. 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.
    15. Kyoungboo Yang, 2020. "Determining an Appropriate Parameter of Analytical Wake Models for Energy Capture and Layout Optimization on Wind Farms," Energies, MDPI, vol. 13(3), pages 1-17, February.
    16. 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).
    17. Kelan Patel & Thomas D. Dunstan & Takafumi Nishino, 2021. "Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response," Energies, MDPI, vol. 14(19), pages 1-16, October.
    18. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    19. 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.
    20. Wang, Yangwei & Lin, Jiahuan & Zhang, Jun, 2022. "Investigation of a new analytical wake prediction method for offshore floating wind turbines considering an accurate incoming wind flow," Renewable Energy, Elsevier, vol. 185(C), pages 827-849.

    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:7:p:2949-:d:1105520. 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.