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Numerical modelling of offshore wind-farm cluster wakes

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  • Ouro, Pablo
  • Ghobrial, Mina
  • Ali, Karim
  • Stallard, Tim

Abstract

Large offshore wind farms are being installed in proximity to other wind farms to maximise seabed use so that ambitious targets for electricity system decarbonisation can be realised in line with the UN SDG 7: Affordable and Clean Energy. When in operation, a wind farm generates a low-velocity wake region downstream, reducing the available wind resource and potentially impacting the performance of wind farms located downstream. Accurate prediction of wake effects from multi-GW wind farms and clusters of closely spaced wind farms is essential to enable reliable forecasts of energy yield, thus informing wind-farm siting and operation. For these scales there is insufficient data available from existing operational wind-farms alone to fully evaluate the applicability of numerical models for capturing the relevant flow physics. Farm-to-farm interactions are expected to have their greatest impact during stable thermal stratification, characterised by low turbulence intensity and small length scales that promote long wakes. Gravity waves may induce secondary effects impacting wind-farm wake recovery but knowledge about these remains limited. Coriolis forcing plays an important factor influencing the far-wake region with wake deflection being particularly important to capture since this determines which downstream farms are impacted. The review identifies five main numerical frameworks widely used by industry and academia for wind-farm flows and evaluates the applicability and limitations of each approach. Subsequently several recommendations are made for improving confidence in predictions of such wakes, and hence forecasts of the energy-yield from multi-GW wind-farm clusters.

Suggested Citation

  • Ouro, Pablo & Ghobrial, Mina & Ali, Karim & Stallard, Tim, 2025. "Numerical modelling of offshore wind-farm cluster wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:rensus:v:215:y:2025:i:c:s1364032125001996
    DOI: 10.1016/j.rser.2025.115526
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    References listed on IDEAS

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    1. Ahmed, U. & Apsley, D.D. & Afgan, I. & Stallard, T. & Stansby, P.K., 2017. "Fluctuating loads on a tidal turbine due to velocity shear and turbulence: Comparison of CFD with field data," Renewable Energy, Elsevier, vol. 112(C), pages 235-246.
    2. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Javier Sanz Rodrigo & Roberto Aurelio Chávez Arroyo & Patrick Moriarty & Matthew Churchfield & Branko Kosović & Pierre‐Elouan Réthoré & Kurt Schaldemose Hansen & Andrea Hahmann & Jeffrey D. Mirocha & , 2017. "Mesoscale to microscale wind farm flow modeling and evaluation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(2), March.
    4. Porchetta, Sara & Muñoz-Esparza, Domingo & Munters, Wim & van Beeck, Jeroen & van Lipzig, Nicole, 2021. "Impact of ocean waves on offshore wind farm power production," Renewable Energy, Elsevier, vol. 180(C), pages 1179-1193.
    5. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    6. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    7. 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).
    8. Mahdi Abkar & Jens Nørkær Sørensen & Fernando Porté-Agel, 2018. "An Analytical Model for the Effect of Vertical Wind Veer on Wind Turbine Wakes," Energies, MDPI, vol. 11(7), pages 1-10, July.
    9. Stieren, Anja & Gadde, Srinidhi N. & Stevens, Richard J.A.M., 2021. "Modeling dynamic wind direction changes in large eddy simulations of wind farms," Renewable Energy, Elsevier, vol. 170(C), pages 1342-1352.
    10. Mahdi Abkar & Fernando Porté-Agel, 2013. "The Effect of Free-Atmosphere Stratification on Boundary-Layer Flow and Power Output from Very Large Wind Farms," Energies, MDPI, vol. 6(5), pages 1-24, April.
    11. Carl R. Shapiro & Genevieve M. Starke & Charles Meneveau & Dennice F. Gayme, 2019. "A Wake Modeling Paradigm for Wind Farm Design and Control," Energies, MDPI, vol. 12(15), pages 1-19, August.
    12. 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.
    13. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    14. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    15. Keane, Aidan, 2021. "Advancement of an analytical double-Gaussian full wind turbine wake model," Renewable Energy, Elsevier, vol. 171(C), pages 687-708.
    16. J. K. Lundquist & K. K. DuVivier & D. Kaffine & J. M. Tomaszewski, 2019. "Publisher Correction: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development," Nature Energy, Nature, vol. 4(3), pages 251-251, March.
    17. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    18. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
    19. Strickland, Jessica M.I. & Gadde, Srinidhi N. & Stevens, Richard J.A.M., 2022. "Wind farm blockage in a stable atmospheric boundary layer," Renewable Energy, Elsevier, vol. 197(C), pages 50-58.
    20. Kale, Baris & Buckingham, Sophia & van Beeck, Jeroen & Cuerva-Tejero, Alvaro, 2023. "Comparison of the wake characteristics and aerodynamic response of a wind turbine under varying atmospheric conditions using WRF-LES-GAD and WRF-LES-GAL wind turbine models," Renewable Energy, Elsevier, vol. 216(C).
    21. 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.
    22. Larsén, Xiaoli Guo & Fischereit, Jana & Hamzeloo, Sima & Bärfuss, Konrad & Lampert, Astrid, 2024. "Investigation of wind farm impacts on surface waves using coupled numerical simulations," Renewable Energy, Elsevier, vol. 237(PC).
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