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Modeling large offshore wind farms under different atmospheric stability regimes with the Park wake model

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  • Peña, Alfredo
  • Réthoré, Pierre-Elouan
  • Rathmann, Ole

Abstract

We evaluate a modified version of the Park wake model against power data from a west-east row in the middle of the Horns Rev I offshore wind farm. The evaluation is performed on data classified in four different atmospheric stability conditions, for a narrow wind speed range, and a wide range of westerly wind directions observed at the wind farm. Simulations (post-processed to partly account for the wind direction uncertainty) and observations show good agreement for all stability classes, being the simulations using a stability-dependent wake decay coefficient closer to the data for the last turbines on the row and those using the WAsP recommended value closer to the data for the first turbines. It is generally seen that under stable and unstable atmospheric conditions the power deficits are the highest and lowest, respectively, but the wind conditions under both stability regimes are different. The ensemble average of the simulations does not approach the limits of the infinite wind farm under any stability condition as such averages account for directions misaligned with the row.

Suggested Citation

  • Peña, Alfredo & Réthoré, Pierre-Elouan & Rathmann, Ole, 2014. "Modeling large offshore wind farms under different atmospheric stability regimes with the Park wake model," Renewable Energy, Elsevier, vol. 70(C), pages 164-171.
    • Handle: RePEc:eee:renene:v:70:y:2014:i:c:p:164-171
    DOI: 10.1016/j.renene.2014.02.019
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    References listed on IDEAS

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    1. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
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    2. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
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    4. Astariz, S. & Perez-Collazo, C. & Abanades, J. & Iglesias, G., 2015. "Towards the optimal design of a co-located wind-wave farm," Energy, Elsevier, vol. 84(C), pages 15-24.
    5. Cranmer, Alexana & Baker, Erin & Liesiö, Juuso & Salo, Ahti, 2018. "A portfolio model for siting offshore wind farms with economic and environmental objectives," European Journal of Operational Research, Elsevier, vol. 267(1), pages 304-314.
    6. Han, Xingxing & Liu, Deyou & Xu, Chang & Shen, Wen Zhong, 2018. "Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain," Renewable Energy, Elsevier, vol. 126(C), pages 640-651.
    7. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    8. 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.

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