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Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms

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  • Bart Matthijs Doekemeijer

    (National Wind Technology Center, National Renewable Energy Laboratory, 19001 W 119th Ave, Arvada, CO 80007, USA)

  • Eric Simley

    (National Wind Technology Center, National Renewable Energy Laboratory, 19001 W 119th Ave, Arvada, CO 80007, USA)

  • Paul Fleming

    (National Wind Technology Center, National Renewable Energy Laboratory, 19001 W 119th Ave, Arvada, CO 80007, USA)

Abstract

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model’s accuracy. We assume a fixed turbulence intensity of I ∞ = 6 % and a standard deviation on the inflow wind direction of σ w d = 3 ° in our Gaussian model. First, we demonstrate the non-uniqueness issue of I ∞ and σ w d , which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses.

Suggested Citation

  • Bart Matthijs Doekemeijer & Eric Simley & Paul Fleming, 2022. "Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms," Energies, MDPI, vol. 15(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1964-:d:766525
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    References listed on IDEAS

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    1. Doekemeijer, Bart M. & van der Hoek, Daan & van Wingerden, Jan-Willem, 2020. "Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions," Renewable Energy, Elsevier, vol. 156(C), pages 719-730.
    2. van der Hoek, Daan & Kanev, Stoyan & Allin, Julian & Bieniek, David & Mittelmeier, Niko, 2019. "Effects of axial induction control on wind farm energy production - A field test," Renewable Energy, Elsevier, vol. 140(C), pages 994-1003.
    3. 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.
    4. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
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    Cited by:

    1. 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.

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