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Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm

Author

Listed:
  • Maarten T. van Beek

    (Department of Wind Energy, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands)

  • Axelle Viré

    (Department of Wind Energy, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
    These authors contributed equally to this work.)

  • Søren J. Andersen

    (Department of Wind Energy, Technological University of Denmark, 2800 Kgs. Lynbgy, Denmark
    These authors contributed equally to this work.)

Abstract

Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%.

Suggested Citation

  • Maarten T. van Beek & Axelle Viré & Søren J. Andersen, 2021. "Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm," Energies, MDPI, vol. 14(5), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1293-:d:506560
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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. 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.
    3. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
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    Cited by:

    1. Antonio Crespo, 2022. "Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization," Energies, MDPI, vol. 15(18), pages 1-3, September.

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