IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1293-d506560.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1293/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/5/1293/
    Download Restriction: no
    ---><---

    References listed on IDEAS

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

    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. Michael F. Howland & John O. Dabiri, 2020. "Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation," Energies, MDPI, vol. 14(1), pages 1-20, December.
    2. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
    3. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    4. Sadek, Zein & Scott, Ryan & Hamilton, Nicholas & Cal, Raúl Bayoán, 2023. "A three-dimensional, analytical wind turbine wake model: Flow acceleration, empirical correlations, and continuity," Renewable Energy, Elsevier, vol. 209(C), pages 298-309.
    5. 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.
    6. 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).
    7. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    8. Bottasso, C.L. & Cacciola, S. & Schreiber, J., 2018. "Local wind speed estimation, with application to wake impingement detection," Renewable Energy, Elsevier, vol. 116(PA), pages 155-168.
    9. Frederik, Joeri A. & van Wingerden, Jan-Willem, 2022. "On the load impact of dynamic wind farm wake mixing strategies," Renewable Energy, Elsevier, vol. 194(C), pages 582-595.
    10. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    11. Mingqiu Liu & Zhichang Liang & Haixiao Liu, 2022. "Numerical Investigations of Wake Expansion in the Offshore Wind Farm Using a Large Eddy Simulation," Energies, MDPI, vol. 15(6), pages 1-19, March.
    12. Ti, Zilong & Deng, Xiao Wei & Zhang, Mingming, 2021. "Artificial Neural Networks based wake model for power prediction of wind farm," Renewable Energy, Elsevier, vol. 172(C), pages 618-631.
    13. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    14. Su, Keye & Bliss, Donald, 2019. "A novel hybrid free-wake model for wind turbine performance and wake evolution," Renewable Energy, Elsevier, vol. 131(C), pages 977-992.
    15. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    16. Aju, Emmanuvel Joseph & Kumar, Devesh & Leffingwell, Melissa & Rotea, Mario A. & Jin, Yaqing, 2023. "The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms," Renewable Energy, Elsevier, vol. 215(C).
    17. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    18. Hegazy, Amr & Blondel, Frédéric & Cathelain, Marie & Aubrun, Sandrine, 2022. "LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models," Renewable Energy, Elsevier, vol. 181(C), pages 457-471.
    19. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    20. Zhang, Jincheng & Zhao, Xiaowei, 2020. "Quantification of parameter uncertainty in wind farm wake modeling," Energy, Elsevier, vol. 196(C).

    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:14:y:2021:i:5:p:1293-:d:506560. 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.