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Collective wind farm operation based on a predictive model increases utility-scale energy production

Author

Listed:
  • Michael F. Howland

    (Massachusetts Institute of Technology)

  • Jesús Bas Quesada

    (Siemens Gamesa Renewable Energy Innovation & Technology)

  • Juan José Pena Martínez

    (Siemens Gamesa Renewable Energy Innovation & Technology)

  • Felipe Palou Larrañaga

    (Siemens Gamesa Renewable Energy Innovation & Technology)

  • Neeraj Yadav

    (ReNew Power Private Limited)

  • Jasvipul S. Chawla

    (ReNew Power Private Limited)

  • Varun Sivaram

    (ReNew Power Private Limited
    US Department of State)

  • John O. Dabiri

    (California Institute of Technology
    California Institute of Technology)

Abstract

In wind farms, turbines are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. Here we operate a wind turbine array collectively to maximize array production through wake steering. We develop a physics-based, data-assisted flow control model to predict the power-maximizing control strategy. We first validate the model with a multi-month field experiment at a utility-scale wind farm. The model is able to predict the yaw-misalignment angles which maximize array power production within ± 5° for most wind directions (5–32% gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 3.0% ± 0.7% and 1.2% ± 0.4% for wind speeds between 6 m s−1 and 8 m s−1  and all wind speeds, respectively. The predictive model can enable a wider adoption of collective wind farm operation.

Suggested Citation

  • Michael F. Howland & Jesús Bas Quesada & Juan José Pena Martínez & Felipe Palou Larrañaga & Neeraj Yadav & Jasvipul S. Chawla & Varun Sivaram & John O. Dabiri, 2022. "Collective wind farm operation based on a predictive model increases utility-scale energy production," Nature Energy, Nature, vol. 7(9), pages 818-827, September.
    • Handle: RePEc:nat:natene:v:7:y:2022:i:9:d:10.1038_s41560-022-01085-8
    DOI: 10.1038/s41560-022-01085-8
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    Citations

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    Cited by:

    1. Cheoljoon Jeong & Ziang Xu & Albert S. Berahas & Eunshin Byon & Kristen Cetin, 2023. "Multiblock Parameter Calibration in Computer Models," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 116-137, October.
    2. Shin, Heesoo & Rüttgers, Mario & Lee, Sangseung, 2023. "Effects of spatiotemporal correlations in wind data on neural network-based wind predictions," Energy, Elsevier, vol. 279(C).
    3. Sara C. Pryor & Rebecca J. Barthelmie, 2024. "Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 17(5), pages 1-30, February.
    4. Esmail Mahmoodi & Mohammad Khezri & Arash Ebrahimi & Uwe Ritschel & Leonardo P. Chamorro & Ali Khanjari, 2023. "A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines," Energies, MDPI, vol. 16(15), pages 1-13, July.
    5. Pawar, Suraj & Sharma, Ashesh & Vijayakumar, Ganesh & Bay, Chrstopher J. & Yellapantula, Shashank & San, Omer, 2022. "Towards multi-fidelity deep learning of wind turbine wakes," Renewable Energy, Elsevier, vol. 200(C), pages 867-879.
    6. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).
    7. Kuichao Ma & Huanqiang Zhang & Xiaoxia Gao & Xiaodong Wang & Heng Nian & Wei Fan, 2024. "Research on Evaluation Method of Wind Farm Wake Energy Efficiency Loss Based on SCADA Data Analysis," Sustainability, MDPI, vol. 16(5), pages 1-16, February.

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