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


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.

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Fig. 1: Schematic of the predictive wind farm flow control model.
Fig. 2: Collective wind farm operation experimental setup.
Fig. 3: Model predictions and field experiment results from the static yaw-misalignment model validation field experiment for three yaw-misalignment values.
Fig. 4: Results from the static yaw-misalignment field experiment for flow control model validation.
Fig. 5: Results from utility-scale collective wind farm operation to maximize energy production.

Data availability

All data generated or analysed during this study are included in the published article and its Supplementary Information. The data are available at: Source data are provided with this paper.

Code availability

The code used during this study is provided in the Supplementary Software Files. Instructions for the code use are provided as examples and comments in the Supplementary Software Files. The code is available at:


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We would like to thank the field site team from ReNew Power who assisted with the experiment. M.F.H. acknowledges partial support from the MIT Energy Initiative and Siemens Gamesa Renewable Energy. J.O.D. acknowledges partial support from the California Institute of Technology. The authors would like to thank the reviewers for their thoughtful comments and contribution to this work. We would also like to thank G. Tregnago for thoughtful comments and contribution to this work.

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Authors and Affiliations



M.F.H., V.S. and J.O.D. conceived the research. M.F.H. developed the flow control model and code and analysed the data. J.B.Q., J.J.P.M. and F.P.L. implemented the yaw offsets, provided feedback and suggestions on the flow control model and contributed codes for data analysis. N.Y. and J.S.C. performed LiDAR installation and wind farm site management. M.F.H. wrote the manuscript. All authors contributed to manuscript edits.

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Correspondence to Michael F. Howland.

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Nature Energy thanks Maarten Paul van der Laan and Eric Simley for their contribution to the peer review of this work.

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Howland, M.F., Quesada, J.B., Martínez, J.J.P. et al. Collective wind farm operation based on a predictive model increases utility-scale energy production. Nat Energy 7, 818–827 (2022).

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