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

Michael F. Howland (), Jesús Bas Quesada, Juan José Pena Martínez, Felipe Palou Larrañaga, Neeraj Yadav, Jasvipul S. Chawla, Varun Sivaram and John O. Dabiri
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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
John O. Dabiri: California Institute of Technology

Nature Energy, 2022, vol. 7, issue 9, 818-827

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41560-022-01085-8

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