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

Howland, Michael F. and Bas Quesada, Jesús and Pena Martínez, Juan José and Palou Larrañaga, Felipe and Yadav, Neeraj and Chawla, Jasvipul S. and Sivaram, Varun and Dabiri, John O. (2022) Collective wind farm operation based on a predictive model increases utility-scale energy production. Nature Energy, 7 (9). pp. 818-827. ISSN 2058-7546. doi:10.1038/s41560-022-01085-8.

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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.

Item Type:Article
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URLURL TypeDescription ItemDiscussion Paper ReadCube access
Howland, Michael F.0000-0002-2878-3874
Pena Martínez, Juan José0000-0001-9395-7976
Yadav, Neeraj0000-0002-1806-119X
Chawla, Jasvipul S.0000-0002-9129-7975
Sivaram, Varun0000-0002-0878-6349
Dabiri, John O.0000-0002-6722-9008
Additional Information: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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Massachusetts Institute of Technology (MIT)UNSPECIFIED
Issue or Number:9
Record Number:CaltechAUTHORS:20220823-625642500.749
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116395
Deposited By: Melissa Ray
Deposited On:25 Aug 2022 14:41
Last Modified:15 Feb 2023 19:10

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