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

Howland, Michael F. and Quesada, Jesús Bas and Martínez, Juan José Pena and Larrañaga, Felipe Palou 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 . ISSN 2058-7546. doi:10.1038/s41560-022-01085-8. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20220823-625642500

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41560-022-01085-8DOIArticle
https://resolver.caltech.edu/CaltechAUTHORS:20220207-90072000Related ItemDiscussion paper
https://rdcu.be/cUhTcPublisherFree ReadCube access
ORCID:
AuthorORCID
Howland, Michael F.0000-0002-2878-3874
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.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Massachusetts Institute of Technology (MIT)UNSPECIFIED
CaltechUNSPECIFIED
DOI:10.1038/s41560-022-01085-8
Record Number:CaltechAUTHORS:20220823-625642500
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220823-625642500
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116395
Collection:CaltechAUTHORS
Deposited By: Melissa Ray
Deposited On:25 Aug 2022 14:41
Last Modified:25 Aug 2022 14:41

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