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Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses

Abstract:
Turbine–wake and farm–atmosphere interactions can reduce wind farm power production. To model farm performance, it is important to understand the impact of different flow effects on the farm efficiency (i.e. farm power normalised by the power of the same number of isolated turbines). In this study we analyse the results of 43 large-eddy simulations (LESs) of wind farms in a range of conventionally neutral boundary layers (CNBLs). First, we show that the farm efficiency ηf is not well correlated with the wake efficiency ηw (i.e. farm power normalised by the power of front-row turbines). This suggests that existing metrics, classifying the loss of farm power into wake loss and farm blockage loss, are not best suited for understanding large wind farm performance. We then evaluate the assumption of scale separation in the two-scale momentum theory (Nishino and Dunstan, 2020) using the LES results. Building upon this theory, we propose two new metrics for wind farm performance: turbine-scale efficiency ηTS, reflecting the losses due to turbine–wake interactions, and farm-scale efficiency ηFS, indicating the losses due to farm–atmosphere interactions. The LES results show that ηTS is insensitive to the atmospheric condition, whereas ηFS is insensitive to the turbine layout. Finally, we show that a recently developed analytical wind farm model predicts ηFS with an average error of 5.7 % from the LES results.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5194/wes-10-435-2025

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-8389-1619
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


Publisher:
Copernicus Publications
Journal:
Wind Energy Science More from this journal
Volume:
10
Issue:
2
Pages:
435–450
Publication date:
2025-02-20
Acceptance date:
2024-12-20
DOI:
EISSN:
2366-7451
ISSN:
2366-7443


Language:
English
Pubs id:
2082874
Local pid:
pubs:2082874
Deposit date:
2025-02-02
ARK identifier:

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