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Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.
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This content will become publicly available on May 1, 2026
Benchmarking Engineering Wake Models for Assessing Wind Farm Wakes Interaction
Abstract As the world transitions towards sustainable energy sources, offshore wind farms are one of the options under consideration in several countries. Some countries, for instance, the Netherlands, Denmark, Germany, the UK, and China, have already constructed multiple offshore wind farms. Other countries, such as the United States in North America and Brazil in South America, are making movements toward offshore wind. One potential problem is that offshore wind farm wakes can extend for longer distances than onshore, placing challenges on potential wake losses due to farm-to-farm interaction effects. This study proposes a farm-to-farm benchmark to complement ongoing experimental and computational efforts. We consider eight engineering wake models and 31 cases of pairs of existing offshore wind farms, totaling 248 simulations. The results varied according to the engineering wake model applied, alignment (or not) of neighboring wind farms with the prevailing wind direction, and wind turbine capacity. The range of AEP loss significantly varied between 0.075% and around 2.3% in the extreme cases.
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- Award ID(s):
- 2347702
- PAR ID:
- 10627274
- Publisher / Repository:
- IOPscience
- Date Published:
- Journal Name:
- Journal of Physics: Conference Series
- Volume:
- 3016
- Issue:
- 1
- ISSN:
- 1742-6588
- Page Range / eLocation ID:
- 012045
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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