Articles | Volume 10, issue 7
https://doi.org/10.5194/wes-10-1471-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-1471-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating long-term annual energy production from shorter-time-series data: methods and verification with a 10-year large-eddy simulation of a large offshore wind farm
Bernard Postema
CORRESPONDING AUTHOR
Whiffle BV, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Meteorology and Air Quality Group, Wageningen University & Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, the Netherlands
Remco A. Verzijlbergh
Whiffle BV, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Department of Engineering Systems and Services, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands
Pim van Dorp
Whiffle BV, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Peter Baas
Whiffle BV, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Harm J. J. Jonker
Whiffle BV, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
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To design safe wind turbines, we need to know how strong the once-in-a-50-year wind could be. However, most wind measurements only last a year, which makes this task challenging. We tested a new method that combines one year of real measurements with detailed computer simulations of the atmosphere. This approach gave results nearly as accurate as using 20 years of measured data. It could help wind energy projects make better decisions with shorter measurement campaigns.
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To design safe wind turbines, we need to know how strong the once-in-a-50-year wind could be. However, most wind measurements only last a year, which makes this task challenging. We tested a new method that combines one year of real measurements with detailed computer simulations of the atmosphere. This approach gave results nearly as accurate as using 20 years of measured data. It could help wind energy projects make better decisions with shorter measurement campaigns.
Peter Baas, Remco Verzijlbergh, Pim van Dorp, and Harm Jonker
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This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
Gerard Schepers, Pim van Dorp, Remco Verzijlbergh, Peter Baas, and Harmen Jonker
Wind Energ. Sci., 6, 983–996, https://doi.org/10.5194/wes-6-983-2021, https://doi.org/10.5194/wes-6-983-2021, 2021
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In this article the aeroelastic loads on a 10 MW turbine in response to unconventional wind conditions selected from a year-long large-eddy simulation on a site at the North Sea are evaluated. Thereto an assessment is made of the practical importance of these wind conditions within an aeroelastic context based on high-fidelity wind modelling. Moreover the accuracy of BEM-based methods for modelling such wind conditions is assessed.
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Short summary
Atmospheric large-eddy simulation is a technique that simulates weather conditions in high detail and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm without having to simulate 10 years of weather and instead simulating much less (1 year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude and that wind observations can be included as well to add more insight.
Atmospheric large-eddy simulation is a technique that simulates weather conditions in high...
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