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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2019-108
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-108
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Jan 2020

Submitted as: research article | 17 Jan 2020

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This preprint is currently under review for the journal WES.

Clustering wind profile shapes to estimate airborne wind energy production

Mark Schelbergen1, Peter C. Kalverla2, Roland Schmehl1, and Simon J. Watson1 Mark Schelbergen et al.
  • 1Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
  • 2Meteorology and Air Quality Section, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands

Abstract. Airborne wind energy (AWE) systems typically harness energy in an altitude range up to 500 m above the ground. To estimate the annual energy production (AEP), measured wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from reanalysis data or long-term LiDAR measurements that include the vertical variation of the wind speed and direction. K-means clustering is used to identify a set of prevailing wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas and LiDAR observations for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the center of the Netherlands, respectively. The resulting wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with literature. Finally, it is demonstrated how a set of wind profile shapes and their statistics can be used to estimate the AEP of a pumping AWE system. For four or more clusters, the site specific AEP error is within a few percent of the converged value.

Mark Schelbergen et al.

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Mark Schelbergen et al.

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Latest update: 26 Feb 2020
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Short summary
We have presented a new methodology to identify a set of characteristic wind profile shapes using clustering for estimating site specific airborne wind resource. The wind resource representation is used for fast AEP calculations for pumping AWE systems. This is necessary because the validity of conventional wind profile parametrisations, such as the logarithmic profile, is limited for the full height range in which these systems operate.
We have presented a new methodology to identify a set of characteristic wind profile shapes...
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