Clustering wind profile shapes to estimate airborne wind energy production


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.

Wind Energy Science Discussions
Mark Schelbergen
PhD Researcher

My research is focused on airborne wind energy resource assessment and performance modeling.