We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and continuously adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 × 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 × 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting height at the approximate hub height of wind turbines, the wind power density which is available for 95 % of the time increases by a factor of two. (Philip Bechtle), m.schelbergen@tudelft.nl (Mark Schelbergen), r.schmehl@tudelft.nl (Roland Schmehl), zillmann@airbornewindeurope.org (Udo Zillmann), s.j.watson@tudelft.nl (Simon Watson)
This paper presents the case study of possible design improvements for 20 MW Vertical Axis Wind Turbine (VAWT) rotors. Structural optimizations of a 3-bladed carbon-fiber H-rotor and Darrieus rotor are performed for different rotor sizes and heights. The results are used to construct rotor mass scaling trends for VAWT rotors. Furthermore, critical failure modes and their driving loads are identified. To mitigate fatigue and buckling in the blade, a non-constant chord distribution is recommended. Furthermore, further research on improving the buckling performance of the H-rotor strut is recommended. Nomenclature A Projected rotor area, m 2 a Curve coefficient of power curve fit b Curve exponent of power curve fit C p Power coefficient c ineq Curve exponent of power curve fit D Rotor diameter, m H Rotor height, m m Mass, kg R Rotor radius, m P Power capacity, W
Abstract. Airborne wind energy (AWE) systems harness energy at heights beyond the reach of tower-based wind turbines. To estimate the annual energy production (AEP), measured or modelled 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 modelled datasets that include the variation in the wind speed and direction with height. K-means clustering is used to identify a set of wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the centre of the Netherlands, respectively. The cluster-mean wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with the literature. Finally, it is demonstrated how a set of wind profile shapes is used to estimate the AEP of a small-scale pumping AWE system located at Cabauw, which requires the derivation of a separate power curve for each wind profile shape. Studying the relationship between the estimated AEP and the number of site-specific clusters used for the calculation shows that the difference in AEP relative to the converged value is less than 3 % for four or more clusters.
The quasi-steady performance model (QSM) has been developed specifically for pumping airborne wind energy systems using flexible membrane wings. In this study, we validate this model using a comprehensive set of flight data that includes 87 consecutive pumping cycles and is acquired with the development platform of Kitepower B.V. The aerodynamic properties of the kite are determined using onboard measurements of the relative flow velocity. We found that neglecting the vertical wind component and straightening and slacking motion of the tether lead to substantial errors in the kite velocity calculated using the system model. A reasonable agreement between the QSM simulations and flight data can be obtained by multiplying the kite’s drag coefficient by a fudge factor and thereby turning the QSM into a grey-box model. The model accuracy is statistically evaluated as opposed to only evaluating a single pumping cycle per system configuration as done in earlier research.
Thank you for the comprehensive comments. We feel that they were very helpful for increasing the quality of the paper to the current level. Your comments, together with those of referee #1, led to a thorough revision of the paper. The most important changes to the paper include:1. Including information on orography 2. Discarding the lidar discussion 3. Using one stability metric: the Obukhov length, and corresponding classification for identifying stability trends within the clusters C1
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