Long-term corrected wind resource estimation for AWE converters

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David Wölfle
Book title
Airborne Wind Energy Conference, Freiburg, Germany
At AWEC2015 an approach has been presented to estimate the potential power yield of airborne wind energy (AWE) systems over larger areas [1]. This approach has been demonstrated for an Enerkíte EK200 system. It has been shown thereby that the potential power yield may depend on certain AWE specific factors, like minimum wind speed for departure, the optimal operational height or de-icing measures of the airborne system. The presented results have only covered a two-year period, 2012 and 2013, due to the limited availability of high resolution meteorological input data. However, it is known from wind resource estimation for conventional wind power plants that coverage of a decade, better 30 years or more, is required for a power predication. This is as the average wind speed and wind speed distribution vary significantly from year to year. To achieve this long-term wind resource estimation for AWE, a technique commonly referred to as MeasureCorrelate-Predict (MCP) has been applied, which is state of the art for conventional wind turbines. Thereby the MERRA2 meteorological reanalysis dataset, which covers 1980 till today, has been employed as long-term reference. The year 2012 of the already presented wind resource estimation for AWE has been used as training data, while 2013 has been kept back as independent evaluation dataset. Transfer functions between the long-term reference values for 2012 and training data have been computed for potential power yield, as well as for the power not produced due to unmet starting conditions and deicing measures. By application of the transfer functions on the full time span covered by MERRA2 virtual longterm data has been computed. The quality of the longterm data has been evaluated by comparing the virtual long-term data of 2013 with the evaluation dataset. This presentation will cover a review of 2015’s results and methods, an introduction to the MCP method as well as the used transfer functions. The long-term results for potential power yield and power not produced due to unmet starting conditions and de-icing measures will be shown and compared to the equivalent measures generated from the two-year period. Finally, the quality of the long-term data will be discussed. References: [1] Brandt, D., et al.: Adapting wind resource estimation for airborne wind energy converters. In: Proceedings of the 6th Airborne Wind Energy Conference, Delft University of Technology, The Netherlands, 15-16 June 2015. http://resolver.tudelft.nl/uuid:cfc030a3- d6d1-4baf-99b3-d89e5fa8aefc
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Published by
David Woelfle