High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Christopher Jung
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Christopher Jung: Environmental Meteorology, Albert-Ludwigs-University of Freiburg, Werthmannstrasse 10, Freiburg D-79085, Germany

Energies, 2016, vol. 9, issue 5, 1-20

Abstract: In this paper a methodology is presented that can be used to model the annual wind energy yield ( AEY mod ) on a high spatial resolution (50 m × 50 m) grid based on long-term (1979–2010) near-surface wind speed ( U S ) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEY mod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The U S values were extrapolated to the height 100 m ( U 100m,emp ) above ground level (AGL) by the Hellman power law. All U 100m,emp time series were then converted to empirical cumulative distribution functions (CDF emp ). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDF emp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level ( U 850hPa ), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance ( PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U 100m,emp and the modeled wind speed at 100 m AGL ( U 100m,mod ), was identified. Results from relative PI -evaluation demonstrate that the most important predictor variables are relative elevation (?) and topographic exposure (?) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEY mod .

Keywords: annual wind energy yield ( AEY ); Wakeby distribution (WK5); least squares boosting (LSBoost); predictor importance ( PI ); wind speed extrapolation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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