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

• Main Author: Christopher Jung
• Format: Article
• Language: English
• Published: MDPI AG 2016-05-01
• Series: Energies
• Subjects: annual wind energy yield (AEY); Wakeby distribution (WK5); least squares boosting (LSBoost); predictor importance (PI); wind speed extrapolation
• Online Access: http://www.mdpi.com/1996-1073/9/5/344

In this paper a methodology is presented that can be used to model the annual wind energy yield (AEYmod) on a high spatial resolution (50 m × 50 m) grid based on long-term (1979–2010) near-surface wind speed (US) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEYmod 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 US values were extrapolated to the height 100 m (U100m,emp) above ground level (AGL) by the Hellman power law. All U100m,emp time series were then converted to empirical cumulative distribution functions (CDFemp). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDFemp 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 (U850hPa), 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 U100m,emp and the modeled wind speed at 100 m AGL (U100m,mod), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEYmod.