Next Article in Journal
Modeling for Three-Pole Radial Hybrid Magnetic Bearing Considering Edge Effect
Previous Article in Journal
An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform
Article Menu

Export Article

Open AccessArticle
Energies 2016, 9(5), 344; doi:10.3390/en9050344

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

Environmental Meteorology, Albert-Ludwigs-University of Freiburg, Werthmannstrasse 10, Freiburg D-79085, Germany
Academic Editor: Frede Blaabjerg
Received: 15 February 2016 / Revised: 21 April 2016 / Accepted: 22 April 2016 / Published: 6 May 2016
View Full-Text   |   Download PDF [4911 KB, uploaded 6 May 2016]   |  

Abstract

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. View Full-Text
Keywords: annual wind energy yield (AEY); Wakeby distribution (WK5); least squares boosting (LSBoost); predictor importance (PI); wind speed extrapolation annual wind energy yield (AEY); Wakeby distribution (WK5); least squares boosting (LSBoost); predictor importance (PI); wind speed extrapolation
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jung, C. High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series. Energies 2016, 9, 344.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top