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Wind resource mapping using landscape roughness and spatial interpolation methods

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Windkracht 13
Abstract
Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium).
Keywords
wind resource mapping, small and medium wind turbines, decision-making tool

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Citation

Please use this url to cite or link to this publication:

Chicago
Van Ackere, Samuel, Greet Van Eetvelde, David Schillebeeckx, Enrica Papa, Karel Van Wyngene, and Lieven Vandevelde. 2016. “Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods.” In Energy Policy and Climate Change, ed. Vincenzo Dovì and Antonella Battaglini, 72–94. MDPI.
APA
Van Ackere, S., Van Eetvelde, G., Schillebeeckx, D., Papa, E., Van Wyngene, K., & Vandevelde, L. (2016). Wind resource mapping using landscape roughness and spatial interpolation methods. In V. Dovì & A. Battaglini (Eds.), Energy policy and climate change (pp. 72–94). MDPI.
Vancouver
1.
Van Ackere S, Van Eetvelde G, Schillebeeckx D, Papa E, Van Wyngene K, Vandevelde L. Wind resource mapping using landscape roughness and spatial interpolation methods. In: Dovì V, Battaglini A, editors. Energy policy and climate change. MDPI; 2016. p. 72–94.
MLA
Van Ackere, Samuel, Greet Van Eetvelde, David Schillebeeckx, et al. “Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods.” Energy Policy and Climate Change. Ed. Vincenzo Dovì & Antonella Battaglini. MDPI, 2016. 72–94. Print.
@incollection{7225469,
  abstract     = {Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth{\textquoteright}s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders (Belgium).},
  author       = {Van Ackere, Samuel and Van Eetvelde, Greet and Schillebeeckx, David and Papa, Enrica and Van Wyngene, Karel and Vandevelde, Lieven},
  booktitle    = {Energy policy and climate change},
  editor       = { Dov{\`i}, Vincenzo and Battaglini, Antonella},
  isbn         = {978-3-03842-158-0},
  language     = {eng},
  pages        = {72--94},
  publisher    = {MDPI},
  title        = {Wind resource mapping using landscape roughness and spatial interpolation methods},
  url          = {http://www.mdpi.com/books/pdfview/book/191},
  year         = {2016},
}