Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1283-1289, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
14 Sep 2017
M. Ma1, B. He1, Y. Guan1, H. Zhang1, and S. Song2 1School of Resources and Environment, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, China
2Chengdu Engineering Corporation Limited, North Huanhua Road, Chengdu, China
Keywords: Wind energy resource, Assessment, Global, Utilization potential, Remote sensing, ERA-Interim, Data mining Abstract. Development of wind energy resource (WER) is a key to deal with climate change and energy structure adjustment. A crucial issue is to obtain the distribution and variability of WER, and mine the suitable location to exploit it. In this paper, a multicriteria evaluation (MCE) model is constructed by integrating resource richness and stability, utilization value and trend of resource, natural environment with weights. The global resource richness is assessed through wind power density (WPD) and multi-level wind speed. The utilizable value of resource is assessed by the frequency of effective wind. The resource stability is assessed by the coefficient of variation of WPD and the frequency of prevailing wind direction. Regression slope of long time series WPD is used to assess the trend of WER. All of the resource evaluation indicators are derived from the atmospheric reanalysis data ERA-Interim with spatial resolution 0.125°. The natural environment factors mainly refer to slope and land-use suitability, which are derived from multi-resolution terrain elevation data 2010 (GMTED 2010) and GlobalCover2009. Besides, the global WER utilization potential map is produced, which shows most high potential regions are located in north of Africa. Additionally, by verifying that 22.22 % and 48.8 9% operational wind farms fall on medium-high and high potential regions respectively, the result can provide a basis for the macroscopic siting of wind farm.
Conference paper (PDF, 3267 KB)

Citation: Ma, M., He, B., Guan, Y., Zhang, H., and Song, S.: ASSESSMENT OF GLOBAL WIND ENERGY RESOURCE UTILIZATION POTENTIAL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1283-1289,, 2017.

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