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Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: A case study

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  • Azadeh, Ali
  • Rahimi-Golkhandan, Armin
  • Moghaddam, Mohsen

Abstract

The use of wind energy as a renewable source of energy is rapidly increasing all over the world as demand for energy is rising. Apart from wind blow, different social and local criteria are important for location optimization of wind power generation–transmission plants. This study presents an integrated fuzzy-DEA approach for decision making on wind plant locations. Besides, an integrated approach incorporating the most relevant indicators of wind plants is introduced. Principal Component Analysis (PCA) and Numerical Taxonomy (NT) are the two multivariate methods used for verification and validation of the results of the DEA model. The proposed model was tested on 25 nominated cities in Iran with 5 regions in each city. In addition, 20 other cities are considered as the consumers of the generated energy. The obtained results indicate the importance of consumers' proximity in wind plant establishment. Moreover, it is shown that fuzzification of uncertain indicators leads to a more realistic approach to this facility location problem.

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  • Azadeh, Ali & Rahimi-Golkhandan, Armin & Moghaddam, Mohsen, 2014. "Location optimization of wind power generation–transmission systems under uncertainty using hierarchical fuzzy DEA: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 877-885.
    • Handle: RePEc:eee:rensus:v:30:y:2014:i:c:p:877-885
    DOI: 10.1016/j.rser.2013.10.020
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