Location optimization of wind plants in Iran by an integrated hierarchical Data Envelopment Analysis

Abstract

Unique features of wind energy have caused increasing demands for such resources in various countries. In order to use wind energy as a natural resource, environmental circumstances and geographical location related to wind intensity must be considered. Different factors may affect on the selection of a suitable location for wind plants. These factors must be considered concurrently for optimum location identification of wind plants. This article presents an integrated approach for location of wind plants by hierarchical Data Envelopment Analysis (DEA). Furthermore, an integrated approach incorporating the most relevant indicators of wind plants is introduced. Moreover, two multivariable methods namely, Principal Component Analysis (PCA) and Numerical Taxonomy (NT) are used to validate the results of DEA model. The prescribed approach is tested for 25 different cities in Iran with 5 different regions within each city. The approach of this study has been validated by the previous studies and actual data of wind plants in Iran. This is the first study that considers an integrated mathematical approach for location optimization of wind plants. Implementation of the proposed approach would enable the energy policy makers to select the best possible location for construction of a wind power plant with lowest possible cost.