An integrated fuzzy AHP- fuzzy DEA approach for location optimization of renewable energy plants

This study presents an integrated approach for optimizing the location of renewable energy plants. The proposed approach is composed of fuzzy analytic hierarchy process (FAHP) and fuzzy data envelopment analysis (FDEA). The FDEA and FAHP methods are used to select the preferred location. The results of FDEA are validated by DEA, and then it is employed for ranking of location of renewable energy plants and the best α-cut is selected based on the test of Normality. Also, FAHP that is a method based on expert opinion is used for ranking. Five kinds of renewable energies including solar, wind, geothermal, biofuel and hydrogen and fuel cell are considered. The most related criteria are identified from the literature. The intelligent approach of this study is applied to an actual location optimization of renewable energy plants in Iran. In the proposed case study, in some cases FDEA and FAHP select the same alternatives, and for some other cases different alternatives are preferred by these two methods. According to the obtained results, the proposed approach of this study is ideal for renewable energy plant location optimization with possible ambiguity and uncertainty. The aim of this study is helping managers to select optimal locations for renewable energy plants when experts’ opinions are available or not.