Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey

Nowadays, energy demand is increasing as a result of growing population all over the world. Current conventional sources are not an adequate level in order to meet this energy requirement. Therefore, it is necessary to consider economic and clean alternative energy sources. In this context, renewable energy sources can be contemplated as a solution for this energy problem. On the other hand, selection among energy alternatives is a multi-criteria decision making (MCDM) problem and it is necessary to make an assessment in terms of several conflicting criteria. Sometimes, it may not be easy to evaluate these criteria by using crisp numbers and we need to evaluate by using human judgements and linguistic terms that can be used for a more flexible and sensitive evaluation. However, the fuzzy sets enable to cope with vagueness of evaluations in decision making process. In this study, an integrated MCDM model based on the fuzzy sets is proposed for prioritization of renewable energy alternatives in Turkey. The suggested fuzzy MCDM model combines analytic hierarchy process (AHP) based on interval type-2 fuzzy sets and hesitant fuzzy TOPSIS methods. Since the type 2 fuzzy sets whose membership functions are also fuzzy and hesitant fuzzy sets that enable to handle situations that an element has several membership value are more able to model uncertainties in decision making process, in this paper a MCDM methodology based on these two methods are suggested to evaluate renewable energy alternatives for Turkey. Interval type-2 fuzzy AHP method is applied to determine the weights of decision criteria, and hesitant fuzzy TOPSIS method is applied to prioritize renewable energy alternatives. A real case application has been presented via expert evaluations to indicate applicability of the proposed model. Besides, a sensitivity analysis has been performed to examine the effects of main criteria weights in ranking.