An extended interval-valued Pythagorean fuzzy WASPAS method based on new similarity measures to evaluate the renewable energy sources

Abstract

The recent decade has arisen a significant issue in the energy sector, which is how to select proper sources of renewable energy as a sustainable substitution for conventional forms of fossil fuels. The way of solving this problem will meaningfully affect the environmental development and economic growth. To handle the issue, various scholars have concentrated on preferring the desirable energy source by employing the decision-making model based on the different fuzzy sets methods. Therefore, the aim of this paper is two folds. Firstly, various renewable resources potential are reviewed, and secondly, an assessment model is developed for prioritizing renewable options. Five major resources, hydropower, solar, wind, biomass, geothermal are considered. The present paper attempted to propose an integrated method on the basis of the Weighted Aggregated Sum Product Assessment (WASPAS) method in a way to provide an effective solution to decision-making problems on interval-valued Pythagorean fuzzy sets (IVPFSs). For the aim of calculating the criteria weights, the subjective weights offered by decision-makers were combined with objective weights achieved by means of the similarity measure method. This combination helped to attain more realistic weights. In the case of objective and subjective weights, new similarity measures and enhanced score functions were applied to IVPFSs. In addition, a renewable energy source selection problem is addressed in order to demonstrate the developed method is completely applicable to the real-world Multi-Criteria Decision-Making (MCDM) problems. This study also involves a sensitivity analysis using various weights of criteria as well as various values of the method’s parameters in a way to approve the developed method stability. As revealed by the performed analysis, the integration of the subjective and objective weights improved the developed method stability with various weights of criteria. To reliably evaluate the performance of the method developed here, its results were compared with those of different methods formerly proposed in the literature. The evaluation results showed that the wind energy with a maximum assessment score degree (0.6259) using the proposed method was found the best option for selecting renewable energy sources over diverse criteria.