A new GDM based AHP framework with linguistic interval fuzzy preference relations for renewable energy planning

Abstract

Renewable energy resources (RER) are one of the most growing energy sources in the world and various researches point out that these resources will have vital importance in the future. On the other hand, limited reserves and negative environmental impacts of fossil fuels make investors to consider RER for their needs in a sustainable way. To select most appropriate RER alternatives, it is essential to prioritize alternatives which provide minimum negative environmental impact caused by RER operations and maximum financial benefits to the investor. This study aims to develop a novel RER evaluation model that can help investors with their critical selection process and identify the priority of RER alternatives. High complexity of socioeconomic environments often makes it difficult for a single decision maker (DM), investor in our case, to consider all important aspects of selection problems. Therefore, group decision making (GDM) is often preferred to avoid the bias and minimize the partiality in this kind of decision process. In some critical situations DMs' judgments can be under uncertainty where it is difficult for them to provide exact numerical values. In these cases, linguistic interval fuzzy preference relations are utilized to better represent DMs' preferences. This paper develops a new GDM approach based on fuzzy analytic hierarchy process (AHP) with linguistic interval fuzzy preference relations for obtaining weights of selected evaluation criteria. Then, the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) approach is applied for ranking suitable RER alternatives. The originality of the paper comes from the application of these combined methodologies in literature for the first time and no previous work has investigated this RER subject using this kind of an integrated method. For demonstrating the potential of the proposed approach, a motivated example is given to select the most suitable RER for Turkey.