Feasibility stage screening for sustainable energy alternatives with a fuzzy multi-criteria decision analysis protocol

Abstract

Many regions and urban areas are becoming more engaged in selecting the optimum future clean energy technology mix to best fit their local power requirements. At the feasibility stage, such analysis is difficult to perform quantitatively due to the substantial uncertainties associated with many of the key influencing criteria. Moreover, changing climate means the renewable energy mix most suited to many regions is also changing as local climates progressively change. A protocol is proposed and evaluated for conducting qualitative multi-criteria decision analysis (MCDA) of multiple clean energy alternatives, suitable for specific regional conditions, using the TOPSIS method. This begins with linguistic assessments of a large number of pertinent criteria (50 or more) taking into account the diverse preferences of the many stakeholders involved. The linguistic assessments are inverted to integer number, fuzzy and intuitionistic fuzzy scoring (IFS) systems. The IFS method is shown to integrate uncertainty in a more flexible way. The fuzzy and IFS TOPSIS methods adjust their impact matrices with three weight factors: (1) objective weights derived from calculated entropy for each criteria, (2) subjective weights associated with preferences expressed by individual representative stakeholders; (3) subjective weights applied to balance the preferences among stakeholder groups. The three methods are applied using regionally specific case studies to illustrate and compare the clean energy rankings they select for the conditions associated with the specific region evaluated. Fuzzy and IFS scoring systems generate slightly different rankings as they capture uncertainty in different ways.