Abstract

Energy storage technology (EST) plays a foundational role for dealing with the intermittency of wind power and accelerating the structural revolution of renewable energy systems. Generally, EST selection is treated as a multiple-criteria group decision-making problem. However, stakeholders are not allowed to express multiple preferences via personalized linguistic distribution assessment and their risk appetites have received less attention in the existing approaches. This study aims at prioritizing ESTs by developing a novel multistage support framework where multi-granular unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) are adopted to depict and quantify stakeholders' opinions based on personalized semantics and granularities. A sustainable index system is devised in four dimensions (economic, technical, environmental and social) and the extended best-worst method (BWM) under multi-granular UHFLTSs environment is combined with maximum deviation method to determine the hybrid weights of criteria. A novel approach linking multi-granular UHFLTSs with double parameters TOPSIS method integrating risk appetite and optimism preference of stakeholders is further proposed by constructing optimization model, which can simultaneously yield the credible experts' weights and prioritize the most desirable technology. The application of the proposed framework is demonstrated through an empirical case. Eventually, sensitivity analysis and comparative analysis are implemented to verify the effectiveness and validity of our proposal.(c) 2022 Elsevier B.V. All rights reserved.