• A quantifiable multi-dimensional framework integrating economic, environmental, safety, and efficiency indicators is established, revealing significant differences among sizing schemes. • A stable hybrid weighting mechanism is proposed by adaptively fusing subjective hesitant fuzzy weights and improved entropy weights, ensuring ranking stability under varying perspectives. • A robust behavioral decision model is developed by enhancing TODIM with a safety-aware value function, identifying superior schemes with clear discrimination across preferences. Effective planning of hybrid electric-hydrogen energy storage systems requires robust assessment and decision-making under multidimensional performance trade-offs and deep uncertainties. To address the persistent challenge of aligning quantitative performance data with risk-aware decision preferences in energy storage planning, this study proposes a preference-aware multidimensional assessment and fuzzy decision-making framework. First, a comprehensive performance indicator system covering economic, environmental, energy-efficiency, and safety dimensions is established. Second, a hybrid information-processing method based on probabilistic hesitant fuzzy linguistic term sets is introduced to represent evaluation uncertainty. Third, an adaptive weighting strategy is developed by combining subjective judgments with an improved entropy-based objective weighting method. Finally, an interactive multi-criteria decision-making method that incorporates prospect theory and a safety-aware value function is applied to rank alternative sizing schemes. The results show that, under the balanced-preference setting, the proposed Adaptive TODIM method yields a standard deviation of 0.3766 and a coefficient of variation of 0.6898 for the composite scores of the five candidate sizing schemes, indicating strong discriminative capability. The top-ranked scheme remains optimal across the tested range of weight-fusion coefficients and shows near-zero elasticity to key behavioral-parameter perturbations, confirming the robustness of both the assessment results and the decision-making process.
Sun et al. (Fri,) studied this question.