Assessment of energy storage systems using a linguistic spherical fuzzy Hamacher aggregation operators-based group decision making model

Energy storage systems (ESSs) play a crucial role in enhancing grid stability, facilitating renewable energy integration, and supporting sustainable energy transitions. However, selecting the most suitable ESS remains a challenging task due to the presence of multiple conflicting criteria and the inherent uncertainty in expert evaluations. To address this issue, this study proposes an integrated multi-criteria group decision-making (MCGDM) framework based on linguistic spherical fuzzy (LSF) information. In the proposed approach, novel Hamacher aggregation operators are developed under the LSF environment to effectively combine expert judgments while capturing complex interactions among decision elements. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is employed to objectively determine criteria weights by considering both contrast intensity and inter-criteria correlations. Furthermore, the Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS) method is extended under the LSF framework to rank alternatives based on compromise solutions. To demonstrate the applicability of the designed LSF-CRITIC-MARCOS MCGDM model, a case study on energy storage systems assessment is conducted, considering five representative alternatives evaluated under technological, economic, environmental, and social criteria. The results identify electrochemical energy storage systems as the highest ranked category among the considered options, indicating their priority for subsequent detailed assessment. The robustness of the proposed framework is examined through sensitivity analysis, which confirms that the preferred alternative remains unchanged across the tested Hamacher parameter values, and through comparative analysis, which demonstrates consistency and improved discriminative capability compared to existing methods. The findings of this study highlight that the proposed LSF-CRITIC-MARCOS framework provides a reliable, flexible, and effective decision-making tool for handling uncertainty in complex assessment problems. The developed approach can support decision-makers in the strategic-level screening and prioritization of energy storage technology categories during early-stage sustainable energy planning, and can be extended to a wide range of real-world decision-making applications.