Energy Storage System Selection by Using Complex Intuitionistic Fuzzy Rough MCDM Technique Based on Schweizer-Sklar Operators

Energy Storage System (ESS) is a talented solution to overcome the intermittency (that they do not produce energy all the time) and demand-supply misalliance problems in different renewable energy systems. Selecting the most optimal ESS requires the consideration of different conflicting criteria under uncertainty. This study presents a novel Multi-Criteria Decision-Making (MCDM) framework based on Complex Intuitionistic Fuzzy Rough Sets (CIFRSs) and Schweizer-Sklar aggregation operators to facilitate a more comprehensive and flexible ESS selection process. Specifically, we develop new aggregation operators namely, the Complex Intuitionistic Fuzzy Rough (CIFR) Schweizer-Sklar weighted average and the CIFR Schweizer-Sklar weighted geometric operators to model imprecise, vague, and inconsistent information. CIFR-MCDM methodology captures the intuitionistic, roughness and extra related fuzzy information in one structure. A case study is performed to illustrate the applicability of the suggested method in ranking different ESS alternatives. Comparative analysis with existing approaches confirms the robustness and effectiveness of the proposed framework in handling complex decision environments. The results highlight the potential of the CIFR-MCDM methodology to support informed and reliable ESS selection in renewable energy applications.