Strategic battery energy storage system site selection in Türkiye's Black Sea region using an intuitionistic fuzzy decision methodology

Rapid population growth, technological developments, and rising living standards have steadily increased global energy demand. Despite the growing interest in battery energy storage systems (BESSs), limited research has addressed their site selection under uncertainty using a comprehensive, uncertainty-aware framework. This study aims to select the most suitable location for BESS deployment by integrating technical, economic, environmental, and resilience-related criteria within a structured decision-making process. A novel hybrid approach combining Intuitionistic Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) and Intuitionistic Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (IF-VIKOR) is employed to handle expert-based evaluations and rank candidate locations in Türkiye's Black Sea region. This hybrid structure offers an advanced and structured solution to complex location selection problems. The findings indicate that renewable energy potential and technical suitability are the most influential factors. A sensitivity analysis further confirms the robustness of this result across varying weights for the criteria. The proposed model not only contributes to the literature but also provides an applied tool to support energy planners and policymakers in making knowledgeable BESS investment decisions. This study contributes a replicable methodology for decision-makers aiming to integrate renewable energy and storage solutions, and it offers a structured path forward for future research in energy infrastructure planning under uncertainty. • A hybrid MCDM model supports BESS site selection under renewable uncertainty. • IF-SWARA and IF-VIKOR are integrated within an intuitionistic fuzzy framework. • Renewable potential and technical suitability rank highest among main criteria. • Grid proximity is identified as the most critical sub-criterion. • The model offers a transferable approach for sustainable energy planning.