Dynamic multicriteria ranking of energy storage systems using a linear programming-based decision framework

AbstractThe increasing diversity of energy storage technologies and their rapidly evolving performance characteristics make robust, transparent, and dynamic decision-support tools essential for technology selection. This study proposes a dynamic multicriteria decision-making framework based on the SIMUS (Sequential Interactive Modeling for Urban Systems) method to rank energy storage systems (ESS) under evolving technical and economic conditions. The approach integrates linear-programming-based optimization with vertical (Efficient Results Matrix) and horizontal (Project Dominance Matrix) aggregation, enabling consistent ranking without subjective weighting. To assess ranking robustness, an Input–Output Sensitivity Analysis (IOSA) is introduced, enabling systematic evaluation of how perturbations in constraint limits affect both optimal objective values and ranking outcomes.The framework is applied to a comparative assessment of ten ESS technologies using datasets representative of two time horizons (2018 and 2025). SIMUS yields consistent top rankings for pumped storage hydro (PSH) and ultracapacitors in both years; in 2018, the top four technologies are PSH (1st), ultracapacitors (2nd), compressed air energy storage (CAES, diabatic) (3rd), and zinc hybrid cathode (4th). In 2025, the ranks are: PSH (1st), ultracapacitors (2nd), CAES (diabatic) (3rd), and flywheel (4th). Between 2018 and 2025, key input updates include total CAPEX reductions of −23% for Li-ion and − 15.5% for lead-acid. IOSA shows that, for the 2025 dataset, tightening the lifetime and rated-energy constraints by −50% reduces the normalized rated-power objective Z5 from ∼0.70 to ∼0.10 and shifts the top rank from PSH to lead-acid, highlighting strong constraint-driven trade-offs. Comparisons with TOPSIS and VIKOR confirm agreement on the two leading options (PSH and ultracapacitors) and reveal divergences among mid-ranked technologies. The proposed methodology offers a robust decision-support tool for policymakers and planners facing uncertainty and temporal evolution in energy storage performance.