This paper presents a practical method to assess the impact of multiple uncertainties in dynamic load models and renewable generation on short-term voltage stability (STVS), with seamless integration into diverse analysis environments and common industry workflows. The proposed method targets deterministic worst-case analysis and does not rely on assumed probability distributions; probabilistic uncertainty quantification is considered complementary but is outside the scope of this paper. Building on trajectory sensitivity analysis, the proposed approach extends conventional sensitivity-based indices by introducing a new metric that accounts for parameter variability. The resulting index quantifies the influence of uncertain parameters, and additional descriptive measures are incorporated to support systematic interpretation of uncertainty impacts. Using the index information, the most influential parameter sets associated with worst-impact conditions are identified and evaluated via targeted nonlinear simulations. This workflow mitigates the limitations of sensitivity-based approximations and enables the assessment of extreme scenarios under substantial uncertainties by capturing nonlinear system behavior. Case studies on the IEEE 39-bus system and a large-scale real-world Korean power system demonstrate that the proposed index-based approach supports reliable uncertainty-aware STVS analysis and improves the practicality of stability studies. • Trajectory sensitivity-based index combining parameter variability and sensitivity. • Index-guided identification of high-impact parameter sets for targeted simulations. • Simulator-independent scalable framework for large multi-parameter uncertainty study.
Jae-Kyeong Kim (Sat,) studied this question.