Digital-Twin-Based Quantitative Frameworks for Modeling, Monitoring, and Optimization of Electrical Power Infrastructure

Abstract

This study addresses a critical problem in electrical power infrastructure operations: many organizations deploy digital-twin technologies, yet lack a validated, quantitative way to determine which digital-twin capabilities actually drive measurable improvements in reliability, response effectiveness, and operational efficiency. The purpose was to test a digital-twin-based quantitative framework that links core capabilities to infrastructure outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 212 power-infrastructure professionals working in enterprise operational contexts where monitoring and decision-support platforms are used. Key independent variables were Digital Twin Modeling Capability (M), Monitoring Capability (N), and Optimization Capability (O), alongside two domain indices: Digital Twin Fidelity and Synchronization Readiness Index (DT-FSRI) and Event Detection and Response Alignment (EDRA); key dependent variables were Infrastructure Reliability/Continuity (Y₁), Response Effectiveness (Y₂), and Operational Efficiency (Y₃). The analysis plan applied descriptive statistics, reliability and validity testing (Cronbach’s alpha, EFA with KMO and Bartlett’s test), Pearson correlations, and multiple regression models. Headline findings show strong measurement quality (α = .82–.88; KMO = .89; Bartlett’s χ² = 2146.3, p < .001) and moderately high capability levels (Modeling M = 3.94, SD = 0.63; Monitoring M = 4.07, SD = 0.58; Optimization M = 3.76, SD = 0.69). All core relationships were positive and significant (p < .001), including Reliability with DT-FSRI (r = .63) and Monitoring (r = .61), Response Effectiveness with EDRA (r = .65), and Efficiency with Optimization (r = .62). Regression results indicate substantial explained variance for Reliability (R² = .60; F(5,206) = 62.7, p < .001), with DT-FSRI as the strongest predictor (β = .34, p < .001), followed by Monitoring (β = .23, p = .002). Implications suggest that utilities should prioritize synchronization discipline and event-to-response alignment before scaling advanced optimization, because these coherence mechanisms most strongly predict operational performance.