A study on digital twin-driven operational decision-making for climate-resilient highway microgrids

Highway microgrids, which power critical transportation corridors, face escalating threats from extreme climate events. Traditional planning models operate in isolation from real-time control systems, creating a dangerous simulation-operation gap. This study introduces a Digital Twin-Informed Decision Support Framework designed to bridge this gap. The framework integrates a high-fidelity co-simulation environment, a stochastic multi-objective optimizer balancing cost, risk, and reliability, and hybrid model-data algorithms for adaptive control and self-healing. Rigorously validated on a 20-node test system using real climate and synthetic traffic data within a MATLAB/Simulink and OpenDSS co-simulation platform, the Digital Twin-Informed Decision Support Framework is compared against five benchmark strategies. Key results demonstrate its superiority: a 54.6% reduction in post-fault recovery time, maintenance of a system resilience index above 0.85 during severe events, and a 49.2% cut in operational costs, culminating in a ten-year Return on Investment of 1.17. The framework is validated through closed‑loop co‑simulation under five benchmark strategies using real climate data and synthetic traffic, with baseline defined as no optimization. By providing a validated pathway from predictive analytics to actionable control, this work delivers a crucial decision-support tool and a foundational step towards fully bidirectional digital twins for resilient transportation energy infrastructure.