DT-Grid: A Digital Twin Framework for Real-Time State Awareness and Operational Optimization of Renewable-Dominated Power Systems

Renewable-dominated power systems need operational digital twins that do more than mirror assets: they must convert streaming evidence into state awareness and secure control actions. This paper presents DT-Grid, a digital twin (DT) framework for real-time state awareness and operational optimization. DT-Grid treats the twin as an evidential control layer with four coupled functions: a topology and data twin, robust temporal state assimilation, confidence-envelope construction, and rolling optimal power flow (OPF). The state-awareness module solves a Huber-weighted, temporally regularized estimation problem and exposes residual information to the optimization layer. The dispatch layer then uses this evidence as a security margin while scheduling conventional generation, renewable acceptance, and corrective actions. We evaluated the framework on the PGLib IEEE 118-bus benchmark driven by Open Power System Data Germany load, wind, and solar profiles over 365 operating points and three measurement seeds. DT-Grid reduced injection root mean square error (RMSE) from 464.6 MW under static weighted least squares (WLS) to 217.0 MW, improved bad-data F1 from 0.140 to 0.149, and reduced the realized overload proxy by 97.9% compared with persistence-driven OPF. The results indicate that state evidence is most valuable when it is carried into dispatch constraints, while pure temporal smoothing can still produce lower phase-angle error in some operating points.