Digital Twin–Enabled Context-Aware Networks: Enhancing Smart Grid Resilience through Predictive Intelligence
S. B. Goyal, Bhaksara Rallabandi, Krishna Kumar Gattupalli, Monish Sai Medarametla
Abstract
The blistering development of smart grids to a complex object of cyber-physical nature has increased the issues of resiliency, scalability and security. In future efforts of overcoming these challenges, the present paper has come up with a Digital Twin-enabled Context-Aware Smart ground structure, which incorporates predictive intelligence, adaptable communications, and secure implementation. The digital twin is a high-fidelity and real-time simulation of grid operation, which allows preemptively identifying failures, demand, and anomalies. The dynamics of context-aware intelligence guarantees the dynamical prioritization of communication and control activities in terms of situational urgency, whereas blockchain and zero-trust systems facilitate the process of strengthening the integrity and cyber resilience of data. The evaluation research on a hybrid simulation testbed proves that the proposed framework can predict faults with a 93% fault prediction F1-score, demand forecasting error can be minimized by more than 40 percent, and the anomaly detection precision is 96. In addition, the system has a shorter response time by 52 per cent and a higher resilience index by 46 per cent, in comparison to conventional SCADA based systems, at a low security overhead cost. The presented findings prove the opportunities of co-locating digital twins and context-aware networking towards providing anticipatory resilience to the next-generation smart grids.