Digital Twin-Enabled Resilient Network Reconfiguration for Cybersecurity of Decarbonized Power Distribution Systems

This paper proposes a digital twin-enabled remediation framework to counter False Data Injection (FDI) cyberattacks targeting power distribution systems integrated with renewable energy-based distributed generation (DG) units. The framework leverages a digital twin to identify the optimal network reconfiguration from a pre-saved archive, mitigating attack impacts by altering the network topology in real-time. Digital twins are particularly relevant in this context, as they create a dynamic, virtual replica of the physical distribution network, continuously updated with real-time data and capable of predictive analysis. This setup allows for the simulation of various attack and reconfiguration scenarios, using data inputs such as system voltage, current, and load patterns to detect anomalies indicative of FDI events.