Deploying a Generic Threat Model for Detecting Anomalies in a Power Grid Digital Twin

Monitoring power grid infrastructures typically generates a massive amount of power consumption data related to different components or communication channels. This is typically employed for power optimization, but does not suffice for conducting other key tasks for guaranteeing desirable properties as reliability, safety and security. In these cases, the grid should be monitored for detecting anomalies due to security threats, component failures, environmental damages, or other hazards. This is the case of the Grid Data’s Digital Twin industrial scenario, which provides an up-to-date grid image that combines actual measurement data and a time-series-based grid model that closely approximates reality. To tackle this, this paper analyzes the state of the art of existing threat models for smart grids, proposing a generic and comprehensive threat and anomaly model that is then used to craft power consumption anomaly detectors for the case study above. This work was conducted by members from academia and industrial partners to show how to deploy power consumption anomaly detectors in the wild, showing a methodology that is generic enough to be applied also by other stakeholders.