TwinEDGE- Edge First digital twin dockerised application for behaviour detection intrusion

A digital twin is a virtual representation of a physical object or system, enabling real-time monitoring and analysis. It enhances efficiency, optimizes performance, and reduces costs by simulating real-world scenarios. Due to their simulation properties, digital twins can be used to detect anomalous behavior.

As the national grid anticipates more than 7 million flexible loads to be digitally connected, the number of cybersecurity vulnerability checkpoints in the system will increase. While existing cybersecurity systems are capable of detecting intrusions from malicious actors, they cannot detect changes in asset behavior. A skilled malicious actor could potentially take control of a critical national asset (or 1 million EV cars), inject false data into the system, and remain completely undetected by current cybersecurity measures.

If such threats go undetected, they could have a detrimental effect on national grid stability, fair energy billing, and overall grid resilience.

This project aims to develop digital twin models for energy asset equipment and substations using highly granular equipment data. These digital twin models will have low configuration requirements and can be deployed on virtual containers for scalable deployment.