A digital twin framework for flexibility management using electric vehicle aggregators with embedded cyber-attack detection

This paper presents a real-time Digital Twin (DT) framework for power distribution networks that leverages Electric Vehicles (EVs) as flexible energy storage to support peak load management. EVs are coordinated through aggregators, serving as an interface between the network operator, energy market, and EV owners. The DT–physical system communication remains vulnerable to cyberattacks and data manipulation. To address this, an Artificial Neural Network (ANN) is trained on historical data to detect and mitigate such attacks, ensuring robust operation. The framework is applied to Jordan’s distribution network with high renewable penetration, demonstrating its ability to deliver flexibility services, detect cyberattacks, and accurately reconstruct corrupted measurements, achieving a maximum Mean Squared Error (MSE) of 3.46 × 10 − 7 .