A Resilient Predictive Control Framework for Frequency Regulation in EV-Integrated Power Systems Against Covert Cyberattacks

Abstract

The growing penetration of renewable energy sources in multi-area interconnected power systems (MAIPSs) poses significant challenges for frequency regulation (FR) due to their variable and intermittent output. Electric vehicles (EVs) equipped with vehicle-to-grid technology have emerged as a promising resource to support FR, offering a fast and flexible response. However, the use of open communication networks for real-time coordination among control centers, measurement units, and EV aggregators exposes the system to cyberattacks, particularly covert attacks. These highly stealthy attacks manipulate control commands to degrade FR performance while simultaneously altering measurements to evade detection. To mitigate these attacks, this paper proposes a resilient model predictive control (MPC) framework for EV-supported FR in MAIPSs under covert attacks. The proposed framework combines a primary one-step delay MPC (PD-MPC) for secure nominal operation with an auxiliary MPC (A-MPC) designed to ensure a smooth transition back to nominal performance immediately after an attack ends. An attackduration assessment is introduced to trigger communication restoration only for prolonged attacks, which avoids unnecessary disconnection–reconnection cycles. Practical limits—governor dead band, generation-rate constraints, and EV frequency regulation capacity—are enforced via tightened MPC constraints to maintain feasibility under normal and attack conditions. The framework also provides formal guarantees that the closed-loop control remains feasible, satisfies these constraints, and is stable under both normal operation and covert attacks. Real-time simulations on the modified New England 39-bus power system, conducted on an OPAL-RT/RT-LAB platform, demonstrate the effectiveness of the proposed framework against covert attacks.