A Fuzzy Logic Approach to Power System Security With Nonideal Electric Vehicle Battery Models in Vehicle-to-Grid Systems

Power systems are becoming increasingly vulnerable to cyberattacks due to extensive information exchange. This article proposes a novel fuzzy inference system (FIS)-enhanced data integrity attack (DIA)-recovery framework and analyzes the combined effects of attacks simultaneously launched by groups with different motivations. These range from market participants seeking a competitive edge for personal economic benefits to outsider groups aiming to drive the system into an uneconomic operation state. A two-stage optimization problem and a fuzzy-Bayesian approach are formulated to counteract the potential attacks on the Electric Vehicle Charging Management Center (EVCMC), where successful manipulation can result in substantial financial gains or disruptions to the power grid. According to the simulation results: 1) the proposed FIS approach provides a dynamic assessment of the attacker’s capability and the vulnerability of EVCMC to potential DIAs; 2) the combined effects of attack launched by market participants and outsider groups can cause more severe economic impacts compared to attack conducted separately; and 3) the proposed attack-recovery scheme can identify the most vulnerable EVCMC to attackers and recover the optimal power dispatch under DIAs.