False Data Injection Attacks on Smart Grid Voltage Regulation With Stochastic Communication Model

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 5; pp. 7122 - 7132
• Main Authors: Liu, Yuan, Ardakanian, Omid, Nikolaidis, Ioanis, Liang, Hao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Charging stations; Constraints; Cyberattacks; distribution system state estimation; Electric potential; Electric vehicle charging; electric vehicles (EVs); Informatics; Optimization; Phasor measurement units; Real-time systems; Smart grid; stochastic optimization; Stochastic processes; Voltage; Voltage measurement
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3209287

With the growing adoption of electric vehicles (EVs) and advent of bidirectional chargers, EV aggregators, such as charging stations, will become a major player in electricity markets, providing voltage regulation (VR) or other services. We present a novel and practical VR scheme that takes advantage of the charging flexibility of EVs in charging stations that are connected to buses in a distribution grid. This VR scheme relies on real-time measurements, as well as estimates of the distribution system state and regulation capacity of each charging station. We then propose a novel false data injection attack against the VR capacity estimation process that exploits the uncertainty in EV mobility and network conditions. We show the attack vector with the largest expected adverse impact is the solution of a stochastic optimization problem, subject to a constraint that ensures it bypasses bad data detection. We determine this attack vector by solving a sequence of convex quadratically constrained linear programs. The case studies examined in a cosimulation platform, based on two standard test feeders, reveal the vulnerability of the VR capacity estimation process.