Vulnerability Assessment of Large-scale Power Systems to False Data Injection Attacks
For power system operators, this work provides practical tools to evaluate worst-case physical consequences of cyberattacks on large-scale grids, addressing a computational bottleneck in existing bi-level optimization approaches.
This paper develops four computationally efficient algorithms to assess the vulnerability of large-scale power systems to false data injection attacks, demonstrating their effectiveness on the IEEE 118-bus and Polish 2383-bus systems by identifying feasible attacks that cause line overflows and providing upper bounds on maximal power flow.
This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical power flow on a target line. This ADBLP can be transformed into a single-level mixed-integer linear program, but it is hard to solve on large power systems due to numerical difficulties. In this paper, four computationally efficient algorithms are presented to solve the attack optimization problem on large power systems. These algorithms are applied on the IEEE 118-bus system and the Polish system with 2383 buses to conduct vulnerability assessments, and they provide feasible attacks that cause line overflows, as well as upper bounds on the maximal power flow resulting from any attack.