Evaluating Power System Vulnerability to False Data Injection Attacks via Scalable Optimization

TL;DR

This paper introduces scalable algorithms to evaluate power system vulnerability to false data injection attacks, enabling analysis of large systems by efficiently estimating maximum power flow impacts.

Contribution

It presents three new computational algorithms that provide bounds on system vulnerability, improving scalability over previous mixed-integer linear programming methods.

Findings

Algorithms successfully assessed IEEE 118-bus system vulnerability.

Algorithms evaluated Polish system with 2383 buses.

Bounds effectively estimate maximum power flow impacts.

Abstract

Physical consequences to power systems of false data injection cyber-attacks are considered. Prior work has shown that the worst-case consequences of such an attack can be determined using a bi-level optimization problem, wherein an attack is chosen to maximize the physical power flow on a target line subsequent to re-dispatch. This problem can be solved as a mixed-integer linear program, but it is difficult to scale to large systems due to numerical challenges. Three new computationally efficient algorithms to solve this problem are presented. These algorithms provide lower and upper bounds on the system vulnerability measured as the maximum power flow subsequent to an attack. Using these techniques, vulnerability assessments are conducted for IEEE 118-bus system and Polish system with 2383 buses.