Visible to the public Evaluating power system vulnerability to false data injection attacks via scalable optimization

TitleEvaluating power system vulnerability to false data injection attacks via scalable optimization
Publication TypeConference Paper
Year of Publication2016
AuthorsChu, Z., Zhang, J., Kosut, O., Sankar, L.
Conference Name2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)
Date Publishednov
KeywordsAlgorithm design and analysis, bi-level optimization problem, composability, false data injection cyber-attacks, Generators, IEEE 118-bus system, integer programming, Linear programming, Load flow, lower bounds, Metrics, mixed-integer linear program, Optimization, physical power flow maximization, Polish system, power grid vulnerability analysis, power system security, power system vulnerability evaluation, pubcrawl, Resiliency, scalable optimization, security of data, Smart grids, state estimation, Upper bound, upper bounds, vulnerability assessments, worst-case attack consequences

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.

Citation Keychu_evaluating_2016