Data Injection Attacks on Electricity Markets by Limited Adversaries: Worst-Case Robustness

Electricity markets consist of multiple look-ahead and real-time spot markets, across which energy price is generally volatile. Moreover, dispatch and pricing decisions in the real-time markets strongly hinge on the quality of the real-time state estimates, which are formed dynamically in order to delineate real-time information about operational state of the grid. Adversaries can leverage price volatility in conjunction with the dependence of the real-time markets on the state estimates in order to carry out profitable financial misconduct, e.g., via virtual bidding on the locational marginal prices. When the adversaries are omniscient (i.e., have full and instantaneous access to grid topology and dynamics), the attack strategies for maximizing financial profits are studied extensively in the existing literature. This paper focuses on limited adversaries who have only partial and imperfect information about the grid, and offers a framework for analyzing the attack strategies for limited adversaries and the associated confidence about profitable attacks. Specifically, adversaries’ information is considered to be within a measure of bounded error, and attack strategies are designed such that the adversaries are guaranteed a certain level of confidence to gain profit. Designing such attacks is investigated analytically and examined in the IEEE 14- and 118-bus systems.