False Data Injection Attacks in Electricity Markets by Limited Adversaries: Stochastic Robustness

Deregulated electricity markets rely on a two-settlement system consisting of day-ahead and real-time (RT) markets, across which electricity price is generally volatile. In electricity markets, locational marginal pricing is widely adopted to set electricity prices and manage transmission congestion. Such locational marginal prices (LMPs) strongly hinge on the reliability and accuracy of the state estimation routines, which are designed to provide RT information about the state of the power grid based on meter measurements. However, state estimation is vulnerable to false data injection attacks (FDIAs), which aim to compromise the readings of measurement meters in order to distort state estimates and, subsequently, mislead the computation of RT LMPs, and thus carry out financial misconduct. Existing studies show that if the adversaries are omniscient, i.e., have full and instantaneous access to grid topology and state, they can design profitable attack strategies without being detected by the residue-based bad data detectors. This paper focuses on a more realistic FD1A setting, in which the attackers have only partial and imperfect information due to their limited resources and restricted physical access to the grid. Specifically, the attackers are assumed to have uncertainties about the state of the grid, and the uncertainties are modeled stochastically. Based on this model, this paper offers a framework for characterizing the optimal stochastic guarantees for the effectiveness of the attacks and the associated economic impacts. Designing such attacks is investigated analytically, and is examined in the standard IEEE 14-bus and 118-bus systems.