Monte-Carlo-based data injection attack on electricity markets with network parametric and topology uncertainties

Abstract

False data injection (FDI) attacks can significantly impact on economic performance of electricity markets in modern power systems. These attacks can be stealthily accomplished by cyber-attackers for the purpose of profitability through financial arbitrage in electricity markets. In this paper, a new strategy of FDI attack based on Monte Carlo is proposed for an attacker participating in an electricity market, who has overmuch imperfect level of the network information. This piece of information, including both the connection /disconnection situation and admittance values of the transmission lines is denominated as topology and parametric uncertainties, respectively. Herein, a probable model is offered for analyzing the uncertainties by the Monte Carlo simulation (MCS). Afterwards, considering the probable errors of uncertainties, the attack strategy is designed in such a manner that the attacker obtains the most profit based on the contribution of each transmission line. The numerical results on two PJM 5-bus and IEEE 30-bus test networks could obviously demonstrate the success of such limited attackers in current electricity markets.