Modelling Financially Motivated Cyber Attacks on Electricity Markets Using Mixed Integer Linear Programming

With the vast expansion of grid network and enhancement of Communication system, Cyber security reinforcement is of paramount importance for reliable and secure power system operation. Cyber-attacks on electricity markets acquire financial profits to the adversary. In this paper a modest attempt is made to model the cyber attacker's objective of profit maximization by injecting false data into Day-ahead and Real Time electricity markets. It is considered that attacker runs a bi-level optimization problem which includes attacker's profit maximization objective and market clearing problem for finding out the optimal attack measurements. While manipulating the measuring devices like RTUs, the attacker takes care to avoid being detected by the bad data detection (BDD) procedure run by the ISO. This paper focuses on financially motivated FDI attacks considering attacker as one of the virtual players in electricity market. A novel attacking model is designed using bi-level optimization problem where attacker aims to gain financial benefits by misleading market clearing problem. Potential impact of financially motivated False Data Injection (FDI) attacks on electricity markets is presented by considering PJM 5-bus system. The simulation results show the sharp impact on Locational Marginal Prices (LMPs) in fulfilling the attacker's objective, and the distinct relationship between LMPs and the market clearing prices during the attack.