Bilevel Cyber-Induced Overloads Mechanism for False Data Injection Attacks Considering Post-Attack Economic Dispatch

False data injection (FDI) attacks can mislead the system operator to conduct incorrect dispatch decisions, causing cyber-induced physical line overloads. However, traditional false data is constructed either further from normal data and easy to detect, or not effective to overload multiple lines. To improve both attack stealth and overload impacts, this paper proposes a bilevel cyber-induced overloads (CIO) mechanism that can cause a predefined number of multi-line overloads considering post-attack economic dispatch, where the injected false data is minimised to improve the attack stealth. Within this mechanism, a detailed CIO attack model is formulated that explicitly incorporates the post-attack economic dispatch, enabling it to design more practical attack strategies. One advanced feature of this CIO attack model is the optimal selection of overloaded lines for the bilevel optimization of cyberattack resources and post-attack impacts in terms of line overloads, operation costs, and load loss. To solve the proposed model, it is converted into a single-level nonlinear model by a strong duality theory, and then CIO attacks are discretised to convert this model into a mixed-integer linear programming (MILP) problem. Case studies conducted on an IEEE 14-bus power system and an industrial 126-bus equivalent power system validate the superiority and effectiveness of our proposed approach.