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Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator's solution without being detected. It is also shown that a stealthy attack on state estimation can increase the OPF cost. However, the impact of stealthy attacks on the economic and secure operation of the system cannot be comprehensively analyzed due to the very large size of the attack space. In this paper, we present a hybrid framework that combines formal analytics with Simulink-based system modeling to investigate the feasibility of stealthy attacks and their influence on OPF in a time-efficient manner. The proposed approach is illustrated on synthetic case studies demonstrating the impact of stealthy attacks in different attack scenarios. We also evaluate the impact analysis time by running experiments on standard IEEE test cases and the results show significant scalability of the framework. 

Wind energy is one of the major sources of renewable energy. Countries around the world are increasingly deploying large wind farms that can generate a significant amount of clean energy. A wind farm consists of many turbines, often spread across a large geographical area. Modern wind turbines are equipped with meteorological sensors. The wind farm control center monitors the turbine sensors and adjusts the power generation parameters for optimal power production. The turbine sensors are prone to cyberattacks and with the evolving of large wind farms and their share in the power generation, it is crucial to analyze such potential cyber threats. In this paper, we present a formal framework to verify the impact of false data injection attack on the wind farm meteorological sensor measurements. The framework designs this verification as a maximization problem where the adversary's goal is to maximize the wind farm power production loss with its limited attack capability. Moreover, the adversary wants to remain stealthy to the wind farm bad data detection mechanism while it is launching its cyberattack on the turbine sensors. We evaluate the proposed framework for its threat analysis capability as well as its scalability by executing experiments on synthetic test cases.