Designing a Security Metric for EV-Based Load-Altering Attacks in Transmission Systems

Due to their cyber vulnerabilities, the increasing integration of electric vehicles (EVs) and their related EV supply equipment (EVSE) makes power grids prone to a variety of cyber attacks. Among possible threats, adversaries can observe frequency measurements and alter the consumption of EVs accordingly, creating an EV-based load-altering attack (EV-LAA). On this basis, this article uses the measurements of the transmission grid and information on its cyber layer to derive a security metric that can be used for diagnosis and condition monitoring of the transmission grid’s security state. First, common vulnerabilities in EV ecosystems are analyzed to devise related attack graphs. Afterward, a Markov decision process (MDP) tree is established based on the obtained attack graphs to display the possible attacker’s actions and their detrimental consequences. In this MDP, to calculate the probabilities of adversaries’ success in each branch, a customized common vulnerability scoring system (CVSS) is developed. Furthermore, control input and measurement signals are used to identify the transmission systems’ model. Using this model, the damping ratio, controllability, and observability of low-damping modes, as well as the number of compromised charging stations, can be obtained for calculating the terms of a reward function. The generated MDP tree is resolved by the Epsilon-Greedy Q-learning algorithm to calculate the value of each state in the MDP tree and the related optimal adversarial action. This metric is integrated into a back propagation neural network (BPNN) to provide a security monitoring framework for attacks originating from the EV ecosystem. The security monitoring framework is evaluated on a testbed to demonstrate its usefulness in quantifying the security status in the case of EV-LAAs. This testbed consists of a virtual sphere (vSphere) of an EV ecosystem with the New England 39-bus transmission system simulated in a real-time simulator (RTS).