Electric Vehicle-Based Load-Altering Attacks and Their Impacts on Power Grids Operations

Broad deployment of electric vehicles (EVs) in power grids necessitates the integration of information and communication technologies (ICTs) into the cyber layers of EV ecosystems. However, such integration makes the power grid prone to cyberattacks originating from these ecosystems. This article investigates potential vulnerabilities in the physical and cyber layers that adversaries can exploit to impact the operation of power grids. Several threat models are developed to introduce EV-based load-altering attacks (EV-LAAs) and observe the impact of such attacks on the operation of the two-area Kundur benchmark. To cope with EV-LAAs, model-based and data-driven approaches can be suggested. In data-driven approaches, adequate data are obtained from smart meters and equipment in power grids to train machine learning models to distinguish between healthy and under-attack scenarios. In model-based approaches, estimators and observers are usually developed based on accurate models of understudied systems to estimate attack vectors. The accuracy of the proposed detection methods is not precisely 100%, and several attacks may not be identified due to overlooking the low-probability attack vectors posed by residential EVs. As such, mitigation techniques, e.g., wide-area damping controllers (WADCs), are suggested to ensure that oscillations are controlled following successful EV-LAAs. Finally, dynamic thermal rating (DTR), as a real-time strategy that uses actual conditions, can be integrated into developed detection methods. After identifying cyberattacks originating from EV ecosystems, the DTR can take preemptive actions, e.g., rerouting power, adjusting line ratings, or disconnecting infected feeders in distribution networks, to mitigate the impact of such attacks.