On the Impact of Data Integrity Attacks on Vehicle-to-Microgrid Services

With the increasing demands on the power grid, more Electric Vehicles (EVs) will be used as mobile storage units to trade energy and avoid power shortages. The integration of EVs and smart grids has expanded the attack surface and paved the way for adversaries to perform novel and intelligent attacks on the system. Therefore, data integrity attacks in modern smart grids are expected to increase in Vehicle-to-Grid (V2G) and Vehicle-to-Microgrid (V2M) applications. In this paper, we propose a novel scheme to model data integrity attacks in V2M applications. By leveraging unsupervised machine learning, we implement an intelligent detector to encounter the data integrity attacks. Although some of the data integrity attacks are able to deceive the detector, they fail to impact the V2M service operation. Through simulations, we show that performing the data integrity attacks against an increasing number of EVs (i.e. backup energy suppliers) results in reducing the attacks' impact by up to 76.5 %. In addition, doubling the original contribution of EVs alleviates the impact of the data integrity attacks by 60%. On the contrary, doubling the number of microgrids (i.e. demand) raises the attacks' impact by at least 75%.