Data manipulation attacks in electricity market with generative adversarial network for electric vehicle aggregator

Abstract

The electrification of the transportation sector is reducing its dependency on fossil fuels and promoting sustainability. Electric vehicles (EVs) play a substantial role in transportation sectors. The growing number of EVs is increasing the opportunity for their participation in the electricity market in an aggregated form. Electric vehicle aggregators (EVAs) participate in the electricity market by submitting electricity bids for power purchases with the help of information and communication technology (ICTs). However, the dependence on ICTs can make the EVAs and EVs vulnerable to cyber-attacks and cyber-threats. The attacker can intercept the transaction data and manipulate electricity bid prices and demands. In this work, the vulnerability of EVA in transactive energy management is addressed. False data is produced using a generative adversarial network (GAN) and injected in the form of the price and energy demand of EVAs to manipulate the market price and power variables. An FDI attack with an application of GAN is showcased in this work for transactive energy management. The results indicate the susceptibility of EVs, EVAs, and DSO in transactive energy management.