False Data Injection Attack Detection with Feedforward Neural Network in Electric Vehicle Aggregator Bidding Price

Electricity market has become flexible in the presence of increasing number of prosumers and has opened opportunity for transactive energy management. Further, information and communication technologies (ICTs) have levitated the interest for transactive energy. Increasing number of electric vehicle (EV) provides the opportunity to participate EV owners in a transactive energy management by submitting price bids to electric vehicle aggregators (EVAs). Further, the EVAs process and submit the final bid price to distribution system operator (DSO) and receive the clearing price for the volume of energy demand. In this process, the large volume of information of bid prices floods over cyber-space and provides the opportunity for cyber-attacker to manipulate the control variables as such price and volume of energy demand by injecting the false data. In this work, false data injection on EVA bidding price is showcased and the feedforward neural network is developed to identify the attacked and falsified bids.