Impact of optimal false data injection attacks on local energy trading in a residential microgrid

This paper illustrates the vulnerability of local energy trading to false data injection attacks in a smart residential microgrid and demonstrates the impact of such attacks on the financial benefits earned by the participants. In a local energy market, the attacker can overhear the energy generation and consumption patterns of legitimate participants and based on this, optimize its attack signal to achieve maximum benefits either as a buyer/seller, while balancing the supply-demand to remain undetected. For such a system, we have formulated an optimization problem at the attacker, to extract the maximum possible benefits from legitimate participants. The numerical results show that the false data injection from the attacker causes significant losses in the benefits of legitimate participants, up to a reduction of 94% in certain hours.