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An attack-resilient distributed DC optimal power flow algorithm via neighborhood monitoring

Distributed DC optimal power flow (DC-OPF) is vulnerable to malicious cyber attacks due to the absence of a control center. In our previous work, we demonstrated a data integrity attack can manipulate the power dispatch result of distributed DC-OPF by compromising the distributed controller on a bus and modifying the information being sent to the neighboring buses. This vulnerability, in turn, could be exploited by attackers for financial arbitrage in a distributed electricity market. Thus, there is a growing need for attack-resilient control techniques that can fit into the distributed power system framework to ensure the global optimality of the power dispatch result in the presence of unexpected adversaries. In this paper, we proposed a resilient distributed DC-OPF algorithm against data integrity attacks by using a neighborhood monitoring scheme. On one hand, the resilient distributed DC-OPF algorithm is an efficient approach to deal with significant increasing amount of distributed energy resources (DERs) thanks to its flexibility and scalability. On the other hand, its neighborhood monitoring scheme enables its built-in defense to identify the misbehaving distributed controllers relying on each bus's local information and recover the optimal power dispatch from the malicious impact of data integrity attacks.