Abstract
In this paper, two approaches are proposed for transactive energy systems with competitive market in order to detect anomalies that occur in the process of information exchange during market interactions. The first approach fully exploits the property of monotonicity associated with the supply or demand curves of rational market participants. It only relies on the data that are normally exchanged through market interaction and does not require any extra information. Therefore, it can be easily adopted by existing system implementation. When the gradients of the supply (or demand) curves have positive lower (or negative upper) bounds, a second approach can be further developed for more accurate anomaly detection if the knowledge of these bounds become available. For both approaches, the impacts of power flow constraints are also taken into account. The detailed simulation studies illustrate the performance and effectiveness of the proposed approaches in detecting anomalies caused by cyberattacks.
