PNNL

Abstract

An attack detection method against false data injection is proposed for distributed optimization problems emerging in transactive energy systems. The detection method is also applicable to general distributed convex optimization problems. Transactive energy systems seek an optimal power allocation through hybrid economic control methods to facilitate the integration of various types of distributed energy resources to a power distribution systems. Communication is necessary for transactive energy systems to find the optimal solution in a distributed scheme. Cyber attacks may be imposed on and negatively impact the performance of transactive energy systems. Thus, attack detection is necessary for successful deployment of transactive energy systems. In transactive energy systems, every participant is assumed to be a rational entity, in which the consumers have diminishing marginal utility and the suppliers have increasing marginal cost. With the proposed method, the convexity of the objective function is examined through the monotonicity of the gradient in distributed optimization problems, which corresponds with the assumption of diminishing marginal utility and increasing marginal cost in transactive energy systems. The proposed detection method does not require any data beyond those necessary to find the optimal solution. Simulation examples show that the detection technique is efficient for detecting and locating false data injection attacks.