Privacy-preserving distributed optimization for economic dispatch in smart grids

Abstract

This paper discusses a distributed economic dispatch problem (EDP) of smart grids while preventing sensitive information from being leaked during the communication process. In response to the problem, a novel privacy-preserving distributed economic dispatch strategy is developed via adding an exponentially decaying random noise to minimize the total cost of the grid while ensuring the privacy of sensitive state information. The quantitative relationship between the privacy and the estimation accuracy of eavesdroppers is profoundly disclosed in the framework of $(\varsigma ,\sigma )$-data-privacy. Furthermore, a sufficient condition on the iteration step size is achieved to ensure that the well-designed algorithm can converge to the optimal value of the addressed EDP exactly by resorting to the classical Lyapunov stability theory. Finally, simulation results verify the effectiveness of the carefully constructed privacy-preserving scheme.