Privacy-Preserving Distributed Economic Dispatch for Microgrids Based on State Decomposition With Added Noises

Abstract

This paper devotes itself to the issue of privacy-preserving distributed economic dispatch (ED) of microgrids. The desired algorithm in a distributed way not only ensures a balance between power supply and demand for the entire microgrid system at minimal cost but also effectively safeguards the confidentiality of sensitive information. To this end, both state decomposition and Laplace noises are embedded into a distributed optimization algorithm in the framework of consensus to improve the privacy-preserving capability. Then, both a sufficient condition about the strength of consensus coupling and the convergence accuracy induced by added noises are disclosed for the proposed algorithm via the Lyapunov stability theory and stochastic analysis. Furthermore, the privacy level is discussed in the framework of differential privacy and the lower bound of the convergence accuracy is found for the given privacy level and noise distribution. Finally, the effectiveness and correctness of our approach are demonstrated by applying an IEEE 39-bus system.