A new privacy-preserving average consensus algorithm with two-phase structure: Applications to load sharing of microgrids☆
Abstract
This paper is concerned with the privacy-preserving average consensus problem for discrete-time multi-agent systems. The main goal of this paper is to develop a privacy-preserving algorithm that ensures all agents in the network to reach exact average consensus while keeping the initial state of each agent private. To achieve this, a novel two-phase privacy-preserving algorithm is proposed. In the first phase, each agent generates a group of independent random signals and creates a mask signal via local data exchange. In the second phase, all agents execute a standard consensus algorithm. Rigorous analysis shows that each agent can converge to exact average consensus while protecting sensitive information of individuals against internal honest-but-curious nodes and external eavesdroppers. Moreover, the developed algorithm is applied to the distributed load sharing of microgrids. Finally, a simulation example is provided to verify the theoretical results. (c) 2024 Elsevier Ltd. All rights reserved.
