Distributed economic dispatch via a predictive scheme: Heterogeneous delays and privacy preservation

This paper studies distributed economic dispatch problems for smart grids, in which a quadratic generation cost is to be minimized over a feasible set that is determined jointly by an equality constraint and a box constraint. Our primary objective is to seek a distributed design that can handle heterogeneous time-delays, while preserving agents' privacy-a fundamental prerequisite that has become gradually important for cyber-physical systems. For this purpose, we design a state predictor for each agent to compensate for the effect of heterogeneous time-delays, which allows the agents to predict the missing states between two consecutive update times. Based upon the predictor, we present a distributed gradient-descent algorithm to locally update the outputs of the generators, which guarantees that the optimal solution is attained in an asymptotic manner. Among other things, we incorporate a privacy preservation scheme to the proposed algorithm in order to preserve agents' privacy and delicately characterize its convergence, differential privacy properties, as well as accuracy. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper studies distributed economic dispatch problems for smart grids with a focus on addressing heterogeneous time-delays and protecting agents' privacy. By designing state predictors for each agent and proposing a distributed gradient-descent algorithm, the optimal solution is guaranteed to be attained in an asymptotic manner. Additionally, a privacy preservation scheme is incorporated to delicately characterize convergence, differential privacy properties, and accuracy of the algorithm.