Dynamic Average Consensus with Privacy Guarantees and Its Application to Battery Energy Storage Systems

TL;DR

This paper introduces a privacy-preserving dynamic average consensus algorithm that maintains convergence while preventing eavesdroppers from inferring reference signals, demonstrated through application to battery energy storage systems.

Contribution

It presents a novel privacy-preserving DAC scheme using sinusoidal masking signals, ensuring convergence and preventing information leakage in practical energy storage applications.

Findings

Preserves convergence of DAC while enhancing privacy.

Effectively prevents external eavesdroppers from inferring signals.

Validated through simulations on battery energy storage systems.

Abstract

A privacy-preserving dynamic average consensus (DAC) algorithm is proposed that achieves consensus while preventing external eavesdroppers from inferring the reference signals and their derivatives. During the initialization phase, each agent generates a set of sinusoidal signals with randomly selected frequencies and exchanges them with its neighboring agents to construct a masking signal. Each agent masks its reference signals using this composite masking signal before executing the DAC update rule. It is shown that the developed scheme preserves the convergence properties of the conventional DAC framework while preventing information leakage to external eavesdroppers. Furthermore, the developed algorithm is applied to state-of-charge (SoC) balancing in a networked battery energy storage system to demonstrate its practical applicability. Simulation results validate the theoretical findings.