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

Mihitha Maithripala, Chenyang Qiu, Zongli Lin

TL;DR

It is shown that the developed scheme preserves the convergence properties of the conventional DAC framework while preventing information leakage to external eavesdroppers.

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.

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

TL;DR

It is shown that the developed scheme preserves the convergence properties of the conventional DAC framework while preventing information leakage to external eavesdroppers.

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.
Paper Structure (12 sections, 5 theorems, 35 equations, 6 figures)

This paper contains 12 sections, 5 theorems, 35 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Graph Theory
  3. Communication Network
  4. Main results
  5. Algorithmic Design for Privacy Protection
  6. Convergence Analysis
  7. Privacy Analysis
  8. Application to SoC balancing for Battery Energy Storage Systems
  9. The SoC Dynamics and Control Objective
  10. Power Allocation and Average Estimation
  11. Simulation Results
  12. Conclusions

Key Result

Lemma 1

kia2019tutorial Let Assumption ass: graph hold and $\dot{z}_i(t)$ be bounded. The estimate $\hat{z}_{{\rm a},i}(t)$ generated by eq:1 converges exponentially to a bounded neighborhood of $\frac{1}{N}\mathbf{1}_N z(t)$ for any $\beta > 0$, that is, where $\gamma = \sup_{\tau \in [t,\infty)} \left\|\left( I_N - \tfrac{1}{N}\mathbf{1}_N \mathbf{1}_N\tt \right)\dot{z}(\tau)\right\|,$$z(t) = [z_1(t)\;

Figures (6)

  • Figure 1: Configuration of a six-unit networked BESS.
  • Figure 2: State-of-charge balancing and power tracking performance.
  • Figure 3: Estimator performance.
  • Figure 4: Eavesdropper estimates $x_{{\rm obs},i}(t)$ vs true states $x_i(t)$
  • Figure 5: Eavesdropper estimates $p_{{\rm obs},i}$ vs true states $p_i(t)$.
  • ...and 1 more figures

Theorems & Definitions (9)

  • Lemma 1
  • Theorem 1
  • proof
  • Remark 1
  • Theorem 2
  • proof
  • Lemma 2
  • Theorem 3
  • proof