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Privacy-Preserving Distributed Control for a Networked Battery Energy Storage System

Mihitha Maithripala, Zongli Lin

TL;DR

This paper addresses privacy risks in distributed SoC balancing for networked BESSs by introducing a privacy-preserving distributed control framework that couples a power allocation law with two estimators built on state-decomposition and leader-following consensus. The two estimators enable local, decentralized operation: a privacy-preserving average unit state estimator that yields a scaled consensus on $x_a(t)$ and a privacy-preserving average desired power estimator that converges to a scaled $p_a(t)$, ensuring $S_i$ balancing and $p_\Sigma$ tracking without leaking private signals. The approach achieves asymptotic convergence to desired performance while providing tunable privacy via scaling factors $\eta$ and $\sigma$, with robustness to disturbances and practical implementation considerations. Simulations on a six-battery network demonstrate effective SoC balancing, accurate total power tracking, and strong privacy protection against external eavesdroppers, highlighting the framework’s potential for secure, scalable energy management in distributed grids.

Abstract

The increasing deployment of distributed Battery Energy Storage Systems (BESSs) in modern power grids necessitates effective coordination strategies to ensure state-of-charge (SoC) balancing and accurate power delivery. While distributed control frameworks offer scalability and resilience, they also raise significant privacy concerns due to the need for inter-agent information exchange. This paper presents a novel privacy-preserving distributed control algorithm for SoC balancing in a networked BESS. The proposed framework includes distributed power allocation law that is designed based on two privacy-preserving distributed estimators, one for the average unit state and the other for the average desired power. The average unit state estimator is designed via the state decomposition method without disclosing sensitive internal states. The proposed power allocation law based on these estimators ensures asymptotic SoC balancing and global power delivery while safeguarding agent privacy from external eavesdroppers. The effectiveness and privacy-preserving properties of the proposed control strategy are demonstrated through simulation results.

Privacy-Preserving Distributed Control for a Networked Battery Energy Storage System

TL;DR

This paper addresses privacy risks in distributed SoC balancing for networked BESSs by introducing a privacy-preserving distributed control framework that couples a power allocation law with two estimators built on state-decomposition and leader-following consensus. The two estimators enable local, decentralized operation: a privacy-preserving average unit state estimator that yields a scaled consensus on and a privacy-preserving average desired power estimator that converges to a scaled , ensuring balancing and tracking without leaking private signals. The approach achieves asymptotic convergence to desired performance while providing tunable privacy via scaling factors and , with robustness to disturbances and practical implementation considerations. Simulations on a six-battery network demonstrate effective SoC balancing, accurate total power tracking, and strong privacy protection against external eavesdroppers, highlighting the framework’s potential for secure, scalable energy management in distributed grids.

Abstract

The increasing deployment of distributed Battery Energy Storage Systems (BESSs) in modern power grids necessitates effective coordination strategies to ensure state-of-charge (SoC) balancing and accurate power delivery. While distributed control frameworks offer scalability and resilience, they also raise significant privacy concerns due to the need for inter-agent information exchange. This paper presents a novel privacy-preserving distributed control algorithm for SoC balancing in a networked BESS. The proposed framework includes distributed power allocation law that is designed based on two privacy-preserving distributed estimators, one for the average unit state and the other for the average desired power. The average unit state estimator is designed via the state decomposition method without disclosing sensitive internal states. The proposed power allocation law based on these estimators ensures asymptotic SoC balancing and global power delivery while safeguarding agent privacy from external eavesdroppers. The effectiveness and privacy-preserving properties of the proposed control strategy are demonstrated through simulation results.

Paper Structure

This paper contains 19 sections, 6 theorems, 86 equations, 21 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Communication Network
  4. Privacy Definition and Attack Model for Dynamic Average Consensus Algorithm
  5. Problem Formulation
  6. Power Allocation Law Based on Average Estimators in a Non-Privacy-Preserving Setting
  7. Privacy-Preserving Power Allocation Algorithm Design
  8. Privacy-Preserving Distributed Average Unit State Estimator
  9. Privacy-Preserving Distributed Average Desired Power Estimator
  10. Power Allocation Algorithm Based on Privacy-Preserving Estimators
  11. Robustness of Privacy Guarantees
  12. Implementation Considerations and Practical Feasibility
  13. Simulation Results
  14. Discharging Mode Performance
  15. Charging Mode Performance
  16. ...and 4 more sections

Key Result

Lemma 1

kia2019tutorial There exists a positive constant $\gamma_{\rm s} > 0$ such that, for every $\beta > 0$, the estimate $\hat{x}_{{\rm a},i}(t)$, generated by the estimator eq:10, converges exponentially to a neighborhood of $x_{\rm a}(t)$, that is, where $\lambda_2$ represents the smallest positive eigenvalue of the Laplacian matrix $L$.

Figures (21)

  • Figure 1: The illustration of a microgrid including a BESSs.
  • Figure 2: Explanation of state-decomposition process: (a) Original state before decomposition. (b) Decomposed state.
  • Figure 3: The communication topology of the BESS.
  • Figure 4: The state of charge of each unit over time.
  • Figure 5: The desired power and the total discharging power.
  • ...and 16 more figures

Theorems & Definitions (11)

  • Lemma 1
  • Lemma 2
  • Theorem 1
  • Theorem 2
  • proof
  • Remark 1
  • Theorem 3
  • proof
  • Theorem 4
  • proof
  • ...and 1 more