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A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Chin-Yao Chang

TL;DR

A distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs and maintains the privacy of local agents by only communicating through dummy variables is proposed.

Abstract

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

TL;DR

A distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs and maintains the privacy of local agents by only communicating through dummy variables is proposed.

Abstract

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.
Paper Structure (12 sections, 2 theorems, 22 equations, 7 figures, 1 algorithm)

This paper contains 12 sections, 2 theorems, 22 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Notations
  4. System modeling with input-output data
  5. Distributed model identification algorithm
  6. System modeling of network systems
  7. Distributed reformulation of the system modeling
  8. Distributed algorithm for model identification
  9. Numerical Studies
  10. A small-scale example
  11. A modified IEEE 37-bus test system
  12. Conclusion

Key Result

Lemma 3.1

(Optimal solutions of eq:data_opt_2 and eq:data_opt_3). If $\mathop{\mathrm{\mathcal{G}}}\nolimits$ is connected and $(\mathop{\mathrm{\mathbf{x}}}\nolimits^\star,\mathop{\mathrm{\mathbf{w}}}\nolimits^\star)$ is an optimal solution of eq:data_opt_3, then $\mathop{\mathrm{\mathbf{x}}}\nolimits^\star$

Figures (7)

  • Figure 1: The absolute value difference for each element of the estimated $A$ and $A^\star$ by implementing \ref{['eq:pd_dis']}.
  • Figure 2: The absolute value difference for each element of the estimated $A$ and $A^\star$ for Algorithm \ref{['alg:D-ADAM']}.
  • Figure 3: Illustration of the modified IEEE 37-bus system with the buses highlighted in red triangles are PV buses
  • Figure 4: The voltage magnitudes (p.u.) over time without control.
  • Figure 5: The voltage magnitudes (p.u.) over time with distributed feedback-based control and known LinDistFlow model.
  • ...and 2 more figures

Theorems & Definitions (4)

  • Lemma 3.1
  • Theorem 3.2
  • proof
  • Remark 3.3