A Privacy-Preserving Distributed Control of Optimal Power Flow

TL;DR

This paper introduces a differentially private distributed algorithm for optimal power flow that ensures data privacy while guaranteeing convergence, addressing privacy concerns in distributed power system optimization.

Contribution

The paper proposes a novel differentially private projected subgradient algorithm with solution encryption for distributed optimal power flow, ensuring privacy without sacrificing convergence.

Findings

The DP-PS algorithm converges in expectation, probability, and almost surely.

Convergence rate is influenced by the chosen privacy level.

Numerical experiments confirm privacy preservation and convergence.

Abstract

We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.