Differentially Private Distributed Optimal Power Flow

TL;DR

This paper introduces differentially private algorithms for distributed optimal power flow using ADMM, balancing privacy guarantees with convergence and optimality in power systems.

Contribution

It develops novel privacy-preserving algorithms for distributed OPF that incorporate Laplacian perturbations and provides theoretical privacy guarantees.

Findings

Achieves differential privacy in distributed OPF algorithms.

Analyzes privacy, convergence, and optimality trade-offs.

Validates approach on IEEE 118-node test case.

Abstract

Distributed algorithms enable private Optimal Power Flow (OPF) computations by avoiding the need in sharing sensitive information localized in algorithms sub-problems. However, adversaries can still infer this information from the coordination signals exchanged across iterations. This paper seeks formal privacy guarantees for distributed OPF computations and provides differentially private algorithms for OPF computations based on the consensus Alternating Direction Method of Multipliers (ADMM). The proposed algorithms attain differential privacy by introducing static and dynamic random perturbations of OPF sub-problem solutions at each iteration. These perturbations are Laplacian and designed to prevent the inference of sensitive information, as well as to provide theoretical privacy guarantees for ADMM sub-problems. Using a standard IEEE 118-node test case, the paper explores the fundamental trade-offs among privacy, algorithmic convergence, and optimality losses.