Privacy-Preserving Obfuscation for Distributed Power Systems

TL;DR

This paper introduces a distributed algorithm for releasing privacy-preserving load data in power systems, ensuring data privacy while maintaining the feasibility of optimal power flow solutions using differential privacy and ADMM.

Contribution

It proposes a novel differentially private distributed algorithm based on ADMM that preserves data utility and system constraints in power system load data sharing.

Findings

The algorithm effectively balances privacy and data fidelity.

Experimental results show high accuracy on OPF benchmarks.

The method maintains AC power flow feasibility with privacy guarantees.

Abstract

This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF) problems and proposes a distributed algorithm that complies with the notion of Differential Privacy, a strong privacy framework used to bound the risk of re-identification. The problem is challenging since the application of traditional differential privacy mechanisms to the load data fundamentally changes the nature of the underlying optimization problem and often leads to severe feasibility issues. The proposed differentially private distributed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) and guarantees that the released privacy-preserving data retains high fidelity and satisfies the AC power flow constraints. Experimental results on a variety of OPF benchmarks demonstrate the effectiveness of the approach.