Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks

TL;DR

This paper presents a privacy-preserving power flow analysis framework for distribution networks that uses local randomisation and zero-knowledge proofs to protect household data while maintaining high accuracy in power flow calculations.

Contribution

It introduces a novel combination of local randomisation and zero-knowledge proof strategies for privacy-preserving power flow analysis in distribution networks.

Findings

Efficiently collects one month of smart meter data within one hour.

Maintains mean errors of 0.005 p.u. and 0.014 p.u. under measurement errors and seasonal load variations.

Robust privacy protection for household data during power flow analysis.

Abstract

Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.