Model-Free Privacy Preserving Power Flow Analysis in Distribution Networks

Abstract

With the increasing availability of smart meter (SM) data and the frequent lack of accurate network topology information, model-free power flow (PF) calculation has gained traction, often leveraging artificial neural networks (ANNs). However, training such models typically requires large volumes of SM data, raising significant privacy concerns for households in distribution networks. To address this challenge, we propose a privacy-preserving PF calculation framework that incorporates two local privacy-enhancing mechanisms: a Local Randomisation Strategy (LRS) and a Zero-Knowledge Proof (ZKP)-based data collection strategy. The LRS provides irreversible transformation of power data, ensuring strong privacy protection while preserving data utility. In parallel, the ZKP-based strategy enables secure and trustworthy voltage data collection, allowing smart meters to interact with distribution system operators without disclosing actual voltage magnitudes. To address performance degradation caused by seasonal variations in load profiles, we further integrate an incremental learning strategy into the online application. Extensive evaluations across three datasets demonstrate that the proposed framework can efficiently collect one month of SM data within one hour while maintaining most voltage magnitude estimation errors lower than 0.01 p.u. under varying measurement noise and seasonal conditions.