Low-Voltage Distribution Network Impedances Identification Based on Smart Meter Data

TL;DR

This paper introduces fast, scalable, and decentralized data-driven methods for accurately identifying low-voltage distribution network impedances using smart meter data, crucial for grid management under high renewable penetration.

Contribution

It proposes novel recursive algorithms that are efficient, scalable, and suitable for decentralized implementation, improving real-time network modeling with smart meter data.

Findings

Algorithms are fast and scalable with linear complexity.

Methods perform well across different measurement accuracy scenarios.

Decentralized implementation is feasible and effective.

Abstract

Under conditions of high penetration of renewables, the low-voltage (LV) distribution network needs to be carefully managed. In such a scenario, an accurate real-time low-voltage power network model is an important prerequisite, which opens up the possibility for application of many advanced network control and optimisation methods thus providing improved power flow balancing, reduced maintenance costs, and enhanced reliability and security of a grid. Smart meters serve as a source of information in LV networks and allow for accurate measurements at almost every node, which makes it advantageous to use data driven methods. In this paper, we formulate a non-linear and non-convex problem, solve it efficiently, and propose a number of fully smart meter data driven methods for line parameters estimation. Our algorithms are fast, recursive in data, scale linearly with the number of nodes, and can be executed in a decentralised manner by running small algorithms inside each smart meter. The performance of these algorithms is demonstrated for different measurement accuracy scenarios through simulations.