A stochastic approach to estimate distribution grid state with confidence regions

TL;DR

This paper introduces a stochastic method to estimate distribution grid states using non-phasor measurements, providing confidence regions for voltages and currents despite measurement errors and limited data resolution.

Contribution

It proposes a novel stochastic grid calculation approach with two measurement models, enabling confidence region estimation for grid state monitoring.

Findings

The estimator performs well with real Danish distribution grid data.

Comparison between PMU and Electric Meter models shows similar accuracy.

The approach effectively accounts for measurement errors and limited data resolution.

Abstract

Widely available measurement equipment in electrical distribution grids, such as power-quality measurement devices, substation meters, or customer smart meters do not provide phasor measurements due to the lack of high resolution time synchronisation. Instead such measurement devices allow to obtain magnitudes of voltages and currents and the local phase angle between those. In addition, these measurements are subject to measurement errors of up to few percent of the measurand. In order to utilize such measurements for grid monitoring, this paper presents and assesses a stochastic grid calculation approach that allows to derive confidence regions for the resulting current and voltage phasors. Two different metering models are introduced: a PMU model, which is used to validate theoretical properties of the estimator, and an Electric Meter model for which a Gaussian approximation is introduced. The estimator results are compared for the two meter models and case study results for a real Danish distribution grid are presented.