Leveraging DERs to improve the inference of distribution network topology

Abstract

Analytics on smart meter data can help distribution utilities to infer and maintain an accurate topology or connectivity model of their network. Prior work has proposed optimization techniques based on the principle of conservation of energy, which utilize a time series of customer and feeder load (KWH) measurements to compute connectivity information. This work analyses real smart meter measurements to show that correlations between customer loads hinders the computation of connectivity information. Based on this finding, we propose a novel approach that exploits the DER flexibility of customers to minimize correlations between their loads, thereby expediting the inversion of connectivity information. Empirical evaluation results are presented based on real smart meter and SCADA data collected from feeders of North American distribution networks.