Enhancing the Spatio-Temporal Observability of Residential Loads release_7spv5kbpl5c4rbnju6uezjow54

by Shanny Lin, Hao Zhu

Released as a article .

2019  

Abstract

Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a low rank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles.
In text/plain format

Archived Files and Locations

application/pdf  1.3 MB
file_xpqhiu4l2rcpfl3tva3fqdfzt4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-10-04
Version   v2
Language   en ?
arXiv  1910.00984v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 44174d71-dabd-4f93-a891-2cd8ff52f035
API URL: JSON