A Data-Driven Approach for Detection and Estimation of Residential PV Installations

The number of photovoltaic (PV) systems in the electric grid is growing at an unprecedented speed. This is rapidly transforming the ways in which the traditional distribution grid is being planned and operated. A problem faced by utilities is that, in many cases, the PV system installed does not cor...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on smart grid 2016-09, Vol.7 (5), p.2477-2485
Main Authors: Xiaochen Zhang, Grijalva, Santiago
Format: Article
Language:English
Subjects:
Algorithms
Change-point detection
Detection algorithms
Energy consumption
Estimation
Gaussian kernel smoothing
Inference
Kernel
Mathematical model
Monitors
Networks
parameter estimation
permutation test
Permutations
Photovoltaic cells
PV system
Smart meters
Solar cells
Spearman’s rank coefficient
Time series analysis
Utilities
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The number of photovoltaic (PV) systems in the electric grid is growing at an unprecedented speed. This is rapidly transforming the ways in which the traditional distribution grid is being planned and operated. A problem faced by utilities is that, in many cases, the PV system installed does not correspond to the size or type filed with the installation permit, or simply the installation took place without a permit. In order to maintain grid reliability and safety, utilities must be able to detect and monitor all PV installations in their network. This paper proposes a data-driven approach for the detection, verification, and estimation of residential PV system installations. We use a change-point detection algorithm to screen out abnormal energy consumption behaviors including unauthorized PV installations. Then the existence of the unauthorized PV installation is further verified through a statistical inference known as permutation test with Spearman's rank coefficient. The proposed hypothesis test takes the customer's load profiles before and after the detected change-point as inputs, which are estimated through Gaussian kernel density method. Finally, the local cloud cover index is integrated with smart meter measurements to estimate the size of the PV system. The proposed method has been tested and validated with actual smart meter measurements under several scenarios.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2016.2555906