Regional non-intrusive electric vehicle monitoring based on graph signal processing

Jiahang Li, Ran Li, Shuangyuan Wang, Yue Xiang, Yunjie Gu

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)
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Abstract

Electricity network is leading to a low carbon future with high penetration of plug-in electric vehicles (EVs). However, it is extraordinarily difficult to acquire detailed information on regional EV electrification with an incomplete monitoring system for network operators. In this study, a flexible graph signal processing (GSP)-based non-intrusive monitoring on aggregated EVs is proposed to enhance the EVs visibility for operating power system safely and cost-efficiently. It can deduce the individual EV charging status with the highest possibility iteratively from the limited dataset using a GSP-based possibility calculation after processing a daytime EV characteristic charging patterns. The experiment is developed with realistic EV charging datasets collected in London, and the results show the daily EVs number in a specific region of 500 EVs daily aggregation can be estimated efficiently with an around 4.77% value of relative mean absolute deviation applying the proposed method.

Original languageEnglish
Pages (from-to)6512-6517
Number of pages6
JournalIET Generation, Transmission and Distribution
Volume14
Issue number26
Early online date17 Feb 2021
DOIs
Publication statusPublished - 17 Feb 2021

Bibliographical note

Funding Information:
Thanks the discussion with Dr.Shiwei Xia from North China Electric Power University through Dr.Yue Xiang's project supported by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources of China (Grant No. LAPS20011).

Publisher Copyright:
© The Institution of Engineering and Technology 2021.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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