Open AccessRegional non‐intrusive electric vehicle monitoring based on graph signal processing
Author(s) -
Li Jiahang,
Li Ran,
Wang Shuangyuan,
Xiang Yue,
Gu Yunjie
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0845
Subject(s) - electrification , electricity , visibility graph , computer science , electric vehicle , graph , visibility , real time computing , automotive engineering , plug in , simulation , electrical engineering , power (physics) , engineering , mathematics , physics , geometry , optics , theoretical computer science , quantum mechanics , regular polygon , programming language
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.