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.
ASJC Scopus subject areas
- Control and Systems Engineering
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering