Charging Load Pattern Extraction for Residential Electric Vehicles: A Training-Free Nonintrusive Method

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 10; pp. 7028 - 7039
• Main Authors: Xiang, Yue, Wang, Yang, Xia, Shiwei, Teng, Fei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant-identification analysis; Approximation; Charging; Data mining; Decomposition; Discrete wavelet transforms; Electric vehicles; Electrical loads; Feature extraction; Harnesses; Household appliances; Indexes; Loess; nonintrusive load extracting; residential electric vehicle; Signal processing algorithms; smart meter; Smart meters; two-stage decomposition
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3060450

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.