Training-Free Non-Intrusive Load Extracting of Residential Electric Vehicle Charging Loads

Abstract

Extracting the charging loads of residential electric vehicle (EV) clusters and identifying their charging patterns can help grid operators develop effective regulation strategies. The duration of the power consumption event (PCE) and the interval between adjacent events are used to characterize the difference in the stochastic behavior of the load pattern between the EV cluster and the air conditioner (AC) cluster. An event detection method based on skipping power difference is proposed, which can effectively identify changing edges of the PCE. A training-free non-intrusive load extracting (NILE) algorithm based on bounding-box fitting and load signatures is proposed, which can automatically identify the start time, the end time and the power amplitude of the charging event. The validity of the NILE algorithm is verified by multiple performance metrics on the real data set.INDEX TERMS Bounding-box fitting, electric vehicle, load pattern, non-intrusive load extracting.

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

“…However, other power events that can interfere with the charging load in step 2). In [5] and [22], a novel algorithm was designed to extract step 2), but the signals containing noise or multi-power appliances cannot be decomposed accurately [23]. The novel NILE algorithms are needed to be designed or improved to overcome the sample data bottleneck, eliminate high-frequency interference, and promote computational efficiency.…”

“…F1 can evaluate the accuracy of the NILE algorithm for identifying the ON/OFF appliances, while the charging load can be approximated as the binary appliances [5], [31]. Therefore, the extracted results can be evaluated using F1.…”

“…However, the large-scale roll-out of EVs will have a significant impact on power grid reliability and stable operation [3]. These negative effects mainly depend on charging behaviors [4], [5]. Thus, the effective identification of possible EV charging load pattern…”

Nonintrusive Monitoring for Electric Vehicles Based on Zero-Shot Learning

“…Recently, with the popularity of EVs, nonintrusive EV monitoring has gradually attracted the attention of scholars. On the basis of NIM, a training-free, nonintrusive load extraction algorithm was proposed based on boundary box fitting and load characteristics (Zhao et al, 2019), which can automatically identify the start time, end time, and power amplitude of charging events. Based on the low-frequency characteristics of the charging load mode, a charging load extraction method based on residential smart meter data was proposed to realize the nonintrusive extraction of the residential EV charging load mode (Xiang et al, 2021).…”