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.

Keywords

• Bounding-box fitting
• electric vehicle
• load pattern
• non-intrusive load extracting