Abstract
The popularization of electric vehicles raises concerns about their negative impact on the electrical grid. Extracting electric vehicle charging load patterns is a key factor that allows smart grid operators to make intelligent and informed decisions about conserving energy and promoting the stability of the electrical grid. This paper presents an unsupervised algorithm to extract electric vehicle charging load patterns nonintrusively from the smart meter data. Furthermore, a method to define flexibility for the collective electric vehicle charging demand by analyzing the time-variable patterns of the aggregated electric vehicle charging behaviors is presented. Validation results on real residential loads have shown that the proposed approach is a promising solution to extract electric vehicle charging loads and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as electric vehicles. Furthermore, a case study on real residential data to analyze electric vehicle charging trends and quantify the flexibility achievable from the aggregated electric vehicle load in different time periods is presented.