Development of EV charging templates: an improved K-prototypes method

作者：Hong, Juhua; Xiang, Yue^*; Liu, Youbo; Liu, Junyong; Li, Ran; Li, Furong; Gou, Jing

来源：IET Generation Transmission & Distribution, 2018, 12(20): 4361-4367.

DOI：10.1049/iet-gtd.2017.1911

• electric vehicle charging
• pattern clustering
• rough set theory
• feature extraction
• EV charging templates
• improved K-prototypes method
• electric vehicle charging behaviour
• EV charging load profiles
• rectangular pulse train
• clustering analysis
• traditional distance calculation
• Euclidean distance
• morphological dissimilarities
• pulse train waves
• mixed data feature extraction

In order to manage the charging behaviour of electric vehicles (EVs), this study for the first time develops a set of EV charging load profiles: EV templates. EV charging profiles have unique waveforms similar to a rectangular pulse train. This characteristics significantly limits the performance of clustering analysis in that traditional distance calculation, such as Euclidean distance, which cannot reflect the morphological dissimilarities. This study proposes a novel clustering method using rough set theory to accurately measure the dissimilarity between the EV profiles. The pulse train waves are firstly extracted as mixed data features, which are partitioned by an improved K-prototypes method based on rough set distance. The proposed method is implemented on the real charging load profiles and compared with K-means and traditional K-prototypes. Their clustering performances are evaluated by diverse validity indices. The results show that the proposed method outperforms other comparison methods.

• 出版日期2018-11-13
• 单位四川大学