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

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...

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Published inIET generation, transmission & distribution Vol. 12; no. 20; pp. 4361 - 4367
Main Authors Hong, Juhua, Xiang, Yue, Liu, Youbo, Liu, Junyong, Li, Ran, Li, Furong, Gou, Jing
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 13.11.2018
Subjects
clustering analysis
electric vehicle charging
electric vehicle charging behaviour
Euclidean distance
EV charging load profiles
EV charging templates
feature extraction
improved K‐prototypes method
mixed data feature extraction
morphological dissimilarities
pattern clustering
pulse train waves
rectangular pulse train
rough set theory
Special Issue: New Trends in the Planning of Distribution Network with High Penetration of Renewables and Flexible Loads
traditional distance calculation
traditional distance calculation
improved K-prototypes method
EV charging load profiles
mixed data feature extraction
pulse train waves
electric vehicle charging behaviour
morphological dissimilarities
EV charging templates
electric vehicle charging
pattern clustering
feature extraction
rectangular pulse train
Euclidean distance
rough set theory
clustering analysis
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Abstract 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.
AbstractList 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.
Author Liu, Youbo
Xiang, Yue
Hong, Juhua
Li, Furong
Liu, Junyong
Gou, Jing
Li, Ran
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Issue 20
Keywords traditional distance calculation
improved K-prototypes method
EV charging load profiles
mixed data feature extraction
pulse train waves
electric vehicle charging behaviour
morphological dissimilarities
EV charging templates
electric vehicle charging
pattern clustering
feature extraction
rectangular pulse train
Euclidean distance
rough set theory
clustering analysis
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Snippet 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....
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iet
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StartPage 4361
SubjectTerms clustering analysis
electric vehicle charging
electric vehicle charging behaviour
Euclidean distance
EV charging load profiles
EV charging templates
feature extraction
improved K‐prototypes method
mixed data feature extraction
morphological dissimilarities
pattern clustering
pulse train waves
rectangular pulse train
rough set theory
Special Issue: New Trends in the Planning of Distribution Network with High Penetration of Renewables and Flexible Loads
traditional distance calculation
Title Development of EV charging templates: an improved K-prototypes method
URI http://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2017.1911
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Volume 12
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