Load variation analysis by bus of distribution systems based on PEVs charging modeling

This paper presents an algorithm that evaluated the load variation analysis influence by bus upon the distribution system by calculating the daily load curve of PEVs charging by bus based on the daily charging patterns of PEVs according to PEVs penetration scenarios. Up to now, PEVs clustering metho...

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Bibliographic Details
Published inElectrical engineering Vol. 100; no. 2; pp. 687 - 694
Main Author Choi, Sang-Bong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2018
Springer Nature B.V
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Summary:This paper presents an algorithm that evaluated the load variation analysis influence by bus upon the distribution system by calculating the daily load curve of PEVs charging by bus based on the daily charging patterns of PEVs according to PEVs penetration scenarios. Up to now, PEVs clustering methodology was simply considered to calculate number of PEVs per household by applying the Stochastic model, thereby failing to reflect charging characteristics by battery type, charging time and number of PEVs by bus of the distribution systems. To overcome these problems, the proposed algorithm calculates the number of PEVs to estimate the number of households by bus; the probability density function of the charging start time of PEVs, considering driving characteristics of PEVs; and the daily load curve of PEVs charging by bus considering battery characteristics according to PEVs penetration scenarios. The algorithm then adds the calculated daily load curve of PEVs charging and the conventional daily load curve to evaluate the load variation influence on the target bus according to PEVs penetration scenarios. To verify the evaluation of the load variation influence by bus on the distribution system in terms of the proposed algorithm, the cases were reviewed on the target bus (apartments, detached houses #1 and detached houses #3) among the feeders of the distribution systems at Dongtan new town in South Korea.
ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-017-0593-4