Resident electric vehicles charging optimization strategy in the smart grid

Duan Ruiqin; Ma Zhongjing

doi:10.1109/ChiCC.2015.7261072

Resident electric vehicles charging optimization strategy in the smart grid

Duan Ruiqin
, Ma Zhongjing

自动化学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

Considering the stochastic nature of electric vehicles (EVs) charging activities, this paper is dedicated to schedule the resident EVs charging load in the smart grid. Three important factors of the EV charging process are taken into account and studied, including the characteristics of EV battery, the start time of EV charging and the initial state-of-charging (SOC) of EV battery. We present a resident EVs charging optimization scheduling strategy to minimize the variation of total power load in the specified time period. And then we propose an approximate evaluation method for the corresponding optimization problem. The simulation results illustrate that the proposed EVs charging scheduling strategy will reduce the total power load curve difference of peak and valley, and the proposed method is very promising to improve the daily load profile of power system.

源语言	英语
主期刊名	Proceedings of the 34th Chinese Control Conference, CCC 2015
编辑	Qianchuan Zhao, Shirong Liu
出版商	IEEE Computer Society
页	9054-9059
页数	6
ISBN（电子版）	9789881563897
DOI	https://doi.org/10.1109/ChiCC.2015.7261072
出版状态	已出版 - 11 9月 2015
活动	34th Chinese Control Conference, CCC 2015 - Hangzhou, 中国期限: 28 7月 2015 → 30 7月 2015

丛书

姓名	Chinese Control Conference, CCC
卷	2015-September
ISSN（印刷版）	1934-1768
ISSN（电子版）	2161-2927

会议

会议	34th Chinese Control Conference, CCC 2015
国家/地区	中国
市	Hangzhou
时期	28/07/15 → 30/07/15

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

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10.1109/ChiCC.2015.7261072

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引用此

@inproceedings{eb9a2221308447c490feb62ecc2ac25e,

title = "Resident electric vehicles charging optimization strategy in the smart grid",

abstract = "Considering the stochastic nature of electric vehicles (EVs) charging activities, this paper is dedicated to schedule the resident EVs charging load in the smart grid. Three important factors of the EV charging process are taken into account and studied, including the characteristics of EV battery, the start time of EV charging and the initial state-of-charging (SOC) of EV battery. We present a resident EVs charging optimization scheduling strategy to minimize the variation of total power load in the specified time period. And then we propose an approximate evaluation method for the corresponding optimization problem. The simulation results illustrate that the proposed EVs charging scheduling strategy will reduce the total power load curve difference of peak and valley, and the proposed method is very promising to improve the daily load profile of power system.",

keywords = "Electric vehicle, Optimization scheduling, Probability model, Smart grid",

author = "Duan Ruiqin and Ma Zhongjing",

note = "Publisher Copyright: {\textcopyright} 2015 Technical Committee on Control Theory, Chinese Association of Automation.; 34th Chinese Control Conference, CCC 2015 ; Conference date: 28-07-2015 Through 30-07-2015",

year = "2015",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2015.7261072",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "9054--9059",

editor = "Qianchuan Zhao and Shirong Liu",

booktitle = "Proceedings of the 34th Chinese Control Conference, CCC 2015",

address = "United States",

}

Ruiqin, D & Zhongjing, M 2015, Resident electric vehicles charging optimization strategy in the smart grid. 在 Q Zhao & S Liu (版本), Proceedings of the 34th Chinese Control Conference, CCC 2015., 7261072, Chinese Control Conference, CCC, 卷 2015-September, IEEE Computer Society, 页码 9054-9059, 34th Chinese Control Conference, CCC 2015, Hangzhou, 中国, 28/07/15. https://doi.org/10.1109/ChiCC.2015.7261072

Resident electric vehicles charging optimization strategy in the smart grid. / Ruiqin, Duan; Zhongjing, Ma.
Proceedings of the 34th Chinese Control Conference, CCC 2015. 编辑 / Qianchuan Zhao; Shirong Liu. IEEE Computer Society, 2015. 页码 9054-9059 7261072 (Chinese Control Conference, CCC; 卷 2015-September).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Resident electric vehicles charging optimization strategy in the smart grid

AU - Ruiqin, Duan

AU - Zhongjing, Ma

PY - 2015/9/11

Y1 - 2015/9/11

N2 - Considering the stochastic nature of electric vehicles (EVs) charging activities, this paper is dedicated to schedule the resident EVs charging load in the smart grid. Three important factors of the EV charging process are taken into account and studied, including the characteristics of EV battery, the start time of EV charging and the initial state-of-charging (SOC) of EV battery. We present a resident EVs charging optimization scheduling strategy to minimize the variation of total power load in the specified time period. And then we propose an approximate evaluation method for the corresponding optimization problem. The simulation results illustrate that the proposed EVs charging scheduling strategy will reduce the total power load curve difference of peak and valley, and the proposed method is very promising to improve the daily load profile of power system.

AB - Considering the stochastic nature of electric vehicles (EVs) charging activities, this paper is dedicated to schedule the resident EVs charging load in the smart grid. Three important factors of the EV charging process are taken into account and studied, including the characteristics of EV battery, the start time of EV charging and the initial state-of-charging (SOC) of EV battery. We present a resident EVs charging optimization scheduling strategy to minimize the variation of total power load in the specified time period. And then we propose an approximate evaluation method for the corresponding optimization problem. The simulation results illustrate that the proposed EVs charging scheduling strategy will reduce the total power load curve difference of peak and valley, and the proposed method is very promising to improve the daily load profile of power system.

KW - Electric vehicle

KW - Optimization scheduling

KW - Probability model

KW - Smart grid

UR - https://www.scopus.com/pages/publications/84946593744

U2 - 10.1109/ChiCC.2015.7261072

DO - 10.1109/ChiCC.2015.7261072

M3 - Conference contribution

AN - SCOPUS:84946593744

T3 - Chinese Control Conference, CCC

SP - 9054

EP - 9059

BT - Proceedings of the 34th Chinese Control Conference, CCC 2015

A2 - Zhao, Qianchuan

A2 - Liu, Shirong

PB - IEEE Computer Society

T2 - 34th Chinese Control Conference, CCC 2015

Y2 - 28 July 2015 through 30 July 2015

ER -

Resident electric vehicles charging optimization strategy in the smart grid

摘要

丛书

会议

联合国可持续发展目标

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其它文件与链接

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