@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",
}
