TY - GEN
T1 - A Data-Driven Framework for Residential Electric Vehicle Charging Load Profile Generation
AU - Yi, Zonggen
AU - Scoffield, Don
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/28
Y1 - 2018/8/28
N2 - Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.
AB - Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due to emerging electrified transportation. This paper introduces a data-driven framework of charging load profile generation for residential plug-in electric vehicles. Real world historical residential charging behavior data is utilized to construct empirical charging decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation is proposed to construct probability density functions for estimating charging duration based on parking duration. A generation algorithm considering parking time and travel demand dependency is introduced to generate residential charging behaviors. This framework is extensible to generate various charging load profiles and simulate varied residential charging scenarios under different number of households and charging rates. This will be crucial for designing and validating residential charging control strategies.
UR - https://www.scopus.com/pages/publications/85053851953
U2 - 10.1109/ITEC.2018.8450228
DO - 10.1109/ITEC.2018.8450228
M3 - Conference contribution
AN - SCOPUS:85053851953
SN - 9781538630488
T3 - 2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
SP - 220
EP - 225
BT - 2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
Y2 - 13 June 2018 through 15 June 2018
ER -