99 0

Prediction of electric vehicle charging-power demand in realistic urban traffic networks

Title
Prediction of electric vehicle charging-power demand in realistic urban traffic networks
Author
배성우
Keywords
Electric vehicle charging-power demand; Markov-chain traffic model; Charging patterns; Real-time closed-circuit television data; Urban area
Issue Date
2017-06
Publisher
ELSEVIER SCI LTD
Citation
APPLIED ENERGY, v. 195, page. 738-753
Abstract
This paper presents a time-spatial electric vehicle (EV) charging-power demand forecast model at fast charging stations located in urban areas. Most previous studies have considered private charging locations and a fixed charging-start time to predict the EV charging-power demand. Few studies have considered predicting the EV charging-power demand in urban areas with time-spatial model analyses. The approaches used in previous studies also may not be applicable to predicting the EV charging-power demand in urban areas because of the complicated urban road network. To possibly forecast the actual EV charging-power demand in an urban area, real-time closed-circuit television (CCTV) data from an actual urban road network are considered. In this study, a road network inside the metropolitan area of Seoul, South Korea was used to formulate the EV charging-power demand model using two steps. First, the arrival rate of EVs at the charging stations located near road segments of the urban road network is determined by a Markov-chain traffic model and a teleportation approach. Then, the EV charging power demand at the public fast-charging stations is determined using the information from the first step. Numerical examples for the EV charging-power demand during three time ranges (i.e., morning, afternoon, and evening) are presented to predict the charging-power demand profiles at the public fast-charging stations in urban areas. The proposed time-spatial model can also contribute to investment and operation plans for adaptive EV charging infrastructures with renewable resources and energy storage depending on the EV charging-power demand in urban areas. (C) 2017 Elsevier Ltd. All rights reserved.
URI
https://www.sciencedirect.com/science/article/pii/S0306261917301459?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/114686
ISSN
0306-2619; 1872-9118
DOI
10.1016/j.apenergy.2017.02.021
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE