Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 배성우 | - |
dc.date.accessioned | 2019-11-26T06:36:40Z | - |
dc.date.available | 2019-11-26T06:36:40Z | - |
dc.date.issued | 2017-06 | - |
dc.identifier.citation | APPLIED ENERGY, v. 195, page. 738-753 | en_US |
dc.identifier.issn | 0306-2619 | - |
dc.identifier.issn | 1872-9118 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0306261917301459?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/114686 | - |
dc.description.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. | en_US |
dc.description.sponsorship | This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161210200560). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ELSEVIER SCI LTD | en_US |
dc.subject | Electric vehicle charging-power demand | en_US |
dc.subject | Markov-chain traffic model | en_US |
dc.subject | Charging patterns | en_US |
dc.subject | Real-time closed-circuit television data | en_US |
dc.subject | Urban area | en_US |
dc.title | Prediction of electric vehicle charging-power demand in realistic urban traffic networks | en_US |
dc.type | Article | en_US |
dc.relation.volume | 195 | - |
dc.identifier.doi | 10.1016/j.apenergy.2017.02.021 | - |
dc.relation.page | 738-753 | - |
dc.relation.journal | APPLIED ENERGY | - |
dc.contributor.googleauthor | Arias, Mariz B. | - |
dc.contributor.googleauthor | Kim, Myungchin | - |
dc.contributor.googleauthor | Bae, Sungwoo | - |
dc.relation.code | 2017002164 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | swbae | - |
dc.identifier.orcid | http://orcid.org/0000-0001-5252-1455 | - |
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