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dc.contributor.author배성우-
dc.date.accessioned2019-11-26T06:36:40Z-
dc.date.available2019-11-26T06:36:40Z-
dc.date.issued2017-06-
dc.identifier.citationAPPLIED ENERGY, v. 195, page. 738-753en_US
dc.identifier.issn0306-2619-
dc.identifier.issn1872-9118-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0306261917301459?via%3Dihub-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/114686-
dc.description.abstractThis 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.sponsorshipThis 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.isoen_USen_US
dc.publisherELSEVIER SCI LTDen_US
dc.subjectElectric vehicle charging-power demanden_US
dc.subjectMarkov-chain traffic modelen_US
dc.subjectCharging patternsen_US
dc.subjectReal-time closed-circuit television dataen_US
dc.subjectUrban areaen_US
dc.titlePrediction of electric vehicle charging-power demand in realistic urban traffic networksen_US
dc.typeArticleen_US
dc.relation.volume195-
dc.identifier.doi10.1016/j.apenergy.2017.02.021-
dc.relation.page738-753-
dc.relation.journalAPPLIED ENERGY-
dc.contributor.googleauthorArias, Mariz B.-
dc.contributor.googleauthorKim, Myungchin-
dc.contributor.googleauthorBae, Sungwoo-
dc.relation.code2017002164-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING-
dc.identifier.pidswbae-
dc.identifier.orcidhttp://orcid.org/0000-0001-5252-1455-
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COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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