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Ratio Analysis of Fast Charger in the Design of an EV Charging Station using MCMC Sampling
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dc.contributor.authorLee, Semi-
dc.contributor.authorWendha, Brigitte-
dc.contributor.authorJung, Jaesung-
dc.date.issued2024-01-01-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/37141-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85212677349&origin=inward-
dc.description.abstractThis study addresses the urgent challenge of expanding the electric vehicle (EV) charging infrastructure to meet the growing demand as EV adoption grows. The cost difference between fast and slow chargers is as much as sevenfold, and EV aggregators require assistance designing more cost-effective charging stations. The proposed method provides a method for determining the ratio of fast chargers in an EV charging station based on simulation analysis. The EV charging demand model is generated by MCMC sampling using Markov Chain characteristics, and a probability distribution based on actual data is applied. Ratio analysis of multiple charger combinations presents an insightful design for the EV aggregator.-
dc.description.sponsorshipThis research was supported by Energy AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea(NIPA) funded by the Ministry of Science and ICT (No. S1601-20-1005)-
dc.language.isoeng-
dc.publisherIEEE Computer Society-
dc.subject.meshCharging infrastructures-
dc.subject.meshCharging station-
dc.subject.meshElectric vehicle charging-
dc.subject.meshElectric vehicle charging infrastructures-
dc.subject.meshFast chargers-
dc.subject.meshGrowing demand-
dc.subject.meshMarkov chain monte carlo samplings-
dc.subject.meshMCMC sampling-
dc.subject.meshProbabilistic models-
dc.subject.meshRatio analysis-
dc.titleRatio Analysis of Fast Charger in the Design of an EV Charging Station using MCMC Sampling-
dc.typeConference-
dc.citation.conferenceDate2024.7.21. ~ 2024.7.25.-
dc.citation.conferenceName2024 IEEE Power and Energy Society General Meeting, PESGM 2024-
dc.citation.titleIEEE Power and Energy Society General Meeting-
dc.identifier.bibliographicCitationIEEE Power and Energy Society General Meeting-
dc.identifier.doi10.1109/pesgm51994.2024.10761074-
dc.identifier.scopusid2-s2.0-85212677349-
dc.identifier.urlhttp://ieeexplore.ieee.org/xpl/conferences.jsp-
dc.subject.keywordCharging Infrastructure-
dc.subject.keywordElectric Vehicle-
dc.subject.keywordFast Charger-
dc.subject.keywordMarkov Chain Monte Carlo Sampling-
dc.subject.keywordProbabilistic Model-
dc.type.otherConference Paper-
dc.description.isoafalse-
dc.subject.subareaEnergy Engineering and Power Technology-
dc.subject.subareaNuclear Energy and Engineering-
dc.subject.subareaRenewable Energy, Sustainability and the Environment-
dc.subject.subareaElectrical and Electronic Engineering-
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Jung, Jaesung 정재성
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