Data-driven cost-effective capacity provisioning scheme in electric vehicle charging facility
DC Field | Value | Language |
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dc.contributor.author | Kim, Jangkyum | - |
dc.contributor.author | Oh, Hyeontaek | - |
dc.contributor.author | Lee, Joohyung | - |
dc.date.accessioned | 2022-12-30T17:40:08Z | - |
dc.date.available | 2022-12-30T17:40:08Z | - |
dc.date.created | 2022-12-16 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 0360-8352 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86357 | - |
dc.description.abstract | In the actual power system, managing the amount of Contract power , which directly related to the capacity of electric vehicle (EV) charging facility, becomes an important issue to stabilize the power system and minimize the financial loss of the EV charging facility. Since, determining the Contract power is one of critical issues to implement the cost-effective charging service in EV charging facility, it influences the charging decision of EV users. To consider the concern of both the operator and EV users, this paper derives analytical models considering not only electricity tariff polices but also EV users' inflow rate and behavior (i.e., the users' willingness to charge and leaving without charging probability) based on an open-dataset about EV users. Then, using the analytical models, this paper proposes a novel method to optimize the capacity of EV charging facility by considering the relationship between various monetary factors and Contract power. Finally, applying the obtained optimal Contract power in actual power system and market environment, we confirm that the proposed scheme could achieve reduction of overall monetary cost 24.6% compared to other benchmark models. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | COMPUTERS & INDUSTRIAL ENGINEERING | - |
dc.title | Data-driven cost-effective capacity provisioning scheme in electric vehicle charging facility | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000876451200004 | - |
dc.identifier.doi | 10.1016/j.cie.2022.108743 | - |
dc.identifier.bibliographicCitation | COMPUTERS & INDUSTRIAL ENGINEERING, v.173 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85140728586 | - |
dc.citation.title | COMPUTERS & INDUSTRIAL ENGINEERING | - |
dc.citation.volume | 173 | - |
dc.contributor.affiliatedAuthor | Lee, Joohyung | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Electric vehicle | - |
dc.subject.keywordAuthor | EV user behavior | - |
dc.subject.keywordAuthor | Contract power | - |
dc.subject.keywordAuthor | Cost minimization | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | RANGE | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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