Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11902
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dc.contributor.authorColak, Aslinur-
dc.contributor.authorFescioglu-Unver, Nilgun-
dc.date.accessioned2024-12-10T19:00:45Z-
dc.date.available2024-12-10T19:00:45Z-
dc.date.issued2024-
dc.identifier.issn0360-5442-
dc.identifier.issn1873-6785-
dc.identifier.urihttps://doi.org/10.1016/j.energy.2024.133637-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11902-
dc.description.abstractThe demand for public fast charging stations is increasing with the number of electric vehicles on roads. The charging queues and waiting times get longer, especially during the winter season and on holidays. Priority based service at charging stations can provide shorter delay times to vehicles willing to pay more and lower charging prices for vehicles accepting to wait more. Existing studies use classical feedback control and simulation based control methods to maintain the ratio of high and low priority vehicles' delay times at the station's target level. Reinforcement learning has been used successfully for real time control in environments with uncertainties. This study proposes a deep Q-Learning based real time resource allocation model for priority service in fast charging stations (DRL-EXP). Results show that the deep learning approach enables DRL-EXP to provide amore stable and faster response than the existing models. DRL-EXP is also applicable to other priority based service systems that act under uncertainties and require real time control.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEnergyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Q-learningen_US
dc.subjectResource allocationen_US
dc.subjectQueue managementen_US
dc.subjectElectric vehicleen_US
dc.subjectFast charging stationen_US
dc.subjectPriority serviceen_US
dc.subjectManagementen_US
dc.subjectGuaranteesen_US
dc.subjectAdmissionen_US
dc.subjectFrameworken_US
dc.subjectCriteriaen_US
dc.subjectSystemsen_US
dc.titleDeep Reinforcement Learning Based Resource Allocation for Electric Vehicle Charging Stations With Priority Serviceen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume313en_US
dc.identifier.wosWOS:001354822600001en_US
dc.identifier.scopus2-s2.0-85208244209en_US
dc.institutionauthor-
dc.identifier.doi10.1016/j.energy.2024.133637-
dc.authorscopusid57288546000-
dc.authorscopusid9639266600-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.4. Department of Industrial Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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