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Organic Computing

dc.contributor.editorKrupitzer, Christian
dc.contributor.editorTomforde, Sven
dc.date.accessioned2023-06-28T13:24:14Z
dc.date.available2023-06-28T13:24:14Z
dc.date.issued2023
dc.identifierdoi:10.17170/kobra-202302107484
dc.identifier.urihttp://hdl.handle.net/123456789/14854
dc.language.isoeng
dc.publisherkassel university press
dc.rightsNamensnennung - Weitergabe unter gleichen Bedingungen 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectAutomatic Computingeng
dc.subjectProActive Computingeng
dc.subjectComplex Adaptive Systemseng
dc.subjectSelf-Organisationeng
dc.subject.ddc004
dc.subject.ddc600
dc.titleOrganic Computingeng
dc.typeBuch
dcterms.abstractElectromobility plays an increasingly important role in the energy transition. Electric vehicle charging poses challenges to the electricity grid due to high peak demand situations. Therefore, it is crucial to leverage the vehicle’s flexibility when charging can be delayed. Conventional charging management systems cannot adapt to frequently changing conditions such as fluctuating renewable energy output, electricity prices and user behavior. In this article, we investigate the application of reinforcement learning approaches for self-adaptive charging management. In particular, we identify challenges regarding realistic environments and adaption to varying topologies and connections among charging stations. We describe related approaches and propose ideas and planned experiments to overcome these problems by utilizing generative models and graph neural networks.eng
dcterms.accessRightsopen access
dcterms.creatorHassouna, Mohamed
dcterms.creatorHetzel, Manuel
dcterms.creatorSmirnov, Nikita
dcterms.creatorPilchau, Wenzel Pilar von
dcterms.creatorCui, Henning
dcterms.creatorFeyrer, Georg
dcterms.creatorHussaini, Mortesa
dcterms.creatorBoysen, Jonas
dcterms.creatorKisselbach, Timo
dcterms.creatorAl-Falouji, Ghassan
dcterms.creatorWazed Ali, Mohammad
dcterms.creatorMeitz, Lukas
dcterms.creatorHenrichs, Elia
dcterms.creatorShmelkin, Ilja
dcterms.extentXVI, 199 Seiten
dc.publisher.placeKassel
dc.relation.isbn978-3-7376-1100-8
dc.subject.swdProaktives Computingger
dc.subject.swdAdaptives Systemger
dc.subject.swdSelbstorganisationger
dc.title.subtitleDoctoral Dissertation Colloquium 2022eng
dc.type.versionpublishedVersion
dcterms.source.seriesIntelligent Embedded Systemseng
dcterms.source.volumeBand 24
kup.iskuptrue
kup.price39,00
kup.seriesIntelligent Embedded Systemseng
kup.subjectNaturwissenschaft, Technik, Informatik, Medizin
kup.typSammelband
kup.institutionFB 16 Elektrotechnik / Informatik
kup.bindingSoftcover
kup.sizeDIN A5
ubks.epflichttrue


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