Organic Computing

Organic Computing

dc.contributor.editor	Krupitzer, Christian
dc.contributor.editor	Tomforde, Sven
dc.date.accessioned	2023-06-28T13:24:14Z
dc.date.available	2023-06-28T13:24:14Z
dc.date.issued	2023
dc.identifier	doi:10.17170/kobra-202302107484
dc.identifier.uri	http://hdl.handle.net/123456789/14854
dc.language.iso	eng
dc.publisher	kassel university press
dc.publisher.place	Kassel
dc.relation.isbn	978-3-7376-1100-8
dc.rights	Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/	*
dc.subject	Automatic Computing	eng
dc.subject	ProActive Computing	eng
dc.subject	Complex Adaptive Systems	eng
dc.subject	Self-Organisation	eng
dc.subject.ddc	004
dc.subject.ddc	600
dc.subject.swd	Proaktives Computing	ger
dc.subject.swd	Adaptives System	ger
dc.subject.swd	Selbstorganisation	ger
dc.title	Organic Computing	eng
dc.title.subtitle	Doctoral Dissertation Colloquium 2022	eng
dc.type	Buch
dc.type.version	publishedVersion
dcterms.abstract	Electromobility 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.accessRights	open access
dcterms.creator	Hassouna, Mohamed
dcterms.creator	Hetzel, Manuel
dcterms.creator	Smirnov, Nikita
dcterms.creator	Pilchau, Wenzel Pilar von
dcterms.creator	Cui, Henning
dcterms.creator	Feyrer, Georg
dcterms.creator	Hussaini, Mortesa
dcterms.creator	Boysen, Jonas
dcterms.creator	Kisselbach, Timo
dcterms.creator	Al-Falouji, Ghassan
dcterms.creator	Wazed Ali, Mohammad
dcterms.creator	Meitz, Lukas
dcterms.creator	Henrichs, Elia
dcterms.creator	Shmelkin, Ilja
dcterms.extent	XVI, 199 Seiten
dcterms.source.series	Intelligent Embedded Systems	eng
dcterms.source.volume	Band 24
kup.binding	Softcover
kup.institution	FB 16 Elektrotechnik / Informatik
kup.iskup	true
kup.price	39,00
kup.series	Intelligent Embedded Systems	eng
kup.size	DIN A5
kup.subject	Naturwissenschaft, Technik, Informatik, Medizin
kup.typ	Sammelband
ubks.epflicht	true