Classification of Economic Approaches for Smart Grid

dc.accessRightsAnonymous
dc.audienceScience
dc.contributor.authorKeller, Corinne
dc.contributor.authorManser, Daniel
dc.contributor.authorVogler, Sandro
dc.contributor.authorWache, Holger
dc.date.accessioned2015-09-21T09:37:35Z
dc.date.available2015-09-21T09:37:35Z
dc.date.issued2015
dc.description.abstractAbstract—Several European countries are increasingly focusing on renewable energy in order to satisfy their demand. A core problem of these sources is their reliability, which means less continuously available energy is accessible. Smart grids are trying to cope with this problem by adding intelligence to the net, which tries to adjust the load according to the current produced amount of electrical energy. Many approaches try to tackle down this problem by technical means. This paper analyses existing economical approaches for smart grid environments and highlights the unique features and important properties of a broad selection of papers. Classification criteria are derived from existing literature. Afterwards, the most prominent papers are used to demonstrate how the classification scheme can be applied.
dc.identifier.urihttp://hdl.handle.net/11654/5068
dc.identifier.urihttp://dx.doi.org/10.26041/fhnw-2787
dc.language.isoenen_US
dc.publisherIEEE
dc.relation.ispartof12th International Conference on the European Energy Market, IEEE Computer Society Press
dc.spatialLos Alamitosen_US
dc.subject.ddc330 - Wirtschaftde
dc.titleClassification of Economic Approaches for Smart Grid
dc.type04 - Beitrag Sammelband oder Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.IsStudentsWorkno
fhnw.PublishedSwitzerlandNo
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.pagination1-5
fhnw.publicationStatePublished
relation.isAuthorOfPublication9a5348f4-47b3-437d-a1f9-7cf66011e883
relation.isAuthorOfPublication.latestForDiscovery9a5348f4-47b3-437d-a1f9-7cf66011e883
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