Hierarchical electric vehicle charging aggregator strategy using dantzig-wolfe decomposition

dc.contributor.author	Amini, M.H.
dc.contributor.author	Mcnamara, P.
dc.contributor.author	Weng, P.
dc.contributor.author	Karabasoglu, O.
dc.contributor.author	Xu, Y.
dc.date.accessioned	2021-01-25T20:48:27Z
dc.date.available	2021-01-25T20:48:27Z
dc.date.issued	2018
dc.identifier	10.1109/MDAT.2017.2759505
dc.identifier.issn	21682356
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030760080&doi=10.1109%2fMDAT.2017.2759505&partnerID=40&md5=933da7624e73b64ccaa6bd50df696753
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/9923
dc.description.abstract	This article focuses on reducing a charging cost for electric vehicles (EVs). A charging strategy is proposed to minimize the charging cost of EVs within the charging station constraints. © 2018 IEEE.
dc.language.iso	English
dc.publisher	IEEE Design and Test
dc.title	Hierarchical electric vehicle charging aggregator strategy using dantzig-wolfe decomposition
dc.type	Article
dc.relation.firstpage	25
dc.relation.lastpage	36
dc.relation.volume	35
dc.relation.issue	6
dc.description.affiliations	Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China; Electricity Research Center, University Colleg

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