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dc.contributor.authorSørensen, Åse Lekang
dc.contributor.authorLindberg, Karen Byskov
dc.contributor.authorSartori, Igor
dc.contributor.authorAndresen, Inger
dc.date.accessioned2021-08-09T08:23:58Z
dc.date.available2021-08-09T08:23:58Z
dc.date.created2021-04-30T11:07:21Z
dc.date.issued2021
dc.identifier.issn2352-3409
dc.identifier.urihttps://hdl.handle.net/11250/2766894
dc.description.abstractThis data article refers to the paper "Analysis of residential EV energy flexibility potential based on real-world charging reports and smart meter data" [1]. The reported datasets deal with residential electric vehicle (EV) charging in apartment buildings. Several datasets are provided, with different levels of detail, aiming to serve various needs. The paper provides real-world EV charging reports describing 6,878 charging sessions registered by 97 user IDs, from December 2018 to January 2020. The charging reports include identifiers, plug-in time, plug-out time and charged energy for the sessions. Synthetic charging loads are provided with hourly resolution, assuming charging power 3.6 kW or 7.2 kW and immediate charging after plug-in. The non-charging idle time reflects the flexibility potential for the charging session, with synthetic idle capacity as the energy which could potentially have been charged during the idle times. Synthetic hourly charging loads and idle capacity are provided both for individual users, and aggregated for users with private or shared charge points. For a main garage with 33% of the charging sessions, smart meter data and synthetic charging loads are available, with aggregated values each hour. Finally, local hourly traffic density in 5 nearby traffic locations is provided, for further work related to the correlation with plug-in/plug-out times. Researchers, energy analysts, charge point operators, building owners and policy makers can benefit from the datasets and analyses, serving to increase the knowledge of residential EV charging. The data provides valuable insight into residential charging, useful for e.g. forecasting energy loads and flexibility, planning and modelling activities.
dc.language.isoeng
dc.publisherElsevier
dc.rightsCC BY 4.0
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectElectric vehicle (EV) charging
dc.subjectResidential electricity demand
dc.subjectLoad profiles
dc.subjectEnd-user flexibility
dc.subjectEnergy management
dc.titleResidential electric vehicle charging datasets from apartment buildings
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.rights.holder©2021 TheAuthors
dc.subject.nsiVDP::Teknologi: 500
dc.source.volume36
dc.source.journalData in Brief
dc.identifier.doi10.1016/j.dib.2021.107105
dc.identifier.cristin1907413
dc.relation.projectNorges forskningsråd: 272402
dc.relation.projectNorges forskningsråd: 257660
dc.source.articlenumber107105
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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