Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

dc.contributor.authorStraub, Florian
dc.contributor.authorStreppel, Simon
dc.contributor.authorGöhlich, Dietmar
dc.date.accessioned2021-05-19T06:31:01Z
dc.date.available2021-05-19T06:31:01Z
dc.date.issued2021-04-08
dc.date.updated2021-05-03T15:01:22Z
dc.description.abstractWith continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m2 area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%.en
dc.description.sponsorshipDFG, 410830482, Multi-Domain Modellierung und Optimierung eines integrierten Energieversorgungs- und Fahrzeugsystems für urbane Ballungsräumeen
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlinde
dc.identifier.eissn1996-1073
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13123
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11917
dc.language.isoenen
dc.relation.ispartof10.14279/depositonce-17741
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherelectric vehicleen
dc.subject.otheractivity-based simulationen
dc.subject.othertransportation electrificationen
dc.subject.othercharging power demanden
dc.subject.otherspatial temporal distributionen
dc.subject.otheropen dataen
dc.titleMethodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Dataen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber2081en
dcterms.bibliographicCitation.doi10.3390/en14082081en
dcterms.bibliographicCitation.issue8en
dcterms.bibliographicCitation.journaltitleEnergiesen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume14en
tub.accessrights.dnbfreeen
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Maschinenkonstruktion und Systemtechnik::FG Methoden der Produktentwicklung und Mechatronikde
tub.affiliation.facultyFak. 5 Verkehrs- und Maschinensystemede
tub.affiliation.groupFG Methoden der Produktentwicklung und Mechatronikde
tub.affiliation.instituteInst. Maschinenkonstruktion und Systemtechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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