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dc.contributor.advisorBordin, Chiara
dc.contributor.advisorMishra, Sambeet
dc.contributor.authorIsaksen, Ole-André
dc.date.accessioned2023-08-23T09:25:33Z
dc.date.available2023-08-23T09:25:33Z
dc.date.issued2023-06-01
dc.description.abstractFor Norway to reach the emission limits in the Paris Agreement, a substantial amount of CO2 must be reduced. Road traffic alone accounts for a high percentage of the total emissions during 2021. This thesis will focus on electrifying the transport sector and analyzing charging infrastructure for heavy-duty electric vehicles. New charging infrastructure for heavy-duty Electric Vehicles (EVs) provides issues regarding profitability due to the currently low adaption rates. However, heavy-duty EVs use the same charging sockets as EVs. As a result, EVs may finance the charging infrastructure needed to increase the adaption of heavy-duty EVs. Projections from Norwegian grid operators suggest that the total electricity surplus is diminishing during the next years and will be negative by 2027. This highlights the importance of modeling the power system in combination with finding optimal locations for charging stations. This study uses prescriptive analytics to suggest optimal locations for charging infrastructure to maximize returned profits to motivate station builders to implement more charging stations. A soft-linking will be done with PyPSA-eur to model the power system, where the new infrastructure is added as an additional load. Analyzing the results, it is possible to see that charging infrastructure has the potential to become profitable as the adaption rate for heavy-duty EVs rise. The collaboration between the models offers an open-source tool for scholars, researchers, and planners to study how new charging infrastructure affects key components in the Norwegian power system and could be useful in modeling state-of-the-art technologies.en_US
dc.identifier.urihttps://hdl.handle.net/10037/30230
dc.language.isoengen_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDEOM-3901
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555en_US
dc.subjectGISen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Analyse: 411en_US
dc.subjectPrescriptive analyticsen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Analyse: 411en_US
dc.subjectOptimizationen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Elektronikk: 435en_US
dc.subjectElectric Vehicleen_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Elektronikk: 435en_US
dc.subjectPower systemsen_US
dc.titleOptimal sizing and placement of Electrical Vehicle charging stations to serve Battery Electric Trucksen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)