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Data · dataset · 2022

SPEECh Model for Study on Grid Impacts of Charging Infrastructure Access

Listed in Teesside University Research Data Repository

Description

This data set accompanies the github repository - github.com/SiobhanPowell/speech-grid-impact - and supports the research paper, "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption", submitted in 2021. This paper uses a novel, data-driven model of electric vehicle drivers' charging behaviours to study the grid impacts of charging infrastructure access and control at deep levels of electric vehicle adoption.

The files here include five folders: (1) a Data folder containing models of driver charging behaviour that should be downloaded to run the Github model; (2) normalized load profiles for each driver group, giving the per vehicle daily load profile in kW; (3) the charging profiles produced for each of the main WECC grid scenarios studied in the paper; (4) the control objects learned to model workplace charging control; and (5) objects needed to run the grid dispatch model.

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Note in (3) those with suffix _20220313 are the main estimate of WECC demand while those with suffix _20211119 give the demand as if there were one timezone in WECC (used for illustration). Authors: Siobhan Powell (siobhan.powell@stanford.edu), Gustavo Vianna Cezar, Liang Min, Prof Ines Azevedo, and Prof Ram Rajagopal (ramr@stanford.edu).

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Catalogue records · 1

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Inferred from text
Audio 65% · Climate change impacts and adaptation 71%
Provenance · 1 source records, 12 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/y872vhtfrc.29 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].local:field:social-sciencemapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[modality].local:modality:audioenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (65%)
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