2026-10-12T22:53:25Z https://data.mendeley.com/oai

oai:data.mendeley.com/y872vhtfrc.2 2022-05-19T18:25:37Z

Powell, Siobhan SPEECh Model for Study on Grid Impacts of Charging Infrastructure Access Mendeley Data This data set accompanies the github repository - https://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. 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). Long-Term Planning of Power System Electric Vehicles Smart Grid Cezar, Gustavo Vianna Min, Liang Azevedo, Ines Rajagopal, Ram Dataset doi:10.17632/y872vhtfrc.2 oai:data.mendeley.com/y872vhtfrc.2 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/y872vhtfrc 2022-05-19T18:25:37Z