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Residential electric vehicle charging datasets from apartment buildings

This 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, w...

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Detalles Bibliográficos
Autores principales: Sørensen, Åse Lekang, Lindberg, Karen Byskov, Sartori, Igor, Andresen, Inger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134705/
https://www.ncbi.nlm.nih.gov/pubmed/34026988
http://dx.doi.org/10.1016/j.dib.2021.107105
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author Sørensen, Åse Lekang
Lindberg, Karen Byskov
Sartori, Igor
Andresen, Inger
author_facet Sørensen, Åse Lekang
Lindberg, Karen Byskov
Sartori, Igor
Andresen, Inger
author_sort Sørensen, Åse Lekang
collection PubMed
description This 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.
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spelling pubmed-81347052021-05-21 Residential electric vehicle charging datasets from apartment buildings Sørensen, Åse Lekang Lindberg, Karen Byskov Sartori, Igor Andresen, Inger Data Brief Data Article This 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. Elsevier 2021-04-28 /pmc/articles/PMC8134705/ /pubmed/34026988 http://dx.doi.org/10.1016/j.dib.2021.107105 Text en © 2021 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Sørensen, Åse Lekang
Lindberg, Karen Byskov
Sartori, Igor
Andresen, Inger
Residential electric vehicle charging datasets from apartment buildings
title Residential electric vehicle charging datasets from apartment buildings
title_full Residential electric vehicle charging datasets from apartment buildings
title_fullStr Residential electric vehicle charging datasets from apartment buildings
title_full_unstemmed Residential electric vehicle charging datasets from apartment buildings
title_short Residential electric vehicle charging datasets from apartment buildings
title_sort residential electric vehicle charging datasets from apartment buildings
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134705/
https://www.ncbi.nlm.nih.gov/pubmed/34026988
http://dx.doi.org/10.1016/j.dib.2021.107105
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