Stochastic load profile generator for residential EV charging
Electric vehicle (EV) charging loads have an impact on the power grid, but also represent a potential for energy flexibility. There is a need for EV data to evaluate effects on the power grid and optimal EV charging strategies. A stochastic bottom-up model is developed for residential EV charging, t...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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EDP Sciences
2022-01-01
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Series: | E3S Web of Conferences |
Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_03005.pdf |
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author | Sørensen Åse Lekang Westad Maria Claire Delgado Benjamín Manrique Lindberg Karen Byskov |
author_facet | Sørensen Åse Lekang Westad Maria Claire Delgado Benjamín Manrique Lindberg Karen Byskov |
author_sort | Sørensen Åse Lekang |
collection | DOAJ |
description | Electric vehicle (EV) charging loads have an impact on the power grid, but also represent a potential for energy flexibility. There is a need for EV data to evaluate effects on the power grid and optimal EV charging strategies. A stochastic bottom-up model is developed for residential EV charging, taking outdoor temperatures into account. The model input is based on real-world data from residential charging in Norway. The load profile generator provides hourly load profiles for any number and combination of small and large EVs, assuming immediate charging after plug-in. It is found that the model generates realistic load profiles for residential EV charging, reflecting today’s charging patterns. Data generated can be used for load and flexibility simulations for residential EV charging. |
first_indexed | 2024-04-11T13:43:26Z |
format | Article |
id | doaj.art-d8dee019076b4aacb28822bdd321953e |
institution | Directory Open Access Journal |
issn | 2267-1242 |
language | English |
last_indexed | 2024-04-11T13:43:26Z |
publishDate | 2022-01-01 |
publisher | EDP Sciences |
record_format | Article |
series | E3S Web of Conferences |
spelling | doaj.art-d8dee019076b4aacb28822bdd321953e2022-12-22T04:21:10ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013620300510.1051/e3sconf/202236203005e3sconf_bsn2022_03005Stochastic load profile generator for residential EV chargingSørensen Åse Lekang0Westad Maria Claire1Delgado Benjamín Manrique2Lindberg Karen Byskov3SINTEF, Department of Architectural EngineeringNorwegian University of Science and Technology (NTNU)SINTEF, Department of Architectural EngineeringSINTEF, Department of Architectural EngineeringElectric vehicle (EV) charging loads have an impact on the power grid, but also represent a potential for energy flexibility. There is a need for EV data to evaluate effects on the power grid and optimal EV charging strategies. A stochastic bottom-up model is developed for residential EV charging, taking outdoor temperatures into account. The model input is based on real-world data from residential charging in Norway. The load profile generator provides hourly load profiles for any number and combination of small and large EVs, assuming immediate charging after plug-in. It is found that the model generates realistic load profiles for residential EV charging, reflecting today’s charging patterns. Data generated can be used for load and flexibility simulations for residential EV charging.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_03005.pdf |
spellingShingle | Sørensen Åse Lekang Westad Maria Claire Delgado Benjamín Manrique Lindberg Karen Byskov Stochastic load profile generator for residential EV charging E3S Web of Conferences |
title | Stochastic load profile generator for residential EV charging |
title_full | Stochastic load profile generator for residential EV charging |
title_fullStr | Stochastic load profile generator for residential EV charging |
title_full_unstemmed | Stochastic load profile generator for residential EV charging |
title_short | Stochastic load profile generator for residential EV charging |
title_sort | stochastic load profile generator for residential ev charging |
url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/29/e3sconf_bsn2022_03005.pdf |
work_keys_str_mv | AT sørensenaselekang stochasticloadprofilegeneratorforresidentialevcharging AT westadmariaclaire stochasticloadprofilegeneratorforresidentialevcharging AT delgadobenjaminmanrique stochasticloadprofilegeneratorforresidentialevcharging AT lindbergkarenbyskov stochasticloadprofilegeneratorforresidentialevcharging |