Agent-Based Modeling of Urban Residential Electricity Demand Incorporating EV Charging Behavior
(2025) 39th ECMS International Conference on Modelling and Simulation, ECMS 2025 In Proceedings - European Council for Modelling and Simulation, ECMS 2025-June. p.455-461- Abstract
By 2050, electric vehicles (EVs) are expected to be 40% of all cars on the road1. In some countries like Norway, more than 90% of the new cars sold are electric2. This rise in EVs presents challenges to the current electricity infrastructure to meet this rapid increase in electricity demand. In this study, we simulate urban residential electricity demand using an agent-based model to investigate the effects of EVs' charging behavior on the electricity demand profile and how using smart charging behavior helps in reducing the peak demand pressure. The simulation results of different scenarios show how it can be effective to schedule the charging time in the off-peak.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c6ebf6c4-0247-41e7-82c3-0ed850415681
- author
- Alaliyat, Saleh and Oucheikh, Rachid LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Agent-Based Modeling, Charging Behaviour, Electricity Demand, EV, Simulation
- host publication
- Proceedings of the 39th ECMS International Conference on Modelling and Simulation, ECMS 2025
- series title
- Proceedings - European Council for Modelling and Simulation, ECMS
- editor
- Scarpa, Marco ; Cavalieri, Salvatore ; Serrano, Salvatore and De Vita, Fabrizio
- volume
- 2025-June
- pages
- 7 pages
- publisher
- European Council for Modelling and Simulation
- conference name
- 39th ECMS International Conference on Modelling and Simulation, ECMS 2025
- conference location
- Catania, Italy
- conference dates
- 2025-06-24 - 2025-06-27
- external identifiers
-
- scopus:105010578101
- ISSN
- 2522-2414
- ISBN
- 9783937436869
- DOI
- 10.7148/2025-0455
- language
- English
- LU publication?
- yes
- id
- c6ebf6c4-0247-41e7-82c3-0ed850415681
- date added to LUP
- 2026-01-20 11:13:07
- date last changed
- 2026-01-21 08:43:58
@inproceedings{c6ebf6c4-0247-41e7-82c3-0ed850415681,
abstract = {{<p>By 2050, electric vehicles (EVs) are expected to be 40% of all cars on the road<sup>1</sup>. In some countries like Norway, more than 90% of the new cars sold are electric<sup>2</sup>. This rise in EVs presents challenges to the current electricity infrastructure to meet this rapid increase in electricity demand. In this study, we simulate urban residential electricity demand using an agent-based model to investigate the effects of EVs' charging behavior on the electricity demand profile and how using smart charging behavior helps in reducing the peak demand pressure. The simulation results of different scenarios show how it can be effective to schedule the charging time in the off-peak.</p>}},
author = {{Alaliyat, Saleh and Oucheikh, Rachid}},
booktitle = {{Proceedings of the 39th ECMS International Conference on Modelling and Simulation, ECMS 2025}},
editor = {{Scarpa, Marco and Cavalieri, Salvatore and Serrano, Salvatore and De Vita, Fabrizio}},
isbn = {{9783937436869}},
issn = {{2522-2414}},
keywords = {{Agent-Based Modeling; Charging Behaviour; Electricity Demand; EV; Simulation}},
language = {{eng}},
pages = {{455--461}},
publisher = {{European Council for Modelling and Simulation}},
series = {{Proceedings - European Council for Modelling and Simulation, ECMS}},
title = {{Agent-Based Modeling of Urban Residential Electricity Demand Incorporating EV Charging Behavior}},
url = {{http://dx.doi.org/10.7148/2025-0455}},
doi = {{10.7148/2025-0455}},
volume = {{2025-June}},
year = {{2025}},
}