A data-driven probabilistic framework for estimating grid impacts of EV charging at scale
Callanan, Alice LU ; Samuelsson, Olof LU and Marquez Fernandez, Francisco J. LU
(2025)
In International Journal of Electrical Power and Energy Systems
172.
- Abstract
- The rapid uptake of electric vehicles presents both opportunities and challenges for modern power systems. As charging demand increases, intelligent and data-driven approaches are essential to predict and manage future grid impacts. This paper presents a probabilistic and data-driven approach for generating realistic electric vehicle charging profiles, which can be integrated into probabilistic load flow simulations to assess their effect on grid capacity. In contrast to many existing studies that focus on low-voltage grids, this work targets the sub-transmission grid, analyzing the impact of aggregated charging demand. The approach is applied to a Swedish sub-transmission grid using a combination of real-world data: travel statistics,... (More)
- The rapid uptake of electric vehicles presents both opportunities and challenges for modern power systems. As charging demand increases, intelligent and data-driven approaches are essential to predict and manage future grid impacts. This paper presents a probabilistic and data-driven approach for generating realistic electric vehicle charging profiles, which can be integrated into probabilistic load flow simulations to assess their effect on grid capacity. In contrast to many existing studies that focus on low-voltage grids, this work targets the sub-transmission grid, analyzing the impact of aggregated charging demand. The approach is applied to a Swedish sub-transmission grid using a combination of real-world data: travel statistics, demographic data, charger usage rates, and two empirical home charging datasets, one representing uncoordinated charging, and another incorporating smart charging technologies. The methodology generates synthetic but realistic charging demand profiles, which are coupled to a full-scale grid model to evaluate the system under 100 % fleet electrification. Results show that substantial congestion may emerge in the primary substation transformers, which may, to some extent, be mitigated by adopting planned or intelligent charging strategies. By combining real-world data with a detailed grid model and a probabilistic modeling approach, the study lays a foundation for analyzing grid impacts under high EV penetration and supports the development of flexibility services to inform more proactive grid planning. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0696064d-062e-4412-8687-b85f6a2c4c82
- author
- Callanan, Alice
LU
; Samuelsson, Olof
LU
and Marquez Fernandez, Francisco J.
LU
- organization
- publishing date
- 2025-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- International Journal of Electrical Power and Energy Systems
- volume
- 172
- article number
- 111204
- pages
- 9 pages
- publisher
- Elsevier
- ISSN
- 0142-0615
- DOI
- 10.1016/j.ijepes.2025.111204
- language
- English
- LU publication?
- yes
- id
- 0696064d-062e-4412-8687-b85f6a2c4c82
- date added to LUP
- 2025-10-02 08:39:47
- date last changed
- 2025-10-02 10:52:07
@article{0696064d-062e-4412-8687-b85f6a2c4c82,
abstract = {{The rapid uptake of electric vehicles presents both opportunities and challenges for modern power systems. As charging demand increases, intelligent and data-driven approaches are essential to predict and manage future grid impacts. This paper presents a probabilistic and data-driven approach for generating realistic electric vehicle charging profiles, which can be integrated into probabilistic load flow simulations to assess their effect on grid capacity. In contrast to many existing studies that focus on low-voltage grids, this work targets the sub-transmission grid, analyzing the impact of aggregated charging demand. The approach is applied to a Swedish sub-transmission grid using a combination of real-world data: travel statistics, demographic data, charger usage rates, and two empirical home charging datasets, one representing uncoordinated charging, and another incorporating smart charging technologies. The methodology generates synthetic but realistic charging demand profiles, which are coupled to a full-scale grid model to evaluate the system under 100 % fleet electrification. Results show that substantial congestion may emerge in the primary substation transformers, which may, to some extent, be mitigated by adopting planned or intelligent charging strategies. By combining real-world data with a detailed grid model and a probabilistic modeling approach, the study lays a foundation for analyzing grid impacts under high EV penetration and supports the development of flexibility services to inform more proactive grid planning.}},
author = {{Callanan, Alice and Samuelsson, Olof and Marquez Fernandez, Francisco J.}},
issn = {{0142-0615}},
language = {{eng}},
month = {{10}},
publisher = {{Elsevier}},
series = {{International Journal of Electrical Power and Energy Systems}},
title = {{A data-driven probabilistic framework for estimating grid impacts of EV charging at scale}},
url = {{http://dx.doi.org/10.1016/j.ijepes.2025.111204}},
doi = {{10.1016/j.ijepes.2025.111204}},
volume = {{172}},
year = {{2025}},
}