Profile of Charging Load on the Grid Due to Plug-in Vehicles
Abstract
The paper presents a study of the profile of the load imposed on a power system by grid-charging of the onboard battery pack of electric and plug-in hybrid vehicles. The study uses a large database of field-recorded driving cycles stamped with parking times and locations to predict realistic driving habits of drivers in an urban setting. A fuzzy-logic inference system is designed to emulate the decision-making process of a driver when deciding to charge the vehicle's battery. The charging load is then estimated on an hourly basis for a number of electric and plug-in hybrid vehicles with different storage capacities. Level-1 and level-2 charging regimes as well as two scenarios for charging, namely charging at home and charging at home and work, are considered. The load profile is presented as an hourly probability of charging for each vehicle type. The results demonstrate how penetration of plug-in hybrid and electric vehicles affects the load on a utility network.
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Level 1 and level 2 chargers take a longer time to charge EVs than level 3 chargers.
“However, level 1 and level 2 chargers take a longer time to charge EVs.”
From Optimal placement of electric vehicle charging stations in the active distribution network (2020) · cites this paper as [5]
Level 3 chargers are super-fast in charging EVs compared with level 1 and level 2 chargers.
“Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs.”
From Optimal placement of electric vehicle charging stations in the active distribution network (2020) · cites this paper as [5]
Capital costs of level 1 and level 2 chargers are less than the cost of level 3 chargers.
“Capital costs of level 1 and level 2 chargers are less than the cost of level 3 chargers.”
From Optimal placement of electric vehicle charging stations in the active distribution network (2020) · cites this paper as [5]
Cited by
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Managing Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey
Rigas, E. S., Ramchurn, S. D., & Bassiliades, N. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2376873 · Cited by 280 · see citing works
Optimal placement of electric vehicle charging stations in the active distribution network
Zeb, M. Z., Imran, K., Khattak, A., Janjua, A. K., Pal, A., Nadeem, M., Zhang, J., & Khan, S. (2020) · IEEE Access · doi:10.1109/access.2020.2984127 · Cited by 263 · see citing works
“The impact of plug-in hybrid EVs on grid load is studied and analyzed in [5] and EV load was measured and used in this study.”
MethodUrban power load profiles under ageing transition integrated with future EVs charging
Zhang, H., Chen, J., Yan, J., Song, X., Shibasaki, R., & Yan, J. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2020.100007 · Cited by 40 · see citing works
Dominguez-Jimenez, J. A., Campillo, J. E., Montoya, O. D., Delahoz, E., & Hernández, J. C. (2020) · Sustainability · doi:10.3390/su12187769 · Cited by 28 · see citing works
“Over the last decades, several approaches have been developed until today to solve problems related to load balancing and capacity planning due to large-scale EVs penetration [13].”
BackgroundHaque, A., Kurukuru, V. S. B., & Khan, M. A. (2019) · IET Power Electronics · doi:10.1049/iet-pel.2019.0048 · Cited by 18 · see citing works
- Authors
- Shahidinejad, S., Filizadeh, S., & Bibeau, E.
- Affiliations
- University of Manitoba (CA)
- ORCID
- Filizadeh, Shaahin
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2012-03
- Volume
- 3
- Issue
- 1
- Pages
- 135-141
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W1981820886
- Cited by
- 187
- References
- 0
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 11 · 2023: 13 · 2022: 14 · 2021: 9 · 2020: 11 · 2019: 21 · 2018: 13 · 2017: 10 · 2016: 11 · 2015: 14 · 2014: 22 · 2013: 13 · 2012: 10
- FWCI
- 10.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- plug-in electric vehicles, plug-in hybrid electric vehicles, grid charging, charging load profiles, driving cycles, fuzzy inference, Level 2 charging, EV penetration
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