Modeling of Load Demand Due to EV Battery Charging in Distribution Systems
Abstract
This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, “smart” domestic charging and uncontrolled public charging-commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging-the “worst-case” scenario.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
When fully charged, the Nissan Altra's maximum range is around 80 miles.
“When fully charged, its maximum range is around 80 miles [4].”
From Optimal placement of electric vehicle charging stations in the active distribution network (2020) · cites this paper as [4]
For a typical distribution system, 10% EV penetration increases peak load by 17.9%, while 20% penetration increases peak load by 35.8%.
“For a typical distribution system, 10% penetration of EVs has proved to increase peak load by 17.9% while 20% penetration increases peak load by 35.8% [4].”
From Optimal placement of electric vehicle charging stations in the active distribution network (2020) · cites this paper as [4]
Cites
Rahman, S., & Shrestha, G. B. (1993) · IEEE Transactions on Power Delivery · doi:10.1109/61.216865 · Cited by 259 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Yilmaz, M., & Krein, P. T. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2212917 · Cited by 3,118 · see citing works
Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109618 · Cited by 1,140 · see citing works
Mastoi, M. S., Zhuang, S., Munir, H. M., Haris, M., Hassan, M., Usman, M., Bukhari, S. S. H., & Ro, J.-S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.011 · Cited by 523 · see citing works
Hybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations
Chaudhari, K., Ukil, A., Kumar, K. N., Manandhar, U., & Kollimalla, S. K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2713481 · Cited by 384 · 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
“When fully charged, its maximum range is around 80 miles [4].”
BackgroundXiao, Y., Zhang, Y., Kaku, I., Kang, R., & Pan, X. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111567 · Cited by 236 · see citing works
Tirunagari, S., Gu, M., & Meegahapola, L. (2022) · IEEE Access · doi:10.1109/access.2022.3217525 · Cited by 141 · see citing works
Xie, R., Wei, W., Khodayar, M. E., Wang, J., & Mei, S. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2018.2849222 · Cited by 131 · see citing works
The impact of plug-in behavior on the spatial–temporal flexibility of electric vehicle charging load
Gschwendtner, C., Knoeri, C., & Stephan, A. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104263 · Cited by 91 · see citing works
Rodríguez-Pajarón, P., Hernández, A., & Milanović, J. V. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.106807 · Cited by 89 · see citing works
“Probabilistic models are also adopted to take into account the uncertainties related to charging modes and location of EVs [11,17,19].”
MethodPashajavid, E., & Golkar, M. A. (2013) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.4822257 · Cited by 68 · see citing works
Mohsenzadeh, A., Pazouki, S., Ardalan, S., & Haghifam, M.-R. (2018) · International Journal of Ambient Energy · doi:10.1080/01430750.2017.1345010 · Cited by 49 · see citing works
Enhanced AIMD-based decentralized residential charging of EVs
Liu, M., & McLoone, S. (2015) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331213494100 · Cited by 36 · see citing works
Optimal location of PEVCSs using MAS and ER approach
Jiang, C., Jing, Z., Ji, T., & Wu, Q. (2018) · IET Generation, Transmission & Distribution · doi:10.1049/iet-gtd.2017.1907 · Cited by 24 · see citing works
Haque, 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
Ikhajangbe, O. E., Dhulipati, H., & Liu, S. (2026) · IEEE Access · doi:10.1109/access.2026.3684508 · Cited by 5 · see citing works
- Authors
- Qian, K., Zhou, C., Allan, M., & Yuan, Y.
- Affiliations
- Glasgow Caledonian University (GB)
- Hohai University (CN)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2011-05
- Volume
- 26
- Issue
- 2
- Pages
- 802-810
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2114461427
- Cited by
- 1,268
- References
- 1
- Citations by year
- 2026: 41 · 2025: 71 · 2024: 84 · 2023: 92 · 2022: 95 · 2021: 102 · 2020: 96 · 2019: 85 · 2018: 98 · 2017: 94 · 2016: 99 · 2015: 86 · 2014: 87 · 2013: 59 · 2012: 66
- FWCI
- 34.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- ResearchOnline (Glasgow Caledonian University) (acceptedVersion)
- 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 › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- electric vehicle charging, distribution systems, peak load, EV penetration, stochastic load modeling, state of charge, smart charging, off-peak charging, workplace charging, lead-acid batteries, lithium-ion batteries, electricity tariffs, load demand prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
