Probabilistic Impact Analysis of Residential Electric Vehicle Charging on Distribution Transformers
Abstract
Transportation electrification by means of increasing plug-in electric vehicle (PEV) adoption presents both opportunities and challenges to power system operators. Although the electrification of transportation offers power utilities the opportunity to increase total load served in an era of flat year-over-year electricity sales, the sustainable integration of PEVs over the long term requires improved understanding of overload risks on existing power distribution systems. In this context, we propose a probabilistic model to study the impacts of residential PEV charging load on distribution systems. More specifically, the proposed model captures the uncertainty of PEV charging load and the uncertainty of residential customer load through Poisson and Gaussian distribution models respectively. These models are then utilized in a Monte Carlo simulation approach to calculate the statistical significance of resulting total load profiles and ultimately quantify the overloading risk levels on distribution transformers. The proposed model is utilized to study multiple PEV integration scenarios using residential customer load profile data of Salt Lake City and PEV charging load data available by Idaho National Laboratory.
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[142] introduced a voltage-constrained-based approach to calculate the HC of EVs under uncontrolled charging scenarios, while the authors in [144] considered both uncontrolled and controlled charging schemes.
“Therefore, [142] introduced a voltage-constrained-based approach to calculate the HC of EVs under uncontrolled charging scenarios while the authors in [144] considered both uncontrolled and controlled charging schemes.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [144]
To quantify the risk of overloading in the network, [144,145] capture the uncertainty of EV and customer loading using Poisson and Gaussian distribution models respectively.
“To quantify the risk of overloading in the network, [144,145] capture the uncertainty of EV and customer loading using Poisson and Gaussian distribution models respectively.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [144]
Cites
Electric vehicles: A review of network modelling and future research needs
Jing, W., Yan, Y., Kim, I., & Sarvi, M. (2016) · Advances in Mechanical Engineering · doi:10.1177/1687814015627981 · Cited by 79 · see citing works
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Coincidence Factors for Domestic EV Charging From Driving and Plug-In Behavior
Bollerslev, J., Andersen, P. B., Jensen, T. V., Marinelli, M., Thingvad, A., Calearo, L., & Weckesser, T. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3088275 · Cited by 78 · see citing works
Umoh, V., Davidson, I., Adebiyi, A., & Ekpe, U. (2023) · Energies · doi:10.3390/en16083609 · Cited by 71 · see citing works
Umoh, V., Adebiyi, A., & Moloi, K. (2023) · Eng · doi:10.3390/eng4030111 · Cited by 12 · see citing works
- Authors
- Palomino, A., & Parvania, M.
- Affiliations
- University of Utah (US)
- ORCID
- Parvania, Masood
- Venue
- 2018 North American Power Symposium (NAPS)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 1-6
- OpenAlex
- W2907037534
- Cited by
- 16
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 5 · 2021: 2 · 2020: 4 · 2019: 1 · 2018: 1
- FWCI
- 3.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- North American Power Symposium (NAPS) (publishedVersion)
- Funders
- U.S. Department of Energy
- Idaho National Laboratory
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- plug-in electric vehicles, residential EV charging, distribution transformers, probabilistic load modeling, Poisson distribution, normal distribution, Monte Carlo simulation, electric mobility, electricity profile, distribution networks
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