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Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks
Open accessAbstract
Increasing electric vehicle (EV) charging demand and residential solar photovoltaic (PV) generation greatly alter traditional distribution system operation and have the potential to overload and otherwise threaten the operating life of legacy infrastructure. The rate and location of adoption of these technologies on residential distribution systems introduce operational uncertainties for which traditional utilities may not be prepared. This paper proposes a user-defined, data-driven risk assessment method to quantify the severity and likelihood of transformer and secondary conductor overload conditions posed by high levels of EV charging demand coupled with rooftop solar PV. The stochasticity inherent in the operation of a distribution secondary system is captured by employing data-driven probability distribution functions for residential loading, EV charging, rooftop solar generation and ambient temperature. Samples then are repeatedly drawn from each function as inputs to a Monte-Carlo, multi-period power flow analysis to calculate secondary line currents, total loading and accelerated transformer aging. The proposed approach is utilized to study transformer and secondary conductor overload risk as well as transformer loss-of-life for multiple EV and PV penetration scenarios using historical EV charging profiles and residential Salt Lake City load profile data for a peak summer load day.
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“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 [145]
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Cites
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Cited by
Sorted by citations
Umoh, V., Davidson, I., Adebiyi, A., & Ekpe, U. (2023) · Energies · doi:10.3390/en16083609 · Cited by 71
Mulenga, E., Bollen, M. H. J., & Etherden, N. (2021) · Electricity · doi:10.3390/electricity2030023 · Cited by 25
Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity
Barros, A. M. P., Angelim, J. H., & Affonso, C. M. (2023) · Energies · doi:10.3390/en16124810 · Cited by 12
Metadata
- Authors
- Palomino, A., & Parvania, M.
- Affiliations
- University of Utah (US)
- Venue
- IEEE Open Access Journal of Power and Energy
- Published
- 2020
- Volume
- 7
- Pages
- 141-150
- ISSN
- 2687-7910 (ISSN-L)
- OpenAlex
- W3014931283
- Cited by
- 32
- References
- 1
- Citations by year
- 2024: 7 · 2023: 9 · 2022: 6 · 2021: 7 · 2020: 3
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Open Access Journal of Power and Energy (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-EE0007997)
- 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
- electric vehicle charging, transformer aging, Monte Carlo simulation, EV charging profiles, household load profiles
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