Adapted Stochastic PV Hosting Capacity Approach for Electric Vehicle Charging Considering Undervoltage
Open accessAbstract
This paper presents a stochastic approach to single-phase and three-phase EV charge hosting capacity for distribution networks. The method includes the two types of uncertainties, aleatory and epistemic, and is developed from an equivalent method that was applied to solar PV hosting capacity estimation. The method is applied to two existing low-voltage networks in Northern Sweden, with six and 83 customers. The lowest background voltage and highest consumption per customer are obtained from measurements. It is shown that both have a big impact on the hosting capacity. The hosting capacity also depends strongly on the charging size, within the range of charging size expected in the near future. The large range in hosting capacity found from this study—between 0% and 100% of customers can simultaneously charge their EV car—means that such hosting capacity studies are needed for each individual distribution network. The highest hosting capacity for the illustrative distribution networks was obtained for the 3.7 kW single-phase and 11 kW three-phase EV charging power.
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In [2], a stochastic approach to single-phase and three-phase EV charging HC for two existing distribution networks including aleatory and epistemic uncertainties is presented.
“In [2], a stochastic approach to single-phase and three-phase EV charging HC for two existing distribution networks including aleatory and epistemic uncertainties is presented.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
Connecting single-phase or three-phase EVs on the distribution network causes violations of various performance limits including thermal limits and transformer overload due to increased demand, harmonics, voltage magnitude, and voltage unbalance.
“Moreover, connecting single-phase or three-phase EVs on the distribution network causes violations of various performance limits including thermal limits and transformer overload due to increased demand, harmonics, voltage magnitude, and voltage unbalance [2,137,138].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
In [2], background voltage and under-voltage are the limiting factors, with the 10th percentile of the worst-case voltage distribution as the performance index and 90% of the nominal voltage as a limit.
“Background voltage and under-voltage are the limiting factors, with the 10th percentile of the worst-case voltage distribution as the performance index and 90% of the nominal voltage as a limit.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
EV charging HC is sensitive to the lowest background voltage and highest power consumption.
“The results show that EV charging HC is sensitive to the lowest background voltage and highest power consumption.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
A stochastic approach to determine the single-phase and three-phase EV charging HC considering both aleatory and epistemic uncertainties is developed in [2], while [15] applied a simplified MCS using limited input data to determine the EV charging HC.
“A stochastic approach to determine the single-phase and three-phase EV charging HC considering both aleatory and epistemic uncertainties is developed in [2], while [15] applied a simplified MCS using limited input data to determine the EV charging HC.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
The method in [2] can be used at any time without detailed knowledge of the charging patterns.
“The method can be used at any time without detailed knowledge of the charging patterns.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [2]
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References 29 · Cited by 25
- Authors
- Mulenga, E., Bollen, M. H. J., & Etherden, N.
- Affiliations
- Luleå University of Technology (SE)
- Corresponding author
- Mulenga, Enock
- Venue
- Electricity
- Publisher
- MDPI AG
- Published
- 2021-09-17
- Volume
- 2
- Issue
- 3
- Pages
- 387-402
- ISSN
- 2673-4826 (ISSN-L)
- OpenAlex
- W3201300066
- Cited by
- 25
- References
- 29
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 4 · 2023: 11 · 2022: 2
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electricity (publishedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Energimyndigheten (NA)
- Skellefteå Kraft Elnät AB (NA)
- Umeå Energi AB (NA)
- 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, hosting capacity, distribution networks, undervoltage, stochastic modeling, aleatoric uncertainty, epistemic uncertainty, low-voltage distribution networks, single-phase charging, charging power
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