Hosting Capacity Assessment of South African Residential Low-Voltage Networks for Electric Vehicle Charging
Open accessAbstract
The necessity for environmentally friendly transportation systems has prompted the proliferation of electric vehicles (EVs) in low-voltage (LV) distribution networks. However, large-scale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid. It is therefore important to assess the impact of connecting EVs for charging in existing distribution networks and determine the hosting capacity (HC) of such a network. This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively, for a South African low-voltage distribution network containing 21 households. Voltage drop and equipment loading are the performance indices (PI) considered for the impact assessment and HC estimation in this study. The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits. The results show that the HC is 5–8 three-phase EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario. Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively. The result showing the seasonal variation in HC and for other EV charging power is also presented. The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC.
AI snapshot
Written from the paper’s full text.
TL;DR
This paper assesses EV charging hosting capacity on a 21-household South African low-voltage network using deterministic time-series and simplified Monte Carlo methods. It finds equipment loading is the main limiting factor, with three-phase HC of 5–8 EVs worst-case and 9–13 best-case, and single-phase 3.7 kW HC of 8 worst-case and 15 best-case.
Key findings
- Three-phase EV charging HC is 5–8 EVs charging simultaneously for the worst-case scenario and 9–13 EVs for the best-case scenario; seasonal analysis shows the network can host more EVs in summer (13 best-case, 8 worst-case) and fewest in winter.
- Equipment loading is the major limiting factor. In the three-phase worst-case, the first loading violation is at 108% at 29% penetration (6 customers); in the best-case, the first loading violation is at 107% when 10 customers (48% penetration) charge.
- For single-phase charging, the popular 3.7 kW charger has HC of 8 EVs worst-case and 15 EVs best-case; Table 2 reports voltage-drop HC of 10 worst-case and 15 best-case, and equipment-loading HC of 8 worst-case and 10 best-case. For an 11.5 kW charger, equipment-loading HC is 0 worst-case and 5 best-case.
- During the best-case scenario, 9 and 10 customers can be hosted simultaneously for 85% and 70% of hours, respectively; during the worst-case, 5 and 6 customers can charge simultaneously for 85% and 60% of the year without violating any performance index.
Methods
- Test network: part of an eThekwini power grid in Durban, South Africa, consisting of 21 high-income homes, supplied by a 350 kVA 11/0.41 kV Dyn11 transformer, with three customers per distribution unit and one EV assumed per customer.
- Data and modelling: one calendar year (2021) of measured hourly average active energy for all 21 customers from eThekwini Municipality; load scaling in DIgSILENT PowerFactory 2022; 15 July 2021 used as the critical day for single-phase assessment.
- Analysis: deterministic/time-series method for three-phase HC; stochastic simplified Monte Carlo simulation for single-phase HC with phase selection as the only uncertainty, 500 scenarios, and 16:00–18:00 on the critical day; performance indices are voltage drop and equipment loading, with limits from NRS097 (loading 100%, voltage not below 0.90 p.u.), assuming uncontrolled charging without state of charge.
Limitations
- Stated: results are specific to one grid, performance index, and PI limits, and cannot be immediately transferred to other non-homogenous distribution grids.
- Stated: the study uses uncontrolled EV charging without considering charging behaviour or battery state of charge, which may underestimate HC; future work should consider controlled charging, state of charge, and different customer charging behaviours.
- Evident from the text: single-phase Monte Carlo considers only phase selection as uncertainty; individual household load data were unavailable, so all customers were assumed to have the same consumption pattern via load scaling; the single-phase assessment relies on one critical day and assumes no violation then implies likely no violation in other periods.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
1 of 7 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Findingown data
Increasing EV penetration level results in a corresponding increase in equipment loading towards the allowable limit in the single-phase network.
“It is observed that increasing the penetration level results in a corresponding increase in the equipment loading towards the allowable limit.”
Quote in the paperSubstantial claim - Findingown data
The single-phase hosting capacity result shows that 48% to 76% of the customers can charge simultaneously when voltage drop is the PI considered.
“Also, the single-phase HC result presented shows that 48% to 76% of the customers can charge simultaneously when the voltage drop is the PI considered.”
Quote in the paperSubstantial claim - Findingown data
For single-phase voltage drop as PI, the hosting capacity reduces to 9.5% and 57% at 6.9 kW charging power.
“The HC reduces to 9.5% and 57% at 6.9 kW charging power.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase best-case scenario, the first voltage violation is observed at 57% PL EV when 12 EVs are simultaneously charging.
“Figure 8b shows the best-case scenario where the first violation is observed at 57% PL EV , when 12 EVs are simultaneously charging.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase worst-case scenario, more customers can charge at hours of lower household load demand until both minimum and maximum equipment loading exceed the limits when nine EVs (43% PL EV) are simultaneously charging.
“More customers can charge at hours of lower household load demand until both minimum and maximum equipment loading exceed the limits when nine EVs (43% PL EV ) are simultaneously charging.”
Quote in the paperSubstantial claim
- Findingown data
Increasing EV charging penetration results in a corresponding movement of the performance indices voltage drop and equipment loading toward the allowable limits.
“The impact assessment result confirms that increasing EV charging penetration will result in a corresponding movement of the PIs toward the allowable limits.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Each customer is capable of connecting only one EV to the grid.
- The authors state this as an assumption: All customers are of the same class with the same consumption pattern.
- The authors state this as an assumption: EV charging is an addition to the domestic load.
- The authors state this as an assumption: An uncontrolled charging pattern is assumed for EV charging.
- Findingown data
In the three-phase worst-case scenario, more customers can charge at hours of lower household load demand until both minimum and maximum voltages exceed the limits when nine EVs (43% PL EV) are simultaneously charging.
“Due to load variations, more customers can charge at hours of lower household load demand until both minimum and maximum voltages exceed the limits when nine EVs (43% PL EV ) are simultaneously charging.”
Quote in the paperSubstantial claim - Findingown data
The probability of a voltage violation increases as more EVs are connected beyond the first violation point in the single-phase network.
“The probability of a violation increases from this point as more EVs are connected.”
Quote in the paperSubstantial claim - Findingown data
In the single-phase worst-case scenario, the first voltage violation is observed at 52% PL EV when 11 customers are simultaneously charging.
“For the worst-case scenario shown in Figure 12a, the first violation is observed at 52% PL EV when 11 customers are simultaneously charging.”
Quote in the paperSubstantial claim - Findingown data
In the single-phase best-case scenario (Figure 13b), the first equipment loading violation at 102% is observed when 11 customers (52% PL EV) are simultaneously connected to the network.
“The best-case scenario in Figure 13b shows the first violation at 102% equipment loading when 11 customers (52% PL EV ) are simultaneously connected to the network.”
Quote in the paperSubstantial claim - Findingown data
Equipment loading is the most limiting performance index for determining the hosting capacity of EV charging in the single-phase network.
“Table 2 shows a comparison of the HC values for worst-case and best-case scenarios when considering both PIs, with equipment loading as the most limiting PI.”
- Also stated in: § 4. Conclusions · plain
“Equipment loading is found to be the major limiting factor in determining the HC of EV charging.”
Quote in the paperSubstantial claim - Also stated in: § 4. Conclusions · plain
- Findingown data
In the three-phase worst-case scenario, the first voltage violation occurs at 29% PL EV when six customers are simultaneously charging.
“For the worst-case scenario shown in Figure 8a, the first violation is observed at 29% PL EV when six customers are simultaneously charging.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase worst-case scenario, at 43% PL EV (nine EVs simultaneously charging) and beyond, there will always be a voltage violation irrespective of the time of year.
“At this point, there will always be a violation, irrespective of the time of year.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase best-case scenario, the first equipment loading violation at 107% is observed when 10 customers (48% PL EV) are simultaneously charging.
“The best-case scenario in Figure 9b shows the first violation at 107% equipment loading when 10 customers (48% PL EV ) are simultaneously charging.”
Quote in the paperSubstantial claim - Findingown data
Increasing EV penetration level results in a corresponding increase in the performance indices towards their allowable limit.
“The result of the impact assessment shows that increasing the EV penetration level results in a corresponding increase in the performance indices towards their allowable limit.”
Quote in the paperSubstantial claim - Findingown data
In the single-phase best-case scenario (Figure 13a), the first equipment loading violation at 100% is observed at 43% PL EV when nine customers are simultaneously charging their EVs.
“During the best-case scenario in Figure 13a, the first violation at 100% equipment loading is observed at 43% PL EV when nine customers are simultaneously charging their EVs.”
Quote in the paperSubstantial claim - Findingown data
Increasing EV penetration level decreases the feeder voltages towards the lower limit in the three-phase LV network.
“It is observed that an increase in the penetration level of EVs (PL EV ) decreases the feeder voltages towards the lower limit.”
Quote in the paperSubstantial claim - Findingown data
Peak consumption in the studied grid was observed in July.
“Figure 4 shows the aggregated historical time-series load data used to model the studied grid with peak consumption observed in July.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase best-case scenario, at 67% PL EV (14 customers) and beyond, there will always be an equipment loading violation for every hour of the year.
“At 67% PL EV (14 customers) and beyond, there will always be a violation for every hour of the year.”
Quote in the paperSubstantial claim - Findingown data
Increasing EV penetration level results in a corresponding increase in equipment loading towards the allowable limit in the three-phase LV network.
“It is observed that increasing the penetration level result in a corresponding increase in the equipment loading towards the allowable limit.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase best-case scenario, at 71% PL EV (15 EVs simultaneously charging) and beyond, there will always be a voltage violation for every hour of the year.
“At 71% PL EV (15 EVs simultaneously charging) and beyond, there will always be a violation for every hour of the year.”
Quote in the paperSubstantial claim - Findingown data
In the three-phase worst-case scenario, the first equipment loading violation at 108% is observed at 29% PL EV when six customers are simultaneously charging their EVs.
“In the worst-case scenario in Figure 9a, the first violation at 108% equipment loading is observed at 29% PL EV , when six customers are simultaneously charging their EVs.”
Quote in the paperSubstantial claim - Findingown data
In the single-phase best-case scenario, the first voltage violation is observed at 76% PL EV when 10 customers (48% PL) are simultaneously charging.
“Figure 12b shows the best-case scenario, where the first violation is observed at 76% PL EV when loading when 10 customers (48% 𝑃𝐿 ) are simultaneously charging.”
Quote in the paperSubstantial claim - Interpretationown data
The difference in hosting capacity values between the two scenarios highlights the effect of the charging location on the hosting capacity of single-phase EV charging.
“The difference in HC values between the two scenarios in this case study also highlights the effect of the charging location on the HC of single-phase EV charging.”
Quote in the paperSubstantial claim - Interpretationown data
The difference in hosting capacity between the worst-case and best-case scenarios shows the effect that the location of charging has on hosting capacity.
“The difference in HC for the worst-case and best-case scenarios portrays the effect that the location of charging has on the HC.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Each customer is capable of connecting only one EV to the grid.
- The authors state this as an assumption: All customers are of the same class with the same consumption pattern.
- The authors state this as an assumption: EV charging is an addition to the domestic load.
- The authors state this as an assumption: An uncontrolled charging pattern is assumed for EV charging.
- The authors state this as an assumption: If there is no violation during the critical peak period, there is likely no violation in other periods.
- Premisecitation
EV charging will have the greatest impact on the distribution grid when there is peak household demand.
“According to [24], EV charging will have the greatest impact on the distribution grid when there is peak household demand.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] Dixon, 2020, Electric vehicles: Battery capacity, charger power, access to charging and the impacts on distribution networks, ETransportation source
- Premisecitation
An uncontrolled charging approach at different penetration levels has the greatest impact on the distribution network.
“An uncontrolled charging approach at different penetration levels has the greatest impact on the distribution network [18,19].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [18] Habib, 2015, Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks—A review, J. Power Sources source
- cites [19] Zamora, 2014, Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches, Renew. Sustain. Energy Rev. source
- May combine two claims. 15
- Classified as probably not a substantial claim. 2
May combine two claims.
PremisecitationThe growing sales of plug-in electric vehicles imply that EV charging in the distribution network is increasing, translating into increased peak power consumption and changes in consumption patterns.
“The growing sales of plug-in electric vehicles [3] imply that EV charging in the distribution network is increasing, and this translates into a corresponding increase in peak power consumption and changes in consumption patterns [4,5].”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [3] (2022, November 11). EV Sales. Available online: https://cleantechnica.com/tag/ev-sales/.
Not checked in the free run.
- cites [4] Ping, 2020, A two-stage autonomous EV charging coordination method enabled by blockchain, J. Mod. Power Syst. Clean Energy source
The Introduction says increasing EV penetration in distribution networks creates overload risks. Coordination Results reports that uncoordinated charging exceeds the network’s load limit during peak hours, while coordination shifts charging to neighboring hours. This supports the peak-load concern, but the paper attributes the change in the load pattern to coordination, not directly to growing EV sales or charging demand. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [5] Shepero, 2018, Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review, Renew. Sustain. Energy Rev. source
May combine two claims.
Findingown dataThe test network can host more customers with EVs during summer (13 for best-case and eight for worst-case) and the fewest during winter.
“The test network can host more customers (13 and eight for best-case and worst-case scenario, respectively) with EVs during the summer while the fewest number of EVs can be connected during the winter.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor single-phase equipment loading as PI, the hosting capacity with a 3.7 kW charger is eight customers for the worst-case and 10 customers for the best-case scenario.
“Moreover, for the popular 3.7 kW charger, the HC is eight customers for the worst-case and 10 customers for the best-case scenario.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe single-phase hosting capacity for the 3.7 kW EV charger is 15 EVs for the best-case scenario and 8 EVs for the worst-case scenario.
“Furthermore, the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Each customer is capable of connecting only one EV to the grid.
- The authors state this as an assumption: All customers are of the same class with the same consumption pattern.
- The authors state this as an assumption: EV charging is an addition to the domestic load.
- The authors state this as an assumption: An uncontrolled charging pattern is assumed for EV charging.
- The authors state this as an assumption: If there is no violation during the critical peak period, there is likely no violation in other periods.
May combine two claims.
Findingown dataFor single-phase voltage drop as PI, the hosting capacity with a 3.7 kW charger is 48% (10 customers) for the worst-case and 71% (15 customers) for the best-case scenario.
“In Figure 14, the HC with a charging power of 3.7 kW, is 48% and 71% (10 and 15 customers) for the worst-case and best-case scenario, respectively, when voltage drop is the PI considered.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe three-phase hosting capacity results show that 6-9 customers can charge their EVs simultaneously in the worst-case scenario without a violation, while 9-12 customers can connect their EVs at the same time in the best-case scenario.
“The three-phase HC results show that 6-9 customers can charge their EVs simultaneously in the worst-case scenario without a violation, while 9-12 customers can connect their EVs at the same time in the best-case scenario.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor single-phase equipment loading as PI, no customers can charge their EVs with an 11.5 kW charger during the worst-case scenario, while five customers can charge during the best case.
“When considering equipment loading as the PI in Figure 15, none of the customers can charge their EVs with an 11.5 kW charger during the worst-case scenario, while five customers can charge during the best case.”
- Also stated in: § 3.2.3. Hosting Capacity Estimation · plain
“The HC is zero and five customers for the worst case and best case, respectively.”
Quote in the paperSubstantial claim- Also stated in: § 3.2.3. Hosting Capacity Estimation · plain
May combine two claims.
Findingown dataThe highest consumption in the studied grid occurred during the winter season, with 15 July 2021 being the critical day with the highest consumption.
“It can be observed that the highest consumption occurred during the winter season, with 15 July 2021 being set as the critical day with the highest consumption, which is subsequently used as the simulated period for single-phase EV charging HC assessment.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataDuring the three-phase worst-case scenario, five and six customers can charge their EVs simultaneously for 85% and 60% of the year, respectively, without violating the limit of any PI.
“Moreover, five and six customers can charge their EVs simultaneously for 85% and 60% of the year, respectively, during the worst-case scenario without violating the limit of any PI.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataDuring the three-phase best-case scenario, nine and 10 customers can be hosted simultaneously for 85% and 70% of the hours, respectively.
“During the bestcase scenario, nine and 10 customers can be hosted simultaneously for 85% and 70% of the hours, respectively.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe hosting capacity for three-phase EV charging is 5-8 EVs charging simultaneously for the worst-case scenario and 9-13 EVs for the best-case scenario.
“The results show that the HC is 5-8 three-phase EVs charging simultaneously for the worst-case scenario and 9-13 EVs for the best-case scenario.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Each customer is capable of connecting only one EV to the grid.
- The authors state this as an assumption: All customers are of the same class with the same consumption pattern.
- The authors state this as an assumption: EV charging is an addition to the domestic load.
- The authors state this as an assumption: An uncontrolled charging pattern is assumed for EV charging.
May combine two claims.
Findingown dataThe hosting capacity of the balanced three-phase network is 5-8 customers for the worst-case scenario and 9-13 customers for the best-case scenario, considering all hours of the year and all performance indices.
“The range of values for the hosting capacity of the balanced three-phase network obtained from the impact assessment conducted is 5-8 customers and 9-13 customers for the worst-case and best-case scenario, respectively.”
- Also stated in: § 3.1.3. Hosting Capacity Estimation · hedged
“This range is generally considering all hours of the year and all the performance indices.”
Quote in the paperSubstantial claim- Also stated in: § 3.1.3. Hosting Capacity Estimation · hedged
May combine two claims.
InterpretationreasoningThe results presented are for a specific grid, PI, and PI limits, and cannot be immediately transferred to another grid because distribution grids are non-homogenous.
“However, the results presented are for a specific grid, PI, and the PI limits, and cannot be immediately transferred to another grid since the distribution grids are non-homogenous.”
Quote in the paperSubstantial claim- Rests on an assumption: Distribution grids are non-homogenous.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIn reality, there may be a higher probability of more EV charging simultaneously if the charging is distributed in time.
“In reality, there may be a higher probability of more EV charging simultaneously if the charging is distributed in time.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe approach of using uncontrolled EV charging without considering charging behavior and state of charge may underestimate the hosting capacity but generates a result that shows the variation in HC throughout the year.
“This approach may underestimate the HC but generates a result that shows the variation in HC throughout the year.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationLarge-scale integration and simultaneous charging of multiple EVs can create power quality challenges and have a high impact on existing electrical networks, disrupting standard operating condition by creating technical challenges such as voltage violations, transformer and line overloading, and increasing electrical losses.
“However, largescale integration and simultaneous charging of EVs can create power quality challenges for the distribution grid.”
- Also stated in: § 1. Introduction · plain
“However, large-scale integration and simultaneous charging of multiple EVs are identified as having a high impact on the existing electrical networks, disrupting the standard operating condition of the grid by creating several technical challenges, such as voltage violations, transformer and lines overloading, and increasing electrical losses [6].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] Ogunboyo, 2018, Effectiveness of Dynamic Voltage Restorer for Unbalance Voltage Mitigation and Voltage Profile Improvement in Secondary Distribution System, Can. J. Electr. Comput. Eng. source
- Also stated in: § 1. Introduction · plain
Probably not a substantial claim: may not pass S4 (based).
PremisereasoningHigh load variability will occur from the installation of higher power chargers because EV charging load ramps up and down over shorter periods.
“However, since high load variability will occur from the installation of higher power chargers because the EV charging load will ramp up and down over shorter periods, it is important to consider key uncertainties like charging patterns and mode of charging in EV charging HC.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
There is a dearth of studies on EV charging hosting capacity in the South African low-voltage distribution network.
“Whereas there is an abundance of studies on solar photovoltaic (PV) HC, there is a dearth of studies on EV charging HC in the South African LV distribution network.”
The authors state this as an assumption.
Each customer is capable of connecting only one EV to the grid.
“It was assumed that each customer is capable of connecting only one EV to the grid.”
The authors state this as an assumption.
All customers are of the same class with the same consumption pattern.
“Since information about the load consumption of individual households is not available and all customers are assumed to be of the same class with the same consumption pattern, the load scaling method is applied to estimate the load for each consumer.”
The authors state this as an assumption.
EV charging is an addition to the domestic load.
“EV charging was considered an addition to the domestic load, and an uncontrolled charging pattern was assumed without considering the state of charge of the battery.”
The authors state this as an assumption.
An uncontrolled charging pattern is assumed for EV charging.
“EV charging was considered an addition to the domestic load, and an uncontrolled charging pattern was assumed without considering the state of charge of the battery.”
The paper relies on this without support.
Utilities in South Africa have not yet specified the allowable EV chargers on the grid.
“However, since utilities in South Africa have not yet specified the allowable EV chargers on the grid, other single-phase charging powers considered included 4.6, 5.75, 6.9, 9.2, and 11.5 kW chargers, corresponding to 20, 25, 30, 40, and 50-A fuses, respectively [22].”
The authors state this as an assumption.
If there is no violation during the critical peak period, there is likely no violation in other periods.
“Therefore, this period with the highest consumption throughout the year was chosen, and it was assumed that if there was no violation during this period, it was likely that there would not be in other periods.”
The paper relies on this without support.
Distribution grids are non-homogenous.
“since the distribution grids are non-homogenous”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Abstract overstates best-case single-phase hosting capacity
MajorThe abstract reports a best-case capacity of 15 EVs for the 3.7 kW charger. The results state that equipment loading limits this capacity to 10 customers when both performance indices are considered.
“the single-phase HC for the popular 3.7 kW EV charger is 15 and 8 EVs for the best-case and worst-case scenarios, respectively.”
“Moreover, for the popular 3.7 kW charger, the HC is eight customers for the worst-case and 10 customers for the best-case scenario.Table 2 shows a comparison of the HC values for worst-case and best-case scenarios when considering both PIs, with equipment loading as the most limiting PI.”
Conclusion gives incompatible three-phase hosting-capacity ranges
MajorThe conclusion says 6-9 customers can charge without a violation in the worst case and 9-12 in the best case. The results give ranges of 5-8 and 9-13, respectively; they also state that a worst-case violation first occurs with six customers.
“The three-phase HC results show that 6-9 customers can charge their EVs simultaneously in the worst-case scenario without a violation, while 9-12 customers can connect their EVs at the same time in the best-case scenario.”
“The range of values for the hosting capacity of the balanced three-phase network obtained from the impact assessment conducted is 5-8 customers and 9-13 customers for the worst-case and best-case scenario, respectively.”
Abstract reverses the methods assigned to the two case studies
MinorThe abstract assigns the deterministic time-series method to single-phase charging and the stochastic method to three-phase charging. The methodology assigns them the other way around.
“This paper uses a deterministic time-series method and stochastic method based on a simplified Monte Carlo simulation to estimate the HC of single-phase and three-phase EV charging, respectively”
“The deterministic and time-series method is used for the three-phase HC determination while a stochastic method based on a simplified Monte Carlo simulation method is adopted for single-phase HC analysis.”
Figure 13a is assigned to both worst and best cases
MinorThe equipment-loading discussion calls Figure 13a the best-case scenario, but both its preceding sentence and the figure caption identify 13a as the worst case.
“During the best-case scenario in Figure 13a, the first violation at 100% equipment loading is observed at 43% PL EV when nine customers are simultaneously charging their EVs.”
“Figure: 13 Figure 13 . Figure 13.Feeder maximum loading (a) Worst case; (b) Best case.”
Results identify the network diagram as the hosting-capacity concept figure
MinorThe results refer to the 21-customer network as Figure 1, whereas the captions identify Figure 1 as the hosting-capacity concept and Figure 3 as the test network.
“The method in Section 2 is applied to the 21-customer LV distribution network in Figure 1.”
“Figure: 1 Figure 1 . Figure 1.Hosting capacity concept.”
Conclusion treats a voltage-violation level as hosting capacity
MinorThe conclusion gives 76% as the upper end of the single-phase capacity when voltage drop is the performance index. The hosting-capacity results give a best-case maximum of 71% (15 customers) for the 3.7 kW charger; 76% is instead identified as the level at which a first violation occurs.
“the single-phase HC result presented shows that 48% to 76% of the customers can charge simultaneously when the voltage drop is the PI considered.”
“In Figure 14, the HC with a charging power of 3.7 kW, is 48% and 71% (10 and 15 customers) for the worst-case and best-case scenario, respectively, when voltage drop is the PI considered.”
Cites
Acharige, S. S. G., Haque, M. E., Arif, M. T., Hosseinzadeh, N., Hasan, K. N., & Oo, A. M. T. (2023) · IEEE Access · doi:10.1109/access.2023.3267164 · Cited by 590 · see citing works
Habib, S., Kamran, M., & Rashid, U. (2015) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.12.020 · Cited by 556 · see citing works
Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches
García-Villalobos, J., Zamora, I., San Martín, J. I., Asensio, F. J., & Aperribay, V. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.07.040 · Cited by 497 · see citing works
Stochastic analyses of electric vehicle charging impacts on distribution network
Leou, R.-C., Su, C.-L., & Lu, C.-N. (2014) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2291556 · Cited by 336 · see citing works
Haque, M. M., & Wolfs, P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.04.025 · Cited by 331 · see citing works
A Comprehensive Assessment of PV Hosting Capacity on Low-Voltage Distribution Systems
Torquato, R. J. S., Salles, D., Pereira, C. O., Meira, P. C. M., & Freitas, W. (2018) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2018.2798707 · Cited by 273 · see citing works
Bollen, M., & Rönnberg, S. (2017) · Energies · doi:10.3390/en10091325 · Cited by 169 · see citing works
A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation
Zain ul Abideen, M., Ellabban, O., & Al-Fagih, L. (2020) · Energies · doi:10.3390/en13112758 · Cited by 161 · see citing works
Dixon, J., & Bell, K. (2020) · eTransportation · doi:10.1016/j.etran.2020.100059 · Cited by 158 · see citing works
Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review
Shepero, M., Munkhammar, J., Widén, J., Bishop, J. D. K., & Boström, T. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.02.034 · Cited by 113 · see citing works
Ogunboyo, P. T., Tiako, R., & Davidson, I. E. (2018) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/cjece.2018.2858841 · Cited by 72 · see citing works
Umoh, V., Davidson, I., Adebiyi, A., & Ekpe, U. (2023) · Energies · doi:10.3390/en16083609 · Cited by 71 · see citing works
Self-citationEV Hosting Capacity Analysis on Distribution Grids
Paudyal, P., Ghosh, S., Veda, S., Tiwari, D. R., & Desai, J. (2021) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm46819.2021.9638170 · Cited by 56 · see citing works
A two-stage autonomous EV charging coordination method enabled by blockchain
Ping, J., Yan, Z., & Chen, S. (2021) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2019.000139 · Cited by 55 · see citing works
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Leou, R., Teng, J., Lu, H., Lan, B., Chen, H., Hsieh, T., & Su, C. (2018) · IET Generation, Transmission & Distribution · doi:10.1049/iet-gtd.2018.0112 · Cited by 42 · see citing works
Mulenga, E., Bollen, M. H. J., & Etherden, N. (2021) · Electricity · doi:10.3390/electricity2030023 · Cited by 25 · see citing works
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Sandström, M., Bales, C., & Dotzauer, E. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1050/1/012008 · Cited by 11 · see citing works
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Fadi, A. K., & Bruno, F. (2022) · IET Conference Proceedings · doi:10.1049/icp.2022.0649 · Cited by 9 · see citing works
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Renewable Energy Source Utilization Progress in South Africa: A Review
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Self-citationSoto, J. D. D., Guillén, D., Pacheco-Cherrez, D. S., Ibarra, L., Avilés, J., Valdez-Reséndiz, J. E., Santibañez‐Aguilar, J. E., & Notholt, A. (2025) · IEEE Access · doi:10.1109/access.2025.3643749 · Cited by 4 · see citing works
Electric Vehicles Hosting Capacity Assessment for a South African Low Voltage Distribution Network
Umoh, V. B., Adebivi, A. A., & Moloi, K. (2023) · International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) · doi:10.1109/ice3is59323.2023.10335304 · Cited by 2 · see citing works
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Umoh, V. B. (2023) · doi:10.51415/10321/5245
Self-citationTools and test systems for hosting capacity assessment
Umoh, V. B., Akpan, J. S., Donaldson, D., & Adebiyi, A. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33912-7.00007-4
Self-citationArévalo-Cordero, P., Ochoa-Correa, D., Benavides, D., et al. (2026) · Applied Sciences · doi:10.3390/app16083718
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Challenges of Electric Vehicle Integration into the South African Power Grid
Ntombela, M. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17060321
- Authors
- Umoh, V., Adebiyi, A., & Moloi, K.
- Affiliations
- Durban University of Technology (ZA)
- Corresponding author
- Umoh, Vincent
- Venue
- Eng
- Publisher
- MDPI AG
- Published
- 2023-07-12
- Volume
- 4
- Issue
- 3
- Pages
- 1965-1980
- ISSN
- 2673-4117 (ISSN-L)
- OpenAlex
- W4384131071
- Semantic Scholar
- 5aa3f6017fe8cc8a81e556335e5131bc7a0de300
- Cited by
- 12
- References
- 21
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 3 · 2023: 2
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Eng—Advances in Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Durban University of Technology
- 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
- hosting capacity, electric vehicle charging, low-voltage distribution networks, residential distribution networks, power quality, voltage drop, equipment loading, single-phase charging, seasonal variation, South Africa
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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-28.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-28.
Cited sources are checked by gpt-6-sol.
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.
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