Hosting capacity of LV residential grid for uncoordinated ... - IEEE Xplore

Hosting capacity of LV residential grid for uncoordinated EV charging Snezana Cundeva Aleksandra Krkoleva Mateska

Math H.J. Bollen

University Ss. Cyril and Methodius Faculty of Electrical Engineering and IT Skopje, Republic of Macedonia

Luleå University of Technology Electric Power Engineering 931 87, Skellefteå, Sweden

Abstract—The envisaged large penetration of electric vehicles (EVs) in the near future, brings new challenges to low voltage (LV) and medium voltage (MV) grids. For uncoordinated EV charging, the impact on the LV grid may be significant, due to the simultaneity between the residential power peak and the charging of EVs. The amount of EV chargers that can be connected to the grid without endangering the reliability or voltage quality for other customers is called the hosting capacity. This paper presents the analyses of the hosting capacity of a realistic LV residential network with 160 households for EV chargers. A stochastic approach is used, assuming that the EV chargers are randomly distributed over the households and the phases. For different EVs penetration rates in the network, the hosting capacity for slow chargers is determined. The results show that the EV penetration rate that the network can safely accept is 45%. Index Terms—Electric vehicle, electric vehicle integration, hosting capacity, LV grid, voltage magnitude deviation

I.

INTRODUCTION

An EV can be defined as “a vehicle which is powered by an electric motor drawing current from rechargeable storage batteries, fuel cells, or other portable sources of electrical current”. There are several types of EVs as hybrid and plug-in electric vehicles (HEV and PHEV), range extended electric vehicles (REEV), battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV) [1]. All of these have in common that they derive all, or part of their energy, from on board rechargeable energy storage systems. The number of electric vehicles on European roads has increased from practically zero in 2010 to more than 500.000 in 2016 [2]. While moving towards sustainability, it is expected that this trend will increase in the coming years having in mind the numerous advantages of EVs compared to internal combustion engine vehicles (ICEV). Some of the proven advantages are that EV’s overall efficiency is a factor of about 3 higher than ICEV; EVs emit no CO2 and other pollutants at the point of use; and they allow consumption of surplus amounts of renewable electricity generation. Furthermore, EVs provide quiet and smooth operation and consequently create less noise and vibration;

EVs contain fewer moving parts and the drive has simpler transmission [1]-[3]. In conjunction with the increased production of electricity derived from renewable sources, the massive penetration of the EVs in the urban areas remains one of the major opportunities to reduce CO2 over the next decade [4]. Using an EV requires safe and easy to use charging of the on-board rechargeable storage system (most frequently a battery). There are different types of charging infrastructure that are used for different charging strategies. For shorter stops, when quick charging is required, dedicated terminals are needed. For longer stops (usually overnight at home) complete charging of the storage system can be done with a household or specific installation. An on-board slow AC charger is included in almost all EVs, as EV owners expect to be able to charge the vehicle at any standard socket or with an inexpensive dedicated slow AC charger at home [5]. The charging modes are defined in the IEC 61851-1 standard [6]. Typically, mode 2 or mode 3 charging is used to charge EVs at their standstill locations. In Europe, single phase charging is commonly rated at 10 A for mode 2 and 16 A for mode 3 [5]-[7]. Overview of the parameters of different charging strategies is shown in Table I. TABLE I. Charging time

6-8 hours 3-4 hours 2-3 hours 1-2 hours 20-30 min 20-30 min

PARAMETERS OF DIFFERENT EV CHARGING STRATEGIES Power supply

Power nomination

Single phase 3.3 kW Single phase – 7 kW Three phase – 10 kW Three phase – 24 kW Three phase 43 kW Continue – 50 kW

Normal power Medium power Medium power High power AC High power AC High power DC

Voltage

Max current

230 VAC

16 A

230 VAC

32 A

400 VAC

16 A

400 VAC

32 A

400 VAC

63 A

400-500 VDC

100-125 A

978-1-5386-0517-2/18/$31.00 ©2018 IEEE

For fast charging strategies of EVs (high power AC and DC) substantial power is needed. Usually these strategies require additional investments in the grid to accommodate the dedicated charging power stations. However, the expectations are that the vehicle charging will be mostly done at home, during the standstill time of the vehicle. The combination of mode 3 charging (with specific socket on a dedicated circuit) and type 3 connectors (single phase or three phase with maximum current of 32 A) for the electrical installation offers the best solution for electric vehicle charging [5]. EV charging will affect the power system e.g. it may increase peak power demand, change load profile and cause voltage magnitude deviations [7]-[15]. For slow charging, the residential LV grid impact may be significant, due to the simultaneity between the residential power peak and the plugging in of EVs when arriving at home. The typical household diagrams for Macedonia [16] show that the peak occurs in the period 5-7 pm, depending on the season. Plugging in the vehicles when arriving from work may increase the peak power. However, the low “off-peak” tariff for households between 10 pm and 7 am applied in Macedonia may have a significant impact in customer’s decision when to charge their vehicles, and possibly, mitigate the effect of simultaneity. Furthermore, charging control strategies may be introduced to contribute to smoothing the load profile (for example peak shaving) as well as for reducing voltage deviation. These control strategies typically require local controls and enabled communication between the distribution system operator and consumers. In cases of absence of highly automated distribution systems and communication, which is the case for Macedonia, rule-based control of the charging process, as described in [12] may be an adequate solution. Clearly, the impact of EVs to distribution grids has to be quantified. In this context, introducing the hosting capacity of the grid is quite significant. By definition [13], “hosting capacity is the amount of new production or consumption that can be connected to the grid without endangering the reliability or voltage quality for other customers.” Recent research shows that hosting capacity estimation approach can be stochastic, thus taking into account numerous uncertainties related to charging of EVs, as distribution of EV chargers over households, charging time variations, charging load distribution per phase, etc. The objective of this paper is to estimate the hosting capacity of a LV grid considering voltage magnitude deviation due to EVs charging. For this purpose, a stochastic approach for hosting capacity estimation is used and the calculations are done for an existing LV urban distribution grid in Macedonia. The reminder of the paper is organized as follows: the network that has been used is described in Section II; the hosting capacity methodology that is used for the calculations is briefly explained in Section III and the application of the methodology is done in Section IV for different cases of EV charging. II.

PARAMETERS OF THE NETWORK

The studied network is a low voltage urban network that supplies 160 domestic households, equally spread in two 10storey buildings, each containing 4 entrances. There are 20 households per entrance. A 400kVA transformer with 8 feed-

ers supplies the buildings. A separate feeder supplies each entrance in the building. The network is three phase down to the customer and the customer has the possibility to connect three phase equipment and to spread the equipment over the three phases. This network is typical for urban areas in Macedonia. For the purposes of this work, each feeder has been modeled to supply one aggregated load. An aggregated load is added to the end of each parallel feeder, where the load equals 20 households. The simplified topology of the network is presented in Fig. 1. Note that the cables lengths are not presented in scale and their disposition is not realistic.

Figure 1. One-line diagram of the LV urban network

The feeders and transformer data are provided by the Macedonian distribution system operator (DSO) EVN. The cable parameters are shown in Table II. TABLE II. CABLE PARAMETERS Cable Type

PPOO -A 4x150 mm PPOO -A 4x70 mm2

R [Ω/km] 2

X [Ω/km]

0.206 0.443

0.080 0.082

The cable lengths of the feeders 1 to 8 are presented in Table III. The length of the connecting cable between the transformer and the cable cabinet is 50 m. The transformer impedance has been calculated for Pcu=5.15 kW and Uk%=4.2%. TABLE III. FEEDER LENGTHS Feeder number Length [m]

1

2

3

4

5

6

7

8

40

30

30

10

10

30

10

30

The load of the transformer varies between the maximum value of 119 kVA (in a cold day in December) and the minimum value of 68 kVA (in a mild day in June). By neglecting reactive power consumption (the households are assumed to be purely resistive loads), these values correspond to average 747.5 W consumption per household when the transformer is loaded to maximum, and average consumption of 425 W per household when the transformer is minimally loaded. The rated neutral-to-phase voltage is 230 V. III.

CALCULATION OF HOSTING CAPACITY FOR EVS IN A STOCHASTIC WAY

The paper presents estimates of the risk of undervoltages due to introduction of single-phase-connected EV chargers for LV urban network that supplies two buildings with 160 indi-

vidual households. The connection of single-phase equipment is more severe for the grid than the connection of three-phase equipment with the same power rating. Not only the voltage drop is higher for single-phase equipment (three times the current and about twice the impedance), but the other phases are also impacted. Connection of a single-phase EV charger in one phase results in a voltage drop in that phase and a voltage rise in the other phases. All the random variables in the model are non-correlated. The consumption per household is randomly distributed between the minimum and maximum values presented in the previous section. No further details are considered in modeling of the load. The reason is that detailed historical data (for example consumption measurements for each household) is unavailable. The chargers for all EVs are considered to be the same in an analyzed case. Also, it is assumed that the EV chargers are randomly distributed over the households and over the phases. The values used for the specific cases analyzed in this paper are presented in the following section. With the aim to provide a large number of combinations of households with chargers connected to various phases, a Monte Carlo simulation approach is used. A simple linear network model is used, consisting of the node-impedance matrix calculated from the positive and zerosequence series impedance of the different branches. Positivesequence and negative sequence impedances are considered equal. The difference between positive and zero-sequence impedance is considered in the calculations. From the injected and consumed power for each customer and phase, a current vector is calculated. This vector is multiplied with the node impedance matrix to obtain the vector with phase-to-neutral voltages for all customers and phases. This model provides a straightforward calculation of the phase-to-neutral voltages, using the node-impedance matrix, which is one of the standard approaches for description of electricity grids. In general, the node-to-impedance matrix uses the lumped resistive and reactive impedances and the network graph to represent the analyzed electricity circuit.

•

No load voltage: uniformly distributed between 230 V and 234 V • Charging power: 3680 W and 7360 W (corresponding to 16 A and 32A). These values are typical for home chargers. A 7360 W charger can charge an electric vehicle from flat to full in 3-4 hours, while for 3680 W the charging time is twice longer. • EV penetration rate: 100% (1 EV with domestic charger per household), 50% (each second household has EV with domestic charger) and 20% (each 5th household has EV with domestic charger). Using a Monte-Carlo simulation the combinations of customers and phases with EV chargers are generated and for each combination, the lowest phase-to-ground voltage is calculated for each feeder terminals. The probability distribution of the voltages is obtained from 10000 samples. A. Case 1 – 100% EV penetration rate, 3680 W charging power, 10% undervoltage percentile In this analysis, each household has an EV with a possibility of uncoordinated charging with 3680W charger. This scenario can be considered as the “worst case scenario” as it actually represents an upper limit of penetration of EVs that the electricity network needs to accommodate. This situation is modeled by adding an aggregated load of 20 chargers per feeder. The calculations showed that in this case, the equivalent hosting capacity for the proposed number of EV chargers is zero, i.e. the network cannot accommodate these chargers. This is clearly shown on Fig. 2, which presents the 10th percentile of the lowest voltage as a function of the feeders that are drawing current at the same time, for 3680 W per charger. As shown in Fig.2, the 90% undervoltage limit is reached for all feeders, confirming that there is not enough hosting capacity in the analyzed grid. The same results as in Fig. 2 would be obtained for 50% EV penetration rate with 7 kW chargers.

The result of the calculations is the probability distribution of the worst-case voltage magnitude. More details about the methodology are presented in [13]. IV.

CALCULATIONS AND RESULTS

The EV chargers are connected to the distribution transformer through the existing feeders that supply the residential households. Several scenarios were considered, for different percentage of EV chargers penetration (from 100% to 20%), different charging power (3680 W and 7360 W, corresponding to 16 A and 32 A respectively) and different voltage magnitude drop as described in EN50160 [17]. The voltage magnitude constraints in the different scenarios determine the number of EV chargers the distribution grid can host in addition to the residential load. The following values have been considered for the calculations: •

Active power consumption per feeder: uniformly distributed between 8500 W and 14950 W

Figure 2 - 10th percentile of the lowest voltage per feeder when 180 chargers are drawing current at the same time

B. Case 2 – 50% EV penetration rate, 3680W charging power, 10% undervoltage percentile In this case the assumption is that each second household has an EV. Case 2 is much more realistic compared to Case 1. This case is modeled by adding an aggregated power of 10 chargers per feeder. The number of EV chargers in the grid in this case equals 80. However, a total of 60 chargers can be accommodated in the network before the 90% undervoltage limit is reached, as shown in Fig. 3.

E. Summary of the results The summarized results from the analyzed cases are presented in Table IV. TABLE IV. HOSTING CAPACITY OF THE GRID FOR THE ANALYZED CASES

Figure 3 - 10th percentile of the lowest voltage per feeder when 80 chargers are drawing current at the same time

C. Case 3 – 20% EV penetration rate, 3680W charging power, 10% undervoltage percentile In this analysis, each 5th household has EV. This case is modeled by adding an aggregated power of 4 chargers per feeder. This additional load from the chargers can be fully accommodated in the network, as shown in Fig. 4. The hosting capacity in this case equals 32 EV chargers.

Figure 4 - 10th percentile of the lowest voltage per feeder when 32 chargers are drawing current at the same time

12-percentile (%)

D. Case 4 - 50% EV penetration rate, 3680W charging power, 12% undervoltage percentile This case differs from Case 2 in the undervoltage percentile allowed in the network. If the network operator is willing or allowed to take higher risk, the hosting capacity in this case will be 80 EV chargers of 3680W in the network, as shown in Fig. 5.

Figure 5 - 12th percentile of the lowest voltage per feeder when 80 chargers are drawing current at the same time

EV penetration rate

Power per charger [W]

percentile

Chargers per feeder

100% 50% 50% 50%

3680 7360 3680 3680

10% 10% 10% 12%

20 10 10 10

Added power per feeder [W]

73600 73600 36800 36800

HC of the grid represented as number of hosted chargers

0 0 60 80

It is obvious that the hosting capacity depends on the charging load and on the adopted percentile of the lowest voltage. From results shown in the table, it can be concluded that the hosting capacity of the analyzed grid for high penetration of EVs, even with lower power charger equals zero. Hosting capacity with 50% penetration of EVs is 60 chargers, considering lower power chargers, which can be further extended by allowing increased risk of undervoltages in the distribution grid. Accommodation of relatively low penetration of 20% of chargers is not a problem for the analyzed grid. F. Generality of the results and of the method The results obtained in this work are not valid beyond the specific network studied. Network operators should use data from their own networks to estimate the hosting capacity as part of their distribution-system planning. Nevertheless, the applied method has a broader applicability. It should be applied to different low and mediumvoltage networks, to find out how general the results are, but also to evaluate the broader applicability of the method. Important advantages of the applied method are: • The method fits closely to existing planning approaches used by distribution companies. •

A limited amount of input data is needed.

•

The results are such that they can be interpreted relatively easy by distribution companies.

•

Different kinds of uncertainties can be added without changing the basic approach.

•

Any power-system analysis tool can be used to perform the actual calculations.

More discussion about the generality of the method and the work towards further developing the hosting-capacity approach can be found in [13]. V. CONCLUSIONS In the paper sensitivity analysis of the hosting capacity of a LV urban network for single-phase EV chargers has been performed. The hosting capacity turns out to be most sensitive to the percentile used and to the consumed power per charger. The percentile used is a matter of how much risk the network operator is willing or even allowed to take in the planning stage.

The LV urban network contains 8 feeders which are uniformly loaded. In the most realistic case of EV penetration rate, where each 5th household has EV with possibility to charge it with standard 3680 W single phase charger, the hosting capacity of the network is big enough to accommodate the extra load from the chargers. For higher penetration rate of EV chargers, representing higher power consumption in the grid, the hosting capacity decreases. For EV penetration rate of 50% the hosting capacity of the network is 60 chargers. For 100% EV penetration rate the hosting capacity of the analyzed network is 0. Increasing the percentile used actually increases the hosting capacity of the network. In the analyzed network, the increase of the percentile for extra 2% showed an increase of the hosting capacity for extra 20 chargers. The general conclusion is that the hosting capacity of the studied network is 72 EV chargers additional to the residential load, uniformly distributed over the feeders. This number of EV chargers corresponds to EV penetration rate of 45%. For 73 and more EVs that are drawing current simultaneously there is a high probability that the 90% undervoltage limit will be exceeded. These results are derived for 3680W single phase EV chargers (i.e. normal power chargers). For 7360W single phase EV charging (i.e. medium power chargers) the hosting capacity of the network will be twice lower.

[5]

[6] [7] [8] [9] [10]

[11] [12]

[13] [14]

REFERENCES [1] [2] [3] [4]

James Larminie, John Lowry, Electric Vehicle Technology Explained 2nd edition, Wiley, 2012 “Global EV Outlook 2017 Two million and counting” OECD/IEA 2017 http://ec.europa.eu/transport/themes/urban/vehicles/road/electric_en as accessed on 22 February 2016 “The Electric Vehicle – A major contributor to EU energy & climate policy objectives”, Prepared by Creara Energy Experts for European Copper Institute, Summer 2017

[15]

[16] [17]

“Connection System on the Recharging Spot: A Key Element for Electric Vehicles” Schneider Electric [16] [Online]. Available https://www.schneider-electric.co.in/documents/support/whitepapers/connection-system-recharging-spot.pdf (accessed on 6 November 2017) Electric vehicle conductive charging system – Part 1: General requirements, IEC 61851-1:2017 Clement-Nyns, K.; Haesen, E.; Driesen, J. “The impact of charging plug-in hybrid electric vehicles on a residential distribution grid”. IEEE Trans. Power Syst., vol. 25, pp. 371–380. 2010 Leemput, N.; van Roy, J.; Geth, F.; Tant, P.; Claessens, B.; Driesen, J. “Comparative analysis of coordination strategies for electric vehicles”. In Proc. 2011 IEEE PES ISGT Europe, pp. 1–8. “Grid Integration of Electric Mobility: 1st International ATZ Conference 2016”, edited by Johannes Liebl, Springer Vieweg “Distribution grid planning and operational principles for electric vehicle mass roll-out while enabling integration of renewable distributed energy resources”, PlanGridEV research FP7 project report Jan 2013 – Feb 2016 Jayakrishnan R. Pillai, Birgitte Bak-Jensen, “Impacts of electric vehicle loads on power distribution systems”, 2010 IEEE Vehicle Power and Propulsion Conference (VPPC) Niels Leemput, Frederik Geth, Juan Van Roy et al., “MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles”, Energies 2015, Vol. 8, pp. 1760-1783; doi:10.3390/en8031760 Math H.J. Bollen, Sarah K. Rönnberg, “Hosting capacity of the power grid for renewable electricity production and new large consumption equipment”, Energies 2017, Vol. 10(9), pp. 1-31 Akif Zia Khan; Abdul Kashif Janjua; Shayan Tariq Jan; Zeeshan Nazir Ahmed, “A comprehensive overview on the impact of widespread deployment of electric vehicles on power grid”, IEEE International Conference on Smart Grid and Smart Cities (ICSGSC), 2017 Xuesong Zhou; Lei Zou; Youjie Ma; Zhiqiang Gao, “Research on impacts of the electric vehicles charging and discharging on power grid”, IEEE 29th Chinese Control And Decision Conference (CCDC), 2017 [Online]. Available https://evn.mk/Za-nas/Legislation-and-rules.aspx Voltage characteristics of electricity supplied by public distribution systems, EN50160