Article
Challenges in Grid Integration of Electric Vehicles in Urban
and Rural Areas
Jakob Gemassmer 1,*,† , Carolin Daam 1,† and Ricardo Reibsch 1,2,†
Citation: Gemassmer, J.; Daam, C.;
Reibsch, R. Challenges in Grid
Integration of Electric Vehicles in
Urban and Rural Areas. World Electr.
Veh. J. 2021,12, 206. https://doi.org/
10.3390/wevj12040206
Academic Editor: Zonghai Chen
Received: 6 October 2021
Accepted: 19 October 2021
Published: 21 October 2021
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1Reiner Lemoine Institut gGmbH, Rudower Chaussee 12, 12489 Berlin, Germany;
2Department of Electrical Energy Storage Technology, Technische Universität Berlin, 10623 Berlin, Germany
*Correspondence: [email protected]
† These authors contributed equally to this work.
Abstract:
The ramp-up of battery electric vehicles (BEVs) could lead to severe grid issues but also
enables flexibility. This paper provides a better understanding of the challenges and potentials of
integrating BEVs in power grids. Three charging strategies were modelled on four use cases and
six low-voltage grids for urban and rural areas. Especially in rural areas, where many cars charge
at home overnight, the charging strategy significantly affects grid issues. Purely market-oriented
strategies can lead to high load peaks and thus to transformer and line overloading, while even a
relatively simple, balanced charging strategy can significantly reduce grid issues.
Keywords: BEV; smart charging; passenger car; power; energy system
1. Introduction
The share of renewable energies in electricity generation is steadily increasing, exceed-
ing 50% in 2020 for the first time in Germany [
1
]. At the same time, the share of battery
electric vehicles (BEVs) is also increasing. In 2020, the share of new electric passenger
cars registrations in Germany almost tripled. Currently, over one per cent of all registered
passenger cars are electric [
2
]. This share will grow strongly in the coming years. According
to own calculations based on [
3
], around 40 million vehicles in Germany will be electrified
in 2040, corresponding to around 85% of all passenger cars. In the wake of these changes,
flexibility options are becoming increasingly important for grid operators. Battery electric
vehicles offer a high potential for this flexibility demand due to the built-in storage and the
possibility to charge flexibly during the entire standing time.
However, this potential varies depending on the charging option and region. For
this purpose, the German Federal Ministry of Transport has defined seven charging use
cases. Because of the different mobility and parking behaviour in urban and rural areas, the
charging demand and, therefore, the distribution among the seven charging use cases also
differs. Together with different charging strategies, this distribution can result in different
impacts on the power grid.
Because power grids are usually designed for several decades, the impacts and poten-
tials of electric vehicle integration need to be included in long-term planning. Therefore,
the primary research questions of this paper are:
1. How does charging demand differ in urban and rural areas?
2.
What impact do different charging strategies have on the load profile of electric
vehicles at different charging use cases?
3.
What is the impact of integrating electric vehicles on the power grid in urban and
rural areas?
These questions are investigated based on the German states of urban Berlin and
rural Brandenburg, which differ in mobility behaviour and grid topology. This paper will
World Electr. Veh. J. 2021,12, 206. https://doi.org/10.3390/wevj12040206 https://www.mdpi.com/journal/wevj
World Electr. Veh. J. 2021,12, 206 2 of 11
compare the impact of three different charging strategies on low-voltage grids for the two
types of region.
2. Methodology
2.1. Number of Electric Vehicles
In line with current developments, the share of electric cars will rise sharply in the
coming years. The increase in electric vehicles can vary from region to region. Currently,
electric cars are more often registered in urban areas than in rural areas. Areas such as Berlin,
which have been well above the national average electrification rate, are therefore expected
to achieve full electrification a few years earlier than rural regions such as Brandenburg [
4
].
The calculations for Figure 1are based on studies by Reiner Lemoine Institute and [3].
Figure 1. Ramp-up of electromobility in Germany, Berlin and Brandenburg
Assuming a constant level of motorisation (number of cars per inhabitant), in 2040,
around 1.12 million cars will be registered in Berlin and 1.38 million cars in Brandenburg.
Different development rates are applied to determine the number of electric passenger
cars following the different electrification development in the urban and the rural federal
state. By 2040, in Berlin, all registered passenger cars are expected to be electric, while in
Brandenburg, this share amounts to around 82%.
The registered passenger cars are distributed very differently among the households.
While in Berlin, only 49% of the households own a car, the figure is 79% in rural Branden-
burg. The number of cars per household also differs. All characteristic values are listed in
Table 1.
Table 1.
Comparison of the electric passenger car development of Berlin and Brandenburg in
2040 [5–7].
Berlin Brandenburg
Population 3,919,000 2,418,000
Degree of motorisation 287 cars per 572 cars per
1000 inhabitants 1000 inhabitants
Number of vehicles 1,124,000 1,383,000
Electrification rate 100% 82.2%
Number of BEVs 1,124,000 1,137,000
Vehicles per household 0.6 1.1
Share of households with 0/1/2/3+ vehicles 51%/43%/6%/0% 21%/54%/22%/2%
Car owners with private parking space 37% 83%
Vehicle classes small/medium/large 39%/46%/14% 39%/49%/12%
2.2. Driving Profiles
In a first step, driving profiles for the vehicles in both regions are created. In Germany,
mobility behaviour is regularly investigated by the study “Mobility in Germany” (MiD—
Mobilität in Deutschland). The survey asks around 150,000 households about their mobility
World Electr. Veh. J. 2021,12, 206 3 of 11
behaviour, including aspects such as daily trips, the trip purpose, the time and distance
travelled and the mode of transportation. The last survey dates back to 2017, with over
900,000 individual trips recorded [5].
At the Reiner Lemoine Institut, the tool SimBEV was developed to create driving
profiles for passenger cars for seven different region types. The basis for the driving profiles
is probability data gained from the analysis of MiD2017. The different region types are
based on the RegioStaR7
-
classification given by the German Federal Ministry of Transport
and Digital Infrastructure (BMVI) [
8
]. The created driving profiles include information
such as the status of the car for every 15 min time step, location of the car and the energy
used during a trip. Additionally, by providing probabilities of how likely it is to charge at
a specific location, the output includes information on whether a charging point exists at
that location and the maximum charging capacity. Possible locations for parked cars are
work, business (business trips starting from work), school, shopping, private/ridesharing,
leisure and home. Probabilities are based on [5,9,10].
Mobility behaviour in rural and urban areas differs mainly in the distances travelled
daily and the parking possibilities. As a large city, distances travelled daily in Berlin are
relatively low, and people who own a private car seldom have a private parking space
available. As a predominantly rural state, Brandenburg distances are higher but fewer trips
per week are made, and most people have access to an own parking space. The two study
areas Berlin and Brandenburg also differ in their degree of motorisation and the parking
possibilities. In Berlin, the degree of motorisation is 28.7%, and around 37% of people have
access to a private parking space. In Brandenburg, the degree of motorisation is almost
twice as high, with 57.2%, and the majority of people has access to an own parking space
(83%) (Table 1).
If a trip is longer than the remaining battery capacity, a fast-charging event is triggered.
At a fast
-
charging hub, the maximum charging capacity of the charging point is defined as
either 150 or 350 kW. Additionally, the destinations from the study MiD2017 are assigned
to four different charging use cases, according to Figure 2: Home, Work, Charging hub
and Public.
Figure 2. Characteristics of different charging use cases
Table 2shows the usable battery capacity, the consumption, and the maximum charg-
ing power for the vehicle classes small, medium and large. The battery and maximum
charging capacity assumptions are taken from the scenario “Increased Electrification” for
2035-
-
2042 from [
11
]. Energy consumptions are taken from [
12
], assuming a 10 per cent
decrease until 2040 due to increases in energy efficiency.
World Electr. Veh. J. 2021,12, 206 4 of 11
Table 2. Characteristics of different vehicle classes in 2040 [11,12].
Battery Capacity Consumption Max. Charging Capacity
BEV small 70 kWh 0.126 kWh/km 120 kW
BEV medium 100 kWh 0.148 kWh/km 350 kW
BEV large 120 kWh 0.171 kWh/km 350 kW
Depending on the region under investigation, a representative sample of 0.1% of the
required number of driving profiles is created for 14 days. For urban Berlin, this adds
up to 1124 driving profiles (441 small, 519 medium and 164 large vehicles), and for rural
Brandenburg, 1137 driving profiles (442 small, 555 medium and 140 large vehicles) are
generated. The resulting charging profiles created by applying the charging strategies are
then scaled up to the entire vehicle fleet.
2.3. Charging Strategies
EVs typically do not need to be charged with maximum power over the entire standing
time. Thus, the charging power can be varied within the standing time, allowing for a
lower charging power or the load to be shifted according to external signals. The effects
on the power grid, therefore, depend heavily on the respective charging strategy. This
paper uses the tool SpiceEV (Simulation program for individual charging events of electric
vehicles) to apply three different charging strategies on the created driving profiles for
urban and rural areas, to investigate the impact on use cases and the power grid.
SpiceEV calculates the actual and the desired State of Charge (SoC) for each charging
event. The initial SoC is taken from SimBEV
-
output. The desired SoC is either defined by
the following trip distance or by setting a minimum required value. Independent from the
charging strategy, the specific load is distributed within the standing time until the desired
SoC of 80% is reached or the standing time is over. Three charging strategies are applied
for this purpose:
1.
Greedy: Charging is performed immediately after plugging in the vehicle at maxi-
mum power.
2.
Balanced: Depending on the standing time, the minimum possible charging power
is calculated in each time step. As long as this charging power is below 10% of the
charging point’s maximum charging power, charging does not occur. As soon as the
calculated charging power exceeds the threshold value of 10%, charging occurs until
the end of the standing time.
3.
Market
-
oriented: This strategy uses the stock exchange electricity price as an external
price signal [
13
]. For the respective charging event, it is determined at which time it
is most favourable to charge. In the case of negative prices, additional charging is
performed beyond the desired SoC.
For this study, the market-oriented charging strategy is based on an immutable (his-
toric) schedule of electricity prices. If the strategy was adopted massively, it can be assumed
that the schedule of electricity prices would change. Figure 3shows the charging profile
for one medium BEV for the three charging strategies at the use case “work” on 18 March.
At 07:15, the car is plugged in at the workplace, standing until 16:30. The stock exchange
electricity price is lowest at 14:00 with 61.15 Euro/MWh. The charged energy within one
charging event can differ between the strategies since the start SoC depends on previous
charging events.
World Electr. Veh. J. 2021,12, 206 5 of 11
Figure 3. Different charging strategies for one passenger car at work.
2.4. Impact on Low-Voltage Grids
EVs are primarily connected at the low
-
voltage grid level. Six representative synthetic
low-voltage grids for rural and urban regions are examined to assess the impact of dif-
ferent charging strategies on grid stability [
14
]. While the three rural grids are typically
characterized by long line distances, the other three suburban and urban grids, in contrast,
are dominated by a higher number of grid consumption points in a smaller grid area.
In this study, suburban and urban grids are defined as urban grids. Thus, the following
assumptions made for urban grids are also applied to the two suburban grids in [
14
]. The
low-voltage grids examined are radial networks. This grid topology is the most frequently
encountered grid type at the low-voltage level due to its cost structure [
15
]. Radial grids
are characterized by feeders to which consumers are connected and radiate from the local
grid transformer. In radial grids, no rings or meshed structures exist. At times of high
consumption, when BEVs charge simultaneously with high charging power, grid issues
may emerge [16]. In these cases, high consumption can cause three kinds of grid issues:
1.
Voltage issues: They occur mainly at the end of a feeder with long line lengths. The
nominal voltage is 1.0 p.u. (Per-unit-system: In electrical power engineering, the
per-unit system represents an auxiliary unit of measurement related to a reference
value. This allows power data to be compared directly without a conversion factor.) It
should not fall below 0.9 p.u. and not exceed 1.1 p.u. at any bus within the grid [17].
2. Line overloads: They occur mainly in feeders near the transformer.
3. Transformer overloads.
To examine which grid issue emerges at which time and to which extent, power flow
calculations are conducted. An own developed model based on the open energy network
calculation program pandapower is used [
18
]. The simulation is based on a 15 min time
resolution and 14 days time scope, defined by the period of the created driving profiles.
Only general household loads are considered within the grids based on real measured
power consumption profiles [
19
]. To better understand the impact of BEVs on grid stability,
other sector-coupled consumers or grid components, such as heat pumps, decentralized
energy production units or stationary energy storage units, are not considered. Additional
consumption or production units may superimpose the effects on the grid stability and
probably lead to indistinct conclusions. Nevertheless, the interaction of different sector-
coupled consumers, decentralized energy production units, and storage units appears
worth investigating for future energy system planning but is not part of this study.
According to Table 1, the number of “vehicles per household”, the “electrification
rate”, and the “car owners with private parking space” are considered in the simulation for
both rural and urban regions. The charging points are statistically distributed across the
network. Furthermore, the examined grids are considered residential areas. Thus, only the
charging profiles for use case “home” are taken into account. Table 3shows the Number of
Households and BEVs within the examined rural and urban grids.
World Electr. Veh. J. 2021,12, 206 6 of 11
Table 3. Number of households and BEVs within the examined grids [14].
Rural Rural Rural Urban Urban Urban
Grid 1 Grid 2 Grid 3 Grid 1 Grid 2 Grid 3
Number
of households 13 99 118 41 104 111
Number of BEVs with
9 69 83 10 23 25
home charging place
3. Results
The examinations of this paper show the influence of the different charging strategies
on peak loads in different use cases and how many charging points are occupied at the
same time. In addition, it was investigated how mobility behaviour differs in urban and
rural areas and what influence this has on charging performance. The power grid analyses
show which effects arise in the low-voltage grid in urban and rural areas.
In the two weeks under consideration, the energy charged by the entire fleet of BEVs
in Berlin sums up to 142,485 MWh (on average: 126 kWh per BEV); in rural Brandenburg,
the sum is 8.60% lower with 130,228 MWh (on average: 114 kWh per BEV). Table 4provides
information on how the total amount of charged energy is distributed among the four use
cases in Berlin and Brandenburg. In Brandenburg, almost half of the total energy is charged
at home, whereas in Berlin, it is less than 20%. In Berlin, on the other hand, most of the
energy is charged in a publicly accessible area.
Table 4. Energy charged by the entire electric vehicle fleet in the four use cases considered in Berlin
and Brandenburg.
Home Work Charging Hub Public
27,620.6 MWh 32,413.3 MWh 13,211.5 MWh 69,240.1 MWh
Berlin 19.38% 22.75% 9.27% 48.59%
58,924.5 MWh 27,415.3 MWh 5,978.9 MWh 37,908.9 MWh
Brandenburg 45.25% 21.05% 4.59% 29.11%
The load profile resulting from the three charging strategies in Berlin and Brandenburg
are show in Figure 4. The resulting load peaks depend strongly on the charging strategy.
The highest load peaks result from applying the market-oriented charging strategy be-
cause the strategy treats all vehicles individually. Thus, each BEV takes advantage of the
favourable charging times. In Berlin, the maximum charging power sums up to 1537 MW
for the strategy “greedy”, to 1159 MW for the strategy “balanced”, and 8678 MW for the
strategy “market-oriented”. In Brandenburg, the maximum charging power for the strategy
“greedy” and “balanced” are 18 to 32% lower, with 1266 MW and 793 MW, respectively. The
strategy “market
-
oriented” in contrast results in a 15% higher power peak of 10,013 MW.
Figure 4. Total load profile for each charging strategy for Berlin and Brandenburg for 2 weeks.
The examination of the individual use cases shows a different potential for load
shifting and thus the applicability of charging strategies. Figure 5shows the load profiles
World Electr. Veh. J. 2021,12, 206 7 of 11
in the private space on the example of Berlin for two representative days. Long standing
times in the private space offer a high potential for load shifting. By applying the strategy
“greedy”, regular load peaks occur at the workplace, where many vehicles are connected
in a short time window in the morning. At home, lower peaks occur because people do
not get home as simultaneously as they go to work. The strategy “balanced” smooths the
load profiles. At home, loads are shifted from evening to morning hours, and at work, the
load is distributed relatively evenly throughout the standing time. The market
-
oriented
charging strategy results in high peak loads both at home and at work.
Figure 5. Load profile for each charging strategy in Berlin for 2 days in the private space.
Figure 6shows the load profiles in the publicly accessible space in Berlin for the
same two days. The load profiles resulting from the charging strategies differ less than in
the private space. Especially at charging hubs, the choice of charging strategy has little
influence on the load profile. The need to recharge a relatively large amount of energy
in a few minutes offers almost no potential to adjust the charging power. At the use case
“public”, the potential for shifting loads is higher than at charging hubs. However, only
minor differences can be observed between the strategies “greedy” and “balanced”. The
balanced strategy manages to lower loads during the daytime by shifting loads into night
hours. Load peaks result mainly from the market
-
oriented charging strategy. The same
pattern can be observed in Brandenburg, except that higher loads occur at home and lower
loads in the public space due to the different distribution of energy demand.
Figure 6. Load profile for each charging strategy in Berlin for 2 days in the public space.
Load peaks influence the voltage stability and the load on transformers and lines
in the grids under consideration. Exemplary, the transformer load of rural grid 2 for
different charging strategies over two representative days is depicted in Figure 7. The total
transformer load comprises the regular household load and the load caused by different
charging strategies. In contrast to the transformer load without BEVs, the transformer
load increases in the “greedy” strategy, especially in the evening hours when many BEVs
are plugged in and charging simultaneously (1). These charging events lead to additional
transformer loading up to 20%. The charging strategy “market-oriented” shows a signifi-
cant increase in the load when the market price is low and cause severe high load peaks
World Electr. Veh. J. 2021,12, 206 8 of 11
and an overload of the transformer (2). Furthermore, Figure 7reveals that the “balanced”
strategy can significantly reduce the transformer load. Load peaks, which mainly occur in
the evening, are decreased, and the charging is shifted to the night when the transformer
load is at its lowest (3).
Figure 7. Load profile for each charging strategy in Berlin for 2 days in the private space.
Figure 8depicts the impact of the three different charging strategies on rural and
urban grids. The region types “rural” and “urban” each contain three grids’ power flow
calculation results. The results show that grid components such as transformers and lines
in rural grids are exposed to much higher stress than urban grid components. This stress
in rural grids is due to the higher number of BEVs with home charging point in 2040 (rural
54%, urban 18%). Furthermore, within the examined urban grids, transformers and lines
can carry a higher power related to the household number within the grid. Longer line
length and the resulting voltage drop over these lines additionally cause higher voltage
deviation in the rural grids.
Figure 8. Load profile for each charging strategy in Berlin for 2 days in the private space.
Especially in rural grids, different charging strategies appear to have a significant
impact on grid issues. Charging strategies such as “greedy” and “market-oriented” lead to
higher loads on transformers and lines. Specifically, within the “market-oriented” scenario,
transformer overloads up to more than 250% occur several times, lines are loaded up
to nearly 100%, and voltage deviations almost reach the lower voltage band of 0.9 p.u.
These crucial grid issues suggest that a pure market-oriented charging strategy is not grid-
serving. In contrast, the “balanced” strategy can reduce the charging peaks significantly
and therefore relieve the grid to a large extent. The maxima of the voltage deviation and
the transformer and line loadings are slightly higher than in the simulation without BEVs.
4. Discussion
This examination shows the impact of different charging strategies on power grids in
urban metropolitan areas and predominantly rural areas using the German states Berlin
World Electr. Veh. J. 2021,12, 206 9 of 11
and Brandenburg. In terms of charging strategies, both “greedy” and “market
-
oriented”
represent extreme examples. The “greedy” strategy illustrates the load peaks that can
occur with entirely uncontrolled charging. “Market-oriented”, on the other hand, shows
the influence of a charging strategy that is purely oriented to the dynamic stock exchange
electricity price. Since all vehicles individually use favourable times for charging without
taking the other vehicles into account, high power peaks occur, which can cause overloads
in the grid. The “balanced” strategy approach leads to an increase in the total charging
power at the end of the idle times, which can be observed mainly in the use case home.
Other approaches are conceivable to take advantage of the idle time and to avoid falling
below the minimum charging power due to the efficiency. For example, charging could be
done directly at the beginning until the desired SoC is reached, or the minimum possible
charging power could also be randomly placed in the standing time, which would flatten
the overall load curve further. Furthermore, the distribution among the use cases depends
strongly on the defined probabilities.
In this paper, only 0.1% of the overall predicted vehicle fleet in 2040 was simulated.
For the whole number of BEVs in the examined regions of more than one million vehicles,
it can be assumed that the load peaks are lower than in this analysis due to a random
distribution. Thus, this study represents an extreme case with relatively high simultaneity
factors. In addition, a vehicle ramp-up was assumed that does not consider changes in
mobility behaviour. However, a change in the total number of vehicles or people’s driving
behaviour by 2040 would directly affect the charging profiles.
The study shows the effects of different charging strategies on charging profiles in
individual use cases. There is little potential to shift loads at locations with short standing
times, mainly in the publicly accessible space. Especially at charging hubs, there is hardly
any flexibility because these fast charging events usually require a relatively large amount
of energy to be recharged in only 15–30 min. At locations with long standing times on the
other hand, the charging strategies have a substantial impact on the charging profiles, as
can be seen for the “balanced” and “market
-
oriented” strategy. Especially at home and
at work, the application of grid-serving charging strategies can lead to grid relief. Even a
relatively simple charging strategy reduces grid issues significantly and could decrease or
avoid expensive and time-consuming grid expansion in the future. In a 100% renewable
energy system, the load will have to be increasingly adapted to the generation to shift it in
times of very high feed
-
in. The strategy “market-oriented” offers the potential to balance
generation and BEV consumption. At the same time, a “market-oriented” strategy must
not lead to grid overloads and should consider local grid restrictions.
5. Conclusions
The analysis of the two federal states has shown that there are apparent differences
between urban and rural regions. Accordingly, different approaches are needed for the grid
integration of electric vehicles. Because of a distinct mobility behaviour in both regions,
the energy demand is distributed differently between private and public use cases. This
different mobility behaviour results in individual potentials for load shifting. More energy
is charged in private locations in rural areas, where long standing times offer a high
potential to adjust the charging profile by applying charging strategies. In urban areas,
on the other hand, people depend more on public charging locations, which offer less
flexibility to apply charging strategies.
The examination shows that a simple balanced strategy can significantly reduce grid
issues such as transformer and line overloads, especially in rural areas, where many
charging processes simultaneously occur at home. Purely market
-
oriented charging, on
the other hand, causes significant load peaks. That suggests that price signals must not be
determined solely by a dynamic stock exchange electricity price but must also consider
the local grid situation, such as voltage violation and overloading of grid components, the
number of consumers, and their charging behaviour.
World Electr. Veh. J. 2021,12, 206 10 of 11
Author Contributions:
Conceptualization: J.G., C.D. and R.R.; Data curation: J.G.; Formal analysis:
J.G. and R.R.; Investigation: J.G., C.D. and R.R.; Methodology: J.G., C.D. and R.R.; Project adminis-
tration: J.G.; Resources: J.G. and R.R.; Software: J.G. and R.R.; Supervision: J.G.; Validation: C.D.;
Visualization: J.G. and R.R.; Writing—original draft: J.G. and R.R.; Writing—review and editing: C.D.
All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments:
The authors gratefully thank the Reiner Lemoine Foundation for supporting
this research work.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
BEV Battery electric vehicle
SoC State of charge
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