Intelligent E-Transportation Management

DistributedGeneration
Briefing Paper
Nynke Verhaegh, Petra de Boer,
Jos van der Burgt
KEMA Consulting , The Netherlands
Available online January 2010
Intelligent E-Transportation Management
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Distributed Generation
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www.leonardo-energy.org
ABSTRACT
This paper describes the performance of the network of a typical future residential
concept area, as has been studied in the Intelligent E-Transportation Management
project. Several scenarios have been elaborated by load flow simulations. The study
investigated what level of introduction of electric vehicles, heat pumps, photovoltaic
systems and micro- combined heat and power plants is feasible in this network. Possible
overload situations are examined and the opportunities of demand side management for
the power grid are investigated. In general load-flow simulations show that transformer
overloads will occur when electric heat pumps and electric vehicles are introduced
together in this specific grid. In that case extension of the grid or of the transformer
capacity is necessary. Alternatively, demand side management can be applied
successfully to mitigate the overload.
1. INTRODUCTION
The ITM-project (Intelligent E-transportation Management) aims at developing network
management concepts and specifications for controling the increasing power fluctuations
in the electricity network. Fluctuations are introduced into the grid by largescale
introduction of electric vehicles (EVs), electric heat pumps (HPs), photovoltaic (PV)
systems and micro-combined heat and power plants ( CHP). On the one hand PV
systems and CHP plants impact the power generation. Especially the supply by PV
systems is intermittent: depending on weather conditions and day-night profile. On the
other hand EVs and HPs demand significant amounts of extra electricity. Interestingly
this power demand can be externally managed. Charging of EVs can be arranged during
the night when the car is connected to the grid. The power demand of HPs can be
controled since the heat capacity of the buildings can serve as a heat buffer. The ITM
project examines the possibilities to use demand side management (DSM) of EVs and
HPs for increased power balance in the electricity grid.
2. BACKGROUND
2.1. Load-flow simulations
All simulations in this study are done with the PowerFactory Program, a simulation tool
made by DigSILENT corporation. The main objective of this interactive software package
is to optimize planning and operation of electrical power systems. The load flow
calculations are run for each hour per year, or each hour per week. The Program
monitored overloading and under- or overvoltage problems.

3
2.2. The Meekspolder
In this paper the electricity grid of a typical Dutch residential area to be built in 2020 is
considered. The layout of this area is given in Figure 1.
Figure 1 Layout of the Meekspolder
The Meekspolder consists of single houses, apartments buildings, a shopping center and
a school. The area was designed for various studies of future electric grids and the ITM
project uses this model to allow comparison of results from different projects. The
distribution network of the Meekspolder is well defined and prepared for future situations.
Details of the Meekspolder area and its basic electricity demand are given in Table 1.
The total annual electricity demand of the Meekspolder is 1604 MWh, providing there are
no electric cars, no electric heat pumps, no photovoltaic generation nor micro-combined
heat and power plants.
Table 1 Details of Meekspolder area and its basic electricity demand
Shop
60 x F
40x G
60x H
5 x J 5 x J3 x J
School
25 x B
25 x C
20 x E
5 x E 10 x E
5 x A
5 x A
20 x A
5 x B
10 x C
10 x B
12 x C
10 x E
MV/LV
type number
annual
energy
consumption/
house [MWh]
annual energy
consumption
[MWh] type number
annual
energy
consumption/
apartment
[MWh]
annual
energy
consumption
[MWh]
annual
energy
consumption
[MWh]
A 40 3,20 128 F 60 3,20 192 school 75
B 40 3,96 158 G 40 3,20 128
shopping
center 200
C 47 4,71 221 H 60 3,20 192
E 45 5,39 243
J 11 6,10 67
total 183 817 total 160 512 275
single houses apartments municipal facilities

4
Table 2 summarises the heat demand of the Meekspolder for central heating and tap
water heating. In the case of central heating there is a distinction between residences
with moderate thermal insulation and with good thermal insulation. The heat demand of
the school and the shopping center for central heating and tap water heating is not taken
into account in this study.
Table 2 Heat demand for central heating and tap water heating for residences with
moderate and with good thermal insulation
The total annual heat demand of the Meekspolder is 2651 MWh in case of residences
with good insulation and 3382 MWh in case of residences with moderate insulation.
Traditionally, this heat demand is supplied by gas. In addition, this study examines
several cases in which heating is supplied by an electric heat pump.
2.3. Electric Heat Pumps
An electric Heat Pump (HP) delivers heat for central heating and tap water heating by
using heat input from ambient air or ground water and electric power input for the
electricity pump. The Coefficient of Performance (COP) is the ratio between output heat
and input electricity. Table 3 shows the electricity for heat demand assuming an average
COP of 4. The total electricity demand for central heating and tap water heating is 663
MWh for residences with good thermal insulation and 845 MWh for residences with
moderate thermal insulation.
annual heat
demand/house
annual heat
demand
annual heat
demand/ house
annual heat
demand
annual heat
demand/ house
annual heat
demand
[MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr]
good good moderate moderate
type number
A 40 5,6 222 8,3 333 2,8 111
B 40 5,6 222 8,3 333 2,8 111
C 47 5,6 261 8,3 392 2,8 131
E 45 5,6 250 8,3 375 2,8 125
J 11 6,9 76 9,7 107 2,8 31
total 183 1.032 1.540 508
annual heat
demand/house
annual heat
demand
annual heat
demand/ house
annual heat
demand
annual heat
demand/ house
annual heat
demand
[MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr] [MWh/yr]
good good moderate moderate
type number
F 60 4,2 250 5,6 333 2,8 167
G 40 4,2 167 5,6 222 2,8 111
H 60 4,2 250 5,6 333 2,8 167
total 160 667 889 444
single houses
apartments
thermal insulation
central heating tap water heating
central heating tap water heating
thermal insulation

5
Table 3 Electricity for heat demand of residences with moderate and good thermal
insulation supplied by electric heat pumps assuming a Coefficient of Performance of 4
2.4. Electric Vehicles
In this study it is assumed that each house has two vehicles and each apartment has one
vehicle. Depending on the simulation scenario a percentage of the vehicles is EV. The
battery of each vehicle is assumed to be 10 kWh. The daily energy consumption is set at
7 kWh between 6 am and 6 pm. In the Netherlands this amount of energy is sufficient for
an average driving distance of 50 km per day. In the basic calculations it is assumed that
all charging is taking place at home starting at 6 pm with a full power of 2.33 kW. Thus,
charging is accomplished in 3 hours. Table 4 shows the annual energy demand of EVs in
the Meekspolder as function of percentage of EVs.
Table 4 Annual energy demand of EVs in the Meekspolder
[MWh/year]
thermal
insulation good moderate
[MWh/year] [MWh/year] [MWh/year]
houses 258 385 127
apartment 167 222 111
sum 425 607 238
electricity for heat demand
central heating
tap water
heating
percentage number
total energy
demand/yr
total power
demand during
charging
[MWh/yr] [kW]
20% 105 268 245
40% 210 537 489
50% 263 671 613
electric vehicles

6
2.5. Photovoltaic generation
Photovoltaic (PV) pannels convert sunlight into electricity. The electric power of a PV
system is expressed in Watt peak (Wp). A Wp is the electric power that a solar cell
supplies at an irradiation of 1000 W/m² and a cell temperature of 25 degrees Celsius.
Under these conditions, a 1 Wp solar cell will produce 1 watt of power. In this study it is
assumed that in case of PV integration each house has a PV system with an electric
power of 2 kWp, which corresponds to a PV area of 8m² per house. The school and shop
together have a PV system of 250 kWp (total area 1000m²). The apartment buildings do
not have a PV system installed. The annual energy generation of a 1kWp system is
about 0.7 MWh. Thus, the total annual energy generation by PV in the Meekspolder is in
the order of 430 MWh (see Table 5).
Table 5 Decentralised electricity generation by PV systems
2.6. Micro-CHP generation
A micro-Combined Heat and Power plant ( CHP) runs on gas and it supplies both heat
and electrical power to consumers. In this study it is assumed that the CHP supplies
20% of the heat demand as electric power. Table 6 shows the electricity generation by
CHP for residences with good and moderate thermal insulation.
Table 6 Electricity generation by mCHP for residences with moderate and good thermal
insulation
PV system
annual
energy
generation
[kWp] [MWh/yr]
houses 366 -256
shop/school 250 -175
PV
tap water
heating
thermal
insulation good moderate
[MWh/year] [MWh/year] [MWh/year]
houses -206 -308 -102
apartments -133 -178 -89
micro CHP power supply
central heating

7
2.7. Demand Side Management
In this study Demand Side Management (DSM) is used in certain cases to control the
power demand of EVs and HPs in order to prevent Low Voltage grid load peaks. DSM is
used for shifting loads from transformer loading peaks to transformer loading valleys.
DSM in combination with HP is possible because of thermal heat storage in the
residence itself. DSM in EV charging is possible because the charging energy need can
be spread out during the night (6 pm-6 am).
2.8. Study cases
In Table 7 the study cases are summarised as studied in the ITM project.
Case 0 Case 1 Case 2 Case 3 Case 4
No HP, no EV HP, no EV HP&EV HP&EV& PV EV&mCHP
HP - 100% 100% 100% -
EV - - 20-50% 20-50% 20-50%
PV - - - 100% -
CHP - - - - 100%

8
3. RESULTS
This chapter describes the results of the load flow simulations. All numeric data are listed
in Table 8.
Table 8 Numeric results of load flow simulations for the Meekspolder
in various study cases
Case 0
In Case 0 there are no heat pumps (HP), no electric vehicles (EV), no photovoltaic
generation (PV) and no micro-combined heat and power generation ( CHP). Only the
basic electricity demand of the Meekspolder is considered. The external energy supply
equals 1632 MWh per year for this base scenario. This is only 30% of the maximum
transformer loading available (5534 MWh), indicating that the transformer has more than
without DSM case 0
thermal insulation moderate good
number of EVs [%] 20 40 50 20 40 50
total energy demand [MWh/yr] 1602 2450 2263 2768 3035 3170 2531 2798 2933
total decentralized energy generation [MWh/yr] 0 0 0 0 0 0 0 0 0
total external energy supply [MWh/yr] 1632 2560 2313 2838 3115 3255 2591 2868 3008
maximum transformer loading per hr [%] 60 81 74 118 157 176 113 152 171
maximum cable loading per year [%] 30 55 50 70 94 107 68 90 105
number of feeders with overloads per year 0 0 0 0 0 2 0 0 2
with DSM case 0
thermal insulation
number of EVs [%] 20 40 50 20 40 50
total energy demand [MWh/yr] 2768 3035 3170 2531 2798 2933
total decentralized energy generation [MWh/yr] 0 0 0 0 0 0
total external energy supply [MWh/yr] 2833 3104 3242 2586 2858 2996
maximum transformer loading per hr [%] 100 100 100 100 100 100
maximum cable loading per year [%] 60 60 61 60 60 61
number of feeders with overloads per year 0 0 0 0 0 0
without DSM
thermal insulation
number of EVs [%] 20 40 50 20 40 50
total decentralized energy generation [MWh/yr] -439 -439 -439 -439 -439 -439
with DSM
thermal insulation
number of EVs [%] 20 40 50 20 40 50
without DSM
thermal insulation
number of EVs [%] 20 40 50 20 40 50
with DSM
thermal insulation
number of EVs [%] 20 40 50 20 40 50
case 2
moderate
case 1
moderate good
case 4
good
case 4
moderate good
case 3
moderate good
case 1
case 3
moderate good
case 2
moderate good

9
sufficient energy for the Meekspolder on average.
Figure 2 shows the transformer loading per hour during one week in each season for
Case 0. The unbalance of transformer loading during the day is higher in winter than in
summer when the power demand is highest. Two clear peaks can be distinguished: one
in the morning when people wake up and one in the afternoon when people return from
work. However, the maximum transformer loading per hour (60%) is well below an
overload situation (100%) indicating that the Meekspolder has a very strong grid. It is
very likely that electric heat pumps and/or electric vehicles can be integrated in the grid.
Figure 2 Transformer loading per hour during one week in
each season, Case 0.
Case 1
In Case 1 every residence has a heat pump (HP), but there are no electric vehicles (EV),
no photovoltaic generation (PV) and no micro-combined heat and power generation
( CHP). In this case the total annual electricity demand of the Meekspolder is 2263 MWh
in case of residences with good insulation and 2450 MWh in case of residences with
moderate insulation (see Table 8). Figure 3 shows the transformer loading per hour
during one week in each season for Case 1 with houses with moderate thermal
insulation. The maximum transformer loading per hour (i.e. peak load) increased up to
81% for houses with moderate insulation. This means that Demand Side Management is
not necessary for Case 1 in which every residence has a HP.
0
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20
30
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60
70
Scenario 0: No HP, No EV
Winter
Spring
Summer
Autumn
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Case 0: no HP, no EV, no PV, no mCHP
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Scenario 0: No HP, No EV
Winter
Spring
Summer
Autumn
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Case 0: no HP, no EV, no PV, no mCHP

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Figure 3 Transformer loading per hour during one week in each season, Case 1: houses
with moderate thermal insulation
Case 2
In Case 2 every residence has a heat pump (HP) and a fraction of the cars in the
Meekspolder is electric vehicle (EV). There is no photovoltaic generation (PV) and no
micro-combined heat and power generation ( CHP). Figure 4 shows the transformer
loading per hour during one week in winter for Case 2 with houses with moderate thermal
insulation as function of percentage EV.
Figure 4 Transformer loading per hour during one week in winter, Case 2: houses with
moderate thermal insulation, with different amounts of EVs. The curve No EV is the
same as in Case 1.
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Case 1 'old': HP, No EVTransformerLoading(%)
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Case 2, 'old': HP and EV (Winter)
50% EV
40% EV
20% EV
No EV
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Case 2: all HP, EV, no PV, no mCHP, moderate thermal insulation
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Case 2, 'old': HP and EV (Winter)
50% EV
40% EV
20% EV
No EV
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Case 2: all HP, EV, no PV, no mCHP, moderate thermal insulation

11
In this case several problems can be identified in the grid (see Table 8). In all sub-cases
(20, 40, 50% EV) there is a transformer overload in winter both for residences with
moderate and good thermal insulation, as can be seen in Table 8. That is because the
charging of EVs happens in the evening on top of the maximum load in winter. In addition
the maximum cable loading is above 100% in the case of 50% EV for residences both
with moderate and good thermal insulation. This results in 2 feeders with overloads per
year. It is noteworthy that in this study the difference in thermal insulation hardly influence
the simulation results.
DSM has been applied to mitigate the overload. The hourly transformer is set at a limiting
value of 100%. Numeric simulation results are presented in Table 8. Figure 5 shows the
results for Case 2 with houses with moderate thermal insulation, with 50% EVs, with and
without DMS. Indeed, DSM solves the grid problems by shifting the overloads to loading
valleys.
Figure 5 Transformer loading per hour during one week in winter, Case 2: 50% of EVs,
residences with moderate thermal insulation, with and without DSM.
Case 3
In Case 3 every residence has a HP, a fraction of the cars is EV and there is no CHP.
Moreover, each house has a photovoltaic generation (PV) of 2 kWp and the school and
shops have a total PV output of 250 kWp. The total decentralized generation by the PV
systems in the Meekspolder equals 436 MWh/year. However, the number of overloads in
Case 2 (without PV) and Case 3 (with PV) are equal since the peak demand does not
coincide with the PV generation profile: the maximum peak demand occurs in winter
evenings when the sun does not shine. This can be seen in Figure 6 showing the
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60
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120
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Case 2, 'old': 50% EV, Winter
Transformerload(%)
Original
With DSM
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Case 2: all HP, 50% EV, moderate thermal insulation, winter
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Case 2, 'old': 50% EV, Winter
Transformerload(%)
Original
With DSM
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Case 2: all HP, 50% EV, moderate thermal insulation, winter

12
transformer loading per hour during one week in winter for Case 3 for houses with
moderate thermal insulation and 50% of EVs. Figure 6 also shows that DSM can be
applied to balance the overloads.
Figure 6 Transformer loading per hour during one week in winter, Case 3: 50% of EVs,
residences with moderate thermal insulation, with and without DSM.
Depending on PV power generation every now and then the generation in the
Meekspolder is larger than the demand, so power is fed back to the external 10 kV grid.
This occurs mainly in summer, occasionally in spring and in autumn and is shown in
Figure 7. It would be good to store this extra energy in the Meekspolder. However, this
cannot be done in the electric vehicles because it is assumed that all cars are out of the
Meekspolder during the day.
-50
0
50
100
150
200
Case 4, 'old': 50% EV, solar PV (Winter)
Case 4 PV
Case 1 No EV, No PV
Case 4 PV with DSM
Solar PV
Mon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat Sun
-50
Case 3: all HP, 50% EV, PV, no mCHP, moderate thermal insulation
Case 3 PV
Case 1 no EV, no PV
Case 3 PV with DSM
Solar PV
-50
0
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100
150
200
Case 4, 'old': 50% EV, solar PV (Winter)
Case 4 PV
Case 1 No EV, No PV
Case 4 PV with DSM
Solar PV
Mon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat SunMon Tue Wed Thu Fri Sat Sun
-50
Case 3: all HP, 50% EV, PV, no mCHP, moderate thermal insulation
Case 3 PV
Case 1 no EV, no PV
Case 3 PV with DSM
Solar PV
-100
-50
0
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150
Case 4, 'old', 50% EV, solar PV, Summer
Transformerload(%)
Case 4 PV
Case 1 No EV No PV
Case 4 with DSM
Solar PV
-100
Case 3: all HP, 50% EV, PV,moderate thermal insulation, summer
Case 3 PV
Case 1 no EV, no PV
Case 3 PV with DSM
Solar PV
-100
-50
0
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Case 4, 'old', 50% EV, solar PV, Summer
Transformerload(%)
Case 4 PV
Case 1 No EV No PV
Case 4 with DSM
Solar PV
-100
Case 3: all HP, 50% EV, PV,moderate thermal insulation, summer
Case 3 PV
Case 1 no EV, no PV
Case 3 PV with DSM
Solar PV

13
Figure 7 Transformer loading per hour during one week in summer, Case 3, residences
with moderate thermal insulation, 50% of EV, with and without DSM. During the day, the
PV generation is higher than the total demand, so power is flowing back from the
Meekspolder into the external grid.
Case 4
In Case 4 every residence has a micro-combined heat and power generation system
(mCHP). There is no HP or PV. A fraction of the cars consists of EV. The total energy
demand is lower than in Case 2 due to the absence of HP. The total decentralized
generation by the mCHP systems in Meekspolder equals 675 MWh/year for residences
with moderate thermal insulation and 530 MWh/year for residences with good thermal
insulation. Consequently, the external energy supply is much less than in Case 2.
Because of the lower demand and the mCHP generation the grid is less vulnerable than
in Case 2. Still some transformer overloads occur with 40 and 50% Evs (see Table 8).
These overloads can be mitigated with DSM as is shown in Figure 8.
Figure 8 Transformer loading per hour during one week in winter, Case 4, with 50% of
EVs, residences with moderate thermal insulatution, with and without DSM.
The surplus of decentrally generated power by the mCHP generation is fed back to the
10 kV grid through the transformer. Since this generation peak is in the night the surplus
can be used for EV battery charging.
-40
-20
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160
Case 5, 'old': No HP, 50% EV, uCHP (Winter)
Case 5 uCHP
EV, No uCHP
Case 5 uCHP w/DSM
u-CHP
-40
Case 4: no HP, 50% EV, no PV, all mCHP, moderate thermal insulation, winter
Case 5: mCHP, no DSM
Case 2: EV, no mCHP, all HP
Case 5: mCHP with DSM
mCHP
-40
-20
0
20
40
60
80
100
120
140
160
Case 5, 'old': No HP, 50% EV, uCHP (Winter)
Case 5 uCHP
EV, No uCHP
Case 5 uCHP w/DSM
u-CHP
-40
Case 4: no HP, 50% EV, no PV, all mCHP, moderate thermal insulation, winter
Case 5: mCHP, no DSM
Case 2: EV, no mCHP, all HP
Case 5: mCHP with DSM
mCHP

14
4. DISCUSSION AND CONCLUSIONS
Nowadays, existing electricity networks encounter problems with large scale integration
of electric heat pumps. In future the electricity networks also have to accomodate large
scale implementation of electric vehicles, photovoltaic systems and micro-combined heat
and power plants. This means that the electricity grid has to be adapted to match future
demand and supply.
In the ITM project, a future residential area, called the Meekspolder is considered. Load
flow simulations show that the grid-design of the Meekspolder is stronger than existing
networks. Even when all residences are provided with an electric heat pump no
overloads are observed. However, additional penetration of electric vehicles can not be
accommodated by the grid. Even when only 20% of all available cars are electric vehicles
overloads are observed. The main problem is in the transformer; the cables and feeders
are less vulnerable. This accounts both for residences with good and moderate thermal
insulation.
Distributed generation by photovoltaic systems can not balance the electricity demand of
electric vehicles because the demand and generation do not coincide in time. That
explains why the amount of overloads in Case 2 (all HP; EV and no PV) and Case 3 (all
HP; EV and PV) is identical. The generation by micro-combined heat and power plants is
mainly in the evening when electric vehicles are being charged. Therefore, there are far
less overloads in Case 5 (no HP; EV and all mCHP) compared to Case 2 (all HP; EV and
no mCHP). Evidently the combination of micro-combined heat and power plants with
electric vehicles is more appropriate than that of photovoltaic systems and electric
vehicles.
The load flow simulations in Case 2, 3 and 5 show that the electricity grid has to be
strengthened when electric heat pumps and electric vehicles or micro-combined heat and
power plants and electric vehicles are introduced on large scales. Alternatively, it has
been demonstrated that demand side management can solve the transformer overloads.
The simulations show that demand side management stabilises the grid by limiting the
hourly transformer load to 100%. This less expensive solution can be applied for electric
heat pumps because of heat storage in the residence itself. Additionally, demand side
management can be applied for charging of electric vehicles because charging can be
spread out during the night.

15
Again it is emphasized that the electricity grid design of the Meekspolder is not
representative for currently existing sub-urban grids. The results of this ITM project based
on the Meekspolder give an indication of requirements for the electricity grid in the future
when
electric vehicles, electric heat pumps, photovoltaic systems and micro-combined heat
and power plants are largely implemented in the grid.
In future simulations it is interesting to see whether overloads can be mitigated by
photovoltaic generation in combination with electrical storage systems. The surplus of PV
power during daytime and mainly in summer can be stored for use during peak demands.
Another possible scenario is the impact of fast charging of electric vehicles. It is
questionable whether fast charging should occur in the residential area or at dedicated
power stations which are not part of the local grid. Furthermore, it is interesting to
consider the electricity generation and demand characteristics of other cultures for the
stability of the grid.

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