intelligent e-transportation management

8/14/2019 Intelligent E-Transportation Management

1/15

DistributedGene

ration

Briefing Paper

Nynke Verhaegh, Petra de Boer,J os van der Burgt

KEMA Consulting , The Netherlands

Available online J anuary 2010

Intelligent E-Transportation Management

Mon Tue Wed Thu Fri Sat Sun0

10

20

30

40

50

60

70

80

90

100Case 1 'old': HP, No EV

TransformerLoading(%)

old, Winter

old, Springold, Summer

old, Autumn

0 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0

Case 1: all HP, no EV, no PV, no mCHP, moderate thermal insulation


10

20

30

40

50

60

70

80

90



old, Winter


old, Autumn




2/15

Distributed Generation

2

www.leonardo-energy.org

ABSTRACTThis 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 andHPs 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.


3/15


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

annualenergy

consumption/house [MWh]

annual energyconsumption

[MWh] type number

annuaenergy

consumption/apartment

[MWh]

annualenergy

consumption[MWh]

annualenergy

consumption[MWh]

A 40 3,20 128 F 60 3,20 192 school 75

B 40 3,96 158 G 40 3,20 128shopping

center 200

C 47 4,71 221 H 60 3,20 192E 45 5,39 243

J 11 6,10 67

tota 183 817 tota 160 512 275

single houses apartments municipal facilities


4/15


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

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]goo goo mo erate mo erate

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 167tota 160 667 889 444

single houses

apartments

thermal insulation

central heating tap water heating

central heating tap water heating

thermal insulation


5/15


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


6/15


6


2.5. Photovoltaic generation

Photovoltaic (PV) pannels convert sunlight into electricity. The electric power of a PVsystem is expressed in Watt peak (Wp). A Wp is the electric power that a solar cell

supplies at an irradiation of 1000 W/m 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 8m per house. The school and shop

together have a PV system of 250 kWp (total area 1000m). 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 bymCHP for residences with moderate and good thermal

insulation

PV system

annual

energy

generation

[kWp] [MWh/yr]

houses 366 -256shop/school 250 -175

PV

tap water

heatingthermal

insulation good moderate

[MWh/year] [MWh/year] [MWh/year]

houses -206 -308 -102apartments -133 -178 -89

micro CHP power supply

central heating


7/15


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%


8/15


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 supplyequals 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 0thermal 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 105number of feeders with overloads per year 0 0 0 0 0 2 0 0 2

with DSM case 0thermal insulationnumber 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 2996maximum transformer loading per hr [%] 100 100 100 100 100 100

maximum cable loading per year [%] 60 60 61 60 60 61num er o ee ers wit overoa s per year 0 0 0 0 0 0

without DSM

thermal insulationnumber of EVs [%] 20 40 50 20 40 50

total energy demand [MWh/yr] 2768 3035 3170 2531 2798 2933total decentralized energy generation [MWh/yr] -439 -439 -439 -439 -439 -439

total external energy supply [MWh/yr] 2444 2727 2871 2187 2474 2619

maximum transformer loading per hr [%] 118 157 176 113 152 171

maximum cable loading per year [%] 71 95 107 69 91 106

number of feeders with overloads per year 0 0 2 0 0 2

with DSMthermal insulation

number of EVs [%] 20 40 50 20 40 50


total decentralized energy generation [MWh/yr] -439 -439 -439 -439 -439 -439


maximum transformer loading per hr [%] 100 100 100 100 100 100maximum cable loading per year [%] 61 61 61 61 61 61


without DSM



total decentralized energy generation [MWh/yr] -675 -675 -675 -529 -529 -529total external energy supply [MWh/yr] 1197 1482 1627 1385 1668 1812

maximum transformer loading per hr [%] 83 122 142 87 126 146maximum cable loading per year [%] 52 75 82 55 77 88


with DSM


total energy demand [MWh/yr] 1869 2137 2271 1869 2137 2271total decentralized energy generation [MWh/yr] -675 -675 -675 -529 -529 -529


maximum transformer loading per hr [%] 83 100 100 87 100 100

maximum cable loading per year [%] 52 60 60 55 60 60

case 2

moderatecase 1

moderate goodcase 4

good

case 4

moderate good

case 3

moderate good

case 1

case 3moderate good

case 2moderate good


9/15


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.


10

20

30

40

50

60

70Scenario 0: No HP, No EV


Winter

SpringSummer

Autumn

0 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun0

Case 0: no HP, no EV, no PV, nomCHP


10

20

30

40

50

60

70Scenario 0: No HP, No EV


Winter

SpringSummer

Autumn

0 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun0

Case 0: no HP, no EV, no PV, nomCHP


10/15


10


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.


10

20

30

40

50

60

70

80

90



old, Winter


old, Autumn




10

20

30

40

50

60

70

80

90



old, Winter


old, Autumn




20

40

60

80

100

120

140

160

180

200

Case 2, 'old': HP and EV (Winter)


50% EV

40% EV

20% EV

No EV

Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0

Case 2: allHP, EV, no PV, nomCHP, moderate thermal insulation


20

40

60

80

100

120

140

160

180

200

Case 2, 'old': HP and EV (Winter)


50% EV

40% EV

20% EV

No EV

Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0

Case 2: allHP, EV, no PV, nomCHP, moderate thermal insulation


11/15


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 noCHP.

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


20

40

60

80

100

120

140

160

180

200Case 2, 'old': 50% EV, Winter

Transformerlo

ad(%)

Original

With DSM

Mon Tue Wed Thu Fri Sat Sun0Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0

Case 2: all HP, 50% EV, moderate thermal insulation, winter


20

40

60

80

100

120

140

160

180

200Case 2, 'old': 50% EV, Winter

Transformerlo

ad(%)

Original

With DSM

Mon Tue Wed Thu Fri Sat Sun0Mon Tue Wed Thu Fri Sat Sun0 Mon Tue Wed Thu Fri Sat Sun0

Case 2: all HP, 50% EV, moderate thermal insulation, winter


12/15


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.

Mon Tue Wed Thu Fri Sat Sun-50

0

50

100

150

200Case 4, 'old': 50% EV, solar PV (Winter)

Transforme

rLoading(%)

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 Sun Mon 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

SolarP V


0

50

100

150

200Case 4, 'old': 50% EV, solar PV (Winter)

Transforme

rLoading(%)

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 Sun Mon 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

SolarP V


-50

0

50

100

150Case 4, 'old', 50% EV, solar PV, Summer

Transformerload(%)

Case 4 PV

Case 1 No EV No PVCase 4 with DSMSolar PV


Case 3: all HP, 50% EV, PV,moderate thermal insulation, summer

Case 3 PV

Case 1 no EV, no PVCase 3 PV withDSM

SolarPV


-50

0

50

100

150Case 4, 'old', 50% EV, solar PV, Summer

Transformerload(%)

Case 4 PV

Case 1 No EV No PVCase 4 with DSMSolar PV


Case 3: all HP, 50% EV, PV,moderate thermal insulation, summer

Case 3 PV

Case 1 no EV, no PVCase 3 PV withDSM

SolarPV


13/15


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.


-20

0

20

40

60

80

100

120

140

160Case 5, 'old': No HP, 50% EV, uCHP (Winter)


Case 5 uCHP

EV, No uCHP

Case 5 uCHP w/DSM

u-CHP


Case 4: no HP, 50% EV, no PV, allmCHP, moderate thermal insulation, winter

Case 5: mCHP, noDSM

Case 2: EV, no mCHP, allHP

Case 5: mCHP with DSM

mCHP


-20

0

20

40

60

80

100

120

140

160Case 5, 'old': No HP, 50% EV, uCHP (Winter)


Case 5 uCHP

EV, No uCHP

Case 5 uCHP w/DSM

u-CHP


Case 4: no HP, 50% EV, no PV, allmCHP, moderate thermal insulation, winter

Case 5: mCHP, noDSM

Case 2: EV, no mCHP, allHP

Case 5: mCHP with DSM

mCHP


14/15


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 existingnetworks. 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.


15/15


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 isquestionable 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.

intelligent e-transportation management

Documents