F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010

24 September 2010
DTU, Copenhagen

Electric Vehicle Integration Into Modern Power Networks

Smart charging strategies for efficient
management of the grid and
generation systems

F. J. Soares
INESC Porto/FEUP

Summary

1. The Electric Mobility Paradigm
a) Motives for EV adoption
b) Expectable benefits
c) Foreseen problems for electric power systems
d) Predicted EV rollout in some EU countries
2. Conceptual Framework for EV Integration Into Electric Power Systems
a) The EV supplier/aggregator
b) Possible EV charging approaches
3. Evaluation of EV Impacts in Distribution Networks – Preliminary Studies
a) Case study A: typical Portuguese LV grid
b) Case study B: typical Portuguese MV grid
c) Overall conclusions
4. Evaluation of EV Impacts in Distribution Networks – A Monte Carlo Method
a) Introduction
b) Case study: Flores Island network (Azores Archipelago)
c) EV motion simulation
d) Monte Carlo Algorithm
e) Results
f) Conclusions
5. Final Remarks


 Extremely volatile oil prices with a rising trend (due to increasing demand)
Source: oil-price.net


 High concentration of GHG in the atmosphere (global problem)

Source: wikipedia.org

Source: wikipedia.org


 High pollution levels in areas with high population density (local problem)
Source: SMH

Source: isiria.wordpress.com

Source: fearsmag.com


 Reduction of the fossil fuel usage in the transportations sector

Immediate reduction of the local pollution levels
(CO2, CO, HC, NOX, PM)

Source: topnews.in

 If EV deployment is properly accompanied by an increase in
the exploitation of renewable endogenous resources

Source: myclimatechange.net
GHG global emissions will be greatly reduced  Important
contribution to eradicate the global warming problematic


 EV capability to inject power into the grid (V2G concept) might be used to
“shape” the power demand, avoiding very high peak loads and energy losses

 EV storage capability might be used to avoid wasting “clean” energy
(wind/PV) in systems with a high share of renewables

During the periods when renewable power available
is higher than the consumption

 Isolated networks might improve their robustness and safely accommodate a
larger quantity of intermittent renewable energy sources

If EV batteries are efficiently exploited as storage devices
and used to mitigate frequency oscillations

c) Foreseen problems for electric power systems

 Depending on the number of EV present in the grid, the increase in the
power demand will lead to:

• Branches overloading

• Under voltage problems

• Significant increase of the energy losses

• Substation transformers overloading

• Need to invest in new generation facilities to face increasing demand

• Aggravation of the voltage imbalances between phases (for single phase
EV/Grid connections)


 Almost no official information available

 Contradictory information from non official sources
Source: Ricardo plc 2010

 Difficult to make accurate network
impact studies

Source: Ricardo plc 2010
ACEA - European Automobile Manufacturers' Association


 Types of EV available:

 Plug-in Hybrid EV  use a small battery
and a generator combined with an ICE

 Fuel Cell EV  store energy in H2 which
feeds a fuel cell that produces electricity
and heat

 Battery EV  powered only by electricity,
which requires a large battery pack


 Single EV do not have enough “size” to participate in electricity markets

 If grouped through an aggregator agent, EV might sell several system services
in the markets

 The EV suppliers/aggregators:

 are completely independent from the DSO

 act as an interface between EV and electricity markets

 group EV, according to their owners’ willingness, to exploit business
opportunities in the electricity markets

 develop their activities along a large geographical area (e.g. a country)


MV Level

 EV CVC

supplier/aggregator CVC

structure: Regional Aggregation Unit
CVC
LV Level

VC EV Owner

• Regional Smart Meter

Aggregation Unit Microgrid Aggregation Unit
Microgrid Aggregation Unit
VC
Smart Meter
EV Owner

(RAU) – located at VC
Smart Meter
EV Owner
SUPPLIER/AGGREGATOR

the HV/MV VC EV Owner

substation level and VC
Smart Meter

EV Owner

covering a region Microgrid Aggregation Unit Smart Meter

(e.g. a large city) with VC
Smart Meter
EV Owner

~20000 clients

• Microgrid MV Level

Aggregation Unit CVC

(MGAU) – located at CVC

Regional Aggregation Unit
the MV/LV substation CVC
LV Level
level and covering a VC EV Owner

LV grid with ~400 Microgrid Aggregation Unit
Smart Meter

clients VC
Smart Meter
EV Owner

VC EV Owner
Smart Meter

VC EV Owner
Smart Meter
VC EV Owner
Smart Meter

VC EV Owner
Smart Meter


Technical Operation Market Operation

CONTROL HIERARCHY PLAYERS
Electric Energy
Generation System GENCO Reserves

Reserves
Transmission System TSO Technical Validation of the Market Negotiation (for the transmission system)

Control
Level 1

Electricity Market
Reserves
DMS DSO
Electric Energy

Operators
Electric Energy

Control
Distribution System

Level 2
CAMC RAU

Electricity Electric Energy
Control Consummer
Level 3
MGCC MGAU
EV Supplier/Aggregator
Battery Battery
Parking Parking
Replacement Replacement

EV
Parking Battery Electricity
CVC VC Owner/Electricity
Consumer Facilities Suppliers Consumer

Controls (in normal system operation) At the level of Sell offer Technical validation of the market results
Controls (in abnormal system operation/emergency mode) Communicates with Buy offer

DMS – Distribution Management System CAMC – Central Autonomous Management System MGCC – MicroGrid Central Controller
CVC – Cluster of Vehicles Controller VC – Vehicle Controller


 EV as uncontrollable static loads:
 EV owners define when and where EV will charge, how much power they will require
from the grid and the period during which they will be connected to it

 EV as controllable dynamic loads:
 EV owners give the aggregator the possibility to manage their charging during the
period they are connected to the grid

 They only inform the aggregator about the time during which their vehicles will be
connected to the grid and the batteries’ SOC they desire at the end of that same period

 EV as controllable dynamic loads and storage devices:
 EV are not regarded just as dynamic loads but also as dispersed energy storage
devices

 They can be used either to absorb energy and store it or inject electricity to grid,
acting in a V2G perspective


 Charging approaches:

Charging
Modes

Uncontrolled Controlled

Dumb Charging Multiple Prices Smart Charging Vehicle-to-Grid
(DC) Tariff (MPT) (SC) (V2G)


 Uncontrolled approaches:
 Dumb charging  EV owners are completely free to charge their vehicles whenever they want;
electricity price is assumed to be constant along the day

 Multiple prices tariff  EV owners are completely free to charge their vehicles whenever they
want; electricity price is assumed not to be constant along the day, existing some periods where its
cost is lower
Market
Responsible for the
grid technical
operation

DSO Aggregator

Billing and
Information about interruptions tariffs
Power
and disconnection orders in consumed
case of grid problems Energy absorbed and
charging period of a single EV

AMM

µG

Charging starts when
EV is plugged-in

µG
Storage

EV Charger EV


 Controllable approaches:
 Smart charging  active management system where there is an aggregator serving as link
between the electricity market and EV owners; enables congestion prevention and voltage control

 V2G mode of operation  besides the charging, the aggregator controls the power that EV might
inject into the grid; EV have the capability to provide peak power and to perform frequency control
Responsible for the
grid technical Market
operation

DSO
Aggregator

Broadcast of information related
with billing, tariffs, set-points to
Power adjust EV control parameters and
Information about interruptions consumed SC/V2G set-points in accordance
and disconnection orders in with the market negotiations
Period during which a single EV will be
case of grid problems connected to the grid and the required
battery SOC at the end of that time
AMM

µG
EV is plugged-in and its owner
defines the disconnection hour
and the required battery SOC

µG
Storage

EV Charger EV

a) Case study A: typical Portuguese MV grid

 Objectives:

 Quantify the maximum percentage of conventional vehicles that can be
replaced by EV, without compromising grid normal operation, using three
different charging approaches:

• Dumb charging

• Dual tariff policy (= multiple prices tariff)

• Smart charging

 Compare grid behaviour when subjected to different percentages of EV
and when different charging approaches are implemented


 Grid architecture:
 Semi-urban MV network (15 kV)
 Two feeding points  voltage 1.05 p.u.
 Consumption during a typical weekday
 271.1 MWh 18
Total
16
 Peak load  16.6 MW Household
Commercial
14
Industrial

Consumption (MW)
12

10

8

6

4

2

0
1 5 9 13 17 21
Hour


 EV characterization and modelling:

 Initially, 635 EV (~5%) were distributed through the grid proportionally to
the residential load installed at each bus

 12700 vehicles

 Annual mileage  12800 km (35 km/day)

 EV assumed charging time  4h

 EV fleet considered:

• Large EV  24 kWh  40% of the EV fleet

• Medium EV  12 kWh  40% of the EV fleet

• Plug-in Hybrid EV  6 kWh  20% of the EV fleet


 Dumb charging and dual tariff policy methodology
Define, in a hourly basis, the nodal conventional load (residential, commercial and industrial) of the grid
Algorithm developed
to quantify the
Define the initial share of conventional vehicles replaced by EV maximum number of
EV that can be safely
integrated into the
Distribute EV through the grid proportionally to the residential power installed in each node
grid with the dumb
charging (without
Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode) grid reinforcements)

Calculate, in a hourly basis, the total nodal load

Run a power flow for the current hour

Feasible operating conditions ?

Yes

End of day was reached ?

No
No
Yes
Next hour

Increase the share of EV in 1% Maximum share of EV was reached

3. Evaluation of EV Impacts in
Define the initial share of conventional vehicles replaced by EV

Distribution Networks – Distribute EV through the grid proportionally to the residential power installed in each node

Preliminary Studies Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb
charging mode)

a) Case study A: typical Portuguese Define the connection period of each EV (*)

MV grid Calculate, in a hourly basis, the total nodal load


 Smart charging methodology No
Yes

Any EV waiting to
Voltage or
resume its charging ?
congestion problem ?

Algorithm developed to Voltage Congestion
Yes

maximize the number of EV No
Halt the charging
Record current grid conditions

Smart Charging
Halt the charging of 2% of the EV
that can be safely integrated of 5% of the EV
connected in the
connected in each
node downstream
Resume the charging of the first 5% of EV on
the halted EV list
in the grid with the smart problematic node the problematic
branch Yes

charging (without grid Update the list of EV whose charging was
Run a power flow with the new load conditions

No
reinforcements) halted (**)

Yes
No
Update the list of EV whose charging was
Feasible operating conditions ? halted

Yes Restore the recorded previous grid conditions

Next hour No End of day was reached ?

Yes
(*) The EV connection period was
defined according to the mobility
Increase the statistical data gathered for Portugal,
List of EV whose charging
share of EV in Yes published in [17].
was halted is empty ?
1% (**) This list is updated and sorted
each cycle, giving priority to EV who
will disconnect first from the grid.
No

Maximum share of EV was reached


 Results regarding the maximum allowable EV integration

 Dumb charging approach – 10% allowable EV integration

 Dual tariff policy – 14% allowable EV integration (considering that 25%
of the EV only charge during the cheaper period – valley hours)

 Smart charging strategy – 52% allowable EV integration (considering
that 50% of EV owners adhered to the smart charging system)


 Scenarios used to evaluate EV impacts in the network  1 power flow for
each hour was performed

Dumb Dual Smart
Test charging tariff charging
case limit limit limit

Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4
N.º of Vehicles 12700 12700 12700 12700 12700
EVs % 0% 5% 10% 14% 52%
Hybrid Share - 20% 20% 20% 20%
Medium EV Share - 40% 40% 40% 40%
Large EV Share - 40% 40% 40% 40%

Total Energy consumption (MWh) 277.1 283.2 294.0 301.7 388.1


 EV electricity demand with the dumb charging (52% EV penetration):
Dumb Charging

 was calculated taking into account mobility statistical data for Portugal
Dumb Charging
35000

30000
EV load
25000
Power demand (kW)

Household load
Total load
20000
EV load
15000
Household load
13 17 10000
21 Total load
Time (h) When people arrive
5000 home from work

0
1 5 9 13 17 21

Time (h)


 EV electricity demand with the dual tariff policy (52% EV penetration):

 was calculated taking into account mobility statistical data for Portugal

 was assumed that 25% of EV owners adhered to this scheme, shifting their EV
Dual Tariff Policy
charging to lower energy price periods Dual Tariff Policy
8
35000 8
7

30000 6 7
Electricity price

5
6
25000 EV load
Power demand (kW)

4

Electricity price
Household load 5
3
20000 Total load
2 Electricity price 4 EV load
15000 1
Household load
3
0
Total load
5 9 13 17
10000
21 2 Electricity price
Time (h)
5000 1

0 0
1 5 9 13 17 21 When electricity is
cheaper
Time (h)


 EV electricity demand with the smart charging (52% EV penetration):

 was assumed that 50% of EV owners adhered to this scheme, being their
charging controlled by the aggregator
Smart Charging Smart Charging
20000 20000
18000 18000
16000 16000
14000 14000
Power demand (kW)

12000 12000
10000 10000 EV load EV load
8000 8000 Household load Household load
6000 6000 Total load Total load
4000 4000
2000 2000
0 0
1 5 9 1 13 5 17 9 21 13 17 21
Time (h) Time (h)
Avoids peak load
increase


 Results  Changes in load diagrams with 52% of EV penetration

35
Without EV
Dumb Charging
30
Dual Tariff Policy

25 Smart Charging
Load (MW)

20

15

10

5

0
1 5 9 13 17 21
Hour


 Results  Voltages obtained for the worst bus during the peak hour

0,98 No EVs Dumb charging Dual tariff policy Smart charging

0,96
0,94
Voltage (p.u.)

0,92
0,90
0,88
0,86
0,84
0,82
No Evs 5% Evs 10% Evs 14% Evs 52% Evs


 Results  Worst branch loading obtained during the peak hour

160 No EVs
Dumb charging
140
Dual tariff policy
120 Smart charging
100
Rating (%)

80
60
40
20
0
No Evs 5% Evs 10% Evs 14% Evs 52% Evs


 Results  Daily losses

30 7%
7%
Losses with no EV (MWh)
Dumb charging losses (MWh)
6%
6%
25 Dual tariff policy losses (MWh)
Smart charging losses (MWh)

Losses relative value (%)
Losses relative value (% of the energy consumption) 5%
5%
20
Losses (MWh)

4%
4%
15
3%
3%

10
2%
2%

5
1%
1%

0 0%
0%
Without EV 10% EV 14% EV 52% EV


 Results  Branches loading overview (peak hour), with 52% EV penetration
No EV Dumb charging

Dual tariff policy Smart charging

b) Case study B: typical Portuguese LV grid

 Objectives:

 Develop a smart charging strategy to:

1. Maximize the number of EV that can be safely connected into the
grid (without reinforcing it)

2. Minimize the renewable energy wasted (in scenarios where
renewable generation surplus might exist)


1st Objective – Maximize the number of
EV that can be safely connected into the
grid (without reinforcing it)


 Grid architecture:
120
Total Household Commercial

100

% of the consumption
 Residential LV network (400 V) 80

60
 Feeding point voltage  1 p.u.
40

 Feeder capacity  630 kW 20

0

 250 households 1 3 5 7 9 11 13 15 17 19 21 23
Hour

 9.2 MWh/day

 550 kW peak load


 EV characterization and modelling:

 Initially, 20 EV (~5%) were distributed through the grid proportionally to
the residential load installed at each bus

 375 vehicles

 Annual mileage  12800 km (35 km/day)

 EV assumed charging time  4h

 EV fleet considered:

• Large EV  24 kWh  40% of the EV fleet

• Medium EV  12 kWh  40% of the EV fleet

• Plug-in Hybrid EV  6 kWh  20% of the EV fleet


 Dumb charging and dual tariff policy methodology (same as in case study A)
Algorithm developed
to quantify the
Define the initial share of conventional vehicles replaced by EV maximum number of
EV that can be safely
integrated into the
Distribute EV through the grid proportionally to the residential power installed in each node
grid with the dumb
charging (without
Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (dumb charging mode) grid reinforcements)

Calculate, in a hourly basis, the total nodal load



Yes

End of day was reached ?

No
No
Yes
Next hour

Increase the share of EV in 1% Maximum share of EV was reached

3. Evaluation of EV Impacts in
Define the initial share of conventional vehicles replaced by EV

Distribution Networks – Distribute EV through the grid proportionally to the residential power installed in each node

Preliminary Studies Define, in a hourly basis, the nodal EV load, if no control over charging is imposed (as in the dumb
charging mode)

b) Case study B: typical Portuguese Define the connection period of each EV (*)

LV grid Calculate, in a hourly basis, the total nodal load


 Smart charging methodology No
Yes

(same as in case study A) Any EV waiting to
Voltage or
resume its charging ?
congestion problem ?

Algorithm developed to Voltage Congestion
Yes

maximize the number of EV No
Halt the charging
Record current grid conditions

Smart Charging
Halt the charging of 2% of the EV
that can be safely integrated of 5% of the EV
connected in the
connected in each
node downstream
Resume the charging of the first 5% of EV on
the halted EV list
in the grid with the smart problematic node the problematic
branch Yes

charging (without grid Update the list of EV whose charging was

No
reinforcements) halted (**)

Yes
No
Update the list of EV whose charging was
Feasible operating conditions ? halted

Yes Restore the recorded previous grid conditions

Next hour No End of day was reached ?

Yes
(*) The EV connection period was
defined according to the mobility
Increase the statistical data gathered for Portugal,
List of EV whose charging
share of EV in Yes published in [17].
was halted is empty ?
1% (**) This list is updated and sorted
each cycle, giving priority to EV who
will disconnect first from the grid.
No

Maximum share of EV was reached


 Results regarding the maximum allowable EV integration

 Dumb charging approach – 11% allowable EV integration

 Smart charging strategy – 61% allowable EV integration (considering
that 50% of EV owners adhered to the smart charging system)


 Scenarios used to evaluate EV impacts in the network  1 three-phase
power flow for each hour was performed

Dumb Smart
charging charging
limit limit

Scenario 0 Scenario 1 Scenario 1
N.º of Vehicles 375 375 375
EVs % 0% 11% 61%
Hybrid Share - 20% 20%
Medium EV Share - 40% 40%
Large EV Share - 40% 40%

Total Energy consumption (MWh) 9.17 9.81 12.74


 Total electricity demand with the dumb and smart charging (61% EV penetration):
 The dumb charging curve was calculated taking into account mobility statistical
data for Portugal
 The smart charging curve obtained assuming that 50% of EV owners adhered to
this scheme, being their charging controlled by the aggregator

Without EVs
1000 Dumb Charging
Smart charging
800 Feeder capacity

600
kW

400

200

0
1 3 5 7 9 11 13 15 17 19 21 23

Hour


 Results  Voltages obtained for the worst bus during the peak hour

Phase R Phase S Phase T
0,97

0,96

0,95
Voltage (p.u.)

0,94

0,93

0,92

0,91

0,90
No EVs 11% - Dumb 11% - Smart 61% - Dumb 61% - Smart
Charging Charging Charging Charging


 Results  Worst branch loading obtained during the peak hour

140

120

100
Congestion Level (%)

80

60 124

40 75
72
63 64

20

0


 Results  Daily losses

11% EVs 61% EVs
Increase in losses due to EVs consumption (%) 140

120

100

80

130
60

40 83

20
17 11
0
Dumb Smart Dumb Smart
charging charging charging charging


 Results  Load imbalance between phases

PMAX,T  PMIN ,T
R,S R,S

16
LI  %   R , S ,T
100
PAVERAGE
Load Imbalance in the MV/LV Transformer (%)

14

12

10

8
14,2 14,0
6

4
6,0
4,8 4,7
2

0


2nd Objective – Minimize the renewable
energy wasted (in scenarios where
renewable generation surplus exist)


 Selected scenario  A wet and windy day in 2011

 Portuguese situation in 2011:
 Around 5 GW of wind power + “must run” of the thermal units  renewable
energy might be wasted (in low demand periods)

Portuguese Generation Profile for a Windy Day in 2011
Installed Capacity (MW)
Installed Capacity (MW) DER - Hydro Hydro - Run of River Coal

Others - 52 NG Fuel Der - Thermal
9000
Hydro (with reservoir) DER - Wind Demand
Wind - 5000 Hydro - 4957
8000

7000

6000

5000
P (MW)

4000
CHP - 1463
3000

2000
Thermal - 5820
1000

0

Wind energy produced - 51 GWh 1 3 5 7 9 11 13 15 17 19 21 23

Hour


 Demand change due to 11% of EV  Results obtained for the LV grid were
transposed to the complete electric power system

LV Grid Load Diagram Portuguese Generation Profile
Without EVs
18000
1000 Dumb Charging DER - Hydro Hydro - Run of River

Smart charging Coal NG
16000 Fuel DER - Thermal
800 Feeder capacity
Hydro (with reservoir) DER - Wind
Demand without EVs Demand with EVs - Smart charging
600 14000
kW

Demand with EVs - Dumb charging

400 12000
Renewable Energy Wasted!
200
10000

P (MW)
0
8000
1 3 5 7 9 11 13 15 17 19 21 23

Hour
6000

Smart Charging 15
4000
Wind Dumb Charging 30
Energy 2000

Wasted No EVs 31
0
0 5 10 15 20 25 30 35 1 3 5 7 9 11 13 15 17 19 21 23
% Hour


 Demand change due to 61% of EV  Results obtained for the LV grid were
transposed to the complete electric power system

LV Grid Load Diagram National Generation Profile
18000 DER - Hydro Hydro - Run of River
Without EVs
Coal NG
1000 Dumb Charging
Fuel DER - Thermal
Smart charging 16000
Hydro (with reservoir) DER - Wind
800 Feeder capacity Demand without EVs Demand with EVs - Dumb charging
14000 Demand with EVs - Smart charging

600
kW

12000

400

10000

P (MW)
200
Large Peak Load Increase! 8000
0
1 3 5 7 9 11 13 15 17 19 21 23 6000
Hour

4000
Smart Charging 1

Wind Dumb Charging 26
2000

Energy 0
No EVs 31 1 3 5 7 9 11 13 15 17 19 21 23
Wasted Hour

0 5 10 15 20 25 30 35
%


 Daily CO2 emissions

70

60
Daily CO2 emissions (kton)

50
30
40 31
Power system emissions
(including: extraction and
30 36 processing; raw material
transport; and electricity
20 generation)
29 26
10 Light vehicles emissions
11 (well-to-wheel)
0
Without EVs 11% EVs* 61% EVs* *Smart charging

c) Overall conclusions

 Losses increase as the number of EV rises

 Overall GHG emissions decrease as the number of EV rises

 Voltages and branches loading worsen as the number of EV increases

 ~10% is the number of EV that can be integrated with the dumb charging

 ~15% is the number of EV that can be integrated with the dual tariff policy

 When comparing with the dumb charging and with the dual tariff policy, the smart
charging allows:
 decreasing grid losses and consequently GHG emissions
 improving voltage profiles and branches’ congestion levels
 safely integrating 50-60% of EV
 avoiding the loss of renewable energy

 Results are highly dependent on where and when EV will charge  A Monte Carlo
simulation method should be used to obtain more accurate results

a) Introduction

 The utilization of a Monte Carlo method to perform impact studies is more
adequate  allows reducing the uncertainties by running a high number of
different scenarios

 This approach allows obtaining average values and confidence intervals for
several system indexes, like buses voltages, branches loading and energy
losses

b) Case study: Flores Island network (Azores Archipelago)

 Grid architecture: Swing Bus
1
Thermal Power Plant Hydro Power Plant
 Isolated MV network
(15 kV) 2 7 8 17 41

3 9 18 42
 Typical winter day
consumption  47.55 4 10 19 28 35 43
Wind Farm
MWh
5 11 20 29 30 31 36 44 45

 2.59 MW peak load 6 12 21 32 37
(occurs at 19:30 h)
13 22 33 38

 Average power factor 
14 23 34 39
0.77
15 24 40

 Island light vehicles
fleet  2285 vehicles 16 25

24 Bus
26 Load
 2 scenarios studied  Power Plant
Line
25% and 50% EV 27

penetration


 EV movement along one day was simulated using a discrete-time non-Markovian
process to define the states of all the EV at each 30 minutes interval (48 time instants)

 In each time instant, EV can be in four different states: in movement, parked in
industrial area, parked in commercial area, parked in residential area

 The EV state for each time instant is defined according to the probabilities specified for
that time instants and according to the discrete-time non-Markovian process

��=1
�� = ��

�� = ��
In Movement
�� = ��
��=1 In Movement
��→�� =1
��→��
In Movement
��=��
��→��
��=1 ��=1
��→�� →��
��=��
��→��
��=1 ��=1
��→�� →��

Parked in Parked in Parked in
Residential Area Industrial Area Commercial Area

Parked in Parked in Parked in
��=1 ��=1
�� =1
Parked in
��
Parked in Parked in
��=�� =��
�� =��

��=�� =��
�� =��


 The state transition probabilities applied were determined by analyzing the common traffic
patterns of Portuguese drivers

 It was gathered information about the number of car journeys made per each 30 minutes
interval, along a typical weekday, as well as the journey purpose and its average duration

 With this data, it was possible to define the probabilities of an EV reside in a given state at a
given time instant


 Define EV location for parked EV:
 all bus loads were classified as industrial, commercial or residential
 the probability of an EV be located at a specific bus was calculated with the
following equations:
��
��
��
��
��
�� = �� = �� =
��

4. Evaluation of EV Impacts in Define EV initial conditions (initial state, bus, battery capacity, slow charging rated
power, initial SOC, energy consumption and driver behaviour)

Distribution Networks – A Monte Draw EV states and the buses where “parked” EV are located, for the next time
instant

Carlo Method
Update EV batteries SOC

d) Monte Carlo algorithm
EV charge at the
What is the EV driver behaviour ? end of the day or
EV charge whenever is
only when convenient and the
it needs EV charge
1. Make the initial characterization of all the EV:

Sample generation and evaluation
driver has time
whenever
possible

• initial state
No No No
•
EV is parked in EV arrived home from the
the bus they are initially located EV battery SOC < 30% ?
residential area ? last journey of the day ?

• battery capacity (kWh) Yes Yes

•
Yes
slow charging rated power (kW) EV is parked in
residential area ?
No

• initial SOC (%) Yes
EV do not charge

• energy consumption (kWh/km)
• owners’ behaviour EV starts charging
No
GAUSSIAN DISTRIBUTIONS FOR INITIAL EV CHARACTERIZATION
Maximum
Standard Minimum Determine the new load at each bus
Average value
deviation value allowed
allowed
Battery capacity (kWh) 24.73 17.19 85.00 5.00
Power flow analysis
Slow charging rated power
3.54 1.48 10.00 2.00
(kW)
Energy consumption No
0.18 0.12 0.85 0.09 End of the day was reached ?
(kWh/km)
Initial battery SOC (%) 50.00 25.00 85.00 15.00
Yes

DRIVERS’ BEHAVIOURS CONSIDERED

Indexes
update
Update of grid technical indexes and vehicle usage indicators in a hourly and daily
Percentage of the basis
responses
EV charge at the end of the day 33%
Monte Carlo finishing criteria was met ?
EV charge only when it needs 30% SOC 23%

EV charge whenever possible 20% Yes

Compile results: power demand, voltages, branches loading, energy losses, peak
EV charge whenever is convenient and the driver has time 24% power, number of voltage and branches ratings violations


2. Samples generation:

• Simulate EV movement along one typical weekday  define EV states

• Attribute a bus location to parked EV

• Update battery SOC for EV in movement:

o if an EV was in movement in time instant t and its battery SOC went below a
predefined threshold (assumed to be 15%) in time instant t+1, it was considered that
the EV would make a short detour to a fast charging station for recharging purposes
GAUSSIAN DISTRIBUTIONS FOR EV MOVEMENT CHARACTERIZATION
Maximum
Standard Minimum
Average value
deviation value allowed
allowed
Travelled distance in
9.01 4.51 27.03 0.90
common journeys (km)
Travelled distance to fast
4.51 2.25 13.52 0.45
charging station (km)

o the fast charging was assumed to be made during 15 minutes with a power of 40 kW

o the fast charging station was considered to be installed in bus 12, as this is located
near one of the more populated areas of the island, with a high number of potential
clients

• Compute the total amount of power required from the network, discriminated per bus and
per time instant


3. Samples evaluation:

• Made by running a power flow for each time instant and by gathering information about:

o Voltage profiles

o Power flows in the lines

o Energy losses

o Highest peak load

4. Terminating the Monte Carlo process  2 criteria used:

• Number of iterations  10000

• Variation in the last 10 iterations of the aggregated network load variances (of each one of
the 48 time instants) < 1�� −4

∆�� = �� − ��−10 < 1�� −4

e) Results

 Power demand:

e) Results

 Voltage profile of one feeder (buses 17 to 27):

e) Results

 Network voltage profiles for the highest peak load identified:

e) Results

 Voltage lower limit violation probability:

�� . ��
��.�� = × 100
��. �� × 48

e) Results

 Branches loading:

No EV

50% EV

25% EV

F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010

F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010

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F. J. Soares, "Smart charging strategies for efficient management of the grid and generation systems," in Electric Vehicle Integration Into Modern Power Networks, DTU, Copenhagen, 2010