The document discusses the impact of electric vehicles on electricity distribution infrastructure at Lappeenranta University of Technology, highlighting the need for network analysis to assess electric vehicle integration. It examines various parameters influencing load flow and energy consumption, and emphasizes the importance of intelligent charging systems to mitigate peak load increase and reduce distribution fees. The study outlines scenarios for network reinforcement and charging strategies to accommodate the growing electric vehicle market.

1. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
Electric Cars
– Challenge or Opportunity for the
Electricity Distribution Infrastructure?
Jukka Lassila
Tero Kaipia, Juha Haakana and Jarmo Partanen
Lappeenranta University of Technology
• Energy Technology ELECTRIFICATION OF MOBILITY
• Electrical Engineering AND THE ELECTRICAL NETWORK
• Environmental Engineering
1 Madrid, Spain
November 20th, 2009

2. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
Target of the studies
• Define method to analyse network effects of electric
vehicles
• Make network analysis using actual distribution network
data and load flows (city area and rural area feeders)
• Define best and worst scenarios and define needed
network reinforcements
• Define network effects to the distribution fees paid by
the end-customers
• Energy Technology
• Electrical Engineering
• Environmental Engineering
2

3. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
Input Parameters on Electric Car Network Simulation
National passenger transport survey
National passenger transport survey Area-specific
Area-specific
-- Spatial and temporal variations in passenger trips additional
additional
Spatial and temporal variations in passenger trips Charging profile
-- Length of daily trips
energy
energy
Length of daily trips
-- Annual length of driving (region dependent)
Annual length of driving (region dependent) __ kWh/day
__ kWh/day
Power
-- Length of daily trips according to housing type
Length of daily trips according to housing type (working hours/
(working hours/
-- Length of daily trips according to residential area
Length of daily trips according to residential area leisure time)
leisure time)
Hours
-- Length of daily trips according to the month of year
Length of daily trips according to the month of year
-- Length of trips according to the time of day
Length of trips according to the time of day
-- Number of cars in households
Number of cars in households
Properties of electric cars
Properties of electric cars Network simulations and analysis results
Network simulations and analysis results
-- Energy consumption, kWh/km
Energy consumption, kWh/km -- Load flow and loss calculations
Load flow and loss calculations
-- Capacity of the batteries, kWh
Capacity of the batteries, kWh -- Estimation of reinforcements required
Estimation of reinforcements required
-- Charging power, kW
Charging power, kW
-- Required charging time, h/day (battery properties)
Required charging time, h/day (battery properties)
Town planning statistics
Town planning statistics
-- Workplaces according to the area and time of day
Workplaces according to the area and time of day
-- Residential areas (detached houses, terraced houses,
Residential areas (detached houses, terraced houses,
apartment houses)
apartment houses) Electricity distribution network
Electricity distribution network
MARTINKYLÄ
-- Network topology and customer information
Network topology and customer information
Penetration of electric cars
Penetration of electric cars -- Feeder and hourly-specific actual load curves
MASSBY
Feeder and hourly-specific actual load curves
KALLBÄCK
LANDBO
-- Development of electric car markets
Development of electric car markets -- Network volume
Network volume 8
7
-- Replacement value
Replacement value 6
5
Power [MW]
Tariffs and supplier
Tariffs and supplier -- Parameters: loss costs, load growth, lifetime,
Parameters: loss costs, load growth, lifetime,
4
3
unit price of network components
unit price of network components
2
-- Distribution fee
Distribution fee
1
0
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
Thursday (hours)
• Energy Technology
• Electrical Engineering
• Environmental Engineering
3

4. Case Network
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
Whole distribution company
- 110/20 kV primary substations: 4 Winter
Winter
- 20 kV feeders: 22
- Habitants/end-customers: 19 470 / 11 000
- Workplaces: 5 333
- Houses: 7 932 (5992 detached houses,
525 terraced houses, 1287 apartment Summer
houses, 128 others) Summer
MARTINKYLÄ - 20/0.4 kV distribution substations: 470
- Peak load: 50 MW
1. - Annual energy: 200 GWh
- 20 kV lines and cables: 433 km
- 20 kV underground cabling rate: 16 %
2.
20 kV feeder 1. (densely populated area) 20 kV feeder 2. (rural area)
- Peak load: 8 MW - Peak load: 2 MW
- Annual energy: 36 GWh - Annual energy: 6 GWh
- Residential 58 %, industry 22 %, - Residential 95 %, agriculture 2 %,
public 13 % and service 7 % industry 3 %
MASSBY
- Habitants/end-customers: 4171 / 2278 - Habitants/end-customers: 1037 / 444
KALLBÄCK
- Workplaces: 1 577 - Workplaces: 84
- Houses: 1 840 (659 detached houses, 266 - Houses: 372 (all detached houses)
LANDBO terraced houses, 888 apartment houses) - 20/0.4 kV distribution substations: 27
- 20/0.4 kV distribution substations: 39 - 20 kV lines and cables: 31 km
- 20 kV lines and cables: 21 km - 20 kV underground cabling rate: 6 %
- 20 kV underground cabling rate: 33 %
• Energy Technology
• Electrical Engineering
• Environmental Engineering
4

5. Case Network - Load curve
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
• Energy Technology
• Electrical Engineering
• Environmental Engineering
5

6. Electric Vehicles - Properties
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
• Needed energy: 0.1 – 0.2 kWh/km
• Capasity: 30 kWh/car
• Charging power: 3.6 kW/car
(Charging power max 3.6 kW = 230 V x 16 A)
Photo presents car pre-heating pole
used widely in Finland and Nordic
countries.
• Energy Technology
• Electrical Engineering
• Environmental Engineering
6

7. Case area
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
• Habitants: 19 470
• Electricity end-customers: 11 000
• Workplaces: 5 333
• Houses: 7 932
– 5992 detached, 525 terraced, 1287 apartment, 128 others)
MARTINKYLÄ
• Personal cars: 11 000
• Travelling distances: 20 900 km/car,a
= 57 km/car,day
• Needed charging energy: 11.5 kWh/car,day
46 GWh/a (all cars)
MASSBY
KALLBÄCK
LANDBO
• Energy Technology
• Electrical Engineering
• Environmental Engineering
7

8. Case Network – Electric car
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
charging profiles
Direct night-time charging Split-level night-time charging
0:00 9:00 16:00 22:00 0:00 9:00 16:00 22:00
Working-hour and Optimised charging
time-off charging
0:00 9:00 16:00 22:00 0:00 9:00 16:00 22:00
The same amount of Transmission capacity in the network?
charging energy in Losses and loss costs?
each profile!
• Energy Technology
• Electrical Engineering
• Environmental Engineering
8

9. Case Network - Losses
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
400
Losses in medium voltage network
350
Direct night-time charging
Working-hour and
300
time-off charging
Split-level
Load losses [kW]
250
night-time
charging
200
150
100
Optimised charging
50
Present losses
0
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00
• Energy Technology
• Electrical Engineering
• Environmental Engineering
9

10. Case Network - Losses
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
200
180
160
Cost of losses [€/day]
140
120
100
80
60
40
20
0
Present losses Direct night- Split-level night- Working-hour Optimised
time charging time charging and time-off charging
charging
No remarkable differences in charging profiles from loss
costs point of view in medium voltage network!
• Energy Technology
• Electrical Engineering
• Environmental Engineering
10

11. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY Case Network – Feeder 1 (city area)
10 10
9 9
Feeder load with
8 8 electric cars City area feeder:
7 7
- Peak load of the day: 6.6 MW
6 E
E
6
- Minimum load of the day: 4.0 MW
5 5
4
4
3
Present load
Peak power [MW]
3 - Number of electric cars: 2000
2 2
Direct night-time charging 1
Split-level night-time charging - Driving distance: 57 km/car,day
1
0
0 - Energy consumption: 0.2 kWh/km
0 2 4 6 8 10 12 14 16 18 20 22
0 2 4 6 8 10 12 14 16 18 20 22 - Charging energy: 11.5 kWh/car,day
22.9 MWh/day for all cars
10 10
9 9
8 8 - Charging power: 3.6 kW/car
7
7 - Additional power: 0 – 3.5 MW
6 6
5 5 (depending on charging method)
4 4
3
3
Working-hour and - Charging energy (E) is equal in each
2 2
time-off charging Optimised charging charging alternative
1 1
0 0
0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22
• Energy Technology
• Electrical Engineering
• Environmental Engineering
11

12. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY Case Network – Feeder 2 (rural area)
4.0 4.0
3.5 3.5 Rural area feeder:
3.0 3.0 - Peak load of the day: 1.25 MW
Direct night-time charging Split-level night-time charging
2.5 2.5 - Minimum load of the day: 0.75 MW
2.0 2.0
Peak power [MW]
1.5
1.5 - Number of electric cars: 750
1.0
1.0 - Driving distance: 57 km/car,day
0.5
0.5 - Energy consumption: 0.2 kWh/km
0.0
0.0
0 2 4 6 8 10 12 14 16 18 20 22
- Charging energy: 11.5 kWh/car,day
0 2 4 6 8 10 12 14 16 18 20 22
8.6 MWh/day for all cars
4.0 4.0
3.5 3.5
Working-hour and - Charging power: 3.6 kW/car
3.0 3.0
time-off charging Optimised charging - Additional power: 0 – 1.75 MW
2.5 2.5
2.0 2.0
(depending on charging method)
1.5 1.5
1.0 1.0 Charging energy is equal in each
0.5 0.5 charging alternative
0.0 0.0
0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22
• Energy Technology
• Electrical Engineering
• Environmental Engineering
12

13. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY Case Network – Whole company
60 60
Direct night-time charging Split-level night-time charging
50 50
40 40
Whole company:
- Peak load of the day: 36 MW
30 30
- Minimum load of the day: 25 MW
20 20
Peak power [MW]
10 10 - Number of electric cars: 11 000
0 0
- Driving distance: 57 km/car,day
0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 - Energy consumption: 0.2 kWh/km
60 - Charging energy: 11.5 kWh/car,day
60
Working-hour and Optimised charging 126 MWh/day for all cars
50 50
time-off charging
40 40 - Charging power: 3.6 kW/car
30 30 - Additional power: 0 – 24 MW
20 20 (depending on charging method)
10 10
0 0
0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22
Using intelligent charging system (Optimised charging)
charging can be adjusted fully into low-load moments
• Energy Technology
• Electrical Engineering
• Environmental Engineering
13

14. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY Case Network – Reinforcement costs
- Network value compared with the peak load in
- low-voltage networks 320 €/kW
- medium-voltage network 300 €/kW
- primary substation level (110/20 kV) 100 €/kW
An example of defining required
reinforcement investments on the medium
voltage feeder
20 kV feeder 1. (densely populated area)
-Peak load of the day: 6.6 MW
-Additional power: + 3.0 MW
- Average marginal cost: 300 €/kW
Estimated need for reinforcement:
300 €/kW x 3000 kW = 900 000 €
Using intelligent charging system (Optimised charging)
charging can be adjusted fully into low-load moments
• Energy Technology
• Electrical Engineering
• Environmental Engineering
14

15. LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY Case Network – Reinforcement costs
- Replacement value: 50 M€ ( 2.9 M€/a calculated by p = 5 % and t = 40 a)
- Annual present delivered energy: 200 GWh/a
Network value per delivered energy 1.46 cent/kWh
- Estimated additional annual charging energy required by electric
cars: + 46 GWh/a ( 11 000 cars, 20 900 km/car,a and 0.2 kWh/km,car)
- Depending on charging method, estimation of the required
investments in a new transformer and transmission capacity in the
whole distribution network is 0 – 20 M€ (0 – 1 060 k€/a).
New distribution fee: 1.18 – 1.66 cent/kWh after the
network reinforcements
• Energy Technology
• Electrical Engineering
• Environmental Engineering
15

16. Conclusions (1/3)
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
1. Description of the overall methodology; what background
information is required and how it is used to determine the need
for electric car charging energy and power and to analyse the
network effects.
2. Charging mode has a significant impact on the peak load level on
the feeder. Without any intelligence included in the system,
charging of electric cars may increase the peak power even three
times higher compared with the present load level on the
medium-voltage feeder. With an intelligent charging system,
charging can be adjusted to a low-load moment, thereby avoiding
overlapping of the existing peak load and the additional charging
load. This may reduce the distribution fees of the electricity end-
users, because more energy is delivered through the same system
without a need for reinforcement investments. This is possible
especially on feeders where the load varies considerably.
• Energy Technology
• Electrical Engineering
• Environmental Engineering
16

17. Conclusions (2/3)
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
3. It is possible to cut the transmission/distribution fees charged to
electricity end-users during the large-scale adoption of electric
cars, if the charging system is well-planned and enough
intelligence is included in it. And opposite situation with
charging system without intelligent control.
4. Load variations of medium-voltage feeders have to be taken
into account in the analyses. The charging mode used on the city
area feeder does not provide the desired end result on the rural
area feeder. An intelligent charging system has to be able to take
into account to which feeder each electric car is connected and
where it is charged.
• Energy Technology
• Electrical Engineering
• Environmental Engineering
17

18. Conclusions (3/3)
LAPPEENRANTA
UNIVERSITY OF TECHNOLOGY
5. Biggest challenges in low-voltage networks. In the worst case
the triple extra capacity has to be invested (home, work,
cottage). However in Finland we have a lot of experience of
transposition of loads (sauna oven, electric space heating, water
heaters, block heaters).
Jukka Lassila
Lappeenranta University of Technology
jukka.lassila@lut.fi
+358 50 537 3636
• Energy Technology
• Electrical Engineering
• Environmental Engineering
18