presentation - Boosting Electromobility

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presentation - Boosting Electromobility
Robert van den Hoed,
Simone Maase
Amsterdam University of
Applied Sciences
Boosting electromobility
Project funded with a
contribution from the Life+
financial instrument of the
European Union.
Utilization of public charging infrastructure
Benchmark of 4 major cities and metropolitan area in the Netherlands
• Benchmark of 4 major cities and Metropolitan area
• Amsterdam, Den Haag, Rotterdam, Utrecht
• MRA (metropolitan region)
• Applying data analysis on rich dataset (June 2015):
• More than 3200 charging points
• More than 1 million charge sessions (filtered: 3,8%)
• Facilitating more than 10.000 unique users per month
Key performance indicators of charging
infrastructure
• # charging points
 # charging points / inhabitants
• # charge sessions
• #users facilitated (RFIDs)
 # frequent users
• # kWh charged
 #kWh of overall charge infrastructure (indicator of electric km’s  CO2)
 #kWh/ charging station
• Capacity utilization
 Too low (limited use)
 Too high (scarcity of charging points)
Rich dataset
Anonymous charge-data generated by charge points
Parameter
Example
RFID
Explanation
charge point address
Charge point Admiralengracht Adress of the charge
44
point
address
Charge point Nuon
operator
Owner
point
of
the
charge
Essent
charge
service
provider
Charge point Amsterdam
city
Charge point 1057EW
postal code
Owner of the used charge
card
ZIP code of the area of
the charge point
Volume
0,86
Charged energy [kWh]
Connection
time
Start Date
0:14:23
18-04-2012
Time
the
car was
connected
Date the session started
End Date
18-04-2012
Date the session ended
Start Time
23:20:55
Time the session started
End Time
23:35:18
Time the session ended
charge time 0:14:23
RFID
60DF4D78
Time the car is actually
charge
RFID code of a charge
card
charged volume
connection time
De data is anoniem, de RFID tag is niet gekoppeld aan persoonsgegevens in onze dataset. De SQL server database
bevindt zich in het HvA dat center achter een VPN en firewall en gebruikers hebben beperkte toegang.
Number of charging stations
Increase to 1600 charge stations (3200 points); Amsterdam frontrunner
# of charging stations
Inhabitants per charging point
Amsterdam has highest coverage of charging infra compared to amount
of inhabitants
Number of monthly charge sessions
Major growth in charge sessions, particularly in Amsterdam (>factor 3)
Frequency of use
Every charging station used 32 to 45 times monthly
# of monthly charge sessions divided by # charging stations
(june 2015)
50
45
40
35
30
25
20
15
10
5
0
Amsterdam
Den haag
Rotterdam
Utrecht
Number of unique users (RFIDs)
Strong growth in unique users
Capacity utilization
Occupation between 20-40%; Amsterdam and Utrecht highest
• Based on capacity utilization: 2,5-5 times more EVs could charge on these networks
• Based on charge utilization (10-15%): 7-10 times more EVs to be facilitated
Strong increase in charge amount
550MWh charged in June 2015 - translating to 2,7 mln e-km’s facilitated
Car2Go & E-Taxis
• Major contribution by Car2Go and E-taxis in Amsterdam
Monthly charge per charging station
Ranging 200-600kWh – relevant for the business case
kWh charged
kWh charged
per charging
per charging
station
station
(G4-excl
(G4)Car2Go/taxi)
700
600
500
Amsterdam
Amsterdam
Den Haag
Den Haag
Rotterdam
Rotterdam
Utrecht
Utrecht
Amsterdam excl CS en Taxi
400
Car2Go / Etaxis (2015)
300
200
100
0
11
22
33
44
5 5
6 6
7 7
2014
2014
8
8
9
9
10 10 11
11 12
12 1
1 2
2 3
34
2015
45
2015
56
67
7
Applying data science to optimize rollout strategies
Introduction of new e-fleets
E-taxis (BIOS) leading to a threefold increase in kWh charged in Nieuw-West
80000
70000
60000
Centrum
kWh
50000
Nieuw-West
40000
Noord
Oost
30000
West
20000
Westpoort
Zuid
10000
Zuidoost
0
2
3
4
5
6
7
2012
8
9 10 11 12 1
2
3
4
5
6
7
8
2013
Maand/Jaar
9 10 11 12 1
2
3
4
5
6
7
2014
8
9 10 11 12
Mapping users and charge clusters
#kWh per session & level of utilization
Small charge, high
utilization
Large charge, low
utilization
Significant overlap in charging between cities can be
discerned
Using charge point user-behaviour profiles
for smart location planning
Combination
of AJ 2014
Ernststraat profile with Abbenesstraat profile
Profiel
AJ Ernststraat
Profiel Abbenesstraat
120
140
120
120
100
Aansluittijd in uren
Aansluittijd in uren
100
100
40
40
20
20
80
80
80
60
0
60
60
40
20
0
0
1
2
3
4
5
6
7
8
9
10
11 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
de dag - 6i
A. J. Ernststraat - 861Uren van
Abbenesstraat
Charge profiles based on occupancy in one month.
Uren van de dag
12
13
14
15
16
17
18
19
20
21
22
23
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Uren van de dag
Plotting: RFID-enabling x ChargeEffectiveness
700
600
•
kWh per paal
500
400
Amsterdam
Den Haag
300
Rotterdam
Utrecht
200
100
0
0
1
2
3
4
5
Aantal RFIDs per paal
6
7
8
9
Conclusions
• The Dutch cities and metropolitan area provide a rich dataset:
 monitoring, modeling, forecasting and simulation
 benchmarking policies - support policy makers (and companies)
 Importance of acquiring valuable data
• Distinct KPI’s differences between cities:
 Amsterdam frontrunner – partly explained Car2Go and E-taxis
 Amsterdam and Utrecht slightly higher in (i) kWh/station, (ii) utilization, (iii)
charge sessions/station
• Policy effects?
 National policies still dominant in stimulating EV sales and infra-utilization
 Local policies to be researched: placement on demand versus grid placement

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