Data analysis on the public charge infrastructure in the ... Session 1 Session 2 Session 3 Session 4 Session 5 Session 6 Session 7 Session 8 Session 9 The crawling algorithm checks
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Data analysis on the public charge
infrastructure in the city of Amsterdam
R. Van den Hoed PhD MSc, J.R. Helmus MSc, R. de Vries MSc, D. Bardok MSc
University of Applied Sciences Amsterdam, Municipality of Amsterdam
November 19, 2013
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• Provide first analysis of use patterns in a strongly
developed public charge infrastructure:
– 520 charge points
– 2100 (PH)Evs including 282 Car2Go
• Discuss implications for policy makers and EV-
stakeholders.
• Present future work on how to optimize the roll-out
public charge infrastructure.
Data mining: Making sense of 135.000
charge sessions.
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‘Amsterdam Electric’ Program: Driven by
air quality & EU limits on urban planning
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• 2009-2011:
– Placement of 100 charge points
– Facilitate early adapters
– Policy measures (a.o. free parking, subsidies)
• 2012-2015
– 1000 charge points (2015) – currently 520
– Focus on market segments
– Large scale EV-demo’s (Car2Go, Nissan Leaf)
‘Amsterdam Electric’ has lead to leading
position in public charge infra
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The data: Gathered by the charge point
operators
Parameter Example Explanation
Charge point
address
Admiralengracht
44
Adress of the charge point
Charge point
operator
Nuon Owner of the charge point
Charging service
provider
Essent Owner of the used charging
card
Charge point city Amsterdam
Charge point
postal code
1057EW ZIP code of the area of the
charge point
Volume 0,86 Charged energy [kWh]
Connection time 0:14:23 Time the car was connected
Start Date 18-04-2012 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
Charging time 0:14:23 Time the car is actually
charging
RFID 60DF4D78 RFID code of a charging cardCharge volume
Charge point address
Connection time
RFID
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Causes of ~30% data removal per error type
0
20000
40000
60000
80000
100000
120000
140000
total before
cleansing
connection time
repair
Import error physicly impossible
charge sessions
null record double records short time Summer Winter
time error
Net usable records
Out of 135,000 records a subset of 90,000
records was suitable for analyis
Note: connection time could be repaired, the rest of the records were deleted.
Source: Charge infrastructure forecast database
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Example of short time algorithm
A data crawling algorithm was used to repair
adjacent short time charge sessions
Note: Our hypothesis is that loose cable connections and information transfer issues cause this
problem.
total charge sesion
Session 1
Session 2
Session 3
Session 4
Session 5
Session 6
Session 7
Session 8
Session 9
The crawling algorithm checks
on adjacent short times.
e.g. session 6 is an null
session, yet adjacent to
several short sessions before
and after.
The algorithm influences the #
charge sessions as well, and
thus the mean session
duration.
Source: Charge infrastructure forecast database
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A comparison between four large districts
and the rest of A’dam was made
The Amsterdam city center is
divided in several districts. The
east, west, south and center have
highest use rates of EV public
charge stations.
Amsterdam is known for its lack
of private parking space in the
districts near the city center,
which leads to higher amounts of
public charge stations.
Map of Amsterdam with city districts highlighted
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Both #stations as wel as #sessions
doubled within one year (all districts)
0
50
100
150
200
250
300
0
2000
4000
6000
8000
10000
12000
14000
16000
4 5 6 7 8 9 10 11 12 1 2 3
am
ou
nt
of
cha
rgin
g s
tati
on
s
am
ou
nt
of
cha
rge
se
ssio
ns
# charge sessions # charge points
# charge sessions vs # charge stations (April 2012-March 2013)
. Source: Charge infrastructure forecast database
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More than 80% of all charge stations are
found in the four largest districts
# charge stations from April-2012 to March-2013
Source: Charge infrastructure forecast database
0
50
100
150
200
250
300
4 5 6 7 8 9 10 11 12 1 2 3
#charge stations A'dam # charge stations 4 districts
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More than 85% of all charge sessions
took place in the 4 largest districts.
Source: Charge infrastructure forecast database
Car2Go is responsible for 66% of all charge sessions, with less than 15% of total amount of EV’s
charged in the region.
# charge sessions 4 largest districts vs. all districts vs. Car2Go
0
2000
4000
6000
8000
10000
12000
14000
16000
4 5 6 7 8 9 10 11 12 1 2 3
am
ou
nt
of
cha
rge
se
ssio
ns
Amsterdam
4 districts
#CAR2GO
sessions
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The amount of energy charged nearly
doubled to 11MWh/month.
Source: Charge infrastructure forecast database
Note: In total 894MWh was charged over the course of the focus year, representing ~4,9Mln
zero emission kilometers facilitated.
Amount of energy charged (All districts; April 2012-March 2013)
0
1
2
3
4
5
6
7
8
9
10
0
20000
40000
60000
80000
100000
120000
4 5 6 7 8 9 10 11 12 1 2 3
kW
h p
er
sess
ion
kW
h
kWh kWh per session
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A significant growth of capacity
utilization is visible (4 large districts)
0
10
20
30
40
50
60
70
80
Centrum Oost West Zuid
Pe
rce
nta
ge
4 5 6 7 8 9 10 11 12 1 2 3
Source: Charge infrastructure forecast database
Capacity utilization degree of charge stations (April 2012-March2013)
Average capacity utilization varies from 28% (East) to 51% (South); the latter implying that half
of the total time its charge points were occupied by (PH)EVs
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Main concerns is Charge Utilization: on
average less than 1 hour a day per
charge point.
0:00:00
1:12:00
2:24:00
3:36:00
4:48:00
6:00:00
7:12:00
8:24:00
9:36:00
4 5 6 7 8 9 10 11 12 1 2 3
mean connection time mean charging time
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Conclusion: The database enables
monitoring and optimizing the rollout
of charge infrastructure1. Steady growth in charge points, charge sessions and kWh’s.
2. Public infrastructure has enabled 4,9 mln zero emission km’s.
3. Capacity utilization is increasing; and supports policy makers in
extension strategies of infrastructure.
4. Charge utilization is main concern; requiring additional incentives.
5. Car2Go; plays a major role in optimizing the use of charge infra
(‘filling the gaps’).
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Future work will focus on forecasting
charge point efficiency
Goal: Analysis and model to enable effective roll out of charge infra:
• Comparing with other cities/regions
• Modeling of influencing factors (integrate databases, statistics).
• Applying mathematical models to forecast effectiveness of newly
installed charge points.
• Translating energy profiles to business models (e.g. load profiles)
• Validate and test particular (academic) metrics concerning charge
models and smart grids.
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Key characteristics Amsterdam Case:
Demand-driven, Subsidies & Car2Go
Demand driven placement of charge points
Car2Go sharing scheme
520 charge points
~1800 (PH)Evs - 282 Car2Go’s
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