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Deriving vehicle-to-grid business models from consumer preferences

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Combining electric cars with utility services seems to be a natural fit and holds the promise to tackle various mobility as well as electricity challenges at the same time. So far no viable business model for vehicle-to-grid technology has emerged, raising the question which characteristics a vehicle-to-grid business model should have. Drawing on an exploratory study amongst 189 Dutch consumers this study seeks to understand consumer preferences in vehicle-to-grid business models using conjoint analysis, factor analysis and cluster analysis. The results suggest that consumers prefer private ownership of an EV and a bidirectional charger instead of community ownership of bidirectional charger, they prefer utility companies instead of car companies as the aggregator and they require home and public charging. The most salient attributes in a V2G business model seem to be functional rather than financial or social. The customer segment with the highest willingness to adopt V2G prefers functional attributes. Based on the findings, the study proposes a business model that incorporates the derived preferences.
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EVS28 International Electric Vehicle Symposium and Exhibition
1
EVS28
KINTEX, Korea, May 3-6, 2015
Deriving vehicle-to-grid business models
from consumer preferences
Bohnsack, René12, Van den Hoed, Robert1 , Oude Reimer, Hugo2
1Amsterdam University of Applied Sciences, Weesperzijde 190, 1097 DZ Amsterdam, The Netherlands,
r.bohnsack@uva.nl (corresponding author)
2University of Amsterdam Business School
Abstract
Combining electric cars with utility services seems to be a natural fit and holds the promise to tackle
various mobility as well as electricity challenges at the same time. So far no viable business model for
vehicle-to-grid technology has emerged, raising the question which characteristics a vehicle-to-grid
business model should have. Drawing on an exploratory study amongst 189 Dutch consumers this study
seeks to understand consumer preferences in vehicle-to-grid business models using conjoint analysis, factor
analysis and cluster analysis. The results suggest that consumers prefer private ownership of an EV and a
bidirectional charger instead of community ownership of bidirectional charger, they prefer utility
companies instead of car companies as the aggregator and they require home and public charging. The
most salient attributes in a V2G business model seem to be functional rather than financial or social. The
customer segment with the highest willingness to adopt V2G prefers functional attributes. Based on the
findings, the study proposes a business model that incorporates the derived preferences.
Keywords: vehicle-to-grid, business model, consumer preferences
1 Introduction
Vehicle-to-grid (V2G) technology is emerging
as a sustainable technology which combines
energy with mobility. Combining electric cars
with utility services seems to be a natural fit
and holds the promise to tackle various
mobility as well as electricity challenges at the
same time. That is to say, batteries of electric
cars (EV) can act as capacitors in the grid and
provide regulation services, while using green
energy, such as solar power. In theory, when
households combine an electric car, solar
panels and a smart meter, they could be
autonomous from the grid, could become
electricity provider and could generate
revenues through smart charging and trading
of electricity. In practice, this scenario is
currently only adopted by a small group of
technology enthusiasts.
To that end, the question is how vehicle-to-grid
technology can be popularized to an audience
beyond technology enthusiasts. So far, V2G has
not been commercialized, raising the question
for actors in the newly emerging industry which
characteristics a V2G business model should
have.
Drawing on an online survey amongst 189
Dutch respondents, this study seeks to explore
consumer preferences in vehicle-to-grid business
models. To that end, the paper sets out to distil
the most salient attributes of V2G value
propositions, explore likely customer segments,
and, explore preferences for a V2G value
network. Before moving to the empirical results,
we describe the main tenets of V2G business
models and the methodology.
EVS28 International Electric Vehicle Symposium and Exhibition
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2 Vehicle-to-grid business
models
Research shows that cars are utilized for
transportation only 4% of the time. This makes
them available for secondary functions for the
remaining 96% of the time. According to
several authors, EVs can even be
complementary to the electric power grid [1]
[4]. When an EV is connected to a
bidirectional charger, it is possible to charge
and discharge electricity to the grid. Various
studies suggest large potential for V2G as a
means to regulate the grid, to provide ancillary
services or even as a backup generator in cases
of power failures [1], [5], [6] . However, apart
from a few pilot projects, no widely available
V2G service has emerged so far [7].
Studies on V2G have mainly focused on
technical aspects, such as what grid-services
V2G technology could provide [1], [2] and the
commercial potential it has [5]. Most studies
suggest only modest potential [8] and also
point to risks such as increased battery wear as
a result of V2G [9].
Nonetheless, Lassila et al. [6] suggest that
there is commercial value yet, it is not clear
how to capture it. There are different types of
V2G applications to create economic value for
consumers. The applications may roughly be
divided into three main categories: Vehicle-to-
Home (V2H), Vehicle-to-Building (V2B) and
Vehicle-to-Community (V2C). Kempton et al.
[7] suggest four different business models,
namely using EVs as an appliance, EV
charging as a service, EV batteries, and
charging as a package service and paying the
owner of the EV for grid services. However,
since the technology is still in its infancy, it is
unclear which business model consumers
would prefer.
For EV owners, V2G holds the promise that
households could be autonomous from the
grid, save electricity costs by charging when
the price is low and use electricity from the
battery when the price is high, and even
generate revenues by selling energy, for instance
to neighbours [4]. This study sets out to explore
consumer preferences and based on the
preferences, derives a possible V2G business
model. The business model is conceptualized on
three dimensions: the value proposition (product
preferences and customer segment), the value
network (who is creating the value) and the
revenue model (how is the value captured) [10]
(see Figure 1).
3 Method
The results of this paper are part of a larger
study on V2G business models. In order to
measure consumer preferences for V2G business
models, the extant consumer research literature
on EVs was scanned for attributes that have
been used to analyze EV consumer preferences.
These were complemented with factors that
deemed relevant regarding V2G technology.
Table 1 provides an overview of attributes and
illustrates the selected items for the survey.
Some attributes have not been considered in the
survey, namely fuel cost/efficiency because
these were already covered in operating costs,
policy incentives since these are not available at
Figure 1: Operational framework
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the moment, and last design, size, and motor
sound because they were not regarded as
important with respect to V2G technology.
Instead, we added some relevant V2G specific
attributes, namely V2G as a source of income,
confidence in technology and easiness to use.
Also, we added social aspects, namely general
trend, image, and freedom of mobility.
In order to explore consumer preferences, an
online survey was designed. Before
dissemination, a pilot among 20 participants
was conducted. In this pilot, issues regarding
the survey or unexpected biases were
corrected. The survey then was spread to a
Dutch population in Dutch language to prevent
bias. The sample was recruited by various
means, e.g. social network sites, Rotary clubs,
universities and work places. Out of 350
respondents, 189 fully answered the survey.
Table 2 summarises the sample characteristics.
Male participants outnumber female
participants, but given the sample size female
respondents are still sufficiently represented.
The minimum age of participants was set at 18
years. The first age group from 18 to 24 is
overrepresented. This can be explained with a
selection bias on online platforms towards
younger participants. The age groups until 64
are well represented. The group of 65 and older
is less represented which we also attribute to the
online platform selection bias. The sample is
considerably higher educated than the average in
the Netherlands which is also somewhat
reflected in the average income and the
possession of EVs.
Attributes/ studies
Ahn, Jeong, & Kim [11]
Caulfield, Farell, & McMahon [12]
Chorus, Koetse, & Hoen. [13]
Daziano & Chiew [14]
Eggers & Eggers [15]
Hackbarth & Madlener ( [16]
Hidrue, Parsons, Kempton, & Gardner [17]
Kudoh & Motose [18]
Lee, Wang, & Lee [19]
Miao, Xu, Zhang, & Jiang [20]
Potoglou & Kanaroglou [21]
Number of times mentioned
Initial purchase price
X
X
X
X
X
X
X
X
X
X
10
Sufficient range
X
X
X
X
X
X
X
X
8
Charging time (fast and slow)
X
X
X
X
X
X
X
7
Public charging network
X
X
X
X
X
X
6
Environmentally friendly
X
X
X
X
X
X
6
Reliable performance
X
X
X
X
X
X
6
Operating/maintenance cost
X
X
X
X
X
X
6
Safe usage
X
X
X
3
Maintenance network
X
1
Comfort
X
1
Fuel cost/efficiency
X
X
X
X
X
X
X
X
8
Policy incentives
X
X
X
X
X
X
X
7
Design/style
X
X
X
X
4
Size/internal space
X
X
X
X
4
Motor sound
X
1
Easy to use
General trend
Source of income
Freedom of mobility
Confidence in technology
Image
Table 1: Overview of performance attributes used in EV consumer research
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Table 2: Characteristics of the sample
Survey (%)
National (%)
Gender
Male
65.4
49.5
Female
37.6
50.5
Age
18-24
41.8
8.7
25-34
15.3
12.6
35-44
6.9
13.6
45-54
18.5
14.6
55-64
14.3
12.8
65 or older
3.2
16.7
Education
Secondary education
8.5
10.5
Interm. Voc. Edu.
5.3
29.8
Bachelors degree
55.0
18.5
Masters degree
25.9
9.8
Professional Degree
5.3
NA
Income
Less than 30.000
47.1
46.7
30.000 39.999
5.8
11.2
40.000 49.999
5.3
7.2
50.000 100.000
21.6
9.1
More than 100.000
20.1
1.5
Possess EV
Yes
3.7
0.3
No
96.3
99.7
N=189
National data from Statistics Netherlands [22][26]
4 Results
This study adopts a three-step-approach to
derive a business model from consumer
preferences. First, the preferred value network
and revenue model is examined by means of a
conjoint analysis. Next, the preferred value
proposition characteristics are explored using a
factor analysis. Last, the respective customer
segment is analyzed in a cluster analysis.
Value network and revenue model preferences
First, we conducted a conjoint analysis to
analyze consumer preferences with regard to
the value network. Conjoint analysis is a tool
to study multi attribute decision-making and
has been applied widely to measure consumer
preferences regarding attributes [27].
The analysis was conducted in two
stages. In the first ‘trade-off’ stage,
respondents were asked to select the most
(score 10) and the least preferred attribute
(score 0), and subsequently rank the remaining
attributes on a scale from 1 to 9 (see Table 6,
column LOP). This was done for three
dimensions, namely preferred ownership,
preferred charging spot and preferred
aggregator, and is reflected in the average level
of preference (LOP). In the second ‘ranking’
stage, the respondents were asked to rank the
importance of the dimensions overall by
allocating in total 100 points over the three
dimensions (see column UCS). The highest
number of points would reflect the most
important dimension and the lowest number of
points would reflect the least important
dimension and is calculated in the utility
constant sum. The result of multiplying LOP and
UCS is the weighted score of level of preference
(WLP).
The most important dimension to the sample
was the charging location (45.12), least
important was the type of aggregator (18.78).
The most important attribute for the sample was
to charge at home (4.27) and to have a public
charging network (2.84). This is also reflected in
the fact that the preferred ownership and revenue
model is to own an EV and discharge at home
(2.75). Least interesting for the sample were
community EVs (1.06). Although the dimension
preferred aggregator was least important, a
closer look reveals interesting insights.
Surprisingly, the energy supplier was ranked as
the most preferred aggregator (7.54) as
compared to the car manufacturer (5.48). This is
interesting for utility companies since this could
be a new source of revenues. Car manufacturers
were even lower ranked than the distribution
network operator (5.5). This is interesting
because the car is an important part of the V2G
business model.
EVS28 International Electric Vehicle Symposium and Exhibition
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Table 3: Results conjoint analysis
Features
Utility
constant
sum
(UCS)
Average
level of
preference
(LOP)
Weighted
level of
preference
(WLP)
LOP x
UCS
Preferred ownership
and revenue model
36.1
Private EV, revenues
through bidirectional
charger at home
7.37
2.75
Company EV, revenues
through bidirectional
charger at home & work
6.57
2.37
Private EV, revenues
through bidirectional
charger shared in a
community
2.88
1.06
EV car sharing, no
revenues, but reduced
costs
3.75
1.31
Preferred location to
charge
45.12
At home
9.23
4.27
At work
6.11
1.51
In the neighbourhood
3.93
1.83
On public places.
3.46
2.84
Preferred aggregator
18.78
Energy supplier
7.54
1.48
Distribution network
operator
5.5
1.1
Your mobile telecom
provider
3.65
0.66
Battery manufacturer
5.06
0.98
Car manufacturer
5.48
1.11
A company that is also
connected to my
employer
3.4
0.65
Value proposition preferences
Next, respondents were asked for their
preferences with regard to V2G, for instance “I
would use V2G if it is safe.” The full list of
items is displayed in the appendix. The
dimension that is most important to
respondents is sufficient range with a mean
score of 4.04 on a five-point scale. There were
only two attributes that received a mean score
below the ‘neutral’ point, namely ‘Charging
time’ and Image’. Table 4 summarizes the
means and standard deviations of the 16
attributes.
Table 4 also shows the results of a rotated
varimax factor analysis of the sample [28],
which identified three factors with an eigenvalue
greater than 1.00 explaining a total of 63.9% of
the sample. The factors were theoretically
labeled to qualitatively describe the attributes
that they include. The attributes that load on the
first factor have in common that they describe
largely functional aspects of a V2G business
model. Attributes that load on the second factor
suggest to be related to financial aspects and the
last factors to be related to social elements.
These characteristics were then applied as the
names of the factors, namely functional,
financial and social.
Table 4 also indicates that the items
comprising the functional attributes scored
highest, resulting in a factor mean of 3.93 and
standard deviation of 0.77. The second factor
combining four items representing the financial
attributes produced a mean of 3.49 and a
standard deviation of 0.88. This shows that the
view on the importance indicates a wider
dispersion on the desirability of these features.
The scale representing the social attributes
received the lowest score at 2.76 (SD .89).
However, the low ratings of ‘Image’ account for
most of this difference. The environmental
aspect on the contrary was highly appreciated
(3.62).
Customer segments
Last, the objective of this study was to explore
whether clear customer segments could be
identified. We therefore undertook a cluster
analysis [29], using the abovementioned three
factors. The inspection of dendrograms, based
on hierarchical cluster analysis suggested a three
cluster solution. Two step clustering was then
applied. Before the analysis, sixteen outliers
were excluded. These had either missing values
or were negative on all factor dimensions and in
a first analysis represented a cluster by itself
which could be labelled as Anti-V2G, however
this cluster was not regarded as relevant to
identify preferences. The exclusion of outliers
resulted in a sample size of 173.
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The cluster analysis resulted in three clusters,
the size of the smallest cluster was 44 (25.4%)
and the size of the largest cluster was
90(40.5%). The ratio of the size between the
largest and smallest cluster was 1.59. All
factors influenced the cluster formation
equally.
Figure 2: Clusters
Cluster 2 is most positive towards the adoption
of V2G with a median of 4.00 for Willingness to
use. This is concomitantly the smallest in size of
the three clusters, accounting for 25.4% of
respondents. Both, cluster 1 and 3, are more
neutral towards V2G with medians each of 3.01
for willingness to adopt. For cluster 1, the most
important factor is the financial aspect whereas
the social factor is not important. This cluster is
the largest, is male dominated, almost 50% of
the respondents have less than 30.000 Euro
income and they are least educated. Cluster 2 is
the smallest one with 44 respondents but has the
highest willingness to use. Their most important
factor are the functional aspects, the financial
aspect is of least importance. It has to be noted
that the explanatory value is limited, as apart
from age, none of the relationships were
significant; however, they provide a good
starting point for further validation.
Factors/Attributes
Mean
Std.
Deviation
Factor
1
Factor
2
Factor
3
Eigenvalue
6.3
2.1
1.8
Variance explained
39.4%
13.2%
11.3%
Functional
3.93
.770
Ease of use
3.80
.736
.791
.149
.220
Sufficient range
4.04
.849
.686
.142
-.075
Public charging network
3.99
.872
.728
.159
-.001
Confidence in technology
3.97
.775
.867
.118
.062
Comfortable ride and dis/charging
3.85
.716
.801
.337
.108
Safe usage
3.89
.723
.802
.263
.163
Reliable usage
3.95
.686
.826
.161
.066
Availability of charging points
3.83
.812
.788
.157
.114
Freedom of mobility
4.04
.804
.777
.014
.051
Financial
3.49
.880
Purchasing price
3.88
.723
.600
.419
.076
Source of income
3.88
.886
.261
.816
.031
Operating costs
3.72
.950
.233
.859
.027
Charging time
2.48
.943
.042
.382
.345
Social
2.76
.890
Image
2.09
.826
-.060
.076
.859
Environmentally friendly technology
3.62
.873
.464
.258
.428
Trend
2.56
.982
.165
-.030
.807
Table 4: Preferred characteristics of V2G business models and factors
Note: The 16 descriptions are presented in full in the Appendix. Factors loadings are based on varimax rotation.
Mean from 1 to 5, 5 being most important, 1 being least important.
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Table 5: Cluster characteristics
Cluster
1
Cluster
2
Cluster
3
Financial
Functional
Social
#
70
44
59
%
40%
25%
34%
Willingness V2G
Average
3.21
3.55
3.42
Median
3.01
4.00
3.01
Factors
Functional
0.05
0.91
-0.24
Financial
0.42
-0.86
0.27
Social
-0.67
-0.17
1.04
Demographics
Age group
until 24
42.9%
43.2%
35.6%
until 44
24.3%
18.2%
22.0%
over 45
32.9%
38.6%
42.4%
Gender
Male
65.7%
61.4%
55.9%
Female
34.3%
38.6%
44.1%
Income
Less than 30.000
48.6%
45.5%
40.7%
30.000 49.999
12.9%
9.1%
13.6%
More than 50.000
38.6%
45.5%
45.8%
Education
Vocational
training
47.1%
56.8%
49.2%
Higher education
52.9%
43.2%
50.8%
Deriving a business model from consumer
preferences
Building on the operational model in Figure 1
and the results of the three analyses, a business
model for this sample could be designed as
follows. First, the conjoint analysis revealed
that the sample would prefer owning a car and
to charge at home. Thus, revenues would be
generated by selling EVs, electricity and a
home (dis)charger. Also, the conjoint analysis
showed that customers would prefer the utility
company to be the aggregator, i.e. the
company that would
sell the product. Next, a factor analysis
revealed that functional aspects, such as range,
comfort, ease of use, are most salient in a
potential V2G business model. Financial and
social attributes are of less importance.
Consequently, functional attributes should be
emphasized. Last, a cluster analysis segmented
the sample into three customer segments with
different preferences. The cluster that was
most likely to adopt the V2G business model
was the male dominated functional cluster which
was most attracted to the functional aspects of
V2G. Table 6 summarizes the business model
characteristics.
Table 6: Derived V2G business model
Value proposition
Value network
Revenue model
& cost model
Based on factor
analysis
Based on conjoint
analysis
Based on conjoint
analysis
- Sample prefers
functional attributes,
e.g. ease of use,
range; financial and
social aspect less
important
- Functional customer
cluster in the sample
has the highest
willingness to adopt
- Utility company
is the preferred
provider/aggregator
- Provide public
charging network
- Sample prefers
to own an EV,
bidirectional
charger and
charge at home
- Revenues
through sales of
EVs, bidirectional
charger,
electricity, grid
regulation
5 Conclusion
This study set out to explore V2G business
models derived from consumer preferences.
Based on an exploratory study of a Dutch
sample in an online survey, the results suggest a
V2G business model with the following
characteristics: an emphasis on functional
attributes, targeted at the functional customer
cluster, provided by the utility company which
should also provide a public charging network,
used by private owners of EVs with
bidirectional chargers at home (see Table 6).
It is surprising that utility companies are the
preferred aggregator for V2G business models,
which points to new revenue sources for that
industry. Also, it seems that the potential
customer is not attracted by the revenue
potential but rather by functional aspects.
Due to the sample size the results of this study
need to be treated with caution. However, the
three-step-approach to derive a business model
from consumer preferences could be further
developed and potentially used in other
industries or studies.
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Appendix
V2G attributes and questions in Dutch in the
survey
Attribute
Question in Dutch
Initial purchase
price
Ik zou V2G gebruiken als de aankoopprijs
naar tevredenheid is.
Sufficient range
Ik zou V2G gebruiken als er geen
beperkingen zijn in de afstand die ik kan
rijden.
Charging time
(fast and slow)
Ik zou V2G gebruiken, ongeacht dat ik
rekening moet houden met het indelen
van mijn tijdschema.
Public charging
network
Ik zou V2G gebruiken als de
beschikbaarheid van publieke
(ont)laadplaatsen hoog is, omdat ik dan
op meer plekken kan (ont)laden.
Environmentally
friendly
Ik zou V2G gebruiken als het een
milieuvriendelijke innovatie is.
Reliable
performance
Ik zou V2G gebruiken als het betrouwbaar
is en fatsoenlijk werkt.
Operating/maint
enance cost
Ik zou V2G gebruiken als ik er 7.500 euro
mee bespaar in 5 jaar vergeleken met
auto's aangedreven door fossiele
brandstof.
Safe usage
Ik zou V2G gebruiken als het veilig is om
te gebruiken.
Maintenance
network
Ik zou V2G gebruiken als
(ont)laadplaatsen toegankelijk zijn als ik
ze nodig heb.
Comfort
Ik zou V2G gebruiken als het comfortabel
rijdt en (ont)laadt (ontladen is energie
terugleveren).
Easy to use
Ik zou V2G gebruiken als het makkelijk te
gebruiken is.
General trend
Ik zou V2G gebruiken als het trendy is.
Source of income
Ik zou V2G gebruiken als ik er per jaar
2.500 euro mee zou verdienen.
Freedom of
mobility
Ik zou V2G gebruiken als ik nog steeds
flexibel ben om te gaan en staan waar ik
wil.
Confidence in
technology
Ik zou V2G gebruiken als ik vertrouwen
heb dat het laden en ontladen werkt.
Image
Ik zou V2G gebruiken als mijn vrienden
denken dat ik hierdoor milieu bewust ben.
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Authors
René Bohnsack is assistant professor
at the University of Amsterdam
Business School, the Netherlands and
research fellow in the Urban
Technologies department at the
Amsterdam University of Applied
Sciences. In his PhD René explored
the role of car companies and
governments in the evolution of
low-emission vehicles. His areas of
research and expertise are in strategy
and sustainability. He has published in
Journal of Product Innovation
Management and Research Policy,
presented papers at several
international conferences, and
consults companies with regard to
business models for sustainable
technologies.
Robert van den Hoed is lector Energy
and Innovation at the University of
Applied Science Amsterdam, and as
such is one of the coordinators of the
CleanTech research program. After
his graduation at the faculty of
Industrial Design Engineering at the
Delft University of Technology he
carried out his PhD studying how
established industries react to radical
technologies, with a case on hydrogen
and fuel cells in the automotive
industry. After finishing his PhD he
worked at Ecofys for 7 years, a large
consultancy agency in the field of
sustainable energy.
Hugo Oude Reimer presently provides
strategic analysis for SIS International
research. Before joining the SIS team,
Hugo worked as investment
consultant for Velthuyse & Mulder
Asset Management. He advised
businesses and private individuals as
regards their investments on the
exchange market. Hugo graduated for
his MBA in Strategy on the
University of Amsterdam. As
graduation project he developed a
new vehicle-to-grid business model
from a consumer perspective. Hugo
also obtained a BA in
Entrepreneurship and Strategy &
Organization with a minor in Web
Science.
ResearchGate has not been able to resolve any citations for this publication.
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