MASTER THESIS EXPLORING BUSINESS MODELS FOR A VIRTUAL POWER PLANT OF INDIVIDUAL ELECTRIC VEHICLES
BRAM PALS
488166 | BUSINESS INFORMATION MANAGEMENT
Coach: Derck Koolen Co-reader: Philipp Cornelius Submission date: 15 June 2018
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Executive Summary The global energy landscape is in a transition towards more renewable energy resources. As a
result of this transition, the demand for electricity and the uncertainty and variability of its
supply pose serious challenges to grid operators. In order to cope with these challenges, the
large-scale penetration of renewable energy requires a power system that is flexible enough to
maintain a continuous balance between supply and demand. This study explores the feasibility
of a business model that offers flexibility through facilitating the participation of individual
electric vehicle (EV)-drivers in short-term electricity markets. In doing so, it combines two
areas of the Green IS domain – a subfield of information systems (IS) research that is focused
on increasing the efficiency of energy demand and supply systems. Firstly, by designing a
model that enables decision making for individual EVs, a contribution is made to the design of
smart electricity markets. Secondly, by exploring the opportunities for implementing this model
into a business model, a contribution is made to the area of sustainable mobility.
Through designing and evaluating the model, this study explores the economic feasibility of
using the storage capacity of individual EVs to act as distributed energy resources that offer
flexibility to the electricity grid – ultimately contributing to more efficient use of renewable
energy sources. The model uses an optimization algorithm to determine, for a predefined period
of time, the power profile strategy (based on charging or discharging) that optimizes the
financial benefit for an individual EV. It includes a best-case scenario - based on perfect price
prediction, and a realistic scenario - in which prices were predicted by a ten-day moving
average. The model is evaluated for two strategies. The first strategy is solely dependent on
price volatility. The second strategy includes cost savings yielded through charging at a cheaper
cost. This model was then evaluated based on data from the German Intraday Auction. The
results showed that the ‘volatility only’-strategy is only profitable under conditions of no
taxation and an optimistic prospect of depreciation costs. In this case it yields annual profits
ranging from €78.11 - €48.91 per EV. The charging-strategy yielded profits under every
scenario – except for longer periods of time combined with high taxes and high depreciation
costs. The maximum results achieved for the charging-strategy amount to annual profits ranging
from €4652.50 in a best-case scenario to €1845.65 in a realistic scenario.
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In regard to the exploration of business models, this study followed a qualitative approach based
on conducting interviews with experts in practices of vehicle-to-grid (V2G), peer-to-peer
(P2P)-carsharing and EV-lease. For the V2G-cases, the interviews in combination with findings
in literature have identified that integrating individual EVs into a system that is sufficiently
robust to guarantee the agreed upon charging- or discharging capacity is considered a primary
condition for implementation. Furthermore, charging technology must mature in order for
bidirectional chargers to become affordable and compatible with a large variety of EVs.
Exploration of the feasibility for implementing the model into a P2P-carsharing organization
indicate that the proposed interest was not supported. P2P-carsharing organizations are
primarily concerned with overall growth, while the scale of EVs is currently not sufficient to
justify a specific focus of investments. Furthermore, respondents from P2P-carsharing
organizations indicate to be restricted in their ability to exercise control – which hinders them
in building a system that is sufficiently robust. On the long term, there may even be a conflict
of interest between P2P-carsharing and EV-participation in electricity markets. Whereas market
participation is only possible if an EV is parked, P2P-carsharing organizations are focused on
maximizing the mobility use of a vehicle to reduce the overall number of cars.
In contrast, the exploration of the feasibility for implementing the model into an EV-lease
organization yields promising results. EV-lease organizations have access to a customer group
of which the majority is expected to find the possibility of participating in an electricity market
appealing. Moreover, these organizations indicate a serious interest in initiatives that allow their
customers to reduce mobility costs – in order to attract more customers. The contractual
agreement that customers engage in is expected to enable EV-lease organizations to exercise
sufficient control to build a robust system. Moreover, the monthly transaction in the form of a
lease-fee provides an opportunity for financial benefits to be delivered to the individual.
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Consequently, the facilitation of market participation for individual EV-drivers may well be
facilitated by EV-lease organizations. Nevertheless, feasibility for long-term implementation
of the model is uncertain, given the likelihood of grid congestion. In order for this feasibility to
be determined, the interests of grid operators must be incorporated into the model. Furthermore,
a limitation of this study is related to its dependency on the dataset used to evaluate the model,
which is strongly dependent on the characteristics of the market – including factors that are
dependent on geographical location and time (e.g. the alignment of renewable energy supply
and demand profiles). Additionally, the qualitative approach may limit the generalizability of
the results found for the V2G-, P2P-carsharing and EV-lease cases. Ultimately, the results are
highly dependent on the development and clarification of (tax) policy. Therefore, this study
also contributes to existing recommendations for V2G-favourable (tax) policy, which remains
an uncertain factor that has a strong impact on the feasibility of such business models.
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Preface Most prominently, I want to thank my coach – Derck Koolen – who guided me through the
process of writing this thesis and helped me to maintain my enthusiasm for the topic. Your
expert insights and opinions have been much appreciated and I would like to express my sincere
gratitude for taking the time and patience to engage in all the discussions we have had.
I would also like to thank my co-reader, Dr. Philipp Cornelius, for his critical notes on my
proposal and draft version.
Furthermore, I thank the respondents that participated in the interviews and many other people
who provided me with feedback and expert opinions.
Lastly, I want to thank the people close to me for their support and patience.
Having said that, I hope you will enjoy reading this thesis.
The copyright of the master thesis rests with the author. The author is responsible for its
contents. RSM is only responsible for the educational coaching and cannot be held liable for
the content.
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Table of Contents Executive Summary ...............................................................................................................1 Preface ...................................................................................................................................4 Table of Contents ...................................................................................................................5
1. Introduction ........................................................................................................................7 1.1 Research goals ............................................................................................................ 11
1.1.1 Individual Electric Vehicles ................................................................................. 12
1.1.2 Electricity Intraday Auction ................................................................................. 13 1.1.3 Virtual Power Plant .............................................................................................. 13 1.1.4 P2P-Carsharing .................................................................................................... 14
1.2 Structure ..................................................................................................................... 14 2. Theoretical Background .................................................................................................... 15
2.1 Green IS ..................................................................................................................... 15 2.2 Electricity Markets ...................................................................................................... 17 2.3 Carsharing .................................................................................................................. 26 2.4 EV-Lease .................................................................................................................... 30
2.5 Cross-case .................................................................................................................. 32 3. Methodology .................................................................................................................... 34
3.1 Data ............................................................................................................................ 34 3.2 Model Description ...................................................................................................... 34
3.2.1 Price Scenarios ..................................................................................................... 36 3.2.2 Session Time and Duration .................................................................................. 37 3.2.3 Battery Capacity and State of Charge ................................................................... 37 3.2.4 Charging- and Discharging Rate........................................................................... 38 3.2.5 Strategies ............................................................................................................. 38
3.3 Interview Study .......................................................................................................... 39 3.4 Model Performance..................................................................................................... 40 3.5 Interview Analysis ...................................................................................................... 42
4. Results .............................................................................................................................. 43 4.1 Data Description ......................................................................................................... 43 4.2 ‘Volatility only’-Strategy ............................................................................................ 44
4.2.1 Hour-sessions ....................................................................................................... 44 4.2.2 Work-sessions ...................................................................................................... 48 4.3.3 Night-sessions ...................................................................................................... 53
4.3 Charging Strategy ....................................................................................................... 58 4.3.1 Hour-sessions ....................................................................................................... 59 4.3.2 Work- and Night-sessions .................................................................................... 60
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4.4 Profitability................................................................................................................. 62 4.4.1 ‘Volatility only’-Strategy ..................................................................................... 62 4.4.2 Charging-Strategy ................................................................................................ 64 4.4.3 Summary ............................................................................................................. 66
4.5 Benchmark ................................................................................................................. 67 5. Discussion ........................................................................................................................ 69
5.1 Vehicle to Grid ........................................................................................................... 69
5.1.1 Conditions............................................................................................................ 70 5.1.2 Summary ............................................................................................................. 75
5.2 P2P-Carsharing ........................................................................................................... 75 5.2.1 Conditions............................................................................................................ 77
5.2.3 Constraints ........................................................................................................... 80 5.2.4 Summary ............................................................................................................. 84
5.3 EV-Lease .................................................................................................................... 85 5.3.1 Conditions............................................................................................................ 87 5.3.2 Summary ............................................................................................................. 91
5.4 Cross-case .................................................................................................................. 92
5.5 Prospect of Intraday Price Volatility ........................................................................... 95 6. Conclusion ....................................................................................................................... 97
6.1 Limitations & Future Research ................................................................................. 100 References .......................................................................................................................... 103
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1. Introduction In the process of limiting the concentration of greenhouse gases, the global energy landscape is
shifting towards more renewable energy resources. An accelerator of this shift is the European
Union policy that requires member states to have a 30% renewable energy content by 2030
(The European Parliament and The Council of the European Union, 2009). More recently, 172
parties agreed to keeping the
increase of the global temperature
to below 2 °C (United Nations,
2015; United Nations, 2016).
Parallel to these policies, figure 1
shows an increase in renewable
energy penetration and a
convergence towards wind and
solar energy sources (Bloomberg
New Energy Finance, 2017). The
resulting transformation of the
energy sector is commonly referred
to as the ‘energy transition’ (Leach, 1992).
While the impact of small scale renewable energy penetration on the grid is negligible, large
scale penetration poses significant challenges to grid operators due to increased variability and
uncertainty (Kassakian et al, 2011). The variation of intermittent renewable energy sources may
even become the single largest source of variability on the power system (Potter, Archambault,
& Westrick, 2009). The challenge of dealing with this variability and uncertainty lies in the
requirement of maintaining a continuous balance between supply and demand (i.e. load-
generation balance) to prevent grid blackouts. On the demand side, the electrification of energy
consumption is considered to contribute to the energy transition by replacing the use of fossil
fuels in buildings and transport with low carbon electricity (Department of Energy & Climate
Change, 2013; Taylor, 2010; Tran et al., 2012). In addition to the related overall increase in
demand for electricity, this implicates an increase in electricity demand peaks that may threaten
the stability of the electric grid (Fawcett, Layberry & Eyre, 2013). On the supply side, the
uncertain nature of renewable energy resources increases requirements for back-up power in
order to facilitate the balancing process (Kassakian et al., 2011; Morales et al., 2014).
Figure 1. Global New Investment in Clean Energy, by Sector (Bloomberg New Energy Finance, 2017)
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In contrast to fossil fuels, the supply of renewable energy resources is considered to be
inflexible. The sun is not always shining, the wind is not always blowing, and these conditions
are beyond the control of organizations.
The resulting fluctuations of supply and demand create volatility in the electricity market. The
low price of renewable energy resources – due to their low marginal cost – makes them the
preferred primary source of electricity for buyers (i.e. first in the merit order). Consequently,
the variability in their supply affects the aggregate supply curve of the electricity system:
shifting it leftward in the case of low production and rightward in the case of high production
(Morales et al., 2014). In order to compensate for demand that is inelastic to short-term
wholesale price signals (Ilic et al., 2007; Lijesen, 2007) and a fluctuating power supply, large
scale penetration of renewables requires a power system with a high degree of flexibility to
maintain a continuous load-generation balance (Denholm & Hand, 2011; Morales et al., 2014).
The study of information systems (IS) plays a crucial role in enabling the transformation
towards such sustainable processes (Melville, 2010). The application of information systems
thinking and skills to increase efficiency of energy demand and supply systems has even been
posed as a new subfield of IS, referred to as ‘Green IS’ (Watson et al., 2010). A particular
contribution can be made through designing and evaluating artifacts – e.g. computational tools
– to better understand the complex environment of electricity markets and facilitate better
decision making (Bichler, Gupta & Ketter, 2010; Hevner et al., 2010). IS-scholars have already
made several contributions in this regard.
Methods of changing demand patterns, like demand side management (DSM), will become
increasingly valuable (Denholm & Hand, 2011). DSM includes everything that is done on the
demand side of the electricity system, ranging from increasing overall efficiency of demand to
installing a sophisticated dynamic load management system (Palensky et al., 2011). It includes
a set of strategies commonly referred to as Demand Response (DR), which aim to increase
participation of end-consumers in setting prices and clearing the market (International Energy
Agency, 2003). By stimulating consumers to reduce demand or shift the time of day at which
they demand power from the grid, DR-programs increase demand flexibility thereby improving
system reliability and reducing price volatility in electricity markets (Albadi & Saadany, 2008).
An example of this may be to decrease the temperature of an electrical heating system at a
certain time to reduce peak loads.
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Furthermore, storage capacity allows for shifting demand from periods with low production to
periods in which production is high (Morales et al., 2014). Although different energy storage
technologies do exist (Denholm et al., 2010), additional progress is required to enable electricity
storage that provides sufficient flexibility for large scale penetration of renewables (Pickard,
Shen & Hansing, 2009). Especially battery storage is valuable because it has no ramp-up costs
and time and can therefore respond quickly (Bessa & Matos, 2012). Whereas bulk energy
storage poses significant challenges in regard to economic feasibility (Pickard, Shen &
Hansing, 2009), the use of software agents that intelligently manage micro-storage capacity
across the grid provide a promising opportunity (Vytelingum et al., 2011). The use of dual
purpose batteries is a particularly interesting line of research, as single purpose battery energy
storage facilities have only proven to be economically feasible for niche applications
(Kiviluoma & Meibom, 2010; Walawalkar, Apt & Mancini, 2006). This provides an interesting
opportunity for electric vehicles (EVs) to offer storage capacity to the grid, as their batteries are
primarily used and charged for driving while standing idle for the remaining time.
While charging many EVs near the same location at the same time will quickly overload the
grid (Kim et al., 2012; Sioshansi, 2012), smart charging – at times where demand (i.e. prices)
is low – has already yielded significant reductions in demand peaks (Valogianni et al., 2014).
Additional flexibility may be provided through the vehicle-to-grid (V2G) concept, which is an
extension of smart charging. The V2G-concept is that EVs, while parked, provide power
capacity to the grid (Kempton & Tomic, 2005a). Kahlen et al. (2017) demonstrate how smart
charging and V2G enable EVs to operate as a virtual power plant and generate profits by
participating in secondary reserve markets. By operating as an intelligent agent, on behalf of
the EV- fleet owner, the best storage profile based on market prices can be determined and
followed (Vytelingum et al., 2011). The continued increase in the number of EVs (CBS, 2017b)
incentivizes the design of additional, innovative business models that include an additional part
EV-storage into smart market design.
In recognition to the statement that the potential of electric mobility deserves serious attention
from IS-scholars (Kossahl et al., 2012), this study takes the perspective of an individual EV. To
incentivize individual contributions to a sustainable environment individual goals must be
related to sustainability goals (Loock et al., 2013). This alignment is established by considering
that individual EV-drivers are motivated to participate in an electricity market by generating
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financial benefit during time at which the EV would otherwise stand idle. It is assumed that
individual EVs can ultimately be aggregated into a VPP in order to facilitate sufficient
scalability to meet market requirements (Pudjianto et al., 2007), which provides an opportunity
to develop this model into a business model. By exploring such innovative business models,
this study is able to make a contribution to a second area of the Green IS-discipline: sustainable
mobility (Hildebrandt et al., 2012).
The individual motivation of yielding financial benefit particularly corresponds with
motivations to engage in peer-to-peer (P2P) carsharing (Hamari, Sjoklint & Ukkonen, 2016;
Shaheen & Cohen, 2007). Moreover, the contribution to enabling increased penetration of
renewable energy closely corresponds to the concerns about climate change that are a major
driver behind carsharing business models (Albinsson & Yasanthi Perrera, 2012; Belk, 2010;
Botsman & Rogers, 2010). Hence, P2P-carsharing organizations are considered a potentially
interesting opportunity for facilitating the participation of individual EV-drivers in electricity
markets.
In addition, lease companies may benefit from facilitating the participation of individual EV-
drivers in electricity markets. Although lease customers acknowledge the sustainability value
of an EV, this value is often outweighed by cost (Egbue & Long, 2012). The premium costs
associated with EVs are even stated as a dominant limitation to consumer adoption (Carley et
al., 2013; Roy, Caird & Potter, 2005). Enabling customers to reduce lease costs by participating
in an electricity market may therefore contribute to attracting additional clientele for EV-lease
organizations. Hence, EV-lease organizations are considered a potentially interesting
opportunity for facilitating the participation of individual EV-drivers in electricity markets.
In terms of resources, P2P-carsharing organizations and EV-lease organizations both have an
existing client base and an operational IT-infrastructure. This further incentivizes the
exploration of opportunities to combine the participation of EVs in short term electricity
markets with these existing business models.
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This study will combine the areas of smart market design and sustainability by designing and
evaluating a model of an individual EV that has participated in the German Intraday Auction
during 2017. Potential income will be calculated under different conditions to determine the
profitability of participating in this market. Subsequently, the opportunities for developing a
business model will be assessed by conducting face-to-face interviews at V2G experts, P2P-
carsharing organizations and EV-lease organizations.
1.1 Research goals The aforementioned motivation results into the following research question:
What are profitable business models for individual EVs to benefit from participation in short-
term electricity markets?
This research question can be subdivided into the following sub-questions:
• How profitable can individual EV participation be in an electricity intraday auction?
• What are the requirements of stakeholders?
• How do these requirements interact?
• How can participation in intraday electricity auctions be combined with P2P carsharing?
• How can participation in intraday electricity auctions be combined with EV-lease?
It is found that profitable participation is possible, particularly through saving costs by charging
against lower costs. Given the need of grid- and transmission operators to control energy
resources for load-generation balance to be maintained, EV-lease organizations provide the
most promising opportunity for implementing the model that yields these results.
The section below will define the elements of the research question in more detail.
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1.1.1 Individual Electric Vehicles
Plug-in hybrid (PHEV) penetration is significantly higher than pure, battery EVs
(Rijksoverheid voor Ondernemen Nederland, 2018). Given the broader target market, both
PHEVs and ‘pure’ battery EVs will be targeted. PHEVs and EVs have been used
interchangeably in the literature that is referred to. Unless mentioned otherwise this does not
alter their implications relevant for this study. For clarity purposes this study will use the term
EVs indistinctly of PHEVs.
This study is explicitly different from Kahlen et al. (2015) as the interests in this study is
decentralized (i.e. spread across individuals), whereas in Kahlen et al. (2015) decisions can be
made on a fleet-level.
Only 6% of EVs can be directly related to private ownership (Rijksoverheid voor Ondernemen
Nederland, 2015a). Similarly, approximately 33% of Dutch vehicle registrations in the 2017
regarded private ownership whereas including registrations that regarded lease-contracts
amounts up to 71% (Bovag, 2018). Therefore, to broaden the target market, this study includes
EVs under a lease contract in the definition of individual EVs. As long as the decision making
an EV available for other purposes than driving is lies with the driver and is decentralized, no
distinction is made between full ownership or lease contracts.
Lease organizations lower up-front costs, by enabling drivers to incur monthly costs rather than
an up-front bulk-sum. These costs generally cover the use of the vehicle (excluding fuel),
maintenance and insurance. However, the vehicle remains in the lease organization’s ownership
rather than the individual’s. The ability to spread out the costs of ‘ownership’ is considered
even if this results in higher net prices (Dasgupta, Siddhardt & Silvo-Rossa, 2008). EV-lease
organizations refer to organizations that engage in lease practices of, specifically, EVs.
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1.1.2 Electricity Intraday Auction
Increased uncertainty and resulting volatility caused by the penetration of renewable energy
call for the trade of more flexible, shorter contracts that are closer to the time of physical
delivery (Weber, 2010). To prevent this call to solely fall upon balancing reserve markets
(Morales et al., 2014), sub-hourly products, of 15 minutes, can be traded in an intraday auction.
These products can then be used to correct sub-hourly forecasting errors. As the German
electricity market is one of the few that has been operational for sufficient time to acquire data
from, this will be the focus of this study. The intraday auction was introduced in December
2014, by the European Power Exchange (EPEX). The auction facilitates the trade in 15-minute
timeslots, at 15.00h each day before delivery. For each timeslot, pricing is set according to the
equilibrium price at which supply meets demand.
Competitive participation in electricity markets goes beyond the ability of a single EV (Kamboj
et al., 2010). Whereas limited services can be provided by a single EV, a significant impact on
the electrical network can only be achieved by a large fleet (Bessa & Matos, 2012). Although
this study takes the perspective of an individual EV, aggregation is crucial to reach a critical
mass that meets the requirements (e.g. minimum trade volumes) for participation in short term
electricity markets. Therefore, the model and the results of this study are assumed to be scalable
to multiple privately owned EVs through the concept of a virtual power plant.
1.1.3 Virtual Power Plant
Pudjianto et al. (2007) state that a virtual power plant (VPP) enables commercial activities (i.e.
market participation) and technical capabilities (i.e. system management and support). By
turning an EV into a virtual power plant, it can technically contribute to grid stability and
commercially generate income for the EV-owner:
“A Virtual Power Plant refers to a flexible representation of a portfolio of distributed energy
resources [that] aggregates the capacity of many diverse distributed energy resources [and]
creates a single operating profile […]” (Pudjianto, Ramsay & Strbac 2007)
Since well managed distributed storage capacity can be regarded as a valuable resource to the
grid (Vytelingum et al., 2011), an EV-battery may be regarded as a DER. Given the current
focus, this study will be referring to EVs specifically and not to DER in general.
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By joining a VPP, EV drivers can delegate the trading responsibility and corresponding
decision-making to a single trader representing the VPP on the market (Andersen et al., 2008).
This study follows a definition that includes many of the common elements in literature,
including the aggregation of distributed energy resources (DER) and market participation. In
addition, it emphasizes the role of a VPP fulfilling the flexibility requirements of the market
and of efficiency, which corresponds to this studies’ focus on profitability.
1.1.4 P2P-Carsharing
Peer-to-Peer (P2P) carsharing allows individual car owners to convert their personal vehicles
into rental cars (Hampshire & Gaites, 2011). Vehicle owners create revenue from transactions
with renters, which are often facilitated by a third-party that connects users and provides
insurance (Ballús-Armet et al., 2014). The P2P model relies on intermediation using
information technology to connect multiple owners (i.e. individuals) of vehicles with potential
drivers (Cohen & Kietzmann, 2014). It is considered to provide an interesting business
opportunity given that it is also focused on generating income from idle vehicles. Moreover, it
is also driven by environmental sustainability factors (Albinsson et al. 2012; Belk, 2010;
Botsman & Rogers, 2011), which corresponds to this study’s relation to the energy transition.
1.2 Structure The rest of this report is structured as follows. Chapter 2 will elaborate on the theoretical
background, based on literature and existing theories a hypothesis and three propositions will
be formulated. The hypothesis will focus on the potential to participate in a short-term
electricity market, whereas the propositions will explore the opportunities to implement the
model into existing business models. Chapter 3 will present the methodology that is followed
in order to test the hypotheses and explore the propositions. It includes the data used, describes
the model and explains how the analysis will be performed. The results of the analysis will be
presented in chapter 4, the chapter tests the hypothesis and concludes with a benchmark of the
results. Chapter 5 will present the qualitative element of this study. The chapter will link the
results of interviews, that have been conducted with several expert practitioners, with findings
in literature to assess the propositions. Chapter 6 will conclude this study with an answer to the
research question and a discussion of this study’s limitations and recommendations for future
research.
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2. Theoretical Background This study draws upon interdisciplinary research of sequential electricity trading, vehicle-to
grid and two business models that allow for interaction with individual EV-drivers: P2P-
carsharing and EV-lease. Based on literature, two hypotheses will be presented in regard to a
model for participation in short-term electricity markets. In addition, three propositions will be
presented in regard to the implementation of this model into existing business models.
2.1 Green IS Digital connectivity is everywhere and adapting to it has become an essential capability to
competitiveness in most sectors of our economy (Iansiti & Lakhani, 2014). Recognition of the
omnipresence of digital connectivity has incentivized many IS scholars to study its relation to
areas like organizational performance (Brynjolfsson & Hitt, 2000; Chen et al., 2014;
Westerman et al., 2014), consumer influence (Goh & Bockstedt, 2013; Li et al., 2014) or the
potential benefits for specific sectors – e.g. health care (Agarwal et al., 2010). Furthermore, the
transformation to a world in which everything is connected enables markets to work towards a
scenario of perfect information by reducing information asymmetries between sellers and
buyers – making them more efficient (Granados & Gupta, 2013).
The application of information systems thinking and skills to increase efficiency of energy
demand and supply systems has been posed as a new subfield of IS, referred to as ‘Green IS’
(Watson et al., 2010). Similarly, Melville (2010) stresses that IS-scholars have an important
contribution to make in the development of innovative environmental strategies and the creation
of systems that benefit environmental sustainability. Loock et al. (2013) make an important
contribution to this field by highlighting the importance of motivating sustainable behavior by
aligning individual goals with sustainability – e.g. through financial incentives. Similarly,
Malhotra et al. (2013) point out the importance of integrating financial and environmental goals
for the benefit of fostering sustainability (Malhotra et al., 2013).
This makes the design of smart markets a crucial component of Green IS. Smart market design
is focused on using theoretically supported computational tools to better understand complex
trading environments and facilitate better decision making in these environments (Bichler,
Gupta & Ketter, 2010).
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Whereas increased computational power enables the optimization of decision making
algorithms, markets have evolved accordingly – leading to increasingly complex environments.
Hevner et al. (2010) pose that it is up to IS-scholars to develop innovative artifacts that are
capable to utilize this increasingly complex information and enable human capabilities to be
leveraged in order to achieve the desired outcomes. Artifacts are “human machine systems
whose purpose is to support operations, management, analysis and decision-making functions
in an organization” (Davis & Olson, 1985; Hevner et al., 2010). This definition implicates a
construct, a model or a method that is represented in a way that enables evaluation and
comparison. The opportunities for developing such artifacts in regard to smart markets
particularly arise in dynamic markets that are characterized by significant uncertainty in regard
to supply and demand – like electricity markets.
Whereas this topic is already receiving attention from IS-researchers, Kossahl et al. (2012)
highlight that specifically the potential of electric mobility is yet to be addressed by IS-scholars.
In recognition to this part of the IS research agenda, Valogianni et al. (2014) successfully
developed an algorithm that learns individual household consumption behavior to schedule EV-
charging accordingly so that fewer resources are required to cover demand peaks. Fazelpour et
al. (2014) developed a set of algorithms for the optimization of a grid-friendly smart parking
lot. And Kahlen et al. (2017) show how distributed storage capacity – in the form of a fleet of
electric vehicles (EVs) – can be used to balance the electricity grid while generating a profit for
the fleet owner. Similarly, this study contributes to the research area of Green IS by designing
a model that enables decision making for the benefit of optimizing the utilization of individual
EVs as an energy resource in an electricity market.
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2.2 Electricity Markets Electricity is a commodity that is constrained by physical and technical laws for its production
and transmission (Stoft, 2002). In addition to the requirement of maintaining a continuous load-
generation balance, large scale electricity storage is in most cases economically unjustifiable
(Pickard et al., 2009). Furthermore, market architecture should be specifically designed to meet
these requirements and simultaneously allow for competition among market participants
(Wilson, 2002). It is therefore that electricity markets are normally forward markets, in which
electricity is traded ahead of its delivery time (Hiroux & Saguan, 2010).
Electricity trading is conducted at various points in time in multiple sequential markets. The
timeline of these markets is illustrated in figure 2. Sequential markets enable the efficient
allocation of resources for the production of electricity when facing market uncertainty. In order
to be efficient, a market should send out correct price signals to market participants in order to
incentivize the desired behavior based on different technologies used and their flexibility
characteristics (Malkiel, 2003).
Electricity trading starts on the futures market, in which products ranging from yearly to weekly
intervals are traded. Products traded in future markets are subcategorized into base (00.00h –
24.00h), peak (08.00h – 20.00h) and off-peak (20.00h – 08.00h) power delivery. Market
participants mainly operate in the futures market to use its low volatility for risk hedging
purposes (Knaut & Paschmann, 2017a).
Figure 2. Sequence of trading in electricity wholesale markets (adapted from Knaut & Paschmann (2017a)).
Balancing Markets
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On a shorter term, the day-ahead auction facilitates the trade in 1-hour products. It is held at
noon on the day before physical delivery (Knaut & Paschmann, 2017a). The day-ahead market
uses a uniform pricing mechanism, as illustrated in figure 3. As the lowest bids will be accepted
at the price of the highest accepted bid, a uniform pricing mechanism incentivizes participants
to pay at marginal cost.
In order to participate in the day-ahead market, suppliers must set a price that is based on the
mean supply for that hour (Knaut & Paschmann, 2016). Historically, prices in the day-ahead
market were the most important reference price for all kinds of participants in electricity
markets (Knaut & Paschmann, 2017b). However, the increased penetration of renewable energy
drives supply and demand to increasingly deviate
from the generation profiles that led to determining
these hourly prices. This is because renewable
energy resources, like solar and wind energy, are
subject to a more volatile load profile than
traditional power plants. Figure 4 illustrates how
sub-hourly generation profiles deviate from the
hourly mean. Whereas the first two quarters in
figure 4 would require additional supply for the
hourly generation profile to be met, the last two
quarters are characterized by an excess of supply.
Figure 3 Example of the bidding process in uniform pricing auctions.
Figure 4. Example of a generation profile of a solar power plant portfolio (Knaut & Paschmann, 2016)
19
These deviations can be responded to by adjusting supply, adjusting demand or using storage
capacity (Borggrefe & Neuhoff, 2011). Demand in electricity markets is regarded to be rather
price inelastic, especially as real time approaches (Lijesen, 2007). Alternatively, the resulting
increase in uncertainty requires the trade of more flexible, shorter contracts that are closer to
the time of physical delivery (Weber, 2010).
Products that provide sub-hourly flexibility are traded in the intraday-auction, which is held at
15.00h on the day before physical delivery and operates under a uniform pricing mechanism
that is similar to the day-ahead market (EPEX Spot, 2018a). As illustrated in figure 5, the
formation of prices in the intraday auction can be explained as a deviation from the settlement
prices for the corresponding hourly products in the day-ahead market (the blue line). Price
setting in the intraday auction (the green line) is characterized by an inclined gradient when
compared to the corresponding day-ahead prices (Knaut & Paschmann, 2016).
Figure 5. Example of the pattern of intraday prices as a consequence of residual demand (Knaut & Paschmann, 2016)
20
The authors explain this through a concept of residual demand (the red line): the difference
between demand and the supply of inflexible renewable energy resources. As residual demand
increases, the gradient of the price curve will tilt upwards relative to the day-ahead price (figure
5). If residual demand decreases, the gradient of the price curve will tilt downwards relative to
the day-ahead price. Since a different day-ahead price is set every hour, this pattern repeats
itself. If residual demand is low, the majority of the demand at that moment can be supplied
through renewable energy resources. Consequently, the need for balancing sub-hourly
deviations is not required – leading to low price variation for that hour.
Forecast uncertainty decreases significantly as the timeframe to delivery decreases (Focken et
al., 2002; Von Roon & Wagner, 2009). This results in deviations between intraday prices and
the day-ahead price for a specific hour and requires products to be traded closer to real time in
order for forecast errors to be balanced. (Kiesel & Paraschiv, 2017). Therefore, the continuous
intraday market enables the trade of hourly and quarterly products until 30 minutes before
delivery. In contrast to the uniform price auction mechanisms in the day-ahead and intraday
auction, the continuous intraday market is characterized by a pay-as-bid pricing mechanism.
This requires market participants to continuously anticipate the clearing price and determine
their bids according to a prediction.
Regardless, the production of renewable energy resources – like wind and solar – can only be
predicted to a certain level of uncertainty (Inman, Pedro & Coimbra, 2013). The imbalances
that remain until the physical delivery are to be resolved by transmission system operators
(TSO’s), who purchase balancing capacity from the balancing reserves market. The market for
balancing reserves is divided into primary, secondary and tertiary operating reserves. Due to its
near real-time operation, these markets are primarily distinctive in their required response times
and the duration of their products – ranging from 30 seconds to 15 minutes for primary reserve
markets and from 15 minutes to one hour for tertiary reserve markets (Borggrefe & Neuhoff,
2011; ENTSO-E, 2015).
21
Markets with gate-closure times that approach real-time enable participants to respond to prices
that include better information in regard to the production of renewable energy (Barth et al.,
2008). Since gate- closure times of balancing markets are closest to real-time, they provide the
best information in regard to the production of renewable energy. As a result, increased
renewable penetration strongly increases the demand for flexibility in balancing markets
(Morales et al., 2014). The options for supplying this short-term flexibility are limited –
especially when it comes to providing sub-hourly flexibility (Knaut & Paschmann, 2016).
One source of that is considered particularly suitable for meeting the increased demand for
flexibility in short-term and real time markets is the storage capacity in EVs. On one hand, a
concept referred to as ‘vehicle-to-grid’ (V2G) is claimed to be the option that offers the most
storage potential in Europe, compared to alternative options like standalone batteries (Després
et al., 2017). On the other, the low ramp-up costs and -time of EV-storage make them
technically suitable to provide flexibility short-term and real-time markets (Bessa & Matos,
2012).
The effect of massive integration of EVs into the electricity grid may go two ways. Even at low
penetration, uncontrolled charging of EVs can threaten the stability of the electricity grid by
dramatically increasing peak loads (Gerbracht, Möst & Fichtner, 2010). In contrast, cost
reductions can be accomplished through smart charging. This means to charge EVs at times
when demand for electricity is low (Mal et al., 2013). The incentive for EV-owners to use smart
charging is based on charging their battery at lower costs. From the utility perspective, smart
charging has already demonstrated its potential to accomplish significant peak reductions
(Valogianni et al., 2014). The V2G-concept extends the notion of smart charging. It shifts the
perspective from merely facilitating additional loads to providing power to the grid (Ford,
1994). Such participation of EVs in electricity markets can yield substantial benefits by
enabling higher utilization rate of utility investments, preventing additional investments and
other benefits that may accrue to society (Sioshansi et al., 2009). V2G assumes the possibility
of a bi-directional flow of electricity: instead of solely charging from the grid an EV would also
be able to discharge into the grid (Kempton & Tomic, 2005a).
22
Whereas limited services can be provided by a single EV, a significant impact on the electrical
network can only be achieved by a large fleet (Bessa & Matos, 2012). Moreover, competitive
participation in electricity markets goes beyond the ability of a single EV (Kamboj et al., 2010).
Kempton et al. (2001) developed an intelligent agent that serves as a middleman between EV-
owners and the market. Brooks & Gage (2001) propose a model in which EV-owners
communicate their mobility needs in order for the ‘virtual power plant’ (VPP) to make
predictions on capacity. Similarly, Pudjianto et al. (2007) state that a VPP enables commercial
activities (i.e. market participation) and technical capabilities (i.e. system management and
support). By turning an EV into a VPP, they can be aggregated in order to meet technical
reliability and availability requirements while commercially generating income for the EV-
owner (Quinn, Zimmerle & Bradley, 2010). More specifically, Kahlen et al. (2015) simulated
profits ranging from 7% to 12% that are to be made by EV-fleet owners by turning EVs into
VPP’s that participate in the secondary operating reserve market.
Although V2G is possible from a technical perspective, as the standard of the International
Electrotechnical Commission IEC 62196 supports V2G-discharging, it remains a concept that
is in the stage of small scale pilot projects. In Japan, a project that was aimed at testing how six
Nissan Leafs can reduce summer peak loads for an office building resulted in annual savings
of ¥500.000 (Nissan, 2013). The first commercial V2G-project that incentivizes individuals to
engage in V2G was launched in Denmark. The project allowed ten EVs to provide balancing
services to the Danish grid (Nuvve, 2016). In the same year, a similar trial has been set up on a
larger scale in the UK. In this project, Nissan and Enel allow 100 EVs to operate as V2G-units
and sell electricity back to the grid (Enel, 2016).
These projects illustrate how additional research is required in order to enable large scale
adoption of V2G-practices. The study at hand contributes to this line of research by addressing
the commercial aspects of such large-scale adoption. Namely the development of a business
model that facilitates V2G-practices for individual EV drivers, while creating an incentive for
these drivers to participate. Regardless of this study’s perspective of an individual EV, the
concept of a VPP serves the purpose of operating as an intelligent agent. It contributes to the
assumption that the model is sufficiently scalable to reach the critical mass required by
electricity markets.
23
As a source of income for individuals and the facilitating organization, the model in this study
will benefit from price volatility in short-term electricity markets that is caused by the current
scarcity of flexibility (Knaut & Paschmann, 2016). By buying electricity at low prices, storing
it and then selling it during price peaks, profits can be yielded. Evidently, markets must me
sufficiently liquid for these transactions to occur. Liquid markets contain a sufficient number
of bids and trading activity to create transparent prices and ensure that individual actors only
have small impacts on the price formation (Borggrefe & Neuhoff, 2011). All else equal, the
higher the market liquidity, the higher the possibility and the lower the transactions costs to
find a trading partner (Neuhoff et al., 2016).
It is important to note that the primary purpose of an EV is related to mobility, which is a
sociotechnical system (Geels, 2004). EV-owners must therefore be able to value their mobility
needs against the potential benefit of offering flexibility in an electricity market. The benefits
for an EV-driver must outweigh the costs or effort that it takes. In addition, the EV-driver must
be sufficiently informed to be able to make this tradeoff.
Despite of their high liquidity (Hiroux & Saguan, 2010) and volatility (Huisman, Huurman &
Mahieu, 2007), EV-participation in day-ahead markets has demonstrated that day-ahead
markets are insufficiently volatile for participation to be economically feasible (Capion, 2009;
Peterson, Whitacre & Apt, 2010b; Wang et al. 2010). Moreover, the day-ahead market is not
restricted to participants that can offer sub-hourly flexibility. Consequently, EVs may not be
able to compete with conventional power plants that are able to supply the same products at a
lower cost. This restricts EVs in fully exploiting from their unique capabilities to provide short-
term, sub-hourly flexibility.
In contrast, EVs are considered to be particularly attractive participants in balancing markets.
This is mainly related to their fast response time, distributed location, their ability to reduce
wear and tear on generators and their possibility of automatic regulation response (Hawkins,
2001). Multiple studies demonstrated how these characteristics enable EV-participation in
balancing markets profitably capture the benefits of high price volatility (Andersson et al.,
2010; Kahlen et al., 2015; Schuller & Rieger, 2013). However, Brandt et al. (2017) concluded
that the viability of EV-fleets to contribute to grid stability in these markets may be
overestimated or economically unfeasible in practice – given the current market design and
investment requirements.
24
In addition to strict technical requirements (ENTSO-E, 2015), participation in balancing reserve
markets is highly regulated through legal requirements (Consentec, 2014). These requirements
may prove to be a barrier to entry – for smaller parties in particular (Pavić et al., 2017). Lastly,
Kempton & Tomic (2005ab) argue that scalability of EV-participation in balancing markets is
limited by the fact that only a small percentage of EVs would saturate these markets (Kempton
& Tomic, 2005ab; Letendre & Kempton, 2002). It is therefore concluded that, although
balancing reserve markets are highly attractive based on their liquidity and volatility, its current
design makes the large-scale participation of individual EVs infeasible.
Participation in the intraday auction is considered to be more feasible. Unlike the pay-as-bid
mechanism in continuous intraday trading, continuous monitoring and negotiating prices is not
necessary to engage in the uniform pricing mechanism of the intraday auction. Hence, the
intraday auction is subject to lower entry barriers for new, small market players that are not
equipped for continuous intraday trading (Neuhoff et al., 2016; Porter, 2008). It is assumed that,
at the auction time of 15.00h the day before delivery, EV-owners have a reasonably accurate
idea of their schedule and corresponding mobility needs for the next day – enabling them to
make the required sociotechnical trade-off (Geels, 2004). Therefore, EV-owners are assumed
to be capable and willing to indicate their availability for the next day. In addition, the technical
restrictions of the intraday auction allow EVs to exploit their capabilities – whereas many
conventional power plants are not flexible enough to provide 15-minute products. As a result
of restricted participation, market prices in the intraday auction are highly volatile (Knaut &
Paschmann, 2017a). The introduction of the intraday auction, in 2014, has significantly
increased liquidity of both the intraday auction and the continuous intraday market. However,
the trade volumes for quarter-hour products in the intraday auction have largely cannibalized
the continuous intraday market’s total trade volumes in these products (Neuhoff et al., 2016).
Moreover, price volatility is higher for the intraday auction than for the continuous intraday
market (Kuo & Li, 2011; Neuhoff et al., 2016).
There is no extensive body of literature in regard to business models for EV-participation in
intraday markets. Given its attractiveness in terms of volatility and liquidity, this is presumably
due to its recent introduction. Using price volatility in the intraday auction is already recognized
to potentially increase profits for hydro-storage plants that use the intraday auction as an
additional market for short-term position management (Braun, 2016). Similar to this study, EVs
can potentially benefit from short-term price volatility.
25
To the best of my knowledge, individual EV participation in intraday markets has never been
studied before. The combination of high liquidity and high price volatility are argued to enable
EVs to yield profits by charging when prices are low and discharging when prices are high. The
unique capabilities of EV-batteries enable them to benefit from short-term flexibility by
operating within the technical constraints that restrict conventional power plants from
participating, consequently increasing potential profitability for market participants (Porter,
2008). It is therefore hypothesized that:
H01: Individual EVs can yield profits by offering flexibility in the intraday auction.
26
2.3 Carsharing In addition to a contribution to the Green IS domain through smart market design (Bichler et
al., 2010), this study recognizes the claim of Hildebrandt et al. (2012) that IS-researchers should
focus on increasing the attractiveness of green practices by developing innovative business
models that allow for large scale adoption by customers. In this regard, Zolnowski et al. (2011)
describe how IS enables data connectivity to drive the development of new service-oriented
business models. The emergence of these business models is commonly referred to as a “sharing
economy” of collaborative consumption, which is driven by a growing concern about climate
change (Albinsson et al. 2012; Belk, 2010; Botsman & Rogers, 2010). These business models
have been enabled by the development of information and communication technologies as is
explicitly mentioned in their definition: “…the peer-to-peer-based activity of obtaining, giving,
or sharing the access to goods and services, coordinated through community-based online
services" (Hamari, Sjöklint & Ukkonen, 2016).
A specific type of business model that falls under the umbrella of the “sharing economy”
consists of a platform that facilitates goods or assets to be used more intensively (Schor, 2016).
A prominent example of an asset used more intensively under such a business model, is the
concept of carsharing. The emergence of carsharing business models is strongly driven by a
combination of connected vehicles, GPS, and customer technology which have enabled a digital
ecosystem that facilitates ‘Mobility as a Service’ rather than ownership (Seeger & Bick, 2013).
As illustrated in figure 6, the carsharing market has strongly developed over the course of the
past decade and is expected to continue growing in the future (Deloitte, 2017). It is assumed
that developments in IS have been a major driver of the expansion (Barth et al., 2003; Wagner
& Shaheen, 1998). As a result of its expansion, carsharing has demonstrated to reduce the
number of private vehicles ranging from 4.6 to
20 per new shared vehicle, yielding significant
environmental and economic benefits (Martin,
Shaheen & Lidicker, 2010). A major reason
for individuals to participate in carsharing is
related to saving costs of ownership (Hamari et
al., 2016; Shaheen & Cohen, 2007).
Figure 6. Development of the European carsharing market (Deloitte, 2017)
27
From the perspective of the renter, cost savings can be made by only incurring mobility costs
on a pay-per-use basis. On the other side, the owner of the vehicle can make a return on the
initial investment of buying the car and share operational costs like insurance and maintenance.
Under the traditional carsharing approach the owner offers a membership program that is an
alternative to car ownership by which persons or entities that become members are permitted
to use vehicles from a fleet on an hourly basis (Celsor & Millard-Ball, 2007). This notion is
similar to what Cohen & Kietzmann (2014) call Business-to-Consumer (B2C) Carsharing, in
which a company acquires vehicles and makes them available for rent at key points throughout
a city. An example of a carsharing organization operating under this model is Car2Go, albeit
they charge on a minute-basis instead of hours.
Hampshire & Gaites (2011) argue that the traditional carsharing model poses two problems that
limit its scalability. Firstly, these models are difficult to scale geographically – especially to
areas with lower population densities. Companies can only make a profit when at least 25 active
members live within 0,25-mile radius of each vehicle (Sullivan & Magid, 2007). This poses a
problem as even the largest metropolitan areas have limited amount of parking spots available
that are suitable as carsharing spots. Secondly, the traditional carsharing model is highly capital
intensive – the company that manages the fleet must bear the upfront cost of leasing or buying
vehicles (Cohen & Kietzmann, 2014; Hampshire & Gaites, 2011). In addition to the barrier to
entry that is formed by the requirement of these investments (Porter, 2008), it also results in
strategic implications in regard to the area of operation. In order to offset investment costs, the
rental company will aim to maximize rental income. Placing cars in areas with lower population
density may decrease the likelihood of a vehicle being rented out and consequently decrease
the return on investment. It is therefore unlikely that these areas will be served.
28
In contrast to a single company owning a fleet of vehicles, the Peer-to-Peer (P2P) model allows
individual car owners to convert their personal vehicles into rental cars (Hampshire & Gaites,
2011). Vehicle owners create revenue from transactions with renters, which are often facilitated
by a third-party that connects users and provides insurance (Ballús-Armet et al., 2014). The
P2P model relies on intermediation using information technology to connect multiple owners
(i.e. individuals) of vehicles with potential drivers (Cohen & Kietzmann, 2014). Hampshire &
Gaites (2011) argue that the P2P-model eliminates many expenses related to carsharing except
for insurance and an IT-infrastructure. This makes them more flexible to meet the entire
carsharing demand. In addition, the peer-to-peer (P2P) model contributes to solving the
situation in which privately owned vehicles sit idle for over 90% of the day (Shoup, 2005).
P2P-carsharing is not without its own adoption barriers. These include insurance cost and
availability, fear of sharing and lack of trust, challenges around balancing revenue and pricing,
the expense of technological solutions, the availability of vehicles and the quality and resulting
reliability of vehicles (Shaheen, Mallery & Kingsley, 2012). Nevertheless, multiple carsharing
and car lease-organizations have recently started to collaborate in order to overcome these
barriers and increase the adoption of P2P-carsharing. Prominent examples of a P2P -carsharing
company are Turo in Germany (formerly known as Relay Rides) and Snappcar in the
Netherlands. Whereas many B2C-carsharing organizations own fleets of EVs (Kahlen et al.,
2015), the share of EVs in P2P-carsharing is currently unknown and studies that research EVs
in P2P-carsharing organizations are lacking.
The P2P-concept is considered to be highly relevant to include in this study. From the
perspective of participants, the financial benefits yielded in the time during which a vehicle is
otherwise idle is an important motivator (Hamari et al., 2016; Shaheen & Cohen, 2007). EVs
are in the unique position of generating revenue in two ways: through P2P carsharing and
through participation in electricity markets. P2P carsharing and participation in electricity
markets serve a similar interest of generating revenue from time during which an EV would
otherwise be idle, thereby increasing the incentive to participate. This may prove to be of
essential strategic advantage, as competition among carsharing operators in the same region is
expected to increase (Shaheen & Cohen, 2007). In addition, Hamari et al. (2016) describe
environmental benefits as an important extrinsic motivation for consumers to participate in
carsharing. Schaefers (2013) recognizes the importance of environmental benefits, but states
they are more a positive side-effect rather than a dominant motive.
29
It is therefore interesting to see whether the similarity between the sharing economy and the
potential of EVs to contribute to an increase in renewables on the grid appear is perceived as
an effective combination for P2P-carsharing organizations to offer their participants. Moreover,
combining organizational a P2P-carsharing’s resources and IT-assets can create a valuable
strategic advantage (Nevo & Wade, 2010). Existing P2P-carsharing organizations already have
an operational IT-infrastructure and client base in place for their renting business. Both a client
base and an IT-infrastructure can be considered a valuable resource.
It is therefore proposed in this study that:
Proposition 1: The business model of P2P-carsharing provides a promising opportunity
for the implementation of intraday market participation.
30
2.4 EV-Lease Alternatively, this study explores a second business model within the field of Green IS for
sustainable mobility services (Hildebrandt et al., 2012). Leasing business models recognize that
despite the increasing popularity of consumption without ownership, ownership is still valued
by consumers (Moeller & Wittkowski, 2010). It is therefore that car lease business models, that
offer a mixture of “ownership” and “non-ownership”, supply approximately 11% of all cars in
the Netherlands (CBS, 2018; VNA, 2018). A major driver for consumers to engage in lease
contracts is the ability to lower up-front costs, even if this results in higher net prices (Dasgupta,
Siddhardt & Silva-Risso, 2008). In regard to EVs, their additional perceived value of
sustainability is often outweighed by cost and performance characteristics (Egbue & Long,
2012). The premium costs associated with EVs are even stated to be the primary limitation of
the widespread consumer adoption (Carley et al., 2013; Roy et al. 2005). Given the low up-
front costs associated with lease contracts, car leasing business models are considered a
valuable tactic to reduce the primary limitation of consumers to choose an EV.
Other important motivators for consumers to engage in lease contracts are related to the
simplification of ‘consuming’ a vehicle – particularly in regard to its maintenance (Trocchia &
Beatty, 2003). This closely corresponds for other important adoption barriers for EVs, which
are related to product uncertainty and battery range (Egbue & Long, 2012). A prime example
of this is related to the development of battery technology and the resulting battery depreciation.
As maintenance is often included in lease contracts, the simplification of consumption drivers
is considered to lower these barriers to consumer EV-adoption. Car manufacturers are already
employing these tactics effectively in their effort to incentivize consumer adoption (Bohnsack
& Pinkse, 2017).
There is currently no extensive body of literature in regard to EVs in lease business models.
Nevertheless, the scale of car leasing and its opportunities to reduce adoption barriers for EVs
indicate an interesting opportunity for effective interaction with individual EV-drivers.
Enabling lease-customers to yield income through participating in short-term electricity
markets may contribute to further decreasing cost barriers for consumers. Consequently,
implementing short-term electricity market participation into an EV-lease business model is
considered to strengthen the value proposition of an EV-lease organization.
31
Similar to P2P-carsharing organizations, existing car leasing organizations already have a
customer-base and often an operational IT-infrastructure to interact with these customers. As
both a customer-base and an IT-infrastructure can be considered a valuable resource, these can
potentially enable P2P carsharing companies to achieve a strategic advantage (Nevo & Wade,
2010).
It is therefore proposed in this study that:
Proposition 2: The business model of EV-lease provides a promising opportunity for the
implementation of intraday market participation.
32
2.5 Cross-case This study aims to contribute to the domain of Green IS by extending the design of an artifact
that is focused on smart market design (Bichler et al., 2010; Hevner et al., 2010). This is
achieved by addressing its application in the domain of sustainable mobility through the
exploration of an innovative business model (Hildebrandt et al., 2012). The two business
models that have been discussed above are inherently different. Hence, the feasibility of
implementation of the model is expected to differ.
P2P-carsharing business models are dependent on a high capacity utilization to obtain a
profitable business (Brook, 2004). This implies that in order for the sharing business to be
profitable, vehicles should be shared as often as possible. Participating in EV-markets can
reduce the incentive to do so in case the conditions (e.g. certainty or value of potential income)
for participating in an EV-market are more favorable for a consumer than sharing it. This will
reduce the number of sharing transactions. Moreover, it potentially decreases the availability of
vehicles on the platform. Carsharing organizations must achieve a critical mass for use of the
service to be perceived interesting for consumers (Litman, 2000). Given the limited share of
EVs relative to overall automobiles (CBS, 2017a), it is likely that P2P-carsharing organizations
are not yet making specific investments to reach this particular part of the market. Similarly,
the certainty of having a vehicle available at all times is considered a main barrier for use of the
service (Katzev, 2003). Therefore, effective implementation of short-term electricity market
participation into P2P-carsharing business models would require measures that mitigate this
risk. In contrast to the high daily volume of transactions, lease contracts are mostly span a
longer period of time. Therefore, the potential interest of an EV-lease business model is to
provide an incentive by lowering customer’s monthly costs for lease contracts – regardless
whether this is through sharing or participation in a short-term electricity market.
33
Based on this reasoning, P2P-carsharing organizations are expected to have other strategic
priorities than specifically focusing on providing an extra service for individual EV-drivers.
Although both P2P-carsharing and EV-lease business models are expected to provide
interesting opportunities for implementation, EV-lease business model are expected to provide
a more interesting opportunity on the short-term. This results in the following proposition:
Proposition 3: In the short term, the business model of EV-lease provides a more
promising opportunity for the implementation of intraday market participation than
P2P-carsharing.
34
3. Methodology The research methodology for this study is dual. A quantitative approach will determine the
profitability of the participation of privately owned EVs in the intraday auction under different
scenarios. This approach includes the development of a model, that will be tested against
historical data to calculate profitability. Subsequently, a qualitative approach will be used to
assess the model’s feasibility in a practical context. The qualitative approach will consist of
semi-structured interviews at P2P-carsharing organizations, EV-lease and vehicle-to-grid
(V2G) professionals. This chapter will elaborate on the methodology followed for this study.
3.1 Data This study uses historical data from the EPEX Intraday Auction Market (EPEX SPOT, 2018b)
that was scraped and cleaned by the Institute of Energy Economics at the University of Cologne.
It contains prices (in euros per MWh) for every quarter-hour product from 10 December 2014
up to and including the 5 January 2018. Given that 2017-2018 is the most recent part of the
dataset, this study will use a subset of the data that ranges from January 1 2017, 8.00 AM, to
January 1 2018, 8:00AM.
3.2 Model Description This study models an individual EV using an optimization algorithm. The model maximizes
the income over a predefined period of consecutive quarter-hour products at a specific time
while adhering to multiple constraints. It is assumed that EV-drivers provide the necessary
information in regard to time and duration of availability. A predefined period of quarter-hour
products will be referred to as a ‘session’.
Income is generated through benefitting from volatility in clearing prices (l): charging (i.e.
buying) electricity during a quarter at which prices are low and discharging (i.e. selling) during
a quarter at which prices are high. As the duration of a session increases, so does the volume of
electricity that is traded during that session. Each session has a strategy that is expected to result
in the maximum income for that session.
35
A strategy consists of a combination of power profiles (PRT). The model maximizes the sum of
power profiles over a predefined period of time (t) based on their (expected) price (l). This is
represented as follows:
𝑓 = 𝑚𝑎𝑥&'(
𝔼*+,(𝑃/0 ∗ 𝜆4)6
478
9
The problem is subject to multiple variables and constraint. The first of which is related to the
fact that the state of charge of a battery (SoC), at any given time, cannot exceed the lower and
upper limit of its capacity (Ω). This is formulated by:
𝑆𝑜𝐶=4> ≤ 𝑆𝑜𝐶6 ≤ 𝑆𝑜𝐶=@A
Where SoCt is determined as follows:
𝑆𝑜𝐶6 = 𝑆𝑜𝐶B6@C6 +,𝑃/0 ∗ ∆𝑡6
This refers to the fact that the state of charge at any given time (SoCt) is determined by the SoC
at the beginning of the time period (SoCStart) and the sum of the power profiles (PRT) that have
been executed over the time that has passed.
A power profile (PRT) can either be positive – in the case of charging, or negative – in the case
of discharging. This is represented by:
𝑃/0 = 𝛾 ∨ −𝛾
Where γ represents that power-profiles are constrained by the capacity of the charging
infrastructure in kW.
The first strategy will solely use price volatility to yield income, by enforcing that SoCEnd must
be equal to SoCStart. This is represented as the following constraint:
𝑆𝑜𝐶B6@C6 = 𝑆𝑜𝐶J>K
(1)
(2)
(3)
36
Alternatively, a charging-strategy will enforce a minimum SoCEnd of 80%. Which is represented
by the following constraint:
𝑆𝑜𝐶J>K ≥ 0.8 ∗ 𝑆𝑜𝐶=@A
The raw code is attached in appendix A.
3.2.1 Price Scenarios
It is important to note that assuming a perfect market is not realistic, as prices (𝜆) are set at the
moment of market clearing. Therefore, this study takes two scenarios into consideration. These
scenarios provide an upper bound and a reasonable lower bound to income.
To provide an upper bound to income, it is assumed that 𝜆 can be predicted perfectly. It uses
the actual clearing prices to determine the optimal strategy for a session and calculates the
related income accordingly.
A more realistic scenario will provide a reasonable lower bound. This scenario uses a simple
dynamic moving average that is based on the price of the same quarter of the day for the ten
days prior to the session. This is represented as follows:
�̅�K = 1𝑛,𝜆KS4
>
478
Where �̅�Krepresents the expected price for a given quarter at day d and n represents the number
of days included in the model. In this particular case, a ten-day moving average, n is determined
at 10. The resulting value �̅�K will be used as input for 𝜆4 in equation 1 to determine the power
profile strategy. Subsequently, the power profiles (PRT) that have been identified as part of the
strategy based on �̅�K will be used in equation 1, where 𝜆4 reflects the actual price, to determine
income for the realistic scenario. Whereas there are studies that research the predictability of
intraday markets in more detail (Panagiotelis & Smith, 2008; Weron, 2014), these prediction
algorithms are considered to be beyond the exploratory scope of this study.
37
3.2.2 Session Time and Duration
Ultimately, the time and duration (t) of a session will determine the income that results from
the model.
This study analyzes the following three session-types:
• Hourly sessions: one-hour session for every hour of the year.
• Work sessions: take place on every working day in 2017 from 09:00h to 17.00h.
Mobility during lunch hours is not taken into account and weekends are not included.
• Night-sessions: take place on every day in 2017 from 23.00h to 07.00h, including
weekends.
3.2.3 Battery Capacity and State of Charge
As a battery cannot be charged over SoCMax = 100% nor can it be discharged below SoCMin =
0% (equation 2), the model is constrained by battery capacity. Therefore, battery size (W)
restricts the bandwidth in equation 2. Consequently, the number of available power-profile
strategies is constrained. Different values for W result in different absolute values for SoCMin
and SoCMax, consequently enabling different power-profile strategies. This study will perform
an analysis for W = 16.7 kWh and W = 100 kWh. The first represents EVs with a small storage
capacity (e.g. Smart for Two 2017), it also covers the possibility that older vehicles with a low
storage capacity will participate. Alternatively, W = 100kWh anticipates the development of
storage technology by representing the storage capacity of a high-end EV (e.g. Tesla Model S
2017).
Similarly, the state of charge at the start of a session (SoCStart) limits the available power-profile
strategies. Depending SoCStart, different power-profiles will be available (equation 3) within the
restrictions of SoCMin and SoCMax (equation 2). E.g. an EV with W = 16 kWh and SoCStart= 75%
will not be able to charge for as many consecutive quarters as the same battery with SoCStart=
75% (equation 3). It is assumed that EV-drivers connect at an SoCStart that lies between 25% and
75%. By aiming for a range that is neither nearly full or nearly empty, an EV is able to
incorporate consecutive charges or discharges depending on what yields the highest income.
Therefore, this study will analyze model outputs with SoCStart -parameters of 25%, 50% and
75% to illustrate the effect.
38
3.2.4 Charging- and Discharging Rate
Similar to Kahlen et al. (2017), PRT is constrained by the charging- and discharging capacity of
charging stations. This constraint limits the volume of electricity that can be traded during a
session. This study analyses the model for charging capacities for γ = 7 kW and γ = 22 kW,
which correspond to contemporary standards and the maximum capacity for regular charging
stations that are not categorized as a fast-chargers (International Energy Agency, 2017). It will
be assumed that every power-profile (dis)charges at full capacity. Kahlen et al. (2017) also take
into account charging and discharging efficiencies, of respectively 96% and 97.4%. However,
on an individual level these values and the difference between them is considered to be
negligible.
3.2.5 Strategies
The amount of possible strategies considered by the model is constrained by including SoCEnd
(equation 5). Including SoCEnd aims to ensure that EVs have sufficient electricity to fulfil
mobility purposes. This study will take two approaches in regard to SoCEnd. The first approach
will isolate the effect of benefitting from volatility in market prices, by stating that SoCEnd must
be equal to SoCStart (D SoC = 0%). This implies that x in equation 5 is determined at SoCStart. It
is assumed that EV-owners are willing to participate under this condition.
The second strategy takes into account that studies that researched EV-participation in other
markets, have demonstrated that benefits from discharging are relatively low when compared
to the potential benefits from charging (Kahlen et al., 2017; Mullan et al., 2012). Literature also
suggests that allowing for a higher SoCEnd may contribute to the assumption of adoption by EV-
owners, as it lowers potential barriers in regard to range anxiety (Franke et al., 2011).
39
To explore the effect of including charging into the model, the second strategy will enforce a
charging preference onto the model by stating that SoCEnd ≤ 80%. Consequently, potential
income should include opportunity benefit (OB), which regards the benefit of charging
electricity from the market without discharging it during the same session. The OB takes into
account the monetary difference between buying electricity from the intraday auction and
buying it at the retail electricity tariff (RT).
OB = (SoCEnd – SoCStart) ∗ RT
The retail electricity tariff is a flat rate, determined at €0.1562 per kWh. This corresponds to
the Dutch household retail-tariff in 2017 (Eurostat, 2017).
3.3 Interview Study Data for the qualitative approach were gathered through 6 semi-structured face-to-face
interviews in April 2018, using a structured topic list. An interview enables a phenomenon to
be studied in context and contributes to gaining insights in the meaning of a phenomenon in
practice (Bleijenbergh, 2013). A semi-structured interview enables respondents to openly
answer questions, although the questions and their sequence are largely predetermined (Boeije,
2005).
Table 1 presents an overview of the interviewees that participated in this study.
Interview Type of organization Company Personal Role
1 P2P-carsharing A Community Manager
2 P2P-carsharing B Product Manager
3 Grid Operator C Chief Electric Mobility
4 V2G Consultancy & Project
Management
D Director
5 EV-lease E CEO
6 EV-lease F Partner Manager
Table 1 Overview of interview participants
Interviews have been conducted according to an interview protocol (Appendix B) that was
aimed at providing guidance without stimulating or limiting the interviewees in their opinion.
All interviews have been recorded and transcribed. Transcripts have been reviewed and
confirmed by interviewees and are attached as appendices (Appendix C).
40
3.4 Model Performance Model performance will be assessed by its financial performance: i.e. the returns of market
participation. This will be operationalized as the average return per session and the total return
of all sessions in 2017. To analyze behavior of the model under different conditions and
constraints, parameters will be adjusted in order to isolate the effect of each condition or
constraint. The analysis will initially determine the gross income for market participation.
Subsequently, operational costs will be included to determine a more realistic potential income
of market participation.
Participation in the EPEX-Spot Intraday Auction requires an entrance investment of €25000
for direct membership. This is a one-time investment and will therefore be regarded as sunk
cost. The annual membership costs are €5000 and transactions are charged with 0.07 €/MWh
(EPEX Spot, 2018a). Since the volume traded by an individual EVs are measured in kWh, the
variable transactions costs are considered to be negligible.
Given that V2G is an alternative to using each kWh for mobility, battery depreciation will be
calculated for every kWh that is discharged. Battery degradation costs are currently determined
at 0.055 €/kWh and expected to decrease to 0.028 €/kWh by 2022 (US Department of Energy,
2013). An optimistic scenario will take into account that, for a lithium-ion battery, the capacity
loss as a result from V2G-operations, is approximately half the capacity loss that results from
using the EV battery for driving purposes (Peterson et al., 2010a; Uddin et al., 2017).
Table 2. Battery depreciation costs under different scenarios.
Given that power profiles under the charging-strategy are predominantly charging, depreciation
costs are lower for this strategy. However, as power profile strategies become restricted by the
SoCMax-constraint, power profiles must discharge. Hence, depreciation costs will increase until
they reach their maximum values - which are listed in table 2. Please note that depreciation
costs are assumed to be exogenous; they have not been incorporated into the model.
Depreciation scenario €/kWh Conditions
Optimistic 0.028 50% for every kWh that is discharged.
Expected 0.028 For every kWh that is discharged
Current 0.055 For every kWh that is discharged
41
Under current legislation, bi-directional charging
may be subject to double taxation, as every kWh
that is charged or discharged is taxable (PwC,
2017) – regardless whether it is kept in the battery
for later consumption or not (figure 7). However,
given the potential of V2G to contribute to
achieving climate policies (United Nations, 2015;
United Nations, 2016) these policies will possibly
be clarified in favor of V2G-initiatives.
Given the current ambiguity in tax policy three scenarios will be analyzed. A scenario of double
taxation will assume that every kWh that is discharged or charged will be subject to taxation.
A scenario of single taxation will assume that only the amount of kWh that is charged will be
subject to taxation. A scenario of half taxation will assume that taxation is favourable towards
to V2G-initiatives and that taxes will be reduced by half for every kWh that is charged by
parties participating in V2G-initiatives.
The rate for energy taxes in the Netherlands is determined at 0.05274 €/kWh. For every session
type presented in this study, the taxation for every scenario is listed in table 3.
Table 3. Cost of taxes under different scenarios.
Taxation scenario €/kWh Conditions
Half 0.02637 For every kWh that is charged.
Single 0.05274 For every kWh that is charged.
Optimistic 0.05274 For every kWh that is traded (discharged or charged).
Figure 7. Possibility of double taxation for V2G (PwC, 2017)
42
3.5 Interview Analysis Based on the results of the quantitative analysis, semi-structured interviews will be conducted.
Upon completion, the interview transcripts will be coded inductively. An inductive approach
aims to establish universal conclusions from a limited amount of observations or respondents
(Vennix, 2010). Therefore, an inductive approach corresponds to the exploratory nature of this
study.
Coding will be executed by following the following three steps (Boeije, 2005):
1. Open coding: assigning a label to each text fragment that best characterizes the content
of the fragment. The label must be explicitly mentioned in the fragment.
2. Axial coding: determine the connections between open codes and determine axial codes
(i.e. themes). Axial coding is for the benefit of abstraction and information reduction.
3. Selective coding: comparing fragments with identical axial codes to determine patterns.
This will result in pattern-codes that enable the connection of empirical findings with
theoretical findings.
The coding is attached in Appendix D.
43
4. Results This chapter will present the results of the model that has been described in the previous chapter.
The first paragraph will elaborate on descriptive statistics of the dataset that was used to
evaluate the model. Subsequently, the results for the ‘volatility only’-strategy will be presented
and sensitivity of the model for different parameters will be discussed. These results regard the
potential gross income that may be yielded solely by benefitting from price volatility. The
results and sensitivity analysis for the charging-strategy will then be discussed. In contrast to
the volatility only-strategy, this strategy aims to charge the EV to a predetermined SoC (≥80%)
– yielding benefits from cost savings related to avoiding the retail tariff. Gross income for both
strategies will then be assessed against the related costs in the form of battery depreciation and
energy taxes in order to determine profitability of the model for both strategies. The chapter
will conclude with a benchmark of the results against similar studies.
4.1 Data Description The dataset includes 35036 data points. The dataset contains
missing pricing data for one hour in the night of March 26 2017.
The entire dataset proves that this is a recurring event that
happens annually, at the last Sunday of March. This is due to
technical maintenance. Sessions that included these data have not
been included in the analysis. Hence, the dataset resulted in 8760
hour-sessions, 260 work-sessions or 364 night-sessions.
A summary of descriptive statistics is presented in table 4.
Additionally, the mean prices and corresponding standard
deviations have been plotted in figure 8. Whereas this will be elaborated upon in the remainder
of this section, the mean values already demonstrate how prices vary greatly during the day and
according to a similar pattern as described by Knaut & Paschmann (2016).
Mean 34.00
Standard Error 0.11
Median 33.97
Standard Deviation 20.04
Variance 401.53
Kurtosis 10.13
Skewness 0.14
Minimum -134.82
Maximum 290.65
Count 35036
Table 4. Summary statistics
Figure 8. Mean values and standard deviation for prices over the course of a day in 2017.
44
4.2 ‘Volatility only’-Strategy This section evaluates the ‘volatility only’-strategy of the model, based on potential gross
income. This section is subdivided into the different sessions that have been analyzed. Each
session type will include a sensitivity analysis for different parameters. Please note that this
section only includes gross income from the market, an additional analysis that includes costs
is provided later in this chapter.
4.2.1 Hour-sessions
The dataset for 2017 contained 8760 hour-sessions, during which 35040 quarter-hour products
were traded. In total, these quarter-hour products add up to volumes ranging from 61.32MWh
to 192.72 MWh per EV, depending on charging capacity (γ). Although this volume is equally
distributed across all sessions, gross income varies per session as prices (l) differ. Figure 9
illustrates how income is distributed across the days of the week. It shows how the income in
the best-case scenario decreases over the course of the week, until it peaks on Sunday. A
possible explanation for the peak on Sunday is that demand is usually at its lowest on Sundays
(Aneiros, Vilar & Raña, 2016). Assuming there is no structural difference in the supply of
renewable energy, this would result in decreasing residual demand resulting in higher price
volatility (Knaut & Paschmann, 2016).
Figure 9. Distribution of gross income for hour-sessions.
45
Time of Day
Analysis of the average income for 1-hour sessions over the course of a day, indicate an upward
trend (figure 10). There are two deviations to be noticed, in the morning and the late afternoon,
which can be attributed to increased electricity demand related to human routines like breakfast
and dinner (Dang-ha, Bianchi & Olsson, 2017). Assuming there are no structural differences in
renewable energy supply, this will temporarily stabilize the trend of increasing residual demand
and consequently lower short-term price volatility (Knaut & Paschmann, 2016).
Figure 10. Distribution of gross income of hourly sessions over the course of day.
State of Charge
For hour-sessions, different SoCStart -values do not result in
different power profile strategies. This is related to their
short duration and the charging capacity constraint (γ = 7
kW), which does not allow for either SoCMin or SoCMax to be
reached by any power profile strategy.
Figure 11 illustrates the strategy followed by the model,
which is identical for each of the analyzed SoCStart-values.
Negative values indicate that, over all hours in 2017, power
profiles discharged more often than they charged during that
quarter. E.g. a value of -0.15 for Quarter 1 indicates that,
over all hours in 2017, the model would discharge 15% more
frequently than it would charge. Positive values implicate
the same for charging.
Figure 11. Average strategy followed for an hourly-session.
46
The pattern is clearly consistent with the price pattern that Knaut & Paschmann (2016) describe,
resulting from fluctuations around the respective hourly prices. The corresponding gross
income is listed in table 5.
SoCStart Optimal Prediction
Annual Average Annual Average
25% / 50% / 75% €327.78 €0.04 €292.31 €0.03
Table 5. Income for hour-sessions under γ = 7 kW and Ω = 16.7kWh.
Battery capacity
For hour-sessions, there is no difference in income for different battery capacities. The results
for a large battery and the power profile strategy followed are identical to that of a small battery
– which are discussed above and listed in table 5. The fact that results are identical is related to
the limited duration (t) of a one-hour session, which is limited to the extent that it is impossible
for any power profile strategy to reach SoCMin or SoCMax.
Charging capacity
In contrast to a charging capacity of γ = 7 kW (table 5, above), SoCStart values have an effect on
income for hourly-sessions under conditions of advanced charging capacity (γ = 22kW). The
results for advanced charging capacity are listed in table 6. SoCStart -values of ≤ 25% and ≥75%
limit power profile strategies by quickly reaching, respectively, SoCMin and SoCMax. The results
indicate that prices (λ) are often higher during the first two quarters of an hour. This makes
discharging the preferred power profiles for these quarters. However, a low SoCStart -value
requires the first power profile to charge the battery, as it will otherwise reach SoCMin.
Nevertheless, SoCStart = 50% allows a broader bandwidth that enables power profile strategies
to acquire an income increase of approximately factor 1.5 compared to a normal charging
capacity (γ = 7kW).
SoCStart Optimal Prediction
Annual Average Annual Average
25% €185.16 €0.02 €141.02 €0.02
50% €532.08 €0.06 €476.58 €0.05
75% €345.97 €0.04 €301.83 €0.03
Table 6. Income for hour-sessions under γ = 22 kW and Ω = 16.7kWh.
47
Whereas increasing battery size (Ω) for a normal charging capacity (γ = 7 kW) does not have a
large effect on income, the results in table 7 demonstrate that a large battery can leverage the
positive effect of an increased charging capacity (γ) on income.
SoCStart Optimal Prediction
Annual Average Annual Average
25% / 50% / 75% €1030.18 €0.12 €918.70 €0.10
Table 7. Income for hour-sessions under γ = 22 kW and Ω = 100kWh.
In the case of a small battery (Ω = 16.7kWh), an advanced charging capacity (γ = 22kW) results
in quickly reaching SoCMin or SoCMax. Although it allows for an increased volume to be traded,
this may restrict the model from using the optimal power profile strategy for that hour (e.g.
discharging during the first two quarters, while recharging in the last two quarters).
In contrast, a large battery (Ω = 100 kWh) enables power profile strategies to fully benefit from
trading larger volumes and volatility across the full hour. This difference is illustrated in figure
12.
A B
Figure 12. Power profile strategies for γ = 22kW and SoCStart = 50%
48
4.2.2 Work-sessions
The dataset for 2017 contained 260 work-sessions, during which 8320 quarter-hour products
were traded. In total, these quarter-hour products add up to volumes ranging from 14.56 MWh
to 45.76 MWh per EV, depending on charging capacity.
Although this volume is equally distributed across all sessions, income varies per session as
prices differ. Figure 13 illustrates how income is distributed across the days of the week. It
shows how the income in the best-case scenario decreases over the course of the week, until it
increases again on Friday. Although resulting in lower income, the realistic scenario is more
consistent across the week, with the exception of Friday.
In addition to the day of the week, income is dependent of multiple parameters. The effect of
these parameters will be discussed in the remainder of this section.
Figure 13. Distribution of gross income for work-sessions over the course of a week.
49
State of Charge
For work-sessions, the results demonstrate that high SoCStart- values contribute to higher annual
income (table 8). However, the €0.03 - €0.04 difference in average income per session is
marginal over the course of 8 hours.
SoCStart Optimal Prediction
Annual Average Annual Average
25% €55.97 €0.22 €43,60 €0.17
50% €61.83 €0.24 €49,61 €0.19
75% €66.15 €0.25 €54,17 €0.21
Table 8. Gross income for work-sessions under different SoCStart -values.
As illustrated in Figure 14, a higher SoCStart enables power profile strategies to consecutively
discharge and charge for a longer duration of time. This allows the EV to benefit from price
differences that occur over a longer period of time (e.g. the morning and the afternoon). This
pattern is consistent with the morning price-peak found in the day-ahead market, after which
day-ahead prices decrease during the afternoon (Paraschiv, Erni & Pietsch, 2014). Since prices
in the intraday auction fluctuate around corresponding day-ahead prices (Knaut & Paschmann,
2016), this indicates that benefits from long term volatility in day-ahead prices outweigh the
corresponding potential benefits from short-term price volatility.
Although income increases with higher SoCStart-values, the graph clearly indicates a preference
of the model to benefit from downward price volatility between 16.00h and 17.00h. This
implicates that the SoCStart -value should allow for charging over SoCStart, which makes starting
with a full battery (SoCStart = SoCMax) would result in a suboptimal power profile strategy for
work-sessions.
Figure 14. Average SoC over the course of a work-session.
50
Battery Capacity
An increase in battery capacity (W) of approximately factor 6 results in an increase of income
that ranges from 10.7% in a realistic scenario to 16.7% in a best-case scenario (table 9). Battery Capacity Optimal Prediction
Annual Average Annual Average
16.7 kWh €61.83 €0.24 €49.61 €0.19
100 kWh €72.17 €0.28 €54.94 €0.21
Table 9. Gross income for work-sessions under different battery capacities.
Increasing the battery capacity (W) implicates an increase in bandwidth: upper and lower
boundaries are further away. This results in an effect that is similar to changing the SoCStart:
enabling an EV to consecutively discharge and charge for a longer duration of time, thereby
allowing it to benefit from price differences that occur over a longer period of time (e.g. the
morning and the afternoon). In addition to the steepness of the slope that is illustrated in figure
15.A, there are minor differences in the average strategy for battery capacity. Figure 15.B
illustrates how often power profiles discharges or charges at a certain time. For each quarter-
hour, a value of -1 indicates that power profiles discharge for 100% of the sessions. Similarly,
positive values indicate that power profiles charge during that quarter-hour. The graph indicates
that a larger battery capacity (Ω = 100kWh) enables a power profile strategy to discharge more
frequently for quarters in the morning, while more frequently charging in the afternoon.
A
B
Figure 15. SoC-values and strategy for work-sessions under different battery capacities.
51
Charging infrastructure
The strategy that is illustrated in figure 16 clearly illustrates how the strategies differ and
demonstrates how the model doesn’t go below SoC = 17 % or above SoC = 83% as this will
result in reaching the respective lower and upper limit of the battery. The results for a small
battery are listed in table 10.
γ Optimal Prediction
Annual Average Annual Average
7 kW €61,83 €0.24 €49.61 €0.19
22 kW €88.26 €0.34 €77,68 €0.30
Table 10. Gross income for work-sessions under different γ -values (Ω = 16.7 kWh).
However, for a large size battery the results demonstrate that increasing the charging- and
discharging speed with approximately factor 3, results in a similar factor-increase in income
during work sessions (table 11).
γ Optimal Prediction
Annual Average Annual Average
7 kW €72,17 €0.28 €54,94 €0.21
22 kW €220.94 €0.85 €173,73 €0.67
Table 11. Gross income for work-sessions under different γ -values (Ω = 100 kWh)
Figure 16. Average SoC for work-sessions under different γ -values.
52
This difference is related to the capacity of trading larger volumes over a longer duration of
time: the battery can discharge large volumes electricity in the morning and recharge in the
afternoon. This effect is illustrated in figure 17.A, which demonstrates a steeper slope for g =
22kW. Nevertheless, the strategies followed during work-sessions over the year are almost
identical (as illustrated in figure 17.B).
A
B
Figure 17. SoC-values and strategy for work-sessions under different γ -values.
53
4.3.3 Night-sessions
The dataset for 2017 contained 365 night-sessions, during which 11680 quarter-hour products
were traded. In total, these quarter-hour products add up to volumes ranging from 20.44 MWh
to 64.24 MWh per EV, depending on infrastructural constraints.
Although this volume is equally distributed across all sessions, income varies per session due
to price variations. Figure 18 illustrates how income is distributed across the days of the week.
Whereas volatility is relatively constant for the first four days of the week, particularly Friday
and Saturday night result in lower income. This is due to a lower electricity demand (e.g. many
factories and offices are closed), which is consistent with findings in literature indicating that
prices and their volatility are lower during weekends than they are during the week (Li & Flynn,
2004).
State of charge
For night-sessions, the results demonstrate that adjusting SoCStart- values has no strong effect
on income (table 12). Particularly in regard to the average income per session, the differences
are negligible. Nevertheless, the average income per session is higher than it is for work
sessions: regardless of their identical duration. This implies that price volatility is higher at
night, than it is during the day.
Figure 18. Distribution of gross income for night-sessions over the course of a week.
54
SoCStart Optimal Prediction
Annual Average Annual Average
25% €138.02 €0.38 €116.37 €0.32
50% €143.78 €0.39 €119.36 €0.33
75% €138.11 €0.38 €112.86 €0.31
Table 12. Gross income for night sessions under different SoCStart -values.
However, in contrast to work-sessions, the optimal strategy for night-sessions does not require
frequent consecutive dis- and recharging. This is due to the absence of major prices differences
over a longer period of time (e.g. late at night and the early morning). Based on these results,
night-sessions does not strongly benefit from a large bandwidth in order to execute the optimal
strategy.
The results indicate that fluctuations around hourly prices are higher during the night than they
are during the day, resulting in higher short-term price volatility. This is consistent with findings
in literature, which relate this increased volatility to a decrease in demand and an increase in
supply from renewable energy resources (particularly wind) during the night (Paraschiv et al.,
2014; Kiesel & Paraschiv, 2017).
Figure 19. Average SoC over the course of a night-session.
55
Battery capacity
An increase in battery capacity (W) of approximately factor 6 results in an increase of income
that ranges from 10.7% in a realistic scenario to 16.7% in a best-case scenario (table 13).
W Optimal Prediction
Annual Average Annual Average
16.7 kWh €143.78 €0.39 €119.36 €0.33
100 kWh €148.13 €0.41 €120.02 €0.33
Table 13. Gross income for night-sessions under different battery capacities.
Increasing the battery capacity (W) implicates an increase in bandwidth: the range between
SoCMin and SoCMax increases. Whereas a larger battery results in a flatter slope in regard to SoC
(figure 20.A), the strategies for both battery sizes are almost identical (figure 20.B). This
indicates that execution of the optimal strategy does not require such a broad bandwidth for the
majority of night-sessions. These effects appear to be similar to that of changing SoCStart.
B
A
Figure 20. SoC-values and strategy for night-sessions under different battery capacities
56
Charging infrastructure
The strategy that is illustrated in figure 21 clearly illustrates how the strategies differ and
demonstrates how the model doesn’t go below SoC = 17 % or above SoC = 83% as this will
result in reaching the respective lower and upper limit of the battery. The corresponding results
are listed in table 14.
g Optimal Prediction
Annual Average Annual Average
7 kW € 143.78 €0.39 €119.36 €0.33
22 kW € 200.73 €0.55 €178.21 €0.49
Table 14. Gross income for night-sessions under different battery capacities.
These results demonstrate how increasing the charging capacity restricts the model in executing
the optimal strategy for a night session. During a night session, the model is repeatedly forced
to execute a suboptimal strategy in order to prevent it from respectively reaching SoCMin or
SoCMax. This implicates that increasing the bandwidth will result in higher income due to the
ability of the model to exercise its optimal strategy (table 15). Figure 22 demonstrates how the
model does so, showing a similar pattern to the strategy of a16.7 kWh-battery in figure 20.A.
g Optimal Prediction
Annual Average Annual Average
7 kW €148.13 €0.41 €120.02 €0.33
22 kW €464.19 €1.27 €377.20 €1.03
Table 15. Gross income for night-sessions under different γ -values.
Figure 21. SoC-values and strategy for night-sessions and a small battery capacity and different γ -values.
57
Figure 22. SoC-values and strategy for night-sessions and a large battery capacity under different γ -values.
58
4.3 Charging Strategy Whereas the results that have been presented in the previous section solely rely on price
volatility benefits to yield income, gross income for the charging strategy includes an
opportunity benefit in the form cost savings for charging an EV. In contrast to the ‘volatility
only’-strategy, the charging-strategy sets SoCEnd at ≥ 80%. The opportunity benefit refers to the
difference in cost between charging from the market vs. charging against the retail tariff.
Setting a minimum desired SoCEnd and including an opportunity implies that the distance
between SoCStart and SoCEnd and SoCMax largely determines how much opportunity value is to be
yielded. Lower (Higher) SoCStart -values allow an EV to charge more (less) often which increases
(decreases) the share of opportunity benefit in gross income. This is illustrated in figure 23,
where SoCStart = 25% yields an income ranging from €798.95 - €382.16, SoCStart = 50% merely
yields €548.81 - €269.73. This difference may be attributed to the fact that SoCStart = 25%
enables a power profile strategy that yields opportunity benefits over a larger volume of charge
power profiles. For the benefit of consistency, the remainder of this section will assume SoCStart
= 50% - unless explicitly mentioned otherwise.
Figure 23. Example of SoCStart restricts the model in yielding opportunity benefit (work-sessions, Ω = 16.7 kWh, γ = 7 kW).
59
Similarly, battery capacity determines how much opportunity benefit is to be yielded. Small
batteries may be restricted in exercising the optimal strategy as the optimal strategy may be to
charge as much as possible. Figure 24 illustrates how a large battery enables more charging
power profiles than a smaller battery that is constrained by SoCMax. To allow for a consistent
illustration of the model’s behavior for the charging strategy, the remainder of this section will
assume a large battery capacity (Ω = 100 kWh).
The remainder of this section will explain how these strategies are related to potential gross
income. The results will be discussed for each session-type the sensitivity of the model to
different γ-values will be assessed.
4.3.1 Hour-sessions
Although the absolute values of annual income are strictly hypothetical (as an individual EV
cannot set its SoC to SoCStart = 50% for every hour), the potential gross income under the
charging strategy (table 16) far outweighs the potential income from the ‘volatility only’-
strategy. In addition, a higher charging capacity (γ = 22 kW) results in higher income. As
illustrated in figure 25, this is due to the fact that a higher charging capacity allows the model
to yield opportunity benefits over a large volume of electricity. Please note that SoCStart is set
to 70%, which illustrates how the model sets power profiles to charging even beyond SoCEnd =
80%. The results indicate this strategy is identical for all hour sessions. This is a clear indication
that the potential opportunity benefit of paying the market price instead of the retail tariff
outweighs the potential income from benefitting from price volatility.
Figure 24. Example of how battery size (Ω) restricts the model in yielding opportunity benefit (work-sessions, SoCStart = 50%, γ = 7 kW).
60
However, as power profile strategies are restricted by SoCMax gross income will decline as
power profile strategies will be forced to discharge.
γ Optimal Prediction
Annual Average Annual Average
7 kW €17071.49 €1.95 €7476.86 €0.85
22 kW €53653.25 €6.13 €23498.71 €2.68
Table 16. Gross income for hour-sessions under charging-strategy.
4.3.2 Work- and Night-sessions
Similar to the results for hour-sessions, the charging strategy yields a higher potential gross
income than the ‘volatility only’-strategy. This difference demonstrates how the opportunity
benefit of paying the market price instead of the retail tariff outweighs the potential income
from benefitting from price volatility.
Interestingly, a low charging capacity (γ = 7 kW) yields a higher potential gross income than a
high charging capacity (γ = 22 kW). Figure 25 illustrates how the finer granularity of a small
charging capacity enables a power profile strategy of continuous charging – thereby
maximizing opportunity benefits. In contrast, high capacity charging will result in quickly
reaching SoCMax. As another charge profile at the end of a session would exceed the SoCMax
constraint the model is forced to discharge resulting in a lower SoCEnd and lower opportunity
benefits than the small charging capacity. P
Figure 25.. SoC for different values op γ during hour sessions under the charging-strategy.
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please note that these results are also dependent on SoCStart. When the model can reach the same
SoCEnd under both charging capacities, a higher charging capacity will yield higher gross
income (table 17) as it is also able to benefit from price volatility (see the similarities between
figure 25/26 and figure 17A/23).
Optimal Prediction
Annual Average Annual Average
7 kW €3481.10 €13.39 €1550.56 €5.96
22 kW €3315.67 €12.75 €1566.27 €6.02 Table 17. Gross income for work-sessions under the charging- strategy, SoCStart = 50% and Ω = 100 kWh.
As illustrated in table 18, night-sessions yield higher potential gross income than work-sessions.
Findings in regard to the strategy and the effect of charging capacity (figure 26) are similar to
those of work-sessions.
Optimal Prediction
Annual Average Annual Average
7 kW €5174.88 €14.18 €2366.85 €6.48
22 kW €5009.61 €13.73 €2438.58 €6.68 Table 18. Gross income under for night-sessions under different battery capacities (charging-strategy).
Figure 25. SoC for different values op γ during work-sessions under the charging-strategy.
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4.4 Profitability Whereas the previous sections determined gross income, this section will assess the profitability
after deducting depreciation costs and taxes from gross income. The results for the ‘volatility
only’- and the charging-strategy will be discussed separately.
4.4.1 ‘Volatility only’-Strategy
The ‘volatility only’-strategy is characterized by an equal number of charging- and discharging
power profiles. As a result, the volume (in kWh) that is subject to depreciation costs is equal to
the volume that is subject to taxes. Gross income and profit after deduction of depreciation costs
and taxes has been illustrated for each type of session on the following page (figure 27-29).
With the exception of marginally positive results for the most optimistic scenario under perfect
prediction, results demonstrate that the costs of taxes alone restrict EV-owners from
participating profitably in the intraday auction. The combination of depreciation costs and taxes
does not allow for profitable participation for any session type under any scenario.
Nevertheless, profits are to be made for work- and night-sessions, but only under the conditions
of optimistic depreciation costs and no taxes. As demonstrated by the results, the majority of
costs that are incurred by trading are related to energy taxes.
Figure 26. SoC for different values op γ during night-sessions under the charging-strategy.
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Figure 27. Total profit in 2017 for work-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50%
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Figure 28. Average profit in 2017 for hour-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50% .
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Figure 29. Total profit in 2017 for night-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50%
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
64
4.4.2 Charging-Strategy
In contrast to the ‘volatility only’-strategy, the volume of electricity that is charged and
discharged varies from one another under the charging-strategy (figure 30-32). The overall
tendency of power profile strategies to maximally charge an EV-battery under the ‘charging-
strategy avoids depreciation costs – which are only incurred for discharging. However, taxation
costs outweigh depreciation costs (table 2 & 3). Therefore, the benefit of avoiding depreciation
costs may be outweighed by the extra of taxes that are related to a higher volume of charging
power profiles.
Indeed, the results demonstrate that depreciation costs are low or non-existent for every session
with a charging capacity of γ = 7 kW. However, a high charging capacity (γ) results in quickly
reaching the SoCMax. This forces the model to incorporate multiple discharging power profiles
into its strategy, thereby incurring depreciation costs. By being forced to incorporate
discharging strategy profiles, more depreciation costs and taxes must be incurred. Figure 31
and 32 demonstrate how the extra costs outweigh the extra benefits of yielding income from
price volatility. This effect is particularly relevant for the scenarios that assume high
depreciation costs and high taxes.
Nevertheless, the charging-strategy remains profitable after deduction of depreciation and taxes
– with the exception of the pessimistic scenario under γ = 22kW.
65
Figure 30. Average profit in 2017 for hour-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50%
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Figure 31. Total profit in 2017 for work-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50%
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Figure 32. Total profit in 2017 for night-sessions after depreciation costs and taxes for γ = 7kW (left) and γ = 22kW (right), under Ω = 100 kWh, SoCStart = 50%
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
Optimistic Depreciation & Half Taxation
Expected Depreciation & Single Taxation
Current Depreciation & Double Taxation
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4.4.3 Summary
Income yielded by the model and the dynamics of the constraints, parameters and costs are
highly dependent on the strategy that is followed. The findings are summarized in table 19.
‘Volatility only’-strategy Charging-strategy
Gross income Optimal Realistic Optimal Realistic
€464.19 €377.20 €5174.88 €2366.85
Transactions Equally distributed over ‘charging’ and
‘discharging’.
Maximize ‘charging’ power profiles.
SoCStart Must not restrict the model from following
the optimal power profile strategy.
Preferably low to yield maximum opportunity
benefit.
Battery size Preferably high battery capacity, but
moderate effect on gross income.
Preferably high to yield maximum opportunity
benefit.
γ Preferably high (22kW) to benefit from price
volatility over larger volumes.
Normal (7kW) does suffice, as a high γ forces
power profile strategies to discharge.
Taxes
Equally distributed over transactions. High under most scenarios due to higher
‘charging’-frequency.
Much higher under γ = 22kW.
Depreciation Equally distributed over transactions. Low due to high ‘charging’-frequency.
Higher under γ = 22kW.
Profitability
Only under optimistic depreciation costs and
no energy tax.
Yes, with the exception of long sessions,
combined with a high γ-value under conditions
of current depreciation costs and double
taxation.
Table 19. Summary of results.
For the ‘volatility only’-strategy, the results demonstrate that profitable participation in the
intraday auction is possible from a technical perspective, under optimistic conditions. However,
these conditions only hold when policy makers rule in absolute favor towards these initiatives.
Given that no electricity is kept inside the battery, it may be argued that policies should exempt
the ‘volatility only’-strategy from energy taxes. Nevertheless, under circumstances where
electricity transactions are subject to current energy tax rates profitable participation is not
possible in any scenario.
For the charging-strategy, the results demonstrate that profitable participation in the intraday
auction is possible for most scenarios. However, it should be noted that profit mostly refers to
cost savings rather than actual income. This indicates that H01 is supported: profitable
participation is technically possible but – particularly for the ‘volatility only’-strategy –
dependent on tax policy.
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4.5 Benchmark The results that have been discussed above have been benchmarked with corresponding
parameter values against comparable studies with in table 20.
Balancing markets
Andersson et al. (2010) Germany €80 - €30 per month
Sweden None
Schuller & Rieger (2013) Germany €730.31 per year
Kahlen et al. (2015) Germany $177 per year
Day-ahead market
Peterson et al. (2010b) United States
(PHL, BOS, ROC)
Perfect Imperfect
$12 - 118 $6 - 72
Schill (2011) Germany $ 176 - $203
Intraday Auction
Study at hand Volatility only -€131 – -€161
Study at hand Charging to ≥80% €5105 – €2298
Table 20. Benchmark of results.
The results for balancing reserve markets are mostly related to regulation down (i.e. charging)
and capacity fees. In addition, Andersson et al. (2010) and Schuller & Rieger (2013) strongly
benefit from capacity fees that are earned by standing by without necessarily utilizing the
battery – thereby avoiding costs. Please note that the volumes that are charged in balancing
reserve markets are lower than the volumes traded under the charging-strategy in this study.
Therefore, a vis-à-vis comparison may not be completely representative. Furthermore, the
different price mechanisms in balancing markets and the intraday auction make a comparison
with the ‘volatility-only’ strategy difficult.
In contrast, Peterson et al. (2010b) use a fairly similar methodology to the ‘volatility only’-
strategy in this study – resulting in a surprising comparison of the results. Whereas intraday
price volatility is – by design – higher than volatility of day-ahead prices, the results that
Peterson et al. (2010b) find for the day-ahead market of Pennsylvania outweigh the results
found in the German Intraday Auction. This is partly related to the fact that Peterson et al.
(2012b) incorporate depreciation costs into the model; preventing the model from making
trades that result in a loss after deduction of depreciation costs. Furthermore, the result is a
maximum income over a period of six years – the average income for Philadelphia is $70.
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The authors highlight that results vary per state: Rochester and Boston yield significantly lower
results. Similar to the difference that Andersson et al. (2010) find for balancing reserve markets,
the difference may also be related to different characteristics between the German and
Pennsylvanian day-ahead market (e.g. share of renewable energy). Whereas Peterson et al.
(2010b) explicitly mention that social welfare benefits have not been included in their analysis,
Schill (2011) includes the economic benefit of efficient utilization of storage capacity.
Including these benefits in the results provides a possible explanation for a higher income in a
market that is generally characterized by lower price volatility than the intraday auction.
In comparison to similar studies that take a fleet-perspective, the benchmark identifies the
charging-strategy as an interesting opportunity for EV-participation in electricity markets. A
comparison with the ‘volatility only’-strategy yields surprising results – particularly in regard
to studies that focus on day-ahead markets. Nevertheless, multiple reasons provide a plausible
explanation for the unexpected discrepancies.
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5. Discussion This chapter discusses the opportunities for implementation. The first three paragraphs will
present insights from the vehicle-to-grid (V2G), P2P-carsharing and EV-lease practice. These
insights will be related to literature to determine opportunities, conditions and potential
constraints for the implementation of the model into these existing business models. The
subsequent paragraph will take a cross-case perspective in which the previous findings will be
compared in order to determine the implementation possibilities and conditions. Insights on the
development of intraday price volatility and its potential impact on the results from the previous
chapter will then be discussed. This chapter will conclude with a discussion of this study’s
limitations.
5.1 Vehicle to Grid Respondents recognize the potential of using EV-storage as a potential low-cost source of
flexibility. It prevents grid operators from having to heavily invest in upgrading their electricity
grid with higher capacity cables. This corresponds to the recognition of EVs as a storage
opportunity that is affordable and scalable (Mal et al., 2013; Després et al., 2017).
“Our electricity grids are not equipped for large scale electrification, like a massive switch to electric
mobility.”
(Interview 3, p. 7)
Opportunity V2G-1: EV-storage is considered as a potential low cost-source of flexibility.
Furthermore, the demand for flexibility is claimed to be on the rise. A major driver of this
increased demand is modern housing construction. Modern construction often includes the
installment of solar panels, which increases the effect of variability and uncertainty in the
system (Kassakian et al., 2011). An example of this trend in modern housing construction is the
promotion of ‘0 on the meter’-houses, that are designed so inhabitants will not have to pay for
electricity consumption (Rijksoverheid voor Ondernemen Nederland, 2015b). As a result, the
use of EV-storage as a buffer is expected to become an increasingly promising opportunity to
meet the increased demand for flexibility (Interview 4, p. 6).
Opportunity V2G-2: The demand for flexibility is expected to increase.
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5.1.1 Conditions
The robustness of the system is considered a major requirement for the implementation of the
model in this study. This is primarily important to maintain a continuous load-generation
balance.
The model currently assumes that individual EVs will deliver according to their indicated
availability – thereby allowing TSO’s and grid operators to schedule accordingly. However,
emergencies and other unforeseen circumstances may occur. Consequently, the model may fail
to deliver according to the indication. In order to meet the requirements of grid operators, the
system must be sufficiently robust to guarantee delivery regardless of unforeseen
circumstances. The limited ability of an individual EV to provide this robustness corresponds
to Kamboj et al. (2010), who state that competitive participation goes beyond the ability of a
single EV. Scaling the model to multiple EVs that are under direct control of a central operator
contributes to building a system that is sufficiently robust to meet grid operators’ requirements.
This corresponds to the findings of Vytelingum et al. (2011), who state that well managed
distributed storage capacity may be regarded as a valuable resource to the grid. In this regard
Kahlen et al. (2015) have demonstrated how to build a system that is sufficiently robust to meet
standards on the secondary operating reserve markets. Given that the technical requirements of
these markets are stricter than the intraday auction (Consentec, 2014; ENTSO-E, 2015; EPEX
Spot, 2018a), it is reasonable to assume that scaling the model can build a system that
sufficiently robust to participate in the intraday auction.
Condition V2G-1: The model should be sufficiently robust to guarantee a certain amount of
capacity – regardless of unforeseen events.
“Grid operators require hard guarantees to keep the system running. […] If a component fails, what will be
corrective measures? What uptime can you guarantee?”
(Interview 4, p. 3)
“Uptime and scale are correlated. If you’re using a car [that’s charging or discharging] and it fails, you’ve
got a problem. But if you have ten, your system will be much more robust.
I think that’s what’s required for this to work.”
(Interview 4, p. 6)
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Another condition is related to the fact that charger technology uses multiple protocols that are
not mutually compatible. Furthermore, charger technologies that enable a bidirectional flow of
electricity are still highly expensive.
Consequently, the limited accessibility of EV-owners to these technologies forms a restriction
to the scalability of the model. “In order for this to work you will need a bidirectional charger that:
A. costs less than €10.000, and
B. is compatible with all EV-brands.”
(Interview 3, p. 5)
Respondents from P2P-carsharing organizations indicate that many of the vehicle owners who
are offering to share their EV are centered in urban areas. As a consequence of lacking private
parking spaces in urban areas, these EV-owners are depending on public charging infrastructure
(Interview 2, p. 9). This infrastructure must improve to facilitate bidirectional charging.
Similarly, EV-lease organizations indicate that many of their customers are not willing to invest
in private charging infrastructure (Interview 6, p. 5). Given the high costs of bidirectional
chargers this makes it unlikely that bidirectional charging technology will be available at the
homes of EV-owners.
These issues are widely recognized in literature as adoption barriers to EVs in general (Brown
et al., 2010; Egbue & Long, 2012; Steinhilber et al., 2013). Kempton et al. (2014) even state
the standardization of infrastructure should be a core dimension of public policy that is aimed
at increasing EV and V2G adoption. Moreover, Yilmaz & Krein (2013) provide a list of charger
technology conditions that will determine the success of EV deployment over the next decade.
Given the currently low technology readiness level that is assigned to bi-directional charging
(Andwari et al., 2017), it is likely that prices will drop as the technology advances (Interview
4, p. 3). Although it is yet uncertain to which extent prices will drop, it is reasonable to assume
that high capacity bi-directional charging technology is going to be affordable for mass
adoption in the future.
Condition V2G-2: Charger protocols should be standardized to enable compatibility.
Condition V2G-3: Bidirectional charging technology should be sufficiently mature (both
technically and financially) to enable mass adoption.
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Given the important role of financial benefit, it is important to include costs as a condition.
Electricity costs are built up of multiple components (CBS, 2015; Interview 4, p. 5):
- The connection fee: a one-time payment for connection to the electricity grid. This will
be considered as sunk costs.
- The capacity fee: periodical payment for the capacity of the connection. The capacity
fee increases with the size of the capacity. E.g. a 22 kW-capacity connection is subject
to a higher capacity-fee than a 7 kW-capacity connection.
- System-services fee: periodical fee for grid operator services. This includes the costs of
the electrons, which is reflected by the market prices, and taxes.
The results in the previous section demonstrate that a higher capacity (γ) yields higher gross
income from volatility. However, increasing the capacity of a connection requires a one-time
connection fee (which also applies if a current connection must be upgraded) and an annual
capacity fee. This capacity fee is significantly more expensive than low-capacity connections
(PwC, 2017). Based on the capacity fee of Stedin (2018), that is determined at an annual cost
of €1676.91, table 21 illustrates how the capacity fee outweighs the gross market income for
work- and night-sessions.
Strategy Work-sessions Night-sessions
Optimal Realistic Optimal Realistic
Volatility only - €1455.97 - €1503.18 - €1212.72 - €1299.71
Charging €1638.76 -€110.64 €3332.70 €761.67
Table 21. Gross income for γ = 22kW after subtraction of the annual capacity fee under SoCStart = 50% and Ω = 100 kWh (Stedin, 2018).
Moreover, the model’s current assumption that such investments guarantee the full capacity of
22 kW at all times may not hold in the long term. Increased adoption of high capacity chargers
and connections is expected to result in congestion of the grid. Congestion refers to a situation
in which some connections (like high capacity EV-chargers) are curbed in order to prevent a
black out of the grid.
“It’s very similar to a highway. […] You may get stuck in traffic.
That’s congestion: that’s the 7 kW or 22 kW.
You’re assuming that you can always fully use that 22 kW-connection. But if you’re [driving] at 8:30 in the
morning your truck is going to get held up in a traffic jam.”
(Interview 4, p. 5)
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Under current market conditions, this is expected to result in a situation where EV-owners pay
for a 22 kW-connection but yield lower income due to not being able to fully use this capacity
at all times. A solution to this restriction is proposed in the form of a more flexible payment
structure.
Grid congestion problems are widely recognized in literature and is the result of a conflict of
interest between grid operators and electricity producers (i.e. the market) (Bakker et al., 2014).
An effective solution to this problem lies in smart charging (Sundstrom & Binding, 2012;
Valogianni et al., 2014). Please note that smart charging goes beyond the scope of this study,
as privatization of electricity markets - in which grid operators and electricity producers are
separate entities - results in a situation where the interests of grid operators are not included in
market prices. Whereas the market price is based on supply and demand, the capacity fees are
based on grid capacity and usage. Hu et al. (2014) propose a framework that integrates direct
control of EVs and price-based coordination – thereby integrating the interests of grid operators
and electricity producers. Such a framework is argued to lower the negative impact of technical
constraints related to congestion. On the one hand, allowing direct control may lower market
income as the model may be restricted to follow the optimal strategy during peak hours. On the
other hand, however, it may enable grid operators to lower capacity fees. Given the degree in
which capacity fees often outweigh market income (table 21), this is considered to be a
promising mitigation strategy.
Condition V2G-4: In order to prevent grid congestion, the model should include smart charging
strategies that take into account the interest of grid operators.
“You want to be in a situation where you’re paying for 7 kW but can trade according to 22 kW as much as
possible - at quiet times. And not the other way around: getting curbed while heavily paying for a 22 kW
connection. Because [under the current capacity-fee structure] that’s what’s going to happen.”
(Interview 4, p. 8)
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In addition to the capacity fee, current tax policies are unclear in regard to V2G-initiatives and
may restrict the annual income of the model (Interview 4, p. 7). Under current legislation, bi-
directional charging may be subject to double taxation, as every kWh that is charged or
discharged is taxable (PwC, 2017) – regardless whether it is kept in the battery for later
consumption or not (figure 7).
Zhang et al. (2014) illustrate how many policies are focusing on financial incentives, mostly in
the form of a purchase incentive for consumers. However, Van der Steen et al. (2015) state that
it is time for policy makers to move beyond this stage and propose a long-term strategy that
enables a self-enforcing loop of EV-adoption. Similarly, Bakker et al. (2014) encourage policy
makers to create circumstances that allow for interesting business cases in regard to enabling
EV-owners to participate as energy buffers to the grid. The incentive for V2G-beneficial
policies is further incentivized by the execution of projects like that of Nissan (2013), Nuvve
(2016), Enel (2016) and many more in the Netherlands and the rest of Europe (for a more
elaborate list see: HvA, 2018). Moreover, many of the projects that are currently operational
are financially dependent on subsidies (Interview 4, p. 3).
Policy uncertainty regarding these tax policies is likely to remain a constraint in the future
adoption of such business models (Gulen et al., 2015). Nevertheless, given policy
recommendations in academic literature and the rise of V2G-projects, it may be expected that
future (tax) policies will be favorable towards the implementation of the model presented in
this study.
Condition V2G-5: Tax policy should be clarified and supportive of V2G-initiatives.
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5.1.2 Summary
The results for the V2G-case have been summarized in table 22.
Opportunities V2G-1 EV-storage is considered as a potential low cost-source of flexibility.
V2G-2 The demand for flexibility is expected to increase.
Conditions V2G-1 The model should be sufficiently robust to guarantee a certain amount of
capacity – regardless of unforeseen events.
V2G-2 Charger protocols should be standardized to enable compatibility.
V2G-3 Bidirectional charging technology should be sufficiently mature (both
technically and financially) to enable mass adoption.
V2G-4 In order to prevent grid congestion, the model should include smart
charging strategies that take into account the interest of grid operators.
V2G-5 Tax policy should be clarified and supportive of V2G-initiatives.
Table 22. Summary of results for V2G-case.
5.2 P2P-Carsharing Carsharing organizations primarily focus on growth to achieve their mission of enabling people
to share their cars in order to decrease the number of cars required to fulfill people’s mobility
purposes. Achieving this growth involves creating awareness among rental customers, while
simultaneously stimulating car-owners to share their car on the platform. This is a costly affair,
and many of these organizations rely on investments to fulfill their mission (Interview 2, p. 2).
The reliance on investments prioritizes the requirement of financial benefit as the dominant
implementation condition. This section will elaborate on the opportunities the implementation
of the model for P2P-carsharing organizations. Subsequently, it will discuss in what regard the
model corresponds with the strategy of P2P-carsharing organizations. Lastly it will elaborate
on how the operations of a P2P-carsharing organization relate to implementation of the model
and vice versa.
An opportunity can be identified in the target group for the model of this study and P2P-
carsharing, which are indicated to share a common interest. Firstly, an interest to generate
income from their vehicle. This corresponds to the participation motive of yielding financial
benefits as described by Hamari et al. (2016) and Shaheen & Cohen (2007). Secondly, an
interest in building a sustainable future – as also discussed by Martin et al. (2010). Both of these
interests are perceived to also explicitly apply to EV-owners (Interview 1, p. 3).
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Whereas Schaefers (2013) proposed that sustainability benefits are regarded as a positive side-
effect rather than a dominant motive, the interviews correspond to Hamari et al. (2016) who
state sustainability as an important extrinsic motivation.
Opportunity P2P-1: Implementation of the model is likely to appeal to P2P-carsharing
customers, based on their characteristics.
In addition, this study assumed that using the client-facing infrastructure of P2P-carsharing
organizations would provide an opportunity for implementing the model. Respondents confirm
this assumption in regard to processing payments, registering availability and contacting
customers.
“Our strength lies in building great software. […] So, if we can plug into an existing hardware infrastructure
of bidirectional chargers, I think we could be of added value.”
(Interview 2, p. 4)
This argues in favor of the statement that combining organizational a P2P-carsharing’s
resources and IT-assets can create a valuable strategic advantage (Nevo & Wade, 2010).
Nevertheless, the qualitative nature of this study does not allow for inferences about the
generalization of this statement.
Opportunity P2P-2: The existing client-facing IT-infrastructure of P2P-carsharing
organizations may benefit the implementation of the model.
“Especially since our target group would be highly interested to extend that notion of sustainability.”
(Interview 1, p. 10)
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A third opportunity is related to the desired booking durations of different P2P-carsharing
organizations. For both respondents, the range in booking duration is very broad; ranging from
a couple of hours to an entire week. Organization A expects to facilitate a relatively high
number of short bookings, due to the inclusion of an option for vehicle owners to set an hourly
price (Interview 1, p. 4). Although Organization B does not set a minimum booking time, they
deliberately charge renters for half a day to discourage short bookings (Interview 2, p. 7). The
differences in rental preference between these organizations provide an opportunity for them
by enabling an alternative use case to their customers.
Opportunity P2P-3: Participation in the model may be offered complementary to preferred
rental behavior (long/short).
5.2.1 Conditions
In order to benefit from the opportunities that have been discussed above, multiple conditions
have been identified. These conditions must be met in order for implementation of the model
to be considered feasible. Many conditions are related to the current mode of operation of P2P-
carsharing and the behaviors of their participants – both renters and vehicle owners.
A vehicle owner may choose to clean the car before rental, but this is not mandatory. Other than
the fees listed under platform income (table 23), the vehicle owner should not incur any costs
when renting a vehicle. In contrast, P2P-carsharing organizations must be able to cover their
costs (e.g. platform development, control, administration) while simultaneously ensuring that
the majority of the earnings go to the EV-owner (Interview 1, p. 8-9). This – once again –
highlights the importance of financial motivation (Hamari et al., 2016; Shaheen & Cohen,
2007).
“The majority of the earnings must go to the car-owner. […] There must be a margin for us. They must be
able to provide us with part of the earnings.”
(Interview 1, p. 8 - 9)
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Moreover, it is in sharp contrast with the use case of Kahlen et al. (2015), where the fleet-owner
is the only party with an interest in the financial income of market participation. Whereas their
use case must only satisfy the financial interest of one party, P2P-carsharing organizations must
be able to yield sufficient income to incentivize EV-owners to participate in the model while
simultaneously securing a margin to cover their own costs (Interview 1, p. 8).
Condition P2P-1: Implementation of the model must yield sufficient income to (a) incentivize
EV-owners to participate in the model and (b) secure a margin to cover their own costs.
A comparison of the results presented in the previous chapter and the potential rental income
(table 23), indicates how the former is outweighed by the latter – at least under the ‘volatility
only’-strategy. In regard to the charging-strategy, it may be difficult for P2P-carsharing to
charge costs for their involvement - given the possibly different perception of yielding cost
savings rather than income. Albeit possible, respondents fear that this would involve
unnecessary complexity for the organization to implement and for customers to use: they
propose other parties (e.g. a grid operator) are more logical intermediaries to do so (Interview
1, p. 10-11; Interview 2, p. 5). Based on these statements, the potential income yielded under
the ‘volatility only’-strategy and the conditions under which financial benefit is yielded for the
charging strategy it is considered that the rental business should be prioritized over
implementation of the model. This is similar to the perspective of EV-fleet owners in Kahlen
et al. (2015), who demonstrate how rental operations should only be minimally disturbed.
Condition P2P-2: The rental business should not be disturbed by the implementation of the
model.
Organization Rental Price Fees Platform income
p. 24h
Owner income
p. 24h
A €3.50 - €5 p. h. 12.5 % insurance fee €2.50 €22 - €32.50
€2.50 p. day platform fee
B €30 per day
(Normal EV)
10 % insurance fee €3 €24
10 % platform fee
€200 per day
(Large battery)
10 % insurance fee €20 €160
10 % platform fee
Table 23. Income and fees for P2P-carsharing (Interview 1; Interview 2).
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Furthermore, implementation of the model may be offered as an additional service to attract
more EV-owners to the platform. However, respondents suggest there are more pressing
strategic priorities than particularly focusing on EV-owners.
Moreover, the concept may be difficult to prove due to the small number of EVs that are
connected to the platform.
“It would be a huge risk, that isn’t worth taking at this moment. It just wouldn’t make sense if you take the
number of EVs into account. Maybe in five years, but that would be speculating [about the scale of EVs on the
platform].”
(Interview 2, p. 5)
Nevertheless, it is reasonable to assume that the continuing increase of EV-adoption (CBS,
2017b) will partly be reflected in the number of EVs that participate in P2P-carsharing
platforms. In addition, Kahlen et al. (2015) have already demonstrated the opportunities for
EVs in a carsharing context.
Condition P2P-3: The scale of EVs in P2P-carsharing should increase for implementation of
the model to become of strategic relevance.
A final condition for implementation is related to the behavior of vehicle owners. Vehicle
owners can indicate the availability of their vehicle in an availability calendar. This allows
potential renters to send out a rental request. The vehicle-owner must accept this request for a
booking to be made. Although organization A stimulates vehicle owners to respond quickly to
booking requests, this often takes several hours (Interview 1, p. 4). Acceptance by the vehicle
owner may be seen as a bottleneck in the booking process.
“Especially because there is so much to gain in regard to our own, current product itself. There is so much
to improve.”
(Interview 2, p. 4)
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These findings correspond to what Ballus-Armet et al. (2014) described as a deterrent for
vehicle owners to participate: it may simple be too much effort or be insufficiently convenient.
The experience of P2P-carsharing organizations with vehicle owners suggests that expecting
vehicle owners to indicate their availability on a daily basis is not feasible. This jeopardizes the
assumption of the model that EV-owners are willing and capable to indicate their availability
at 15.00h the day before delivery in order to meet the corresponding market requirement.
Instead, participating in the model should not require any substantial effort from vehicle
owners.
Condition P2P-4: Participation in the model should require no substantial effort from EV-
owners.
5.2.3 Constraints
In addition to the conditions that have been identified and the discussion in regard to the extent
by which these conditions can be met, several constraints remain. The first of which is related
to a perceived potential risk of implementing the model: engaging in competition with their
current business model and corresponding investments. As argued above, the rental business
currently provides income for the organization. Initially, this risk is related to the availability
for either renting purposes or market participation according to the model in this study.
“You’re getting into competition with rental availability. If you’re available for renting, you’re not available
for the market. Because otherwise you’d risk that your vehicle will be rented [while you made it available to
the market] and you’ll have to pay a penalty for not delivering accordingly. That way your benefit from
rental income may be lost.”
(Interview 2, p. 6)
“Even if you can earn €30 per day by renting your vehicle, we’re seeing that it’s apparently still too much
trouble for vehicle owners to respond to booking requests. […] Let alone if you’re only getting a tiny bit of
income. […] And I think people who drive a car with a large battery, like Tesla-owners, will certainly not be
interested to put any effort in it - let alone with these figures.”
(Interview 2, p. 8)
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This suggests that a condition for implementing the current model in a P2P-carsharing
organization is to find a balance between rental availability and availability to participate in
electricity markets (Interview 1, p. 10). A problem that may well be solved by using the model
as a complement to renting business. E.g. by stimulating participation in the intraday auction at
times when it is highly unlikely for a car to be rented. However, the risk of internal competition
goes beyond the availability calendar. Even with equal financial benefits, the mission of both
concepts is inherently different.
P2P-carsharing organizations prioritize the mobility utilization of a vehicle over income
generation for its users. Should market participation be equally profitable, it would provide an
incentive for EV-owners to use the model instead of sharing the vehicle. This might incentivize
people to buy an EV, which is counterproductive to the carsharing-ideology of reducing the
number of vehicles on the road. The risk of internal competition may therefore be considered
as a constraint to implementation of the model in the long term. This conflict of interest may
prove to be a constraint in the long term.
Constraint P2P-1: On a strategic level (mission), there is a potential conflict of interest
between operation of the rental business and implementation of the model.
In order to maximize the mobility utilization of vehicles, P2P-carsharing organizations are
investing in incentives that stimulate specific behavior from renters and vehicle owners.
Currently, no clear structures in rental frequency have been identified – other than the
importance of the type of vehicle, rental price and parking location (Interview 2, p. 6). A major
reason for the lack of any additional knowledge is related to the fact that many vehicle owners
are active on multiple platforms (Interview 1, p. 7). Consequently, a vehicle may appear not to
be rented for one organization while it is rented out at another. The time at which a booking
request is sent out by a potential renter also varies greatly. Whereas the majority of booking
requests is sent out by renters a day in advance, there appears to be a trend towards last-minute
bookings.
“What we want is to have cars driving around. For your model, it’s important that they’re standing still.”
(Interview 2, p. 6)
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“We’re seeing last-minute bookings quite a lot. […]
People just want to rent a car without thinking about it a day in advance.”
(Interview 2, p. 6)
These findings correspond to recent work by Shaheen & Martin (2018) who identified
convenience as a key motive to join a P2P-carsharing organization. Moreover, they identify the
process of scheduling as a perceived disadvantage by renters.
In line with last-minute booking, a major focus lies on improving the ease of use of the platform
through facilitating keyless entry – gaining access to a rented vehicle through a mobile
application. Keyless entry aims to substitute the traditional form of handing over the keys. This
will make it unnecessary for the car-owner and the renter to be physically present, improving
the conditions for car-owners to make their vehicle available on the platform.
In addition, instant booking enables vehicle owners to increase the efficiency of the booking
process. Traditionally, the vehicle owner had to personally accept an incoming booking request.
Instant booking enables a vehicle owner to instantly accept incoming booking requests that
meet their requirements (e.g. pick-up time). This focus on convenience is similar to the findings
of Schaefers (2013) and Ballús-Armet et al. (2014).
“Instant booking is convenience.”
(Interview 2, p. 6)
Although these developments have been identified in literature (Shaheen & Cohen, 2007;
Ballus-Armet et al., 2014; Cohen & Kietzmann, 2013), some P2P-carsharing organizations
deliberately chose not to participate in order to save the costs on technology and preserve the
face-to-face interaction between customers and vehicle-owners (Geron, 2013). Interestingly the
implementation of these technologies is prioritized by both Dutch P2P-carsharing organizations
In addition, recent work of Shaheen, Martin & Bansal (2018) have identified a trend in P2P-
carsharing of enabling reservations without a predetermined end-time.
“Those people just hop in and drive. So, there shouldn’t be any additional criteria to their use of the
service.”
(Interview 1, p. 10)
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Although open-ended reservations have not been explicitly mentioned in the interviews, it is
reasonable to assume that implementation of this trend will at least be taken into consideration
by P2P-carsharing organizations at some point in the future. As a result, uncertainty in regard
to the availability of EVs for participation may increase further.
The focus areas of keyless-entry, instant-booking and open-ended reservations illustrate how
P2P-carsharing organizations are moving towards a scenario that allows for a wide range of
booking conditions; ranging from long term to real-time. However, effective implementation
of the model is dependent on the ability of the system to guarantee a certain capacity ahead of
time (see condition V2G-1). The trend towards a real-time booking process makes it difficult
to meet this condition – particularly the market requirement of indicating availability by 15.00h
a day ahead. Predicting availability to solve this uncertainty is perceived to be difficult, given
the characteristics of vehicle-owners that affiliate with a P2P-carsharing platform. This closely
corresponds to Ballús-Armet et al. (2014), who found that people who drive every day are
significantly less open to P2P-carsharing than people who travelled by public transport once a
week.
“If you’re talking about predictable use of vehicles, […] you’re talking about people who frequently use their
vehicle. Those are not the people who use a carsharing platform like ours.”
(Interview 2, p. 6)
Using penalties as an alternative control mechanism is perceived to be risky, as it disincentives
EV-owners to participate in the first place (Interview 2, p. 6). This is little incentive for EV-
owners to restrict their rental availability (see condition P2P-2). It is therefore considered that
P2P-carsharing organizations face serious challenges in creating a system that is sufficiently
robust to participate in an electricity market. Respondents explicitly state the fact that their
organizations do not own the vehicles, which makes it difficult to control them (Interview 1, p.
8; Interview 2, p. 3).
Constraint P2P-2: P2P-carsharing organizations are restricted in their capability to exercise
sufficient control to guarantee a robust system (Condition V2G-1).
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5.2.4 Summary The opportunities, conditions and constraints for implementation of the model into a P2P-
carsharing organization have been summarized below, in table 24.
Opportunities P2P-1 Implementation of the model is likely to appeal to P2P-carsharing
customers, based on their financial en environmental interest.
P2P-2 The existing client-facing IT-infrastructure of P2P-carsharing
organizations may benefit the implementation of the model.
P2P-3 Participation in the model may be offered complementary to preferred
rental behavior (long/short).
Conditions P2P-1 Implementation of the model must yield sufficient income to
(a) incentivize EV-owners to participate in the model and
(b) secure a margin to cover their own costs.
P2P-2 The rental business should not be disturbed by the implementation of the
model.
P2P-3 The scale of EVs in P2P-carsharing should increase for implementation
of the model to become of strategic relevance.
P2P-4 Participation in the model should require no substantial effort from EV-
owners.
Constraints P2P-1 On a strategic level (mission), there is a potential conflict of interest
between operation of the rental business and implementation of the model.
P2P-2 P2P-carsharing organizations are restricted in their capability to exercise
sufficient control to guarantee a robust system (Condition V2G-1).
Table 24. List of identified opportunities, conditions and constraints for the implementation of the model in a P2P-carsharing organization.
Several opportunities for implementation of the model within a P2P-carsharing context have
been identified, these opportunities confirm the assumptions made in this study. While it is
regarded that conditions related to income and complementary operation to the rental business
can be met, the trend towards more uncertain reservations (real-time and open-end) requires a
market design that allows for these uncertainties to be managed.
In addition, implementation may result in a conflict of interest on a strategic level. Whereas the
model benefits from vehicles standing still, both the organizations that were interviewed
emphasized their mission to maximize the use of vehicles for mobility purposes. A second
constraint is derived from the limited ability of P2P-carsharing organizations to exercise
sufficient control for guaranteeing a robust system.
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Based on these considerations it may be argued that P2P-carsharing organizations are a
promising channel for customer acquisition. However, they do not provide a promising
opportunity for implementation of the model.
Proposition 1: The business model of P2P-carsharing provides a promising opportunity
for the implementation of intraday market participation. – Not supported
5.3 EV-Lease In regard to the EV-lease case, interviews suggest that the target group of EV-lease
organizations provide a promising opportunity for implementing the model. This is mainly
related to the ideology of sustainability that characterizes electric mobility.
Opportunity EVL-1: Implementation of the model is likely to appeal to EV-lease customers,
based on their characteristics.
Interestingly, respondents frequently compared implementation of the model to the
implementation of carsharing-concepts in lease contracts. “Someone can lease an EV for €99 per month […] As a condition to this low fee, the driver is obliged to
share the vehicle for a minimum number of times in a month. Drivers can even earn money by sharing it more
often than we require.”
(Interview 6, p. 3)
Both EV-lease organizations and P2P-carsharing organizations demonstrate a positive attitude
towards this concept. For EV-lease organizations, it provides an opportunity to offer low-cost
contracts to incentivize customers who would otherwise not consider engaging in a lease
contract. For P2P-carsharing organizations, drivers who share their vehicle as part of a lease
contract are considered to be highly active participants on the platform.
Opportunity EVL-2: EV-lease organizations indicate an interest in offering their customers an
opportunity to reduce monthly fees.
“I think about 50% would be willing to participate in such a model, based on how the attitude of our current
customer base towards V2G-initiatives.”
(Interview 5, p. 1)
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Furthermore, this study assumed that using the client-facing infrastructure of EV-leasing
organizations would provide an opportunity for implementing the model. Respondents confirm
this assumption in regard to communication and documentation
“We have an app that enables our customers to register their mileage, documentation etc. […] It’s a portal
for us to quickly and easily communicate with customers.”
(Interview 6, p. 2)
This argues in favor of the statement that combining an EV-lease organization’s resources and
IT-assets can create a valuable strategic advantage (Nevo & Wade, 2010). However, the
qualitative nature of this study does not allow for inferences about the generalization of this
statement.
Opportunity EVL-3: The existing client-facing IT-infrastructure of EV-lease organizations
may benefit the implementation of the model.
A fourth opportunity is identified in regard to the ability of EV-lease organizations to use
control mechanisms, which helps them in building a system that is sufficiently robust to meet
V2G-requirements (Condition V2G-1). EV-lease organizations maintain ownership of the
vehicle and its battery – regardless of who is its’ driver. This enables them to set terms in their
leasing contracts, that enable clients to benefit from the model while obliging them to meet
certain criteria to meet market requirements (Interview 5, p. 1).
Opportunity EVL-4: By remaining ownership of EVs, EV-lease organizations can use control
mechanisms to build a robust system (see Condition V2G-1).
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5.3.1 Conditions
In order to benefit from the opportunities that have been discussed above, multiple conditions
have been identified. These conditions must be met in order for implementation of the model
to be considered feasible.
Similar to the importance of financial benefits in regard to overall adoption of EVs (Sierzchula
et al., 2014), financial benefit is claimed to be a major criterion for the development of EV-
lease organizations (Interview 5, p. 2). The experience of organization F explains how financial
aspects often outweigh the sustainability aspects for potential customers, in a way that highly
corresponds to customers’ considerations described by Egbue & Long (2012).
Consequently, providing the driver with sufficient financial benefit to incentivize participation
is considered a primary condition for implementation. If this condition is met, EV-lease
organizations perceive the model to be potentially beneficial in acquiring a larger customer base
by lowering the costs of lease contract.
Condition EVL-1: Participation in the model should yield sufficient financial benefit to
incentivize customer participation.
Similar to P2P-carsharing organizations, EV-lease organizations must be able to secure a
margin to cover costs incurred by implementation and operation of the model (Interview 5, p.
1). However, EV-lease customers have a contractual obligation towards EV-lease organization.
In contrast to P2P-carsharing organizations, participation in the model can be integrated into
the regular interaction between EV-lease organizations and their customers. This enables EV-
lease organization to simply deduct the financial benefits that have been yielded from the
monthly lease fee, while additional costs can also be taken into account. This implicates that
little additional complexity is required, even for the charging-strategy that yields higher
financial benefits but does so in the form of cost savings rather than income. In case of the
charging-strategy, this may take the form of an integration of mobility costs (lease) and fuel
costs into a single contract – whereby the EV-lease organization secures a margin from the cost
savings incurred.
“We can see very clearly that people are highly motivated [in electric vehicles] from a sustainability
perspective. But as soon as this includes higher prices their interest quickly subsides.”
(Interview 6, p. 1)
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In order to yield sufficient financial benefits to justify implementation, EV-lease organizations
require the model to be scalable (Interview 5, p. 2). According to Trocchia & Beatty (2003) an
important motive for individuals to engage in lease agreements is related to a simplified and
convenient way of consuming a mobility product. This implies that scaling the model among
lease-customers will only be possible if it does not hinder an EV-drivers’ primary use of the
vehicle and requires no substantial amount of effort from the EV-driver (Interview 5, p. 1).
Similar to the behavior of vehicle-owners in P2P-carsharing, the model is only considered to
be feasible if it takes minimum effort to participate.
“It must be as easy as ticking a box. Drivers shouldn’t have to think about it other than that. If a driver is
required to perform an action every day, it’s not feasible to scale this sufficiently. Business customers in
particular, are not willing to perform an action every day.”
(Interview 5, p. 1)
Condition EVL-2a: Participation in the model should not restrict the drivers’ alternative use of
the vehicle (i.e. mobility).
Condition EVL-2b: Participation in the model should require no substantial effort from EV-
drivers.
The design of the intraday auction, that requires availability to be indicated at 15.00h a day
before delivery, makes it difficult to meet the conditions related to convenience (EVL-2ab).
Meeting these conditions would require a reduction of the timeframe for indicating availability
to real-time. Although EV-lease organizations are able to contractually enforce certain criteria
to exercise control, the sociotechnical tradeoff that is related to mobility (Geels, 2004) requires
a careful tradeoff between exercising control at the cost of offering sufficient convenience to
customers.
“We must be able to exercise full control over the battery at times when it is connected, within certain
conditions like ensuring that enough battery capacity is present to fulfill mobility purposes.”
(Interview 5, p. 2)
Condition EVL-3: EV-lease organization should make a careful tradeoff between offering
convenience to customers and exercising control through contractual obligations.
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Scalability is also dependent on the scale of the customer group that can be targeted. Despite
the success of lease-carsharing collaboration, such concepts are restricted to a limited number
of private lease contracts. The majority of the lease market, business lease, appear to be less
interested.
“Business lease is more difficult to include. Their clients are often other businesses, and a business has even
fewer interest in letting someone else than their own employees use a vehicle that they are leasing.”
(Interview 2, p. 9)
Interestingly, a participant in this study who represents an EV-lease organization indicated how
they encourage their business lease clients to engage in carsharing (Interview 5, p. 2).
Furthermore, he explains how cost ownership plays a role in the limited incentive for business
lease organization to do so more frequently.
The concept of cost ownership is claimed to play a major role for incentivizing (business) lease
customers to engage in carsharing concepts and – similarly – the implementation of the model
in this study. This situation is a classic example of the principal-agent problem that Graus &
Worrell (2008) put forward in the context of fuel consumption for business lease cars. As long
as individual drivers (the agents) are not responsible for their fuel consumption, there is no
incentive for them to minimize fuel costs for the benefit of the fleet-owner (the principal). Many
businesses employ fleet-managers, who are responsible for managing the vehicle fleet of a
company. Fleet-managers may be regarded as the agents, whereas the business is regarded as
the principal. Fleet-manager roles are relatively expensive human resources for a company,
while modern technology enables individual employees to operate as individual agents. In
addition, the employment of fleet-managers is argued to remove an individual employees’ sense
of responsibility and accountability for costs related to a vehicle. The relationship between an
individual employee and the fleet-manager is similar to the case described by Graus & Worrell
(2008): there is no incentive for the individual (agent) to minimize the costs of a lease contract
for the benefit of the fleet-manager that represents the interests of the business. In this scenario,
there is little to no incentive for an individual to engage in practices that reduce the costs on a
lease contract.
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In contrast, a situation in which individual employees are assigned a ‘car allowance’ would
provide an incentive for these employees to engage in concepts that reduce the costs of their
contract. In this case, the agent-role of the fleet-manager is abandoned as individual all become
agents with a mutual interest between the individual and the business: maximizing the use of a
lease-budget. Presumably because it can enable them to drive a more expensive car or use part
of the car allowance for other purposes. Moving cost ownership to the individual is therefore
considered to be serious value to the scalability of the model within EV-lease organizations.
“That’s going to make it interesting to save money: both for V2G and for carsharing. As long as the company
is paying for everything, you’re lacking the incentive to do so.”
(Interview 5, p. 2)
Condition EVL-4: Individual employees should be assigned a ‘car allowance’ as an alternative
to centralized fleet management.
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5.3.2 Summary The opportunities, conditions and constraints for implementation of the model into a P2P-
carsharing organization have been summarized below, in table 25.
Opportunities EVL-1 Implementation of the model is likely to appeal to EV-lease customers,
based on their characteristics.
EVL-2 EV-lease organizations indicate an interest in offering their customers an
opportunity to reduce monthly fees.
EVL-3 The existing client-facing IT-infrastructure of EV-lease organizations
may benefit the implementation of the model.
EVL-4 By remaining ownership of EVs, EV-lease organizations can use control
mechanisms to build a robust system (see Condition V2G-1).
Conditions EVL-1 Participation in the model should yield sufficient financial benefit to
incentivize customer participation.
EVL-2 A. Participation in the model should not restrict the drivers’ alternative
use of the vehicle (i.e. mobility).
B. Participation in the model should require no substantial effort from
EV-drivers.
EVL-3 EV-lease organization should make a careful tradeoff between offering
convenience to customers and exercising control through contractual
obligations.
EVL-4 Individual employees should be assigned a ‘car allowance’ as an
alternative to centralized fleet management.
Table 25. List of identified opportunities, conditions and constraints for the implementation of the model in a P2P-carsharing organization.
Several opportunities for implementation of the model within the context of EV-lease have been
identified, these opportunities confirm the assumptions made in this study. The current
interaction between EV-lease organization is considered to provide a promising opportunity for
administration of operations and the administration of financial benefits. In addition, initiatives
that enable customers to lower monthly fees are already operational in both EV-lease
organizations that participated in this study. Moreover, the contractual obligation between EV-
lease organizations and their customers provide them with a control mechanism that is expected
to be effective.
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However, the focus on convenience is considered to be misaligned with the current design of
the intraday auction. Given this explicit characteristic of lease-customers (Trocchia & Beatty,
2003) it may be argued that another market design is required for implementation of the model
to succeed.
EV-lease organizations are recommended to be careful in making the sociotechnical tradeoff
(Geels, 2004) when specifying the conditions for participation. Nevertheless, these
organizations are considered to be an interesting opportunity for implementation of the model.
Proposition 2: The business model of EV-lease provides a promising opportunity for the
implementation of intraday market participation. - Supported
5.4 Cross-case Clearly, the conditions for the vehicle-to-grid (V2G) case must be met in order for the model
to be implemented in any (existing) business model, resulting in a tradeoff between control and
convenience. Whereas the V2G-perspective requires a high degree of control (Condition V2G-
1), customers of both P2P- and EV-lease organizations are argued to be focused on convenience
(Conditions P2P-4 & EVL-2AB). The electricity market and grid operators require guarantees
while participants must make a socio-technical trade-off to put in effort and possibly restrict
their mobility use of an EV (Geels, 2004). Given the current design of the intraday market,
respondents for every case state that the feasibility of implementing the model is questionable.
The redesign of electricity wholesale markets, towards gate-closure times that are closer to real-
time, has already been proposed in multiple studies. Kassakian et al. (2011) argue that
uncertainty in these markets will increase with the expected increase of renewable energy
penetration (Kassakian et al., 2011). Since this restricts prices in reflecting all relevant
information the market may become less efficient. However, given the fact that uncertainty
decreases as gate closure times approach real-time (Focken et al., 2002; Von Roon & Wagner,
2009), allowing participants to trade near real-time will enable them to respond to prices that
include better information in regard to the production of renewable energy (Barth et al., 2008).
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In order to prevent this demand to exceed the capacity of balancing reserve markets (Morales
et al., 2014), Musgens & Neuhoff (2006) demonstrate that a gate-closure that is close to real-
time reduces the costs for balancing reserves – since the uncertainties that fuel the demand for
balancing reserves are already accounted for in electricity wholesale markets. It may therefore
be argued that a redesign of electricity wholesale markets is necessary with increased
penetration of renewable energy.
Given the reasonable assumption that gate-closure times are moving towards real-time, multiple
opportunities have been identified for implementation of the model in P2P-carsharing and EV-
lease organizations Both organization types offer interesting opportunities in regard to a
customer group that is expected to find the model appealing and to resources (e.g. IT-
infrastructure) that allow them to interact with customers.
In addition to opportunities, multiple conditions have been identified and described in the
chapter above. While it has been argued that the majority of these conditions can be met, two
constraints remain for implementation of the model in P2P-carsharing organizations
specifically:
• There is a potential conflict of interest between the mission of P2P-carsharing
organization and implementation of the model. Both have contrasting perspectives on
vehicle utilization.
• P2P-carsharing organizations are restricted in their capability to exercise sufficient
control to guarantee a robust system (Condition V2G-1).
Moreover, P2P-carsharing organizations indicate to have no specific focus on EVs. This is
mainly due to the strong growth ambition of P2P-carsharing organizations combined with the
currently limited scale of EVs. Although respondents expect the adoption of EVs to increase,
this is not yet reflected in the strategy for P2P-carsharing organizations. Alternatively,
opportunities in regard to a similar target group may be captured by using P2P-carsharing
organizations as an acquisition model for participants.
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In contrast to this first constraint, EV-lease organizations have no interest in vehicle utilization
by EV-drivers. Moreover, these organizations are already offering their customers an
opportunity to reduce monthly fees – by engaging in P2P-carsharing. Implementation of the
model is argued to coexist with this practice. For EV-lease organizations they serve a similar
interest: attracting customers by providing them with an opportunity to reduce costs.
In regard to the second constraint, EV-lease organizations are argued to be in a unique position
to exercise control, given that the organization maintains ownership of an EV. Although EVs
are geographically distributed they are owned by the EV-lease organization. This enables the
organization to incorporate certain conditions in a contractual agreement with an EV-driver.
Presumably this would be implemented as an option in the contract, as not all EV-lease
customers are expected to be interested (Interview 5, p. 1).
Given that a similar initiative is already being offered to customers, it is considered reasonable
to assume that EV-lease organizations are capable to facilitate this kind of interaction with
customers. From a technological perspective, current partnerships of EV-lease organizations
with suppliers of charging technology, make it reasonable to assume that similar partnerships
can be established with suppliers of bi-directional charging technology and parties that
implement the algorithm to facilitate market participation.
Given the constraints that apply to P2P-organizations and the unique opportunities that apply
to EV-lease organizations, it is concluded that proposition 3 is supported.
Proposition 3: On the short term, the business model of EV-lease provides a more
promising opportunity for the implementation of intraday market participation than
P2P-carsharing. – Supported
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5.5 Prospect of Intraday Price Volatility Given the dominant role of price volatility in meeting conditions related to financial benefit,
this section will discuss likely developments in the intraday auction and their implications for
price volatility.
Intraday price volatility is largely fueled by restricted participation: both in terms of technical
requirements and the lack of cross-border trade (Knaut & Paschmann, 2017a). The restriction
to cross-border trade clearly restricts Dutch EV-owners from participating in the German
intraday auction. However, projects that enable cross-border trade of sub-hourly intraday
products are already being implemented by the European Power Exchange (EPEX SPOT SE,
2017). Whereas enabling cross-border trades is expected to yield substantial welfare benefits in
terms of market efficiency, market coupling is expected to reduce price volatility by a factor
four (Knaut & Paschmann, 2017ab). As a result, it will be possible for Dutch EV-owners to
participate in the German intraday auction. Moreover, Knaut & Paschmann (2017ab) do not
take into account that the energy transition that drives price volatility is also ongoing in other
European markets. Hence, the reduction of price volatility as a result from market coupling is
expected to be limited.
In contrast, it is even argued that German intraday price volatility will rise. Given that demand
is rather inelastic, increased renewable penetration is likely result in peaking intraday prices
(Ilic et al., 2007; Knaut & Paschmann, 2016; Lijesen, 2007). However, despite an increase in
renewable energy penetration, figure 33 demonstrates how intraday price peaks have flattened
over the course of the past two decades (Oxford Institute for Energy Studies, 2016). This is
likely an indication that demand profiles are generally well aligned with the supply of
renewable energy. Given the similarity between demand profiles and the supply of renewable
energy in Germany (Bundesnetzagentur, 2018), residual demand is generally low leading to an
erosion of intraday price peaks with the increased penetration of RES (Knaut & Paschmann,
2016). It was even claimed that, on 1 January 2018, there was a moment of no residual demand
– 100% of electricity was supplied by renewables (Amelang, 2018).
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An example of how demand and the generation of renewable energy (particularly photovoltaic)
align over the course of ten days is illustrated in figure 34. Alternatively, there are multiple
factors that may result in a strong misalignment between demand profiles and the supply of RE
– e.g. areas with high air-conditioning loads or a high dependency on electricity for winter
heating. Nevertheless, it is argued that the alignment between demand and RE-supply
implicates historically low volatility in intraday prices, as the expected increase in renewable
energy penetration will increase the demand for ramping capacity and consequently increase
price volatility in the German electricity market (Moody’s, 2017).
Participation in the intraday auction based on volatility provides opportunities in regard to the
expected increase in renewable energy and the resulting expected increase in price volatility.
However, these opportunities are not without risks as they are dependent on a variety of factors.
In addition, the magnitude of this increase is currently unknown.
Figure 33. Peak erosion of German intraday prices (Bloomberg; Oxford Institute of Energy Studies 2016)
Figure 34. Example of corresponding demand- and renewable energy generation profiles in Germany (Bundesnetzagentur, 2018).
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6. Conclusion This study aimed to answer the following research question:
What are profitable business models for individual EVs to benefit from participation in short-term electricity
markets?
Following a strategy that only benefits from price volatility is only feasible for sessions of
multiple hours, at night, under circumstances of optimistic depreciation costs and no taxation.
In this case participation yields an annual profit of €78.11 - €48.91 per EV. Recommendations
in literature and developments in the V2G-practice argue for this scenario to be reasonable
(Bakker et al., 2014; Van der Steen et al., 2015). Moreover, it may be argued that intraday price
volatility will rise in the future (Moody’s, 2017) – resulting in a higher income for this strategy.
Nevertheless, the development of policy remains a major uncertainty that should be taken into
account by potential investors (Gulen et al., 2015). Although is it possible and future prospects
indicate opportunities, the current potential profit for a ‘volatility only’-strategy is low and
investments in participation under this strategy are not without serious risks.
Following a strategy that allows EVs to yield cost savings by charging from the market instead
of a retail tariff yields a substantially higher gross income. These results remain positive even
after subtraction of depreciation and taxes – with the exception of long sessions under high
taxes and depreciation costs. For an 8-hour session profits range from €4652.50 to €1845.65 in
a scenario of low deprecation and low taxes to €4027.45 - €1220.58. Similar to multiple other
studies (Capion, 2009; Kahlen et al., 2017; Schuller & Rieger, 2013), most of this financial
benefit is yielded by charging at a lower cost rather than price volatility. Hence these results
are strongly dependent on the volume electricity that can be charged. E.g. a higher battery
capacity enables an EV to save costs over a higher volume of charged electricity.
For both strategies, profits are most strongly restricted by taxes rather than battery depreciation
costs. For both strategies, costs of taxes outweighed the costs for depreciation. This contributes
to findings in literature (Bakker et al., 2014; Gulen et al., 2015; Van der Steen et al., 2015) that
recommend V2G-favourable policy development and clarification to enable V2G-adoption.
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Implementation of the model primarily relies on the fulfillment of multiple conditions in the
V2G-practice. Firstly, bidirectional charging technology should be sufficiently mature to enable
large scale adoption. This condition is likely to be met over time (Anwari et al., 2017).
Secondly, individual EVs should be aggregated and sufficiently controllable to build a system
that is capable of guaranteeing uptime.
With these requirements in mind, EV-lease organizations are considered to be in a more
advantageous position to facilitate market participation of individual EV-drivers than P2P-
carsharing organizations. Given the strategic focus on overall growth and the limited scale of
EVs, P2P-carsharing organizations indicate to have other investment priorities. In addition, they
expect to have difficulties in exercising sufficient control to meet V2G-conditions.
Furthermore, there is a potential conflict of interest in vehicle utilization. Whereas participation
in electricity markets requires EVs to be parked at a charging station, P2P-carsharing
organizations aim to reduce the number of vehicles on the road by maximizing their mobility
use. In contrast, EV-lease organizations appear to have an interest in offering their customers
an opportunity to reduce costs – particularly when this is also related to sustainability issues.
Interestingly, EV-lease respondents have already implemented an opportunity to do so by
enabling customers to reduce monthly costs in exchange for their participation in P2P-
carsharing. This implicates that a business model that allows individual EVs to participate in
short-term electricity markets may well be implemented by EV-lease organizations. It is argued
that these practices and the model presented in this study may well coexist in EV-lease
organizations as they both allow customers to reduce monthly fees in exchange for engaging in
additional contractual obligations (i.e. agreement to delegate control of an EV-battery to the
EV-lease organization at times when it is connected to a charging station).
Ultimately, this is may result in a business model where individuals with a frequent, predictable
mobility need will consume mobility through semi-ownership – under a lease contract. Their
mobility costs can be reduced by renting the leased vehicle to consumers with a less frequent
and less predictable pattern of mobility consumption – through P2P-carsharing. EV-lease
customers may further reduce costs by offering the EVs storage capacity to the grid – through
market participation.
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In answering the research question, this study makes several contributions to the research area
of Green IS (Melville, 2010; Watson et al., 2010). It particularly corresponds to the claim, made
by Kossahl et al. (2012), that IS-researchers should address the potential of electric mobility
more strongly. By designing and evaluating a model that enables decision making for individual
EVs – as distributed energy resources, a contribution has been made to the area of smart market
design (Bichler et al., 2010). This study proposes that EVs can be utilized as distributed storage
capacity in the intraday auction, thereby capturing part of the increasing demand for balancing
reserves– ultimately contributing to the efficiency of electricity markets (Morales et al., 2014).
Multiple conditions (e.g. development of favorable tax policy) have been identified that must
be fulfilled for this to become economically feasible. Whereas, multiple studies have researched
the economic potential of such ‘vehicle to grid’(V2G) -initiatives in day-ahead (Capion, 2009;
Peterson et al., 2010) and balancing reserve markets (Andersson et al., 2010; Kahlen et al.,
2015; Kamboj et al., 2010; Brandt et al., 2017), this study is the first to explore the opportunities
of EV-participation in an intraday auction. This contribution is perceived to be particularly
relevant given the increased demand for products traded in these markets (Weber, 2010).
Whereas the participation of EVs in electricity markets has repeatedly been studied from the
perspective of EV-fleet owners (Andersson, 2010; Capion, 2009; Kahlen et al., 2015; Schuller
& Rieger, 2013; Schill, 2011), not much attention has been paid to the perspective of individual
EV-drivers. The individual perspective is also unique in the Green IS research area of
sustainable mobility (Hildebrandt et al., 2012). Although Kahlen et al. (2017) present a similar
combination of smart market design and the trend towards service-oriented business models,
this study is unique in exploring the individual perspective to these models through P2P-
carsharing. In doing so, it provides an example of how of aligning individual and sustainable
goals, highlighted by Loock et al. (2012) as an important element of Green IS, takes shape for
electric mobility and service-oriented business models. This is considered a valuable
contribution to the call, made by Kossahl et al. (2012), for further research into electric mobility.
This is emphasized by the increasing amount of EVs and the share of vehicles that are privately
owned or driven by individuals under lease conditions (Bovag, 2018; CBS, 2017).
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6.1 Limitations & Future Research In addition to academic contributions, this study also acknowledges multiple limitations, many
of which are considered interesting opportunities for future research. These limitations are
primarily related to the validity of the model and the reliability of the dataset that was used to
evaluate it.
Firstly, the model assumes that costs related to depreciation and taxes are exogenous – they are
not incorporated into the model. The model in this study solely focuses on yielding the highest
result by benefitting from price volatility. Whereas Peterson et al. (2010b) have included
depreciation costs into the algorithm, the model in this study may even engage in trades that
are not profitable after subtraction of depreciation costs or taxes. Extending the optimization
algorithm by including the costs that are incurred specifically with charging or discharging (e.g.
depreciation only applies to discharging) is expected to result in more effective strategies and
corresponding income.
The model may be further refined through more flexible use of the charging capacity. Whereas
the model assumes that it (dis)charges at maximum capacity, strategies may benefit by charging
at a finer granularity – e.g. by charging a low volume of electricity during the first three quarters
while discharging at full capacity during the last quarter.
Furthermore, the current model does not incorporate the interests of grid operators. On the long
term, the large-scale adoption of EVs and their participation in electricity markets is expected
to result in grid congestion. Grid congestion problems are widely recognized in literature and
is the result of a conflict of interest between grid operators and electricity producers (i.e. the
market) (Bakker et al., 2014). Grid congestion prevents EVs from (dis)charging at the desired
capacity and possibly restricts the optimal strategy from being executed. The model presented
in this study merely takes into account the interests of EV-drivers, the electricity market and an
organization that enables their interaction. Although crucial, the interests of grid operators have
only been assessed based on a qualitative analysis. Although the findings in this study provide
a useful initial assessment of economic feasibility, long-term feasibility will require the interests
of grid operators to be integrated in the model – presumably through the concept of smart
charging.
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The presence of conflicting interests (Bakker et al., 2014) implicates the need of an independent
party for the integration of these interests. Bessa & Matos (2012) propose that an aggregator in
the form of a VPP would be a suitable party to facilitate such integration. This provides a
potential opportunity for incorporation of the model into the business model of a third party
(e.g. an EV-lease organization). However, including interests of grid operators may affect the
terms of the sociotechnical trade-off for EV-drivers (Geels, 2004). On the side of financial
benefits, additional constraints may prevent the execution of optimal strategies; resulting in
lower gross income. On the side of costs, participation is possibly made less convenient. Hence,
the feasibility may be affected. To determine long-term feasibility, it is crucial to assess how
the terms of the socio-technical tradeoff change when additional interests are included in the
model.
Similarly, by solely focusing on the financial benefits to individuals and organizations, the
model in this study ignores the interests of society in general. However, the participation of
EVs in electricity markets has demonstrated to yield substantial social welfare benefits – e.g.
through increased rates of utilization of utility investments (Sioshansi et al., 2009; Schill, 2011).
This is particularly interesting given the expected increase of these benefits with the growing
penetration of renewable energy (Peterson et al., 2010a). These welfare benefits can provide an
additional incentive for policy makers to develop policies that are favorable to V2G-initiatives,
thereby contributing to the feasibility of the model presented in this study. An interesting area
of future research would therefore be to study the social welfare benefits of adopting the model
in this study, possibly identifying the degree of which these benefits can partly be delivered
back to individuals.
In addition to refinement and extension of the model, the results are highly dependent on the
data that is used to evaluate it. The data used in this study are from the intraday auction in
Germany, which limits the extendibility of the results to other markets. A comparison of the
results in this study with results of similar studies for day-ahead markets identifies unexpected
discrepancies (Peterson 2010b; Schill 2011). Although the volatility of intraday prices is – by
definition – higher than the volatility of day-ahead prices, the proposed explanation for relating
these differences to different market characteristics has not been tested. The North-American
market is therefore considered to be an interesting starting point for a geographical comparison
of the behavior and results of the model presented in this study.
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Given the ongoing energy transition the characteristics of the intraday auction will likely change
over time – e.g. the degree of renewable energy and the alignment of their supply with demand
profiles). Knaut & Paschmann (2017ab) claim that opening the intraday auction for cross-
border trade will result in decreasing price volatility. However, they do not take into account
that the energy transition and the resulting impact on price volatility spans across all European
markets. Moreover, the expected increased penetration of renewable energy is expected to
increase price volatility (Moody’s, 2017). Although these developments are likely to affect the
financial results of the model, the magnitude of the impact is unknown. In order to reduce the
risks in regard to volatility prospects, research into the development of a unified, European
intraday market (e.g. through a longitudinal approach) would be valuable.
Additionally, the interaction between EVs and electricity markets has a reciprocal effect.
Therefore, the scale at which EVs participate in the intraday auction will affect its price
volatility. Based on simple supply and demand logic, large scale participation of EVs will
increase the supply of flexibility resulting in lower residual demand and consequently lower
prices and lower price volatility (Knaut & Paschmann, 2016). It would be interesting to see
where the saturation point lies and to what extend this will move over time.
Lastly, although multiple interviews have been conducted at different organizations, the results
found in the qualitative approach of this study may not be representative of the entire V2G-,
P2P-carsharing or EV-lease practice. This limits the generalizability of the results found in this
study. A quantitative approach to research the propositions found in this study is therefore
considered an interesting area of future research.
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References Agarwal, R., Gao, G., DesRoches, C., & Jha, A. K. (2010). Research commentary—The digital transformation of healthcare: Current status and the road ahead. Information Systems Research, 21(4), 796-809. Alanne, K., & Saari, A. (2004). Distributed energy generation and sustainable development. Renewable and Sustainable Energy Reviews, 539-558. Albadi, M. H., & El-Saadany, E. F. (2008). A summary of demand response in electricity markets. Electric power systems research, 78(11), 1989-1996. Albinsson, P. A., & Yasanthi Perera, B. (2012). Alternative marketplaces in the 21st century: Building community through sharing events. Journal of consumer Behaviour, 11(4), 303-315. Amelang, S. (2018) "Renewables cover about 100% of German power use for first time ever" Clean Energy Wire. Accessed 6 June 2018, from: https://www.cleanenergywire.org/news/renewables-cover-about-100-german-power-use-first-time-ever Andersen, P. B., Hu, J., & Heussen, K. (2012). Coordination strategies for distribution grid congestion management in a multi-actor, multi-objective setting. In Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on (pp. 1-8). IEEE. Andersen, P. B., Poulsen, B., Decker, M., Træholt, C., & Ostergaard, J. (2008, December). Evaluation of a generic virtual power plant framework using service-oriented architecture. In Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International (pp. 1212-1217). Andersson, S. L., Elofsson, A. K., Galus, M. D., Göransson, L., Karlsson, S., Johnsson, F., & Andersson, G. (2010). Plug-in hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany. Energy policy, 38(6), 2751-2762. Aneiros, G., Vilar, J., & Raña, P. (2016). Short-term forecast of daily curves of electricity demand and price. International Journal of Electrical Power & Energy Systems, 80, 96-108. Andwari, A. M., Pesiridis, A., Rajoo, S., Martinez-Botas, R., & Esfahanian, V. (2017). A review of Battery Electric Vehicle technology and readiness levels. Renewable and Sustainable Energy Reviews, 78, 414-430. Asmus, P. (2010). Microgrids, virtual power plants and our distributed energy future. The Electricity Journal, 23(10), 72-82. Bakker, S., Maat, K., & van Wee, B. (2014). Stakeholders interests, expectations, and strategies regarding the development and implementation of electric vehicles: The case of the Netherlands. Transportation Research Part A: Policy and Practice, 66, 52-64. Ballús-Armet, I., Shaheen, S., Clonts, K., & Weinzimmer, D. (2014). Peer-to-peer carsharing: Exploring public perception and market characteristics in the San Francisco Bay area,
104
California. Transportation Research Record: Journal of the Transportation Research Board, (2416), 27-36. Barth, R., Weber, C., & Swider, D. J. (2008). Distribution of costs induced by the integration of RES-E power. Energy Policy, 36(8), 3107-3115. Barth, M., Todd, M., & Shaheen, S. (2003). Intelligent transportation technology elements and operational methodologies for shared-use vehicle systems. Transportation Research Record: Journal of the Transportation Research Board, (1841), 99-108. Belastingdienst, (2018) Belasting op elektriciteit. Accessed 12 May 2018, from: https://www.belastingdienst.nl/wps/wcm/connect/bldcontentnl/belastingdienst/zakelijk/overige_belastingen/belastingen_op_milieugrondslag/tarieven_milieubelastingen/tabellen_tarieven_milieubelastingen?projectid=6750bae7-383b-4c97-bc7a-802790bd1110 Belk, R. (2009). Sharing. Journal of consumer research, 36(5), 715-734. Bessa, R. J., & Matos, M. A. (2012). Economic and technical management of an aggregation agent for electric vehicles: a literature survey. International Transactions on Electrical Energy Systems, 22(3), 334-350. Bhangu, B. S., Bentley, P., Stone, D. A., & Bingham, C. M. (2005). Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles. IEEE transactions on vehicular technology, 54(3), 783-794. Bichler, M., Gupta, A., & Ketter, W. (2010). Research commentary—designing smart markets. Information Systems Research, 21(4), 688-699. Bleijenbergh, I. (2013). Kwalitatief onderzoek in organisaties. Den Haag: Boom Uitgevers Bloomberg New Energy Finance, (2017) Clean Energy Investment Trends: 3Q 2017. Boeije, H. (2005). Analyseren in kwalitatief onderzoek. Amsterdam: Boom Lemma Uitgevers. Bohnsack, R., & Pinkse, J. (2017). Value Propositions for Disruptive Technologies: Reconfiguration Tactics in the Case of Electric Vehicles. California Management Review, 59(4), 79-96. Borggrefe, F., & Neuhoff, K. (2011). Balancing and intraday market design: Options for wind integration. Botsman, R., & Rogers, R. (2011). What's mine is yours: how collaborative consumption is changing the way we live. Botsman, R. (2013). The sharing economy lacks a shared definition. Fast Company, 21, 2013. Bovag, (2018). Mobiliteit in Cijfers Auto's 2017 - 2018. Accessed 6 June 2018, from: https://bovagrai.info/auto/2017/bezit/1-5-personenautopark-naar-brandstof/
105
Bundesnetzagentur, (2018). Electricity generation and consumption in Germany. Accessed 6 June 2018, from: https://www.smard.de/blueprint/servlet/page/en/5790 Brandt, T., Wagner, S., & Neumann, D. (2017). Evaluating a business model for vehicle-grid integration: Evidence from Germany. Transportation Research Part D: Transport and Environment, 50, 488-504. Braun, S. (2016). Hydropower Storage Optimization Considering Spot and Intraday Auction Market. Energy Procedia, 87, 36-44. Brook, D. (2004, January). Carsharing–start up issues and new operational models. In Transportation Research Board Annual Meeting. Brooks, A., Gage, T., & Propulsion, A. C. (2001, October). Integration of electric drive vehicles with the electric power grid—a new value stream. In 18th International Electric Vehicle Symposium and Exhibition, Berlin, Germany (pp. 20-24). Brown, S., Pyke, D., & Steenhof, P. (2010). Electric vehicles: The role and importance of standards in an emerging market. Energy Policy, 38(7), 3797-3806. Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: Information technology, organizational transformation and business performance. Journal of Economic perspectives, 14(4), 23-48. Caldon, R., Patria, A. R., & Turri, R. (2004). Optimal control of a distribution system with a virtual power plant. Bulk power system dynamics and control, Cortina. d’Ampezzo, Italy. Carley, S., Krause, R. M., Lane, B. W., & Graham, J. D. (2013). Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites. Transportation Research Part D: Transport and Environment, 18, 39-45. Capion, K. (2009). Optimized charging of electric drive vehicles in a market environment. M. Sc., Tech. Univ. Denmark, RisØ National Laboratory for Sustainable Energy, Lyngby, Denmark. CBS, (2015) Elektriciteit in Nederland. CBS, (2017a) Jaarmonitor Wegvoertuigen: Aantallen. CBS, (2017b) Toename elektrische auto’s' zet door. Accessed 16 May 2018, from:https://www.cbs.nl/nl-nl/nieuws/2017/21/toename-elektrische-auto-s-zet-door CBS, (2018) Statline: Motorvoertuigenpark; type, leeftijdsklasse, 1 januari. Accessed 16 May 2018, from: https://opendata.cbs.nl/statline/#/CBS/nl/dataset/82044NED/table?ts=1526470015772 Celsor, C., & Millard-Ball, A. (2007). Where does carsharing work?: Using geographic information systems to assess market potential. Transportation Research Record: Journal of the Transportation Research Board, (1992), 61-69.
106
Charkhgard, M., & Farrokhi, M. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE transactions on industrial electronics, 57(12), 4178-4187. Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT capability and organizational performance: the roles of business process agility and environmental factors. European Journal of Information Systems, 23(3), 326-342. Cohen, B., & Kietzmann, J. (2014). Ride on! Mobility business models for the sharing economy. Organization & Environment, 27(3), 279-296. Consentec, (2014) Description of load-frequency control concept and market for control reserves. Dang-Ha, T. H., Bianchi, F. M., & Olsson, R. (2017). Local short-term electricity load forecasting: Automatic approaches. arXiv preprint arXiv:1702.08025. Dasgupta, S., Siddarth, S., & Silva-Risso, J. (2007). To lease or to buy? A structural model of a consumer's vehicle and contract choice decisions. Journal of Marketing Research, 44(3), 490-502. Dauensteiner, A. (2007). European Virtual Fuel Cell Power Plant–Management summary report. Germany, NNE5-2000-208. Davis, G. B., & Olson, M. H. (1984). Management information systems: conceptual foundations, structure, and development. McGraw-Hill, Inc.. Denholm, P., Ela, E., Kirby, B., & Milligan, M. (2010). The role of energy storage with renewable electricity generation. Denholm, P., & Hand, M. (2011). Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy, 39(3), 1817-1830. Deloitte, (2017). Carsharing in Europe: Business Models, National Variations and Upcoming Disruptions. Department of Energy & Climate Change. (2013). The Future of Heating: Meeting the Challenge Després, J., Mima, S., Kitous, A., Criqui, P., Hadjsaid, N., & Noirot, I. (2017). Storage as a flexibility option in power systems with high shares of variable renewable energy sources: a POLES-based analysis. Energy Economics, 64, 638-650. Egbue, O., & Long, S. (2012). Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy policy, 48, 717-729. Enel, (2016). Nissan and Enel launch groundbreaking vehicle-to-grid project in the UK. Enel. Accessed 14 May 2018, from: https://www.enel.com/media/press/d/2016/05/nissan-and-enel-launch-groundbreaking-vehicle-to-grid-project-in-the-uk
107
ENTSO-E. (2015). Continental Europe Operation Handbook. Accessed February 11, 2018, from https://www.entsoe.eu/publications/system-operations-reports/operation-handbook/Pages/default.aspx EPEX SPOT SE (2017). Xbid cross-border intraday market project. Accessed 6 June 2018, from: https://www.epexspot.com/de/Marktkopplung/xbid_cross_border_intraday_market_project EPEX SPOT (2018a) EPEXSPOT Operational Rules: Version 008 EPEX SPOT (2018b) EPEX SPOT SE: Day-Ahead Auction Market Data. Accessed February 18, 2018, from https://www.epexspot.com/en/market-data/dayaheadauction/chart/auction-chart/2018-02-16/DE EPEX SPOT (2018c) EPEX SPOT SE: Intraday Auction Market Data. Accessed February 18, 2018, from https://www.epexspot.com/en/market-data/intradayauction/chart/quarter-auction-chart/2018-02-18/DE Erlinghagen, S., & Markard, J. (2012). Smart grids and the transformation of the electricity sector: ICT firms as potential catalysts for sectoral change. Energy Policy, 51, 895-906. European Commission (2011) Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. Accessed May 14 2018, from: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52011DC0144&from=EN European Parliament, Council of the European Union (2009) Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union, L 140 (1), 16-62. Eurostat (2017) Electricity prices for household consumers - bi-annual data (from 2007 onwards). Accessed February 23, 2018, from: http://appsso.eurostat.ec.europa.eu/nui/submitViewTableAction.do Fawcett, T., Layberry, R., & Eyre, N. (2014). Electrification of heating: the role of heat pumps. In BIEE Conference Oxford. Fazelpour, F., Vafaeipour, M., Rahbari, O., & Rosen, M. A. (2014). Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics. Energy Conversion and Management, 77, 250-261. Focken, U., Lange, M., Mönnich, K., Waldl, H. P., Beyer, H. G., & Luig, A. (2002). Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects. Journal of Wind Engineering and Industrial Aerodynamics, 90(3), 231-246. Ford, A. (1994). Electric vehicles and the electric utility company. Energy Policy, 22(7), 555-570.
108
Franke, T., Neumann, I., Bühler, F., Cocron, P., & Krems, J. F. (2012). Experiencing range in an electric vehicle: Understanding psychological barriers. Applied Psychology, 61(3), 368-391. Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research policy, 33(6-7), 897-920. Gerbracht, H. U., Möst, D., & Fichtner, W. (2010, June). Impacts of plug-in electric vehicles on Germany's power plant portfolio-A model-based approach. In Energy Market (EEM), 2010 7th International Conference on the European (pp. 1-7). IEEE. Geron, T. (2013) "RelayRides Focuses On Longer Rentals As Sharing Economy Focuses On Travel." Forbes. October 1, 2013. Goh, K. H., & Bockstedt, J. C. (2013). The framing effects of multipart pricing on consumer purchasing behavior of customized information good bundles. Information Systems Research, 24(2), 334-351. Granados, N., & Gupta, A. (2013). Transparency strategy: Competing with information in a digital world. MIS quarterly, 37(2). Graus, W., & Worrell, E. (2008). The principal–agent problem and transport energy use: case study of company lease cars in the Netherlands. Energy Policy, 36(10), 3745-3753. Gulen, H., & Ion, M. (2015). Policy uncertainty and corporate investment. The Review of Financial Studies, 29(3), 523-564. Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67(9), 2047-2059. Hampshire, R., & Gaites, C. (2011). Peer-to-peer carsharing: Market analysis and potential growth. Transportation Research Record: Journal of the Transportation Research Board, (2217), 119-126. Hawkins, D. (2001, December). Vehicle-to-grid: a control area operators perspective. In Presentation at the EVAA Electric Transportation Industry Conference, Sacramento, CA (Vol. 13). Hevner, A., & Chatterjee, S. (2010). Design science research in information systems. In Design research in information systems(pp. 9-22). Springer US. Hildebrandt, B., Hanelt, A., Piccinini, E., Kolbe, L., & Nierobisch, T. (2015). The Value of IS in Business Model Innovation for Sustainable Mobility Services-The Case of Carsharing. In Wirtschaftsinformatik (pp. 1008-1022). Hiroux, C., & Saguan, M. (2010). Large-scale wind power in European electricity markets: Time for revisiting support schemes and market designs?. Energy Policy, 38(7), 3135-3145.
109
Huisman, R., Huurman, C., & Mahieu, R. (2007). Hourly electricity prices in day-ahead markets. Energy Economics, 29(2), 240-248. Hu, X., Sun, F., & Zou, Y. (2010). Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive Luenberger observer. Energies, 3(9), 1586-1603. Hu, J., You, S., Lind, M., & Ostergaard, J. (2014). Coordinated charging of electric vehicles for congestion prevention in the distribution grid. IEEE Transactions on Smart Grid, 5(2), 703-711. HvA (2018) A V2G Repository: 18 European Vehicle-to-grid Projects. Retrieved 12 April 2018, from: http://www.hva.nl/binaries/content/assets/subsites/urban-technology/vehicle2grid-repository.pdf?1518609633302 Iansiti, M., & Lakhani, K. R. (2014). Digital ubiquity:: How connections, sensors, and data are revolutionizing business. Harvard Business Review, 92(11), 19. Ilic, M. D., Makarov, Y., & Hawkins, D. (2007, June). Operations of electric power systems with high penetration of wind power: Risks and possible solutions. In Power Engineering Society General Meeting, 2007. IEEE (pp. 1-4). IEEE. Inman, R. H., Pedro, H. T., & Coimbra, C. F. (2013). Solar forecasting methods for renewable energy integration. Progress in energy and combustion science, 39(6), 535-576. International Energy Agency. (2003). The power to choose: demand response in liberalised electricity markets. OECD Publishing. International Energy Agency. (2017). Global EV Outlook 2017: Two million and counting. OECD Publishing. Kahlen, M., & Ketter, W. (2015, January). Aggregating Electric Cars to Sustainable Virtual Power Plants: The Value of Flexibility in Future Electricity Markets. In AAAI (pp. 665-671). Kahlen, M., Ketter, W., & Gupta, A. (2017). FleetPower: Creating Virtual Power Plants in Sustainable Smart Electricity Markets. Kamboj, S., Decker, K., Trnka, K., Pearre, N., Kern, C., & Kempton, W. (2010, May). Exploring the formation of electric vehicle coalitions for vehicle-to-grid power regulation. In AAMAS workshop on agent technologies for energy systems (ATES 2010). Kassakian, J. G., Schmalensee, R., Desgroseilliers, G., Heidel, T. D., Afridi, K., Farid, A. & Pérez-Arriaga, I. (2011). The future of the electric grid. Massachusetts Institute of Technology, Tech. Rep, 197-234. Katzev, R. (2003). Car sharing: A new approach to urban transportation problems. Analyses of Social Issues and Public Policy, 3(1), 65-86. Kempton, W., Perez, Y., & Petit, M. (2014). Public Policy for Electric Vehicles and for Vehicle to GridPower. Revue d'économie industrielle, (4), 263-290.
110
Kempton, W., Tomic, J., Letendre, S., Brooks, A., & Lipman, T. (2001). Vehicle-to-grid power: battery, hybrid, and fuel cell vehicles as resources for distributed electric power in California. Institute of Transportation Studies. Kempton, W., & Tomić, J. (2005a). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of power sources, 144(1), 268-279. Kempton, W., & Tomić, J. (2005b). Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy. Journal of power sources, 144(1), 280-294. Kim, E. L., Tabors, R. D., Stoddard, R. B., & Allmendinger, T. E. (2012). Carbitrage: Utility integration of electric vehicles and the smart grid. The Electricity Journal, 25(2), 16-23. Kiesel, R., & Paraschiv, F. (2017). Econometric analysis of 15-minute intraday electricity prices. Energy Economics, 64, 77-90. Kiviluoma, J., & Meibom, P. (2010). Influence of wind power, plug-in electric vehicles, and heat storages on power system investments. Energy, 35(3), 1244-1255. Kossahl, J., Busse, S., & Kolbe, L. M. (2012). The Evolvement of Energy Informatics in the Information Systems Community-a literature Analysis and Research Agenda. In ECIS (p. 172). Knaut, A., & Paschmann, M. (2016). Short-term Price Volatility in Commodity Markets Explained-Evidence from the Power Sector. In European Association of Environmental and Resource Economists 22nd Annual Conference. Knaut, A., & Paschmann, M. (2017a). Price volatility in commodity markets with restricted participation (No. 17/02). EWI Working Paper. Knaut, A., & Paschmann, M. H. (2017b). Decoding restricted participation in sequential electricity markets (No. 17/05). EWI Working Paper. Kuo, W., & Li, Y. C. (2011). Trading mechanisms and market quality: call markets versus continuous auction markets. International review of Finance, 11(4), 417-444. Leach, G. (1992). The energy transition. Energy policy, 20(2), 116-123. Letendre, S. E., Kempton, W., & Stavros, R. (2002). The V2G concept: A new model for power?. Public Utilities Fortnightly, 140(4), 16-16. Li, Y., & Flynn, P. C. (2004). Deregulated power prices: comparison of volatility. Energy Policy, 32(14), 1591-1601. Li, T., Kauffman, R. J., Van Heck, E., Vervest, P., & Dellaert, B. G. (2014). Consumer informedness and firm information strategy. Information Systems Research, 25(2), 345-363.
111
Logenthiran, T., Srinivasan, D., & Shun, T. Z. (2012). Demand side management in smart grid using heuristic optimization. IEEE transactions on smart grid, 3(3), 1244-1252. Loock, C. M., Staake, T., & Thiesse, F. (2013). Motivating Energy-Efficient Behavior with Green Is: An Investigation of Goal Setting and the Role of Defaults. Mis Quarterly, 37(4). Lopes, J. A. P., Soares, F. J., & Almeida, P. M. R. (2011). Integration of electric vehicles in the electric power system. Proceedings of the IEEE, 99(1), 168-183. Lijesen, M. G. (2007). The real-time price elasticity of electricity. Energy economics, 29(2), 249-258. Litman, T. (2000). Evaluating carsharing benefits. Transportation Research Record: Journal of the Transportation Research Board, (1702), 31-35. Mal, S., Chattopadhyay, A., Yang, A., & Gadh, R. (2013). Electric vehicle smart charging and vehicle-to-grid operation. International Journal of Parallel, Emergent and Distributed Systems, 28(3), 249-265. Malhotra, A., Melville, N. P., & Watson, R. T. (2013). Spurring impactful research on information systems for environmental sustainability. MIS Quarterly, 37(4), 1265-1274. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82. Martin, E., Shaheen, S., & Lidicker, J. (2010). Impact of carsharing on household vehicle holdings: Results from North American shared-use vehicle survey. Transportation Research Record: Journal of the Transportation Research Board, (2143), 150-158. Melville, N. P. (2010). Information systems innovation for environmental sustainability. MIS quarterly, 34(1), 1-21. Moeller, S., & Wittkowski, K. (2010). The burdens of ownership: reasons for preferring renting. Managing Service Quality: An International Journal, 20(2), 176-191. Moody's, (2017). Germany's electricity market likely to see moderating power prices, renewables growth and capacity closures over next five years. Morales, J. M., Conejo, A. J., Madsen, H., Pinson, P., & Zugno, M. (2013). Integrating renewables in electricity markets: operational problems (Vol. 205). Springer Science & Business Media. Mullan, J., Harries, D., Bräunl, T., & Whitely, S. (2012). The technical, economic and commercial viability of the vehicle-to-grid concept. Energy Policy, 48, 394-406. Musgens, F., & Neuhoff, K. (2006). Modelling dynamic constraints in electricity markets and the costs of uncertain wind output.
112
Navigant Research (2016) Annual Revenue for the Residential Distributed Energy Resources Market Is Expected to Exceed $92.7 Billion in 2025 [Press release] Retrieved December 12, 2017, from: https://www.navigantresearch.com/newsroom/annual-revenue-for-the-residential-distributed-energy-resources-market-is-expected-to-exceed-92-7-billion-in-2025 Nevo, S., & Wade, M. R. (2010). The formation and value of IT-enabled resources: antecedents and consequences of synergistic relationships. Mis Quarterly, 163-183. Neuhoff, K., Ritter, N., Salah-Abou-El-Enien, A., & Vassilopoulos, P. (2016). Intraday markets for power: Discretizing the continuous trading?. Nissan, (2013) Nissan LEAFs can now power the office, as well as the home. Nissan News. Accessed 14 May 2018, from: http://nissannews.com/en-CA/nissan/canada/releases/nissan-leafs-can-now-power-the-office-as-well-as-the-home?mode=print Nuvve, (2015). Nuvve operate world's first fully commercial vehicle-to-grid hub in Denmark. Nuvve. Accessed 14 May 2018, from: http://nuvve.com/portfolio/nissan-enel-and-nuvve-operate-worlds-first-fully-commercial-vehicle-to-grid-hub-in-denmark/ Oxford Institute for Energy Studies [OIFES], (2016) Energy markets are broken - can they be fixed? Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE transactions on industrial informatics, 7(3), 381-388. Panagiotelis, A., & Smith, M. (2008). Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions. International Journal of Forecasting, 24(4), 710-727. Paraschiv, F., Erni, D., & Pietsch, R. (2014). The impact of renewable energies on EEX day-ahead electricity prices. Energy Policy, 73, 196-210. Pasaoglu, G., Fiorello, D., Martino, A., Zani, L., Zubaryeva, A., & Thiel, C. (2014). Travel patterns and the potential use of electric cars–Results from a direct survey in six European countries. Technological Forecasting and Social Change, 87, 51-59. Pavić, I., Beus, M., Pandžić, H., Capuder, T., & Štritof, I. (2017, June). Electricity markets overview—Market participation possibilities for renewable and distributed energy resources. In European Energy Market (EEM), 2017 14th International Conference on the (pp. 1-5). IEEE. Peterson, S. B., Apt, J., & Whitacre, J. F. (2010a). Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization. Journal of Power Sources, 195(8), 2385-2392. Peterson, S. B., Whitacre, J. F., & Apt, J. (2010b). The economics of using plug-in hybrid electric vehicle battery packs for grid storage. Journal of Power Sources, 195(8), 2377-2384.
113
Pickard, W. F., Shen, A. Q., & Hansing, N. J. (2009). Parking the power: Strategies and physical limitations for bulk energy storage in supply–demand matching on a grid whose input power is provided by intermittent sources. Renewable and Sustainable Energy Reviews, 13(8), 1934-1945. Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard business review, 86(1), 25-40. Potter, C., Archambault, A., & Westrick, K. (2009). Power Systems Conference and Exposition. Building a Smarter Smart Grid Through Better Renewable Energy Information (pp.1-5). Seattle, WA, USA: IEEE. Pudjianto, D., Ramsay, C., & Strbac, G. (2007). Virtual power plant and system integration of distributed energy resources. IET Renewable Power Generation, 1(1), 10-16. Quinn, C., Zimmerle, D., & Bradley, T. H. (2010). The effect of communication architecture on the availability, reliability, and economics of plug-in hybrid electric vehicle-to-grid ancillary services. Journal of Power Sources, 195(5), 1500-1509. PwC, (2017). Smart Charging van Elektrische Voertuigen: Institutionele knelpunten en mogelijke oplossingen. Regelleistung.net, Transnet BW, (2018), Daten zur Regelenergie -- RZ_Saldo -- concatenated, 16 February [Data set]. Retrieved February 18, 2018, from: https://www.regelleistung.net/ext/data/ Roy, R., Caird, S., & Potter, S. (2007). People Centred Eco-design: Consumer adoption and use of low and zero carbon products and systems. Governing technology for sustainability, 41. Rijksoverheid voor Ondernemen Nederland, (2015a). Analyse eigendom elektrische personenauto's. Rijksoverheid voor Ondernemen Nederland, (2015b). Nul op de meter Ervaringen van vernieuwers in de woningbouw Rijksoverheid voor Ondernemen Nederland, (2018). Statistics Electric Vehicles in the Netherlands Schaefers, T. (2013). Exploring carsharing usage motives: A hierarchical means-end chain analysis. Transportation Research Part A: Policy and Practice, 47, 69-77. Schor, J. (2016). DEBATING THE SHARING ECONOMY. Journal of Self-Governance & Management Economics, 4(3). Schuller, A., & Rieger, F. (2013). Assessing the economic potential of electric vehicles to provide ancillary services: the case of Germany. Zeitschrift für Energiewirtschaft, 37(3), 177-194.
114
Seeger, G., & Bick, M. (2013). Mega and Consumer Trends-Towards Car-independent Mobile Applications. In ICMB (p. 27). Shaheen, S., & Cohen, A. (2007). Growth in worldwide carsharing: An international comparison. Transportation Research Record: Journal of the Transportation Research Board, (1992), 81-89. Shaheen, S. A., Mallery, M. A., & Kingsley, K. J. (2012). Personal vehicle sharing services in North America. Research in Transportation Business & Management, 3, 71-81. Shaheen, S., Martin, E., & Bansal, A. (2018). Peer-To-Peer (P2P) Carsharing: Understanding Early Markets, Social Dynamics, and Behavioral Impacts. Sioshansi, R., Denholm, P., Jenkin, T., & Weiss, J. (2009). Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy economics, 31(2), 269-277. Sioshansi, R. (2012). OR Forum—modeling the impacts of electricity tariffs on plug-in hybrid electric vehicle charging, costs, and emissions. Operations Research, 60(3), 506-516. Shoup, D. (2005). The high cost of free parking. American Planning Association. Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. (2014). The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, 183-194. Stedin (2018) Bereken de kosten van uw aansluiting. Accessed 12 May 2018, from: https://www.stedin.net/rekenmodule/wijzigen/capaciteit Steinhilber, S., Wells, P., & Thankappan, S. (2013). Socio-technical inertia: Understanding the barriers to electric vehicles. Energy policy, 60, 531-539. Stoft, S. (2002). Power system economics. Journal of Energy Literature, 8, 94-99. Sullivan, E., & Magid, L. (2007). Bringing Carsharing to Your Community–Long Guide. San Francisco City CarShare. Sundström, O., & Binding, C. (2010, October). Planning electric-drive vehicle charging under constrained grid conditions. In Power System Technology (POWERCON), 2010 International Conference on (pp. 1-6). IEEE. Taylor, P. (2010). Energy technology perspectives 2010–scenarios and strategies to 2050. International Energy Agency, Paris, 74. Tran, M., Banister, D., Bishop, J. D., & McCulloch, M. D. (2012). Realizing the electric-vehicle revolution. Nature climate change, 2(5), 328-333. Trocchia, P. J., & Beatty, S. E. (2003). An empirical examination of automobile lease vs finance motivational processes. Journal of Consumer Marketing, 20(1), 28-43.
115
Uddin, K., Jackson, T., Widanage, W. D., Chouchelamane, G., Jennings, P. A., & Marco, J. (2017). On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system. Energy, 133, 710-722. United Nations (2015) 2015 United Nations Climate Change Conference. Paris, France. United Nations (2016) Paris Agreement Retrieved January 8, 2018, from: https://treaties.un.org/pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-d&chapter=27&lang=en US Department of Energy (2013) EV Everywhere Grand Challenge Blueprint Valogianni, K., Ketter, W., Collins, J., & Zhdanov, D. (2014, January). Effective Management of Electric Vehicle Storage Using Smart Charging. In AAAI (pp. 472-478). Van der Steen, M., Van Schelven, R. M., Kotter, R., Van Twist, M. J. W., & Peter van Deventer, M. P. A. (2015). EV policy compared: An international comparison of governments’ policy strategy towards e-mobility. In E-Mobility in Europe (pp. 27-53). Springer, Cham. Vennix, J. A. M. (2010). Theorie en praktijk van empirisch onderzoek. Pearson/Custom Publishing. VNA, (2018) Autoleasemarkt in cijfers 2017. Von Roon, S., & Wagner, U. (2009). The interaction of conventional power production and renewable power under the aspect of balancing forecast errors. In Proceedings of the 10th IAEE European Conference on Energy, Policies and Technologies for Sustainable Economies. Vytelingum, P., Voice, T. D., Ramchurn, S. D., Rogers, A., & Jennings, N. R. (2011). Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid. Journal of Artificial Intelligence Research, 42, 765-813. Wagner, C., & Shaheen, S. (1998). Car sharing and mobility management: facing new challenges with technology and innovative business planning. World Transport Policy and Practice, 4, 39-43. Walawalkar, R., Apt, J., & Mancini, R. (2007). Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy, 35(4), 2558-2568. Wang, L., Lin, A., & Chen, Y. (2010). Potential impact of recharging plug-in hybrid electric vehicles on locational marginal prices. Naval Research Logistics (NRL), 57(8), 686-700. Watson, R. T., Boudreau, M. C., & Chen, A. J. (2010). Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. MIS quarterly, 23-38. Weber, C. (2010). Adequate intraday market design to enable the integration of wind energy into the European power systems. Energy Policy, 38(7), 3155-3163.
116
Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International journal of forecasting, 30(4), 1030-1081. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Press. Wilson, R. (2002). Architecture of power markets. Econometrica, 70(4), 1299-1340. Yilmaz, M., & Krein, P. T. (2013). Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Transactions on Power Electronics, 28(5), 2151-2169. Zhang, X., Xie, J., Rao, R., & Liang, Y. (2014). Policy incentives for the adoption of electric vehicles across countries. Sustainability, 6(11), 8056-8078. Zolnowski, A., Schmitt, A. K., & Böhmann, T. (2011). Understanding the impact of remote service technology on service business models in manufacturing: From improving after-sales services to building service ecosystems. ECIS. Zou, Z., Xu, J., Mi, C., Cao, B., & Chen, Z. (2014). Evaluation of model based state of charge estimation methods for lithium-ion batteries. Energies, 7(8), 5065-5082.
