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TVE-STS; 19004
Examensarbete 15 hpJuni 2019
A package deal for the future: Vehicle-to-Grid combined with Mobility as a Service
Amanda BränströmJonna Söderberg
Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student
Abstract
A package deal for the future: Vehicle-to-Gridcombined with Mobility as a Service
Amanda Bränström, Jonna Söderberg
The aim of this report is to evaluate how a future commercially owned fleet ofself-driving electric vehicles (EV:s) would be able to provide power in order to avoidpower exceedances in the power grid. Exceedances occur when network agreementsbetween grid operating companies are exceeded. Exceedances are problematic, sincethey infer penalty fees for the paying company and make dimensioning the gridcapacity more difficult for the supplying company. Capacity deficiency regarding theinfrastructure of the grid is expected to increase, likely resulting in higher penalty fees.Integrating transport and power systems by using self-driving EV:s as Mobility as aService combined with Vehicle-to-Grid (V2G) technology is a potential solution forthis problem. By modeling the EV-fleet as the New York City taxi fleet, a usagepattern deemed to resemble Mobility as a Service is created. An economic value forthe V2G service is estimated by comparing the availability of the EV-fleet with localexceedances from Uppsala as well as regional occurring exceedances. The highestincome during the first quarter of 2019 is 96 000 SEK for the whole fleet, or 1100SEK per EV and hour-long exceedance. The time of exceedance and the powermagnitude have to interplay with the availability of the EV-fleet in order to enable thesystem. The EV battery capacity highly impacts the system, but is concluded to not bea limiting factor due to market logic. Lastly, key features such as market formation aswell as geographical and technical aspects are presented and discussed.
ISSN: 1650-8319, TVE-STS; 19004Examinator: Joakim WidénÄmnesgranskare: Umar Hanif RamadhaniHandledare: Oskar Fängström
1
Table of contents
Acknowledgements ..................................................................................................................... 3
1. Introduction.............................................................................................................................. 5
1.1 Project aim ......................................................................................................................... 6
1.2 Research questions .......................................................................................................... 6
1.3 Delimitations and limitations ........................................................................................... 6
1.4 Overview of the report ...................................................................................................... 6
2. Background ....................................................................................................................... 7
2.1 Managing capacity deficiency today ............................................................................... 7
2.1.1 Electricity grid operators ............................................................................................ 7
2.1.2 Capacity deficiency ..................................................................................................... 8
2.1.3 Network agreements ................................................................................................... 8
2.2 Today’s outlook for the future ......................................................................................... 9
2.2.1 System services .......................................................................................................... 9
2.2.2 The future power grid ............................................................................................... 10
2.2.3 Batteries in the power system ................................................................................. 10
2.2.4 Development of cities ............................................................................................... 11
2.3 The role of an EV-fleet in the power system ................................................................ 12
2.3.1 Vehicle-to-grid (V2G) ................................................................................................. 12
2.3.2 EV battery challenges ............................................................................................... 14
3. Theory, Data and Methodology ..................................................................................... 15
3.1 Exceeding network agreements .................................................................................... 16
3.1.1 Local case .................................................................................................................. 16
3.1.2 Regional case ............................................................................................................ 17
3.2 Capacity of EV:s .............................................................................................................. 19
3.2.1 Theory ......................................................................................................................... 19
3.2.2 Data ............................................................................................................................. 21
3.2.3 Methodology .............................................................................................................. 21
3.3 Taxi fleet ........................................................................................................................... 22
3.3.1 Theory ......................................................................................................................... 22
3.3.2 Data ............................................................................................................................. 22
3.3.3 Methodology .............................................................................................................. 23
3.4 Sensitivity analysis ......................................................................................................... 24
3.5 Methodology summary ................................................................................................... 25
4. Results ............................................................................................................................. 27
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4.1 The economic value ........................................................................................................ 27
4.1.1 Local case .................................................................................................................. 27
4.1.2 Regional case ............................................................................................................ 28
4.2 Availability for grid services .......................................................................................... 29
4.2.1 Availability of the EV-fleet during an average winter day ..................................... 29
4.2.2 Local case .................................................................................................................. 29
4.2.3 Regional case ............................................................................................................ 30
4.3 Sensitivity analysis ......................................................................................................... 32
5. Discussion ....................................................................................................................... 34
5.1 The economic value ........................................................................................................ 34
5.2 Availability for grid services .......................................................................................... 36
5.3 Impact of battery capacity .............................................................................................. 38
5.4 Key features ..................................................................................................................... 38
5.5 The road ahead ................................................................................................................ 43
6. Conclusions ..................................................................................................................... 44
References ................................................................................................................................. 46
Appendix A ................................................................................................................................. 50
Appendix B ................................................................................................................................. 52
Appendix C ................................................................................................................................. 54
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Acknowledgements
This report has been put together with guidance from Sweco Uppsala. The authors
wishes to express their sincere gratitude to supervisors Oskar Fängström and Anna
Lundgren, as well as the Energy group at Sweco Uppsala. A lot of helpful assistance has
also been provided by Håkan Österlund at Upplands Energi and Inga-Lill Åkerström at
Svenska kraftnät.
Thank you!
4
Syllabus
Capacity deficiency Challenges in the power grid in two different ways: The
power lines are “full” and can not transport any higher
amount of power at the given time, or there is not enough
electricity produced at a given time. The former challenge
will be of interest in this report
EV Vehicle, or car, running on electricity supplied by an
internal battery
(Power) Exceedance Overstep of network agreement regarding power
consumption, resulting in additional penalty fees
Mobility as a Service A shared transport service which enable people to access
transport on an as-needed basis
Network Agreement Agreement between power grid operating companies
regulating power and energy consumption as well as the
associated fee
SvK Svenska kraftnät, the Swedish transmission grid operating
company
V2G Vehicle-to-Grid, technology for creating a bidirectional
communication and power flow between the EV and the
power grid, enabling an EV to charge from and discharge
to the power grid
Peak shaving Leveling out peaks in electricity consumption
Power peak When power output from the power system is high in
relation to the consumption of the rest of the day
Power shaving Reducing power output from the power grid, not
necessarily related to a power peak
5
1. Introduction
You are on your way to work. In your left hand, you have a nice cup of coffee. With
your right hand, you are texting a colleague. Outside the car window, you see the city
pass by and people riding around in cars, just like yourself. Once outside your
workplace, the self-driving car stops smoothly by the curb and an automated voice
wishes you a nice day. You grab your bag and get out the car. Once the door shuts
behind you, the car drives off to one of the power hubs nearby and docks with it, ready
to provide power to the grid through its internal battery. Without anyone noticing, the
omnipresent self-driving cars will make sure you and your fellow citizens’ lives are
balanced and powered.
This scenario is futuristic, but not impossible. Our society today is completely
dependent on electricity, and this does not seem to change in the foreseeable future
(Energimyndigheten, 2016). The Swedish power grid is faced with handling power
consumption and production that it was not dimensioned for, resulting in capacity
deficiency in several areas in Sweden. Challenges regarding the grid capacity is also
expected to grow. For the society, it limits how much cities can grow and develop, both
economically and geographically. In order to avoid overloads, the usage of the grid is
regulated in network agreements between the transmission grid operating company
Svenska kraftnät and underlying grid operators, as well as between underlying grid
operators, where maximum power output and input is determined (Svenska kraftnät,
2018). The challenges presented pose important questions on how the energy system
will look in the future, and give reasons to think beyond the solutions available today.
The electric vehicles (EV:s), mentioned above, could be one of these solutions.
The system of combining bidirectionally charging EV:s with services to the power grid
is called Vehicle-to-Grid (V2G). By assuming these EV:s to be self-driving, the system
studied in this report can be considered flexible and a model for future implementation
of new ways of transport – Mobility as a Service. In order to evaluate how the power
capacity available in a fleet of EV:s can be combined with daily power needs, the EV:s
power capacity can be juxtaposed with power peaks and valleys. The service provided
by the EV-fleet will be power shaving, in the form of power compensation to avoid
power peaks. In this report, power peaks exceeding the agreed power consumption, with
penalty fees as a consequence, will be examined. By doing this, an economic value of
the potential grid services provided by the EV:s will be presented, both per EV and for
the whole fleet. An evaluation of how viable the interplay between the EV:s and the
exceedances is will also be made, as well as evaluations of both the impact of battery
capacity and key features when implementing the system.
6
1.1 Project aim
The aim of this project is to evaluate how a future commercially owned fleet of self-
driving electric vehicles (EV:s) would be able to provide power in order to avoid power
exceedances in the power grid.
1.2 Research questions
In order to achieve the project aim, the report will answer the following questions:
1) What is the potential economic value of a fleet of EV:s providing service to the
grid?
2) What is the interplay between the EV-fleet and the grid, regarding availability
for grid services?
3) If the battery capacity of the EV is changed, what is the impact on the system?
4) Based on today’s discussion and results from previous research questions, which
key features are important to address when implementing the system1?
1.3 Delimitations and limitations
Delimitations
▪ Regulations linked to the presented energy system are not discussed.
▪ How a future market for the presented energy system would look like will only
be discussed briefly.
▪ Prerequisites for the implementation of the system, such as charging stations,
smart grids and communication software etc., are regarded as fulfilled and
therefore not discussed. The EV:s are also assumed to be charged in a way not
adding to already existing power peaks. The charging pattern is therefore not
discussed.
Limitations
▪ Since the data is sourced, there is no way to ensure the correctness of the data.
▪ Due to legal constraints, only some data regarding electricity usage is available
for analysis.
▪ The pricing structure of the future fees follows today’s pricing.
1.4 Overview of the report
An overview of the electricity grid infrastructure and actors, as well as challenges
connected to capacity deficiency and role of the EV-fleet are presented in the
1 The system consisting of an EV-fleet and the power grid.
7
Background chapter. In the chapter Theory, data and methodology, more technical
specifications and theory regarding the system will be presented. In short, the method
behind the results consists of calculating the number of EV:s involved in power shaving
services and comparing this to exceedance fees. Two cases are examined, with
exceedances originating from different levels of the electricity grid. A sensitivity
analysis is accomplished by changing the battery capacity of the EV:s and evaluating
the system again. Finally, results will be given in the Result chapter, followed by a
Discussion chapter where results will be discussed. The report ends with a Conclusion,
answering the research questions.
2. Background
This section gives an introduction to the power system structure and network
agreements in Sweden as well as information about the growing concern of capacity
deficiency. Future outlooks as well as a solution involving V2G and Mobility as a
Service will also be presented.
2.1 Managing capacity deficiency today
Capacity deficiency is a term often used in the Swedish debate of power supply.
(Energimarknadsinspektionen, 2018). A large number of grid operators form and
maintain the structure of the power supply, and the linkages defining power flow in the
system between these actors are network agreements. How do the contracts relate to
capacity deficiency? In this chapter, an overview of the grid structure and its operators
as well as capacity deficiency and network agreements will be presented, with focus on
the region of Uppsala.
2.1.1 Electricity grid operators
Svenska kraftnät (SvK) is a government authority who owns the transmission grid. SvK
also has the responsibility of operating the system, i.e. to make sure that the
transmission capacity and reliability is sufficient (Svenska kraftnät, 2017a). Customers
of SvK are almost solely grid operating companies who own the regional grids. There
are around 170 grid owning companies in Sweden today, with E.ON Elnät Sverige,
Vattenfall Eldistribution and Fortum Distribution being the largest. The regional grid
operating company has a monopoly, but also responsibility, to provide electricity to its
geographical region. Regional grid operating companies contract power consumption to
local grid operating companies (Södra Hallands Kraft, n.d.). The main featured regional
grid operator discussed in this report is Vattenfall Eldistribution and the main featured
local grid operator is Upplands Energi.
8
2.1.2 Capacity deficiency
Capacity deficiency means that the power lines lack the capacity needed to deliver the
desired amount of electricity to the user, i.e. transmission capacity deficiency. One way
to express it is that the lines are “full” and can not transport any higher amount at the
given time. Another cause of capacity deficiency is that there is not enough electricity
produced at the given time (Energimarknadsinspektionen, 2018). The former type of
capacity deficiency will be of most interest when evaluating the system presented in this
report. Today, urban regions like Uppsala, Västerås, Stockholm, and Malmö are
affected by transmission capacity deficiency (Kellner, 2019). It affects the cities in
terms of having to deny establishment of new factories and server halls, as well as new
neighbourhoods (Energimarknadsinspektionen, 2018). Today in Uppsala, capacity
deficiency occurs during approximately 200 hours a year. The region of Uppsala has a
capacity need of around 300 MW, and there is a limit of how much power the grid
operator Vattenfall can provide (Lindblom, 2018).
Persson (2018), the Chief Financial Officer at Energimarknadsinspektionen, stresses
that the capacity deficiency is a smaller problem at a national level, and that Sweden in
total has a grid capacity that is sufficient. Instead, problems derived from capacity
deficiency mostly occur on local and regional levels (Energimarknadsinspektionen,
2018). One reason could be that the capacity of the transmission grid limits the allowed
power consumption stated in network agreements with regional grid operating
companies. Upgrading the infrastructure takes time and big economic investments.
Persson means that the way of making a greater capacity available is a combination of
smart technologies, demand flexibility and a well-functioning cooperation between grid
operators and municipalities (Energimarknadsinspektionen, 2018).
2.1.3 Network agreements
Network agreements exist between both transmission and regional grid operating
companies, as well as between regional and local grid operating companies. The
structure of the agreements differ depending on which actors are involved (Svenska
kraftnät, 2019). On transmission level, the agreements serve as a tool for SvK to plan
which capacity is needed for delivering power to a geographical area, as well as a way
for SvK to cover costs of maintenance and operations of the grid (Svenska kraftnät,
2018a).
The exceeding of a network agreement might affect the grid in negative ways. Today,
exceedances at some power grid stations might get so high above planned capacity that
the grid components become overloaded. With an increasing capacity deficiency in the
transmission grid, especially in connection with bigger, expanding cities, this problem
has potential to grow. SvK sees a trend indicating that the amount of exceedings will
increase before actions to develop and upgrade the grid will be taken. In fact, there is an
acute need of decreasing the exceedances due to capacity deficiency. According to SvK,
one way to counteract the increasing amount of exceedances and to make an even
9
clearer stand that exceeding a network agreement is not acceptable, is to raise the
penalty fee. The intentions are to make the grid operators take actions to avoid the
higher costs, and to make the fee reflect the seriousness of the capacity deficiency. SvK
points out that it has nothing to do with an economical gain for SvK itself (Svenska
kraftnät, 2018a). From January 1st 2019, SvK has changed the structure of the penalty
fee (Svenska kraftnät, 2018b). SvK mentions in a referral from 2018 that there is no
guarantee that the structure of the fee will look and function the same way in the future,
but it is necessary to address the capacity deficiency by making exceedances more
expensive (Svenska kraftnät, 2018a).
One consequence of raising the fee of exceeding network agreement is that the cost for
the regional grid operator will increase. In turn, end-consumers might be affected too if
the regional grid operator decides to raise the cost of the contract with the local grid
operator in order to compensate the greater fee charged by SvK (Svenska kraftnät,
2018a).
Just as regional grid operating companies pay fees for using electricity from the
transmission grid, local grid operating companies pay fees for using electricity from the
regional grid (Österlund, 2019a). One example is the network agreement Upplands
Energi has with the overlaying regional grid operated by Vattenfall Eldistribution. At
the time of April 2019, Upplands Energi has already exceeded the agreement with
Vattenfall Eldistribution multiple times. For the company, finding solutions for
regulating power consumption is economically motivated. Therefore, Upplands Energi
in cooperation with the software company Ngenic AI, has started to control whether
heat pumps in houses are on or off in order to adjust the power consumption during cold
winter hours. This method has contributed to making it possible for Upplands Energi to
shave power peaks by 2 MW (Österlund, 2019a).
2.2 Today’s outlook for the future
In order to investigate the role of a V2G EV-fleet in the power system, a future outlook
is conducted. What will the energy system look like and which challenges will be
faced? In this part of the report, predictions from SvK about the energy system and
plans for the future of Uppsala will be presented.
2.2.1 System services
Today, SvK stresses that the power system is facing major changes. New production
methods of electricity and the way electricity is used and stored are some of these
changes. This opens up for new ways of managing the grid through new types of system
services. According to SvK, it is not obvious what these system services will look like.
With the technical development taking place, it is difficult to say whether new system
services will be performed by production facilities or by network components.
According to SvK, it is unclear whether the system services will be implemented with
10
the help of, for example, regulations or market solutions. Another question is how to
divide the responsibility and costs regarding the system services between SvK,
electricity producers and grid operating companies. The system services may be
provided by commercial operators on market terms (Svenska kraftnät, 2017a).
2.2.2 The future power grid
SvK has formulated a scenario of the Swedish power grid in the year 2040, based on
current national and international politics, driving forces and decisions made today. In
the scenario, no revolutionary technology breakthroughs, big market changes or big
extension of the national power grid is assumed (Svenska kraftnät, 2017a). The most
central challenges in this scenario are stated in the Table 1 below.
Table 1. Some challenging aspects of SvK:s scenario of a possible outcome for the
Swedish power grid year 2040. The aspects are selected by relevance to this report.
Scenario Outcome
Decommissioning of nuclear power. Decreasing the power and frequency
stability in the grid.
Increasing share of intermittent
electricity production in terms of wind
power and, to some extent, solar
power.
Increasing demand of flexibility and
balancing in the power grid.
Increasing power consumption and
reducing production capacity.
Degrading of the power supply capacity in
the south of Sweden, with possible power
deficiency as a result.
Increasing production and
consumption flexibility, as well as
energy storage in the system.
Improving the power adequacy.
(Svenska kraftnät, 2017a)
In the scenario for the power grid 2040, wind power will more or less replace the loss of
nuclear power. In total, the energy production is large enough to cover the nuclear
power loss, but the weather dependence makes the production unpredictable. Without
the nuclear power, the south of Sweden risks a power deficiency of 400 hours a year.
The power deficiency will demand an electricity market with flexibility (Svenska
kraftnät, 2017a).
2.2.3 Batteries in the power system
The Royal Swedish Academy of Engineering Sciences (IVA) stresses that a
combination of different energy sources can be used both on transmission grid level to
improve the quality of the electricity, and in the distribution grids to improve the local
stability of power supply. By using batteries, power is obtained from the batteries
11
instead of the grid. This can be used for frequency regulation and local peak shaving.
Other ways batteries can integrate with the grid today is by balancing fluctuations in
electricity production, to avoid bottlenecks, and to ensure an uninterrupted power
supply (Nordling, 2016). In this report, peak shaving will be the central feature
investigated, although this does not exclude the possibility of any mentioned feature.
However, services mentioned above are energy-intensive and require characteristics of
the batteries they do not possess today. According to Vattenfall, new markets for
making battery storage economically viable will develop. But to reach a future of these
battery services, the batteries have to be optimized for the services they are meant to
perform. Put in the words of Vattenfall’s Batteries Director; “the constant cycling of the
batteries are very energy-intense and affect the lifespan” (Nasner, 2019). Also, market
incentives and cheaper production need to fall into place to make the development of
these new energy services a viable solution (Nasner, 2019). According to IVA, price
drops are occurring regarding lithium-ion batteries in the vehicle industry. With the use
of batteries with reasonable price, expensive upgradings of the grid can be avoided
(Nordling, 2016).
2.2.4 Development of cities
To understand how the V2G EV fleet may operate and how it can be implemented, the
way cities are planning for the future is of great interest.
Local changes
One of the cities experiencing capacity deficiency today is Uppsala (Lindblom, 2018).
On the 28th of May 2018, the Uppsala municipal board adopted “Energiprogram 2050”.
Energiprogram 2050 is the plan and vision of the municipality regarding the
development of the energy system, as a part of making Uppsala fossil free in 2030 and
climate positive in 2050. One of the aims is to develop the energy system and to
integrate it with other systems in society, such as the transportation system (Uppsala
kommun, 2018).
The municipality is aware of the fact that with a higher amount of local and renewable
energy, the importance of managing and decreasing power peaks will grow. An aim to
use renewable energy sources in combination with smart usage and energy storage
integrated with the grid has therefore been formulated. By storing energy, it can be used
when the power demand exceeds the production (Uppsala kommun, 2018). The
discussion regarding capacity deficiency in section 2.2.1 is in other words present in
Uppsala as well.
In the Energiprogram it is also stated that an important part of future energy storage will
be integrated in the infrastructure of the transport sector, and forecasts suggest that the
transport system will be completely electrified. The municipality predicts that with
technological development regarding energy storage and usage, commercial solutions
12
may develop for both the power grid and power consumers (Uppsala kommun, 2018).
One of these technological developments could be the development of new transport
services, such as V2G integrated with Mobility as a Service.
Mobility as a Service
New technology and development of solutions for shared mobility, such as self-driving
cars, is likely to affect how the public transports itself. The public’s travel pattern
influence how the town or city itself develops, in terms of attractiveness as a place to
live. However, there are big changes needed in order to enable more people to
participate in the public transportation system. Mobility, or transport, as a service is one
of these potential developments, which would enable people to access transport on an
as-needed basis. Shared mobility can take many forms, but the trends now point away
from peer-to-peer platforms, such as car-pooling, towards a future with integrated
services from several mobility providers into one single service. This development is
aided by the development and use of digital solutions (Polle et al., 2018).
One of the possible developments mentioned above, include self-driving vehicles.
Studies have predicted that fully autonomous vehicles will start being phased into
transport systems around year 2020-2025. Autonomous solutions such as these, may be
a way to make public transportation more efficient as a system, providing transportation
at a low cost for more people (Polle et al., 2018). A growing research and policy
consensus that transport systems based on privately owned internal combustion engine
vehicles have a finite lifespan (Cooper et al., 2019), indicate that a future transportation
system could be based on electric, autonomous vehicles. Further on, the system in the
form of a commercially owned, self-driving EV-fleet with potential to interact with the
electric grid has interesting potential for the future (Nelder et al., 2017). The fleet would
essentially be enabling a new way of transportation.
2.3 The role of an EV-fleet in the power system
One way of managing a growing capacity deficiency and a way to even out power peaks
in the grid might be to use energy storage in the form of V2G technology. In this
section, a presentation of how an EV-fleet could integrate the transportation and power
systems by providing V2G services will be made. Challenges connected to using energy
storages in the form of EV batteries will also be presented briefly.
2.3.1 Vehicle-to-grid (V2G)
In a future with smart electrical grids as system standard, EV batteries, which have a
quick response rate, could be an asset to the grid in the form of providing charge to
meet power demands at peak times. If a large number of EV:s could be centrally
coordinated, the vehicles would be able to provide grid services as well as transport
services, skipping manual intervention as would happen with vehicles owned by private
13
persons (Cooper et al., 2019). Consumer acceptance of V2G as well as attitudes are
social challenges linked to V2G (Noel et al., 2019).
An ordinary EV is charged by connecting to the electricity grid, but unable to supply
power back to the grid. With V2G technology, it would be possible to to create a “/.../
bidirectional communication and power flow between the EV and the power grid.”
(Noel et al., 2019). To make the V2G system work, there must be a way of connecting
the EV to the grid bidirectionally, i.e. a specialized charger, as well as a way of
communicating to the EV when to charge from and discharge to the grid (Noel et al.,
2019).
Some interesting aspects in a future scenario are the ways the V2G system may
integrate energy and transportation systems and what kind of services the system could
offer the grid. With the ability to get information on the state of the power supply EV:s
can “/.../ offer stability and flexibility as a market participant /.../” (Noel et al., 2019)
and with a well-functioning synchronization, the EV:s can offer services that balances
energy flows (Noel et al., 2019). In order to compensate instead of contributing to
power peaks, a well-managed implementation is needed, especially on regional and
local grid levels (Nordling, 2016).
In order to be part of the energy market, the EV-fleet has to meet the demands from the
grid operators. The grid operators need to be ensured that offered power capacity will be
charged and discharged at the right time. Highlighted advantages of V2G are that the
EV:s together have a high capacity at a relatively low price, can react quickly when
needed and have a high availability. At the same time, the EV:s have a limited energy
supply capacity and the cost per unit of energy is higher in comparison with competitive
solutions (Noel et al., 2019).
When the energy market has been charted regarding V2G, the considered highest valued
service for today is stabilizing the imbalance of momentary power production and
consumption, i.e. frequency regulation. Serving as a baseload power is considered
unsuitable, with reasons such as not being able to provide continuous energy long-term.
With the prediction of a greater amount of intermittent energy sources, the mismatch
between the electricity demand and generation might also be a problem that can be
solved by the EV:s backing up the system. Another potential service is providing power
compensation to the grid (Noel et al., 2019). In this report, power compensation due to
capacity deficiency caused by transmission capacity deficiency will be focused on. The
general idea of V2G examined in this report is illustrated in Figure 1 below.
14
Figure 1. The V2G system examined in this report.
The EV-fleet can offer its service to grids on different scales, even as small as micro
grids. On a local level, the EV-fleet can improve the power supply and help avoid
critical situations that otherwise might occur. However, it is important to remember that
this kind of market allowing these kind of services does not exist today, and there is no
way of knowing exactly how the future will develop regarding how the EV-fleet might
integrate with the market. Thus, some potential services might still be unknown (Noel et
al., 2019).
V2G solutions today are in a stage of development and there are only a few examples of
the technology being used to provide services to an electricity market. The first example
took place at the University of Delaware in the US, where the EV:s successfully
participated in frequency regulation (Noel et al., 2019). Another successful V2G-project
took place in Denmark. Today, the company Nuvve runs the first commercial V2G
project providing frequency regulation to the grid (Noel et al., 2019). Some conclusions
from the now ended “Parker project” are that V2G technology is scalable and the
market is ready, but also that the supply chain for both vehicles and charging
infrastructure, as well as a clear business case are not yet in place (Andersen et al.,
2019). Pilot projects involving V2G are also running in Sweden. Nissan, together with
the municipality of Kungsbacka and E.ON, have initiated a project with the aim to
install ten V2G units in Kungsbacka (Nissan News, 2018).
2.3.2 EV battery challenges
For an EV, there are limitations in the ways it can be used. The battery has a given
energy storage that can not be exceeded. With services to the grid taken into account,
the whole energy capacity can not be used (Mohammed et al., 2017). Another limitation
is the fact that due to reducing stress on the battery, it is recommended not to charge or
discharge the battery to its highest and lowest energy capacity. It is recommended to
charge the battery 80 % of its maximum capacity (Nissan USA, n.d). The charging and
discharging cycle pattern the V2G system would demand affects the battery in a
negative way; it reduces its lifetime and performance (Mohammed et al., 2017).
15
3. Theory, Data and Methodology
In order to answer the research questions, theory, data and methodology for different
components of the modeled system have been compiled. The chapter is divided into
sections presenting each component of the modeled system based on its corresponding
theory, data and methodology. The chapter is summarized in the last section.
Overview
An overview of this chapter is provided in Figure 2 below. Sections 3.1-3.4 aim to
answer research question 1, 2 and 3. How the sections come together to answer all four
research questions is presented in a Methodology summary, see section 3.5.
Figure 2. Overview of methodology sections.
Notes on methodology
Estimating future developments is always a tricky business. In this report, conditions
that apply today together with assumptions that are continuously presented throughout
the chapter have been used as an estimation of a future scenario. Locating the system to
Uppsala and its estimated taxi fleet is motivated by the fact that capacity deficiency is a
present and addressed problem in the area. Data obtained from Upplands Energi,
operating near Uppsala, will therefore be the local scenario examined in this report. The
system will also be examined by using the taxi fleet of Uppsala for V2G services to the
transmission grid. Where the EV:s will connect to the power grid is delimited to only
being briefly discussed in chapter 5.
3.1 Exceedances
• Localexceedances
• Regional exceedances
• Quarter one 2019
3.2 Capacity ofEV:s
• Capacity ofNissan Leaf e+
• Comparecapacity withexceedances, powerwise and energywise
3.3 Taxi fleet
• New York City taxi fleet scaledto Uppsala
• Compareavailability ofthe fleet withtime ofexceedances
3.4 Sensitivity analysis
• How willbatterycapacityimpact the system?
• Capacity ofTesla Model 3
• Capacity needduringexceedances
16
The softwares used for data handling and calculations in this project are Microsoft
Excel and Matlab R2018b. In addition to this, literature on the topics of V2G, Mobility
as a Service and power exceedances has been used.
3.1 Exceeding network agreements
As mentioned in section 2.1.3, penalty fees apply when network agreements are
exceeded. The structure of the fees differs depending on which actors are involved;
transmission and regional grid operators, or regional and local grid operators.
In this report, two cases will be examined; a Local case and a Regional case. The EV:s
will connect to the electricity grid on a local level in order to perform power shaving
services on higher levels of the grid, such as transmission level (Noel et al., 2019). The
exceedances examined in the two cases are real exceedances of network agreements
from different levels of the grid. The reason behind looking at a local and a regional
case is to get a picture of the potential income and availability for the EV-fleet
providing power shaving services to grid operating companies at different levels of the
power system. Since the magnitude of exceedances in the Regional case is considerably
higher than exceedances in the Local case, not to mention the capacity of an EV battery,
it is reasonable to not compare the stations of the two cases directly. Instead, three
stations from the Local case will be compared to each transmission station. Details
regarding magnitude of exceedances can be found in Table 2 and Appendix C.
3.1.1 Local case
Theory
As mentioned in section 2.1.3, Upplands Energi has to pay fees when exceeding
network agreements with the overlaying regional grid operator. Agreements are signed
one year at a time (Österlund 2019c). Upplands Energi has agreements for individual
stations, as well as a joint agreement for the total power consumption. Penalty fees
apply when exceeding agreements at individual stations, as well as the joint agreement.
The former fee depends on the amount of exceeding power at a given time. The latter
fee depends on the mean power consumption of two consecutive months. In this report,
only exceedances at individual stations will be considered. The fee for exceeding the
joint network agreement will not be considered since it does not represent a power peak
in time (Österlund, 2019a). Exceedances occur almost exclusively during winter and are
highly weather dependent (Österlund, 2019c).
Data
Data for exceedances during the first quarter of 2019 was sourced from Upplands
Energi and is presented in Table 2 below. Three out of a total number of four
exceedances happened during the same day, with simultaneous exceedances at stations
1 and 2.
17
Table 2. Exceedances of network agreement during the first quarter of 2019, Local
case.
Station Date and Time Exceedance [MW] Exceedance Fee [SEK]
1 190121, 18-19 1.163 52 335
2 190121, 18-19 1.680 75 600
2 190204, 07-08 0.070 3150
3 190121, 17-18 0.386 17 370
Total exceedance fee:
148 455
(Österlund, 2019a)
The data presented in Table 2 above is a good estimation of how quarter one usually
looks like, according to Håkan Österlund, maintenance engineer at Upplands Energi.
Depending on weather conditions, exceedances may vary +/- 10 %, which affects the
cost (Österlund, 2019b).
Methodology
Since the cost and time of exceedance at each individual station are known and
considered a good general estimation of exceedances during the first quarter of a year,
results from using this data is presented in Table 10 and Figure 8, combined with the
needed number and usage pattern of the EV:s. How the data is handled in order the
answer research question 1 and 2 is described in more detail in section 3.5.
3.1.2 Regional case
Theory
When a regional grid operating company is connected to the transmission network, the
operator needs to pay a fee to SvK. SvK has a transmission grid tariff, which determines
the fee an operator has to pay for transporting electricity on the transmission grid. It
depends on both the amount of energy and power used. The fee related to power
depends on how many kW the network agreement covers (Svenska kraftnät, 2019). The
regional grid operator is not allowed to exceed its network agreement, the only accepted
way of using a higher amount of power than allowed is to get a temporary network
agreement (Svenska kraftnät, 2018a).
Data
The regional grid operating company pays hourly fees when exceeding the network
agreement (Svenska kraftnät, 2019). The data was sourced from SvK and the details
regarding fees 2019 are shown in Table 3 below.
18
Table 3. Penalty fees for exceeding a network agreement, depending on the hour of
exceedance, for 2019.
Hour of exceedance (during 24 hours) Exceedance fee [kr/MWh]
First hour 560
Second hour 1400
Third hour and following hours 2800
(Svenska kraftnät, n.d)
Data for exceedances during the first quarter of 2019 at three different transmission
stations was sourced from SvK. The three stations were chosen to show varying patterns
of exceedance magnitude and occurrence. The geographic locations of the three stations
are unknown, due to the confidentiality of the data. The station data is presented in
Tables 1A, 2A and 3A in Appendix A.
Methodology
As stated above in section 3.1, the EV:s will connect to the grid on a local level in order
to perform power shaving on higher levels of the grid. When examining the
exceedances of the network agreement between regional and transmission grid
operating companies, the EV:s are assumed to be able to power shave seemingly at the
point of exceedance and without losses. This is done in order to enable comparison of
income with the Local case.
To determine an income for the EV-fleet providing power shaving at the transmission
stations, data on exceedances at each station is used. The results from using this data are
presented in Tables 1C, 2C and 3C in Appendix C as well as Table 11. How the data is
handled in order to answer research question 1 is described in more detail in section
3.5.
To compare the exceedances at the transmission stations with the usage pattern of the
EV:s, for each station, a day consisting of compiled mean values of all exceedances
during the first quarter of 2019 was created, see Tables 1B, 2B and 3B in Appendix B.
All exceedances were added, and the mean value of each minute of occurring
exceedances was calculated by dividing the accumulated exceedance with the number
of days with exceedance at that particular minute. By doing this, the data could be
represented in a more comprehensible way and in addition to this, give an indication on
the time and mean value of exceedances. Thus, note that the day of compiled mean
exceedances does not show exceedances happening during the same day, but rather
when in time they happen. By taking a mean value of simultaneous exceedances,
extreme exceedances might be smoothed out, eliminating the most extreme cases. These
compiled days of exceedances combined with the usage pattern of the EV:s is presented
19
in Figure 9, 10 and 11. How the data is handled in order to answer research question 2 is
described in more detail in section 3.5.
3.2 Capacity of EV:s
To determine the economic value the grid compensation services might bring to the
company operating the EV-fleet, as well as the interplay of the EV-fleet and
exceedances, it is essential to know how many EV:s are involved in power shaving. To
determine the number of EV:s, technical specifications regarding power and energy
storage capacity have to be defined. In this section, the theory, methodology and data
behind the results will be presented.
3.2.1 Theory
To determine the number of EV:s needed to match a particular exceedance, in both the
Local and Regional case, calculations regarding the EV capacity are needed. Two
characteristics of the EV are essential when comparing its capacity to an exceedance;
how the EV can match the exceedance powerwise, and how the EV can match the
exceedance energywise, i.e. power over time. The specification demanding the higher
number of EV:s will be the deciding factor. This entails investigating the specifications
of the EV battery.
To calculate the amount of EV:s needed to match an exceedance powerwise, the
following quantities, shown in Table 4, are needed:
Table 4. Quantities needed to calculate the amount of EV:s, powerwise.
Quantity Unit Description
Power exceedance MW Occurring exceedance in
the transmission or
regional grid
Maximum charge rate MW Maximum charge rate
With these quantities mentioned above known, the number of EV:s can be calculated as
follows: The Maximum discharge rate, i.e. the maximal power that an EV is capable of
supplying to the grid, is assumed to be the same as the Maximum charge rate, see
equation (1):
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 𝑟𝑎𝑡𝑒 = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑐ℎ𝑎𝑟𝑔𝑒 𝑟𝑎𝑡𝑒 (1)
The Number of EV:s, powerwise, can now be calculated using equation (2):
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉: 𝑠, 𝑝𝑜𝑤𝑒𝑟𝑤𝑖𝑠𝑒 =𝑃𝑜𝑤𝑒𝑟 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 𝑟𝑎𝑡𝑒 (2)
20
To calculate the amount of EV:s needed to match an exceedance energywise, the
following quantities, shown in Table 5, are needed:
Table 5. Quantities needed to calculate the amount of EV:s, energywise.
Quantity Unit Description
Power exceedance MW Occurring exceedance in the transmission or regional grid,
during one hour
Energy storage MWh Maximal energy storage in an EV battery
Percentage energy
storage
- The percentage of the storage of the battery that is ideal to
charge
Percentage to
grid
- The percentage of how much of the storage of the battery
that can be discharged to the grid; this is dependent on
how much energy that should be left in the battery to
perform transport services
When calculating the amount of EV:s involved energywise, Power exceedance needs to
be translated into required energy, i.e. power over time. Today, penalty fees are charged
one hour at a time. This means that the measured power at every new hour is assumed
to have been constant over the past hour. As a response to this simplification, the EV:s
featured in this report need to supply a constant power during one hour. An assumption
is thus made; to cover an exceedance, the amount of EV:s needed to cover the whole
exceedance is deemed to be the “sufficient” amount of EV:s. The required energy is
calculated by multiplying the value of Power exceedance with one hour, as equation (3)
shows:
𝐸𝑛𝑒𝑟𝑔𝑦 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒 = 𝑃𝑜𝑤𝑒𝑟 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒 ∙ 𝑂𝑛𝑒 ℎ𝑜𝑢𝑟 (3)
The energy storage capacity is not used to its maximum, in order to extend the battery
life. Further on, the battery does not discharge completely to the grid when power
shaving, due to the fact that the EV must have the capability to perform transportation
services. Thus, the Available energy storage to the grid is given in equation (4):
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 = 𝐸𝑛𝑒𝑟𝑔𝑦 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 ∙ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 ∙ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑡𝑜 𝑔𝑟𝑖𝑑 (4)
The Number of EV:s, energywise, can now be calculated using equation (5):
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉: 𝑠, 𝑒𝑛𝑒𝑟𝑔𝑦𝑤𝑖𝑠𝑒 =𝐸𝑛𝑒𝑟𝑔𝑦 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑡𝑜𝑟𝑎𝑔𝑒 (5)
To determine the Numbers of EV:s needed to match exceedances both powerwise and
energywise, the largest output of equations (2) and (5) will be chosen as the resulting
number of EV:s.
21
3.2.2 Data
Data regarding technical specifications of the EV battery has been based on the current
EV model 2019 Nissan LEAF e+. The model has been selected due to the price
plausibility for a company investing in a large number of EV:s, as well as being a well
known EV model of today. Nissan is also a company involved in projects regarding
V2G today, see section 2.3.1. The data is presented in Table 6 below.
Table 6. Data regarding the EV model 2019 Nissan LEAF e+ and assumed Percentage
to grid unique to this report.
Quantity Value
Maximum charge rate 0.050 MW*
Energy storage 0.062 MWh*
Percentage energy storage 0.8*
Percentage to grid 0.5
(*Nissan USA, 2019)
The value of Percentage to grid is determined based on assumptions made in section
3.3.1, i.e. the primary operations of the EV-fleet is transportation, which limits the
energy available for grid services. With the given data, the following quantities in Table
7 have been calculated with equations (1)-(5) mentioned in section 3.2.1. The value of
Available energy storage is rounded off in Table 7.
Table 7. Calculated quantities based on previous equations and known data regarding
the EV model 2019 Nissan LEAF e+ in Table 6.
Quantity Value
Maximum discharge rate 0.050 MW
Available energy storage 0.025 MWh
Together with specifications concerning the exceedances presented later in the report,
the number of EV:s needed to match each exceedance will be calculated.
3.2.3 Methodology
By using the calculated quantities in Table 7 combined with known hourly exceedances,
the number of EV:s needed to match an exceedance can be calculated as shown in
Figure 3 below.
22
Figure 3. Method for calculating number of EV:s needed to match an exceedance.
3.3 Taxi fleet
In order to determine the availability of the EV-fleet, the usage pattern of the taxi fleet
of New York City has been examined. The reason for examining this usage pattern is
based on the assumption that this might resemble the usage pattern of the EV-fleet in
the future, with relatively short and frequent trips in a limited area.
3.3.1 Theory
No matter the economic value of the grid service, an assumption has been made that the
EV-fleet will remain a taxi fleet, in other words; the EV:s will not stop driving people
around. This assumption is made to ensure that the EV-fleet can still be considered
offering Mobility as a Service. Therefore, it is reasonable to assume that the EV:s
available for grid services will be the ones not performing transport services at that
moment.
3.3.2 Data
To examine the usage pattern of the New York City taxi fleet, a dataset provided by the
Illinois Data Bank has been used. The dataset consists of data from almost 700 million
taxi trips during the years 2010-2013, in the form of pickup and drop-off dates, times,
and coordinates, the metered distance reported by the taximeter, taxi identification
number (medallion number), fare amount, and tip amount (Donovan and Work, 2016).
The authors of this report believe that in a future with self-driving EV:s that are
commercially owned and operated, the usage of the service will result in more, but
Available power
•𝑃𝑜𝑤𝑒𝑟 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 𝑟𝑎𝑡𝑒= 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉: 𝑠, 𝑝𝑜𝑤𝑒𝑟𝑤𝑖𝑠𝑒
Available energy
•𝐸𝑛𝑒𝑟𝑔𝑦 𝑒𝑥𝑐𝑒𝑒𝑑𝑎𝑛𝑐𝑒
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑡𝑜𝑟𝑎𝑔𝑒= 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉: 𝑠, 𝑒𝑛𝑒𝑟𝑔𝑦𝑤𝑖𝑠𝑒
Determine number of EV:s
• Determine Number of EV:s as the largest output from previous steps
23
shorter trips. This corresponds well to the usage pattern in the dataset, and motivates
using it for modelling a future taxi fleet. In order to scale the fleet to Uppsala, the
number of taxis operated by the two largest taxi companies in Uppsala was used to
estimate the number of taxis in Uppsala. The total came down to an approximation of
150 (Uppsala taxi, 2019) plus 180 (Taxi kurir, 2019) taxis. In total 330 taxis were
estimated to operate in Uppsala, with an estimated hourly income of 370 SEK per hour
(Axelsson, 2019). The method used was telephone call to the companies.
3.3.3 Methodology
In order to calculate the availability of the taxi fleet, the following data extracts were
chosen: pickup and drop-off dates and times, as well as the medallion number. From
this, the activity of each active taxi during each minute of a certain day could be
determined. The activity is here defined as the number of taxis performing transport
service during a particular minute. By dividing the activity each minute with the total
amount of active taxis during a day, a percentage of active taxis during each minute was
calculated.
Due to the magnitude of the dataset and the fact that access to big data-software was
unavailable, delimitations had to be made. This means that only a few days were
sampled, and some of the days do not have 24 hours of data, since some parts of the
data was not read. Winter days were chosen due to the fact that power peaks are more
frequent during cold winter months (Svenska kraftnät, 2017b). Data from the first two
days of the months November, December and February were chosen. Only one day
from January was chosen, since the first day of January involves taxi usage during New
Year’s, which can be considered exceptional usage. As mentioned in 3.3.2, not all days
had 24 hours of sampled data. These are data points lacking information, and thus not
included in calculating a mean day, as Figure 4 below shows.
Figure 4. Method for calculating the activity of the taxi fleet during a mean winter day.
Since the activity is now a percentage, the availability during each minute of the mean
winter day is defined as 1-percentage of active cars. This is a simplification due to the
fact that data only exists for when the taxis are occupied. Activity occurring when taxis
are off-duty is therefore unknown.
The population in New York City in 2010 was 8 175 133 (NYC Government, n.d.) and
the total number of taxis in the dataset is 13 164 (Donovan and Work, 2016). The quota
Calculate the activity each minute of each
day
Sum up the activity of all minutes of all days, omit data points that
lack information due to delimitations
Calculate a mean day by dividing the summed
up activity for each minute by the sampled
amount of days
24
taxis/inhabitant in New York City is approximately 0.0016. The current population in
Uppsala is 376 354 (SCB, 2019) and using the estimated number of taxis concluded in
section 3.3.2, the corresponding quota for Uppsala is 0.00088. The higher quota in New
York City motivates using this usage pattern to represent a more frequent use of taxi
services in Uppsala, i.e. an EV-fleet providing Mobility as a Service.
Finally, the number of taxis available was scaled to Uppsala by multiplying the
percentage of available taxis with the estimated number of taxis in Uppsala. When
plotting the result, the first and last 20 minutes of each day were cut, since these
represent a non-realistic increase and decrease indicating that all taxis stand still at
midnight, which is not the case. Trimming the data like this does not affect the V2G
system, since no exceedances investigated occur during this time.
3.4 Sensitivity analysis
In order to evaluate one aspect of the system’s sensitivity, the impact of the EV
batteries, i.e. charge rate and energy storage, will be evaluated based on the following
questions:
▪ If the capacity of the EV battery were to change in accordance to probable
development of batteries, what would the impact on the system2 be?
▪ In order to fully cover the exceedance with an unchanged number of EV:s, what
would the needed EV battery capacity have to be?
By doing this, how sensitive the system is to battery capacity can be further
investigated. The exceedances in the most extreme scenario, illustrated in Figure 11,
will be examined. When answering the second question, exceedances that can not be
covered will be examined, since these represent situations needed to be resolved for the
system to improve.
The method for answering the first question involves using data for an EV battery
considered to represent the general future development of EV batteries. A sound
estimation is to use the battery of Tesla Model 3, since the model is in the same price
range and market as the Nissan Leaf e+, but with better range (Shepero, 2019). In this
sensitivity analysis, a Tesla supercharger with a charging rate of 120 kW is assumed to
be used (Shepero, 2019). The specifics regarding the EV model is presented in Table 8
below. Note that the last two parameters are unchanged. The values can be compared to
specifics regarding the Nissan Leaf e+ model in Table 6.
2 The system consisting of an EV-fleet and the power grid.
25
Table 8. Data regarding the EV model Tesla Model 3.
Quantity Value
Maximum charge rate 0.120 MW*
Energy storage 0.075 MWh*
Percentage energy storage 0.8**
Percentage to grid 0.5
(**Lambert, 2017; *Nissan USA, 2019)
The variable Percentage to grid is based on assumptions stated in section 3.3.1. By
using equations (1)-(5) in section 3.2.1, the following quantities were calculated, see
Table 9.
Table 9. Calculated quantities based on previous equations and known data regarding
the EV model Tesla Model 3 in Table 8.
Quantity Value
Maximum discharge rate 0.120 MW
Available energy storage 0.030 MWh
The number of EV:s are determined by using the method presented in Figure 3. The
result is compared to the availability of the EV-fleet and presented in section 4.3.
The method for answering the second question includes using specifics for an ideal EV
according to Table 5. The number of EV:s available for power shaving is determined as
the mean number of available EV:s during the hour of exceedance.
By using equation (4), Available energy storage is calculated. Energy exceedance is
calculated by using equation (3). The result is presented in Table 12.
3.5 Methodology summary
To answer the first research question: “What is the potential economic value of a fleet
of EV:s providing service to the grid?”, the income must be estimated. By knowing the
actual cost of exceeding a network agreement at a given time, the income of each
exceeding for the EV-fleet operating company can be determined as in equation (6).
𝐼𝑛𝑐𝑜𝑚𝑒 𝑝𝑒𝑟 𝑒𝑥𝑐𝑒𝑒𝑑𝑖𝑛𝑔 =𝐴𝑣𝑜𝑖𝑑𝑒𝑑 𝑓𝑒𝑒
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉: 𝑠 (6)
26
Since the Avoided fee is an hourly fee, the Number of EV:s must be sufficient to cover
the exceedance the whole hour to earn that hourly income. Therefore, Number of EV:s
will be compared to the mean number of EV:s available during the hour of exceedance
to determine if Number of EV:s is sufficient at the time. The income will be presented as
both the total income during the first quarter of 2019 and the average income per EV
and exceedance. The income will be determined for both the Local case and Regional
case. For an overview, see Figure 5 below.
Figure 5. Flowchart of how to answer research question 1. The method is applied in
both the Local and Regional case.
To answer the second research question: “What is the interplay between the EV-fleet
and the grid, regarding availability for grid services?” the usage pattern can be
compared with the number of EV:s needed for power shaving, distributed over 24
hours. The interplay between the transport and grid service can be illustrated by plotting
the usage pattern and the needed number of EV:s to power shave in the same graph.
This will be determined for both the Local case and Regional case. In the former case,
every exceedance during the first quarter of 2019 will be presented in the same graph, to
illustrate which exceedances could potentially be covered. In the latter case,
exceedances of a day consisting of compiled mean values of all exceedances during the
first quarter of 2019 for each transmission station will be presented in separate graphs,
to illustrate which of these exceedances could potentially be covered. Representing the
exceedances this way is done to show occurrence and magnitude of exceedances in
order to illustrate the interplay of exceedances and the EV-fleet at each individual
station. The number of EV:s needed to match each of these exceedances is presented in
Tables 1B, 2B and 3B in Appendix B. For an overview, see Figure 6 below.
Figure 6. Flowchart of how to answer research question 2. The method is applied in
both the Local and Regional case.
To answer the third research question: “If the battery capacity of the EV is changed,
what is the impact on the system3?” a sensitivity analysis will be made, see section 3.4.
3 The system consisting of an EV-fleet and the power grid.
Cost of exceedance
Required number of EV:s
involved
Is a sufficient number of EV:s
available? If yes, income
Plot availability of the EV-fleet
of Uppsala
Needed number of EV:s to
match exceedances
Plot needed number of EV:s
at time of exceedances
Determine interplay
27
To answer the fourth research question: “Based on today’s discussion and results from
previous research questions, which key features are important to address when
implementing the system?”, results from the three former research questions will be
discussed in a wider context, with themes stemming from the Background (chapter 2).
4. Results
In this part of the report the results of research question 1, 2 and 3 is presented in
different sections. In order to answer research question 1 and 2, the Local case and the
Regional case is presented in sections 4.1 and 4.2. The third research question is
answered in the Sensitivity analysis, section 4.3. The fourth research question is
answered in the Discussion, chapter 5.
4.1 The economic value
To answer the first research question: “What is the potential economic value of a fleet
of EV:s providing service to the grid?”, the income of the EV-fleet in the Local and
Regional cases, respectively, has been determined. Power shaving services generating
income for the fleet can only be provided if there is a sufficient amount of EV:s
available at the time of the exceedance, i.e the fleet must cover a whole exceedance. The
availability of the EV-fleet is illustrated in Figure 7, section 4.2.1. The determined
number of EV:s involved in covering exceedances was, in both cases, determined as the
number of EV:s energywise.
4.1.1 Local case
The total income for the EV-fleet during the first quarter of 2019 in the Local case is
presented in Table 10. The Table shows the total income for the EV-fleet and the
average income per EV and exceedance. Note that two exceedances occurred
simultaneously at station 1 and 2. In this case, the income is related to which of these
exceedances, (a) or (b), the company operating the EV-fleet prioritize; the highest
income per EV or the highest total income per exceedance. In Table 10, the index (a)
means that this exceedance, together with the unlabeled exceedances, are considered
when calculating the “Total income” and “Average”. In the same way, the index (b)
means that this exceedance, together with the unlabeled exceedances, are considered
when calculating the “Total income” and “Average”.
28
Table 10. Exceedances of network agreements during the first quarter of 2019 in the
Local case, shown for each station. Note that two exceedances occurred simultaneously
190121, (a) and (b). Income per EV is rounded off to whole numbers. Average and
Total income are rounded off to two significant numbers.
Station Date and
Time of
exceedance
Exceedance
[MW]
Exceedance
Fee [SEK]
# EV:s
needed to
match
exceedance
#EV:s
available
Income
per EV
[SEK]
1 190121,
18-19
1.163 52 335 (a) 47 149 1114 (a)
2 190121,
18-19
1.680 75 600 (b) 109 149 694 (b)
2 190204,
07-08
0.070 3150 3 255 1050
3 190121,
17-18
0.386 17 370 16 184 1086
Total
income:
73 000 (a)
96 000 (b)
Average:
1100 (a)
940 (b)
4.1.2 Regional case
The total income for the EV-fleet during the first quarter of 2019 and the average
income per EV and exceedance based on covering exceedances at three transmission
stations, A, B and C, are presented below in Table 11. The results come from the results
in Table 1C, 2C and 3C presented in Appendix C. Each of these tables contains the
number of EV:s needed to match an exceedance, the number of available EV:s at the
time of an exceedance and resulting total income and income per EV and exceedance.
An average of the latter result is presented in Table 11.
Table 11: Total income for the EV-fleet, average Total income and Average income per
EV and exceedance. Exceedances occurred during the first quarter of 2019. Results are
presented for each station. Average income per EV and exceedance and Total income
are rounded off to whole numbers. Average is rounded off to two significant numbers.
Station Total income [SEK] Average income per EV and exceedance [SEK]
A 11 760 19
B 15 680 42
C 17 360 17
Average 15 000 26
29
4.2 Availability for grid services
To answer the second research question: “What is the interplay between the EV-fleet
and the grid, regarding availability for grid services?”, the number of EV:s needed to
match each exceedance is compared with the usage pattern of the EV fleet in the Local
and Regional case, respectively.
4.2.1 Availability of the EV-fleet during an average winter day
The estimated average number of EV:s available in Uppsala during an average winter
day is presented in Figure 7 below. The mean value of the availability of the taxi fleet in
New York City an average winter day has been scaled to the amount of taxis in
Uppsala.
Figure 7. Estimated average number of EV:s of the commercially owned EV-fleet
available in Uppsala during an average winter day.
4.2.2 Local case
The number of EV:s needed to match every exceedance during the first quarter of 2019,
see Table 10, compared with the average availability of the EV:s during a winter day is
shown in Figure 8 below. Note that two exceedances occurred simultaneously at station
1 (**) and 2 (*), during 18-19 190121. Figure 8 shows that the number of EV:s are
sufficient to cover three out of four exceedances.
30
Figure 8. Comparison of EV-fleet availability and EV:s needed to match exceedances in
the Local case. Note that during 18-19 190121, 109 or 47 EV:s are involved, depending
on exceedance covered. The red* and yellow** bars are stacked.
Figure 8 shows that the interplay between the EV-fleet and the grid regarding
availability for grid service seems to be sufficient in the Local case if not considering
covering both exceedances during the time 18-19, see Figure 8. The largest exceedances
occurs during the time when the EV-fleet is most occupied with transport service,
18-19. During 07-08, the margin is the greatest, with an additional 200 EV:s idle.
4.2.3 Regional case
The number of EV:s needed to match exceedances at station A during a day consisting
of compiled mean values of all exceedances occuring during the first quarter of 2019,
see Table 1B, compared with the average availability of the EV:s during an average
winter day is shown in Figure 9 below. The figure shows that the number of EV:s are
not always sufficient to cover every exceedance, given the assumption made in section
3.5.
31
Figure 9. Comparison of EV-fleet availability and EV:s needed to match compiled mean
exceedances in the case of transmission station A.
The number of EV:s needed to match exceedances at station B during a day consisting
of compiled mean values of all exceedances occuring during the first quarter of 2019,
see Table 2B, compared with the average availability of the EV:s during an average
winter day is shown in Figure 10 below. The figure shows that the number of EV:s are
sufficient to cover every exceedance, given the assumption made in section 3.5.
Figure 10. Comparison of EV-fleet availability and EV:s needed to match compiled
mean exceedances in the case of transmission station B.
The number of EV:s needed to match exceedances at station C during a day consisting
of compiled mean values of all exceedances occuring during the first quarter of 2019,
see Table 3B, compared with the average availability of the EV:s during an average
winter day is shown in Figure 11 below. The figure shows that the number of EV:s are
seldomly sufficient to cover every exceedance, given the assumption made in section
3.5.
32
Figure 11. Comparison of EV-fleet availability and EV:s needed to match compiled
mean exceedances in the case of transmission station C.
The available EV:s are sufficient to cover every mean exceedance at Station B (Figure
10), but not at stations A (Figure 9) or C (Figure 11). The least amount of EV:s are
needed to match the exceedances at station B. These exceedances happen during the
hours 02-10, which corresponds well to when the fleet is available for transport
services. During 05-06, the margin is the greatest, with an additional 250 EV:s idle. The
margins are smaller at station A and the exceedances occur when the EV-fleet is
occupied to a higher extent, 07-09 and 16-18. Between 08-09 and 17-18, exceedances
can not be covered. At station C, the majority of exceedances require a number of EV:s
greater than the whole fleet. As an example, almost three times the existing EV-fleet is
needed during 06-07.
4.3 Sensitivity analysis
To answer the third research question: “If the battery capacity of the EV is changed,
what is the impact on the system4?”, a sensitivity analysis based on the following
questions has been made.
▪ If the capacity of the EV battery were to change in accordance to probable
development of batteries, what would the impact on the system5 be?
▪ In order to fully cover the exceedance with an unchanged number of EV:s, what
would the needed EV battery capacity have to be?
The result of evaluating the first question is presented in Figure 12 and 13. Figure 12
and 13 are based on the same compiled day of mean values of exceedances at station C,
4 The system consisting of an EV-fleet and the power grid.
5 Ibid.
33
as previously illustrated in Figure 11. A comparison between using the EV models
Nissan Leaf e+ and Tesla Model 3 is made to illustrate the difference in capacity and
how a battery with a higher capacity impacts the system. In Figure 12, the capacity to
cover exceedances of the fleet consisting of Nissan Leaf e+ is represented, both
powerwise (red) and energywise (blue). In Figure 13, the capacity to cover exceedances
of a fleet consisting of Tesla Model 3 is represented in the same way.
Figure 12. Number of EV:s needed to match compiled mean exceedances at station C,
both energywise and powerwise. The EV model used is Nissan Leaf e+. The red bars
are placed in front of the blue bars.
Figure 13. Number of EV:s needed to match compiled mean exceedances at station C,
both energywise and powerwise. The EV model used is Tesla Model 3. The red bars are
placed in front of the blue bars.
Figure 12 and 13 show that the needed number of EV:s to match each exceedance is
determined by the EV energy storage, since matching the exceedance energywise
requires the highest number of EV:s. Figure 12 and 13 show that both models manage
34
to cover the same exceedances, occurring 05-06 and 11-12. During the hour between
05-06, an amount of 162 Nissan Leaf e+, or 134 Tesla Model 3 are needed. During
11-12, 81 Nissan Leaf e+, or 67 Tesla Model 3 are needed. In other words, using a
battery representing a probable development of batteries impacts the system
marginally.
The result of evaluating the second question is presented in Table 12 below. Only
exceedances neither a fleet of Nissan Leaf e+ nor Tesla Model 3 can cover are
evaluated.
Table 12. Calculated needed EV battery energy storage based on known available EV:s
and mean exceedances at station C.
Time of exceedance
during compiled day
of mean exceedances
Exceedance
[MWh]
Mean number of available
EV:s during exceedance
Needed battery
energy storage
[MWh]
06-07 24.5 288 0.21
07-08 9.1 255 0.09
08-09 12 220 0.14
10-11 8 209 0.14
16-17 11.3 199 0.14
17-18 15 184 0.20
5. Discussion
The discussion presented below is divided into sections based on the research questions
examined in this report, followed by a section presenting future outlooks and ideas for
further studies.
5.1 The economic value
In this section, the result of research question 1: “What is the potential economic value
of a fleet of EV:s providing service to the grid?” will be discussed.
In the Local case, the total income during the first quarter of 2019 is 73 000 or 96 000
SEK, depending on which exceedances are covered. The average income per EV and
hour-long exceedance is approximately 1100 or 940 SEK, respectively. As Österlund
points out in 3.1.1, depending on weather conditions, exceedances may vary +/- 10 %,
which affects the cost from year to year, and gives a hint of how general this income is
during quarter one. In this case, and considering the assumption that whole exceedances
need to be covered, the EV-fleet operating company has to decide which exceedance to
35
target by deciding whether to prioritize a high total income or a high income per EV.
This decision depends on the strategy and size of the company. As an example, it is
reasonable to assume a smaller sized fleet will aim to maximize income per EV, since
this type of fleet can not guarantee covering a whole exceedance due to limited offered
capacity.
In the Regional case, when taking all three stations into account, the average total
income during the first quarter of 2019 is 15 000 SEK. The highest income is derived
from station C, and the lowest from station A. As seen in Appendix C, seven out of 24
exceedances could be covered at station C, and six out of eight exceedances at station
A. The exceedances that can be covered at station C and A have similar magnitude, 1-6
MW, but exceedances of this magnitude occur more frequently at station C. This means
the EV capacity in combination with the availability of the EV-fleet limits the fleet’s
ability of providing grid service. This shows a potential in not having to cover whole
exceedances due to the fact that there is a significant number of exceedances that can
not be covered completely.
In the Regional case, when taking all three stations into account, the mean average
income per EV and exceedance during the first quarter of 2019 is 26 SEK. As
mentioned in 3.1, to investigate the potential income of the EV-fleet, it is interesting to
know what the income derived from different levels of the grid would be. In this report,
the way this has been examined is by comparing a Local and Regional case with the
assumption that the EV-fleet will be dimensioned to cover exceedances of the
magnitude of all three regional stations in the Local case. Since the magnitude of
exceedances in the Regional case is considerably higher, and the size of the fleet
unchanged, it is reasonable to only apply the system at one transmission station at a
time. This is the reason three stations of Upplands Energi are compared to one
transmission station. However, it is important to remember the system will look
differently dependent on which and how many stations are considered, and at what
levels of the grid. A general case is not achieved by using this method, rather an
indication of where in the grid this system is profitable. The total income as well as the
average income per EV and exceedance in the Regional case indicate that this is not a
profitable level of the grid to operate on. This can be explained by a combination of the
magnitude of the exceedances and the comparably low pricing. Derived from these
cases, targeting local exceedances is recommended.
The value is determined by splitting the cost of an exceedance fee with the involved
number of EV:s to power shave. The value represents a threshold value the grid
operating company must offer the EV-fleet operation company for providing grid
service. It is further reasonable to assume the EV-fleet operator will influence the
pricing of the service, due to market conditions. The hourly income for a taxi in Uppsala
today is estimated to 370 SEK per hour, see section 3.3.2. Compared to this hourly rate,
providing grid service in the Local case is profitable, and not profitable in the Regional
case. At the same time, assuming current Swedish taxi fares to be applicable in this
36
future system is a less-than-good solution given the assumption behind Mobility as a
Service, i.e. that this service will more or less replace personally owned vehicles and
thus fundamentally change the transport sector, including the taxi sector. Given the
balancing power of supply and demand, it is reasonable to assume a different pricing of
this service compared to current Swedish taxi fares.
The income of the EV-fleet has been based only on exceedances that the fleet can
compensate completely. This is a strict condition and does probably not represent a
market environment. Thus, there could be different constraints of how the fleet would
operate. One less strict constraint would be for the fleet to utilize the capacity available
at a given time in order to cover exceedances partly. The total income would in this case
increase. As an example, if inspecting Table 3C in Appendix C, regarding station C, and
using this constraint instead of the original one, income could be sourced from all
exceedances, but to different extent. By looking at available capacity of the EV-fleet at
a given time of exceedance and estimating the portion of exceedance that can be
covered, the value from quarter one 2019 could be summed up to approximately
100 000 SEK. This is a significant increase in total income, compared to the income of
17 360 SEK stated in Table 11. Since the constraints for the system can be formulated
in different ways and the strategy of the fleet will probably influence these constraints,
determining an economic value for a today unknown service is a tricky business. A
further discussion regarding market and flexibility will be done in 5.4.
In this report, the EV:s used for power shaving are assumed to be fully charged when
initiating grid service. In reality, this would not be the case. As an example, some of the
capacity of the EV would probably have to be used just to transport the vehicle to where
it can connect to the grid. The impact of this simplification is that the economical value
per EV would decrease, since a higher number of EV:s would be required for power
shaving. In conclusion, it is hard to predict an economic value for the EV-fleet
providing power shaving service.
5.2 Availability for grid services
In this section, the result of research question 2: “What is the interplay between the EV-
fleet and the grid, regarding availability for grid services?” will be discussed.
By analyzing Figures 8-11 of the Local and Regional cases, it can be derived that the
performance of the system looks different depending on the station. When and how
large the exceedances are greatly affect the system. The situation at station B is the most
favourable in order to make the system work effectively, since the exceedances are of a
reasonable magnitude and synchronized with the fleet. It would never be a problem for
the EV-fleet to fulfil both transport and grid services. The situation at station C is the
least favourable, for the same reasons. In this situation, planning the operation of the
EV-fleet would be a challenge. Overall, the majority of exceedances can not be covered
since they are unfavourably synchronized with the EV-fleet and/or demand more
37
capacity than available. In other words, the exceedances are too big and/or happen
during taxi rush hour.
An important factor of the performance of the system is the availability of the EV-fleet.
In this report, the number of available EV:s is determined as the number of EV:s not
providing transport service. Since the method does not take running shifts into account,
see section 3.3.3, the percentage of available EV:s is higher than for a realistic taxi fleet
of today. However, the higher availability can be considered an estimation of the self-
driving EV:s, with no need of running shifts. A more realistic case would also be to
assume other activities, such as charging when idle and transportation between grid
connections points. This would decrease the number of available EV:s. A delimitation
of this report is that the prerequisites needed for the system to work are in place. This
includes the assumption of charging, i.e. the EV:s are charged when idle in a way that
will not affect the system. Finally, it is reasonable to assume a more dynamically
dimensioned fleet in the future, based on predicted power and transport need. This
requires more insight in how and where the actual system is implemented, and is not
considered in this report.
The use of the New York City taxi fleet as a model for V2G in Sweden may not be
ideal, since the behaviour of the vehicles might differ due to factors such as usage
pattern and geographic location. Also, it is reasonable to assume Mobility as a Service
will impact the system by changing the usage pattern. Assuming that a population
consuming transport instead of using their own means of transportation will have the
same usage pattern as the current portion of the same population riding taxis is not
completely reasonable. The number of taxis active around midnight is an example of
when the usage pattern of the EV-fleet riding population and the portion of people
riding taxis today will not match. How sound the estimation of using the taxi fleet as a
model for Mobility as a Service is, only time will tell. Also, since the data sourced is
limited, the mean winter day used as reference is a rough estimation. The number of
existing EV:s, which in this report is estimated to 330 in the Uppsala vicinity, has a
direct impact on the system. It is reasonable to assume this number will grow as
Mobility as a Service become more widespread. At the same time, a higher transport
service demand might lead to a lower availability for the EV-fleet to provide grid
service, since more people will use the service.
In the Regional case, the day consisting of complied mean exceedances at each
transmission station consists of an extreme amount of exceedances due to the chosen
method, see section 3.1.2. In addition to this, since the exceedances are mean
exceedances, extreme cases are smoothed out. An example is found in Table 3C in
Appendix C, where an exceedance of 57 MW can be found. This results in illustrations,
such as Figures 9, 10 and 11, not showing the actual range of exceedances.
38
5.3 Impact of battery capacity
The result of the sensitivity analysis shows that the battery of the EV model Tesla
Model 3 improves the performance of the system only marginally, as the same number
of exceedances can be covered as when Nissan Leaf e+ is used. Further on, the ideal
battery featured in Table 12 must have a capacity in the range of approximately
20-180 % better than the battery in Tesla Model 3 to cover the different exceedances,
given the pattern of available EV:s stays the same. This indicates that development of
better batteries with a larger energy storage is needed, but also hard to dimension to the
system, since the range of needed capacity varies greatly. Worth noting is that the
examined exceedances represent the most extreme compilation of mean exceedances in
this report and might not be representative to the average case. This is important to keep
in mind when determining the reasonableness of the battery capacity needed.
The biggest difference in capacity of the Nissan Leaf e+ and Tesla Model 3 battery is
the discharge rate. Would this parameter be the only parameter affecting the
performance of the system, the latter model would outshine the former, and all
exceedances in Table 13 could be covered. But this would mean only taking the
momentary power into account, which is not the case for the system in this report. Due
to the assumption mentioned in 3.2.1 and equation (3), an exceedance can only be
covered if a sufficient number of EV:s are available during the whole hour of
exceedance. This means the energy, i.e. the power discharged during an hour,
determines the performance of the system. The importance of this parameter will
decrease if the time of grid service is shortened.
Worth mentioning is the fact that the Percentage energy storage and the Percentage to
grid remains the same in the sensitivity analysis as in Table 6. These variables have a
direct impact on the Available energy storage, see equation (4), and therefore also the
results in the Sensitivity analysis. Percentage energy storage remains the same, since it
is reasonable to assume that the ambition to extend battery lifetime is unchanged. The
variable Percentage to grid is unique to this report and based on assumptions mentioned
in 3.3.1; the main operation of the EV-fleet is Mobility as a Service, and therefore a
portion of the energy storage is reserved for this activity. This parameter would
probably be unique to different settings, e.g. urban or countryside, depending on the
demand for transport service in the area of operation. In addition to this, dimensioning
the EV-fleet for covering exceedances is not reasonable, since the primary service is
transport and the magnitude of exceedances is hard to predict.
5.4 Key features
To answer the fourth and last research question: “Based on today’s discussion and
results from previous research questions, which key features are important to address
when implementing the system?” the discussion regarding research question 1, 2 and 3
will be combined with a discussion and analysis of the background material.
39
Supply and demand
Identifying a demand big enough for the system solution is essential when
implementing the system. As mentioned in 2.1.2, the infrastructure of the grid creates
capacity deficiency resulting in cities having to limit growth. Further,
Energimarknadsinspektionen addresses that capacity deficiency as first and foremost a
local issue, derived from the problematic infrastructure of the transmission grid.
Flexible and smart solutions are needed, as well as cooperation. This is important when
designing the future infrastructure, since the system services of tomorrow are unknown,
as mentioned in 2.2.1. However, action must be taken to solve this problem, as SvK
stresses in section 2.1.3. In the same section, it is made clear that SvK’s way of
handling the problem today is to aim for increasing the exceedance fees. Together with
the long process of upgrading the infrastructure of the grid, mentioned in section 2.1.2,
this creates a market for new and system integrating solutions.
A brand new market
As stated by SvK in section 2.2.1, the system services needed to counteract capacity
deficiency might be provided by commercial operators on market terms. The market for
using energy storage in the electricity system will grow, according to actors such as
Vattenfall, as Nasner mentions in 2.2.3. This idea is also present during Uppsala
municipality board meetings, as mentioned in section 2.2.4, and thus an accepted part of
the solution to manage capacity deficiency. Present during these meetings is also a
desire to streamline solutions and integrate systems, such as transport and energy
systems – the two cornerstones of V2G. This reasoning indicates that Uppsala is most
likely open for cooperation with grid operators, something
Energimarknadsinspektionen’s Chief Financial Officer Persson highlights as an
important factor when making a greater capacity available, as stated by
Energimarknadsinspektionen in section 2.1.2. Nordling, representing IVA, stresses in
section 2.2.3 that energy storage is a way of avoiding expensive upgradings of the grid.
Considering the EV-fleet consists of mobile batteries, the capacity needed might be
available through the fleet instead of power grid upgradings. A V2G solution can also
be considered more flexible than extending the grid, because of the EV’s mobility.
Local capacity deficiency can therefore be counteracted where and when it occurs. The
market for the V2G system presented in this report is still forming, and how this is done
highly affects the system.
Building a market starts with building trust for the service and its availability.
Therefore, an important aspect of implementing the system and creating a market case is
the dimensioning. Should the system be able to cover all the exceedances occuring at a
station? The whole magnitude of an exceedance? Should it be able to offer grid service
during a whole hour? By considering the discussion of research question 1 and 2, see
section 5.1 and 5.2, the answer seems to be: “No”. As an example from Figure 8; if the
constraint of covering the whole magnitude of the exceedance did not exist, the income
40
during the time 18-19 would increase. Also, as seen in section 5.3, the discharge rate in
combination with limited energy storage seems to benefit a shorter interval of supplying
grid service. Grid operating companies seek system stability and redundancy, and in a
V2G context, see Noel et al. in section 2.3.1, this means that a grid operating company
needs to be ensured the right capacity is charged and discharged at the right time. Thus,
maybe an optimized way to integrate the V2G system benefitting both the EV-fleet
operating company as well as the grid operating companies could be to implement the
system as a subsystem. This could entail a number of EV-fleets in combination with
other energy storage and power regulation solutions operating in a market environment.
An example is the project mentioned in 2.1.3, where power regulation is put in place to
regulate power consumption in the grid operated by Upplands Energi.
As mentioned above, the different actors in the system must interact and cooperate.
Since the operation and ownership of the EV-fleet is undecided, as motivated by SvK’s
reasoning regarding system services in section 2.2.1, it might even turn out to be an
integrated service of the grid operating company. This might be a way to create new
energy systems, by integrating power regulation and transportation, and thus changing
the way we look at the energy market as well as how we transport ourselves. If society
continues down the path of Mobility as a Service implemented on a large scale, the
market actor running the EV-fleet will most likely have an impact on how the fleet is
perceived as Mobility as a Service. One can speculate that if Vattenfall ran operations
similar to taxi services today, the general public would be a bit confused about what
kind of company Vattenfall is. A new market actor, only running the fleet, might be
easier to accept. However, the disruptive nature of the system, and the change in the
way we think about energy and transportation it brings, might change this.
The economic value of the EV-fleet providing grid services is, as mentioned in section
5.1, hard to determine. The energy market will affect the value in other ways than this
report has considered. As an example, it is reasonable to believe that an economic value
is linked to just being an available resource. Assuming a fleet consisting of the model
Nissan Leaf e+ and being the size of Uppsala, that is 330 EV:s, the total capacity comes
down to around 8 MWh. But the availability of this capacity is not constant. Because of
this, availability will probably influence the pricing of the service.
As mentioned in 2.1.3, SvK plans to increase the penalty fees of exceeding network
agreements, so there is reason to believe that the economic value of power shaving will
grow. Today, the fees on regional-transmission level seem to be significantly lower than
the ones on local-regional level. The awareness of the capacity deficiency considered, it
is reasonable to assume the cost at all levels of the grid will change in the future and
strengthen a business case involving the EV-fleet featured in this report. More
exceedances will create a greater demand for power shaving and drive up the income for
providing this service. As mentioned earlier, the EV-fleet will be part of the solution by
operating as a subsystem and aim to cover exceedances partly, which creates a market
for several competing operators. As an example, the situation illustrated in Figure 11
41
would enable competing market actors to operate simultaneously. As mentioned in 5.1,
the potential economic value has a big range, depending on constraints and terms of
operation, as well as strategy of the EV fleet operating companies. Maximizing using
available capacity without having to guarantee covering whole exceedances seems to
result in a higher economic value. The worst case for the isolated system in this report,
as seen in Figure 11, might in other words be the best case for motivating a market for
the same system.
Location matters
Since the system offers a package deal – both V2G and Mobility as a Service – it would
be convenient to implement the system where both services are required. In other
words, the system will be implemented where capacity deficiency and a need for
transport of a larger number of people are present. The situation in urban areas, such as
the case of Uppsala described in section 2.2.4, fit this description well. Situating the
system in an urban area will further limit the area of operation for the fleet. This will
make it easier to decide where to place connecting points to the grid.
To create a flexible and well coordinated fleet, commercial ownership is preferred as
opposed to coordinating EV:s owned by private persons, as motivated by Cooper et al.
in section 2.3.1. In the same section, Noel et al. stress that consumer acceptance and
attitudes are social challenges linked to V2G. Making V2G a commercially owned
system might evade these challenges and facilitate easier implementation. With a self-
driving fleet, the system can be considered flexible, as there is no need of drivers taking
shifts or having to receive instructions on where to drive next, limiting the operating
time of an EV. The coordination of the fleet is therefore made easier by using self-
driving vehicles. Another advantage of using EV:s is, as mentioned earlier, that they
essentially are mobile batteries, which means they can be placed for grid service where
they are most needed.
In this report, power shaving has been examined on a hypothetical but also simplified
level. This has been motivated by assuming prerequisites for the functioning of the
system are in place. However, the place where the EV:s are able to connect to the grid is
of interest in making this system work. In this report, the Regional case is based on the
fact that the EV:s power shave the exceedances indirectly at an individual transmission
station by connecting to the local grid. Based on the discussion above, it seems
reasonable to implement the V2G system in an urban area, but there are still questions
in need of answering regarding the actual implementation. How does the geographical
location of the transmission stations affect the power shaving by the EV:s? The stations
A, B, and C mentioned in this report are placed in unknown areas. Is it possible for all
these stations, no matter where they are placed, to be served by an EV-fleet operating in
an urban area? To what extent will the distance between the urban area and the
transmission station impact the system? The exceedances at station B, on a regional
level are easy to cover, but does not represent an urban power consumption pattern. The
42
station might therefore be situated far from an urban area. Therefore, an EV-fleet might
not even exist close enough to provide its service. This reasoning indicates that there
exists a wider range of parameters regarding geographical impact on the system than
considered in this report.
As stated in section 4.1, the result of examining the two cases in this report shows that
the potential income for the EV-fleet is significantly higher when local exceedances are
considered and whole exceedances must be covered, due to lower magnitudes of
exceedances enabling complete coverage. However, when considering covering
exceedances partly, the level of the grid where the highest total income can be derived
from varies depending on strategy.
Power-up
There are challenges for making a company owning a big EV-fleet providing grid
services a reality. Nasner, representing Vattenfall, stresses in section 2.2.3 that market
incentives and cheaper production need to form to make the development of these new
energy services a viable solution. But at the same time, Nordling, representing IVA
stresses in the same section that the price of lithium-ion batteries is dropping. The
development of the battery market is therefore an essential part of realizing this system.
Also, there should be questions asked regarding the usage of the batteries in the
combined transport and grid service. In section 2.2.3, Vattenfall’s Batteries Director is
quoted by Nasner, highlighting the fact that constant cycling of batteries affect the
lifespan, which Mohammed et al. also point out in relation to V2G in section 2.3.2.
How will this impact the performance of the batteries and the market strategy of the
company owning the fleet?
As the results from the Sensitivity analysis show, see section 4.3, the development of
battery capacity has an impact on the system, in the form of involved number of EV:s
for power shaving. Overall, capacity in the form of energy storage is the critical factor
when deciding this number. As Noel et al. mention in 2.3.1, the cost per unit of energy
is relatively high in EV batteries compared to competitive solutions, and Table 12
points towards a need of larger energy storages. These energy storages could be the
mentioned competitive solutions in other forms than EV batteries. However, these
results are only valid for the isolated system in this report. Considering the suggested
solution of a subsystem, this development would not necessarily hinder an
implementation. Rather, it would differentiate the market for battery solutions. As
Nordling, representing IVA, states in section 2.2.3, using a combination of different
energy sources on different levels of the grid will improve the local stability of the
power supply. Therefore, the question is not whether or not EV:s will have a place on
the electricity market, rather where on the market it will be found. As mentioned earlier
in 5.3, dimensioning the EV-fleet according to available battery capacity for power
shaving services will not be the case, since the primary service is transport. The battery
43
capacity will therefore most likely not be a limiting factor for implementing the
system.
5.5 The road ahead
The implementation of new systems is more or less always a gamble. No one will know
the exact outcome, and sometimes the purpose of the system becomes clear first after
implementation and maturing. As stated in Table 1, the power grid faces challenges
regarding a number of factors ranging from new ways of producing electricity to
ensuring flexibility in the electricity system. The presented scenarios relate to a large
extent to power production deficiency. In this report, the V2G system performing power
shaving has been examined as a solution to capacity deficiency in the grid, but what if
there could be more to the system? Broadening the scope of what the system could do
would create even stronger incentives to pursue implementation. As an example, the
V2G system could smooth out both power peaks and valleys originating from the
implementation of more intermittent electricity production, which is a probable scenario
stated in Table 1. As mentioned in 2.3.1, there are already projects and companies
working towards making V2G a reality. In these examples, V2G is used for frequency
regulation to increase grid balance, which is a future challenge featured in Table 1.
Thereby, V2G can provide multiple services to the grid. The market for these kind of
services does not exist today, as Noel et al. mention in section 2.3.1, and therefore there
is no way to predict what the future marketplace will look like. For further studies, it
would be interesting to investigate the different ways of applying V2G as a grid service.
What pros and cons come with the different cases and what will the economic value of
providing the services be?
For more further studies, topics in this report can be extended to include research and
data on, for example, cost of implementation, how seasonal changes impact the system,
what a yearly income for the EV-fleet could be and how transport patterns as well as
pricing structures look like and evolve. Another interesting aspect would be to use
models of future predictions of electricity production and try the system in those
environments. It would also be interesting to further research the topic of this report’s
sensitivity analysis, i.e. the battery capacity and how this impacts the system. How is
capacity and pricing of batteries evolving? How will the cycling of the batteries affect
the system? How will the fact that the EV:s must charge in order to function impact the
system? Which other battery trends and solutions are evolving, and how are these
competing with the system presented in this report? These are only some questions
worth further studies.
44
6. Conclusions
The aim of this project has been to evaluate how a future commercially owned fleet of
self-driving electric vehicles (EV:s) would be able to provide power in order to avoid
power exceedances in the power grid. This has been achieved by investigating four
research questions, from which the following conclusions have been derived.
The potential economic value of a fleet of EV:s providing service to the grid, based on
exceedances of the first quarter of 2019, is:
▪ In the Local case:
o total income for the EV-fleet: 73 000 SEK or 96 000 SEK depending on
strategy of the EV-fleet operating company when covering exceedances
o average income per EV and exceedance: 1100 SEK or 940 SEK
depending on strategy of the EV-fleet operating company when covering
exceedances
▪ In the Regional case:
o average total income for the three stations: 15 000 SEK
o mean average income per EV and exceedance: 26 SEK
These results show that the economic value differs depending on covering exceedances
on local or regional level, where covering local exceedances seem more profitable. A
general economic value is hard to determine, due to different patterns of occuring
exceedances at differents stations, as well as the fact that exceedances are highly
weather dependent and therefore hard to predict. In addition, constraints of the system
and the strategy of the EV-fleet operating company influence the potential income. It is
reasonable to assume that the stated income above can potentially be higher, depending
on constraints and strategy of how to cover exceedances.
The interplay between the EV-fleet and the grid, regarding availability for grid services,
depends on which exceedances can be covered. For an exceedance to be covered, the
time of exceedance must happen when the availability of the EV-fleet is sufficient to
offer grid service of the right capacity. In conclusion, this is the reason a majority of the
examined exceedances can not be completely covered.
Changes in battery capacity of the EV impacts the system regarding how many EV:s are
involved in performing grid service. A larger energy storage impacts this number the
most in the system examined in this report. However, if the time of performing grid
service was shorter, the discharge rate could impact the system to a larger extent. In
conclusion, the EV battery capacity is concluded to not be a limiting factor of the
system, due to market logic.
Based on today’s discussion and results from previous research questions, important
key features to address when implementing the system presented in this report are:
45
▪ The impact of an energy market in constant change as well as an increasing
capacity deficiency in the power grid, resulting in new market actors, pricing
and system solutions.
▪ Realizing this system is a subsystem in a larger system consisting of different
market actors of different sizes.
▪ Deciding which exceedances to cover, depending on strategy and grid level to
target.
▪ Evaluating the area of implementation, making sure the need of both capacity
and transport services is sufficient, presumably urban areas.
▪ Addressing the question regarding fleet ownership.
▪ Taking the development of battery capacity into account, since it is a prominent
factor of the system, as well as the market development of energy storages.
▪ Addressing the issue of battery stress through cycling of the batteries.
▪ Realizing the potential in Mobility as a Service and the value self-driving
vehicles bring as easily coordinated assets.
In conclusion, the system presented in this report represent a future solution daring to
aim for something new. Flexibility and stability are two sought after aspects when the
future of the power system is discussed; mobility and power capability are the means to
achieve this.
46
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50
Appendix A
This appendix is part of section 3.1.2.
Table 1A. Exceedances during the first quarter of 2019 at station A.
Date and Time Exceedance [MW] Exceedance Fee [SEK]
190121, 07-08 4 2240
190123, 16-17 4 2240
190123, 17-18 1 1400
190130, 07-08 19 10640
190212, 07-08 2 1120
190220, 17-18 14 7840
190301, 07-08 1 560
190301, 08-09 3 4200
(Åkerlund, 2019)
Table 2A. Exceedances during the first quarter of 2019 at station B.
Date and Time Exceedance [MW] Exceedance Fee [SEK]
190202, 04-05 1 560
190303, 05-06 1 560
190303, 06-07 1 1400
190303, 07-08 1 2800
190303, 08-09 1 2800
190303, 09-10 1 2800
190312, 02-03 1 560
190312, 03-04 1 1400
190312, 04-05 1 2800
(Åkerlund, 2019)
51
Table 3A. Exceedances during the first quarter of 2019 at station C.
Date and Time Exceedance [MW] Exceedance Fee [SEK]
190103, 16-17 9 5040
190107, 10-11 4 2240
190115, 16-17 12 6720
190116, 07-08 6 3360
190121, 07-08 7 3920
190124, 07-08 12 6720
190128, 06-07 14 7840
190128, 07-08 30 42000
190204, 06-07 57 31920
190204, 07-08 4 5600
190204, 08-09 12 33600
190211, 06-07 9 5040
190212, 11-12 2 1120
190218, 06-07 18 10080
190220, 16-17 13 7280
190220, 17-18 11 15400
190222, 07-08 1 560
190227, 10-11 12 6720
190301, 05-06 4 2240
190301, 06-07 39 54600
190312, 17-18 26 14560
190319, 17-18 8 4480
190322, 06-07 10 5600
190328, 07-08 4 2240
(Åkerlund, 2019)
52
Appendix B
This appendix is part of section 3.1.2. The values of “Exceedance” in Tables 1B, 2B,
and 3B are rounded off. The exact values of are plotted in the Figures 9,10 and 11.
Table 1B. Exceedances occurring during a compiled day of mean exceedances during
the first quarter of 2019 at station A and the number of EV:s needed to match them.
Time of mean exceedance Exceedance [MW] # EV:s needed to match
exceedance
07-08 6.5 263
08-09 3 121
16-17 4 162
17-18 7.5 303
Table 2B. Exceedances occurring during a compiled day of mean exceedances during
the first quarter of 2019 at station B and the number of EV:s needed to match them.
Time of mean exceedance Exceedance [MW] # EV:s needed to match
exceedance
02-03 1 41
03-04 1 41
04-05 1 41
05-06 1 41
06-07 1 41
07-08 1 41
08-09 1 41
09-10 1 41
53
Table 3B. Exceedances occurring during a compiled day of mean exceedances during
the first quarter of 2019 at station C and the number of EV:s needed to match them.
Time of mean exceedance Exceedance [MW] # EV:s needed to match
exceedance
05-06 4 162
06-07 25 988
07-08 9.1 369
08-09 12 484
10-11 8 323
11-12 2 81
16-17 11 457
17-18 15 605
54
Appendix C
This appendix is part of the result in section 4.1.2 and based on the data presented in
Appendix A. Power shaving services generating income for the fleet can only be
provided if there is a sufficient amount of EV:s available at the time of the exceedance,
i.e the fleet must cover a whole exceedance. If this is not the case, the exceedance will
be marked with gray in the table.
Table 1C: Exceedances during the first quarter of 2019 at station A. Average income
per EV is rounded off to whole numbers.
Date and Time Exceedance
[MW]
Exceedance
Fee [SEK]
#EV:s needed
to match
exceedance
#EV:s
available
Income per
EV [SEK]
190121, 07-08 4 2240 162 255 14
190123, 16-17 4 2240 162 199 14
190123, 17-18 1 1400 41 184 34
190130, 07-08 19 10640 767 255 14
190212, 07-08 2 1120 81 255 14
190220, 17-18 14 7840 565 184 14
190301, 07-08 1 560 41 255 14
190301, 08-09 3 4200 121 220 35
Total
exceedance
fee: 30 240
Total
income:
11 760
Average
income per
EV: 19
55
Table 2C: Exceedances during the first quarter of 2019 at station B. Average income
per EV is rounded off to whole numbers.
Date and Time Exceedance
[MW]
Exceedance
Fee [SEK]
#EV:s needed
to match
exceedance
#EV:s
available
Income per
EV [SEK]
190202, 04-05 1 560 41 289 14
190303, 05-06 1 560 41 302 14
190303, 06-07 1 1400 41 288 34
190303, 07-08 1 2800 41 255 68
190303, 08-09 1 2800 41 220 68
190303, 09-10 1 2800 41 205 68
190312, 02-03 1 560 41 256 14
190312, 03-04 1 1400 41 276 34
190312, 04-05 1 2800 41 289 68
Total
exceedance
fee: 15 680
Total
income:
15 680
Average
income per
EV: 42
56
Table 3C: Exceedances during the first quarter of 2019 at station C. Average income
per EV is rounded off to whole numbers.
Date and
Time
Exceedance
[MW]
Exceedance
Fee [SEK]
#EV:s needed
to match
exceedance
#EV:s
available
Income per
EV [SEK]
190103, 16-17 9 5040 363 199 14
190107, 10-11 4 2240 162 209 14
190115, 16-17 12 6720 484 199 14
190116, 07-08 6 3360 242 255 14
190121, 07-08 7 3920 283 255 14
190124, 07-08 12 6720 484 255 14
190128, 06-07 14 7840 565 288 14
190128, 07-08 30 42000 1210 255 35
190204, 06-07 57 31920 2299 288 14
190204, 07-08 4 5600 162 255 35
190204, 08-09 12 33600 484 220 69
190211, 06-07 9 5040 363 288 14
190212, 11-12 2 1120 81 203 14
190218, 06-07 18 10080 726 288 14
190220, 16-17 13 7280 525 199 14
190220, 17-18 11 15400 444 184 35
190222, 07-08 1 560 41 255 14
190227, 10-11 12 6720 484 209 14
190301, 05-06 4 2240 162 302 14
190301, 06-07 39 54600 1573 288 35
190312, 17-18 26 14560 1049 184 14
190319, 17-18 8 4480 323 184 14
190322, 06-07 10 5600 404 288 14
190328, 07-08 4 2240 164 255 14
Total
exceedance
fee: 278 880
Total
income:
17 360
Average
income
per EV: 17