A package deal for the future: Vehicle-to-Grid combined ...

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

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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!

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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

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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.

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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.

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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.

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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

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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

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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

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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

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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

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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.

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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).

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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.


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.


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.


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




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




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.


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,


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.


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.


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


the first quarter of 2019 at station B and the number of EV:s needed to match them.


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


the first quarter of 2019 at station C and the number of EV:s needed to match them.


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


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


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

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