The Impact of Customer Battery Storage on the Smart Grids and how Power Tariffs can increase Battery Storages’ penetration percentage

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IN

DEGREE PROJECT ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS

STOCKHOLM SWEDEN 2017,

The Impact of Customer Battery Storage on the Smart Grids and how Power Tariffs can increase Battery Storages’ penetration percentage.

EI270X

HIMANSHU GAUTAM

KTH ROYAL INSTITUTE OF TECHNOLOGY

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A

BSTRACT

T

he battery storage will play an important role in future smart distribution grids. At the same time, there should be availability of varying tariff structures, from which customers can choose according to their requirement. This research thesis focuses on the study of impact of battery storage in the distribution grid and how power tariffs can help increase the battery storage’s penetration percentage.

The research is done to assess the impact of both home batteries and EVs on the distribution grid, and how much can they increase or decrease the demand in the region. Also a part of thesis is dedicated to create new power tariff structures for Stockholm region of Ellevio, and then electricity bills of the consumers are compared with existing tariffs and new suggested tariffs.

For the thesis a residential area of Stockholm Royal Seaport/Norra Djurgårdenstaden is chosen.

Ellevio is electricity distribution responsible for the area. Home batteries of Powervault U.K and Tesla Powerwall 2 are chosen and for EV, Tesla Model S with 60 KWh battery size is selected.

One of the most interesting findings is that a group of 480 customers with home battery can bring the power demand during peak hours down by up to 11%, but on the other hand a 50%

penetration of EV in the area can increase demand at certain hours by more than 250%. One of other finding was that if customers shift their charging pattern of EV by couple of hours they can increase the demand in the grid, emphasizing on the role of customers in future distribution systems. Suggested Power tariffs show an increase in monetary amount saved by customer if they opt for home batteries. The most amount saved by the customer is in case of the strictest power tariff suggested, i.e. Power tariff with critical time component and time of use component.

This thesis will become a foundation for future study of impact of batteries on a larger region and impact of batteries owned by DSO in the grid. It also opens new path ways to study varying retail contracts for the customers and how combination of varying retail contract and power tariffs can result in better demand flexibility.

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S

AMMANFATTNING

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atterilager kommer att spela en viktig roll i framtida smarta eldistributionsnät. Sam- tidigt bör det finnas möjlighet till varierande eltariffstrukturer för elkonsumenter. Detta examensarbete fokuserar påstudier av effekten av batterilagring i eldistributionsnätet och hur eltariffer kan bidra till att öka genomslaget av batterilager. Studier har även gjorts för att bedöma effekten påeldistributionsnätet av hembatterier och elfordon med studier av hur efterfrågan påel inverkas. Specifikt föreslås nya eltariffer för ett område där elräkningar för elkunder jämförs med existerande och föreslagna nya eltariffer.

Arbetet har utförts i samarbete mellan Ellevio, den lokala eldistributören i Stockholm, och KTH. Fallstudier har utförts för bostadsområdet Norra Djurgårdsstaden. Vidare har tvåolika typer av hembatterier valts för studien vilka är Powervault respektive Tesla Powerwall 2. För studie av elfordon har Tesla Model S valts med 60 kWh batteristorlek.

Resultat från fallstudierna visar att en grupp om 480 hushållskunder med hembatteri, kan minska totala efterfrågan påel vid topplast med upp till 11%. Resultaten visar att om 50%

av personbilsparken i samma område var elfordon skulle efterfrågan av el vid topplast öka med mer än 250%. Studierna visar hur olika laddningsmönster för elbilar inverkar påtotala belastningen i elnätet. Därmed ges exempel påden centrala rollen elkonsumenten får i det framtida eldistributionsnätet. Föreslagna energitarriffer för el visar påmöjligheten till ekonomisk vinst för elkonsumenter vilka använder hembatterier.

Arbetet ligger till grund för framtida studier av inverkan av batterier i större områden och batterier som ägs av eldistributören. Ett annat område för framtida studier är hur elkon- sumenternas efterfrågeflexibilitet kan ökas erhållas genom varierande lösningar för elavtal och energitariffer.

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A

CKNOWLEDGEMENT

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his Master Thesis has been conducted during the Spring Semester of 2017 at the Electrical Engineering department of KTH Royal Institute of Technology in collaboration with Ellevio AB. The thesis was fully conducted at the Valhallavägen Office of Ellevio in Stockholm. The thesis has been supervised by Lina Bertling Tjernberg, Prof. at School of Electrical Enginnering, KTH Royal Institute of Technology and Olle Hansson, Chef Teknikutveckling AMDNP, Ellevio AB. Also Henrik Bergström, Head of Public Affairs, Ellevio AB provided supportive supervision for thesis work.

During the course of thesis, I came into contact with number of people and would like to thank every one. I want to specially thank my supervisor/examiner Lina Bertling Tjernberg, KTH Royal Institute of Technology for patiently listening to all my queries and supervising me in the best way possible. I also want to specially thank Olle Hansson, Ellevio AB for giving me full freedom to explore the field of thesis and giving positive feedbacks and suggestions for the new approaches suggested in the thesis. Henrik Bergström, Ellevio AB gave great insights about customer behaviour, which was necesaary for the thesis work and I am gratefully thankful to him for answering all my questions.

For the thesis, I got the opportunity to interview many people from inside Ellevio and outside in many different organizations. I want to thank Fredrik Hartman, ex-Ellevio for support in battery informations; Paul Göransson, Ellevio AB for explaining necessities in tariff structure.

I want to thank Olle Johansson, Powercircle; Karin Alvehag and Kristina Östman, EI; Daniel Kullin and Annica Gustafsson, Swedish Energy Agency; Magnus Lindén and Martin Nilsson, Sweco for the time they provided me to have discussions related to thesis topic.

At last I want to acknowledge the strength of my mother and my two elder sisters, otherwise coming back to Stockholm to finish my Masters would have been a difficult task. I once again want to thank Lina Bertling Tjernberg for a huge support during the thesis. To my friends, Sri Ram and Waheb , I will forever be grateful!

Himanshu Gautam Stockholm, October 2017

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T

ABLE OF

C

ONTENTS

Page

List of Tables ix

List of Figures xi

1 Introduction 1

1.1 Background . . . 1

1.2 Objective . . . 2

1.3 Thesis Boundaries and Overview . . . 3

2 Battery Storage and Battery Models 7 2.1 Home Batteries and Electric Vehicles . . . 7

2.2 Battery Models . . . 10

2.3 Approach and Software . . . 17

3 Simulations 19 3.1 Individual Customer Case . . . 19

3.2 All Customers Together . . . 21

3.3 Understanding of Results . . . 33

4 Tariffs 37 4.1 Tariffs . . . 37

4.2 Existing Network Tariffs of Ellevio . . . 38

4.3 Suggested Power Tariffs . . . 40

4.4 Dynamics of Home Batteries and Power Tariffs Together . . . 45

5 Closure 49 5.1 Conclusion And Motivation . . . 49

5.2 The Way Ahead . . . 50

A Appendix A 51

References 81

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L

IST OF

T

ABLES

TABLE Page

3.1 Load profiles of individual customers on Summer Weekday 2016/09/03 . . . 20

3.2 Load Profile Data for Winter Weekday 2017/02/03 . . . 22

3.3 Load Profile Data for Winter Weekday with Home Batteries . . . 23

3.4 Load Profile Data for Winter Weekday with Home Batteries and EV . . . 24

3.5 Load Profile Data for Autumn Weekend 2016/12/03 . . . 26

3.6 Load Profile Data for Autumn Weekend with Home Batteries . . . 27

3.7 Load Profile Data for Autumn Weekend with Home Batteries and EV . . . 28

3.8 Load Profile Data for Spring Weekday 2016/04/01 . . . 30

3.9 Load Profile Data for Spring Weekday with Home Batteries . . . 31

3.10 Load Profile Data for Spring Weekday with Home Batteries and EV . . . 32

3.11 Load Profile Data for Autumn Weekend with two set of charging of home battery and EV 33 4.1 Time Tariff For Weekdays (Simple Power Tariff) . . . 40

4.2 Time tariff For Weekdays (Time of Use) . . . 41

4.3 Time tariff For Winter Weekdays (Critical Time and Time of Use) . . . 42

4.4 Time tariff For Autumn Weekdays (Critical Time and Time of Use) . . . 43

4.5 Time tariff For Spring/Summer Weekdays (Critical Time and Time of Use) . . . 44

4.6 Load Profiles chosen for all Seasons . . . 46

4.7 Load Profiles chosen for all Seasons after Powervault . . . 47

4.8 Comparison of Electricity Bills . . . 48

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L

IST OF

F

IGURES

FIGURE Page

1.1 (a) & (b) Newly Constructed Buildings in the Stockholm Royal Sea Port residential

area of Stockholm. . . 3

1.2 The Area Boundary outside Ropsten Metro T-Bana . . . 4

1.3 (a) The electric chargers for the EVs on one side of the road outside the buildings (b) The cranes for the construction of the new buildings . . . 5

2.1 Individual Customers Models . . . 11

2.2 Winter Models . . . 13

2.3 Autumn Models . . . 13

2.4 Summer Models . . . 14

2.5 Spring Models . . . 16

3.1 Summer Weekday Battery plus EV . . . 21

3.2 Winter Weekday with Home Batteries . . . 24

3.3 Winter Weekday with Home Batteries plus EV . . . 25

3.4 Autumn Weekend with Home Batteries . . . 28

3.5 Autumn Weekend with Home Batteries plus EV . . . 29

3.6 Spring Weekday with Home Batteries . . . 31

3.7 Spring Weekday with Home Batteries plus EV . . . 32

3.8 Autumn Weekend with two charging pattern . . . 34

3.9 Autumn Weekend with EV charging in daytime and no Home Batteries . . . 35

4.1 Existing Network Tariffs of Ellevio for Stockholm . . . 39

A.1 Summer Weekend Home Battery plus EV . . . 52

A.2 Autumn Weekday Home Battery plus EV . . . 53

A.3 Autumn Weekend Home Battery plus EV . . . 54

A.4 Winter Weekday Home Battery plus EV . . . 55

A.5 Winter Weekend Home Battery plus EV . . . 56

A.6 Spring Weekday Home Battery plus EV . . . 57

A.7 Spring Weekend Home Battery plus EV . . . 58

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A.8 Autumn Weekday- 7 Buildings Home Battery plus EV . . . 59

A.9 Autumn Weekday- 14 Buildings Home Battery plus EV . . . 60

A.10 Autumn Weekend- 7 Buildings Home Battery plus EV . . . 61

A.11 Winter Weekday- 16 Buildings Home Battery plus EV . . . 62

A.12 Winter Weekend- 8 Buildings Home Battery plus EV . . . 63

A.13 Winter Weekend- 16 Buildings Home Battery plus EV . . . 64

A.14 Spring Weekday- 11 Buildings Home Battery plus EV . . . 65

A.15 Spring Weekday- 11 Buildings Home Battery (Different Charging) plus EV . . . 66

A.16 Spring Weekday- 6 Buildings Home Battery plus EV . . . 67

A.17 Spring Weekend- 11 Buildings Home Battery plus EV . . . 68

A.18 Spring Weekend- 11 Buildings Home Battery (Different Charging) plus EV . . . 69

A.19 Spring Weekend- 6 Buildings Home Battery plus EV . . . 70

A.20 Spring Weekend- 6 Buildings Home Battery (Different Charging) plus EV . . . 71

A.21 Summer Weekday- 13 Buildings Home Battery plus EV . . . 72

A.22 Summer Weekday- 13 Buildings Home Battery (Different Charging) plus EV . . . 73

A.23 Summer Weekday- 7 Buildings Home Battery plus EV . . . 74

A.24 Summer Weekday- 7 Buildings Home Battery (Different Charging) plus EV . . . 75

A.25 Summer Weekend- 13 Buildings Home Battery plus EV . . . 76

A.26 Summer Weekend- 13 Buildings (Different Charging) Home Battery plus EV . . . 77

A.27 Summer Weekend- 7 Buildings Home Battery plus EV . . . 78

A.28 Summer Weekend- 7 Buildings (Different Charging) Home Battery plus EV . . . 79

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You gave me wings to FLY!

I Promise, I Will FLY;

Reach new HEIGHTS!!

I Promise, I Will Become a TOUCHSTONE,

For this World to Follow

But I Promise, I Will Be Kind and Compassionate;

A Better Human!!

I Promise, I Will

I Promise, I Will Make YOU Proud!!

-: H. Gautam

Dedicated To My Late Father, O. P. Bhuwalia (who left unexpectedly on 31st Dec 2016)

PAPA, I MISS YOU!

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C

HAPTER

1

I

NTRODUCTION

T

he first section of the chapter gives a brief background about storage and tariffs. The later sections gives a overview of the thesis, aim and questions covered and the boundary limitations for the same.

1.1 Background

Electricity Storage is a crucial solution to a number of problems which plague the modern day power system. The advancement of technologies and constant effort to move towards a smarter grid will remain unfulfilled if storage is not incorporated at greater extent in the system.

According to a report developed for U.S. Department of Energy, "Storage is perhaps the most important smart grid advanced component because of its key role in complementing renewable generation. With the proper amount and type of storage broadly deployed and optimally controlled, renewable generation can be transformed from an energy source into a dispatchable generation source".[1]

Electricity Storage can be the bridge to match the difference between supply and demand and will help to regulate the frequency of the grid. [2] Storage can make the grid more flexible as well, but for doing so, storage needs to be decentralized. The decentralized storage system will change the functioning and management of the distribution grid in the areas of energy management, system services and the internal business of the DSO.[3] As eurelectric report states, "A future smart grid without decentralised electricity storage could be like a computer without a hard drive: seriously limited"[3].

There are various types of storages which includes pumped hydro, flywheels, CAES, battery storage etc. Even storing heat in a household by warming the room during off-peak hours and turning the heat pump off during peak demand is a form of storage. But among all of the storage

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options, the effectiveness of battery storage stands out. It has applications on all levels of power systems from generation, transmission, distribution to customers and as the system move towards smarter grids, these applications of battery storage will be more prominent. On generation level, it can help with arbitrage, capacity firming and curtailment reductions [4].At transmission and distribution level battery storage can help in frequency and voltage control, investment deferral, curtailment reduction and black starting[4].

At customer level, battery storages can help customers for time of use management, peak shaving and off grid supply [4]. All these will help to manage the distribution grid more efficiently and investments will be done effectively. The customers have to take an active part for making the grid smarter. The difference between producers and consumers is vanishing and prosumers are emerging. European Union in the "Clean Energy For All Europeans" package, which is also known as "Winter Package", talks about strengthening the role of customers and empowering them.[5][6]

The important parts in Winter package to empower customers is to provide them with reliable and clear information on the prices, various different tariff contracts, to switch easily between suppliers, to entitled to produce, store or sell electricity.[5] [7] To make customers more engaging and to empower them, the distribution network tariff system also need to be changed. As customers opt for time of use management and incorporates storage, the network tariff should be set to motivate customers to do so. According to Council of European Energy Regulators (CEER), the new tariffs have to recover cost, to be cost reflective, non-discriminatory, non-distortionary, simplistic, transparent and predictable. [8]

1.2 Objective

The objective of thesis can be summarized as:

1. To study the impact of the customer battery storage (home batteries and EV’s) on the smart grid

2. To form different scenarios of battery storage penetration percentage in the grid and evaluate the change in the load pattern

3. To study the need for new power tariffs in the distribution network

4. To suggest new power tariffs

5. To study the dynamics of battery storage and power tariffs together

6. Can battery storage and power tariffs together enables customer to be flexible?

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1.3. THESIS BOUNDARIES AND OVERVIEW

1.3 Thesis Boundaries and Overview

1.3.1 Thesis Boundaries

The impact of battery storage is a wide area, so thesis boundaries were set for finishing the research project. The thesis boundaries provided the “Main Focus”for the research area.

The impact of battery storage is studied only on the consumer level of the grid, i.e. on 230V . For this, an upcoming residential area, Norra Djurgårdenstaden/ Stockholm Royal Sea Port is considered, for which Ellevio is network responsible. Stockholm Royal Sea Port is an upcoming residential block in the Stockholm. [9] It will be the largest urban development area in Sweden with at least 12,000 new homes and 35,000 workplaces.[10] It is estimated to be fully finished by the year 2022 and it will cost a total investment of 2.2 billion euros. [10]. Fig 1.1 shows the new residential buildings in the upcoming area. Fig 1.2 is the advertisement board outside the Ropsten T-Bana depicting starting of the residential area boundary.

(a) (b)

Figure 1.1: (a) & (b) Newly Constructed Buildings in the Stockholm Royal Sea Port residential area of Stockholm.

Fig 1.3 (a) shows the electric chargers on one side of the road near the parking spots. It is important to know that only half of the parking spots had electric chargers and forms the basis of assumption for maximum 50% of penetration of EVs in the area (Chapter 2). In the thesis the impact of batteries is only studied on load profiles and not on reliability, voltage quality and stability.

For the section of suggestion of power tariffs, inspirations are taken from existing power tariffs at Sollentuna Energi & Miljö [11] and Karlstads Nät [12]. This was done so that the suggestions remain realistic to existing tariffs in Sweden. But the tariffs are modeled in such a way that the customer pays the same amount of money by existing network tariff structures of Ellevio [13] and new suggested power tariff structure.

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Figure 1.2: The Area Boundary outside Ropsten Metro T-Bana

1.3.2 Overview of Thesis

The work in this Master Thesis can be divided into three parts, which are:

1. In the first part, research will be done to study the impact of the customer battery storage on the distribution smart grid. The customer battery storage will include both home batteries and EVs. This will be done by forming different scenarios of battery storage penetration percentage in the distribution grid and to study the change in the overall load pattern,

2. The second part of thesis will focus on the need for new power tariffs in the distribution network and why they will become a necessity as the grid becomes smarter day by day.

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1.3. THESIS BOUNDARIES AND OVERVIEW

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Figure 1.3: (a) The electric chargers for the EVs on one side of the road outside the buildings (b) The cranes for the construction of the new buildings

Comparisons of tariffs within Sweden and Internationally will be done and new power tariffs for Ellevio will be suggested.

3. The last part of the thesis will be incorporating above two study research into one, i.e. how new power tariffs can motivate customers to invest in new and efficient battery storage so that they can play an active part as a responsible player in the new developed smart grid of the future. This part will include comparison of electricity bills with existing tariff structure of Ellevio and new suggested power tariffs for Ellevio.

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C

HAPTER

2

B

ATTERY

S

TORAGE AND

B

ATTERY

M

ODELS

T

his chapter gives a brief preview of few home battery technologies and Electric Vehicles available in the international market. The one selected for the thesis models are described in detail in respective sections.

2.1 Home Batteries and Electric Vehicles

Storage batteries can be differentiated based on the battery chemistry. For eg: Lead-acid batteries, Lithium-ion batteries, Flow batteries and Sodium Nickel Chloride Batteries and few others.

Among all of the listed batteries, Lithium ion batteries are widespread used from small cell batteries to Electric vehicles battery. Lead-acid batteries are tried and tested batteries available in the market for decades, and Flow batteries and Sodium Nickel Chloride batteries are new market entrants and are competition of Li-ion batteries. [14]

Li-ion batteries are among one of the most commonly used batteries in the market today. The cost of Li-ion batteries have dropped more than 73% since 2010 [15] [16] and it is fore-casted that the cost will continue to fall in future as well.

Bloomberg New Energy Finance fore casts that prices of lithium-ion battery pack will fall to as little as $73/KWh. [17]

2.1.1 Home Batteries

Home battery storages are not new in the market. But the focus towards the home batteries have increased in past few years. It may be attributed to

• the increase in small scale renewable generation, like solar,

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• the improvement in the quality and functioning of batteries.

• the adaptability of batteries with the grid prices and electricity markets, Smart Batteries as commonly known can charge and discharge according to electricity price

• reducing battery price [15], [16] and [17]

Tesla all over the world is pushing the envelope of creation and invention to higher levels.

It unveiled Powerwall home battery to the world in April 2015, and Powerwall 2 in October 2016. It boasts an efficiency of up to 90% [18] and gives an independence to the home owner to be completely off-grid. Tesla Powerwall has created a friendly competitions and opened up a pathway for all the major companies in the world to bring their own home batteries. Nissan and Mercedes are no longer only car manufacturers, but they are rolling out battery solutions for home.[19] [20]

But afore said batteries are generally very big in size. Powerwall 2 is a 14 KWh battery which is very big in size for most of the households. Medium and small apartments or houses doesn’t need such big size batteries. The home battery by Fronius, Sonnen, Powervault, Panasonic, LG, Orison are few of the other home batteries options available in the market. Orison boasts of first ever intelligent energy solution with plug in and play solution.[21] Some of these companies provide battery sizes as low as 2 KWh, [22] [23] which can be useful to small households.

For the scope of this thesis, Powervault is chosen when creating models for individual customers and Tesla Powerwall 2 is chosen when all the customers are considered together for battery models (Chapter 3). The important technical specifications of these two home batteries has been provided in sub-sections.

Powervault

Powervault is a home battery storage company from U.K.. It has two battery types, (i) Lithium- ion Phosphate cells Battery and (ii) Lead Acid batteries. Li-ion battery are available from the capacity of 2.2 KWh −6.6kWh .Lead Acid batteries are available from the capacity of 3.25kWh − 8.8kWh. For the thesis, Home battery size of 4.4 Kwh (Li-ion) is being selected, the important technical specifications are as follows [23]

• Home Battery- Powervault U.K. 4.4 KWh

• Usable Capacity is 4KWh

• Efficiency is greater than 95%, means 3.8 KWh available during discharge after fully charged

• Charging is fixed and is 1.2 Kwh and Discharging is 1.6 KWh, also a fixed value

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2.1. HOME BATTERIES AND ELECTRIC VEHICLES

Tesla Powerwall 2

Powerwall 2 is state of the art home battery provided by Tesla. It has a large operating temperature from −20 degree to 50 degree celcius. It supports applications of demand side flexibility, off grid applications and can be clubbed with solar panels or Tesla solar roof tiles. The main technical specification of Powerwall 2 are as follows. [18] [24]

• Tesla Powerwall 2

• 14 KWh storage capacity and 13.2KWh usable capacity

• It has 89% to 90% Efficiency, i.e. will provide 11.75KWh while discharging

• Charging/Discharging pattern are flexible and can be set while mounting the battery at home, but it cannot be more than 5KWh.

• Setting chosen for charging/discharging 3.3KWh. (By individual customer datasheet)

• It means it will take 4 hours for charging and 3.5 hours for discharging

2.1.2 Electric Vehicle

Electric Vehicles are the future and reality for which we have to be prepared. The ongoing battle to tackle the Global Warming has motivated the Car makers to invest and design best efficient electric cars. Almost all of the big car manufacturers are rolling out there models of electric car.

Out of the big names, Tesla has created a big market for Electric Vehicles. Its models of Electric Cars are taking the world by the storm [25]. The marketing team of the company have created common interest among public to think about future and opt for Electric Vehicles.Mercedes has already showcased its first electric model to the world and it will be true to say that it redefines luxury [26]. GM have rolled out their small electric car especially for China [27], and Mahindra is pushing envelope in Indian markets [28].

The penetration of Electric Vehicles in Sweden is on constant rise and with new Climate Act in action where the goal is to reach zero net emission of greenhouse gases by the year 2045, the penetration of electric vehicle will rise drastically in upcoming decades.[29]. The new Climate Act will come in effect from January 1, 2018.

For the thesis research, Tesla Model S- 60 KWh battery size is chosen. The specifications for which are given in sub section.

Tesla Model S

Tesla Model S is one of the three commercial Electric Car models of Tesla. Other two being Tesla X and Tesla 3. The Tesla S model comes with various battery sizes, 60 , 70, 80, 90 and 100 KWh. For the thesis the battery size of 60 KWh is chosen. [30]

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Other important technical specifications of Tesla Model S are as follows:

• Tesla Model S-60 KWh battery size

• The protection of battery does not allow complete discharge of it. Typically 18 to 20% of charge is always there.

• Hence 50 KWh of 60 KWh battery is charged and discharged

• Charging losses (on board charger losses) are nearly 10%, hence we need 55KWh for charging 50 KWh [31]

• Charging rate can vary and can be set while installing the charger at home. For the thesis, 7KWh charging pattern is considered, which means 8 hrs to fully charge the car

2.2 Battery Models

This section will present the research models designed for gauzing the impact of customer home batteries and EV’s on the distribution grid of Ellevio for the region of Norra Djurgården in Stockholm. The later subsections will give a preview of approach taken and the softwares used to study the impact of batteries.

2.2.1 Load Data

Ellevio has provided load data of bulk customers of Stockholm Royal Seaport area of Stockholm.

The data set provided is for Q2 of 2016 (Spring), Q3 of 2016 (Summer), Q4 of 2016 (Autumn) and Q1 of 2017 (Winter).

As Stockholm Royal Seaport is an upcoming residential area of the Stockholm Royal Seaport , the number of customers from Q2 of 2016 to Q1 of 2017 have considerably increased. In Q2 of 2016, the no. of customers in the area were on an average near around 330, while on the other hand the average number of customers in Q1 of 2017 were approximately 480.

The load data sheet did not disclose the load pattern of individual customer, but provided information on the maximum, minimum, average and median consumption by any generic customer per hour on particular day. It means if any of the customers used a maximum consumption for a particular hour, than that consumption is shown in the maximum consumption column on that particular hour. For another hour, it might so happen that another customer has utilized maximum consumption, and hence that will be chosen for that hour and so on.

For the research, and to study the impact of batteries, the maximum consumption column for the day is chosen.

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2.2. BATTERY MODELS

2.2.2 Individual customers

For individual customers, a weekday and weekend is selected for all four seasons from the data sheet provided.

As already discussed in section 2.2.1 that the data sheet has a column of maximum consumption of every day. That column is treated as the load profile of an individual customer. For the days chosen to study the impact, a model is created first to study the impact of home batteries and then a second model is created, which studies impact of both EV and home battery on the load profile. In total there are 16 models for individual customers as shown in Fig 3.1.

The home battery chosen for the individual customers is Powervault U.K. and EV chosen is Tesla Model S- 60 KW (section 2.1).

Figure 2.1: Individual Customers Models

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2.2.3 All Customers Together

For the research of impact of batteries when considering bulk customers, a weekday and weekend is chosen for every season from the data-sheet of load provided by the Ellevio. The data-sheet has the maximum consumption column. It is assumed that all the customers in the area have the same maximum consumption for the entire day chosen. This is done so as to test the maximum load conditions of the distribution grid.

While creating different models it is assumed that customers either live in a building with 60 apartments or in a building with 30 apartments. As the number of customers vary for every quarter of data-sheet, the number of buildings for every season also varies. When it is assumed that a building has 60 apartments then it is assumed that 6 Tesla Powerwall 2 are being mounted in the building which are common for all residents. For EV it is assumed that there are only half the places of the number of apartments in the building. Hence a maximum penetration of 50 % for EV. This means that for a building with 60 apartments, there are maximum 30 EV for that building.

When it is assumed that a building has 30 apartments then it is assumed that 3 Tesla Powerwall 2 are being mounted in the building which are common for all residents. For a building with 30 apartments, there are maximum 15 EV for that building.

Winter

The models for winter weekday and weekend are shown in Fig 2.2. As the no. of average customers are 480, their are either 8 buildings of 60 apartments each or 16 buildings of 30 apartment each.

After selection of buildings, first model is created when there are only home batteries, and a second model when there are both home batteries and EV.

Autumn

The models for autumn weekday and weekend are shown in Fig 2.3. As the no. of average customers are near 420, their are either 7 buildings of 60 apartments each or 14 buildings of 30 apartment each.

After selection of buildings, first model is created when there are only home batteries, and a second model when there are both home batteries and EV.

Summer

The models for summer weekday and weekend are shown in Fig 2.4. As the no. of average customers are near 390, their are either 7 buildings of 60 apartments each or 13 buildings of 30 apartment each. When there are only 7 buildings, the 7th building has although 60 apartments but only 30 customers live in it. For these models it was assumed that the first 6 buildings will have 6 Tesla Powerwall 2 each and the 7th building will only hve 3 Tesla Powerwall 2, as the number of customers is only 30.

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2.2. BATTERY MODELS

Figure 2.2: Winter Models

Figure 2.3: Autumn Models

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After selection of buildings, the models are created based on charging time of home batteries.

In the first set, the charging time of batteries in all buildings is kept same and then the first model is created when there are only home batteries, and a second model when there are both home batteries and EV. In the second set, the charging time of home batteries varies in buildings, i.e. different buildings have different charging time for Tesla Powerwall 2 and then the first model is created when there are only home batteries, and a second model when there are both home batteries and EV.

Figure 2.4: Summer Models

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2.2. BATTERY MODELS

Spring

The models for summer weekday and weekend are shown in Fig 2.5. The battery models have either 6 buildings of 60 apartments each or 11 buildings of 30 apartment each. The building can be of either 60 apartments or 30 apartments but it can have less number of customers living in it.

It has been clearly stated separately for all models that how many customers live in the building.

After selection of buildings, the models are created based on charging time of home batteries.

In the first set, the charging time of batteries in all buildings is kept same and then the first model is created when there are only home batteries, and a second model when there are both home batteries and EV. In the second set, the charging time of home batteries varies in buildings, i.e. different buildings have different charging time for Tesla Powerwall 2 and then the first model is created when there are only home batteries, and a second model when there are both home batteries and EV.

In total 48 Models are created for all the customers together.

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Figure 2.5: Spring Models

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2.3. APPROACH AND SOFTWARE

2.3 Approach and Software

2.3.1 Approach

For all the scenarios when a model is created only to study the impact of home battery, the penetration of batteries in the model is increased building wise. For example in the model of Winter Weekday with home batteries, with 8 buildings of 60 apartment each, the battery penetration is increased one building by one building. Different load profiles are gathered when home batteries are incorporated one building by one building from when only only one building has home batteries ill all the buildings have home batteries.

In the scenarios with home battery plus EV. the penetration of EV is increased from 10% to 50% penetration per building. Here it is assumed that all the buildings will have home batteries and then EV are incorporated. So with a 10% penetration of EV, all buildings have 10 % of EV penetration. The maximum penetration of EV is considered as 50 %, as the parking available for any building is half the number of apartments.

2.3.2 Software

• MS-Excel is used for the data analytics and creation of all the battery models. The charging and discharging patterns of home batteries and EV are added to the base load profiles of the customers. For all the models we get different load profiles according to penetration percentage of home batteries and EV in the system

• To compare various load profile with the base load profile MATLAB is used. MATLAB helps to provide a graphical representation of the comparison and clearly points out the huge changes in the load pattern for all the battery models.

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C

HAPTER

3

S

IMULATIONS

T

his chapter includes simulation of battery models of individual customer and when all customers are considered together. In total results of simulation of models for four days, one day of each respective season, have been provided. In the last section a detailed understanding of result has been incorporated to understand the extent of impact of batteries and more importantly to understand the power of customers in future smart distribution grid.

3.1 Individual Customer Case

For an individual customer case, summer weekday of 2016-09-03 is taken. The load profile taken is that of maximum consumption column as explained in section 2.2.2.

Powervault [23] and Tesla Model S [30] are considered for individual customers.It is assumed that Powervault is charged from 2 to 6 am and discharged from 17-20. Whereas Tesla Model S-60 KWh is charged from 10 pm to 6 am. Whenever an EV is charged from 10 pm to 6 am, it is assumed that it is charged in two parts from 12 am to 6 am and then in night from 10 pm to 12 am. This is done so as to keep the changes in load profile in one single day.

As Powervault is generally provided to one-phase connection in U.K., it is assumed that the individual customer has one-phase connection.

The difference in load profiles after incorporating Powervault and Tesla Model S is shown in Table 3.1 and the graphical representation is shown in fig. 3.1

It is interesting to note that during peak hour from 17:00 to 18:00, the load demand has decreased by 71% because of Powervault. But on the other hand during off-peak demand hour of 04:00 to 05:00, the demand has increased by 440%.

This shows us that an individual customer has power to reduce the load during peak hours.

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But the incorporation of EV on other hand will lead to increase in demand by huge proportions in the night time.

Table 3.1: Load profiles of individual customers on Summer Weekday 2016/09/03 Time Base Scenario With Powervault With Powervault +EV

00:00 1.87 1.87 8.87

01:00 1.88 1.88 8.88

02:00 1.95 3.15 10.15

03:00 1.94 3.14 10.14

04:00 1.86 3.06 10.06

05:00 1.87 2.27 9.27

06:00 1.96 1.96 1.96

07:00 1.93 1.93 1.93

08:00 2.05 2.05 2.05

09:00 1.98 1.98 1.98

10:00 1.93 1.93 1.93

11:00 1.98 1.98 1.98

12:00 1.96 1.96 1.96

13:00 1.93 1.93 1.93

14:00 1.91 1.91 1.91

15:00 2.01 2.01 2.01

16:00 1.98 1.98 1.98

17:00 2.25 0.65 0.65

18:00 2.57 0.97 0.97

19:00 2.07 1.47 1.47

20:00 1.89 1.89 1.89

21:00 2.06 2.06 2.06

22:00 1.87 1.87 8.87

23:00 2.06 2.06 9.06

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3.2. ALL CUSTOMERS TOGETHER

Figure 3.1: Summer Weekday Battery plus EV

3.2 All Customers Together

The impact of batteries is analyzed for all customers together for a Winter Weekday of 2017-02-03, Autumn Weekend of 2016-12-03 and a Spring Weekday of 2016-04-01.

Powerwall 2 [18] and Tesla Model S [30] are considered for all customers together.

3.2.1 Winter Weekday

For the Winter Weekday of 2017-02-03, the load data gathered was of near about 480 customers.

Table 3.2 shows the load of one customer and all 480 customers. As discussed in section 2.2.3 the load of all customers is assumed to be maximum.

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Table 3.2: Load Profile Data for Winter Weekday 2017/02/03

Time Max. Vol for one Customer Maximum Volume for 480 Customers

00:00 1.48 710.4

01:00 1.48 710.4

02:00 1.48 710.4

03:00 1.48 710.4

04:00 1.48 710.4

05:00 3.81 1828.8

06:00 1.62 777.6

07:00 2.02 969.6

08:00 2.03 974.4

09:00 2.03 974.4

10:00 2.21 1060.8

11:00 2.28 1094.4

12:00 1.85 888

13:00 4.07 1953.6

14:00 4.36 2092.8

15:00 2.88 1382.4

16:00 2.61 1252.8

17:00 2.98 1430.4

18:00 3.28 1574.4

19:00 2.91 1396.8

20:00 1.68 806.4

21:00 2.24 1075.2

22:00 1.94 931.2

23:00 1.93 926.4

The basic assumptions are as follows:

• 480 customers

• Maximum volume considered for each customer (maximum demand of system)

• 8 buildings considered

• 60 customers in each building

• Each building has 6 Tesla Powerwall 2

• Charging of Powerwall 2 from 01:00-05:00, 06:00-07:00

• Discharging of Powerwall 2 from 05:00-06:00, 13:00-15:00 and 17:00-19:00.

• EV’s are charged from 10 pm to 6 am, with maximum 50% penetration

As there is spike in load from 5 am to 6 am, the general assumption is that Powerwall 2 will switch from charging to discharging and then back to charging after 6 am.

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Table 3.3 and Table 3.4 depicts the change in the load profile of all the customers. In Table 3.3 the various profile due to home battery penetration is presented, whereas in Table 3.4 the load profiles after various EV penetration is presented. (EV penetration after all buildings have home battery Powerwall 2). Fig 3.2 and Fig 3.3 provides the graphical representation of the load profiles.

Table 3.3: Load Profile Data for Winter Weekday with Home Batteries

Time Max. Vol for one Customer Maximum Volume for 480 Customers 1 Building with Batteries 4 Building with Batteries 8 Building with Batteries

00:00 1.48 710.4 710.4 710.4 710.4

01:00 1.48 710.4 730.2 789.6 868.8

02:00 1.48 710.4 730.2 789.6 868.8

03:00 1.48 710.4 730.2 789.6 868.8

04:00 1.48 710.4 730.2 789.6 868.8

05:00 3.81 1828.8 1809 1749.6 1670.4

06:00 1.62 777.6 797.4 856.8 936

07:00 2.02 969.6 969.6 969.6 969.6

08:00 2.03 974.4 974.4 974.4 974.4

09:00 2.03 974.4 974.4 974.4 974.4

10:00 2.21 1060.8 1060.8 1060.8 1060.8

11:00 2.28 1094.4 1094.4 1094.4 1094.4

12:00 1.85 888 888 888 888

13:00 4.07 1953.6 1933.8 1874.4 1795.2

14:00 4.36 2092.8 2073 2013.6 1934.4

15:00 2.88 1382.4 1382.4 1382.4 1382.4

16:00 2.61 1252.8 1252.8 1252.8 1252.8

17:00 2.98 1430.4 1410.6 1351.2 1272

18:00 3.28 1574.4 1563.4 1530.4 1486.4

19:00 2.91 1396.8 1396.8 1396.8 1396.8

20:00 1.68 806.4 806.4 806.4 806.4

21:00 2.24 1075.2 1075.2 1075.2 1075.2

22:00 1.94 931.2 931.2 931.2 931.2

23:00 1.93 926.4 926.4 926.4 926.4

It is interesting to note from Table 3.3 that the peak hour load is dropped by 11% in the hour of 17:00 to 18:00.

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Figure 3.2: Winter Weekday with Home Batteries

Table 3.4: Load Profile Data for Winter Weekday with Home Batteries and EV

Time Max. Vol for 480 Customer 8 Buildings with Batteries Batteries + 10% EV Batteries + 30% EV Batteries + 50% EV

00:00 710.4 710.4 1046.4 1718.4 2390.4

01:00 710.4 868.8 1204.8 1876.8 2548.8

02:00 710.4 868.8 1204.8 1876.8 2548.8

03:00 710.4 868.8 1204.8 1876.8 2548.8

04:00 710.4 868.8 1204.8 1876.8 2548.8

05:00 1828.8 1670.4 2006.4 2678.4 3350.4

06:00 777.6 936 936 936 936

07:00 969.6 969.6 969.6 969.6 969.6

08:00 974.4 974.4 974.4 974.4 974.4

09:00 974.4 974.4 974.4 974.4 974.4

10:00 1060.8 1060.8 1060.8 1060.8 1060.8

11:00 1094.4 1094.4 1094.4 1094.4 1094.4

12:00 888 888 888 888 888

13:00 1953.6 1795.2 1795.2 1795.2 1795.2

14:00 2092.8 1934.4 1934.4 1934.4 1934.4

15:00 1382.4 1382.4 1382.4 1382.4 1382.4

16:00 1252.8 1252.8 1252.8 1252.8 1252.8

17:00 1430.4 1272 1272 1272 1272

18:00 1574.4 1486.4 1486.4 1486.4 1486.4

19:00 1396.8 1396.8 1396.8 1396.8 1396.8

20:00 806.4 806.4 806.4 806.4 806.4

21:00 1075.2 1075.2 1075.2 1075.2 1075.2

22:00 931.2 931.2 1267.2 1939.2 2611.2

23:00 926.4 926.4 1262.4 1934.4 2606.4

It is important to note from Table 3.4 that the load demand increased by 259% in the hour

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from 01:00 to 05:00.

Figure 3.3: Winter Weekday with Home Batteries plus EV

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3.2.2 Autumn Weekend

For the Autumn Weekend of 2016-12-03, the load data gathered was of near about 418 customers.

Table 3.5: Load Profile Data for Autumn Weekend 2016/12/03

00:00 1.73 723.14

01:00 1.39 581.02

02:00 1.39 581.02

03:00 1.38 576.84

04:00 1.4 585.2

05:00 1.38 576.84

06:00 1.39 581.02

07:00 2.02 844.36

08:00 2.56 1070.08

09:00 2.33 973.94

10:00 2.51 1049.18

11:00 3.1 1295.8

12:00 2.4 1003.2

13:00 2.57 1074.26

14:00 2.42 1011.56

15:00 2.52 1053.36

16:00 3.31 1383.58

17:00 3.9 1630.2

18:00 3.67 1534.06

19:00 2.95 1233.1

20:00 3.72 1554.96

21:00 2.74 1145.32

22:00 2.23 932.14

23:00 3.22 1345.96

The basic assumptions are as follows:

• 418 customers

• 14 buildings considered

• In first 13 buildings there are 30 customers each

• In Fourteenth building there are 28 customers

• Each building has 3 Tesla Powerwall 2 and charging of powerwall 2 is from 2 am to 6 am

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• Discharging of Powerwall 2 is from 16-19, 20-21 (in first 3 hours 3.3 KWh discharge and in fourth hour remaining power is discharged)

• EV’s are charged from 12am to 8 am, maximum 50% penetration

Table 3.6: Load Profile Data for Autumn Weekend with Home Batteries

00:00 1.73 723.14 723.14 723.14 723.14

01:00 1.39 581.02 581.02 581.02 581.02

02:00 1.39 581.02 590.92 650.32 719.62

03:00 1.38 576.84 586.74 646.14 715.44

04:00 1.4 585.2 595.1 654.5 723.8

05:00 1.38 576.84 586.74 646.14 715.44

06:00 1.39 581.02 581.02 581.02 581.02

07:00 2.02 844.36 844.36 844.36 844.36

08:00 2.56 1070.08 1070.08 1070.08 1070.08

09:00 2.33 973.94 973.94 973.94 973.94

10:00 2.51 1049.18 1049.18 1049.18 1049.18

11:00 3.1 1295.8 1295.8 1295.8 1295.8

12:00 2.4 1003.2 1003.2 1003.2 1003.2

13:00 2.57 1074.26 1074.26 1074.26 1074.26

14:00 2.42 1011.56 1011.56 1011.56 1011.56

15:00 2.52 1053.36 1053.36 1053.36 1053.36

16:00 3.31 1383.58 1373.68 1314.28 1244.98

17:00 3.9 1630.2 1620.3 1560.9 1491.6

18:00 3.67 1534.06 1524.16 1464.76 1395.46

19:00 2.95 1233.1 1233.1 1233.1 1233.1

20:00 3.72 1554.96 1554.96 1554.96 1554.96

21:00 2.74 1145.32 1145.32 1145.32 1145.32

22:00 2.23 932.14 932.14 932.14 932.14

23:00 3.22 1345.96 1345.96 1345.96 1345.96

It is interesting to note from Table 3.6 that the peak hour load is dropped by 10% in the hour of 16:00 to 17:00.

It is important to note from Table 3.7 that the load is increased by up to 253% in the night hours.

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Figure 3.4: Autumn Weekend with Home Batteries

Table 3.7: Load Profile Data for Autumn Weekend with Home Batteries and EV

Time Max. Vol for 418 Customer 14 Buildings with Batteries Batteries + 10% EV Batteries + 30% EV Batteries + 50% EV

00:00 723.14 723.4 1017.14 1605.14 2193.14

01:00 581.02 581.02 875.02 1463.02 2051.02

02:00 581.02 719.62 1013.62 1601.62 2189.62

03:00 576.84 715.44 1009.44 1597.44 2185.44

04:00 585.2 723.8 1017.8 1605.8 2193.8

05:00 576.84 715.44 1009.44 1597.44 2185.44

06:00 581.02 581.02 875.02 1463.02 2051.02

07:00 844.36 844.36 1138.36 1726.36 2314.36

08:00 1070.08 1070.08 1070.08 1070.08 1070.08

09:00 973.94 973.94 973.94 973.94 973.94

10:00 1049.18 1049.18 1049.18 1049.18 1049.18

11:00 1295.8 1295.8 1295.8 1295.8 1295.8

12:00 1003.2 1003.2 1003.2 1003.2 1003.2

13:00 1074.26 1074.26 1074.26 1074.26 1074.26

14:00 1011.56 1011.56 1011.56 1011.56 1011.56

15:00 1053.36 1053.36 1053.36 1053.36 1053.36

16:00 1383.58 1383.58 1373.68 1314.28 1244.98

17:00 1630.2 1630.2 1620.3 1560.9 1491.6

18:00 1534.06 1534.06 1524.16 1464.76 1395.46

19:00 1233.1 1233.1 1233.1 1233.1 1233.1

20:00 1554.96 1554.96 1554.96 1554.96 1554.96

21:00 1145.32 1145.32 1145.32 1145.32 1145.32

22:00 932.14 932.14 932.14 932.14 932.14

23:00 1345.96 1345.96 1345.96 1345.96 1345.96

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Figure 3.5: Autumn Weekend with Home Batteries plus EV

3.2.3 Spring Weekday

For the Spring Weekday of 2016-04-01, the load data gathered was of near about 329 customers.

Basic Assumptions are as follows:

• 329 customers

• 6 buildings of 60 apartments considered

• First 5 buildings 60 customers each; 6 Tesla Powerwall 2

• Sixth building 29 customers, 3 Tesla Powerwall 2

• Charging 2 am to 6 am for first 4 buildings and 10 pm to 2 am for 5th and 6th building

• Discharging 16-20 (in first 3 hours 3.3 KWh discharge and in fourth hour remaining power is discharged)

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Table 3.8: Load Profile Data for Spring Weekday 2016/04/01

00:00 1.67 549.43

01:00 1.68 552.72

02:00 1.67 549.43

03:00 1.68 552.72

04:00 1.66 546.14

05:00 1.68 552.72

06:00 1.83 602.07

07:00 1.89 621.81

08:00 2.63 865.27

09:00 1.81 595.49

10:00 2.17 713.93

11:00 1.71 562.59

12:00 2.27 746.83

13:00 1.78 585.62

14:00 2.29 753.41

15:00 1.96 644.84

16:00 3.72 1223.88

17:00 2.78 914.62

18:00 2.95 970.55

19:00 2.58 848.82

20:00 2.09 687.61

21:00 1.86 611.94

22:00 1.87 615.23

23:00 1.69 556.01

• EV’s are charged from 10 pm to 6 am, maximum 50% penetration

Whenever an EV is charged from 10 pm to 6 am, it is assumed that it is charged in two parts from 12 am to 6 am and then in night from 10 pm to 12 am. This is done so as to keep the changes in load profile in one single day.

It is interesting to note from Table 3.9 that the peak hour load is dropped by 11.9% in the hour of 16:00 to 17:00.

It is interesting to note from Table 3.10 that the load is increased by up to 225% in the night hours.

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Table 3.9: Load Profile Data for Spring Weekday with Home Batteries

00:00 1.67 549.43 549.43 549.43 579.13

01:00 1.68 552.72 552.72 552.72 582.42

02:00 1.67 549.43 569.23 608.83 628.63

03:00 1.68 552.72 572.52 612.12 631.92

04:00 1.66 546.14 565.94 605.54 625.34

05:00 1.68 552.72 572.52 612.12 631.92

06:00 1.83 602.07 602.07 602.07 602.07

07:00 1.89 621.81 621.81 621.81 621.81

08:00 2.63 865.27 865.27 865.27 865.27

09:00 1.81 595.49 595.49 595.49 595.49

10:00 2.17 713.93 713.93 713.93 713.93

11:00 1.71 562.59 562.59 562.59 562.59

12:00 2.27 746.83 746.83 746.83 746.83

13:00 1.78 585.62 585.62 585.62 585.62

14:00 2.29 753.41 753.41 753.41 753.41

15:00 1.96 644.84 644.84 644.84 644.84

16:00 3.72 1223.88 1204.08 1164.48 1114.98

17:00 2.78 914.62 894.82 855.22 805.72

18:00 2.95 970.55 950.75 911.15 861.65

19:00 2.58 848.82 837.82 815.82 788.32

20:00 2.09 687.61 687.61 687.61 687.61

21:00 1.86 611.94 611.94 611.94 611.94

22:00 1.87 615.23 615.23 615.23 644.93

23:00 1.69 556.01 556.01 556.01 585.71

Figure 3.6: Spring Weekday with Home Batteries

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Table 3.10: Load Profile Data for Spring Weekday with Home Batteries and EV

Time Max. Vol for 329 Customers 6 Buildings with Home Batteries Batteries +10%EV Batteries +30% EV Batteries +50% EV

00:00 549.43 579.13 810.13 1272.13 1734.13

01:00 552.72 582.42 813.42 1275.42 1737.42

02:00 549.43 628.63 859.63 1321.63 1783.63

03:00 552.72 631.92 862.92 1324.92 1786.92

04:00 546.14 625.34 856.34 1318.34 1780.34

05:00 552.72 631.92 862.92 1324.92 1786.92

06:00 602.07 602.07 602.07 602.07 602.07

07:00 621.81 621.81 621.81 621.81 621.81

08:00 865.27 865.27 865.27 865.27 865.27

09:00 595.49 595.49 595.49 595.49 595.49

10:00 713.93 713.93 713.93 713.93 713.93

11:00 562.59 562.59 562.59 562.59 562.59

12:00 746.83 746.83 746.83 746.83 746.83

13:00 585.62 585.62 585.62 585.62 585.62

14:00 753.41 753.41 753.41 753.41 753.41

15:00 644.84 644.84 644.84 644.84 644.84

16:00 1223.88 1114.98 1114.98 1114.98 1114.98

17:00 914.62 805.72 805.72 805.72 805.72

18:00 970.55 861.65 861.65 861.65 861.65

19:00 848.82 788.32 788.32 788.32 788.32

20:00 687.61 687.61 687.61 687.61 687.61

21:00 611.94 611.94 611.94 611.94 611.94

22:00 615.23 644.93 875.93 1337.93 1799.93

23:00 556.01 585.71 816.71 1278.71 1740.71

Figure 3.7: Spring Weekday with Home Batteries plus EV

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3.3. UNDERSTANDING OF RESULTS

3.3 Understanding of Results

Let us take the case of autumn weekend with EV. In Section 3.2.2 it is assumed that EV charges from 12 to 8 am and home batteries from 2 to 6 am. But What if all customers in all buildings charge batteries from 10 pm to 2 am and all EV’s from 10 pm to 6 pm. The final load profiles with all buildings with home batteries and 50% of EV penetration with two set of charging pattern are depicted in Table 3.11

Table 3.11: Load Profile Data for Autumn Weekend with two set of charging of home battery and EV

Time Max. Vol for 418 Customer All customers with Batteries and 50% EV All customers with Batteries and 50% EV with new charging

00:00 723.14 2193.14 2331.74

01:00 581.02 2051.02 2189.62

02:00 581.02 2189.62 2051.02

03:00 576.84 2185.44 2046.84

04:00 585.2 2193.8 2055.2

05:00 576.84 2185.44 2046.84

06:00 581.02 2051.02 581.02

07:00 844.36 2314.36 844.36

08:00 1070.08 1070.08 1070.08

09:00 973.94 973.94 973.94

10:00 1049.18 1049.18 1049.18

11:00 1295.8 1295.8 1295.8

12:00 1003.2 1003.2 1003.2

13:00 1074.26 1074.26 1074.26

14:00 1011.56 1011.56 1011.56

15:00 1053.36 1053.36 1053.36

16:00 1383.58 1244.98 1244.98

17:00 1630.2 1491.6 1491.6

18:00 1534.06 1395.46 1395.46

19:00 1233.1 1233.1 1233.1

20:00 1554.96 1554.96 1554.96

21:00 1145.32 1145.32 1145.32

22:00 932.14 932.14 2540.74

23:00 1345.96 1345.96 2954.56

The findings are as follows:

• The maximum demand rose from 2314 KWh (07:00 to 08:00, Case 1) to 2955 KWh (23:00- 24:00, Case 2).

• 27.7% Increase!

By shifting the charging pattern, the demand in the grid increase by nearly 28 %. A customer group of 418 can vary demand by such large margins in future, if EV penetration is only 50% .

But the EV battery size can be more, as all customers won’t buy same car. In that case the demand rise will be higher.

“But this is not even the worst case scenario. ”What if there are no home batteries and all customers decide to charge the car in daytime.