DEGREE PROJECT IN ENERGY AND ENVIRONMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2021

Energy Usage of Smart Wayside Object Controllers An investigation and theoretical implementation of feasible energy storage and energy harvesting technologies in connection to a railway system

ALEXANDRA JERRESAND

MICAELA DIAMANT

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT i Abstract

With climate change threatening life, countries worldwide have united to limit the damages generated by humanity to the world. For a sustainable future, it will be essential to utilise energy in more efficient ways. Energy harvesting in the form of renewable resources and innovative solutions for industries are some answers to an improved tomorrow. The net-zero greenhouse emission target by 2045 in Sweden puts pressure on the energy-demanding sectors to find intelligent new ways to support their processes. For the railway industry, this results in digitalisation and new smart systems to optimise the railway operations to create an updated, more competitive and resource-efficient transport system in Sweden and Europe.

This master thesis work is developed through a European research program to investigate the energy usage for wayside objects. In order to analyse these subjects, two primary objectives were decided. The first main objective was to distinguish and create an energy system in connection to the railway system, and also implement Smart Wayside Object Controllers, SWOCs. The second objective was to find such a system’s potential, specifically for a case study in Sweden.

To reach the objectives, the thesis work has been combining both a quantitative and qualitative research method. Data have been collected partly from a literature review and partly from gatherings by the cooperating companies, Alstom and Trafikverket. The combined data collection was further used in the modelling and applied in the case study. Following this implementation, an analysis was conducted, leading to results, discussion, and conclusion.

In order to establish the demand from the energy system, non-disclosure values of the power consumption for each object were given by the cooperating company Alstom. Lithium-ion batteries, wind power, and solar photovoltaic were, from the literature review, found as the technologies most feasible for implementation after the limitations for the scope were decided. Suitable batteries were found, where the storage capacity was one crucial factor for the storage to function, which resulted in Tesla Powerwall 2 being the most feasible battery. For solar PV, different modules were compared in the software System Advisor Model, with the smallest possible configuration, which led to the manufacturer SunPower having the highest annual energy production per installed ground area. Different sizes, manufacturers, and constellations for wind turbines were investigated, resulting in a yearly overproduction for all configurations. However, two solutions were found for an installation with only wind power regarding the daily production and demand. Configurations combining both solar PV and wind power were also investigated, resulting in an additional solution consisting of one turbine, PV modules and a battery storage system.

The final recommendations for the case study resulted in configurations consisting firstly of one 10 kW turbine with three battery units, secondly of one 6 kW turbine and one 10 kW turbine with two battery units and lastly of one 10 kW turbine combined with a solar PV configuration with two battery units. These installations all met the demand from the railway system and were within the frames of the limitations as well as the set assumptions.

It was apparent that several factors affected the outcome of the case study. By implementing the energy system at a different location, the system showed substantial improvements and several more feasible solutions. By also changing the railway system’s demand, new possible solutions could be found. Concluding, the energy system is highly location dependant, and if implemented at a different place, the research should be performed from the beginning for optimal results.

ii Sammanfattning

Med hotande klimatf¨or¨andringar, har l¨anderruntom i v¨arldenf¨orenatsf¨oratt begr¨ansadess skada och p˚averkan p˚av¨arlden.F¨oratt n˚aen h˚allbarframtid ¨ardet viktigt att minska energif¨orlusterna och nyttja energianv¨andningp˚ab¨attres¨att.Detta kan g¨orasgenom anv¨andning av f¨ornybara energik¨allorsamt genom att hitta nya innovativa l¨osningarf¨orindustrier. Sverige har som m˚al att till ˚ar2045 vara netto noll vad g¨allerv¨axthusgaser, vilket leder till att man s¨atterpress p˚ade energikr¨avande sektorerna att hitta nya energismartare s¨att.F¨orj¨arnv¨agsindustrinresulterar detta i digitalisering och smarta nya system f¨oratt optimera verksamheten. D¨arigenomskapas ett uppdaterat, mer konkurrenskraftigt och resurseffektivt transportsystem i Sverige och Europa.

Detta examensarbete utvecklades genom ett europeiskt forskningsprogram med fokus p˚aj¨arnv¨ags- systemet och dess h˚allbaraframtid. F¨oratt analysera dessa ¨amnenbest¨amdestv˚ast¨orre m˚al. Det f¨orstam˚aletvar att urskilja och skapa ett energisystem f¨orSWOC, med andra ord signals¨akerhetssystemet, i samband med j¨arnv¨agssystemet.Det andra m˚aletvar att hitta potentialen i ett s˚adant system, s¨arskiltf¨oren fallstudie i Sverige.

F¨oratt n˚am˚alenmed arbetet har b˚adeen kvantitativ och kvalitativ forskningsmetod kombinerats. Data har samlats in, delvis fr˚anen litteraturstudie, och delvis fr˚ande samarbetande f¨oretagen Alstom och Trafikverket. Datainsamlingen vidareutvecklades i modelleringen av fallstudien, vilket ledde till en analys f¨oljtav resultat, diskussion och slutsats.

F¨oratt fastst¨allaefterfr˚aganfr˚anenergisystemet gavs sekretessbelagd information, g¨allande objektens energif¨orbrukning,fr˚anf¨oretagetAlstom. Litiumbatterier, vindkraft och solceller fastst¨alldesfr˚anlitteraturstudien som de teknologier som var mest l¨ampadef¨orimplementer- ing efter att begr¨ansningarnaf¨orkonfigurationen hade beslutats. L¨ampligabatterier f¨oren- ergif¨orvaringssystemet hittades, d¨arlagringskapaciteten var en viktig faktor, vilket resulterade i att Tesla Powerwall 2 var det mest optimala batteriet. F¨orsolceller j¨amf¨ordesolika moduler i programvaran System Advisor Model, med minsta m¨ojligakonfiguration, vilket visade att tillverkaren SunPower hade den h¨ogsta˚arligaenergiproduktionen per installerad markyta. Olika storlekar, tillverkare och konstellationer f¨orvindkraftverk unders¨oktesunder arbetet. Detta resulterade i en ˚arlig¨overproduktion f¨oralla konfigurationer, men vid unders¨okningav daglig produktion och efterfr˚aganhittades tv˚aolika l¨osningarmed endast vindkraft som energik¨alla. F¨orde kombinerade konstellationerna kunde en l¨osningp˚avisatillr¨acklig produktion f¨oratt m¨ota energiefterfr˚agan,vilket var en l¨osningbest˚aendeav en vindturbin i kombination med solceller och ett energif¨orvaringssystem.

De slutgiltiga rekommendationerna f¨orfallstudien resulterade i konfigurationer best˚aendef¨orst av en 10 kW turbin med tre batterienheter, den andra av en 6 kW turbin och en 10 kW turbin med tv˚abatterienheter och slutligen av en 10 kW turbin och en solcellskonfiguration med tv˚a batterienheter. Dessa ans˚agsvara de b¨astaalternativen d˚ainstallationerna m¨otteenergibehovet fr˚ansignalssystemet, men f¨oljdesamtidigt begr¨ansningarnaoch de satta antagandena.

Det blev uppenbart att flera olika faktorer p˚averkade resultatet av fallstudien. Genom att implementera energisystemet p˚aen annan plats visade systemet p˚abetydande f¨orb¨attringaroch fler genomf¨orbaral¨osningar.Genom att ocks˚a¨andra j¨arnv¨agssystemetsenergikrav blev resultaten betydande och nya m¨ojligal¨osningarkunde hittas. Sammanfattningsvis, energisystemet ¨armycket platsberoende, och om det skulle implementeras p˚aen annan plats, b¨orforskningen utf¨orasfr˚an start f¨oratt n˚aoptimala resultat.

iii Acknowledgements

We would like to thank our supervisors Zhendong Liu and Mats Berg, for all the guidance and help throughout this work. A special thanks to our examiner Justin NW Chiu, for his support and counselling. Additionally, we want to give thanks to Trafikverket and Alstom for giving us this opportunity to write this master thesis. A particular thank you to Jorge Sanchez de Nova, Mihail Zitnik, J¨orgenMattisson and Fredrik Bonnevier from Alstom, who have guided us in the right direction when needed and who have provided us with helpful knowledge for the work. Lastly, we would like to thank our families and friends for their support and help during this semester and during our time at KTH. We could not have done this without you.

iv List of Figures

Figure 1.1 An overall program structure of the IPs in S2R (Shift2Rail, 2020)..... 2 Figure 1.2 Schematic picture of the working process...... 4

Figure 2.1 A map of the railway system in Sweden (Trafikverket, 2019e)...... 8 Figure 2.2 A simplified explanation of the three levels (Andersson et al., 2018).... 9 Figure 2.3 A map of the planned ERTMS implementation (Trafikverket, 2020b). . . 10 Figure 2.4 A schematic over the different levels (Andersson et al., 2018)...... 11 Figure 2.5 Different types of main signals in Sweden (Andersson et al., 2018). . . . . 12 Figure 2.6 A schematic over a lithium-ion battery (Balasubramaniam et al., 2020). . 15 Figure 2.7 A schematic over a flywheel storage (Nikolaidis and Poullikkas, 2017). . . 16 Figure 2.8 A schematic over an EDLC (Zhou et al., 2018)...... 17 Figure 2.9 Schematic picture of a solar cell (Osmanbasic, 2019)...... 19 Figure 2.10 Schematic explanation of PV cells, panels, modules and arrays (Abed et al., 2020)...... 20 Figure 2.11 The PV potential around the world (Solargis, 2019b)...... 21 Figure 2.12 The PV potential in Sweden (Solargis, 2019a)...... 21 Figure 2.13 The principle of the wind turbine’s drive train (Breeze, 2014)...... 23 Figure 2.14 The principle of a fuel cell (Dicks and Rand, 2018)...... 25 Figure 2.15 The AC current’s movements (Sandqvist, n.d.)...... 26

Figure 3.1 Legend to the energy storage methodology...... 33 Figure 3.2 The process of selecting a battery energy storage...... 33 Figure 3.3 Legend to the energy harvesting methodology...... 43 Figure 3.4 The process of selecting energy harvesting technologies...... 44 Figure 3.5 A schematic of how GCR is calculated...... 47 Figure 3.6 Turbine specifications for the 3 kW and 5 kW turbines from Enair (ENAIR ENERGY S.L, 2021c; ENAIR ENERGY S.L, 2021d)...... 49 Figure 3.7 Turbine specifications for the 6 kW turbine (Halo Energy, n.d.) and 10 kW turbine (ENAIR ENERGY S.L, 2021b)...... 49

Figure 4.1 Hourly wind speed data at Eskilstuna (SMHI, Sveriges meteorologiska och hydrologiska institut, 2020)...... 53 Figure 4.2 Solar radiance at V¨aster˚as(National Renewable Energy Laboratory, n.d.). 53 Figure 4.3 Location of Tortuna...... 54 Figure 4.4 Schematic picture of the system today with the power cable...... 55 Figure 4.5 Schematic picture of the future system without the power cable...... 55 Figure 4.6 A schematic of Tortuna railway system...... 57 Figure 4.7 A hypothetical time schedule for Case 2, two meeting trains...... 61

v Figure 5.1 Solar PV production...... 66 Figure 5.2 Function of the BESS with two BUs...... 67 Figure 5.3 Function of the BESS with three BUs...... 67 Figure 5.4 Energy production managed by the 3 kW turbine...... 69 Figure 5.5 Energy production managed by the 5 kW turbine...... 69 Figure 5.6 Energy production managed by the 6 kW turbine...... 70 Figure 5.7 Energy production managed by the 10 kW turbine...... 70 Figure 5.8 Days of usage for the BESS with two BUs...... 71 Figure 5.9 Amount of inadequate days with two BUs...... 71 Figure 5.10 Days of usage for the BESS with three BUs...... 72 Figure 5.11 Amount of inadequate days with three BUs...... 72 Figure 5.12 Energy production with the 3 kW turbine and solar PV...... 74 Figure 5.13 Energy production with the 5 kW turbine and solar PV...... 75 Figure 5.14 Energy production with the 6 kW turbine and solar PV...... 75 Figure 5.15 Energy production managed by the 10 kW turbine with solar PV. . . . . 76 Figure 5.16 Days of usage for the BESS with two BUs...... 76 Figure 5.17 Amount of inadequate days with two BUs...... 77 Figure 5.18 Days of usage for the BESS with three BUs...... 77 Figure 5.19 Amount of inadequate days with three BUs...... 78

vi List of Tables

Table 3.1 Electrical and grid connections of the signalling system...... 30 Table 3.2 Cable resistance (Nexans, 2001)...... 30 Table 3.3 Energy storage technologies not chosen...... 31 Table 3.4 Fixed values for energy storage calculations...... 35 Table 3.5 Investigated batteries and their capacity...... 36 Table 3.6 Relion RB300 specifications (RELiON Batteries, 2021)...... 36 Table 3.7 Topband Lithium 12 V 200 Ah TB12200 specifications (Batteriexpressen, n.d.; Shenzen Topband Battery Co., 2018)...... 37 Table 3.8 Smart LITHIUM One 100 Ah specifications. (SUNBEAMsystem, 2021a). . 37 Table 3.9 LFP Smart 12,8/300 specifications (Victron Energy, n.d.[a]; Victron Energy, n.d.[c])...... 38 Table 3.10 LFP Smart 25,6/200 specifications (Victron Energy, n.d.[a]; Victron Energy, n.d.[c])...... 38 Table 3.11 Tesla Powerwall 2 specifications (Marsh, 2020b; Tesla, 2018)...... 39 Table 3.12 sonnenBatterie eco 9.43/5 specifications (Electric Car Home Ltd, 2021; sonnen GmbH, 2018)...... 40 Table 3.13 sonnenBatterie eco 9.43/10 specifications (Electric Car Home Ltd, 2021; sonnen GmbH, 2018)...... 40 Table 3.14 LG Chem RESU 10 specifications (LG Energy Solution, 2019; LG Energy Solution Europe GmbH, 2018; Marsh, 2020a)...... 41 Table 3.15 LG Chem RESU 13 specifications (LG Energy Solution, 2019; LG Energy Solution Europe GmbH, 2018; mg-solar-shop, 2021)...... 41 Table 3.16 Energy harvesting technologies not chosen...... 42 Table 3.17 Manufacturers for solar PV modules...... 46 Table 3.18 Fixed values for input in SAM...... 47 Table 3.19 Size specifications for the modules...... 48 Table 3.20 Chosen wind turbines...... 48 Table 3.21 Fixed values for wind power calculations...... 50 Table 3.22 Cases for the sensitivity analysis...... 51

Table 4.1 Investigated single energy harvesting installation designs...... 54 Table 4.2 Investigated combined energy harvesting installation designs...... 54 Table 4.3 Given values for level crossings...... 57 Table 4.4 Given values for point machines...... 58 Table 4.5 Given values for optical signals...... 58 Table 4.6 Given values for internal cabinet logic...... 58 Table 4.7 Cable lengths for each object...... 59

vii Table 5.1 Energy usage reduction for the new system compared to the conventional system...... 62 Table 5.2 Reduction of cable losses for the new system compared to the conventional system...... 62 Table 5.3 Cable losses for cable 1.5 and 2.5 mm2, with their low and high current. . 63 Table 5.4 Cable losses for cable 4.0 and 6.0 mm2, with their low and high current. . 63 Table 5.5 Total cable losses...... 63 Table 5.6 Results of the SunPower installation...... 64 Table 5.7 Results of the LG installation...... 64 Table 5.8 Results of the CSUN solar installation...... 65 Table 5.9 Results of the REC solar installation...... 65 Table 5.10 Installation set up of the modules...... 65 Table 5.11 Amount of days the battery system is used or inadequate during the year. 68 Table 5.12 Energy production for single wind power turbines...... 68 Table 5.13 Energy production for double wind power turbines...... 68 Table 5.14 Battery usage for single wind power installations...... 73 Table 5.15 Battery usage for double wind power installations...... 73 Table 5.16 Energy production for combinations of single wind power turbines and solar PV...... 73 Table 5.17 Energy production for combinations of double wind power turbines and solar PV...... 74 Table 5.18 Battery usage for single wind power and solar PV installations...... 78 Table 5.19 Battery usage for double wind power and solar PV installations...... 78 Table 5.20 Total number of batteries for each combined configuration to meet demand. 79 Table 5.21 Technologies used for the first solution...... 79 Table 5.22 Technologies used for the second solution...... 80 Table 5.23 Technologies used for the third solution...... 80 Table 5.24 Energy production from wind turbines and solar PV with two BUs at Tortuna. 80 Table 5.25 Energy production from wind turbines and solar PV with two BUs at Copenhagen...... 81 Table 5.26 Energy production from wind turbines and solar PV with two BUs at Lisbon. 81 Table 5.27 Energy production from wind turbines and solar PV with three BUs at Tortuna...... 81 Table 5.28 Energy production from wind turbines and solar PV with three BUs at Copenhagen...... 81 Table 5.29 Energy production from wind turbines and solar PV with three BUs at Lisbon. 82 Table 5.30 Amount of days the BESS was used for five different loads...... 82 Table 5.31 Amount of days the BESS was inadequate for five different loads...... 82 Table 5.32 Amount of days the BESS was used for five different loads...... 83 Table 5.33 Amount of days the BESS was inadequate for five different loads...... 83 Table 5.34 Amount of days the BESS was used for five different loads...... 83 Table 5.35 Amount of days the BESS was inadequate for five different loads...... 84

viii List of Equations

3.1 Energy ...... 29 3.2 Resistance in cables...... 29 3.3 Voltage drop in cables...... 29 3.4 Energy drop in cables...... 29 3.5 Power electricity...... 34 3.6 Capacity of a battery...... 34 3.7 Battery power...... 34 3.8 Capacity demand ...... 35 3.9 Total capacity...... 35 3.10Number of batteries ...... 35 3.11Ground Coverage Ratio ...... 47 3.12Power from wind...... 50 3.13Real power from wind...... 50 3.14Wind speed at hub height...... 50

ix Abbrevations

AC Alternating Current ATC Automatic Train Control ATP Automatic Train Protection BESS Battery Energy Storage System BUs Battery Units CAES Compressed Air Energy Storage CSP Concentrated Solar Power CTC Centralized Traffic Control Center DC Direct Current DoD Depth of Discharge EDLC Electric Double Layer Capacitor ERA European Union Agency for Railways ERTMS European Rail Traffic System ETCS European Train Control System EU European Union FESS Flywheel Energy Storage System Controller GCR Ground Coverage Ratio GSM Global System for Mobile Communications GSM-R Global System for Mobile Communications - Railway IEA International Energy Agency IP Innovation Program L1 Level 1 L2 Level 2 L3 Level 3 Mono Mono-crystalline PE Polythelene Poly Poly-crystalline PP Polypropylene PSH Pumped-Storage Hydropower PV Photovoltaic R&D Research and Development SAM System Advisor Model SDG Sustainable Development Goal SDP Sustainable Development Principles SoC State of Charge SWOC Smart Wayside Object TD Technology Demonstrators UNFCCC United Nations Framework Convention on Climate Change WP Work Plan

x Nomenclature

Sign Name Unit U Electricity Voltage V I Current A f Frequency Hz E Energy Wh P Power W t Time hrs W Watt J/s L Length m N Amount - A Area m2 v Velocity m/s w With m θ Module tilt angle ◦ Ω Resistance Ohm Rc Cable resistance Ω/m 2 ρc Resistance in materials Ωmm /m 2 Ac Area of cable mm Udrop Voltage drop V Edrop Energy drop Wh Eb Energy demand Wh Cb Capacity of a battery Ah Pb Battery power W ηef f Total efficiency % Cd Capacity demand Ah Ub Battery voltage V Ctot Total battery capacity Ah Nb Number of batteries - DoD Depth of discharge % GCR Ground coverage ratio - r Total module coverage and row distance m b Module ground coverage m c Row distance m Pw Generated power from wind W ρ Density kg/m3 cp Power coefficient - vw Wind speed m/s Pwreal Real power W ηm Mechanical efficiency % ηel Electrical efficiency % vhub Wind speed at hub height m/s va Wind speed at anemometer height m/s zhub Hub height m z0 Surface rougness m za Anemometer height m µ Micro 10−6 m Milli 10−3 k Kilo 103 M Mega 106 G Giga 109

xi Contents

List of Figures v

List of Tables vii

List of Equations ix

1 Introduction 1 1.1 Background...... 1 1.2 Aim and Objectives ...... 3 1.3 Method ...... 4 1.4 Limitations ...... 5

2 Literature Study7 2.1 Railway System...... 7 2.1.1 Today...... 7 2.1.2 SWOC...... 8 2.1.3 ERTMS...... 9 2.2 Wayside Objects ...... 10 2.2.1 Object Controller System...... 11 2.2.2 Interlocking system...... 11 2.2.3 Point machines...... 11 2.2.4 Level crossings ...... 12 2.2.5 Optical signals ...... 12 2.2.6 Radio communication system...... 12 2.3 ETALON ...... 13 2.4 Energy Storage...... 13 2.4.1 Chemical energy storage...... 14 2.4.2 Mechanical energy storage...... 15 2.4.3 Electrical energy storage...... 16 2.4.4 Other technologies...... 18 2.5 Energy Harvesting ...... 18 2.5.1 Renewable energy ...... 18 2.5.2 Mechanical energy...... 23 2.5.3 Chemical energy ...... 24 2.6 Electronics and cables ...... 25

3 Modelling 28 3.1 Assumptions ...... 28 3.2 Energy for Railway System ...... 29

xii CONTENTS

3.2.1 Electrical ...... 29 3.3 Energy Storage...... 30 3.3.1 Design of battery storage system...... 32 3.4 Energy Harvest...... 42 3.4.1 Software System Advisor Model ...... 45 3.4.2 Solar PV ...... 46 3.4.3 Wind power...... 48 3.5 Sensitivity Analysis...... 51

4 Case Study 52 4.1 Tortuna...... 54 4.2 SWOC and Wayside Objects ...... 56 4.2.1 Signalling system...... 57 4.2.2 Cases ...... 59 4.2.3 Train schedule ...... 60

5 Results and Discussion 62 5.1 Results...... 62 5.1.1 Case study ...... 62 5.1.2 Sensitivity analysis...... 80 5.2 Discussion...... 84 5.2.1 Energy storage...... 84 5.2.2 Energy harvest...... 85 5.2.3 Case study ...... 86 5.2.4 Sensitivity analysis...... 91 5.2.5 Sustainability...... 92 5.2.6 Sources of errors ...... 93

6 Conclusion and Future Work 95 6.1 Conclusion ...... 95 6.2 Future Works...... 96

Bibliography 98

Appendix I A Cable Specifications...... I B Battery Datasheets...... II C Solar Modules Datasheets...... X D Wind Turbine Datasheets ...... XIV E Time Schedules...... XVII F Energy Production for the Additional Solution ...... XIX

xiii Chapter 1

Introduction

With climate change endangering the natural environment and life, countries worldwide have merged to fight and prevent future damage. Ambitious policies and goals have been set through the United Nations Framework Convention on Climate Change (UNFCCC), where the Paris Agreement and the Sustainable Development Goals (SDG) originated from. While the Paris Agreement centres around environmental factors working towards limiting the global temperature rise, the SDGs include a broader aim towards sustainability for all humanity and nature, with different focus areas reaching from elimination of poverty to increasing equality (United Nations, n.d.; United Nations Framework Convention on Climate Change, 2021).

The European Union (EU) is set to become the first climate-neutral continent by 2050 and aims to cut its greenhouse emissions by 55 % until the year 2030, compared to the year 1990’s levels. The climate actions range from cutting emissions to investing in research and innovations to achieve the goals set within the union. Besides actively working with other countries to achieve the Paris Agreement, the EU also provides developing countries with financial aid to support their efforts to work towards a sustainable life (European Union, n.d.).

Sweden, being a front-runner in sustainability, has higher ambitious goals within energy and climate. By 2045, the target is to be net-zero in emitting greenhouse gases and by 2030 have a 50 % more effective energy usage. Moreover, should the electricity generation be 100 % from renewable resources by 2040, resulting in innovative new ways of utilising and distributing energy (Naturv˚ardsverket, 2021).

To reduce the environmental impact on the world and meet the goals set, significant investments, research, innovations, and changes in human lives will be required. More intelligent systems are created, and a more digital globe is elaborating every day. As most sectors are becoming more intelligent and more digitised, the railway industry is not late to follow. According to the Swedish Government, from 2021 to 2023, there will be tremendous investments made for maintaining the railways. There is a need to ensure higher reliability but also make the trains and railway sector more sustainable and climate-neutral (Alm, 2020).

1.1 Background

Today, products transported by the railway account for 8 % of the EU’s exports of service and more than 20 % of the exports of goods. The railway system has been a backbone of

1 CHAPTER 1. INTRODUCTION transport since the industrial revolution. However, the evolving social status of other means of transportation has increased and developed, taking shares from the industry. For the railway to remain one of the essential transportation methods, it needs to change with the upcoming innovations and digitalisation surrounding other transportation sectors. The ”Shift2Rail” (S2R) program was created for the industry to become once again ”the most sustainable, cost-efficient, high-performing, time-driven, digital and competitive customer-driven transport mode for Europe” (Shift2Rail, 2020).

By optimising the railway and train control system, for example, by reducing the number of times the train accelerates and break, energy usage and carbon emissions can decrease (Trafikverket, 2019b). Moreover, are the carbon emissions minimised as the electrification of the railway system is more elaborated. For the remaining non-electrified routes, sustainable and energy- efficient solutions are to be implemented. Overall, Sustainable Development Principles (SDP) are embedded in the future design and construction of the railway infrastructure and thereby increasing the resilience towards climate change (Shift2Rail, 2020).

This master thesis work is developed as a part of a research program initiated by the EU and railway stakeholders called S2R (Shift2Rail, n.d.[b]). The program involves multiple projects with different aims and five asset-specific Innovation Programs (IP). The overall goal is to create a more competitive and resource-efficient transport system in Europe, which will address major social issues such as traffic demand, security of energy supply, climate change and traffic congestion (Shift2Rail, 2020; Trafikverket, 2019b). The program structure and its IPs can be seen in Figure 1.1.

Figure 1.1: An overall program structure of the IPs in S2R (Shift2Rail, 2020).

A part of the program is the project X2RAIL-4 which is developed to enhance the Traffic Management System function and improve the performance of the systems. This project focuses on the IP2 - ”Advanced Traffic Management & Control Systems” (Shift2Rail, n.d.[b]), and is addressing the ”TD2.10 Smart radio-connected wayside object controller”. TD2.10 is one of the 11 Technology Demonstrators (TD) defined in the IP2. The TDs aim is to develop autonomous, intelligent and self-sufficient smart equipment which will be able to connect to control centres,

2 CHAPTER 1. INTRODUCTION other wayside objects and to communicate between them all (Shift2Rail, n.d.[a]). These types of equipment are presented as Smart Wayside Object Controller, or SWOC, and are essential parts of the future railway system. Their primary purpose is to communicate and control through wireless connections between and with the wayside objects.

The TD2.10’s main goal is to develop and test a prototype able to demonstrate certain focus areas. It involves wireless communication and data transfers between the SWOCs, control centre, interlocking and other devices. This data transfer includes increasing the possibility of diagnostic data to facilitate preventive maintenance, and thereby reducing possible maintenance and operation cost together with increasing the overall railway performance (Shift2Rail, n.d.[a]; Shift2Rail, n.d.[b]). Another objective is to reduce the energy usage of the SWOCs, including possible reduction of cabling for data transmission, communication and electricity. If deemed possible, it could decrease transmission losses and reduce the reliability of the electricity grid. This master’s thesis objective is to determine the energy usage of the wayside objects in connection with the SWOCs, and create an energy system separated from power cabling. The created system will then be compared to a conventional system to present possible innovative implementations for the future railway system.

1.2 Aim and Objectives

Sweden has an aspiring target to be a net-zero emission country by 2045, which creates challenges and obstacles to overcome (Minister for Local Government Finances and Financial Market Issues, 2018). Studies of self-sufficient buildings have shown that going off-grid and produce power locally, in connection with batteries, can make this energy transition easier (Balcombe et al., 2015; Pulido et al., 2019). S2R are implementing more self-sufficiency in the railway system by replacing the existing power cable along the tracks with local power supply and energy storage (Shift2Rail, n.d.[a]). As this project is a pioneering idea for the railway industry, the feasibility and possibility of implementing a new energy system are to be investigated.

The scope of this work consists of two main research objectives where; the first objective is to identify and create a feasible energy system in connection to the railway system and implement SWOC. The second objective is to evaluate the potential of such an energy system in a case study in Sweden. In order to reach the two research objectives, the following intermediate objectives have been defined.

– Identify the energy usage for objects such as switches and level crossings, before and after SWOC is implemented. – Investigate potential energy storage technologies. – Examine feasible trackside (wayside) energy harvesting technologies. – Size the energy system, including storage and harvesting, for the energy demand of the railway system. – Evaluate the system performance regarding weather data input and energy parameters. – Suggest a process or an algorithm for creating an energy system depending on different inputs.

3 CHAPTER 1. INTRODUCTION

1.3 Method

The objectives of this work were conducted through an unbiased perspective and an exploratory type of research. This research has been done to investigate the potential of a more sustainable energy system to power wayside object controllers in the railway system. In ”RESEARCH METHODOLOGY a step-by-step guide for beginners” by Kumar (Kumar, 2011) it is explained that research striven to be well-structured and specific is referred to as quantitative research. Research more focused on understanding and explaining situations is qualitative. By combining these two methods, where one approach were physical measurements of the wayside objects and the other the profound knowledge of the railway system found in the literature review; the energy demand could be quantified. Secondary data was gathered from the literature study, creating a foundation for how energy systems operate and suitable locations for implementation. Moreover, the quantitative part was mainly generated from firsthand simulations and measurements of the objects in the scope from the company Alstom. Since the thesis consists of two main objectives, where the first has a more deductive reasoning than the case study, inductive reasoning was also applied. The chosen research strategy and structure can be seen in Figure 1.2.

Figure 1.2: Schematic picture of the working process.

Firstly, during the pre-study, an unstructured and exploratory approach was applied to identify potential energy demands of objects, energy storage and harvesting technologies. This approach and execution were chosen to understand the aim, objectives and investigate potential limitations in the scope. The pre-study was also necessary to limit the literature study to essential technologies and targets of the thesis. Previous studies of similar work were limited, with the project Etalon being the most alike, further explored in Section 2.3 (Smilek et al., 2018).

4 CHAPTER 1. INTRODUCTION

In parallel with deciding the aim and limitations for the thesis, data was collected from Alstom and Trafikverket. These given values were estimated from the companies since an active railway system does not exist today. The data made it possible to create a load profile for the railway system, hence how much energy has to be produced to cover the system’s need. Firstly, a hypothetical train schedule was decided to achieve a variation of the energy need from this data. Next, cable losses to each object were calculated from cable data collected from Alstom. The received data was afterwards expanded to an analysis consisting of information and facts gathered from the literature study. Collectively, these different data sets made it possible to design a system constituting of an energy storage and harvesting technologies.

Modelling and calculations were carried out to estimate the system’s potential, with suggested energy storage and harvesting technologies. The input values of the calculation model were foremost from primary data provided by cooperating companies and manufacturers of the different technologies investigated as well as employing secondary data in the literature. This structured approach and method were chosen to quantify the potential energy system for a more standardised solution, regardless of the location but with similar constraints. In order to systematically analyse the energy system, calculations were performed and equations were written into Excel. This system of equations aimed to determine the feasibility of the energy system and to meet the demand of a specific application. A few feasible energy systems were created by applying the equations at a specific location in a case study at Tortuna. After that, the created process was used at other locations with other input data, further to establish the feasibility of the generated energy system.

As assumptions were made and specific data were explicitly used for the case study, a sensitivity analysis was essential. The sensitivity analysis was carried out to identify sensitive parameters which could affect the results and the suggested solutions. Technical and environmental parameters were tested, and the results were discussed, analysed and compared to the referenced results.

Lastly, all the results were discussed and analysed before conclusions could be drawn.

1.4 Limitations

The objectives of this work, as stated above, are to establish and investigate an energy system for SWOC objects. Therefore, will the main focus be to conduct an energy analysis of a theoretical energy system created specifically for railway applications. Hence, this scope will only manage energy-related subjects and the modelling will, therefore, not be dependent on the costs related to the system.

The only objects analysed in this thesis are level crossings with signals, point machines, optical signals and interlocking. All other railway wayside objects are excluded. A case study at a specific location will be included in the scope; hence some solutions will be limited to location-based data.

In terms of modelling the energy system, the scope will not include new technologies in the market since one aim is to implement the created system in the near future. The implemented solutions will rely only on sustainable solutions to secure energy supply and help Sweden achieve the future climate goals. Technologies that are too location-dependent will not be investigated

5 CHAPTER 1. INTRODUCTION in order for the cooperating companies to implement the solution regardless of the surrounding environment. Hence, ensuring a solution applicable at as many locations as possible.

This scope will not include the energy or electricity needed for train driving; this also includes traffic planning and management. Frequency transformers, voltage converters from the grid and other electronic objects specifically interconnected with grid operations will not be included in this thesis. The heating of objects is also excluded for the load profile regarding wayside objects and their operations.

In order to utilise the ground, a certificate or contract of some sort will need to be established. This approval of ground usage will not be included in this scope as well as implementation impacts for surrounding residential buildings and inhabitants.

The assessment of the potential energy system has been limited to theoretical investigations only, and no measurements have been carried out. Thereby, will the calculations on energy storage and harvesting be limited to the availability of appropriate data provided by manufacturers and data availability in the literature.

Energy demand profiles of different scenarios have been developed throughout this scope. These scenarios have been created through the limited data given from the company Alstom and found through Trafikverket. The demand profile and energy usage assumptions are based on these values combined with findings throughout the literature review. The data received from Alstom is confidential and will not be presented in this scope.

The thesis work will be investigated for one year of production, therefore will degradation of the systems be excluded in this scope.

6 Chapter 2

Literature Study

2.1 Railway System

In Sweden, railway construction started in the mid-1850s, and unlike today’s electrified railways, the trains were driven by locomotives powered by coal. The railway industry and other industries were extremely dependent on imported coal, leading to an energy supply highly vulnerable to wars and conflicts in the exporting countries. Therefore, at the beginning of the 1900s, the Swedish Government decided to investigate if it could use another energy source, preferably domestic. After an investigation and some test operations, it was concluded that it was possible to use other energy sources, electricity being the main one. This lead to the first electrified railway being that of the Iron Ore Line, where operation commenced at the beginning of 1915 (Berger and Enflo, 2013; Vattenfall AB, 2015).

After the first world war, the railway system developed in Sweden, and new lines were opened. Among those were some on the Inlandsbanan and others further north. During this time a standard track width did not exist, differentiating from 891 mm to 1524 mm (J¨arnv¨agsmuseet, 2018), the nominal and most common being 1435 mm (Trafikverket, 2021). Between 1950 and 1970, travelling by train decreased; the indirect effect of the increasing use of cars. The reduction changed because of the oil crisis in the 70s, leading to an increase in travel by train. By the 80s, this version of travelling stagnated because of the expansion of flights within the Swedish borders. Later, in the 1990s, taxes were raised for travelling, leading to another increase. Fortunately, the development of travelling by train has been positive during the past years because of faster trains, and a higher awareness of climate change (Berger and Enflo, 2013; Sweco and Branschf¨oreningen T˚agoperat¨orerna, 2015).

2.1.1 Today The railway system today in Sweden consists of 15 600 km of tracks, of which 11 900 km are used tracks, and today, more than 80 % of the tracks are electrified. The trains are fed by 16 kV 2 through an overhead contact line with a frequency of 16 3 Hz (Olofsson, 1993) and a standardised track width of 1435 mm. Today’s railway has been modified and updated compared to the original tracks installed several years ago but is mostly the same. Trains travelling these railways are passenger trains with a speed of 200 km/h, to freight trains with a speed of 100 km/h. Figure 2.1 presents a map of the railway system in Sweden today. The tracks shown are the more extensive railway systems called Stambanan.

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Figure 2.1: A map of the railway system in Sweden (Trafikverket, 2019e).

Unlike the trains in the 19th century, trains have a safety system that also controls the operations. This system is called Ebicab 700 and consist of a security system called Automatic Train Control (ATC) or Automatic Train Protection (ATP). Another software system allowed in Sweden today is the L 10000, which will disappear in the upcoming years. Ebicab was developed around 1980 for the Swedish railways and is often referred to as ATC. This system is foremost used to supervise the general speed of the trains, and that brakes are applied correctly (Andersson et al., 2018).

2.1.2 SWOC A SWOC can be defined as an item linked to the wayside objects and the interlocking system. This item can be compared to a ”smart box”, which controls and communicates between the different objects and systems. With the installation of SWOCs, the railway will require less

8 CHAPTER 2. LITERATURE STUDY maintenance and become more intelligent and independent than the existing conventional system. The implementation will lead to a considerable cost reduction related to both installation and maintenance. Moreover, can cabling be reduced, resulting in better communication and fewer transmission losses. The communication will be wireless for this new implementation, unlike before, which increases the redundancy resulting in higher availability and dependability (Jourdain et al., 2019; Trafikverket, 2019c).

With the system that exist today, there are no clever ways of reducing energy usage. By instead using SWOCs, the energy usage can be more controlled as it is possible to choose when to turn the objects on and off. These standby or sleeping modes let the railway systems become more energy efficient by using the power smarter. Furthermore, does the implementation of SWOC produce an opportunity for the railway industry and power supplies to become more sustainable (Jourdain et al., 2019; Trafikverket, 2019c).

2.1.3 ERTMS The EU has decided to create and implement a new ATP system called European Rail Traffic Management System (ERTMS). ERTMS is a safety system which aim is to make rail transportation once again competitive with other transportation methods and safer than previous safety systems (European Union, 2018). This system is divided into two; one train safety system such as ATC and one signalling/communication system where SWOC will be a primary part. Today the countries around Europe have different railway systems, and by implementing ERTMS everywhere, the EU hopes to increase competitiveness with other transportation sectors and create a seamless railway system (Trafikverket, 2019d; unife, n.d.).

The safety system in ERTMS is called European Train Control System (ETCS) with a communi- cation system called the global system for mobile communication designed for railway (GSM-R). The EU establishes standards for these systems, creating a system with three levels. Level 1 (L1) is the most similar to today’s system and constructed with optical signals, balises and cable connections. Level 2 (L2), a development from L1, mainly focuses on radio transmissions leading to optical signalling being removed if possible. Cabling between the different components has been decreased significantly in L2. Level 3 (L3) is similar to that of L2, but with a train integrity check onboard the trains passing (Andersson et al., 2018; Trafikverket, 2019d). A simplified explanation of these levels can be seen in Figure 2.2 below.

Figure 2.2: A simplified explanation of the three levels (Andersson et al., 2018).

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As seen in Figure 2.3, some railway lines already have ERTMS installed in Sweden. Others are currently under construction or planned in the upcoming years.

Figure 2.3: A map of the planned ERTMS implementation (Trafikverket, 2020b).

Most of the lines installed in Sweden for ERTMS will be of the L2 type since the system prevails for more functions and a flexible system. Thereby, will some of the previously installed objects, such as optical signals, still be used (Trafikverket, 2019d).

2.2 Wayside Objects

In order to understand the devices this scope will analyse, this section will include a short presentation and description about the wayside objects and the control system levels used today

10 CHAPTER 2. LITERATURE STUDY in the railway system. When referring to wayside or trackside throughout this report, the area next to the tracks is indicated.

2.2.1 Object Controller System The railway system and its objects are controlled foremost by the Centralised Traffic Control centre (CTC). Eight bigger centres and some smaller exists in Sweden, where most of the functions are digitalised today. CTC is the general level of the control system, and everything is communicating with it. Underneath CTC is the interlocking system, which can be bigger or smaller depending on which objects it controls. In Figure 2.4 is a simplified schematic over the different control system levels (Andersson et al., 2018).

Figure 2.4: A schematic over the different levels (Andersson et al., 2018).

The interlocking house has several names; among those are the interlocking system and internal cabinet logic.

2.2.2 Interlocking system There are different types of interlocking systems existing today; electrical, computer-based or mechanical. The operation principle of the interlocking system is that it picks up input values from the different objects, such as the level crossings or signals, in the system. These inputs are then read and returned to each respective object. It is used to ensure safe traffic operation and to make sure that all objects work together (Andersson et al., 2018).

2.2.3 Point machines For a train to change track or adjust its direction, point machines are installed, also known as switches or turnout machines. They can be controlled through the internal cabinet logic or manually, the first more common in newer systems. These objects are crucial for meeting trains to pass each other and are used throughout the railway system (Andersson et al., 2018).

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2.2.4 Level crossings In Sweden, level crossings only exist on railways without high-speed trains, defined as trains with speeds over 200 km/h. For the lines with high-speed trains, the crossings are different variations of grade separations, either underpass or overpass (Mornell, 2006). Depending on the security of the railway’s level, different crossing configurations exist; only optical signals, optical signals and audio signals or full-on security with added movable barriers. There are different ways of informing the train driver if they can proceed over the level crossing or not. Either by a self-governing system that starts the level crossing objects when a train is arriving, or if a train is approaching but does not have permission to pass the level crossing, the barriers will not be activated. The train can, therefore, not pass (Andersson et al., 2018; Trafikverket, 2020c).

2.2.5 Optical signals There are several signals along the railway which indicate different types of warnings and information. The systems are often nationally determined, which leads to different countries having different signalling systems. Main signals and distant signals are two examples of optical signals (Andersson et al., 2018). The main signal can appear in different ways, Figure 2.5 is showing four different versions of these signals. As Sweden’s focus lies on elaborating L2 in ERTMS, dwarf signals are most likely the optical signals used in these systems, especially if the signals connect with point machines (Trafikverket, 2019a).

Figure 2.5: Different types of main signals in Sweden (Andersson et al., 2018).

Distant signals are foremost used as a warning and reporting that the main signal is coming along the tracks to the driver. These distant signals are used for high-speed trains with long braking distance or when there is a turn, and it is not possible to see the main lights in time (Andersson et al., 2018).

2.2.6 Radio communication system Radio communication and other data transmissions are using the ”Global System for Mobile Communications” (GSM). An extension of this system, specifically for the railway industry, is the GSM-R used for daily operations and this network coexists with the public mobile network technologies. Previously, when using GSM-R, it resulted in interference between other users. In order to prevent these interferences, GSM-R utilises specific frequencies such as 900 MHz, which is only used for railways (European Union Agency for Railways (ERA), n.d.).

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2.3 ETALON

The Shift2Rail IP2 projects, together with the Horizon 2020 Programme by the European Commission, created a project called ETALON which primary focus was to enhance the train integrity functionalities. Included in the project was creating a suitable energy supply for on board integrity and trackside harvesting technologies.

This project is structured in seven Work Packages (WP), divided into essential parts of a Research and Development (R&D) project. The fourth WP is about energy harvesting solutions and how an adaption could lead to more reliable power. This specific project about trackside harvesting technologies is included in the fourth WP and includes several new technologies on the market (Etalon Consortium, 2017).

The report on ”Trackside Energy harvester solutions” (Smilek et al., 2018) includes harvesting technologies such as displacement harvester, variable reluctance harvester, vibration harvester, solar power and wind power. Certain conditions and criteria were created for this scenario because of the high variation of locations in energy harvesting performance. Geographic conditions were a track moving from east to west in Central Europe and on ground level. The railway infrastructure conditions were; electrified straight tracks, 69 kg/m rail, and track sections welded together. The traffic pattern was put to medium density traffic pattern with an express passenger train, stopping passenger trains and freight trains with different speeds. In addition to the physical environment, some cost, maintenance and reliability parameters were considered, such as reliant power supply technologies, low life cost and vulnerability to other factors such as theft (Smilek et al., 2018).

The technologies presented are introduced from functional to historical standpoints and can potentially be used as energy harvesting devices. Some of these technologies were tested through simulation software and in laboratories, while others had been tested at different test sites at specific locations (Smilek et al., 2018).

Feasible candidates for trackside energy harvesting were determined through an analysis that indicated that various technologies could be used to meet the energy demand. The more conventional harvesting technologies, wind and solar, provided more power and showed a higher vulnerability to the surrounding environment. Trackside combined harvesting installations indicated functional solutions; however, these technologies are on a low technological readiness level and are not enough for implementation today (Smilek et al., 2018).

The report’s main focus was, in the end, to present feasible energy harvesting technologies for railway applications without a deeper analysis on possible energy storage and implementation today. The aim was to work more as a guide for further future work and to demonstrate opportunities and limitations for this application. Most of the information presented shows feasible solutions, but for future implementation (Smilek et al., 2018).

2.4 Energy Storage

The main focus of this section is to find feasible energy storage technologies applicable to the railway system. As many off-grid systems rely on renewable energy harvesting technologies, energy storage can support otherwise volatile energy productions. The technologies found will be used for further investigations within this work.

13 CHAPTER 2. LITERATURE STUDY

2.4.1 Chemical energy storage Most chemical energy storage are different versions of a ”classic” battery, which was developed during the 19th century. Today, there is a wide range of batteries on the market specialised for different applications. Most of the batteries work similarly and consists of a cathode, an anode, a material of electrolyte and two current collectors of a positive and negative type (Svoboda, 2009; Wenzl, 2009).

There are several different batteries available on the market today. However, the lithium-ion batteries have a high energy density and high efficiency compared to other batteries (Svoboda, 2009). Further on, are they used today as batteries in the evolving electric car industry, which could lead to additional price drops in the future, which is yet another advantage with lithium-ion (Renewable Energy World, 2020). Flow batteries, zinc-hybrid batteries and nickel-metal hybrid batteries are other common batteries used in energy storage systems today.

Some important terminology needs to be clarified before presenting the research of chemical storage. For batteries, cycle life is highly dependent on the depth of discharge (DoD), which represents the quantity of the battery that can be used. This value is expressed in % and is a percentage of the nominal capacity of a battery. To preserve a long cycle life, lower DoD is recommended not to stress the battery and avoid the end of the charge region. Cycle life means the number of charge/discharge cycles a battery can be subjected to before decreased performance or break down. One cycle is referring to the loop from an ultimately charged battery to complete discharge. Higher temperatures for the cell battery and the risk of overcharges is increased by having a lower DoD; consequently, an almost empty battery. State of Charge (SoC) is the opposite of DoD and presents the amount stored in the battery compared to fully charged in %. SoC is the present capacity in the battery as a percentage of the maximum nominal capacity (Svoboda, 2009; Wenzl, 2009).

To utilise batteries as energy storage, some extra components are vital to providing a functioning system. The battery management system is one of these components and is often used to monitor larger battery installations, often referred to as Battery Energy Storage Systems (BESS). This management system helps monitor the batteries’ parameters and ensure proper performance. The monitoring includes keeping track of cell voltages, temperature, SoC and DoD to ensure that these values remain within the requirements. Thereby prolonging the batteries’ lifetime and increase feasible performance. In addition, the battery system needs a power conversion system, often an inverter or converter, to transform the current to or from the battery or energy system. Thus, will the power conversion system indirectly control the flow in and out of the storage system (Starke et al., 2019; Yang et al., 2015).

Lithium-ion battery Today, lithium-ion batteries dominate in portable electronics and the electric vehicle markets and are on the verge of being adapted into the utility market for grid energy storage. The commercialised lithium-ion technology has an energy density of <250 Wh/kg, but there are ongoing projects which tries to push the energy density to around 500 Wh/kg and >1000 Wh/L (Manthiram, 2017).

The negative electrode (anode) is made out of graphite for the most common lithium-ion battery, and the cathode is made of a lithium-oxide material. The electrolyte is often a mixture between a lithium salt and a nonaqueous liquid (often organic), where lithium-ions are the critical component

14 CHAPTER 2. LITERATURE STUDY of its electrochemistry. During the discharge cycle, the lithium atoms are ionised and move through the electrolyte to the cathode. The free electrons from the ionisation at the anode creates a charge at the positive current collector. The electrical current created is then used to power up the device; the electrons are recombined with the lithium at the cathode and neutralised. During charging, the lithium-ions make the reverse journey. An advantage of the material is that the lithium-ions are small enough to move through micro-permeable separators (between the cathode and anode); this results in that lithium-ion batteries are capable of having high voltage and charge storage per unit mass and volume (Gladysz and Chawla, 2014).

A separator between the anode and cathode is often a porous membrane, constructed out of polypropylene (PP), polyethylene (PE), or a mixture between those or other membranes. The separator controls the movement of the ionised lithium atoms and works as a separator between the anode and cathode, thereby preventing an electrical contact (Gladysz and Chawla, 2014). Figure 2.6 presents a schematic of a simplified lithium-ion battery, where the cathode side consists of a lithium cobalt oxide.

Figure 2.6: A schematic over a lithium-ion battery (Balasubramaniam et al., 2020).

The figure also illustrates how the ions move depending on charge/discharge in a liquid electrolyte and solid electrodes.

2.4.2 Mechanical energy storage Among the most efficient sustainable energy storage systems today are the ones linked to mechanical energy storage. In this category, three main types exist; flywheel, pumped hydro and compressed air. The main advantages of these systems are economic, environmental impact and sustainability (Mahmoud et al., 2020).

Flywheel energy storage The Flywheel Energy Storage System (FESS) stores energy short-term by utilising kinetic energy, which rotates a mass. This storage system has a fast response and is seen as having the fastest response time compared to other versions of energy storage systems. FESS can be used in different applications such as in hybrid vehicles, railway and wind power systems. The most studied applications today are that of regenerating power from braking in trains and cars. This saved energy is then foremost used in the vehicle’s acceleration, aiming to reduce the power peak

15 CHAPTER 2. LITERATURE STUDY utilisation. Moreover, could the energy stored from breaking be used to increase the overall efficiency and reduce the fuel consumption (Abdin and Khalilpour, 2019; Breeze, 2018a; Mahmoud et al., 2020).

The amount of stored energy is proportional to the moment of inertia of the rotor and the rotational speed. Increasing either mass or the rotational speed leads to an increased storage capacity, but higher speeds also lead to higher demands on materials used in the construction. Conventional types of flywheels are often constructed with metal disks made of iron or steel, used on the crankshafts of piston engines. These types of disks are more capable of rotating at lower speeds, and for power applications, lighter composite materials are used instead. These new lighter materials give the disks a possibility of rotating at 10,000-100,000 rpm without getting destroyed by the centrifugal force. Materials used are carbon or glass fibre and lets the flywheel store more energy at higher rotational speeds. Today the disk could be more cylindrical and modified to fit the specific use, such as moving the mass centre from the rotational centre. Whether the flywheel is more cylindrical or disk-shaped depends on the application (Abdin and Khalilpour, 2019; Breeze, 2018a; Mahmoud et al., 2020). A simplified schematic picture on a cylindrical flywheel connected to the grid can be seen in Figure 2.7.

Figure 2.7: A schematic over a flywheel storage (Nikolaidis and Poullikkas, 2017).

To reduce friction and energy losses, the flywheel mechanism is placed inside a high-vacuum environment, reducing wind shear and thereby friction from air resistance. A magnetic bearing is used in the high-performance flywheel system to reduce further friction and, in some cases, give more control to the rotor as it spins (Abdin and Khalilpour, 2019; Mahmoud et al., 2020).

2.4.3 Electrical energy storage This type of storage is similar to chemical energy storage but mainly focuses on storing electricity without chemical reactions (Zhou et al., 2018).

Capacitors Batteries store energy in a chemical reaction, while capacitors and supercapacitors use static electricity. The capacitor consists of two conducting metal plates with an insulating material in

16 CHAPTER 2. LITERATURE STUDY between. During charging, positive and negative electrical charges build up on the plates, unlike a battery where a reaction creates ions. The lack of chemical reaction in the capacitor leads to a faster charge/discharge time and a longer lifetime (Yang et al., 2015; Zhou et al., 2018).

Electric Double Layer Capacitors (EDLC) started to emerge in the 1970s and have shown promising results as alternative energy storage to batteries and other types of capacitors. Today, this type of storage has already been successfully implemented in some electronic products, mainly as a backup system. This technology could be used during higher power usage, such as accelerating and moving in electric vehicles to reduce the overall power consumption. Three different types of capacitors can be distinguished depending on the utilisation; EDLC, pseudocapacitors, and hybrid supercapacitors. The first mentioned appears, with today’s technology, the most promising if adapted into the railway system (Zhou et al., 2018).

EDLC is also referred to as a supercapacitor or ultracapacitor and is an electrochemical capacitor that has shown great results in terms of acting as power storage. The energy density of the capacitor is not as high as a regular battery, especially when compared to a lithium-ion battery. However, it enables large power capacity per weight (Funabashi, 2016). Other positive applications of EDLC is the fast charge/discharge time and long cycle life. In design and assembly, this capacitor resembles a lithium-ion battery since it constitutes of two electrodes, one electrolyte and a separator (Zhou et al., 2018). A simple schematic can be seen in Figure 2.8.

Figure 2.8: A schematic over an EDLC (Zhou et al., 2018).

The electrical capacitance derives from the separation between electrons and the positive atom nuclear of the electrode material and electrolyte. This separation creates electrons and ions, and the excess electric charges accumulate on the electrode’s surface. At the same time, the ions in the electrolyte, on the positive or negative electrode’s surfaces, do the same in the solution. This counter-reaction is to generate and maintain overall electrical neutrality in the capacitor. When the system is charged and given an external load, the cations in the electrolyte migrate toward the negative electrode, and the anions migrate to the positive electrode. The electrons, on the other hand, travel through an external circuit (Zhou et al., 2018).

17 CHAPTER 2. LITERATURE STUDY

2.4.4 Other technologies Another technology not possible to include in the mentioned categories is presented in this section. This is new to the market but could be implemented into an energy system used for a railway system.

Hydrogen energy storage Hydrogen can be stored in many ways. Either physically, as liquid or gas, or stored within or on other materials via adsorption and absorption. When used in a fuel cell, hydrogen is stored as a gas in high-pressure tanks with a pressure of around 350-700 bars or as a liquid that requires cryogenic temperatures. Hydrogen has a low boiling point of -252.8◦C and therefore requires energy to achieve liquidity of the material (Salehabadi et al., 2020; U.S. Department of Energy, n.d.[a]; U.S. Department of Energy, n.d.[b]).

For the more extensive applications of renewable energy harvesting, the surplus energy created can be used to separate hydrogen from other materials; this process is called electrolysis. An electrical current is passed through a chemical solution, leading to hydrogen separation from the rest of the material. Hydrogen can then either be used directly in a fuel cell or stored (Salehabadi et al., 2020; U.S. Department of Energy, n.d.[a]).

2.5 Energy Harvesting

The main idea with energy harvesting is to make use of energy that otherwise would have been wasted. Another benefit is that the energy harvesting often can be locally produced, leading to more self-dependency and less grid usage (Dervojeda et al., 2014). There are several available sources and methods today; some will be presented in this section together with new technologies.

2.5.1 Renewable energy Renewable energy is an energy source that is unlimited in amount but limited in how much can be utilised during a specific time. The most common renewable energy sources are solar power, wind power, hydropower, ocean power, biomass and geothermal (U.S. Energy Information Administration, 2020a).

Solar photovoltaics Solar photovoltaics (PV) is one way of transforming energy from solar radiation to electricity. Solar cells produce energy through the so-called photovoltaic effect. This effect is a process in which dissimilar materials in close contact creates electrical energy through conversions of radiance such as light. The energy from radiance sets some of the electrons free in the materials, which creates a current. The majority of solar cells are fabricated from silicon materials, and unlike batteries, the process of creating electricity does not utilise a chemical reaction (Ashok et al., 2020; The Editors of Encyclopaedia Britannica, 2008).

The solar cell is structured in different layers, which all serve different purposes. Light enters the device through the first layer, which is an optical coating or anti-reflection layer, where the primary function is to minimise the losses from reflection. The layer traps the light coming into the cell by encouraging the light transmission towards the other energy-conversion layers underneath. Next are the energy-conversion layers, where the top is called the top-junction layer,

18 CHAPTER 2. LITERATURE STUDY or top electrode, followed by the absorbing layers, which are the device’s core. These layers are composed of semiconductors, often silicon, and their primary purpose is to absorb the light beams entering the cell. The core of the device carries the electrical current back and forth in the cell. In between these semiconducting materials is a junction layer. The top electrode often referred to as the top junction layer, is usually composed of a conductive material such as a metal. This layer is often in the form of a grid pattern, and these grid lines are thin and widely distributed since the metal blocks the light beams. The last of the electric contact layers works as an electrical contact, and therefore does not have the same constraints as the first and covers the entire back surface; this is referred to as the bottom electrode. This layer is the last one in the cell, and the material used for this layer is also made out of a conductive one, usually metal (Ashok et al., 2020; The Editors of Encyclopaedia Britannica, 2008). In Figure 2.9 the different layers and parts of the solar cell are presented.

Figure 2.9: Schematic picture of a solar cell (Osmanbasic, 2019).

The electrons in the semiconducting absorbing layers acquire a higher excited state than the solid layer’s atoms. This excited state creates the movement of the electrons through the solid, movements in a random motion. Thus, can no oriented current be created. Therefore are the junction-forming layers added since they can induce an electric field that creates a collective motion of the electrons, creating a direct current (DC). Both junction layers, or electrodes, are constituted of different semiconductors than the semiconducting silicon layers. The layers can also be from the same material but with different conduction, thereby creating a dissimilar effect from the silicon layers in which an electric field can be created. The DC is then transferred to the electric contact layer and from there to an external circuit (Ashok et al., 2020; The Editors of Encyclopaedia Britannica, 2008).

Solar cells are connected in series to create better power production capacity. If connected in series, these cells increase the voltage output whilst in parallel the current is increased. These cells are then placed between transparent or opaque covers for protection against the weather, dust, humidity and other environmental substances. Since the cell’s operating temperature directly affects the solar panel’s output, where the output decreases with the increasing temperature, the materials in these modules should help prevent high temperatures. This package of cells is referred to as PV modules, designed for various power outputs depending on the number of cells and how they are connected. The PV module’s top layers are transparent to not influence the radiance from entering the cells, and this layer is often made out of hardened glass or plastic. These layers, or top covers, protects the internal cells from environmental conditions and keeps

19 CHAPTER 2. LITERATURE STUDY pollutants of different sorts from penetrating the module. The backside of the modules is covered with either Tedlar or glass to protect the cells from outer impacts. A frame made of a light material, such as aluminium or composite, is then put around the module to ensure mechanical stability and assist the mounting. These modules can then be mounted and connected in series, which creates a string of modules or, in parallel, creating an array. This mounting can be seen in Figure 2.10. How the modules are connected and mounted is a complete PV system which is created to accommodate different electrical loads, circuits and battery storages (Asdrubali and Desideri, 2018; Ashok et al., 2020).

Figure 2.10: Schematic explanation of PV cells, panels, modules and arrays (Abed et al., 2020).

PV systems can be adapted for both small-scale residential use and large-scale implementation. As of today, there are three more evolved types of PV cells known on the market; crystalline silicon consisting of the two common types Mono-crystalline (Mono) and Poly-crystalline (Poly), and thin-film materials (Duffie and Beckman, 2013). Most common are crystalline silicon applications. Mono has, in general, a higher efficiency but are in turn more complicated to manufacture. Poly, on the other hand, are more known for a cheap manufacturing cost but has on the other side lower efficiencies (Plante, 2014; Solar Energy Technologies Office, n.d.).

The amount of installed solar PV has grown drastically in the latest years, resulting in one of the most significant energy generation growths within renewable energy sources in 2019 (International Energy Agency (IEA), 2020). When installing solar PV, several factors are determining how much energy can be generated, for example, how much shading there is, the tilt angle of the solar panels and solar radiation (Rahim et al., 2014).

The potential PV production differs around the world and is greater closer to the equator. This is visualised through Figure 2.11. These values are only based on solar radiance and do not factor in the ambient temperature. High temperature results in a higher loss because of the cell’s temperature dependency, impacting PV energy production.

20 CHAPTER 2. LITERATURE STUDY

Figure 2.11: The PV potential around the world (Solargis, 2019b).

Sweden’s potential is higher around the coastline, with Gotland and Oland¨ having the highest potential. As seen in Figure 2.12, the northern part of Sweden is not taken into account because of the low PV potential compared to the rest of the country. The lower potential does not mean that it is impossible to install PV modules in northern Sweden but indicates that the potential could be too low to successfully implement solar PV modules.

Figure 2.12: The PV potential in Sweden (Solargis, 2019a).

In terms of environmental factors which can influence solar PV production, snow is one prominent factor in Sweden. There have been some studies on this, but the conclusions have been somewhat

21 CHAPTER 2. LITERATURE STUDY divergent. Snow affects the production, which is concluded throughout the studies, but the exact effects and values differ. One reason why the studies have had different results could be because they have been conducted during different years and at different locations. In order to prevent or minimise the losses, tilt angles should be preferably above 40◦. However, to establish which tilt angle is preferred at the location, a proper prediction of snow should be configured (Andrews et al., 2013; Heidari et al., 2015; Townsend and Powers, 2011).

Wind power The renewable energy that has been growing most in recent years is wind power. In general, off-shore wind power can generate more energy as nothing is disturbing the wind. However, the on-shore wind power industry is more developed and produces most of the total wind power generated today. Wind turbines have existed for more than a century, and since electric generators were invented, engineers have attempted to use wind power for electricity production. The modern wind power with the predecessor of turbines are considered being invented first in Denmark, and the first wind turbines began operating in the later 19th century (International Renewable Energy Agency, n.d.).

The principle of wind turbines is to use the kinetic energy created by the wind or air in motion and transform the movement into electrical energy. When the wind hits the turbine blades, the motion causes them to rotate, turning the turbine in connection. Located within the nacelle, which is a cover for the generating components, is the turbine. Within the nacelle, the bigger wind power turbines have a gearbox installed, increasing the rotational speed of the drive shaft. The driveshaft itself connects to a generator where the electricity is produced. This generator is synchronised with the grid’s frequency. Some wind turbines have a direct-drive generator instead of the gearbox, and this installation tends to be more expensive than using a gearbox but benefits because of higher reliability. The rotor, the assembled name of all the blades of a turbine, and its design will indirectly determine the efficiency of the whole structure. Three-blade rotors are the majority of modern wind turbines, and the overall size is a balance between the cost of manufacturing and installing them with the performance efficiency. In general, this means that the bigger the rotor is, the higher the cost will be. Bigger turbines and increasing wind speed will put a higher strength on the durability of the turbine, and therefore wind turbines also have a maximum size (Breeze, 2014; International Renewable Energy Agency, n.d.).

The general drive train of a wind turbine is presented in Figure 2.13. Since the wind force is uneven, the drive train needs to endure more than the rotational torque produced by the rotor. The wind creates lateral, bending forces, which are transferred into the generating components. The structure, therefore, needs to have shock-absorbing components to reduce this effect; otherwise, these forces can lead to early failure (Breeze, 2014).

22 CHAPTER 2. LITERATURE STUDY

Figure 2.13: The principle of the wind turbine’s drive train (Breeze, 2014).

The power production of a wind power turbine is highly dependent on its rotational speed. Lower wind speed generates a slower rotor movement since the lift produced by each rotor blade is proportional to the wind speed squared. This phenomenon results in parts of the kinetic energy not being extracted, inducing lower output. If the wind is too great, turbulence will be created by the blades, which also causes a decreased production. Turbulence between two wind turbines can also occur if they are mounted too close to each other. The optimum rotational speed is often defined from a parameter called the tip speed ratio, which is simply explained by the ratio of the speed of the blade tips and the wind speed. This ratio varies with the wind speed, regardless of the turbine size (Breeze, 2014).

There are several different ways to design a wind turbine, both for large-scale and small-scale. There are four main types of wind turbine concepts available today. Type A or fixed speed, Type B or limited variable speed, Type C or variable speed with partial scale frequency converter and Type D or variable speed with full-scale frequency converter. Today there are three ways to control the turbine during high wind speeds to reduce destruction; stall control, pitch control and active stall control. The maximum output of a wind turbine is called the Betz limit and is approximately 59 % of the theoretical output. Hence, even if the wind power production could perform without losses, only 59 % would still be extracted (Ackermann, 2005). The turbine is turned on typically when the wind speed exceeds 3.5 m/s and turns off at wind speeds of 25 m/s. However, these numbers can vary a lot depending on the manufacturer and surrounding conditions (Office of Energy Efficiency and Renewable Energy, n.d.).

2.5.2 Mechanical energy As renewable sources are volatile, it could result in providing too little energy for the demanding objects. Insufficient energy production could have significant effects, and most likely, lead to another energy source needed to secure the energy supply. Mechanical energy can be created in several different forms, most commonly through the forms of piezoelectric, electrostatic, magnetoelectric and electromagnetic (Elvin and Erturk, 2013).

23 CHAPTER 2. LITERATURE STUDY

Piezoelectric technology Along with the movement of a vehicle, a train, or similar, vibration and pressure follow. This mechanical energy can, with correct technologies, lead to electricity generation. Piezoelectricity is one method in which pressure is used to create electricity, or in other words; voltage is induced due to an applied force. Piezoelectricity is often used for security reasons, such as when the produced energy is insufficient or when applications of other energy sources with batteries are not an option (Kulkarni et al., 2018). The generation process is simple. Between two metal plates, a crystal is placed. Pressure is applied to the metal plates, creating an electrical imbalance in the crystal. The metal plates either receive the positive charge or the negatively charged part of the structure, leading to voltage creation. Some common materials for this crystal are quartz, Rochelle salt, topaz and composites, to mention a few (Fleischer, n.d.; Uchino, 2010). Today the concept of piezoelectricity can be found anywhere; actuators, automotive and bicycle pressure sensors are some examples (Kulkarni et al., 2018; Uchino, 2010).

Electromagnetic energy Electromagnetic energy, based on Faraday’s law, has been around for many years. The theory behind this law is that a conductor is transported through a magnetic field, creating a voltage. The main idea with electromagnetic energy is converting energy available as kinetic, often in vibrations, into electrical energy. One disadvantage with this kind of harvesting method is that when having limitations in size, other techniques such as piezoelectricity is preferable used (Beeby and O’Donnell, 2009).

2.5.3 Chemical energy Chemical energy is a process where electricity is produced from a chemical reaction. Energy is stored inside different objects, coal and biomass being two examples, and through a chemical reaction, often presented as heat, energy is created (U.S. Energy Information Administration, 2020b).

Fuel cell Fuel cells have in recent years been developed rapidly, which has led to implementation in the energy market. Today the technology is mainly available in different types of transportation, i.e. buses and trains and in privately owned vehicles. Fuel cells have a similar working cycle as batteries, with the main difference that there is a need for continuous fuel supply. The most common fuel is hydrogen, and the main principle can be seen in Figure 2.14.

24 CHAPTER 2. LITERATURE STUDY

Figure 2.14: The principle of a fuel cell (Dicks and Rand, 2018).

The most basic fuel cell consists of two electrodes, one anode and one cathode, an electrolyte solution, and an external load. Hydrogen is supplied to the anode side at the same time as oxygen is supplied to the cathode. When hydrogen is supplied, the protons and electrons are separated. The protons pass through the solution to the cathode side, while the electrons are passing through a circuit that generates electricity. The only byproducts created from this construction are heat and water (Fuel Cell & Hydrogen Energy Association, n.d.; Hydrogen and Fuel Cell Technologies Office, n.d.).

There are several primary benefits with fuel cells; high efficiency, close to zero emissions during operation and simple construction. Another advantage is that fuel cells can often be used for many different construction sizes, from fuelling small energy objects to very large. However, while fuel cells have many advantages, some drawbacks have slowed down worldwide usage. It is foremost the high capital costs, and difficulties in storing that are the reasons behind this (Dicks and Rand, 2018; U.S. Department of Energy, n.d.[a]; U.S. Department of Energy, n.d.[b]).

2.6 Electronics and cables

There are separate cables for supplying train operations and railway objects, each connected to the electricity grid. The cable distributing electricity to the objects has an actual voltage of between 22 kV to 11 kV. Through a transformer, the voltage is decreased to 230 V or 400 V depending on the connected objects. The distributed electricity has the same frequency as the rest of Sweden’s power grid, 50 Hz (Trafikverket, 2014; Trafikverket, 2020a) and this frequency is used in a majority of Europe with the standard connection of 230 V alternating current (McGrayne and The Editors of Encyclopedia Britannica, 2016). For installing an energy system suitable for SWOCs, the electronics behind are essential to understand. Today, most railway systems are connected to the electricity grid, a grid that is not standardised worldwide.

Electric circuits are divided into two types of circuits, alternating current (AC) or DC. Unlike AC, DC’s flow of electric charge never changes direction, meaning that the current flows only in

25 CHAPTER 2. LITERATURE STUDY one direction. This type of current is produced and used by batteries, fuel cells and rectifiers. From the beginning, DC was used for standard commercial power in the 1880s, but because of difficulties of transporting high voltages long distances, DC was supplanted by AC. Another difficulty was also the conversion from low to high voltages. Technologies have been evolving since the 1960s, and today a high voltage DC cable is currently being installed between Norway and England. Even though the technology nowadays exists for DC distribution, it has to ordinarily be converted to AC before application and final distribution (ABB Ltd, 2015; Johansson et al., 2013; McGrayne and The Editors of Encyclopedia Britannica, 2016; The Editors of Encyclopedia Britannica, 2016).

AC is a flow of charges that alternate and periodically reverse with time. This alternation is similar to a sinus curve, as seen in Figure 2.15, and the interval of time between the attainment of the cycles is called a period. The number of cycles, also referred to as periods per second, is the frequency. RMS stands for the root-mean-square of the effective value of the current, which is defined as the amount of AC power that produces the same effect as a DC power, or the square root of the time average of the voltage squared. This value is what often is referred to when talking about the size of the AC (Johansson et al., 2013; McGrayne and The Editors of Encyclopedia Britannica, 2016; The Editors of Encyclopedia Britannica, 2021).

Figure 2.15: The AC current’s movements (Sandqvist, n.d.).

The maximum value in either direction is the current amplitude, and today those of low frequencies are used for distributing power around the world. In television and radar communication, frequencies of 100,000,000 cycles per second, or 100 MHz, are used, and for cellular operating towers, this frequency is often 1 GHz (Johansson et al., 2013; McGrayne and The Editors of Encyclopedia Britannica, 2016; The Editors of Encyclopedia Britannica, 2021).

A wire or a cable is essentially a conductor of electric charges often surrounded by different types of sheaths depending on the application. The most common material used for conductors are either copper or aluminium, both used because of their high electrical conductivity. These conductors are the core of the cable and consists of a single wire or many wires stranded together. Around the wires is an insulation that protects the wires from moist and external influences such as chemical components or prevents a person from touching the live metal. An inner sheath then surrounds the insulated wires and works as extra protection. The inner sheath is embedded in an armouring or an extra protected covering; in bigger cables used for electricity distribution or cables with higher current. Lastly, the cables have outer protection called over-sheath, which gives extra mechanical strength to the cables beside the protection against damage. Around the insulation, cables can also have a layer of filler in order to create a round and correct cross-section

26 CHAPTER 2. LITERATURE STUDY

(nkt cables AB, 2015).

The names of the cable, or the nature of contents, describe what materials the different layers are made off. These numbers or letters also explains the structure or construction of the cable and characteristics (nkt cables AB, 2015).

Resistance in the cables is highly dependent on the length, area and material of the conductor. The materials have different resistivity per unit of length, and the geometric aspects of resistance in cables can be easily interpreted as; the longer the wire, the higher the resistance. Resistance is also very temperature-dependent and increases with the temperature. This phenomenon is vital to observe whenever calculations or adaption of cables is needed, and a way of decreasing potential resistance to the flow is to increase the cross-sectional area. Temperature also increases whenever a current flows through a cable, and it is crucial to dimension the cable to the load current. If a cable becomes overheated, not only do the losses increase, but the lifetime of the cable decreases (McGrayne and The Editors of Encyclopedia Britannica, 2016; nkt cables AB, 2015).

27 Chapter 3

Modelling

This section presents the modelling, including methodology and processes, used throughout this scope. Firstly are the assumptions used for all modelling parts stated, and following is the methodology and modelling for the energy system, including the electrical part. Thirdly is the energy storage with investigated storage technologies, how to design a battery system, and modelling for sizing the storage system. Next is the energy harvesting part with the investigated harvesting techniques, the elaboration of an energy harvesting system and modelling. Finally, the sensitivity analysis is presented.

3.1 Assumptions

Assumptions and other estimations involving the operation of the railway and energy systems are stated below and will be used for the case study.

• The electrical components connected to the system are simplified, this includes: – Phase angle difference for the AC/DC connections will not be included in the calcula- tions. – Simplifications of inverters/converters, since the effects of them often are included in the data. – The cable losses should not surpass 10 % during one day of operation. • The conventional system is always in standby mode; thus, will the steady power never be shut off. • The new system can be turned off when not in use, excluding the cabinets and internal logic. • The energy of the system demand has a safety factor of 1.2; thus, will the energy demand used for calculations be higher than the actual demand. • The installation will aim for the smallest possible installation area of the systems with the lowest amount of devices possible to meet the demand. • Exchange rates from dollar to euro is used from FOREX bank’s numbers the 11/5-2021 and equals $ 1 to € 0.84 (FOREX Bank AB, 2021). • Costs are rounded up to the closest zero. • The batteries should have an autonomy reserve of a minimum of three days. • Primary production will be used for the railway objects.

28 CHAPTER 3. MODELLING

• Overproduction will be used to charge the batteries when the batteries are not full. • The solar cells are placed in a direction towards the south. • Tilt angle will be optimised between 44-48◦ to minimise losses due to snow. • The solar PV system will be designed foremost for a rooftop or mounted on the ground close to the interlocking system.

More assumptions explicitly related to the technologies are presented further on in the report.

3.2 Energy for Railway System

In order to determine the energy demand a system requires, the power each object requires has to be known or can be calculated by using other factors. Non-disclosure values of the power were given from Alstom, which were the foundation for the whole created system. Through Equation 3.1 the energy demand can be calculated when the time usage of each object has been decided.

E = P t (3.1)

E is the energy needed in Wh, P is the power in W, and t is the time in hours (hrs) the object or device is utilised.

3.2.1 Electrical It is crucial to include cable losses in the total energy consumption in order to estimate an accurate energy need. Firstly, the cables included in the system are established and investigated, as the resistance varies depending on the cable area. The voltage drop is then calculated, where the drop is highly dependent on the cable length, the cable resistance and most importantly, the current. Most objects have a high and low current depending on if the object is in steady mode or transient mode. When the voltage drop is decided, the power drop for each object can be calculated, and this power drop depends on how long the object stays in steady mode, transient mode or sleep mode.

The resistance in cabling is calculated with Equation 3.2 (nkt cables AB, 2015).

ρc Rc = (3.2) Ac

2 2 ρc is the resistance in the material in Ωmm /m and Ac is the area of the cable in mm . This results in Rc being measured in Ω/m. However, these values are often given for each cable, but if not, this equation can be used. Equation 3.3 shows how the voltage drop is calculated in the different cables (nkt cables AB, 2015).

Udrop = RcLIN (3.3)

Udrop is the voltage drop, L is length in m, I is current in A and N is the amount of twisted cables in one. Equation 3.4 shows how to calculate the energy drop in the cable (Kirchev, 2015).

Edrop = UdropIt (3.4)

29 CHAPTER 3. MODELLING

Edrop is the energy drop in Wh, and as previously mentioned I is the current and t is the time in hours.

The summation of the different parameters and connections of the signalling system can be seen in Table 3.1.

Table 3.1: Electrical and grid connections of the signalling system.

Parameter Value Unit Single phase 230 V Frequency 50 Hz

Since the objects use AC or DC voltages, a converter/inverter or transformer will need to be installed. These devices are used daily to power computers, for example, which utilises DC voltages, unlike the residentials, which utilises AC voltages. Since most batteries run on DC, a power conversion system of some sort is needed to meet the AC demand. These conversion systems usually have an efficiency of between 93-95 % (Energimyndigheten, 2015). An inverter converts DC to AC, while converters do the opposite. As mention in Section 3.1, these kind of calculations will be simplified.

Table 3.2 shows four standard cables and their resistances which could be installed at the railway system. These will be investigated in this thesis for establishing the system’s losses.

Table 3.2: Cable resistance (Nexans, 2001).

Area [mm2] Resistance [Ω/km] 1.5 12.1 2.5 7.41 4.0 4.61 6.0 3.08

As seen above, the electrical resistance changes significantly with changes in area size. The smaller the cable, the higher the resistance. These values can also be seen in AppendixA .

3.3 Energy Storage

For the energy storage calculations, execution and modelling have been performed in Microsoft Excel.

In order to ensure that the quantity of energy is being delivered to the system, the battery capacity needs to be big enough to ensure a minimum of three days of autonomy reserve. Thus, being able to deliver enough energy to meet the demand even if the production is zero. The DoD is assumed to 80 % as not to exert the batteries too much.

For the upcoming modelling, lithium-ion has been chosen for the continuing work, while other storage options have been excluded. Batteries are chosen as the energy storage technology to

30 CHAPTER 3. MODELLING provide and ensure long-term energy storage or supply. Storage technologies that have been investigated throughout this thesis but excluded are presented in Table 3.3.

Table 3.3: Energy storage technologies not chosen.

Technology Compressed Air Storage Pumped-Storage Hydropower Thermal Energy Storage Redox-flow batteries Other batteries Flywheels Capacitors Future technologies

Compressed-Air Energy Storage (CAES) is a storage technology suitable for gas power plants since surplus power is used to compress air into storage units. These storage units are underground chambers, and only two commercial storage exist today. Since this technology is new to the market and mostly planned on being used for storing energy in utility sizes, this type of storage will not be used in this scope (Breeze, 2018b).

Pumped-Storage Hydropower (PSH) is another mechanical energy storage that utilises two water reservoirs at different elevations. When the water moves down from the higher reservoir (discharge), the water moves to a turbine which generates energy. Some energy created is then used to pump back water from the lower to the higher reservoir (recharge). PSH can be used in connection to other hydropower plants; and can be characterised as an open-loop if it is connected to a natural body of water. Characterisation of a closed-loop is where the reservoirs are disconnected, with a body outside of the PSH (U.S. Department of Energy, n.d.[c]). Since this technology is highly dependent on the closeness to water, it is excluded further on in this work.

The most common types of thermal energy storage are named latent heat and sensible heat storage, but there is a third type of storage called thermochemical. These thermal energy storage systems are, however, primarily used in industries or constructions and not in smaller configurations (L.F.Cabeza et al., 2014; The Energy Technology Systems Analysis Program and International Renewable Energy Agency, 2013), so these will not be investigated further.

Carnot batteries is an innovative technology that utilises thermal energy when storing through a resistive heater or a heat pump. Whenever the electrical production is high, this electricity is used to establish a temperature difference between the two environments inside the battery. The electric energy is used to move heat from a low-temperature reservoir to a high by using a heater or heat pump. In the discharge phase, the heat is moved from the high-temperature reservoir to the low, where the heat flow then powers a heat engine which converts it into electricity or the energy form desired. This technology is new and experimental, but some prototype exists around the world. The exact performance indicators are unclear, and because of the innovative classification of the technology, it is further excluded in this work (Dumont et al., 2020).

Redox-flow batteries is a technology with promising potential for battery energy storage applica-

31 CHAPTER 3. MODELLING tions. However, the flow batteries have an overall lower energy density than lithium-ion batteries, and few commercial implementations exist today. There have been extensive field testing for large-scale applications, which showed promising results of being adequate for many stationary applications. Recent advances in the development of battery components present considerable improvements and could be a possible future implementation for similar scopes. However, because of the lower energy density and scarce commercial implementations, this technology was excluded in this work (Skyllas-Kazacos and McCann, 2015).

Flywheels and capacitors are good solutions for peak power reduction; however, it is more important to provide the system with a longer storage time than what is possible with flywheels and capacitors. Hence, they are excluded from further investigations. New and future technologies will not be investigated further either, as the solution should be implemented in the near future.

Lithium-ion batteries can achieve storage efficiencies of almost 100 % and have the highest energy density compared to other batteries such as lead-acid, nickel-cadmium and nickel-metal-hybrid. Drawbacks of the technology are the high investment costs and some limitations in operational specifications. There are currently many ongoing R&D projects aiming to reduce the costs and decrease the limitations of the technology, thereby increasing its performance. The technology is mature and has been implemented in small-scale energy storage applications and is therefore chosen for further investigation in this work (Jiang et al., 2019; Nair and Garimella, 2010).

3.3.1 Design of battery storage system The process of designing a feasible energy storage system for the case study can be seen in Figure 3.2, together with its legend in Figure 3.1. This process has been created to guide other similar implementations in the future. Different shapes and colours have different meaning in this flowchart of the process. Green represents the start and the finish of the process, yellow stands for decision-making and grey for when calculations are needed. The light blue colour represents analysis steps, darker blue for steps of applications, white for the actual sizing and red for checking the system and calculations. The navy coloured shapes are whenever the steps require reviews and research of devices, technologies or manufacturers.

Rhomb shaped steps are whenever data is managed; oval shapes stand for procedures when environmental factors, limitations or research is needed. Rectangular shapes represent application processes or steps that will be directly correspondent to implementations. Diamond-shaped steps are decision and checks for continuing to the next steps. These are symbolic of necessary inquires for the process to establish the demand.

32 CHAPTER 3. MODELLING

Figure 3.1: Legend to the energy storage methodology.

Figure 3.2: The process of selecting a battery energy storage.

First, the energy demand of the system needed to be established. The energy required from

33 CHAPTER 3. MODELLING the battery system needed to be calculated with the safety factor, and the losses for the system included after the initial step. This process is necessary since there is a need for understanding the total requirement of the storage system. Hence, these values calculated were the minimum demand the battery storage system had to deliver. The next step was to decide how many days the system should go offline without any energy input. When this was established, the following process was to research and review potential manufacturers on the market and their available batteries with the potential of being sufficient for the system. Specification of the battery explains the optimal performance; depending on the system’s requirements, environment or clientele, some batteries were more suited for the specific application than others. Lithium-ion batteries are temperature sensitive, and some are adapted for installation outside or inside. There is usually a preferred DoD for each battery, and to ensure a longer lifetime, it is necessary to adapt the energy storage system to this factor. DoD will have an impact on the number of batteries needed, together with other vital inputs. If some limitations exist, whether it is implementation limits or environmental factors, there is a need to ensure that the measurements of the batteries could function for the desired application. After analysis and decisions, the number of batteries were calculated, and the next step was to establish the location of the storage system. Sizing the energy storage will give information on the installation area, and before applying the energy storage system, an extra check was required. If the stored energy is not sufficient to meet the demand, the process must be repeated until an energy storage system functions correctly with the requirements.

The total power of a battery, and the total power demand from the system, can be calculated through Equation 3.5 (Johansson et al., 2013; Kirchev, 2015).

P = UI (3.5)

Where P is the power demanded by the system in W, U is the voltage, and I is the current in A.

Capacity of the battery is calculated through Equation 3.5, but instead of using the current in A, there is a time variable as well. The equation for capacity therefore is similar as seen in Equation 3.6 (Johansson et al., 2013; Kirchev, 2015).

Eb = UCb (3.6)

Where Eb is the energy in Wh, U is the voltage and Cb is the capacity in Ah.

Total power for the battery is then calculated through Equation 3.7 (Ali et al., 2018; Asian Development Bank, 2018).

Pb = UIηef f (3.7)

Where Pb is the required battery power in W and ηef f is the efficiency for the battery mentioned above, including transmission losses and converter/inverter efficiencies. See Table 3.4 for specific values.

The capacity required from the batteries is calculated through Equation 3.8 (Ali et al., 2018; Asian Development Bank, 2018).

34 CHAPTER 3. MODELLING

Pb Cd = (3.8) Ub

Where Ub is the voltage from the battery, or in some cases, the voltages needed from the battery, and Cd is the capacity in Ah required from the batteries.

Equation 3.9 shows how to calculate the total battery capacity, including the autonomous days.

Ctot = CdN (3.9)

Ctot is the total battery capacity and N is amount of self-sufficient days.

Below, Equation 3.10 shows how to calculate the amount of batteries needed to meed the demand. C C N = tot b (3.10) b DoD

The number of batteries needed to meet the demand, Nb is calculated through Cd which is the capacity demand in Ah, Cb which is the capacity from the battery in Ah and DoD is the depth of discharge.

Table 3.4 shows the fixed input values that have been used for the energy storage modelling. These values are standard for many battery implementations and have therefore been used here.

Table 3.4: Fixed values for energy storage calculations.

Input Value Daily losses 2 % Converter efficiency 90 % Battery efficiency 90 % DoD 80 %

Some feasible batteries were chosen for further investigation in this work, and they are presented in Table 3.5.

35 CHAPTER 3. MODELLING

Table 3.5: Investigated batteries and their capacity.

Company Type Capacity Relion Relion RB300 300 Ah Shenzhen Topband Battery Co. Topband Lithium 12 V 200 Ah 200 Ah SUNBEAMsystem Smart LITHIUM One 100 Ah 100 Ah Victron Energy LFPSmart 12.8/300 300 Ah Victron Energy LFPSmart 25.6/200 200 Ah Tesla Powerwall 2 13.5 kWh sonnenBatterie Eco 9.43/5 5 kWh sonnenBatterie Eco 9.43/10 10 kWh LG Energy Solution RESU10 10 kWh LG Energy Solution RESU13 13.1 kWh

To create a thorough foundation for the BESS, different sizes, specifications, and battery manu- facturers have been investigated. The following section will manage a more thorough presentation of these and their specifications. In AppendixB the respective datasheets can be found.

Potential lithium-ion batteries There are several different batteries on the market today. Some are designed for certain purposes, while others are designed for more of a general application. Similar for all of them are the chemical construction of LiFePO4 and the possibility of recharging. For this scope, the batteries are assumed to be fully charged when installed in the feasible BESS. The entire BESS consists of one or several battery units (BUs).

The first battery is the Relion RB300 12V 300Ah Lithium Deep Cycle Battery. Table 3.6 presents some of the specification to be analysed further in the report (RELiON Batteries, 2021).

Table 3.6: Relion RB300 specifications (RELiON Batteries, 2021).

Parameter Value Nominal voltage 12.8 V Capacity 300 Ah Efficiency 99 % Discharge temperature -20 to 60 ◦C Charge temperature -20 to 45 ◦C Storage temperature -5 to 35 ◦C Cycle life @80 % DoD 7000 cycles Cost € 2930 Weight 34.36 kg Size 520x267x228 mm

The Relion battery is smaller in volume than the other batteries within this scope but still has a high capacity for its size. This battery is to be used indoors and will need heating during colder

36 CHAPTER 3. MODELLING days to properly function.

Another battery chosen for the scope is the Topband Lithium 12 V 200 Ah TB12200. Specification for this battery can be seen in Table 3.7.

Table 3.7: Topband Lithium 12 V 200 Ah TB12200 specifications (Batteriexpressen, n.d.; Shenzen Topband Battery Co., 2018).

Parameter Value Voltage 12 V Capacity 200 Ah Discharge temperature -20 to 60 ◦C Charge temperature 0 to 45 ◦C Storage temperature -20 to 60 ◦C Cycle life @100 % DoD 2000 cycles Cost € 1872 Weight 19.8 kg Size 485x170x24 mm

Shenzhen Topband Battery Co is a company that focuses on LiFePO4 batteries for different utilities. Their batteries range from supplying energy to forklifts, telecom, residential scale and to utility-scale. The company, and its batteries, is one of the leading manufacturers in China, but they are used all over the world (Shenzhen Topband Battery Co., 2019).

Smart LITHIUM One 100Ah from SUNBEAMsystem is the battery with the lowest capacity and smallest in volume studied within this scope. Specifications for the Smart LITHIUM One battery can be seen in Table 3.8.

Table 3.8: Smart LITHIUM One 100 Ah specifications. (SUNBEAMsystem, 2021a).

Parameter Value Voltage 12 V Capacity 100 Ah Cycle life @100 % DoD 2000 cycles Cost € 1690 Weight 14.5 kg Size 328x172x222 mm

SUNBEAMsystem foremost manufactures batteries for boat life and trailer usage. The batteries can be used for other applications and are easy to install but not created for cold climates. Ideally, these batteries are to be used indoors or in slightly warmer climates. Therefore will heating be needed during colder days if used in an energy system (SUNBEAMsystem, 2021b).

Two batteries of different voltages and capacities were chosen to be applied in the feasible energy storage system from the company Victron Energy. Table 3.9 presents specifications for the battery with the higher capacity of the two (Victron Energy, n.d.[b]).

37 CHAPTER 3. MODELLING

Table 3.9: LFP Smart 12,8/300 specifications (Victron Energy, n.d.[a]; Victron Energy, n.d.[c]).

Parameter Value Nominal voltage 12.8 V Nominal capacity 300 Ah Discharge temperature -20 to 50 ◦C Charge temperature 5 to 50 ◦C Storage temperature -45 to 70 ◦C Cycle life @80 % DoD 2500 cycles Cost € 3378 Weight 51 kg Size 347x425x274 mm

LFP Smart 12,8/300 battery is created for outdoor usage and weighs more than the previously presented. Victron Energy is a company that manufactures different electronics, all from solar modules to inverters and batteries. Thereby is it possible to design a complete system to fit a specific purpose at the same company. Presented in Table 3.10 are the specification for a battery with slightly less capacity but with higher voltages than the former from Victron Energy. Both batteries were chosen to investigate if a higher voltage would lead to a smaller sized energy storage system or if capacity has a greater effect on the sizing (Victron Energy, n.d.[a]; Victron Energy, n.d.[b]).

Table 3.10: LFP Smart 25,6/200 specifications (Victron Energy, n.d.[a]; Victron Energy, n.d.[c]).

Parameter Value Nominal voltage 25.6 V Nominal capacity 200 Ah Discharge temperature -20 to 50 ◦C Charge temperature 5 to 50 ◦C Storage temperature -45 to 70 ◦C Cycle life @80 % DoD 2500 cycles Cost € 4000 Weight 56 kg Size 317x631x208 mm

Tesla has been the forefront company in electric vehicles and has lately focused on batteries for other applications. For residential use, this includes the battery called Powerwall 2, which specifications can be seen in Table 3.11.

38 CHAPTER 3. MODELLING

Table 3.11: Tesla Powerwall 2 specifications (Marsh, 2020b; Tesla, 2018).

Parameter Value Nominal AC voltage 230 V Internal battery voltage DC 50 V Usable Energy 13.5 kWh Round trip efficiency 90 % Operating temperature -20 to 50 ◦C Warranty 10 years Cost € 13104 1 Weight 125 kg Size 1150x755x155 mm

The Powerwall 2 battery is the battery with the highest capacity, and it is possible for outdoor installation. Unfortunately, it is not possible for installation in northern parts of the world where, during winter, the temperature drops well below -20 ◦C. Depending on the temperature changes at the location, a heater could be needed. Alternatively, the battery could need ventilation or possibly shadowing for locations with higher temperatures or a high amount of solar radiance. The costs of the batteries are highly dependent on the different parts needed when adapting the battery to the system. Installation costs are also highly dependent on the fitters and the country’s access to parts and fitters available. Prices can, therefore, easily differ between € 7890 to € 12860 for a complete system installation (Marsh, 2020b).

Sonnen is a company which has specialised in home energy storage. The manufacturer designs different batteries called sonnenBatterie, and these come in different sizes and are often connected to solar PV systems. These batteries have the most extended lifetime but are foremost designed for indoor climates. Thus, will these batteries need to be installed in the kiosk where the cabinet is located. Table 3.12 and 3.13 shows the specifications for the Sonnen batteries investigated.

1Without installation costs € 6720.

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Table 3.12: sonnenBatterie eco 9.43/5 specifications (Electric Car Home Ltd, 2021; sonnen GmbH, 2018).

Parameter Value Nominal capacity 5 kWh Nominal discharge power 2.5 kW Nominal charge power 2.5 kW Depth of discharge 90 % Ambient temperature range -5 to 45 ◦C Lifetime 10 000 cycles Warranty 10 years Battery service life 20 years Costs € 7030 2 Weight 97 kg Size 1370x630x230 mm

In design, the smaller batteries from Sonnen are similar to the bigger ones. However, the capacity is 50 % higher, and it weighs and costs more. As seen in Table 3.13, the specifications for the two batteries are otherwise the same.

Table 3.13: sonnenBatterie eco 9.43/10 specifications (Electric Car Home Ltd, 2021; sonnen GmbH, 2018).

Parameter Value Nominal capacity 10 kWh Nominal discharge power 2.5 kW Nominal charge power 2.5 kW Depth of discharge 90 % Ambient temperature range -5 to 45 ◦C Lifetime 10 000 cycles Warranty 10 years Battery service life 20 years Costs € 9850 2 Weight 143 kg Size 1370x630x230 mm

Another company which focus has been directed towards home batteries are LG Energy Solutions. From the beginning, the company mainly focused on batteries for electric vehicles, but have during the past years developed batteries for other applications. Other applications include investments in grid supportive batteries in the U.S. to batteries for residential use (Jin, 2020; LG Energy Solution, n.d.). The battery models exist in many different sizes and can be combined with up to two units for more customised needs. Table 3.14 shows specifications on the first of the two batteries from LG.

2Installation costs and value added tax of 20% is included.

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Table 3.14: LG Chem RESU 10 specifications (LG Energy Solution, 2019; LG Energy Solution Europe GmbH, 2018; Marsh, 2020a).

Parameter Value Energy 10 kWh Capacity 189 Ah Nominal voltage 51.8 V Battery round-trip efficiency 95 % Operating temperature -10 to 50 ◦C Storage temperature -30 to 60 ◦C Expected lifetime @25 ◦C 10 years Costs € 10920 Weight 75 kg Size 1452x484x227 mm

These two batteries are designed to function outdoor. However, for the most optimal operation conditions, the ambient temperature should be between -10 to 50 ◦C. Higher or lower temperatures can affect the performance and lifetime of the battery. The preferred operating temperature does not have anything to do with the battery’s capacity but the construction. As seen in Table 3.15, the capacity is higher compared to the other version, but the temperature preferences are still the same. Complete system installation costs could vary between € 7810 to € 10690 (Marsh, 2020a).

Table 3.15: LG Chem RESU 13 specifications (LG Energy Solution, 2019; LG Energy Solution Europe GmbH, 2018; mg-solar-shop, 2021).

Parameter Value Energy 13.1 kWh Capacity 252 Ah Nominal voltage 51.8 V Battery round-trip efficiency 95 % Operating temperature -10 to 50 ◦C Storage temperature -30 to 60 ◦C Expected lifetime @25 ◦C 10 years Costs € 5860 3 Weight 99 kg Size 1452x626x227 mm

In total, ten different batteries with varying capacity, volume and specification will be implemented in the theoretical constructed case study. These batteries will also be the foundation of the energy storage calculations for the more generalised solution. Prediction of the installation costs are difficult since additional devices may be needed, and what exactly is already included in the battery itself sometimes is not mentioned. Therefore the costs of the batteries are slightly misleading and highly dependent on the application.

3Installation costs not included.

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3.4 Energy Harvest

The methodology for energy harvesting of the system is slightly different from previous sections. The modelling has been performed in different softwares depending on technologies and which software would be the most suitable to perform the analysis in. Both technologies included in the scope will be analysed separately and together in a combined hybrid system. Some technologies excluded in this thesis will briefly be presented in this section, followed by the most significant reasons why. These technologies are presented in Table 3.16.

Table 3.16: Energy harvesting technologies not chosen.

Technology Concentrated Solar Power Hydro Power Ocean Energy Piezoelectric Electromagnetic Hydrogen Fuel Cells/Fuel Cells Geothermal

Some energy harvesting technologies such as Concentrated Solar Power (CSP) are most commonly used for large-scale energy harvesting. Projects for small-scale have been implemented, but the results of those often have low overall efficiency. Since the railway system in this scope utilises energy below 100 kW, CSP will not be investigated further (Giovannelli, 2015; McCue, 2018). Moreover, does CSP often need close vicinity to water and good grid connections (Solar Energy Industries Association, 2021).

Hydro power can be designed for microscale (< 100 kW). However, since this type of energy harvesting needs close vicinity to water (U.S. Department of Energy, n.d.[d]), it will not be possible to implement this technology all around Sweden or at other locations. Therefore will it not be suitable for a generalised solution or for the case study.

Ocean energy includes wave energy, tidal energy, salinity gradient energy and ocean thermal energy conversion. Wave energy is a technique in which it is possible to generate electricity from waves. With tidal energy, the difference in water depth caused by tidewater is used to create electricity. Salinity gradient energy, uses different salt concentrations, for example, when an ocean and a river meet, and use this difference in salt concentration to create electricity. Lastly, ocean thermal energy conversion makes use of differences in temperature that appear when comparing deep water to the surface (International Renewable Energy Agency, 2019). These types of ocean energy have been excluded in further investigations as they are dependant on being close to the ocean and are hence not suitable for a generalised solution.

Both piezoelectric and electromagnetic harvesting technologies are relatively new to the market. These technologies provide independent sources of energy which can theoretically be used for specific applications within the railway system. The energy produced can power electronics which requires a low power input since the technologies produce in the range of 10 µW – 100 mW (Smilek et al., 2018). However, because of the low energy production, and as these technologies

42 CHAPTER 3. MODELLING are defined as new, they were excluded in this thesis work.

Fuel cells are foremost using hydrogen to produce energy, and this technology is relatively new on the market and very complex. Today, there are some difficulties regarding both storing and production of the fuel, making this technology unsuitable for this usage. Therefore was it left out in this thesis work.

Geothermal is an energy source that could be suitable if the signalling system is placed where there is a lot of geothermal activity. Therefore, this harvesting method would be hard to generalise and was chosen not to be investigated further.

The process of designing a feasible energy harvesting system can be seen in the Figure 3.4, the belonging legend can be seen in Figure 3.3. This process was implemented when designing the energy harvesting system in the case study.

Figure 3.3: Legend to the energy harvesting methodology.

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Figure 3.4: The process of selecting energy harvesting technologies.

The figure represents a simplified process where the data gatherings from literature studies were used. The shapes and colours represent similar steps as the energy storage process in this flowchart, and it is possible to combine those two processes. After establishing the demand

44 CHAPTER 3. MODELLING and load needed for the railway system, for which the energy harvesting technologies are to distribute to, environmental factors are studied to determine which technologies are feasible for implementation. After finding the theoretical harvesting technologies, some boundaries were set. These boundaries could represent installation limitations, ground coverage and economic factors. These steps were the first in order to find the technologies implementable for the system. Therefore, potential energy harvesting technologies are researched, and the next step was a more thorough weather data analysis. In this step, certain harvesting technologies found in the previous process ultimately became excluded because of weather limitations. Limitations could be temperature, radiance, wind speed or humidity, which influences the harvesting technologies. After analysing potential technologies, a more thorough application of weather data to the theoretically feasible technologies was applied. If the technologies were applicable, the following process was to research manufacturers who can contribute with specific technologies. This research was done in order to get more accurate calculations for a harvesting system. The findings then lead to an adaption of the weather data input to the devices, which resulted in power/energy production calculations. The aforementioned process was the number of devices needed before the actual sizing of the harvesting system began. When this was established, the total production was calculated before the decision of location was determined. This generates the process of sizing the energy harvesting system before a check was needed, where the production was compared to the demand of the applied harvesting system. When this was deemed sufficient for the energy need, the energy harvesting system was applied before analysed over time. Since renewable energy sources are volatile, an energy storage system is often needed, and the analysis over some time often shows whether this is needed. This step was the last before the energy harvesting system was deemed feasible for the application.

3.4.1 Software System Advisor Model For the modelling of solar PV, the System Advisor Model (SAM) software is used and can be applied for optimisation and for creating different energy harvesting systems. Moreover, can different conditions be simulated, as well as different scenarios. This software can help users determine whether the modelling meets their needs and help provide information about the probable energy harvesting system. SAM’s performance models are foremost used for PV systems with optional battery storage. However, they can also be used for CSP, industrial process heat, solar thermal with water heating, wind, geothermal, biomass and other conventional power systems. These systems can either be modelled to deliver electricity directly to the grid or modelled for self-usage to create an off-grid facility. SAM is an open-source project which means that the public can use the software and its coding. Researchers can also study the algorithm, and software programmers can contribute with their enhancement if desired (Blair et al., 2018; U.S. Department of Energy, 2020).

To model a renewable energy project, a performance model is chosen together with a financial one (if needed) to represent the specific potential project. Input values are then assigned to help provide any needed information about the project’s location, the type of equipment needed or chosen in the system, the cost of installing and operating as well as incentives and financial assumptions. Once the inputs meet the user’s satisfaction, simulations are run, and created results can then be examined. The results can be repeated multiple times until the system meets its needs or to examine what inputs can affect the results (Blair et al., 2018).

The performance model of SAM creates a time step, called step-by-time. It is used to calculate the created power system’s electric output and generate time-series data representing the electricity

45 CHAPTER 3. MODELLING production over a year. The simulation’s time steps are highly dependent on the resolution of the weather input in the weather data file. The size of the system can both be modelled to small residential applications or large utility-scale applications.

Different possible outputs, simulations and analysis in the software includes:

• Parametric Analysis • Stochastic Analysis • Probability of Exceedance Analysis • Excel Exchange • LK Script In the software, the assigned input values and variables are used to show different output metrics for each of the simulated ones, thereby contributing to a thorough theoretical application of an energy system (Blair et al., 2018; U.S. Department of Energy, 2020).

SAM has an extensive library of different solar modules and inverters from several manufacturers, which can be used for specific theoretical applications of solar harvesting. In module specifics, nominal efficiency, maximum power, maximum current and other characteristics are already contributed to the software. Other characteristics such as physical appearance and temperature of the cell are also included in the software, and it is, therefore, possible to get a correct picture of how the module reacts to other inputs (Blair et al., 2018; U.S. Department of Energy, 2020).

Inverter specifics are similar to that of the module specifics, and inputs such as the load, power ratings and losses exist within the software. It is also possible to design an inverter with specific characteristics to meet the demand of the solar module. Other system design inputs, such as the ground coverage ratio of the panels, subarrays, tilt angle and the number of inverters needed, can be simulated to the most optimal or chosen ones (Blair et al., 2018; U.S. Department of Energy, 2020).

This thesis has its main focus on the solar PV applications of this software, for a small-scale application. Different modules are investigated in the software together with different installation sizes.

3.4.2 Solar PV The manufacturers and models for the chosen solar PV modules can be seen in Table 3.17.

Table 3.17: Manufacturers for solar PV modules.

Manufacturer Module SunPower Corporation X22-360-C-AC LG Electronics LG350N1C-V5 Csun Solar Tech CSUN-380-72M REC Solar Holdings AS REC340TP3M

The chosen modules are those with the highest efficiency on the market and can be used for small-scale applications. The respective solar module’s datasheets can be found in AppendixC .

46 CHAPTER 3. MODELLING

For simulations in SAM, the modules’ installation area will be calculated together with the annual energy production. The one with the best energy production per installed area of the modules will be chosen for further analysis.

Fixed input parameters for SAM can be seen in Table 3.18, and are used for every simulation. These values are assumed after some established parameters, the software’s recommendations and assumptions based on the literature study.

Table 3.18: Fixed values for input in SAM.

Input Value Average wiring losses 2 % Transmission losses 2 % Degradation rate 0.3 % Width between module length 0.3 m Mounted South (180 ◦) Mounted Landscape Tilt angle 44◦

To calculate the area of the modules, trigonometry is used, as seen in Figure 3.5, where the Ground Coverage Ratio (GCR) is calculated. The calculations are performed to see how much ground is covered for the module installation.

Figure 3.5: A schematic of how GCR is calculated.

GCR is then calculated through Equation 3.11, which is shown below. w w L GCR = = = (3.11) r b + c (cos(θ)w) + c

Where w is the width of the landscape mounted module, r is the distance from the end of the module before the next, b is the ground covered by the module, and c is the distance in between the rows of the modules, and θ is the tilt angle of the module. All distances are measured in meters, m.

47 CHAPTER 3. MODELLING

The size specifications of each module are presented in Table 3.19.

Table 3.19: Size specifications for the modules.

Module Length [m] Width [m] SunPower Corporation X22-360-C-AC 1.559 1.046 LG Electronics LG350N1C-V5 1.683 1.016 CSUN Solar Tech CSUN-380-72M 1.956 0.992 REC Solar Holdings AS REC340TP3M 1.683 0.997

The chosen GCR, and the sizing of the module system, are optimised for the rest of the calculations. The optimisation aims to provide an adequate energy production to meet the demand for as many months as possible for an entire year.

3.4.3 Wind power During modelling of wind power, the calculations are conducted in the software Microsoft Excel. A wind analysis consisting of hourly wind speed data corresponding to the specific location is collected and performed.

Four different turbines have been chosen to further investigate and analyse throughout this work. Moreover, to broaden the scope, these specific turbines have been chosen from different manufactures. They can be seen in Table 3.20. The corresponding turbine datasheets can be seen in AppendixD .

Table 3.20: Chosen wind turbines.

Company Type Size Turbine area enair E30PRO 3 kW 11.3 m2 enair E70PRO 5 kW 14.5 m2 enair E200L 10 kW 75.4 m2 Halo Energy Halo 6kW 6 kW 10.8 m2

The power coefficient, or cp, for the turbines, are presented in the following figures. Rated power, maximum possible power, cut-in speed and cut-off speed is dependent on the turbine itself. As seen, the power coefficient curve differs, with Figure 3.6a having the lowest rated power, and Figure 3.7b having the highest. Rated power is the capacity at which the turbine will produce power when the wind is ideal. Figure 3.6b represents the power coefficient for the 5 kW turbine. The power coefficient for this turbine is higher than the 3 kW and lower than the 10 kW turbine.

Unlike the enair turbines, the power output for the 6 kW turbine from Halo Energy is presented in Figure 3.7a. These values are not as easily interpreted or applied for the different parameters that affect the real power output, such as the air density. Power coefficient is a measurement of the turbine efficiency, while the power output is the result of production.

48 CHAPTER 3. MODELLING

(a) Power coefficient for the 3 kW turbine. (b) Power coefficient for the 5 kW turbine.

Figure 3.6: Turbine specifications for the 3 kW and 5 kW turbines from Enair (ENAIR ENERGY S.L, 2021c; ENAIR ENERGY S.L, 2021d).

(a) Power output for the 6 kW turbine. (b) Power coefficient for the 10 kW turbine.

Figure 3.7: Turbine specifications for the 6 kW turbine (Halo Energy, n.d.) and 10 kW turbine (ENAIR ENERGY S.L, 2021b).

The surplus electrical energy that can not serve the load or charge the batteries is often either dumped or curtailed. Therefore, to avoid this, the turbines can be turned off during times of overproduction. Thus, will extra installations of grid connections, resistors or other applications not be needed. This phenomenon will be discussed and analysed further in the discussion but will not be presented in the results.

For installation safety, enair recommend a foundation of 3x3x3 meters for the turbines with tower heights between 16 to 22 meters (ENAIR ENERGY S.L, 2018). Distances between turbines are overall highly dependent on the wind speed and the turbulence created behind them. Distances vary between the lengths of the tower and less, with some installations being on top or next to buildings (ENAIR ENERGY S.L, 2021a) as seen in previous researched installations. Since the towers are built to withstand higher wind speed than the location presents, the distances to nearby objects should not be affected by the implementation. As the company have manufactured the turbines to create minimal sound, this should not affect the surrounding environment noticeably.

Weather data gathered for the wind power modelling are from 2020, which is the latest updates

49 CHAPTER 3. MODELLING from SMHI.

Equation 3.12 presents the mechanical power extracted from the wind, and it also shows the relation between the power output and the wind speed (Ackermann, 2005). 1 P = ρAc v3 (3.12) w 2 p w

3 2 Where Pw is the power in W extracted, ρ is the air density in kg/m , A is rotor area in m , cp is the performance coefficient or power coefficient, and vw is the wind speed in m/s at a certain height above ground (Ackermann, 2005).

Real wind power is highly dependant on various losses, so the actual power output through an energy converter is given by Equation 3.13 below. 1 P = c η η ρAv3 (3.13) w,real p m el 2 w

ηm is the mechanical efficiency, ηel is the electrical efficiency; both values are affected by the installation and the turbine performance.

Equation 3.14 shows how to calculate the wind speed at a specific hub height (Homer Energy, n.d.).

V ln zhub hub = z0 (3.14) za Va ln z0

Vhub is the wind speed at hub height in m/s, Va is the wind speed at the anemometer height in m/s, zhub is the hub height above ground in m, z0 is the surface roughness in m, and za is the anemometer height above ground in m. The surface roughness depends on the ground conditions.

Table 3.21 shows the fixed input values that have been used for the wind power modelling.

Table 3.21: Fixed values for wind power calculations.

Input Value Air density 1.24 kg/m3 Turbine height for 10 kW 24 m Turbine height for 3 kW, 5 kW, 6 kW 20 m Surface roughness 0.25 m Electrical efficiency 98 % Mechanical efficiency 98 %

The air density is interpolated from ”Sustainable energy utilisation” and the surface roughness estimated from Homer Energy (Havtun et al., 2018; Homer Energy, n.d.). These assumed values are used for each of the wind turbines and the following calculations.

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3.5 Sensitivity Analysis

Throughout identifying feasible technologies in the literature review, it was determined that they are highly dependent on the weather data and location. Thus, difficulties regarding creating a generalised solution were identified. Therefore, to understand the impact on the different input values, it was decided that the system was to be compared with other locations. Thereby fortifying that creating an energy system for a specific location and demand needs to be modified for each new implementation. Wind and solar radiance for two other locations were determined; Lisbon in Portugal and Copenhagen in Denmark. Lisbon has a higher feasible solar radiance, and Copenhagen has a higher feasible wind potential. These weather data inputs will be compared to the system designed for the case study at Tortuna and thereby strengthen the theory on how the location will influence the production and the developed energy system. Moreover, will different energy requirements, later on referred to as loads, be analysed to see how this changes the energy system. In addition to the energy demand used in the location-based calculations, four additional loads will be investigated as well to see how this change affects the battery usage and the number of inadequate days. Table 3.22 presents the cases constructed and investigated for the sensitivity analysis.

Table 3.22: Cases for the sensitivity analysis.

Sensitivity analysis cases Lisbon in Portugal Copenhagen in Denmark 90 % of load 95 % of load 100 % of load 105 % of load 110 % of load

The load profile used for the sensitivity analysis will be based on the same as for the railway system in the case study.

51 Chapter 4

Case Study

The technologies presented in the previous chapter will be further investigated and applied to create the necessary energy system to meet the requirements of the railway system. Moreover, a simplified version of the wayside objects and how they connect in the system will be presented. The case study’s identified energy storage and harvesting system is defined by literature studies and supplementary data by the cooperating companies.

The aim with the calculation model of the case study is to compare different energy solutions with an energy demand profile. The new railway system represents the future adaption of ERTMS and SWOC at a location with few inhabitants close-by, and the calculations are mostly carried out in Microsoft Excel. The analysis will be conducted for a year with the latest weather data input for wind and solar radiance.

Since the weather station at V¨aster˚asis no longer in use, the wind data used for this scope will be collected from the nearby weather station at Eskilstuna. As the monthly average wind speeds for Eskilstuna are more similar to those at V¨aster˚as,compared to the other close city Enk¨oping,Eskilstuna was the more obvious choice of location (Windfinder, 2021a; Windfinder, 2021b; Windfinder, 2021c). These values are used to better view how the energy harvesting system acts during each hour of the day. Figure 4.1 shows the hourly wind speed for 2020 at the weather station in Eskilstuna since that year’s values are the latest data available.

52 CHAPTER 4. CASE STUDY

Figure 4.1: Hourly wind speed data at Eskilstuna (SMHI, Sveriges meteorologiska och hydrologiska institut, 2020).

During the summer season, the wind speeds are lower, as seen in the figure. Overall the wind varies greatly and shows that the power production will differ from day to day.

Solar radiance for V¨aster˚as during a year is presented in Figure 4.2, and these values are from the year 2019 since this is the latest data available.

Figure 4.2: Solar radiance at V¨aster˚as(National Renewable Energy Laboratory, n.d.).

Configurations of the energy harvesting installations investigated for the case study will be the following, as presented in Table 4.1 and Table 4.2.

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Table 4.1: Investigated single energy harvesting installation designs.

Single harvesting technologies 3 kW turbine 5 kW turbine 6 kW turbine 10 kW turbine 2x3 kW turbines 2x5 kW turbines 2x6 kW turbines 2x10 kW turbines Solar PV

Table 4.2: Investigated combined energy harvesting installation designs.

Combined harvesting technologies 3 kW turbine + PV 5 kW turbine + PV 6 kW turbine + PV 10 kW turbine + PV 2x3 kW turbines + PV 2x5 kW turbines + PV 2x6 kW turbines + PV 2x10 kW turbines + PV

A BESS installation will complement the investigated energy harvesting design, and this installa- tion will compose of one or more BUs, depending on the energy requirements.

4.1 Tortuna

The location for this thesis work is placed outside of V¨aster˚as,in Tortuna; see Figure 4.3 for a more specific site. This figure is taken from Google Maps.

Figure 4.3: Location of Tortuna.

54 CHAPTER 4. CASE STUDY

Figure 4.4 shows all objects included in the system existing today, the so-called conventional system, with the power cable connected. The current tracks are not in use and are therefore not updated; hence the cables are older than the rest of the soon to be updated system. In this conventional system, the objects can not be turned off; hence there will be a constant steady current running through the cables even though the objects are not in use.

Figure 4.4: Schematic picture of the system today with the power cable.

In the conventional system, the communication between the objects, interlocking and control centres are via radio communication and cabling. By installing SWOC, the communication foremost befall through wireless connections and thereby increasing data transfer between the devices. The flow of information will, therefore, be more continuous and reliable.

Figure 4.5 shows how the system could look like in the future if a reasonable solution can be found. The power cable is removed, and the power supply instead comes from renewable sources connected to a suitable storage.

Figure 4.5: Schematic picture of the future system without the power cable.

The cabling from the interlocking system will be the same between the wayside objects as before the implementation of ERTMS and SWOC. Added will be the cabling needed for the new feasible harvesting technologies and energy storage system, and the system will rely more on wireless communication.

55 CHAPTER 4. CASE STUDY

The main difference between the conventional and new railway system is the implementation of SWOC. When implemented, wayside objects can be put in sleep mode, resulting in better energy usage. Moreover, cabling can be reduced because of the wireless operations and communications, leading to a more cost-effective system.

4.2 SWOC and Wayside Objects

The representation of the railway system will consist of a certain number of objects installed. Since this is a fictional system, the following assumptions and data are compiled from data gathering on other locations or the literature. Since there exists a number of wayside objects, this thesis will be focused on the most common and model the following objects:

– Point machines – Optical signals – Level crossing – Interlocking system

The number of these depend on the railway system’s location and how the tracks and objects are connected. Tortuna is located in an area with few buildings and lower traffic volume, which is planned to become a test site for the ERTMS and possible sustainable energy management. The total energy demand of objects will be the foundation on which energy storage and energy harvesting technologies will be most suited. By the literature and data gathering findings, the objects with the highest power demand are point machines, while optical signals demand the least. Data received from Alstom will be used to model the different objects and establish the energy demand of the railway system. These numbers are confidential, as previously mentioned, and will not be presented in the results.

SWOC will be used to control and communicate between the objects and supply them with their energy need. Thereby, will it be acting as a management and power system for the wayside objects and the implemented energy system. The fictional railway system at Tortuna consists of the following wayside objects:

• Point machines: 2, one at each track change. • Optical signals: 6, diverted at different locations. • Level crossing: 1 crossing which is connected to bells and optical signals. • Interlocking system: 2 cabinets installed in one small building.

Figure 4.6 shows a schematic of the Tortuna railway system. This figure is not in scale, so the distances and location could differ from reality. It is yet to decide if both the cabinets are located within one building or two, but for this case study, it will be assumed to be located at the building which already exists at the location, according to the Eniro map.

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Figure 4.6: A schematic of Tortuna railway system.

One part of the railway consists of double tracks, and these are represented in the figure. Before and after this location, the line consists of single tracks where the trains can not pass each other. In the green bigger circle, the signals called SIG are located, facing left. The smaller black circles are the two point machines, P1 and P2. The level crossing is presented by the ellipse-shaped circle in the middle, with each optical signal facing the theoretical road directions. Cabinet 1 and Cabinet 2, the interlocking system, is represented by the black squares in the top, and in the big red circle are the optical signals called SIG-E1. These signals are facing right and are foremost for the trains arriving from that direction.

4.2.1 Signalling system In order to calculate and create an energy system for which the signalling system can work, it is important to know the existing connections to the electricity grid. This information includes the nominal current, phase connections and cables. Table 4.3, 4.4, 4.5 and 4.6 presents the four objects the scope focuses on and their electrical specifications. The lower current in each table represents the steady current, while the higher represents the transient current. Transient current meaning the current needed for the start-up of the objects when they are activated.

Table 4.3: Given values for level crossings.

Level crossing - 230 V Nominal current Time 5 A 20 s 7.2 A 250 ms

The level crossing and point machines are the devices that utilise the most energy and have a higher load profile. They are both driven with 230 V AC, which is the same as the feed from the electricity grid.

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Table 4.4: Given values for point machines.

Point Machine - 230 V Nominal current Time 5.2 A 20 s 26 A 250 ms

Optical signals, as seen in Table 4.5 uses 110 V AC, and depending on the signal, different currents are needed. In the current system, the signals are always active, and the energy is always in use, whether or not a train is passing.

Table 4.5: Given values for optical signals.

Optical signals - 110 V Nominal current Signal type 1.4 A Stop 6.2 A Proceed

Some internal cabinet logic is always active because of safety and communication reasons, called sleep mode. Certain applications are turned on when the object connected is in use, which leads to peak energy usage. Steady current is when the system is on, but the connected objects are not activated. The interlocking system is driven by the internal cabinet logic and is necessary to keep the railway system functioning properly. Table 4.6 shows the currents needed for the different electrical loads.

Table 4.6: Given values for internal cabinet logic.

Internal cabinet logic - 24 V Nominal current Electrical load 6.2 A Steady 7.7 A Peak 2.7 A Sleep

The cabling differs depending on location but is somewhere between 1.5-4.0 mm2. The 6.0 mm2 cable is only used for comparison and to broaden the scope. The cables will be assumed to be of copper material to achieve the necessary current. By using Eniro maps, the distances for the cables have been assumed to the following lengths presented in Table 4.7.

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Table 4.7: Cable lengths for each object.

Object Length Level crossing 450 m Point machine 1 120 m Point machine 2 650 m Signal E-1 1 220 m Signal E-1 2 300 m Signal E-1 3 300 m Signal 1 750 m Signal 2 600 m Signal 3 600 m

These are distances from the small building existing at the location where the cabinets and interlocking system could be installed according to Alstom.

4.2.2 Cases Different traffic situations can appear on the tracks; these can be found in the bullet-point list below.

• Two meeting trains followed by two meeting trains. • Two meeting trains followed by one single train. • One single train followed by one single train. • One single train followed by two meeting trains.

These different occupations on the tracks are divided into four different cases for easier application and conversion to the schedule. The cases are presented below.

• Case 1: Two meeting trains with the first arriving from the right. • Case 2: Two meeting trains with the first arriving from the left. • Case 3: Single train arriving from the right. • Case 4: Single train arriving from the left. For instance, by adding Case 1 and Case 2 together, they are summed up to two meeting trains followed by two meeting trains, as the first occurring situation described previously. So, different configurations of these cases are summarised and merged into the occurring situations, further on named scenarios. These scenarios are examples of the traffic volume that can appear in one day. Below are the scenarios listed and what cases they consist of.

• Scenario 1: – Case 1 + Case 2 – Case 1 + Case 4 – Case 2 + Case 3 – Case 3 + Case 4 • Scenario 2: – Case 1 + Case 2

59 CHAPTER 4. CASE STUDY

– Case 2 + Case 3 – Case 1 + Case 4 • Scenario 3: – Case 1 + Case 2 – Case 2 + Case 1 – Case 2 + Case 3 – Case 1 + Case 4 For instance, Scenario 1 firstly has two meeting trains, with the first from the right and second from the left, followed by two meeting trains, with the first from the left and the second from the right. In this scenario, this is followed by two meeting trains, first from the right, followed by a train from the left, and one single train from the left. Thirdly, two meeting trains with the first from the left are followed by a single train from the right. Lastly is one single train from the right followed by a single train from the left. Scenario 1 has 12 trains a day, Scenario 2 has ten trains a day, and Scenario 3 has 14 trains a day. The energy demand for Scenario 3 is the highest; thus, the energy demand from this scenario is used for the upcoming calculations. The scenarios consist of different cases determined beforehand, while the order they appear in was randomised in Excel. The train schedule, where the cases are used, will be presented in the section below.

4.2.3 Train schedule As these tracks are not in use today, a train schedule is non-existent. Thereby is a hypothetical schedule designed for the case study location Tortuna. Below are some assumptions that have been made in order to create this.

• When the switch has been moved, it is reset after the trains have passed. • Stop signals occur in each direction right before the level crossing. • Every time a train passes, the barriers go down. • Equal amount of trains in each direction. • When one single train passes on the tracks, the signals in the opposite direction are off. • At operation, when the corresponding objects for the cabinet are turned on, results in a power peak with a 25% margin for the cabinet. • Two trains can not drive simultaneously on the tracks, even in the opposite direction on the different tracks; hence a stop is included in each direction. • The objects are always running on peak power when operating. Figure 4.7 shows Case 2 and how the different objects are turned on. The different names of the objects can be related to Figure 4.6.

60 CHAPTER 4. CASE STUDY

Figure 4.7: A hypothetical time schedule for Case 2, two meeting trains.

The first train arrives from the left in this schedule, leading to the objects in that direction being turned on first. SIG 1 and SIG 2 are first going from offline mode to proceed mode. These are followed by the level crossing barriers, and lights are being turned on so the train can move along the tracks without pedestrians or cars being in the way. As the first train arrives on the double tracks, SIG 2 is turned from proceeding to stop, at the same time SIG-E1 1 goes from offline mode to proceed mode. Just before the second train arrives on the double tracks, SIG-E1 3 is set to proceed, and switch 1 is turned on to lead the second train to the other parallel track so that the trains do not collide. A short moment later SIG-E1 3 is turned from proceeding to stop. In a short sequence, both trains stand still on the tracks, as they can not be driving at the same time. Shortly before SIG 2 is set in proceed mode again, switch 1 is turned back to its original position so train 1 can leave the area. When train 1 has left the area, train 2 can move along, hence SIG-E1 3 is set to proceed and switch 2 is turned on to get in position for train 2 to leave the area. A little while after train 2 has left the area, level crossing barriers and lights are turned off, and at the same time, switch 2 is turned back to its original position. The internal cabinet logic, shown in yellow, is turned on whenever the corresponding object is turned on. The time schedule for the other three cases can be found in AppendixE .

61 Chapter 5

Results and Discussion

5.1 Results

The following chapter presents this work’s results and is based on the modelling and case study described in previous chapters. Specific values will not be presented because of confidentiality.

5.1.1 Case study Results specifically for the case study are presented in this section with data input for the location and data gathering from Alstom.

New wayside system compared to the conventional A comparison between the new system, with energy smart solutions, and the conventional system can be found below. Table 5.1 indicates how much energy that can be saved by using the new system instead of the conventional one. Table 5.2 shows the reduction in cable losses that is possible when going from a conventional system to a more energy intelligent system, which means that the new system will have fewer losses compared to the former system.

Table 5.1: Energy usage reduction for the new system compared to the conventional system.

Cable 1.5 mm2 Cable 2.5 mm2 Cable 4.0 mm2 Cable 6.0 mm2 94.8 % 94.9 % 94.8 % 94.8 %

Table 5.2: Reduction of cable losses for the new system compared to the conventional system.

Cable 1.5 mm2 Cable 2.5 mm2 Cable 4.0 mm2 Cable 6.0 mm2 95.4 % 96.3 % 96.3 % 96.2 %

As shown in the tables, the new system required less energy than the old system, resulting in a significant decrease in cable losses. Nevertheless, even though the cable sizes were different, the reduction for all was similar.

62 CHAPTER 5. RESULTS AND DISCUSSION

Cable losses Table 5.3 and 5.4 presents the energy drop in percentage for all investigated cables and for each case. These values represent the energy loss during the electricity distribution between the objects, referred to as cable losses.

Table 5.3: Cable losses for cable 1.5 and 2.5 mm2, with their low and high current.

Cable 1.5 mm2 Cable 2.5 mm2 Case Low current High current Low current High current Case 1 26 % 55 % 5.3 % 42 % Case 2 26 % 55 % 5.3 % 42 % Case 3 30 % 33 % 2.0 % 24 % Case 4 30 % 58 % 2.5 % 45 %

Table 5.4: Cable losses for cable 4.0 and 6.0 mm2, with their low and high current.

Cable 4.0 mm2 Cable 6.0 mm2 Case Low current High current Low current High current Case 1 3.4 % 31 % 2.3 % 23 % Case 2 3.4 % 31 % 2.3 % 23 % Case 3 1.3 % 16 % 0.9 % 11 % Case 4 1.6 % 34 % 1.1 % 26 %

The system had different demands depending on if steady or transient power were used. Low values are for when the steady power is utilised, and high represents peak power situations for when the transient power was turned on. Bigger cables resulted in a decreasing loss, which is correspondent to the literature review findings.

The total cable losses for all scenarios are presented in Table 5.5.

Table 5.5: Total cable losses.

Scenario Cable 1.5 mm2 Cable 2.5 mm2 Cable 4.0 mm2 Cable 6.0 mm2 Scenario 1 25 % 12 % 6.3 % 5.0 % Scenario 2 21 % 10 % 6.5 % 4.4 % Scenario 3 28 % 14 % 8.6 % 5.9 %

As presented, the losses for the 4.0 mm2 resulted in losses below 10 %. The remaining calculations and simulations were based on the data retrieved from this cable configuration. Hence, will the 4.0 mm2 be used for the system since the 6.0 mm2 cable was researched only to broaden the scope.

Battery storage From Table 3.6 - 3.15 the best battery was chosen to be Tesla Powerwall 2. The predominant factors for this choice were that the battery could be kept outside, has a large operating temperature

63 CHAPTER 5. RESULTS AND DISCUSSION span, high real power for charge/discharge, high capacity, and a possibility of connecting many units. Calculations based on the demand confirmed that the BESS consisting of this battery could be smaller than the other. Therefore will from now on, the abbreviation BU in calculations and results be the Tesla battery.

Solar PV The Solar PV installation of the different modules can be seen in the Tables 5.6, 5.7, 5.8 and 5.9. Where the GCR, the total installation area, annual energy production and the energy production per installed square meter is presented.

Table 5.6: Results of the SunPower installation.

SunPower Corporation X22-36-C-AC GCR Total area [m2] Annual Energy Production [kWh] [kWh/m2] 0.8 10.82 1373 126.9 0.7 12.09 1430 118.3 0.6 13.80 1483 107.5 0.5 16.18 1526 94.32 0.4 19.75 1557 78.82 0.3 25.71 1582 61.53 0.2 37.63 1601 42.55 0.1 73.38 1615 22.01

With a GCR of 0.8, the smallest installation area and the most significant annual energy production per square meter instalment was achieved.

Table 5.7: Results of the LG installation.

LG Electronics LG350N1C-V5 GCR Total area [m2] Annual Energy Production [kWh] [kWh/m2] 0.8 15.95 1820 114.1 0.7 17.95 1898 105.8 0.6 20.61 1964 95.28 0.5 24.34 2015 82.77 0.4 29.94 2051 68.50 0.3 39.27 2084 53.07 0.2 57.92 2109 36.41 0.1 113.9 2126 18.67

LG’s modules had the second smallest installation area, and the GCR of 0.8 resulted in the most prominent energy production per square meter.

64 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.8: Results of the CSUN solar installation.

Csun Solar Tech CSUN-380-72M GCR Total area [m2] Annual Energy Production [kWh] [kWh/m2] 0.8 17.86 1965 110.0 0.7 20.10 2047 101.8 0.6 23.09 2119 91.79 0.5 27.26 2173 79.70 0.4 33.53 2212 65.97 0.3 43.98 2247 51.09 0.2 64.87 2274 35.05 0.1 127.5 2293 17.98

This implementation resulted in the highest production annually of all the modules and the GCR of 0.8 induced in the most extensive installation area. The production per square meter was, therefore, the lowest out of the investigated solar modules.

Table 5.9: Results of the REC solar installation.

REC Solar Holdings AS REC340TP3M GCR Total area [m2] Annual Energy Production [kWh] [kWh/m2] 0.8 15.63 1772 113.4 0.7 17.58 1847 105.0 0.6 20.20 1912 94.68 0.5 23.85 1962 82.26 0.4 29.33 1997 68.08 0.3 38.47 2028 52.72 0.2 56.75 2053 36.18 0.1 111.57 2070 18.55

As with the other modules, the GCR of 0.8 resulted in the highest production per square meter but had a lower value than LG’s and SunPower’s modules. The simulations resulted in SunPower being the best of the chosen modules with the highest system production with the smallest area. This module was used for the feasible implementation of an energy harvesting system for the case study. The total number of modules, rows and modules per row is presented in Table 5.10. It shows that SunPower needs a lower amount of modules than the others to reach a higher feasible production.

Table 5.10: Installation set up of the modules.

Module Per row Rows Total SunPower Corporation X22-360-C-AC 2 3 6 LG Electronics LG350N1C-V5 2 4 8 Csun Solar Tech CSUN-380-72M 2 4 8 REC Solar Holdings AS REC340TP3M 2 4 8

65 CHAPTER 5. RESULTS AND DISCUSSION

Modules per row in this table signifies how many modules are installed next to each other, and rows represent how many parallel rows the installation needs. This table does not explain how the modules are installed and connected electrically in terms of strings and arrays. Because of electrical limitations between solar cells and modules, these configurations were the smallest possible for each module to function correctly.

Figure 5.1 shows how much the solar panels could produce with the set conditions per month, compared to the monthly energy the system requires. The y-axis has been removed from the results because of confidentiality.

Figure 5.1: Solar PV production.

The energy production for the solar PV system was enough to meet the demand during the summer months but not sufficient for the colder months. This PV implementation fulfilled the demand during slightly more than 40 % of the year’s months. However, this does not mean that the daily demand was met during these months. Since the production fluctuates day-to-day, a more thorough investigation of the daily demand was conducted, seen in the upcoming figures.

Implementation of two BUs Figure 5.2a and 5.2b show monthly data of how many days the installed BESS, with two BUs, was used as well as how many days the battery system was inadequate and could not cover the energy need. The capacity of one battery unit was based on the chosen battery specifications, efficiencies, losses, the demand of the railway system and the number of autonomous days.

66 CHAPTER 5. RESULTS AND DISCUSSION

(a) Days of usage. (b) Days of inadequate storage system.

Figure 5.2: Function of the BESS with two BUs.

As can be seen above, in Figure 5.2a, the BUs were used frequently over the year, with a slightly higher usage during the colder months. During January, February, March, October, November and December, the BESS had to be used every day as the production from solar PV is not high enough. Figure 5.2b shows that from April to August, the BESS was sufficient, while it was undersized during the other months.

Implementation of three BU Figure 5.3a and 5.3b shows monthly data of how many days the installed battery system, with three BUs, was used and the number of days when the BESS was inadequate and could not cover the energy need.

(a) Days of usage. (b) Days of inadequate storage system.

Figure 5.3: Function of the BESS with three BUs.

Figure 5.3a shows a similar result as for the two BUs, but now with a higher battery usage during April, due to longer time re-charging the batteries. In Figure 5.3b it is clear that the BESS worked for a larger part of the year. The entire month of September was now well-sized; an improvement for January and October have also happened.

In Table 5.11 a yearly summation of the battery usage and amount of inadequate days can be found.

67 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.11: Amount of days the battery system is used or inadequate during the year.

2 BUs 3 BUs Installation design Days of usage Inadequate days Days of usage Inadequate days Solar PV configuration 291 161 293 158

The battery usage became a bit higher with more batteries because of longer charging time, which is correspondent with bigger BESS. A more extensive energy storage system will increase utilisation since the storage system requires more energy. As seen in the table, over 40 % of the year’s days did not meet the demand from the railway system with the larger battery system. The difference between the larger and smaller battery systems in terms of inadequate days was minor and befell during the darker winter and autumn season in Sweden.

Wind power Production of the wind turbines can be seen in Table 5.12 and 5.13, and these values presented are the yearly energy production for the configurations.

Table 5.12: Energy production for single wind power turbines.

Installation design Energy Production [MWh] 3 kW turbine 2.59 5 kW turbine 4.01 6 kW turbine 4.34 10 kW turbine 18.4

Table 5.13: Energy production for double wind power turbines.

Installation design Energy Production [MWh] 2x3 kW turbines 5.17 2x5 kW turbines 8.02 2x6 kW turbines 8.68 2x10 kW turbines 36.9

The results presented in the table shows that the larger turbine implementation produced the most during the entire year, and the smallest installation of a 3 kW turbine produced about 7 % of the 2x10 kW turbines. Production during an entire year was enough to meet the total energy requirements for all different turbines, but further calculations were necessary to determine if the configurations reached the requirements. Energy production per month compared to the energy requirements will be presented in the upcoming figures.

Figure 5.4a to 5.7b indicates the monthly energy production for each wind turbine configuration, compared to the monthly energy need. The y-axis has been removed from the results because of confidentiality for the eight figures below. The scaling on the axis is the same for one 3 kW turbine as for two 3 kW turbines for easier comparison.

68 CHAPTER 5. RESULTS AND DISCUSSION

(a) Energy production with a single turbine. (b) Energy production with double turbines.

Figure 5.4: Energy production managed by the 3 kW turbine.

Above, in Figure 5.4a, it is apparent that the energy need was higher than the energy output for May, June, August, October and December for one 3 kW turbine. In Figure 5.4b, for two 3 kW turbines, the energy production covered the demand for every month of the year besides August and December but showed a large over-production during the majority of the year.

The energy production, compared with the energy demand, for the 5 kW installations are presented in Figure 5.5a and 5.5b.

(a) Energy production with a single turbine. (b) Energy production with double turbines.

Figure 5.5: Energy production managed by the 5 kW turbine.

For only one 5 kW turbine, the energy production for every month did not meet the demand. However, the configuration of two 5 kW turbines presented a result where the production meets the requirements for each month. Both configurations presented a result in which they were overproducing many months of the year.

Figure 5.6a and 5.6b presents energy production compared to energy need for the installations including the 6 kW turbine.

69 CHAPTER 5. RESULTS AND DISCUSSION

(a) Energy production with a single turbine. (b) Energy production with double turbines.

Figure 5.6: Energy production managed by the 6 kW turbine.

These results demonstrate that implementing a single 6 kW turbine was insufficient to meet the energy demand over the entire year. However, installing an extra turbine was sufficient to meet the railway system’s monthly need. Overproduction compared to the demand still occurred most of the months, and further analysis of daily production was required before establishing a conclusion.

Figure 5.7a and 5.7b shows the result for the configurations with the 10 kW turbine.

(a) Energy production with a single turbine. (b) Energy production with double turbines.

Figure 5.7: Energy production managed by the 10 kW turbine.

For the single 10 kW turbine, as shown in Figure 5.7a, energy production met the demand for every month. The configuration of two 10 kW turbines also met the railway system’s monthly requirements, but further analysis on the daily production was required to establish its feasibility. However, this does not mean that the daily demand was met during these months. Since the production fluctuates day-to-day, a more thorough investigation of the daily demand was conducted, seen in the upcoming figures.

Implementation of two BUs For the upcoming results, batteries have been implemented to the configurations. In Figure 5.8a

70 CHAPTER 5. RESULTS AND DISCUSSION and 5.8b monthly data of how many days the batteries were used per wind turbine installation are presented.

(a) Single turbine installation. (b) Double turbine installation.

Figure 5.8: Days of usage for the BESS with two BUs.

As seen in the figures, the month of December had the highest usage of the energy storage system. During the summer months and during October, the production was low, hence a higher utilisation of BESS. The summer season, October and December, were the most critical periods for this configuration and installation of two wind turbines resulting in a reduction of days when the energy storage was in need. The critical months of the BESS remained the same, despite a decrease in battery use.

Figure 5.9a and 5.9b shows monthly data of how many days the installed BESS is inadequate for the energy demand.

(a) Single turbine installation. (b) Double turbine installation.

Figure 5.9: Amount of inadequate days with two BUs.

Inadequate days befell throughout the year for a single turbine installation, with the most critical configuration being the smallest 3 kW. The most critical month for every turbine was December, when the power production was at its lowest. By installing two turbines at the location, the BESS was enough for the 2x10 kW implementation. For the rest, the production generated did not meet the requirements.

71 CHAPTER 5. RESULTS AND DISCUSSION

Implementation of three BUs The results after implementation of an extra battery for the configurations are presented in Figure 5.10a and 5.10b.

(a) Single turbine installation. (b) Double turbine installation.

Figure 5.10: Days of usage for the BESS with three BUs.

For single turbine installations, and energy storage of three BUs showed a similar utilisation pattern throughout the year compared to that of the minor storage installation. A similar result was shown for the double turbine installations, with the utilisation pattern being correspondent to that of the two BUs implementation.

Figure 5.11a and 5.11b presents the months of which the extra implemented battery still is inadequate together with production to meet the demand.

(a) Single turbine installation. (b) Double turbine installation.

Figure 5.11: Amount of inadequate days with three BUs.

For the single turbine configuration with three batteries, the most critical months were during the summer and December. The only configuration large enough to meet the demand was a 10 kW turbine combined with the battery storage. For double turbine configurations, the results showed that more months could meet the demand, and the critical month still being December. November was the second most critical month, with the 2x10 kW turbine installation being the only configuration working.

72 CHAPTER 5. RESULTS AND DISCUSSION

Below, presented in Table 5.14 and 5.15 are a summation for all investigated wind turbine installations, with their amount of battery usage and amount of days the BESS was inadequate and did not provide enough energy.

Table 5.14: Battery usage for single wind power installations.

2 BUs 3 BUs Installation design Days of usage Inadequate days Days of usage Inadequate days 3 kW turbine 269 138 277 124 5 kW turbine 246 97 251 71 6 kW turbine 232 50 234 33 10 kW turbine 72 3 72 0

Table 5.15: Battery usage for double wind power installations.

2 BUs 3 BUs Installation design Days of usage Inadequate days Days of usage Inadequate days 2x3 kW turbines 226 68 230 40 2x5 kW turbines 183 32 187 16 2x6 kW turbines 138 10 140 5 2x10 kW turbines 37 0 37 0

The only configurations that worked during the entire year were the 10 kW turbine implementations. For a BESS with two BUs, the 2x10 kW turbines were adequate to meet the demand for every day. Another result was that a single 10 kW turbine was sufficient when installing an extra BU. The smaller turbine installations with the BESS applied was inadequate for everyday demand.

Solar PV and wind power combination Below, Table 5.16 and 5.17 shows the yearly energy production when combining different wind turbine installations with solar PV.

Table 5.16: Energy production for combinations of single wind power turbines and solar PV.

Installation design Energy Production [MWh] 3 kW turbine + PV 3.97 5 kW turbine + PV 5.34 6 kW turbine + PV 5.72 10 kW turbine + PV 19.8

73 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.17: Energy production for combinations of double wind power turbines and solar PV.

Installation design Energy Production [MWh] 2x3 kW turbines + PV 6.55 2x5 kW turbines + PV 9.40 2x6 kW turbines + PV 10.0 2x10 kW turbines + PV 38.3

As can be seen, the highest output was for 2x10 kW turbines and the lowest for the single 3 kW turbine. The energy production presented is the total annual, and for concluding the feasibility of the configurations, further analysis was needed. The monthly energy production for a single 3 kW turbine and two 3 kW turbines combined with the solar PV installation are presented in Figure 5.12a and 5.12b.

(a) Single turbine and solar PV installation. (b) Double turbines and solar PV installation.

Figure 5.12: Energy production with the 3 kW turbine and solar PV.

The results showed that the monthly energy need was met every month except for December for both the single and double combined wind turbine configuration. Implementation of solar PV to the system resulted in a decrease of critical months since the production was at its peak during the summer season.

In Figure 5.13a and 5.13b, monthly energy production for one 5 kW turbine and two 5 kW turbines together with solar PV are presented.

74 CHAPTER 5. RESULTS AND DISCUSSION

(a) Single turbine and solar PV installation. (b) Double turbines and solar PV installation.

Figure 5.13: Energy production with the 5 kW turbine and solar PV.

The energy need in December was not met for the single 5 kW turbine combined with solar PV. However, with two 5 kW turbines combined with solar PV, the monthly energy need was met during the entire year. Further analysis was needed to determine whether the production would be adequate for every day of the year; in order to establish if this solution was feasible for the railway system.

Figure 5.14a and 5.14b presents the monthly energy production compared to the monthly energy need for the combined wind and solar configurations of a 6 kW turbine and two 6 kW turbines.

(a) Single turbine and solar PV installation. (b) Double turbines and solar PV installation.

Figure 5.14: Energy production with the 6 kW turbine and solar PV.

The total production of the combined cases for one 6 kW turbine was similar to the production from the combined 5 kW turbine, where the monthly energy production was above the demand for all year except for December. Regarding the system with two 6 kW turbines and solar PV, the energy production was adequate to meet the demand for the entire year.

Below, the monthly energy production for the combined cases of the 10 kW wind turbines and solar PV are presented in Figure 5.15a and 5.15b.

75 CHAPTER 5. RESULTS AND DISCUSSION

(a) Single turbine and solar PV installation. (b) Double turbines and solar PV installation.

Figure 5.15: Energy production managed by the 10 kW turbine with solar PV.

These were the first installations each month with enough monthly production for both a single turbine and a double turbine installation with solar PV. During the year for these combined cases, December had the lowest production and January produced the highest.

Implementation of two BUs for combined wind power and solar PV For the combined system, Figure 5.16a and 5.16b show how many days per month the batteries were used, both for a single turbine and double turbine installation together with solar PV.

(a) Single turbine and solar PV installation. (b) Double turbine and solar PV installation.

Figure 5.16: Days of usage for the BESS with two BUs.

By combining the two harvesting technologies, there was still a need for battery usage. The battery usage decreased during the summer months, but the need was still high in the last few months during the year.

Figure 5.17a and 5.17b present how many days per month that the BESS was inadequate, both for a single turbine and double turbine installation together with solar PV.

76 CHAPTER 5. RESULTS AND DISCUSSION

(a) Single turbine and solar PV installation. (b) Double turbine and solar PV installation.

Figure 5.17: Amount of inadequate days with two BUs.

For the single turbine combined system, the results demonstrated a solution that nearly met the railway system’s requirements. Both one 10 kW and two 10 kW turbines together with solar PV and BESS acquired sufficient energy for the railway system, as presented in the figure.

Implementation of three BUs for combined wind power and solar PV In Figure 5.18a and 5.18b it is shown how many days of each month the batteries were being used, this solution included three BUs, six solar PV modules and a single and double wind power configuration.

(a) Single turbine and solar PV installation. (b) Double turbine and solar PV installation.

Figure 5.18: Days of usage for the BESS with three BUs.

As seen in Figure 5.18a, the batteries were being used the least during April and most during December. Therefore was the most decisive month also the last of the year; December. Figure 5.18b indicates the battery usage for a double wind turbine configuration. The number of days the batteries were used were decreased; April was still the least used month, and December the highest.

Figure 5.19a and 5.19b present how many days monthly the BESS, with three BUs, was inadequate in connection with a single and double turbine installation in combination with solar PV.

77 CHAPTER 5. RESULTS AND DISCUSSION

(a) Single turbine and solar PV installation. (b) Double turbine and solar PV installation.

Figure 5.19: Amount of inadequate days with three BUs.

The energy system was adequate the majority of the year; however, during October, November and December, the BESS was too small, or production too low, for all solutions except for a 10 kW turbine. In Figure 5.19b results show how many days a month the system was inadequate for a double turbine installation in connection with solar PV. It was clear that the system was inadequate during two months of the year, November and December, when installing two 3 kW turbines or two 5 kW turbines. An installation including a 10 kW turbine was sufficient all year, while a system with two 6 kW turbines worked all year except one day during December.

In Table 5.18 and 5.19 a summation of the results for the BESS in connection with both wind power and solar PV can be seen.

Table 5.18: Battery usage for single wind power and solar PV installations.

2 BUs 3 BUs Installation design Days of usage Inadequate days Days of usage Inadequate days 3 kW turbine + PV 151 42 155 32 5 kW turbine + PV 132 30 133 24 6 kW turbine + PV 110 16 111 12 10 kW turbine + PV 43 0 43 0

Table 5.19: Battery usage for double wind power and solar PV installations.

2 BUs 3 BUs Installation design Days of usage Inadequate days Days of usage Inadequate days 2x3 kW turbines + PV 124 24 125 16 2x5 kW turbines + PV 105 14 106 10 2x6 kW turbines + PV 65 6 67 1 2x10 kW turbines + PV 29 0 29 0

Noted in the table above is that when installing either a 10 kW turbine or two 10 kW turbines, the size of the battery system did not affect the number of inadequate days. When installing the larger BESS, two 6 kW turbines were almost sufficient to supply the entire system with energy.

78 CHAPTER 5. RESULTS AND DISCUSSION

To meet the system’s demand, one solution was to implement more BUs and therefore create a bigger energy storage system. The quantity of BUs and the capacity of these can be seen in Table 5.20.

Table 5.20: Total number of batteries for each combined configuration to meet demand.

Installation design BUs Capacity [kWh] 2x6 kW + PV 4 54 2x5 kW + PV 6 81 6 kW + PV 6 81 2x3 kW + PV 8 108 5 kW + PV 9 122 3 kW + PV 14 189

As the table presents, the system needed to be more than seven times bigger for all systems to manage than the case study’s BESS presented. By installing an extra battery, another configuration would be deemed feasible to meet the requirements of the railway system. The capacity presented are the capacities seen in the battery specification sheet.

Mixed wind power configurations Other solutions with mixed sizes of wind turbines will be presented in this section. These are solutions that were investigated and concluded to work with the BESS stated. Since the results showed that configurations with one 10 kW turbine combined with three BUs were deemed sufficient to meet the demand, if configurations including an extra turbine were to be installed, it would work as well.

Mixed wind power and solar PV configurations In addition to the configurations of mixed wind turbine configurations, solutions with mixed configurations in combination with solar PV were found. However, because of limitations stated earlier in this work, these are not presented in the results. Only the configurations with the smallest number of devices and the least used area are deemed working solutions.

Feasible solutions The BESS and energy harvesting technologies results showed that three solutions existed for the feasible energy system in the case study. The storage capacity shown in the tables below is the capacity stated by the manufacturers without losses and other factors affecting the capacity.

For the first solution, presented in Table 5.21, three BUs and one 10 kW turbine from enair were deemed adequate to meet the demand of the railway system.

Table 5.21: Technologies used for the first solution.

Technology Type Energy produced Storage capacity Wind Power 10 kW 18.4 MWh - Battery Storage 3 BUs - 40.5 kWh

79 CHAPTER 5. RESULTS AND DISCUSSION

For the second solution, presented in Table 5.22, two BUs, one 6 kW turbine and one 10 kW turbine from enair could work as a theoretical implementation for the railway system. This solution was found in addition to the decided installation designs mentioned in Section4 . The energy production for this solution can be seen in AppendixF .

Table 5.22: Technologies used for the second solution.

Technology Type Energy produced Storage capacity Wind Power 6 kW + 10 kW 22.8 MWh - Battery Storage 2 BUs - 27 kWh

For the third solution, presented in Table 5.23, two BUs and one 10 kW turbine from enair together with the solar PV installation from SunPower showed a feasible possibility to meet the demand of the railway system.

Table 5.23: Technologies used for the third solution.

Technology Type Energy produced Storage capacity Wind Power 10 kW 18.4 MWh - Solar PV SunPower 1373 kWh - Battery Storage 2 BUs - 27 kWh

Results showed that either a wind power production system with BESS or a hybrid system with solar PV, wind power, and BESS would be sufficient to meet the railway system’s demand. The smaller wind turbines with a BESS or a combined hybrid solution were never adequate for the energy need.

5.1.2 Sensitivity analysis As mentioned in Section 3.5, two other locations were chosen to investigate how a location, and thereby weather data inputs, impacted the results. The investigated locations were Copenhagen and Lisbon. Load variations have also been investigated to see the impact on the energy system.

Location comparison Table 5.24, 5.25 and 5.26 shows the energy production, days of battery usage and amount of days the battery system was inadequate when two BUs were installed.

Table 5.24: Energy production from wind turbines and solar PV with two BUs at Tortuna.

Tortuna 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 2.59 4.01 4.34 18.4 5.17 8.02 8.68 36.9 1.37 Days of usage 269 246 232 72 226 183 138 37 291 Inadequate 138 97 50 3 68 32 10 0 161

Table 5.24 shows numbers already shown previously in the report, however, they are added here again for easy comparison.

80 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.25: Energy production from wind turbines and solar PV with two BUs at Copenhagen.

Copenhagen 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 9.98 16.2 17.7 56.3 20.0 32.3 35.5 113 1.60 Days of usage 39 24 8 1 13 7 1 1 260 Inadequate 0 0 0 0 0 0 0 0 162

Table 5.25 shows results indicating that Copenhagen has better wind conditions compared to Tortuna, hence the power output was higher and amount of inadequate days were zero for all wind power configurations. When only implemented solar PV there were 162 inadequate days a year and the overall energy production was higher than at Tortuna.

Table 5.26: Energy production from wind turbines and solar PV with two BUs at Lisbon.

Lisbon 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 7.06 10.4 9.90 48.1 14.1 20.9 19.8 96.2 2.58 Days of usage 64 37 19 5 26 16 8 4 160 Inadequate 0 0 0 0 0 0 0 0 37

In Table 5.26 the energy production from solar PV was higher than both previous cases, leading to the BESS being inadequate for only 37 days a year. The wind power output was higher than Tortuna, but lower than Copenhagen, and this resulted in a battery usage higher than Tortuna and lower than Copenhagen.

Table 5.27, 5.28 and 5.29 shows the yearly energy production, days of battery usage and the amount of days the battery system was inadequate with three BUs installed.

Table 5.27: Energy production from wind turbines and solar PV with three BUs at Tortuna.

Tortuna 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 2.59 4.01 4.34 18.4 5.17 8.02 8.68 36.9 1.37 Days of usage 277 251 234 72 230 187 140 37 293 Inadequate 124 71 33 0 40 16 5 0 158

Above, in Table 5.27, these results have also been shown previously in the report but are presented for an easier comparison with the other locations.

Table 5.28: Energy production from wind turbines and solar PV with three BUs at Copenhagen.

Copenhagen 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 9.98 16.2 17.7 56.3 20.0 32.3 35.5 113 1.60 Days of usage 39 24 8 1 13 7 1 1 263 Inadequate 0 0 0 0 0 0 0 0 159

Table 5.28 shows the Copenhagen results with three BUs installed instead of two. Even though the battery capacity was higher, this did not affect the usage of the BESS for the wind power

81 CHAPTER 5. RESULTS AND DISCUSSION installations. For solar PV, the inadequate days were slightly decreased while the usage was higher, since batteries with higher capacities requires more energy to recharge.

Table 5.29: Energy production from wind turbines and solar PV with three BUs at Lisbon.

Lisbon 3 kW 5 kW 6 kW 10 kW 2x3 kW 2x5 kW 2x6 kW 2x10 kW Solar PV Production [MWh] 7.06 10.4 9.90 48.1 14.1 20.9 19.8 96.2 2.58 Days of usage 64 37 19 5 26 16 8 4 162 Inadequate 0 0 0 0 0 0 0 0 29

In Table 5.29 the results for Lisbon can be seen. In combination with wind power, the battery usage was, as for Copenhagen, the same with three BUs. However, for solar PV, the inadequate days decreased more, 29 days compared to 37 with two batteries.

Load variation A load variation has been investigated in the tables below to see its impacts on the energy system. Table 5.30 and 5.31 shows the result for only the wind power installations. The standard load is further referred to as load, which is the demand from the railway system that has been used for previous calculations.

Table 5.30: Amount of days the BESS was used for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 3 kW turbine 263 273 268 277 269 277 276 283 277 285 5 kW turbine 238 242 243 247 246 251 249 254 253 257 6 kW turbine 218 224 227 229 232 234 235 237 240 242 10 kW turbine 67 67 71 71 72 72 75 75 79 79 2x3 kW turbine 222 225 225 228 226 230 227 233 228 233 2x5 kW turbine 176 178 180 183 183 187 191 195 195 199 2x6 kW turbine 124 126 127 129 138 140 141 143 145 145 2x10 kW turbine 35 35 37 37 37 37 37 37 39 39

Table 5.30 above shows how frequently the batteries were used for all different wind power configurations, with the different BESS and with a varying load. With an increasing load, the battery usage was also increased. For instance, for the 3 kW turbine and two BUs, the battery usage increased by 5 % when changing the load from 90 % to 110 % of the original load.

Table 5.31: Amount of days the BESS was inadequate for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load Installation design 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 3 kW turbine 124 109 132 114 138 124 144 133 148 135 5 kW turbine 76 55 243 64 97 71 105 82 112 92 6 kW turbine 31 19 40 25 50 33 65 42 78 52 10 kW turbine 0 0 1 0 3 0 3 0 4 0 2x3 kW turbine 51 32 62 37 68 40 77 50 82 59 2x5 kW turbine 23 12 28 15 32 16 38 20 42 25 2x6 kW turbine 7 2 9 3 10 5 12 6 13 8 2x10 kW turbine 0 0 0 0 0 0 0 0 0 0

82 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.31 instead shows the amount of inadequate days and how it varied with a changed load for all wind power configurations. A 10 kW turbine with two BUs could manage the demand at 90 % of the original load, but with a 110 % load there were four inadequate days in the year.

Table 5.32 and 5.33 shows results for the solar PV installation.

Table 5.32: Amount of days the BESS was used for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load Installation design 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs Solar PV configuration 274 276 284 286 291 293 298 299 305 312

As can be seen in Figure 5.32 the usage of the BESS increased with several days when the load changed from the lowest to the highest. An almost 10 % increase in battery usage was necessary when the BESS consisted of two BUs and the load changed from 90 % to 110 %.

Table 5.33: Amount of days the BESS was inadequate for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load Installation design 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs Solar PV configuration 152 147 156 150 161 158 167 163 176 169

Table 5.33 shows how the amount of inadequate days changed for different loads. With two BUs, the amount of inadequate days increased with approximately 13 % from a 90 % load to a 110 % load.

In the following two tables, Table 5.34 and 5.35, the results for the combined cases can be seen.

Table 5.34: Amount of days the BESS was used for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load Installation design 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 3 kW turbine + PV 138 140 143 146 151 155 157 161 164 169 5 kW turbine + PV 121 122 126 127 132 133 136 137 142 143 6 kW turbine + PV 101 105 104 106 110 111 120 122 124 125 10 kW turbine + PV 41 41 43 43 43 43 44 44 46 46 2x3 kW turbine + PV 117 118 121 122 124 125 130 132 134 136 2x5 kW turbine + PV 94 94 101 102 105 106 111 112 113 114 2x6 kW turbine + PV 57 58 59 61 65 67 69 71 73 75 2x10 kW turbine + PV 27 27 29 29 29 29 30 30 31 31

In Table 5.34 the number of days the batteries had to be used can be seen for the combined energy technologies. As the tables above shows, the trend was the same in this table. With an increasing load, the usage of the BESS increased. For the 3 kW turbine combined with solar PV, almost 16 % of the battery usage increased between 90 % of the original load to 110 %.

83 CHAPTER 5. RESULTS AND DISCUSSION

Table 5.35: Amount of days the BESS was inadequate for five different loads.

90 % of Load 95 % of Load Load 105 % of Load 110 % of Load Installation design 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3 BUs 2 BUs 3s 2 BUs 3 BUs 3 kW turbine + PV 39 30 42 31 42 32 50 39 51 41 5 kW turbine + PV 24 16 27 19 30 24 34 28 39 30 6 kW turbine + PV 12 6 14 9 16 12 18 12 19 12 10 kW turbine + PV 0 0 0 0 0 0 0 0 2 0 2x3 kW turbine + PV 21 14 22 14 24 16 27 19 29 23 2x5 kW turbine + PV 12 8 12 10 14 10 16 11 22 14 2x6 kW turbine + PV 5 0 6 0 6 1 8 3 9 4 2x10 kW turbine + PV 0 0 0 0 0 0 0 0 0 0

Table 5.35 shows how the number of inadequate days for the combined installations changed with the increasing load. Noted was when installing three BUs, it was possible to create a sufficient BESS for two 6 kW turbines with both 90 % and 95 % of the original load.

5.2 Discussion

In this section, the results of this work will be discussed and analysed. The results from the established process, estimated energy usage of SWOC’s, the developed energy harvesting system and energy storage system are discussed from an individual and a holistic perspective. Also, the sustainability and ethical aspects of the results will be discussed.

5.2.1 Energy storage Creating a design process for potential energy storage technologies for a generalised solution was not fully achieved because of the many varying variables that directly affected the energy storage system. The process was modified to suit battery storage applications, and not all the storage technologies currently available on the market. Thus, there might be a gap between the different technologies and their needed data inputs compared to battery storage and the flowchart. To validate the contribution to this created process, further investigation of the many batteries on the market and practical applications needs to be carried out.

As previously presented, some technologies such as CAES, PSH and capacitors, as presented in Section 3.3, were excluded from the scope because of the environmental factors or the highly complex knowledge of the different technologies. Some storage systems are new to the market; hence a limited knowledge was accessible during this thesis. Other technologies were abandoned because of the big and complex system needed to support the storage. Some technologies have a high need for energy input, which in some cases could still be deemed feasible for a similar application. However, since this scope is time-limited, not all technologies could be investigated and were therefore left out. The chosen storage technology is dependent on the energy demand of the wayside objects, more specifically for the Tortuna case study. Hence, if the signalling system was deemed to have a higher energy demand, other technologies could be preferred. Higher energy demand could indicate that large-scale storage technologies would be preferred or the opposite if the energy demand was lower. In those situations, smaller-scale storage would be more suitable. Included in this analysis and exclusions was storage dependent on a specific natural resource such as a water body close by, as PSH. Since PSH overall have high efficiency, and if occurring naturally, it could have a low impact on the surrounding areas, this storage technology could be adapted at other locations more fitted for application. Thermal storage is a good fit for

84 CHAPTER 5. RESULTS AND DISCUSSION storing energy when the energy conversion focuses on thermal differences. Any energy conversion leads to transmission losses, and minimising the conversion directly affects the losses. Therefore should this type of technology be used foremost during temperature differences or for larger-scale application. Thermal energy is, simply put, a lower form of energy compared to electricity or kinetic energy and should be used at places where electricity is produced in large quantities or whenever heating or cooling can be applied directly. These situations are preferred but not necessarily applied in all cases where thermal storage is used, as seen in Carnot batteries. The chosen technology was, in the end, dependent on the available environmental resources, which differs between locations. Hence, should the storage be chosen for the specific application and location for optimal usage.

Battery storage, especially lithium-ion, is a mature and efficient technology suitable for many applications. However, for long term storage and seasonal variations, other storage could be more optimal for implementation.

Degradation of the battery was excluded in this scope but will have a prominent effect on how long these configurations could work for the overall system. As mentioned previously in the literature review, the cycle life of lithium-ion batteries is affected significantly by the DoD. By having a lower DoD, the lifetime prolongs and decreases the strain on the BESS. Batteries with higher cycle life could be better suited for systems at remote locations or if the requirements have a need for it.

5.2.2 Energy harvest Similar to the process of energy storage systems, the energy harvesting process and the aim of creating a generalised solution was not fully achieved. Unlike the battery storage process, this flowchart was developed through the two harvesting technologies applied for the case study. Hence, the presented flowchart was more adaptable for different technologies, not only for one. Not included in this slightly modified flowchart were the manufacturers who did not provide commercialised technologies currently available on the market. Many of these are designed when the need for the energy harvesting system is established and not beforehand. Thus, more manufacturers could present a viable solution if contacted and if a practical solution was to be designed. Theoretically, other technologies not included in this scope follow a relatively similar process, but the solution could be lacking since their needed data inputs were not investigated. Hence, the flowchart presented could have gaps between different technologies and their needed analysis and input before an application. Further investigation of other technologies and a practical adaption needs to be carried out to validate the contributed energy harvesting process.

Limitations in this scope excluded technologies, which at other locations would be theoretically possible. The energy need from the railway’s signalling system was more correspondent to small- scale harvesting technologies, and therefore had a direct impact on the technologies included in this thesis work. Since many of the sustainable energy harvesting technologies need environmental resources, this led to the exclusion of some potential technologies. Included in these limitations are the technologies which utilise water resources such as hydropower and ocean energy. Hydropower is one of the oldest technologies still used today but is highly location-dependent and therefore impossible to apply to a generalised solution. Ocean energy, a niche that has high potential in energy production, is still new to the market for similar adaption, thus, excluded from this thesis work. Another technology with extremely high potential for energy harvesting is geothermal- adaptable technologies. Included in those technologies are heat pumps for heating SWOCs,

85 CHAPTER 5. RESULTS AND DISCUSSION wayside objects and for feasible energy production.

The overall efficiency for solar panels is not high, so even at locations with high solar radiance, the actual energy output is significantly lower than theoretically possible. It is also highly weather dependent, as it requires sun. When looking into wind power, it became clear that this technology also is weather dependent. Most small-scale wind turbines require a wind speed of at least 2-2.5 m/s to function. This condition excludes many locations with lower wind speed. It is essential to always remember how volatile renewable energy resources are, such as wind power and solar PV. Even if the yearly production is high and can cover the yearly demand, the production can differ from very high to low in a matter of hours.

5.2.3 Case study New wayside system compared to the conventional The comparison between the new and conventional system resulted in significant energy reductions, which was highly affected by the periods the devices were used. Mainly, in the former system, the objects are constantly on, and the standby mode utilises the steady power or current even if the objects were not activated. The primary energy demand was the steady power, and compared to that of the new system, this function had the highest energy usage. Even devices with lower energy demand congregate a large energy need over time. This became apparent when comparing the systems since the only new function added was the ability to turn off particular objects.

As for energy usage, the cable losses decreased significantly when changing from the conventional to the new system. As stated previously, the steady power was always on in the conventional system, and the only change in power was between steady and transient current. As there was always a need for energy to the system in the conventional, there was a constant current through the cables. Thus, it created a high cable loss difference of between 95.4 % to 96.3 % depending on the used cable between the conventional and new system, seen in Table 5.2. To finalise, when the system could not be turned off for a majority of the day, the losses increased considerably.

These results were based on the demand from the created scenario with the highest energy usage, thereby directly corresponding to the scenarios. Since these scenarios were randomised through software, a new iteration could lead to another result. Although the results presented a prominent decrease, this is unlikely to change independently on the scenarios since the main effect of the increased energy usage in the conventional system was influenced by the constant steady power. Thereby, by implementing modes where the objects can be turned off, the new system can save a large quantity of energy. The system’s security is still high, even if objects are turned off, as the internal cabinet and its logic are always activated. Thereby, will the objects always communicate and can promptly be switched from sleep to active mode.

Different traffic volumes were examined during this work, and it became apparent that the device which had the highest impact on the demand was the internal cabinet. Train schedules with different cases and scenarios were not equally as necessary over time as the energy usage of the cabinet. This could be the case for the railway system in rural areas, whereas a system in urban areas, where a higher traffic load appears, could have had a different result.

86 CHAPTER 5. RESULTS AND DISCUSSION

Cable losses During the calculations, the results presented that the longer the cable length, the higher the power loss. The same result was found for the current and the cable area. If a cable with a bigger diameter was used, or if the cables were divided into several smaller cables, the losses could likely be reduced remarkably. If the optical signals furthest away from the internal cabinet were always in proceed, the cable loss would be high due to the long distance, the high current and the long time the objects are in proceed mode. As could be seen in Table 5.3, there was a significant cable loss drop from a 1.5 mm2 to a 2.5 mm2 cable, so by installing one size bigger cable, the losses can be reduced notably. Case 4 had a higher loss than Case 3 since the activated signals are the ones furthest away from the cabinet, while Case 3 had activated signals closer to the cabinet. When comparing the different scenarios and their cable losses, the losses between the different scenarios did not differ much. From this, it could be concluded that the traffic volume and amount of trains that pass by does not influence the cable losses perceptually. So what mainly affects the cable losses could be summarised to cable length, cable area and current.

Battery storage The calculations showed that the higher the capacity, the lower the number of batteries for the installation would be needed. The different batteries from the manufacturers were applied to the configurations, but several were left out. Excluded were those batteries which required a large number in the BESS implementation. The number of batteries differed between two to 16 for the different battery types, where LG’s RESU13 and Tesla Powerwall 2 were the most feasible. Since Powerwall 2 had a higher possible power output and a more comprehensive operating temperature range, this became the most optimal choice for the rest of the thesis work.

Since it is unclear if the BESS can be installed indoors or not, the operating temperature became an essential factor. Depending on the possibility, or the need, of heating, this could lead to another battery being more suitable or another BESS configuration. Implementation of heat pumps could be an answer to this possible problem.

Installation and maintenance costs were hard to predict since these differ depending on the retailers and companies offering installations and trade. For the Powerwall 2, values differed from around € 7890 to € 12860 and could change depending on a country’s subsidies and policies. Today, many installations include inverters and other electrical appliances needed for the residential use of batteries. Often these installations befall in correspondent with solar PV installations since residential or commercial use are relatively new to the market. Thus, applications included in the installation cost or needed in the application of the batteries changes. Analysing economic influences on the choice is therefore problematic, and further investigation needs to be carried out.

When only lithium-ion batteries were investigated, it is of importance to inform that the batteries have a degrading battery performance. How frequently the batteries are used, working temperature and DoD being three factors affecting the lifetime. This degradation is different for each battery, but as this case study only was simulated during a year, this factor was not considered in calculations.

87 CHAPTER 5. RESULTS AND DISCUSSION

Solar PV As this work presents, the annual energy production between the different solar PV module installations differs despite the same weather data. This could be an effect of the different efficiencies or electrical circuits within the modules that exist, as well as cell performances since they are different between the manufacturers. Another reason is the installation area, and the number of modules in the configuration. How many modules depends on the cell performance and how they could be connected electrically. SunPower has the capacity and possibility of a smaller installation being implemented, which interprets directly to the advantage of having a decreased number of total modules compared to other configurations. The total number of modules corresponds to a smaller total installation area since fewer can be used. Thus, leading to apparent production differences.

CSUN produced the most but needs the biggest area. Since it is unclear how much land the installation can utilise, the value production per square meter installation area was created as a direct way to compare the different configurations. The SunPower modules had the highest possible production per installation area and were therefore chosen as the most optimal modules for the rest of the calculations.

GCR, or ground coverage ratio, is how close or far away these modules can be installed next to each other. A higher GCR indicates that the modules are installed closer, leading to a higher shadowing effect and a decrease in production. The smaller the GCR, the larger the installation area becomes. The value of 0.8 was chosen as the most optimal since the production still could meet the demand for the highest number of months per year. A larger value resulted in too great of a decrease in production and was therefore excluded from more simulations.

Other simulations were run to investigate whether a larger installation could be enough to meet the demand of the railway system. Unfortunately, these simulations showed that the size was not the issue since most of the months’ radiance was too low. Hence, the size of the configuration would not change the production enough to meet the requirements of the railway system during the entire year. Without implementing BESS, this resulted in five months of adequate energy production during the entire year.

By implementing two BUs, the results showed inadequate energy production despite high values during the summer season. Implementation of an extra BU showed that one more month was able to meet the demand, and the extra BU corresponded to only three days extra worth of energy production to be distributed to the railway system. In total, even if three BUs were installed, over 40 % of the year were deemed insufficient to support the railway system. Solar PV was not applicable as the only energy harvesting resource for Tortuna.

Wind power Presented in Figure 3.6a - 3.7b were the different power coefficients for the enair turbines, as well as the power output for the Halo Energy turbine of 6 kW. When using the power coefficient during calculations, the result will be more exact as it is a factor added into Equation 3.12. Without the coefficient, this resulted in a possible calculation error or lower accuracy compared with the other turbines. With the cp not being available for the Halo turbine, the power output for that turbine has been used instead. This meant that the possible wind power has been, at the decided turbine height, rounded to the wind power output values found in Figure 3.7a. The rounded values and results presented for this turbine are not as reliable as for the other three

88 CHAPTER 5. RESULTS AND DISCUSSION turbines. Even though the Halo Energy turbine specification differs from the rest, it was chosen because it has been elected as one of the most efficient small-scale wind turbines available today.

Regarding the used weather data for the case study, available wind speed data for that exact location was not available. However, SMHI had weather data for the near stations of Eskilstuna and Enk¨opingMo. When comparing the monthly average wind speeds of these locations with V¨aster˚as,the values of Eskilstuna were closer to the values of V¨aster˚asthan Enk¨opingMo; hence the wind speed for Eskilstuna was used for all presented results. Even if the monthly values are similar for V¨aster˚asand Eskilstuna, this affected the results. Other data inputs that affected the results were the chosen turbine heights shown in Table 3.21. These heights were estimated, so the turbine’s hub height will be placed above any trees nearby. The different efficiencies in the same table were also estimated together with the surface roughness. The surface roughness was estimated from looking at the maps of the location.

The possible energy production from the different turbines differed a lot between the smallest and largest turbines. As all wind turbines in this report have a high maximum wind speed, the power coefficient and power output of the turbines have been interpolated for wind speeds higher than given in the datasheets, which can affect the results. With the installations including double turbines, the energy production was multiplied by the respective single turbine installation. The energy production was considerably higher for the configurations with 10 kW turbines compared to the others. It is, however, important to emphasise that this turbine swept area is much bigger than the others. The 10 kW turbine was almost seven times larger than the 6 kW turbine, which had the smallest swept area even though it had the second largest rated power and energy production of the investigated turbines.

In Figure 5.4a - 5.7b the monthly energy production for each single and double configuration are presented together with the monthly energy demand of the system. For all different turbine configurations, it is apparent that the production was higher at the beginning of the year while December was the worst month and only functioning for the double installation of 5 kW, 6 kW or both configurations of the 10 kW turbine. Though it was possible to meet the demand for most of the year’s months, the wind speed and consequently the energy production can vary a lot from hour to hour, leading to a result that was not sufficient to explain if the system was adequately sized. A more detailed result was when the daily demand had been compared to the daily energy production in connection with a BESS, and these results are found in Figure 5.8a - 5.11b.

Firstly the different wind turbines, single and double configuration, can be seen together with two BUs. Noted is that the battery usage did not decrease much, even if the energy production was doubled. Further on in Figure 5.9a and 5.9b, the number of inadequate days the system experienced in a year can be seen for single and double installation. These figures simply stated how many days the production is not high enough to cover the demand, even with a BESS connected. For the single turbine installations, it was only in February that all turbines were functioning. When a second 10 kW turbine was added, the system was sufficient all year.

Figure 5.10a - 5.11b have a BESS consisting of three BUs. The battery usage was similar to the result with two BUs. The results were expected as the batteries would be used equally if the system was larger; implementing an extra battery would not affect the system demand or power production. When comparing the number of inadequate days, on the other hand, the numbers have decreased noticeably, especially for the double turbine installations. With three BUs installed instead of two, January, April and September, resulted in zero inadequate days.

89 CHAPTER 5. RESULTS AND DISCUSSION

The summation of the different figures is seen in Table 5.14. As the days of usage increased for most installations with more installed BUs, as it takes a longer time recharging the BESS, it was evident that the entire energy system had fewer inadequate days. Hence, the system could supply the objects for a larger part of the year. As seen, it was almost possible to create a sufficient system with one 10 kW turbine with only two connected BUs or two 6 kW turbines with three BUs. Hence, an optimal energy system must find the ratio between the battery storage and the wind turbines. This ratio is dependent on the battery’s specification and the harvesting technology.

As an energy shortage only occurred for a few days of the year for some configurations, possible short-term solutions are available for this issue. One solution could be to have a backup generator that starts supplying the railway system with energy when the batteries are close to empty. Alternatively, if there is a possibility to recharge the BESS manually. The lack of energy was one issue, another issue that appears was the yearly overproduction as shown in Figure 5.4a - 5.7b. For large parts of the year, the production was high above the monthly energy demand for all turbines. If it is decided to install two turbines, one could possibly be turned off when the wind conditions are good; if one turbine is enough to supply the system. Another possible solution could be to connect the energy system with the nearest city or settlement. This solution could lead to useful usage of the overproduction and at the same time reduce pressure on the electricity grid.

Combined system The yearly energy production increased for all configurations when implementing a combined installation of both wind turbines and solar PV. An effect of combining the two harvesting technologies was the increasing overproduction during the months. Excluded in this work were the possibilities of utilising the overproduction for other applications or by implementing larger BESS. More extensive battery storage could lead to increased losses since lithium-ion batteries self-discharge when not in use. Thus, energy will be lost over time if not applied in the system or elsewhere. Other energy storage could therefore be a solution for storing during a longer period of time.

A combination of the two different harvesting technologies presented more feasible solutions since the solar PV could support the smaller turbines during the critical season. The number of inadequate days for the entire configuration decreased tremendously when the wind was combined with solar PV. As the wind power configurations resulted in more possible solutions, the BESS would need charging from a different source than the installed energy harvesting technologies. The charging can be either from a backup generator or a similar manual solution leading to increased maintenance.

Another solution to the insufficient configurations was to install more BUs and thereby creating a bigger BESS. As Table 5.20 presented, that by adding only a few BUs extra resulted in more feasible solutions. Since batteries utilise a smaller area than turbines and solar installations, these solutions could be more applicable at places where area usage is limited. Depending on the number of BUs installed, this could be deemed cheaper than installing another extra large turbine or extra modules. Installation for a BU is overall not as complex as installing a wind turbine and does not require large ground modifications. Thus, could only the 10 kW turbine, instead of a 10 kW turbine with solar PV, together with an extra battery, be the preferred solution in the end if no other devices should be added to the configurations.

90 CHAPTER 5. RESULTS AND DISCUSSION

Feasible solutions Without any additional configuration within this work, three solutions were presented as feasible for application at Tortuna. The first solution was the configuration with a single 10 kW turbine together with three BUs. Energy production during the year is the smallest of the solutions but has the biggest BESS. The second solution is a configuration of one 10 kW turbine and one 6 kW turbine with two BUs. Out of the solutions, this was the solution with the highest yearly production. Thirdly, a combined solution was established with an implementation of one 10 kW turbine and the solar PV installation of SunPower with a BESS consisting of two BUs.

The optimal solution is highly dependent on the requirements for the specific project. Whether there would be area limitations, economic or overall implementation limitations, certain installa- tions will require heating or specifications for mounting. For ground limitations, the first solution would be the most optimal. An extra BU requires less area compared to that of an extra turbine installed. The second solution could be the preferred one because of the high yearly production, while the BESS is smaller. Economic factors could therefore be of importance when choosing between these. The solution which required the most area is the third and last. The technologies completed each other as solar PV produced most during summer, while wind power had the lowest production during these months. This installation could create a more stable yearly production and give more security regarding energy to the objects.

5.2.4 Sensitivity analysis The first part of the sensitivity analysis results was how the energy system would function when implemented at a different location. The theoretical energy production by the wind turbines was distinctively higher for both Copenhagen and Lisbon compared to Tortuna. The production led to a higher degree of overproduction but also resulted in fewer days being inadequate. How many days the batteries were used and the number of inadequate days were the same when installing two or three BUs for both Copenhagen and Lisbon. Hence, all systems consisting of wind turbine configurations were adequate with only two BUs. When the BUs were used due to low energy production, it was still large enough to meet the demand and will not change by installing one more BU. Interestingly, Lisbon and Copenhagen could supply the objects the full year when using only wind power; even the 3 kW turbine could sustain enough production. When comparing the energy production by the solar modules, Tortuna had the lowest production, but the number of inadequate days were similar for Copenhagen. When implementing the system further south, the solar radiance increased, which led to higher energy production. Both Tortuna and Copenhagen are cities in the Nordics with big seasonal changes and winter with a decreased solar radiance, thereby a similar solar PV production. If the same system were to be moved to Lisbon, by only relying on solar production, Lisbon could supply the objects approximately 120 days more every year than Tortuna could. These results showed that location has an impact on the system results. Sweden, in general, especially locations far from the coast or up north, do not have the best conditions for using renewable energy. As the energy sources are volatile and change much from day to day, some kind of backup system, such as a large BESS, is often necessary for a more reliable energy system.

Investigated in the second part of the sensitivity analysis were how the battery usage and amount of inadequate days changed with an increasing load, found in Table 5.30 - 5.35. This investigation was made to see how different sized railway systems would work together with these configurations of energy systems. A general trend from this was that both the battery usage and inadequate days increased with an increasing load. With only a slight change in demand, the effect on

91 CHAPTER 5. RESULTS AND DISCUSSION inadequate days was rather big. When only installing wind power, a decrease in load resulted in more possible solutions. By installing only one 10 kW turbine together with two BUs for a 90 % load, a full year of production was sufficient to meet the demand. On the other hand, if the load was increased, there were four inadequate days for the 110 % load. For the 6 kW turbine, the results presented that the number of inadequate days more than doubled from a 90 % load to a 110 % load for both BESS systems. When only overlooking the changes for solar PV, almost half of the year, the system was inadequate when the load increased to 110 %, and two BUs were installed. When analysing the combined installations, several solutions were created depending on the load and the size of the BESS. When the load was decreased by 5 % or more, compared to the original load, the 2x6 kW turbines with solar PV was a possible solution when three BUs were connected. The 10 kW turbine with solar PV was working for all loads with different amounts of BUs, except when the load reached 110 % and was connected with two BUs. So with an even higher load, even the 10 kW turbine with solar PV could have issues supplying the objects during the entire year.

5.2.5 Sustainability When implementing a new energy system somewhere, several factors have to be considered from a sustainable perspective. Not only analysing if the investigated technology is feasible, but also how the installation can affect the surroundings, the possible political aspects, environmental aspects and lastly economic aspect. These perspectives are essential to investigate to deem if an energy system should be implemented.

Social and ethical aspects In this thesis, solar PV and wind power have been investigated in terms of energy feasibility. When studying the social aspects, wind power has some disadvantages. It is known that wind turbines usually make a lot of sounds which could be disturbing the people living close-by, but it can also affect the wildlife. However, the enair investigated turbines are known to make minimal sound to be able to place them closer to people’s home. Another factor that disfavours wind power is the visual impact it makes. As wind turbines are placed several meters above ground, they are easily noticeable. Even if the created energy is good for the environment, close-by inhabitants will most likely not support installing wind power in the surrounding area. Installations could create a shadowing effect on the ground, affecting ground-life other than only being a disturbance. Nevertheless, wind power does not require as much land area in comparison to solar PV. If a building for placing the solar panels on is not available, the panels have to be mounted on the ground, which takes up a lot of ground coverage. Solar panels are, like wind turbines, not visually attractive to look at either. The lithium-ion batteries that foremost have been explored in this thesis do not make any specific impact socially. They will probably be placed on the cabinet’s walls and not make any intrusion for people living close.

The material lithium is often mined in developing countries where the conditions are not optimal for the workers. The mining and extraction of lithium are limited to a few places in the world and are therefore scarcer than solar and wind. If the system is highly dependent on this material, this can create a vulnerability for future usage if sudden changes happen in the world, such as war or environmental catastrophe.

92 CHAPTER 5. RESULTS AND DISCUSSION

Economic Another prominent factor, limited to this thesis work, are the economic factors. Certain tech- nologies require high maintenance or have a high installation cost, which could lead to different choices. Included in the economic effects, local subsidies and policies could contribute to some technologies being considered more suitable than others. Subsidies granted for a specific solution are used today to help keep the prices of the technologies or services low. Thus, leading to a lower maintenance or installation cost. This phenomenon is also correspondent to certain technologies being invested in, thereby increasing the demand; the government or companies could provide these economic helping tools.

From an economic point of view, as presented and applied in this work, battery storage might not be the most profitable option for supporting energy harvesting technologies. The batteries with larger capacities and bigger potential for storing during a longer period of time are expensive. The installation cost varies and lithium-ion batteries, when compared to other batteries, are more expensive. Therefore, an implementation can be costly but could still be deemed optimal since the technology has other advantages.

The overall costs for renewable energy have decreased in the last years, resulting in even more advantages following when implementing solar PV, for instance. It is often common to have subsidies for people wanting to install privately owned solar modules since the overall emissions have to be drastically decreased in the near future to reach the Paris Agreement. Renewable resources are more often invested in and are front-runners in R&D, thereby resources to venture on for tomorrow.

Environmental Regarding energy harvesting, only renewable energy sources have been investigated, so the environmental effects are not that distinct compared to other fossil fuel-powered technologies. As mentioned previously, when implementing a wind turbine, for example, the effect on wildlife and surrounding areas are hard to predict. The effect often presents itself after some time and not directly when implemented. Both wind power and solar PV release zero emissions when operating and are therefore deemed environmentally friendly. During production and after their lifetime, however, emissions and effects on the environment are hard to avoid. Batteries are not known as being environmentally friendly as they contain material like lithium that is not good for the environment, and the components are difficult to recycle.

The combined configuration overproduces during the year, as seen in Figure 5.12a to 5.15b. This theoretical energy production is currently not applied elsewhere, and for the installation to be effective, this energy needs to be used. If the preferred solution is not to overproduce, there is a possibility to turn off the turbines or the modules. It seems unnecessary to turn off devices if the energy production can be used elsewhere, especially if the country or company aims to achieve the SDGs. Therefore could the energy production be applied as a power producer for a local grid. This grid would not feed the railway system any energy but simply sell the energy production during times of excess; thus, utilising the energy than simply neglecting it.

5.2.6 Sources of errors Through this report, other decisions and assumptions could have been made to receive an even better and trustworthy result. The decided limitations consisted of economic factors which can

93 CHAPTER 5. RESULTS AND DISCUSSION affect the feasible solutions. If an economic point of view was added in the modelling, the most optimal solutions, energy-wise, could be deemed insufficient than as presented in this work.

The weather data was gathered from the closest weather stations that provide this type of information. The result would be more thorough if the actual location could be used. Different years have been used because of the limited weather data available for the public, which could also correspond to imprecise results. Further, is this work conducting its calculations and simulations during a single year. Thus, can the results be different when modelling another year because of the volatile energy resources used.

Noticed throughout the work was the problems involving values and data of some of the wayside objects. Confidentiality of these created difficulties in establishing the energy usage of the entire system and for a generalised application. In the end, the results showed that these objects were not the biggest energy user as the internal cabinet with its circuits was. These operation values of the trackside objects were confidentially shared from one of the cooperating companies and could differ from manufacturer to manufacturer. The gathering of data in the literature review showed that even if the cabinet would differ, in remote locations, this would still be the biggest energy user since the object is always turned on. Devices with the highest power peaks are the wayside objects with prominent mechanical properties such as the level crossings and point machines. These objects involve the movement of another object or movement of itself. Nevertheless, since these movements are restricted to a particular time, which is significantly little compared to the overall total operation time, the total energy used for these objects were small.

94 Chapter 6

Conclusion and Future Work

Conclusions drawn based on the discussed results will be presented in this chapter, and suggestions for future works.

6.1 Conclusion

Batteries were regarded as the most suitable technology used for energy storage, creating a BESS consisting of one or several BUs. Of the batteries examined, lithium-ion was established as the most suited because of its high energy density. Out of the energy harvesting technologies investigated, solar PV and wind power were the best for implementation. These technologies are mature, and have been deemed the cheapest renewable resources today and can easily be adapted to suit a specific need. Since the feasible technologies are weather dependent and therefore volatile, the implementation will need a BESS during periods with low wind speed and low radiance. Therefore, a conclusion is that even though sustainable resources can produce enough energy, these are very volatile and need to be supported by an energy storage system to secure supply.

Different sizes of the two chosen energy harvesting technologies were investigated to determine the most optimal solution for the case study. As the results showed, the most optimal solution is dependent on the requirements established by the potential company. These requirements could either be limitations of area usage, economic, environmental, or the nearby residents’ constraints. Three solutions were produced so that the company has an opportunity to investigate them further and implement the solution deemed most suited to their standards.

Performance of the system was deemed volatile during the months with large excess production during specific periods and low during others. Since the utilisation of the excess power production was excluded in the scope, the system’s overall performance was difficult to establish. Therefore, the objective of evaluating the system performance became an evaluation of whether or not the production met the demand and what solutions were deemed sufficient. Cable and electrical losses were the most significant losses, and the electrical losses were simplified since the data gathered were not enough to conclude anything. The system today consists of small cables and long distances, which created high transmission losses. By evaluating distances and possible electricity running through the cables, a new system would decrease losses if cables were dimensioned correctly. A total loss of 10 % seems unnecessary since by installing a thicker cable, the losses would decrease by almost half.

95 CHAPTER 6. CONCLUSION AND FUTURE WORK

Since many variables and inputs significantly affected the results, a generalised process could not be established for both energy storage and energy harvesting applications. The presented flowcharts are modified to suit the specific technologies chosen in this work for the case study. Finding and establishing feasible technologies leads to a non-linear process because of the previously mentioned perspectives. It is essential to underline that the location where an energy system is needed has a high impact on the energy solution. As the renewable energy sources investigated in this thesis fluctuate, data from another year can give a completely different result.

When designing an energy system, several factors are affecting the results. Discussed previously are the energy usage of the objects, cable losses, traffic volume and location. It was, however, obvious that what drew most energy from the new system was not how many trains per day that traffic the tracks. What did cause the significant energy need was the wayside objects, in this case, the internal cabinet logic, that has to be active all the time. In the conventional system, where SWOC has not been implemented, the energy usage is significantly higher. It is not possible to regulate or control the objects and power usage to the same extent as after implementation. Furthermore, can transmission losses and costs be reduced, thereby concluding that it is beneficial to install SWOC.

As a final conclusion, the case study presented that Tortuna may not be the most optimum location for implementing these technologies to the railway system. These solutions could, in theory, work, but practical experiments are needed to ensure the findings. Other locations, such as Lisbon or Copenhagen, would be more suited for these implementations, and the supporting energy system needs to be explicitly constructed for every installation. Hence, a generalised solution is not to be recommended.

6.2 Future Works

Investigations for future work regarding technologies used for power peak reduction, such as fly-wheels or capacitors, could contribute to a broader scope and more feasible solutions. Some technologies investigated in this thesis are technologies relatively new to the market but have potential for future applications for the railway system. These are foremost the piezoelectric harvesting technologies and electromagnetic. These devices can utilise the tracks and trains’ vibration and movements and, therefore, utilise the energy created during the railway operations. The technologies need to be manufactured for the specific railway track and could become costly since the construction of these devices is use-case specific. However, since many railway tracks are similar in construction, this can still be worth the extra time and money. Piezoelectric and electromagnetic harvesting devices produce a small quantity of energy, which could be sufficient to meet the demand during periods when other technologies are insufficient.

Other technologies which are theoretically feasible for applications are hydrogen storage and hydrogen fuel cells. Hydrogen fuel cells are a new investment in some markets, and currently, many different projects utilise the technology, including applications for cars and larger power production installations as for combined heat and power production plants. These technologies are constantly under development, and even if it is hard to implement today, this may not be the case in the near future.

Another possible future work is to do practical experiments with the configurations investigated in this scope. The focus in this work was for theoretically feasible configurations and was only

96 CHAPTER 6. CONCLUSION AND FUTURE WORK conducted for one year. Since weather data changes from year to year, a practical implementation of the technologies could present a more detailed result. Secondly, a practical application would create accurate measurements of the system’s energy demand, thereby reducing errors instead of relying only on data gathering from literature reviews and data received from the cooperating companies.

The process and flowchart could be modified to suit a more generalised solution by investigating other feasible technologies. An alternative to the flowchart process is to create an algorithm in which specific input values could result in a feasible solution for further investigations of the technology suggested.

A possible local grid connection, where the produced electricity could be sold, could increase the benefits of certain more costly technologies. Thereby not wasting any possible excess production, which was excluded in this work. These installations could therefore be used to support the electricity grid and help reduce the peaks or reduce some of the losses created because of long cables. The distribution of electricity could, therefore, not only benefit investments economically but also help the country meet some of the environmental goals.

As the sensitivity analysis presented, the results in this scope are highly dependent on the location and its possible resources. A more thorough design process could then be established by designing and implementing other technologies at different locations than the modified ones presented, thereby producing a process suited for all different sustainable technologies.

The environmental effects and influences of the technologies have not been investigated throughout this work. Manufacturing processes and installations at locations could therefore be interesting to investigate in future works.

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108 Appendix

A Cable Specifications

7 - Electrical Data

Resistance of copper conductors and design of the conductors cores according to IEC 60228 for classes 2 and 5

Class 2 Class 5 Cross section Maximum Resistance Max. diameter Maximum Resistance 2 Number (mm ) 20°C Ohm/km of wires 20°C Ohm/km D.C. of wires Bare copper Tinned copper inconductor Bare copper Tinned copper

0.35 7 57.6(1) 59.3(1) --- 0.5 7 40.4(2) 41.6(2) --- 0.75 7 26.0(2) 26.6(2) --- 1718.1 18.2 --- 1.5 7 12.1 12.2 --- 2.5 7 7.41 7.56 --- 474.61 4.70 --- 673.08 3.11 --- 10 7 1.83 1.84 --- 16 7 1.15 1.16 --- 25 7 0.727 0.734 --- 35 7 0.524 0.529 --- 50 19 0.387 0.391 0.41 0.386 0.393 70 19 0.268 0.270 0.51 0.272 0.277 95 19 0.193 0.195 0.51 0.206 0.210 120 37 0.153 0.154 0.51 0.161 0.164 150 37 0.124 0.126 0.51 0.129 0.132 185 37 0.0991 0.100 0.51 0.106 0.108 240 61 0.0754 0.0762 0.51 0.081 0.0817 300 61 0.0601 0.0607 0.51 0.0641 0.0654

R20 x L Rt = Resistance in ohms at a temperature t in °C for a length in meters Rt = R20 = Resistance in ohms at 20°C for a length of 1 km Kt x 1000 L = Length in meters Kt = Temperature correction factor for a temperature t in °C

(1) Values according to IEC 60092-374 (2) Values according to IEC 60092-375 March 2001 - Copyright © 2000 Nexans

55

Cable specifications (Nexans, 2001).

I B Battery Datasheets

DATA SHEET

ENGINEERED IN THE USA RB300 Voltage: 12.8V | Capacity: 300 Ah | Energy: 3840 Wh | Group: 8D

ELECTRICAL SPECIFICATIONS CHARGE SPECIFICATIONS Nominal Voltage 12.8 V Recommended Charge Current 15 A - 50 A Nominal Capacity 300 Ah Maximum Charge Current 100 A Charge Current 14 to 32 ºF Capacity @ 25A 720 min ≤0.1 C (-10 to 0 ºC) Charge Current -4 to 14 ºF Resistance ≤30 mΩ @ 50% SOC ≤0.05 C (-20 to -10 ºC) Efficiency 99% Recommended Charge Voltage 14.2 V - 14.6 V

Self Discharge <3% per Month 15.6 V (3.9 ±0.025 vpc) BMS Charge Voltage Cut-Off Maximum Modules in Series 6 (1.1 ±0.4 s) Reconnect Voltage 15.2 V (3.8 ±0.05 vpc) DISCHARGE SPECIFICATIONS Balancing Voltage 14.4 V (3.6 ±0.025 vpc) Maximum Continuous Discharge Current 100 A Peak Discharge Current 200 A (7.5 s ±2.5 s) COMPLIANCE SPECIFICATIONS BMS Discharge Current Cut-Off 280 A ±50 A (9 ±4 ms) UN38.3 (battery) Certifications CE (battery) Recommended Low Voltage Disconnect 11 V UL1642 & IEC62133 (cells) BMS Discharge Voltage Cut-Off 8 V (2.0 ±0.08 vpc) (140 ±60 ms) Shipping Classification UN 3480, CLASS 9 Reconnect Voltage 9.2 V (2.3 ±0.1 vpc) Short Circuit Protection 200-600 µs DIMENSIONAL SPECIFICATIONS

TEMPERATURE SPECIFICATIONS Discharge Temperature -4 to 140 ºF (-20 to 60 ºC) Charge Temperature* -4 to 113 ºF (-20 to 45 ºC) Storage Temperature 23 to 95 ºF (-5 to 35 ºC)

BMS High Temperature Cut-Off 176 ºF (80 ºC)

Reconnect Temperature 122 ºF (50 ºC)

*Refer to charge currents below 32°F (0°C)

MECHANICAL SPECIFICATIONS 20.5 x 10.5 x 9.0” Dimensions (L x W x H) 520 x 267 x 228 mm

Weight 75.8 lbs (34.36 kg) Terminal Type M8 x 1.25 x 2mm Terminal Torque 80 - 100 in-lbs (9 - 11 N-m) Case Material ABS Enclosure Protection IP66

Cell Type - Chemistry Cylindrical - LiFePO4

relionbattery.com +1.803.547.7288 • TOLL FREE: (855) 931-2466 | 1433 Dave Lyle Blvd • Rock Hill, SC 29730

RB300 DATA SHEET • 10.30.20

RELiON datasheet (RELiON Batteries, 2020).

II Topband Lithium 12 V 200 Ah TB12200 datasheet (RELiON Batteries, 2020).

III Topband Lithium 12 V 200 Ah TB12200 datasheet (RELiON Batteries, 2020).

IV battery management & Protection system Upgraded BMS

The SMART LITHIUM ONE peak is truly a unique battery. This discharge includes the ability for quick up to 450A charge in less than an hour. New bolted connection terminals The SMART LITHIUM BASIC advantage is the unbeatable vibration price per Ah for anybody proof with a need for a big storage capacity.

After 5000 cycles (normal usage) the battery will still be able to store 80% of its original capacity

2020 upgrades offers an upgraded BMS with better prognosis sent to the APP. Better temperature handling due to heavy duty connections. Basic with slightly higher current allowance. Slightly smaller exterior with both integrated handles as well as a detachable strap.

WHY IS SMART LITHIUM SAFER THAN YOUR OLD BATTERIES? Lithium phosphate (LiFePO4) is the safest chemistry available

• Multiple electronic safeguards Due to our multi-layered safety, the SMART • Mechanical fire safety valves LITHIUM is one of the few batteries that’s certified under UN 38.3 legislation, certified for air safest LiF ePO4 guaranteeing it’s safe for air transport. It means transport cells that the battery is secured against penetration (or crushing) of the cells, fire, changing air pressure, repeated vibrations, shock impact, short-circuiting and overcharging. In case there is a risk to the system, the built-in BMS will automatically protect and shut down the PENETRATION & battery, isolating itself from the casing. SAFETY VENT CRUSH PROOF

Art Model Capacity System Max. cont. Max. cont. Pea discharge Weight Dimensions Voltage charge discharge (1s) SmartLi1One SMART LITHIUM ONE 1Ah 12V 15A 15A 5A 1.5g 32 x 172 x 222 mm SmartLi1Basic2 SMART LITHIUM BASIC 2x1Ah 12V 2x5A 2x5A 2x3A 1.5g 32 x 172 x 222 mm

Smart LITHIUM One 100 Ah datasheet (Batteriexpressen, 2020).

V

Our LFP batteries have integrated cell balancing and cell monitoring. Up to 5 batteries can be paralleled and up to four 12V batteries or two 24V batteries can be series connected, so that a 48V battery bank of up to 1500Ah can be assembled. The cell balancing/monitoring cables can be daisy-chained and must be connected to a Battery Management System (BMS).

Battery Management System (BMS) The BMS will: 1. Generate a pre-alarm whenever the voltage of a battery cell decreases to less than 3,1V (adjustable 2,85-3,15V). 2. Disconnect or shut down the load whenever the voltage of a battery cell decreases to less than 2,8V (adjustable 2,6V-2,8V). 3. Stop the charging process whenever the voltage of a battery cell increases to more than 4,2V. 4. Shut down the system whenever the temperature of a cell exceeds 50°C. See the BMS datasheets for more features

Battery specification

LFP- LFP- LFP- LFP- LFP- LFP- LFP- LFP- LFP- Smart LFP- Smart VOLTAGE AND CAPACITY Smart Smart Smart Smart Smart Smart Smart Smart 12,8/330 25,6/100 12,8/50 12,8/60 12,8/100 12,8/160 12,8/200 12,8/300 25,6/200 25,6/200-a Nominal voltage 12,8V 12,8V 12,8V 12,8V 12,8V 12,8V 12,8V 25,6V 25,6V 25,6V Nominal capacity @ 25°C* 50Ah 60Ah 100Ah 160Ah 200Ah 300Ah 330Ah 100Ah 200Ah 200Ah Nominal capacity @ 0°C* 40Ah 48Ah 80Ah 130Ah 160Ah 240Ah 260Ah 80Ah 160Ah 160Ah Nominal capacity @ -20°C* 25Ah 30Ah 50Ah 80Ah 100Ah 150Ah 160Ah 50Ah 100Ah 100Ah Nominal energy @ 25°C* 640Wh 768Wh 1280Wh 2048Wh 2560Wh 3840Wh 4220Wh 2560Wh 5120Wh 5120Wh *Discharge current ≤1C CYCLE LIFE (capacity ≥ 80% of nominal) 80% DoD 2500 cycles 70% DoD 3000 cycles 50% DoD 5000 cycles DISCHARGE Maximum continuous 100A 120A 200A 320A 400A 600A 400A 200A 400A 400A discharge current Recommended continuous ≤50A ≤60A ≤100A ≤160A ≤200A ≤300A ≤300A ≤100A ≤200A ≤200A discharge current End of discharge voltage 11,2V 11,2V 11,2V 11,2V 11,2V 11,2V 11,2V 22,4V 22,4V 22,4V OPERATING CONDITIONS Operating temperature Discharge: -20°C to +50°C Charge: +5°C to +50°C Storage temperature -45°C to +70°C Humidity (non-condensing) Max. 95% Protection class IP 22 CHARGE Charge voltage Between 14V/28V and 14,4V/28,8V (14,2V/28,4V recommended) Float voltage 13,5V/27V Maximum charge current 100A 120A 200A 320A 400A 600A 400A 200A 400A 400A Recommended charge ≤30A ≤30A ≤50A ≤80A ≤100A ≤150A ≤150A ≤50A ≤100A ≤100A current OTHER Max storage time @ 25°C* 1 year BMS connection Male + female cable with M8 circular connector, length 50cm Power connection M8 M8 M8 M8 M8 M10 M10 M8 M8 M8 (threaded inserts) 199 x 188 x 239 x 286 197 x 321 x 237 x 321 x 237 x 321 x 347 x 425 x 265 x 359 x 197 x 650 x 317 x 631 237 x 650 x Dimensions (hxwxd) mm 147 x132 152 152 152 274 206 163 x 208 163 Weight 7kg 12kg 14kg 18kg 20kg 51kg 30kg 28kg 56kg 39kg *When fully charged

Victron Energy B.V. | De Paal 35 | 1351 JG Almere | The Netherlands General phone: +31 (0)36 535 97 00 | E-mail: [email protected] www.victronenergy.com

LFP Smart 12,8/300 and LFP Smart 25,6/200 datasheet (Victron Energy, n.d.[a]).

VI POWERWALL

Tesla Powerwall is a fully-integrated AC battery system for residential or light commercial use. Its rechargeable lithium-ion battery pack provides energy storage for solar self-consumption and time-based control.

Powerwall’s electrical interface provides a simple connection to any home or building. Its revolutionary compact design achieves market-leading energy density and is easy to install, enabling owners to quickly realise the benefits of reliable, clean power.

PERFORMANCE SPECIFICATIONS MECHANICAL SPECIFICATIONS

AC Voltage (Nominal) 230 V Dimensions 1150 mm x 755 mm x 155 mm

Feed-In Type Single Phase Weight 125 kg

Grid Frequency 50 Hz Mounting options Floor or wall mount

Total Energy1 14 kWh 755 mm 155 mm Usable Energy1 13.5 kWh (29.7 in) (6.1 in)

Grid Standards (UK) G83 / G59

Real Power, max continuous 3.68 kW / 5 kW (charge and discharge)

Apparent Power, max continuous 3.68 kVA / 5 kW (charge and discharge)

Power Factor Output Range +/– 1.0 adjustable

Power Factor Range (full-rated power) +/– 0.85

Internal Battery DC Voltage 50 V 1150 mm Round Trip Efficiency1,2 90% (45.3 in)

Warranty 10 years

1 Values provided for 25°C, 3.3 kW charge/discharge power. 2 AC to battery to AC, at beginning of life.

COMPLIANCE INFORMATION ENVIRONMENTAL SPECIFICATIONS

Certifications IEC 62109-1, IEC 62109-2, IEC 62619, Operating Temperature –20°C to 50°C UN 38.3 Optimum Temperature 0°C to 30°C Grid Connection Worldwide Compatibility Operating Humidity (RH) Up to 100%, condensing Emissions IEC 61000-6-1, IEC 61000-6-3 Maximum Elevation 3000 m Environmental RoHS Directive 2011/65/EU, Environment WEEE Directive 2012/19/EU, Indoor and outdoor rated Battery Directive 2006/66/EC, Ingress Rating IP67 (Battery & Power Electronics) REACH Regulation IP56 (Wiring Compartment) Seismic AC156, IEEE 693-2005 (high) Noise Level @ 1m < 40 dBA at 30°C

TESLA.COM/ENERGY

Tesla Powerwall 2 datasheet (Tesla, 2018).

VII Technical Data sonnenBatterie eco 9.43

eco 9.43/5 eco 9.43/7.5 eco 9.43/10 eco 9.43/12.5 eco 9.43/15

Nominal battery capacity in kWh 5.0 7.5 10.0 12.5 15.0

Depth of discharge (DoD) 90 %

Cell technology LFP (Lithium Iron Phosphate)

Cabinet option 1 (5.0 kWh)

Weight in kg 81 – – – –

Dimensions (H/W/D) in cm 88/67/23 – – – –

Cabinet option 2 (5.0 kWh – 10.0 kWh)¹

Weight in kg 97 120 143 – –

Dimensions (H/W/D) in cm 137/67/23 137/67/23 137/67/23 – –

Cabinet option 3 (5.0 kWh – 15.0 kWh)¹

Weight in kg 108 131 154 177 200

Dimensions (H/W/D) in cm 186/67/23 186/67/23 186/67/23 186/67/23 186/67/23

Nominal power 2.5 3.3 3.3 3.3 3.3 (charging/discharging) in kW

Max. inverter efficiency 95 %

Max. battery efficiency 98 %

Ambient temperature range -5 °C – +45 °C²

Dust and water protection IP 30

Operating mode single phase

[email protected] · sonnenbatterie.de/en

sonnenBatteries datasheet (sonnen GmbH, 2018).

VIII CHANGE YOUR ENERGY, CHARGE YOUR LIFE

48V Models RESU3.3 RESU6.5 RESU10 RESU13 Total Energy [kWh] 1) 3.3 6.5 9.8 13.1 Usable Energy [kWh] 2) 2.9 5.9 8.8 12.4 Capacity [Ah] 63 126 189 252 Nominal Voltage [V] 51.8 Voltage Range [V] 42.0~58.8 Max Power [kW] 3.0 4.2 5.0 5.0 7.0 Peak Power [kW] (for 3 sec.) 3.3 4.6 7.0 11.0 (Backup Mode) Dimension [W x H x D, mm] 452 x 403 x 120 452 x 656 x 120 452 x 484 x 227 452 x 626 x 227 Weight [kg] 31 52 75 99 Enclosure Protection Rating IP55 Communication CAN2.0B Cell UL1642 Certificates Product UL1973 / TUV (IEC 62619) / CE / FCC / RCM TUV (IEC 62619) / CE / FCC / RCM Compatible Inverter Brands : SMA, SolaX, Ingeteam, GoodWe, Sungrow, Victron Energy, Selectronic - More brands to be added 1) Total Energy is measured at the initial stage of battery life under the condition as follows : Temperature 25℃ 2) Usable Energy is based on battery cell only

RESU Plus is an expansion kit specially designed for 48V models of the RESU series. With RESU Plus, all 48V models can be cross-connected with each other. • Dimension : 216 x 156 x 121 (W x H x D, mm) • Number of Expandable Battery Units : Up to 2EA • IP55

400V RESU7H RESU10H Models Type-R Type-C Type-R Type-C Total Energy [kWh] 1) 7.0 9.8 Usable Energy [kWh] 2) 6.6 9.3 Capacity [Ah] 63 63 Voltage Range [V] 350~450 430~550 350~450 430~550 Max Power [kW] 3.5 5.0 Peak Power [kW] 5.0 (for 5 sec.) 5.0 (for 10 sec.) 7.0 (for 10 sec.) Dimension [W x H x D , mm] 744 x 692 x 206 744 x 907 x 206 744 x 907 x 206 744 x 907 x 206 Weight [kg] 75 87 97 99.8 Enclosure Protection Rating IP55 Communication RS485 CAN2.0B RS485 CAN2.0B Cell UL1642 Certificates TUV (IEC 62619) / TUV (IEC 62619) / Product UL1973 / TUV (IEC 62619) / CE / FCC / RCM CE / FCC / RCM CE / RCM Compatible Inverter Brands : SMA, SolarEdge, Fronius, Huawei - More brands to be added 1) Total Energy is measured at the initial stage of battery life under the condition as follows : Temperature 25℃ 2) Usable Energy is based on battery cell only

LG Chem RESU datasheet (LG Energy Solution, 2019).

IX C Solar Modules Datasheets

X-Series: X22-370 | X22-360 SunPower® Residential AC Module

AC Electrical Data

Inverter Model: Type E (IQ 7XS) @240 VAC

Peak Output Power 320 VA

Max. Continuous Output Power 315 VA

Nom. (L–L) Voltage/Range2 (V) 240 / 211–264

Max. Continuous Output Current (A) 1.31

Max. Units per 20 A (LL) Branch Circuit3 12 (single phase)

CEC Weighted Efficiency 97.5%

Nom. Frequency 60 Hz

Extended Frequency Range 47–68 Hz

AC Short Circuit Fault Current Over 3 Cycles 5.8 A rms

Overvoltage Class AC Port III

AC Port Backfeed Current 18 mA

Power Factor Setting 1.0

Power Factor (adjustable) 0.7 lead. / 0.7 lag.

No active phase balancing for three-phase installations

DC Power Data Warranties, Certifications, and Compliance SPR-X22-370-E-AC SPR-X22-360-E-AC • 25-year limited power warranty Warranties • 25-year limited product warranty Nominal Power 5 (Pnom) 370 W 360 W • UL 1703 Power Tolerance +5/−0% +5/−0% • UL 1741 / IEEE-1547 5 Certifications Module Efficiency 22.7% 22.1% • UL 1741 AC Module (Type 2 fire rated) and • UL 62109-1 / IEC 62109-2 Temp. Coef. (Power) −0.29%/°C −0.29%/°C Compliance • FCC Part 15 Class B

• Three bypass diodes • ICES-0003 Class B Shade Tolerance • Integrated module-level maximum • CAN/CSA-C22.2 NO. 107.1-01 power point tracking • CA Rule 21 (UL 1741 SA)4 (includes Volt/Var and Reactive Power Priority) Tested Operating Conditions • UL Listed PV Rapid Shutdown Equipment6

Operating Temp. −40°F to +185°F (−40°C to +85°C) Enables installation in accordance with: Max. Ambient Temp. 122°F (50°C) • NEC 690.6 (AC module) Wind: 154 psf, 7400 Pa, 754 kg/m² back • NEC 690.12 Rapid Shutdown (inside and outside the array) Max. Test Load7 Snow: 208 psf, 10000 Pa, 1019 kg/m² front • NEC 690.15 AC Connectors, 690.33(A)–(E)(1) Wind: 62 psf, 3000 Pa, 305 kg/m² back Design Load Snow: 125 psf, 6000 Pa, 611 kg/m² front When used with InvisiMount racking and InvisiMount accessories (UL 2703): Impact Resistance 1 inch (25 mm) diameter hail at 52 mph (23 m/s) • Module grounding and bonding through InvisiMount • Class A fire rated Mechanical Data When used with AC module Q Cables and accessories (UL 6703 and UL 2238)6 : Solar Cells 96 Monocrystalline Maxeon Gen III • Rated for load break disconnect High-transmission tempered glass with Front Glass anti-reflective coating PID Test Potential-induced degradation free

Environmental Rating Module: Outdoor rated Inverter: NEMA Type 6 Class II Frame Class 1 black anodized (highest AAMA rating) Weight 42.9 lb (19.5 kg) Recommended Max. 1.3 in. (33 mm) Module Spacing

1 SunPower 360 W compared to a conventional module on same-sized arrays (260 W, 16% efficient, approx. 1.6 m2), 4% more energy per watt (based on third-party module characterization and PVSim), 0.75%/yr slower degradation (Campeau, Z. et al. “SunPower Module Degradation Rate,” SunPower white paper, 2013). 2 Based on search of datasheet values from websites of top 10 manufacturers per IHS, as of January 2017. 3 #1 rank in “Fraunhofer PV Durability Initiative for Solar Modules: Part 3.” PVTech Power Magazine, 2015. Campeau, Z. et al. “SunPower Module Degradation Rate,” SunPower white paper, 2013. 4 Factory set to 1547a-2014 default settings. CA Rule 21 default settings profile set during commissioning. 5 Standard Test Conditions (1000 W/m² irradiance, AM 1.5, 25°C). NREL calibration standard: SOMS current, LACCS FF and voltage. All DC voltage is fully contained within the module. 6 This product is UL Listed as PVRSE and conforms with NEC 2014 and NEC 2017 690.12; and C22.1-2015 Rule 64-218 Rapid Shutdown of PV Systems, for AC and DC conductors; when installed according to manufacturer’s instructions. 7 Please read the safety and installation instructions for more information regarding load ratings and mounting configurations.

See www.sunpower.com/facts for more reference information. For more details, see extended datasheet www.sunpower.com/datasheets Specifications included in this datasheet are subject to change without notice. ©2020 SunPower Corporation. All Rights Reserved. SUNPOWER, the SUNPOWER logo and MAXEON are registered trademarks of SunPower Corporation in the U.S. and other countries as well. 1-800-SUNPOWER. Please read the Safety and Installation Instructions for details. 531945 RevC

sunpower.com

SolarPower Corporation module specification (Sunpower, 2018).

X LG350N1C-V5

General Data Electrical Properties (STC*) Cell Properties (Material/Type) Monocrystalline/N-type Model LG350N1C-V5 Cell Maker LG Maximum Power (Pmax) [W] 350 Cell Configuration 60 Cells (6 x 10) MPP Voltage (Vmpp) [V] 35.3 Number of Busbars 12EA MPP Current (Impp) [A] 9.92

Module Dimensions (L x W x H) 1,686mm x 1,016mm x 40 mm Open Circuit Voltage (Voc, + - 5%) [V] 41.3 Weight 17.1 kg Short Circuit Current (Isc, +- 5%) [A] 10.61 Glass (Material) Tempered Glass with AR Coating Module Efficiency [%] 20.4 Backsheet (Color) White Bifaciality Coefficient of Power [%] 10 Frame (Material) Anodized Aluminium Power Tolerance [%] 0 ~ +3 Junction Box (Protection Degree) IP 68 with 3 Bypass Diodes *STC (Standard Test Condition): Irradiance 1000 W/m², cell temperature 25°C, AM 1.5 **Measurement Tolerance: + 3% Cables (Length) 1,000mm x 2EA - Connector (Type/Maker) MC 4/MC Operating Conditions Operating Temperature [°C] -40 ~+90 Certifications and Warranty Maximum System Voltage [V] 1,000 IEC 61215-1/-1-1/2:2016, IEC 61730-1/2:2016, Maximum Series Fuse Rating [A] 20 UL 1703 Certifications Mechanical Test Load (Front) [Pa/psf] 5,400/113 ISO 9001, ISO 14001, ISO 50001 Mechanical Test Load (Rear) [Pa/psf] 4,000/83.5 OHSAS 18001 *Mechanical Test Load 5,400Pa/4,000Pa based on IEC 61215-2 : 2016 Salt Mist Corrosion Test IEC 61701:2012 Severity 6 Test Load = Design Load x Safety Factor (1.5) Ammonia Corrosion Test IEC 62716:2013 Module Fire Performance Type 1 (UL 1703) Packaging Configuration Fire Rating Class C (UL 790, ULC/ORD C 1703) Number of Modules per Pallet [EA] 25 Solar Module Product Warranty 25 Year Limited Number of Modules per 40' Container [EA] 650 Solar Module Output Warranty Linear Warranty* Number of Modules per 53' Container [EA] 850 *Improved: 1st year 98%, from 2-24th year: 0.33%/year down, 90.08% at year 25 Packaging Box Dimensions (L x W x H) [mm] 1750 x 1,120 x 1,221 Packaging Box Dimensions (L x W x H) [in] 69 x 44.25 x 48.25 Temperature Characteristics Packaging Box Gross Weight [kg] 485 NMOT* [°C] 42 ± 3 Packaging Box Gross Weight [lb] 1,070 Pmax [%/°C] -0.36 Voc [%/°C] -0.26 Isc [%/°C] 0.03 Dimensions (mm/inch) *NMOT (Nominal Module Operating Temperature): Irradiance 800 W/m2, Ambient temperature 20°C, Wind speed 1 m/s, Spectrum AM 1.5

Electrical Properties (NMOT) Model LG350N1C-V5 Maximum Power (Pmax) [W] 262 MPP Voltage (Vmpp) [V] 33.2 MPP Current (Impp) [A] 7.91 Open Circuit Voltage (Voc) [V] 38.9 Short Circuit Current (Isc) [A] 8.52

I-V Curves (Refer to the table)

12.0

10.0

8.0

6.0 Current (A) Current 4.0

2.0

0.0 0.0 10.0 20.0 30.0 40.0 Voltage (V)

LG Electronics USA, Inc. Product specifications are subject to change without notice. Solar Business Division LG350N1C-V5.pdf 2000 Millbrook Drive 051520 Lincolnshire, IL 60069 www.lg-solar.com © 2020 LG Electronics USA, Inc. All rights reserved.

LG module specification (LG Electronics, 2021).

XI CSUN Solar Tech module specification (CSUN Power, 2019).

XII REC Solar Holdings AS module specification (REC Solar Holdings AS, n.d.).

XIII D Wind Turbine Datasheets

Enair 3 kW turbine specification (ENAIR ENERGY S.L, 2021c).

XIV Enair 5 kW turbine specification (ENAIR ENERGY S.L, 2021d).

XV Enair 10 kW turbine specification (ENAIR ENERGY S.L, 2021b).

XVI E Time Schedules

Time schedule for Case 1.

Time schedule for Case 3.

XVII Time schedule for Case 4.

XVIII F Energy Production for the Additional Solution

Energy production with the 6 kW and 10 kW turbine.

XIX TRITA -ITM-EX 2021:272

www.kth.se