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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