Energy Saving Potentials in Railway Operations Under Systemic Perspectives

Research Collection

Doctoral Thesis

Energy Saving Potentials in Railway Operations under Systemic Perspectives

Author(s): Bomhauer-Beins, Axel

Publication Date: 2019-06

Permanent Link: https://doi.org/10.3929/ethz-b-000345776

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ETH Library Axel Bomhauer-Beins

Energy Saving Potentials in Railway Operations under Systemic Perspectives

Schriftenreihe 185

Institut für Verkehrsplanung und Transportsysteme Institute for Transport Planning and Systems

Diss. ETH No. 25937

DISS. ETH NO. 25937

Energy Saving Potentials in Railway Operations under Systemic Perspectives

A thesis submitted to attain the degree of

DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich)

presented by

AXEL BOMHAUER-BEINS

MSc ETH in Electrical Engineering and Information Technology

born on May 9, 1989 Citizen of Germany and Uster ZH

accepted on the recommendation of

Prof. Dr. sc. techn. Ulrich Weidmann Prof. Dr.-Ing. Arnd Stephan

2019

The Whole is More Than The Sum of Its Parts

Aristotle, 384–322 BC

Science has become a Church

Ernst Mach, 1838–1916

For all those who believed in me—and still do.

Dedicated to all who still remember that science and engineering are only capable to fulﬁl their duties towards society if sanity and reason as well as consideration of the entire system —its internal and external interactions, and its boundaries— are applied as guiding qualities.

In loving memory of my grandparents, Ursula & Dr. Volkmar Schmidt, from whom I learned a lot and where I always found interest in this work, but who left this world the year before this thesis was completed.

Acknowledgement

During my work for this thesis, I had the great pleasure to meet many different people, of whom most supported my work in one or another way. I would like to express my gratefulness for this support to all the major and minor supporters who stood by my side whenever I needed them.

My first and very special thanks go to my thesis supervisor, PROF.DR.ULRICH WEIDMANN, who accepted me—an electrical engineer—as scientific assistant and doctoral student in the field of traffic engineering and railway operations. He offered me the outstanding opportunity to add a lot of traffic engineering knowledge to my profile, provided many very special chances, and supported my work and my personal development in an admirable way. Thanks to his strong belief in my final success and many interesting and fruitful discussions, he supported this thesis in the best imaginable way. A special credit is given to him for continuing the mentoring of his doctoral students after his election into the executive board of ETH Zurich. Moreover, I would like to give thanks to PROF.DR.-ING.ARND STEPHAN for taking over the duty as my co-examiner. In interesting discussions he opened my mind for additional ideas and approaches, for considerations to be included, and thereby shaped this work to a certain degree. In addition, many thanks go to PROF.DR.IOANNIS ANASTASOPOULOS for taking the chair of my defence.

During my work in the group of Prof. Weidmann, I had different projects partly supporting this thesis, which were carried out with external partners. One of these projects was done in the context of the SBB Research Grant; for a good collaboration during this project, I would like to thank DR.STEFFEN SCHRANIL,DR.SIMON GINSBURG, and MR.MARKUS HALDER, from whom I also learnt a lot about the railway system in general and its energy demand in particular. Special thanks go to all of them for giving me the allowance to use the SBB data from that project for my doctoral thesis as well. Additionally, I would like to especially thank Dr. Steffen Schranil for his support and co-authorship for publications resulting from this project, as well as for his sarcastic and partly cynical comments on research, practice, and the every day life, which tended to make the latter somewhat easier. In different persons, I found supporters who provided some very useful information and/or data: Thank you, MR.DANIEL STEILING, for a highly interesting discussion on aerodynamics in railways. Thank you, MR.MATTHIAS TUCHSCHMID, for the helpful insight into the energy demand of comfort systems of different trains. Thank you to DR.MARTIN FENGLER and the entire METEOMATICS AG,ST.GALLEN for the straightforward allowance to use the weather data they collect.

– VII – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Also, I would like to mention MR.BETRAM HENNING,DR.MARCO LÜTHI, and MR.MARKUS ENZLER with whom I had interesting discussions on possible measurements and data delivery by different railway companies—SZU, RhB, and RBS. Even though we did not ﬁnd any possibility for a collaboration: Thank you for your interest and time!

Especially mentionable are my remarkable colleagues that I had the great pleasure to meet in Prof. Weidmann’s group. Dear all, thank you for a good time in this research group, thank you for good times off work, thank you for good col- laborations on projects, and thank you for productive and fruitful discussions concerning my thesis. Special thanks in this context go to my former colleagues DR.AMBRA TOLETTI,MR.MARTIN SOJKA,DR.MICHAEL SCHWERTNER, and DR.ERNST BOSINA, who delivered one or another interesting idea that helped me a lot in continuing my thesis. Also, I would like to thank DR.HERMANN ORTH for his continuous and successful efforts to create a team out of individually working doctoral students, thereby establishing a remarkable atmosphere in the group that still lasted after he had left the institute.

However, none of the above mentioned would have been possible without the strong, reliable, and lasting support of my parents. I would like to thank you, ASTRID and DR.RALF BOMHAUER-BEINS, so much for giving me the possibility to develop all the basic skills that are necessary to even start a doctorate. Thank you for giving me the opportunity to study electrical engineering and for your support when changing the field afterwards to traffic engineering. Thank you for supporting me by providing a ready ear, some space for the easing of tension, good meals compensating the canteen, and so much more—thank you for all that you have done for me! Also, I would like to give warm and special thanks to all of my dear friends who stood by my side during this thesis. All of you supported me in an outstanding way, each of you in your very own way, and each of you infinitely valuable. It would exceed this acknowledgement’s scope to name all of your merits that were much more than just stabilising me and supporting my volition to continue—but be assured, I am glad to know you, and I am grateful for what you have done for me. KEVIN CALUSER,MOANA RUSCH,CLAUDIA KRAWIETZ, ANNINA MOSER,KAROLINA KLUCZNIK, and MELODY GREMINGER: Thank you so very much! In addition, there were many people who supported this work—some know- ingly, others unintentionally. I would like to thank these people too, of whom in many cases I do not even know their name. Thank you for a smile in the street, for a nice word, or any other kind of support that somehow helped to keep my world revolving and gave me the necessary strength to continue my work!

To all the people out there that supported this work to smaller or larger extent: Thank you, thank you, thank you!

– VIII – Abstract

This thesis deals with the comprehensive topic of energy saving in railway operations. In contrast to prevailing literature, it focuses on systemic aspects and interactions, given the research question “which energy-oriented optimisations in subsystems show positive effects considering the entire railway system—and which additional saving potentials disclose by holistic analysis?”

To approach this question, a more comprehensive system understanding has to be gained. Due to the high degree of complexity, a closed analytic system description is not possible. Thus, based on literature and some data, the—to the author’s best knowledge—first model describing the entire energy chain from primary energy to wheel—connecting the domains of energy generation, energy transmission, operational decisions, vehicle driving dynamics, and drive chain—is developed. The model is built hierarchically, consisting of the five major subsystems vehicle, energy supply, track, operation control, and the environment, which are themselves built up from sub-subsystems. Naturally, a that comprehensive model requires some simplifications in order to keep the scope manageable. While the driving dynamics—as core domain of railway operations—are described quite precisely, especially in energy transmission and drive chain modelling, significant simplifications are applied. For the prior, a constant catenary voltage is used to determine catenary current and losses, which is valid for 15 kV,16.7 Hz and, with some limitations, for 25 kV,50 Hz systems—but not for DC systems, in which the catenary voltage is strongly depending on the actual operational situation. Also, the description of the drive chain as cascade of efficiencies is limited in precision—however, electro-magneto-mechanical models are required for a higher degree of precision, not being feasible for a first approach to a model of the entire system. Altogether, the definition and implementation of this comprehensive model is successful, fulfilling a task that prior projects—as Railenergy—formulated as goal, but did not reach it.

Possible approaches to energy saving are then collected from literature as first source. Thereby, the number of available publications found on this topic is that large that a complete review and treatment is impossible—which results in an expansive but not comprehensive study presented in this thesis. Additionally, the developed system model is analysed in order to understand systemic interrelations and identify possible additional saving potentials. Based on these results, the approaches of including environmental influences in operational decisions, synchronising braking and acceleration phases, and different applications of energy storage systems and supply system modifications are tested in case studies.

– IX – Energy Saving Potentials in Railway Operations under Systemic Perspectives

In the end, it is found that subsystem optimisations show a positive effect on primary energy level as long as they are properly engineered—i.e., as long as no side-effects of the measure’s implementation reduce or even annihilate the optimisation effect (e.g., additional weight vs. increased efficiency). In terms of systemic potentials, only few promising approaches are found; the most important measure in a systemic context proved to be the interconnection of electric supply systems. Then, especially regenerated braking energy can be used to a higher degree, but also transmission distances can be lowered, thus reducing losses. As this is intrinsically fulfilled in (most) AC systems, this study—which focuses on 15 kV,16.7 Hz systems—discloses significantly lower saving potentials as most of the literature, which focuses on DC metro systems that are typically fed unidirectionally. Additionally, it is found that the most suitable measure strongly depends on the actual situation, comprising the general technical-operational characteristic of the system (AC, DC; long-distance, commuter, ...) but also the specific implementation. Consequently, the future of research on energy saving in railway operations is mainly seen in applied research. Moreover, it has to be kept in mind that energy saving might conflict with other relevant aspects influencing the system’s attractiveness for its users, as time of travel or connecting services. Thus, a subtle balance between all stake- holders’ interests has to be found for each individual case.

–X– Zusammenfassung

Die vorliegende Arbeit widmet sich dem umfassenden Thema des Energie- sparens im Bahnbetrieb. Im Gegensatz zur gängigen Literatur konzentriert sie sich dabei auf Systemaspekte und -interaktionen mit der Forschungsfrage “Welche energieorientierten Optimierungen in Teilsystemen zeigen positive Ef- fekte unter Berücksichtigung des gesamten Bahnsystems – und welche zusätzli- chen Energiesparpotentiale ergeben sich durch ganzheitliche Betrachtung?”

Zwecks Beantwortung ist zuerst ein umfassendes Systemverständnis zu ge- winnen. Aufgrund des hohen Komplexitätsgrades ist eine geschlossene ana- lytische Systembeschreibung nicht möglich. Auf Grundlage der Literatur und einiger Daten wird deshalb das – nach bestem Wissensstand des Autors – ers- te Modell, welches die gesamte Energiekette von der Primärenergie bis zum Rad abbildet, entwickelt. Dabei werden die Bereiche Energieerzeugung, Ener- gieversorgung, Betriebsführung, Fahrdynamik und Antriebsstrang verbunden. Das Modell ist hierarchisch aus den fünf Haupt-Teilsystemen Fahrzeug, Ener- gieversorgung, Gleis, Betriebsführung und Umwelt aufgebaut, die selbst wie- derum aus Teilsystemen bestehen. Natürlich erfordert ein solch umfassendes Modell diverse Vereinfachungen, um es handhabbar zu halten. Während die Fahrdynamik als Kernfunktion des Bahnbetriebs sehr genau beschrieben ist, werden insbesondere bei Energie- übertragung und Antriebsstrang signiﬁkante Vereinfachungen in Kauf genom- men. Bei ersterem wird zur Bestimmung des Fahrdrahtstroms eine konstante Fahrdrahtspannung verwendet, was für 15-kV-16,7-Hz-Systeme und, mit ei- nigen Einschränkungen, 25-kV-50-Hz-Systeme zulässig ist – jedoch nicht für Gleichstromsysteme, bei denen die Fahrleitungsspannung stark von der ak- tuellen Betriebssituation abhängt. Auch die Beschreibung der Antriebskette als Wirkungsgradkaskade ist hinsichtlich Präzision begrenzt; für eine höhere Genauigkeit würden jedoch komplexe elektromagnetisch-mechanische Modelle benötigt, die für ein erstes Gesamtsystemmodell nicht realisierbar sind. Insgesamt ist die Deﬁnition und Umsetzung dieses umfassenden Modells er- folgreich, womit die Arbeit ein Ziel erreicht, das bereits in früheren Projekten wie Railenergy formuliert, jedoch nicht realisiert wurde.

Mögliche Energiesparansätze werden zunächst aus der Literatur gesammelt. Die Anzahl der zugehörigen Veröffentlichungen ist so groß, dass eine vollstän- dige Behandlung unmöglich ist – was sich in einer umfangreichen, trotzdem nicht umfassenden Literaturstudie äußert. Zudem wird das entwickelte Modell analysiert, um systemische Zusammenhänge und Sparpotentiale zu erkennen. Basierend auf diesen Ergebnissen werden verschiedene Ansätze in Fall- studien untersucht: Einbezug von Umwelteinﬂüssen in die Betriebsführung, Brems-Beschleunigungs-Synchronisation und Anwendungen von Energiespei- chern bzw. Modiﬁkationen des Versorgungssystems.

– XI – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Es zeigt sich, dass Teilsystemoptimierungen grundsätzlich einen positiven Ef- fekt auf Primärenergieebene haben, solange sie korrekt ausgeführt werden – d. h. solange keine Nebenwirkungen der Umsetzung den Optimierungseffekt reduzieren oder gar zunichte machen (z. B. zusätzliches Gewicht vs. Wirkungs- gradsteigerung). Im Hinblick auf systemische Potentiale werden nur wenige erfolgversprechende Ansätze gefunden; als wichtigste Maßnahme erweist sich die Vernetzung elektrischer Versorgungssysteme. Dann kann insbesondere re- generierte Bremsenergie in höherem Maße genutzt, aber auch die Übertra- gungsdistanzen können verkürzt und damit Verluste reduziert werden. Da dies inhärent in (den meisten) Wechselstromsystemen erfüllt ist, liefert die- se Arbeit, die sich auf 15-kV-16,7-Hz-Systeme konzentriert, deutlich geringere Sparpotentiale als die meisten Veröffentlichungen, welche typischerweise DC- Metrosysteme thematisieren – die üblicherweise unidirektional gespeist sind. Darüber hinaus wird festgestellt, dass die bestgeeignete Maßnahme stark von der jeweiligen Situation abhängt; von der technisch-betrieblichen Ausprä- gung des Systems (AC, DC; Fernverkehr, S-Bahn, ...) zum einen, von der spezi- ﬁschen Systemumsetzung zum andern. Die Zukunft der Energieforschung im Bahnbetrieb wird daher vor allem in der angewandten Forschung gesehen. Weiterhin ist zu beachten, dass Energiesparen im Widerspruch zu anderen relevanten Aspekten stehen kann, welche die Systemattraktivität in den Au- gen seiner Nutzer beeinﬂussen – Reisezeiten, Anschlüsse etc. Daher ist ein Gleichgewicht zwischen allen beteiligten Interessen anzustreben.

– XII – Table of Contents

Acknowledgement VII

Abstract IX

Zusammenfassung XI

List of Tables XIX

List of Figures XXI

List of Abbreviations XXIII

List of Units XXV

List of Symbols XXVII

1 Introduction 1 1.1 Research Motivation ...... 1 1.2 Literature Review—in Brief ...... 2 1.2.1 Introduction to Energy Research in Railway Applications ...... 2 1.2.2 Current Focus of Research ...... 3 1.2.3 Prevailing Literature under Systemic Criteria ...... 4 1.2.4 System Modelling and Analysis in Literature ...... 5 1.3 Research Question and Hypotheses ...... 6 1.4 Research Scope ...... 7 1.5 Research Structure ...... 9 1.6 Applied Methods ...... 11

2 System Modelling 13 2.1 Introduction ...... 13 2.2 Literature Based System Analysis ...... 14 2.2.1 System Overview ...... 14 2.2.2 Motorised Vehicle ...... 16 2.2.2.1 Vehicle Overview ...... 16 2.2.2.2 Traction Technologies ...... 19 2.2.2.3 Energy Preparation ...... 24 2.2.2.4 Drive Chain ...... 25 2.2.2.5 Force Transmission to Rail ...... 28 2.2.2.6 Acceleration Resistances ...... 30 2.2.2.7 Motion Resistances ...... 31 2.2.2.8 Deceleration (Braking) ...... 46

– XIII – Table of Contents

2.2.2.9 Traction Auxiliary Systems and Their Control ...... 50 2.2.2.10 Comfort Systems and Their Control ...... 52 2.2.2.11 Motion Control ...... 54 2.2.3 Non-Motorised Vehicle ...... 57 2.2.4 Energy Supply System ...... 57 2.2.4.1 System Structures ...... 57 2.2.4.2 Railway Power Grid ...... 60 2.2.4.3 Catenary Supply ...... 62 2.2.4.4 Energy System Modelling ...... 63 2.2.5 Track ...... 65 2.2.6 Operation Control ...... 67 2.3 Influences on Energy Demand ...... 69 2.3.1 Introduction and Overview ...... 69 2.3.2 Technical Factors ...... 69 2.3.3 Operational Interactions ...... 71 2.3.3.1 Operation and Dispatching ...... 71 2.3.3.2 Driving Strategy ...... 72 2.3.3.3 Passengers ...... 73 2.3.4 External Influences ...... 74 2.3.4.1 Topography ...... 74 2.3.4.2 Loading and Passengers ...... 74 2.3.4.3 Wind, Air Pressure, and Air Density ...... 74 2.3.4.4 Precipitation, Humidity, and Dust ...... 75 2.3.4.5 Outside Temperature and Solar Radiation ...... 75 2.4 System Model Formulation ...... 76 2.4.1 Intermediate Summary of Literature and Influences ...... 76 2.4.2 Model Specification for Additional Phenomena ...... 81 2.4.2.1 Wind Speed and Direction ...... 81 2.4.2.2 Tunnel Influence ...... 83 2.4.2.3 Auxiliary Systems ...... 84 2.4.2.4 Heating, Ventilation, and Air Conditioning (HVAC) ...... 87 2.4.3 Final Formulation ...... 89 2.4.3.1 Granularity and Structure ...... 89 2.4.3.2 Vehicle Model ...... 90 2.4.3.3 Energy Supply System ...... 98 2.4.3.4 Operation Control ...... 101 2.4.3.5 Track ...... 101 2.4.3.6 Environment ...... 102 2.4.3.7 Model Summary, Strengths, and Weaknesses ...... 102

3 Implementation, Calibration, and Validation 105 3.1 Program Implementation ...... 105 3.1.1 Concept and Structure ...... 105 3.1.2 The Vehicle Class ...... 108 3.1.3 The Environment Class ...... 109 3.1.4 The Energy Supply Class ...... 110 3.1.5 System Coordination: Operation Control ...... 112 3.1.6 Evaluation of Results ...... 113

– XIV – Table of Contents

3.2 Determination of Parameters ...... 114 3.2.1 Parameters of the Vehicle ...... 114 3.2.2 Parameters of the Environment ...... 114 3.2.3 Parameters of the Energy Supply ...... 116 3.3 Model Calibration ...... 117 3.3.1 Calibration Method and Measures ...... 117 3.3.1.1 Real World Data ...... 117 3.3.1.2 Calibration Method ...... 120 3.3.1.3 Model Quality Indicators ...... 122 3.3.2 Base Unit Problem ...... 123 3.3.3 Calibration Result Discussion ...... 124 3.4 Validation ...... 127 3.4.1 Programming Style ...... 127 3.4.2 Comparison to Real World Data ...... 130 3.5 Sensitivity Analysis ...... 132 3.5.1 General Idea and Procedure ...... 132 3.5.2 Aerial Resistance Coefﬁcient ...... 133 3.5.3 Wind Speed and Direction ...... 134 3.5.4 Vehicle Mass ...... 134 3.5.5 Rail Condition ...... 135 3.5.6 Drive Chain Efﬁciency ...... 136 3.5.7 Adhesion Mass ...... 137 3.5.8 Outside Air Temperature ...... 137 3.5.9 Energy Supply System ...... 138

4 Optimisation Potentials 139 4.1 Overview and Classification ...... 139 4.2 Targeting the Vehicles’ Energy Demand ...... 142 4.2.1 Technical Aspects of a Vehicle in Terms of Energy ...... 142 4.2.2 Drive Chain ...... 143 4.2.3 Comfort Systems ...... 144 4.3 Targeting the Vehicles’ Energy Balance ...... 145 4.3.1 Timetable Optimisation ...... 145 4.3.2 Conflict Avoidance ...... 146 4.3.3 Speed Profile Optimisation ...... 148 4.3.3.1 Overview on the Field ...... 148 4.3.3.2 Nature Inspired Algorithms ...... 149 4.3.3.3 Alternative Algorithms and Dynamic Programming ...... 150 4.3.3.4 Extended Observation of Constraints ...... 151 4.3.3.5 Synopsis ...... 152 4.3.4 Combinatory Approaches ...... 152 4.4 Improving the Substations’ Energy Balance ...... 154 4.4.1 Timetable and Speed Profile Optimisation ...... 154 4.4.2 Coordinated Control ...... 155 4.4.3 Track-Side Energy Storage ...... 156 4.5 Improved Usage of Braking Energy ...... 157 4.5.1 General Considerations ...... 157 4.5.2 Timetable and Speed Profile Optimisation ...... 158

– XV – Table of Contents

4.5.3 On-Board Energy Management ...... 159 4.5.4 Supply Network Adjustment ...... 160 4.6 Supply System Measures ...... 161 4.6.1 Technical Approaches ...... 161 4.6.2 Management and Smart Grid Approaches ...... 164 4.7 Further Studies and Proposals ...... 165 4.7.1 The EU Railenergy Project ...... 165 4.7.2 VDE Energieoptimaler Bahnverkehr ...... 166 4.7.3 Potentials in Rarely Considered Subsystems ...... 167 4.8 Literature Approaches’ Summary ...... 168 4.9 Systemic Potentials ...... 172 4.9.1 Identiﬁcation of Systemic Potentials ...... 172 4.9.2 Addressing Systemic Potentials ...... 173 4.9.2.1 Reduction of Transmission Losses ...... 173 4.9.2.2 Reduction of Losses from Drive Chain and Motion Resistances . 174 4.9.2.3 Improved Usage of Regenerated Energy ...... 174 4.9.2.4 Synopsis ...... 175

5 Intermediate Conclusion 177 5.1 Reason and Purpose ...... 177 5.2 Modelling the Railway System ...... 177 5.3 Energy Saving in Railway Systems ...... 178 5.4 Implications for the Empirical Evaluation ...... 182

6 Empirical Evaluation 185 6.1 Introduction to the Case Studies (CS) ...... 185 6.2 CS1: Considering Environmental Conditions in Operations ..... 186 6.3 CS2: Vehicle Synchronisation ...... 191 6.4 CS3: On-Board Energy Storage ...... 195 6.5 CS4: Power Supply Modiﬁcations ...... 202 6.6 CS5: Energy Storages in Unidirectional Supply Systems ...... 209 6.7 Summary and Conclusion ...... 211

7 Synthesis 215 7.1 Overall Summary and Conclusion ...... 215 7.2 Answer to the Research Question ...... 216 7.2.1 Hypotheses Discussion ...... 216 7.2.2 Research Answer ...... 219 7.2.3 Discussion of Methods ...... 220 7.3 Perspectives for Further Research ...... 221

A Wordings and Deﬁnitions 225

B Numbers and Parameters 227

C Line Characteristics 233

D Derivation of the Extended Wind Model 237

E Program Methods Documentation 247

– XVI – Table of Contents

F Results of the Sensitivity Analysis 263

Bibliography 271

Declaration of Pre-Publications 295

Author’s Curriculum Vitae 297

– XVII –

List of Tables

2.1 Traction Systems’ Subsystem Boundaries between Energy Preparation and Energy Usage ...... 23 2.2 Friction Coefficients for Braking (Wende 2003) ...... 30 2.3 Classification of Outer Aerial Resistances (Wende 2003) ...... 34 2.4 Equivalent Surfaces of Different Trains (Sachs 1973a) ...... 38 2.5 Some Tunnel Factors ...... 40 2.6 Breakaway Resistance Factors as proposed by Weidmann (2011) and Rotter (1966) ...... 42 2.7 Relevant System Model Parameters Influencing the Energy Demand . 70 2.8 Rail Conditions and Corresponding Friction Coefficient µTr (Filipovic´ 2015) ...... 75 2.9 Most Important Parameters of the Vehicle Model ...... 78 2.10 Most Important Parameters of the Energy Supply System ...... 80 2.11 Condensed Overview on Auxiliary Powers ...... 84 2.12 Parameters Used in the Railway System Model ...... 103

3.1 Parameters of the Vehicle Class and Their Determination ...... 115 3.2 Parameters of the Environment ...... 116 3.3 Energy Supply System Parameters and Their Values ...... 117 3.4 Implications of the Model Quality Indicators ...... 122 3.5 Final Calibration Quality Overview ...... 125 3.6 Validation Quality Indicators ...... 130 3.7 Parameter Conﬁguration for the Standard Train Run ...... 132 3.8 Travel Times and Energy Demands of the Standard Train Runs .... 132

4.1 Speed Proﬁle Optimisation: Synoptic Table ...... 152 4.2 Systemic Potentials, Affected Subsystems, and Measures ...... 176

6.1 CS1: Energy Demand for Different Operational Strategies and Wind Situations ...... 189 6.2 CS1 Variation: Results ...... 190 6.3 Timetables of Case Study 2 ...... 192 6.4 CS3: Energy Balances for the Different ESS ...... 198 6.5 CS3: Detailed Energy Demands with Different ESS ...... 200 6.6 Energy Demands of Case Study 4 ...... 207 6.7 Results of Case Study 5 ...... 210

B.1 Initial Parameter Values of the ICN ...... 229 B.2 Standard Values of the Major Classes’ Parameters ...... 230

D.1 Case Studies’ Main Properties ...... 243

– XIX – List of Tables

D.2 Relative Variation of Energy Demand due to Wind Inﬂuences ...... 244

F.1 Influence Quantification: Aerial Resistance Coefficient cw ...... 264 F.2 Influence Quantification: Mass Variation ...... 264 ...... F.3 Influence Quantification: Drive Chain Efficiency ηD 264 F.4 Influence Quantification: Air Temperature T ...... 265 ...... F.5 Influence Quantification: Rail Condition (fRC) 265 F.6 Wind Influences on the Olten–Solothurn Line ...... 266 F.7 Wind Influences on the Solothurn–Olten Line ...... 267 F.8 Wind Influences on the Biel–Yverdon Line ...... 268 F.9 Wind Influences on the Yverdon–Biel Line ...... 269

– XX – List of Figures

1.1 Illustration of the Research Structure ...... 10

2.1 Energy Flow in Railway Systems: Sankey Diagrams ...... 15 2.2 Scheme of Today’s Rail Operation (Lüthi 2009) ...... 17 2.3 General Qualitative Railway System Model ...... 18 2.4 Energy Flow Diagram of a Motorised Vehicle ...... 19 2.5 Schematic Representation of Different Electric Drive Topologies .... 20 2.6 Schematic Representation of the Diesel-Electric Drive System ..... 22 2.7 Efficiency of the Induction Machine over the Vehicle’s Speed Range .. 27 2.8 Friction Coefficient according to Curtius and Kniffler (1950) ...... 29 2.9 Motion Resistances and Their Classification ...... 32 2.10 Schematic Illustration of the Defined Quantities ...... 35 ...... 2.11 Geometric Situation to Derive Arib (Projection) 37 2.12 Wind Influence on the Total Resistance (Sachs 1973a) ...... 40 2.13 Classification of Brake Systems (Janicki, Reinhard, and Rüffer 2013) . 46 2.14 Vehicle Motion Control Scheme, Simplified ...... 54 2.15 Simplified Heat Model of an Electric Machine (W-tech 2017) ...... 56 2.16 Structure of Railway Energy Supply Systems (Biesenack, Braun, et al. 2006) ...... 59 2.17 Different Electric Models to describe the Catenary ...... 62 2.18 DC Traction Power Network Equivalent Circuit (Tian, Weston, et al. 2017) 64 2.19 Example of a Route Profile (Wägli 2010) ...... 66 2.20 Change of Slope with Transition Curve (Freystein, Muncke, and Schollmeier 2008) ...... 67 2.21 Interpretation of Signal Aspects as Modifications of Speed Limits ... 68 2.22 Levels of Operation Control ...... 68 2.23 Schematic Overview of the Vehicle Model ...... 77 2.24 Illustration of the presented Extended Model of Aerial Resistance ... 83 2.25 Illustration of the Tunnel Factor Estimation ...... 85 2.26 Illustration of HVAC Power Demands ...... 87 2.27 Architecture of the Railway System Model for Electric Systems ..... 91

3.1 Flow Chart Representation of one Time Step ...... 107 3.2 Flow Chart of an Energy Supply Class Calculation Step (simpliﬁed) .. 111 3.3 Examples for Result Tables from the Calculation Program ...... 113 3.4 Exemplary Real World Data for Calibration ...... 118 3.5 Merging of Distance and Time Based Data ...... 119 3.6 Exemplary Data Table of a Train Run ...... 119 3.7 Schematic Illustration of the Base Unit Problem ...... 123 3.8 Origin of Power Oscillations in Calibration and Validation (schematic) . 124 3.9 Speed and Power Results obtained in Calibration ...... 126

– XXI – List of Figures

3.10 (Pseudo) Code Snippet for Aerial Resistance Calculation ...... 128 3.11 Code Documentation Example ...... 129 3.12 Speed and Power Results obtained in Validation ...... 131 3.13 Influence of cw Variation on the Energy Demand ...... 133 3.14 Wind Influence on Energy Demand for the Yverdon–Biel Line ..... 134 3.15 Influence of the Rail Condition on Energy Demand and Travel Duration 135 3.16 Change in Energy Demand as Function of Drive Chain Efficiency ... 136 3.17 HVAC Energy Demand as Function of Air Temperature and Operation Duration ...... 137 3.18 Relative Energy Demand for Different Air Temperatures ...... 138

4.1 Functional Classiﬁcation of Energy Saving Approaches in Literature .. 141 4.2 Power Electronic Traction Transformer (Claessens, Dujic, et al. 2012) . 144 4.3 Integrated Real-Time Rescheduling (Lüthi 2008) ...... 147 4.4 Moving Horizon Approach (Yan, Cai, et al. 2015) ...... 153 4.5 Exemplary Application of the ADL System (Schranil and Keiser 2017) . 154 4.6 Examples for Train Cooperation (Sun, Cai, et al. 2014) ...... 159 4.7 Future Railway Energy Supply Grid and its Control (Pilo de la Fuente, Mazumder, and González Franco 2014) ..... 164 4.8 Rail Energy Harvester (Gao, Wang, et al. 2017) ...... 167

6.1 Geographic Overview on the Yverdon–Neuchâtel Line ...... 187 6.2 Speed Proﬁles around Neuchâtel Stop ...... 192 6.3 Excerpt from the Graphic Timetable 2018 around Neuchâtel (SBB AG 2018) ...... 194 6.4 Selected Power and Energy Proﬁles of On-Board Storages ...... 197 6.5 Additional Source Code for ESS Inclusion into Substations ...... 203 6.6 Geographic Overview on the Biel–Neuchâtel–Yverdon Line ...... 204 6.7 Illustration of the (Sub-)Cases Investigated in CS 4 ...... 205

B.1 Standard Settings of the Energy System’s Substations ...... 231

C.1 Characteristics of the Olten–Solothurn Line ...... 234 C.2 Characteristics of the Biel–Neuchâtel–Yverdon Line ...... 235

D.1 Deﬁnition of Yaw Angle α ...... 238 D.2 Behaviour of the Three Presented Models ...... 241 D.3 Illustration of the Aerial Resistance Model ...... 241 D.4 Wheel-Rail-Geometry ...... 242 D.5 Route Traces of the Case Study Routes ...... 244 D.6 Relative Energy Demand from Case Studies A and B ...... 244

– XXII – List of Abbreviations

Please note that the following list only contains frequently used abbreviations; all other abbreviations are explained in their respective context.

ABS Auxiliary Battery Substation EMU Electrical Multiple Unit AC Alternating Current EN European Norm (Eurocode) ADL Adaptive Lenkung ESS Energy Storage System a DAS of SBB EU European Union ASM Asynchronous Machine also “Induction Machine” GIS Geographic Information System ATO Automatic Train Operation GPS Global Positioning System ATP Automatic Train Protection HVAC Heating, Ventilation, and BLS Bern–Lötschberg–Simplon Air Conditioning a Swiss railway company BR Baureihe IEC International Electrotechnical German for “(Vehicle) Series” Commission ICE Inter City Express CH Switzerland a high-speed train of DB CO2 Carbon Dioxide ICN Inter-City Neigezug CS Case Study a tilting EMU of SBB IT Italy D Deutschland IVT Institut für Verkehrsplanung Germany und Transportsysteme DAS Driver Advisory System Institute for Transport Planning DB Deutsche Bundesbahn / and Systems, ETH Zurich Deutsche Bahn German State Railways 1949–1993 / LED Light Emitting Diode since 1994 LPC Line Power Converter DC Direct Current LSF Line Side Filter DIN Deutsches Institut für LV03 Landesvermessung 1903 Normung Swiss Geographic Coordinate System German Institute for Standardisation MBS Moving Block Signalling DMU Diesel Multiple Unit MC Motor Converter DRG Deutsche Reichsbahn- Gesellschaft OOP Object Oriented Programming DB predecessor, 1924–1937 OPC Operation Control

– XXIII – List of Abbreviations

PIS Passenger Information System PMSM Permanent Magnet Synchronous Machine prEN Draft of an European Norm

RhB Rhätische Bahn a Swiss railway company

SBB Schweizerische Bundesbahnen Swiss Federal Railways SM Synchronous Machine SNCF Société Nationale des Chemins de Fer Français French state railways

TGV Train à Grande Vitesse French high speed train TSI Technical Speciﬁcations for Interoperability

UIC Union Internationale des Chemins de Fer International Union of Railways UK United Kingdom

VEI Vehicle’s Energy Input

WGS84 World Geodetic System 1984 A geographic coordinate format

– XXIV – List of Units

The following list contains the units used in this thesis. However, units composed from those listed below (fractions, multiplications) are not mentioned separately.

° Degree angle m Meter length °C Degree Celsius temperature mbar Millibar pressure mF Millifarad electric capacity A Ampere current mH Millihenry inductance min, Min Minute time bar Bar pressure mm Millimeter length MJ Megajoule energy g Gram mass mph Miles per Hour speed GWh Gigawatt-Hour energy MVA Megavolt-Ampere apparent power mW Milliwatt active power h Hour time MW Megawatt active power H Henry inductance MWh Megawatt-Hour energy Hz Hertz frequency mΩ Milliohm electric resistance

J Joule energy N Newton force

K Kelvin temperature rpm Rotations per Minute angular speed kA Kiloampere current kg Kilogram mass s Second time kHz Kilohertz frequency km Kilometer distance t Tonne mass kN Kilonewton force kV Kilovolt voltage V Volt voltage kVA Kilovolt-Ampere apparent power VA Volt-Ampere apparent power kW Kilowatt active power kWh Kilowatt-Hour energy W Watt active power l Liter volume Ω Ohm electric resistance

– XXV –

List of Symbols

Please note that the following list of symbols is not comprehensive: Only frequently used symbols are listed here; all others are explained in their respective context.

Greek Latin

α (Wind) Yaw Angle a Acceleration αw Obstruction Coefﬁcient A Area or Constant Davis Coefﬁcient β Angle of Slope Ab Reference Area (air resistance) ∆ Difference Atun Tunnel Cross Section

ζ Rotational Mass Factor B Linear Davis Coefﬁcient

η Efficiency ⊂ Subset of ηD Drive Chain Efficiency c Coefficient or Factor ηEP Energy Preparation Efficiency cw Aerial Resistance Coefficient ηgen Electricity Generation Efficiency C Capacity or ηgrid Transmission Grid Efficiency Quadratic Davis Coefficient ηUW Substation Efficiency ηw2p Well-to-Plant Efficiency d Distance drt Distance of the Running Treads ϑ Retardation E Inline-Notation for power of ten µ Friction Coefficient ∈ Element of µBr Friction Coefficient, Braking E Energy µSl Friction Coefficient, Sliding Ekin Kinetic Energy µTr Friction Coefficient, Tractive Epot Potential Energy

ρ Air Density f Factor/Coefﬁcient or ρn Norm Air Density at 15°C Frequency % Speciﬁc Resistance f(...) Function of ...

fRC Friction Factor for Rail ϕ Heading (Course Angle) Condition Inclusion F Force ψ1 Wind Direction FAR Aerial Resistance Force ψ2 Wind Source Direction FAR,i Inner Aerial Resistance Force

– XXVII – List of Symbols

FCR Curve Resistance Force Pn Nominal Power Rating Fel Electric (Traction) Force Pprim Primary Power Fmech Mechanical (Traction) Force PTr Tractive Power FR Resistive Force PUW Substation Input Power FRR Rolling Resistance Force PVEI Overall Vehicle Power FSR Slope Resistance Force FTr Tractive Force Q Reactive Power Qtot Total Air Flow within a Train g Gravitational Acceleration r Radius i0 Inclination ( ) rc Curve Radius I Current h R Electric Resistance IR Integral Ratio R0 Electric Resistance per Length R Set of Real Numbers kt Tunnel Factor s Distance l Length SE Error Standard Deviation la Wheelbase t Time m Mass T Temperature madh Adhesion Mass Ta Air Temperature mload Load Mass mpayload Maximum Payload Mass U Voltage mt Current Train Mass mtare Empty Weight v Velocity / Speed mtot Total Mass v0 Initial Speed ME Mean Error vmax Maximum Speed vset Set/Target Speed n Number of ...; n ∈ N vt Train Speed N Set of Natural Numbers vw Wind Speed (0 6∈ N) V Volume p Pressure wl,r Rolling Resistance Coefﬁcient pa Absolute Air Pressure P Power x Coordinate in the Train’s Paux Auxiliary Systems Power Direction of Movement; or PBr Braking Resistor Power Variable / Unknown Value Pcomf Comfort Systems Power PD Drive Chain Input Power y Coordinate Across the Train’s PFP Feeding Point Power Direction of Movement; or Ph Hourly Power Rating Variable / Unknown Value PHVAC HVAC Power PL Power Losses z Vertical Coordinate

– XXVIII – 1 Introduction

1.1 Research Motivation

Energy efficiency and other topics related to energy are highly present in today’s world, literature, and research. Thus, one could ask whether another research study on energy saving in railways is necessary. Well, it is. First of all, energy efficiency is not just “a topic”. It is a topic of environmental and societal importance, as we can be hold responsible for our legacy—be it positive or negative—by future generations. This duty also comprises railway transportation, even though it can be regarded as an energy efficient, sustainable, and environmentally friendly mode of transportation. Second, railways are an important part within the transportation system— for passenger as well as for freight services. In the former, they play a key role especially in commuter systems in urban areas. Thereby, steadily increasing numbers of passengers motivate the system operators to push the system closer to its capacity limit; on the other hand, decreasing prices of road and air put the system under financial pressure. Additionally, transportation in general is one of the most significant users of energy: 35 % of the energy consumed in Switzerland are used by transport means (Bundesamt für Energie 2014); approximately 3 % of this energy—roughly 2400 GWh per year—are delivered to railway systems connected to the 16.7 Hz grid of the Swiss Federal Railways (SBB). Until 2030, the SBB forecast this demand to increase by about 25 %; in future, the energy demand of railways will be billed according to the real (measured) demand and no longer based on a mean value, at least in Switzer- land (Schweizer Bundesrat 2018). This trend towards an increasing energy demand together with a more precise billing—including additional fees if no energy meter is used—is becoming more and more expensive and potentially economically dangerous: for train operating companies, their customers, and the infrastructure operators. Third, there is a certain lack of research not only considering energy demand (and possibly some arbitrary parameters) but also operations and systemic perspectives—cf. section 1.2 and chapter 4. There are many researchers investigating subsystems and finding different possibilities, but rarely any study on how the highly complex system of railways would react on these proposed optimisations. Moreover, the railway system does not only consist of urban rail, but the research is practically exclusively focused on this kind of railways.

– 1 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Consequently, a systemic analysis of the entirety of a generic railway system is to be seen as a necessary approach. The necessity of a systemic analysis is supported by the fact that only this way system-optimal solutions can be found by combining the relevant subsystems. Contrariwise, the sum of optimal subsystem solutions does not coactively lead to the system-optimal solution— even worse, an optimisation within one subsystem might lead to a counterproductive reaction of another subsystem. By development of a comprehensive model and combination of the newest available technologies, this can be avoided, and the overall energy demand from primary energy to wheel in a railway network can be analysed. Altogether, there are more than sufficient reasons to push research into the direction of including systemic perspectives, as only a holistic treatment can deliver integral solutions. Even more as the—originally similarly focused— EU project Railenergy (cf. section 4.7.1) can be seen more as a basis for sub- systemic optimisations than as holistic analysis. Moreover, the systemic perspective is identified as most promising approach to energy saving in railway operations by Dube, Fraas, et al. (2013, p. 75), while today’s knowledge on systemic level is stated to be insufficient—see also section 4.7.2. Thus, this thesis is meant to be a step into the direction of holistic system analysis.

1.2 Literature Review—in Brief

1.2.1 Introduction to Energy Research in Railway Applications The idea of reducing energy demand in railway operations is most probably as old as the system itself; also, considerations on a better use of capacity have been made for a long period in time. Both together resulted, for example, in signals that gave speed advises to the driver, aiming to avoid unnecessary stops— which are, as generally known, toxic for good capacity usage and low energy demand. In Germany, these signals existed 1935–2006 (Behmann 2015). Also in research, the topic is not as new as sometimes considered. For instance, Ichikawa (1968) proposes the application of optimisation theory in order to minimise the energy demand of a train run, which could be regarded the first speed profile optimisation. Talukdar and Koo (1979) follow a similar approach, formulating a multi-objective optimisation problem with conflicting objectives energy demand and total time. Mellitt, Mouneimne, and Goodman (1984) propose the usage of inverting substations in DC transit systems, proving their concept by simulation. The usage of fuzzy control for Automatic Train Operation (ATO) systems—basically speed profile generation—is discussed by Yasunobu, Miyamoto, and Ihara (1984). And genetic algorithms, today’s number one topic, appear towards the late 1990s, as e.g. by Chang and Sim (1997). In-depth research programs emphasise the increasing importance of train run optimisations (Howlett 1990, 2000; Howlett and Cheng 1997; Howlett, Milroy, and Pudney 1994; Howlett and Pudney 1995).

– 2 – Chapter 1: Introduction

A publication of Meyer and Aeberhard (1997) can be seen as kind of an early overview on the field. Starting from the fact that a locomotive’s energy demand costs are in the same order of magnitude as its acquisition costs, possible approaches to energy demand reduction are shown. As a first factor, the train’s running resistances are identified—the energy spent to overcome those cannot be retrieved, the same applies to the kinetic energy converted to heat using mechanical brakes. Vice versa, kinetic energy is basically retrievable as long as the vehicle is equipped with an electric brake—and this brake is used. A similar consideration is valid for potential energy. These identifications imply the energy conversion’s efficiency as one human- controllable influence factor on energy demand: The more efficient the (electric) energy is converted into kinetic energy and back, the lower the losses and the total energy demand are. Secondly, mass and resistance reduction have positive effects in terms of a lower energy demand. As additional point, control is mentioned: The better the regenerative brake usage, the higher the amount of retrieved energy. The better the control system itself, the lower the energy demand, as e.g. parts of the drive system might be switched off. Outside the vehicle, losses occur within the energy supply system, i.e. transformation and transmission. Also, influences of comfort systems as well as driving style are considered non-negligible.

1.2.2 Current Focus of Research At the time being, research on energy demand reduction in railway systems is highly topical, resulting in a huge amount of publications available. As it would be inappropriate to deal with this extensive topic in an introductory context, the reader is kindly requested to refer to sections 4.1–4.8 for details. Summarising the current literature, the following nine basic approaches can be identiﬁed:

1. Speed Proﬁle Optimisation,

2. Timetable Adjustment,

3. Conﬂict Avoidance,

4. Application of Energy Storage Systems (ESS),

5. Bidirectional Feeding Systems (especially in DC supplied networks),

6. Drive Chain Improvement,

7. Mechanical Vehicle Improvements,

8. Ancillary (i.e., auxiliary and comfort) Demand Reduction, and

9. Reduction of Transmission Losses.

– 3 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

On some of them, good fundamental, review, or overview publications exist. One of these is by Albrecht (2008), who approaches the topic by minimising the mechanical energy consumption (including speed profile optimisation) under practical billing systems and in terms of energy efficient driving—basically dealing a bit more in detail with some of the potentials already shown by Meyer and Aeberhard (1997)—see section 1.2.1. Similar work is done by Koseki (2010), who presents a general discussion on energy saving technologies for railways, and Hillmansen (2012), who describes the fundamental vehicle kine- matics and discusses the approaches of regeneration, energy storage, train control optimisation, and operational effects. Schmid and Goodman (2014) conduct a strengths-weaknesses analysis of the rail transport mode in order to derive possible approaches to energy saving, while Douglas, Roberts, et al. (2015) come to similar conclusions starting from a technical point of view, allocating the potentials to subsystems as vehicle or operations. Finally, a publication by Schranil and Grossenbacher (2016) is to be mentioned. Focusing on production (i.e., operation), a high usage of available capacity, centralised control, and automated operation are identified as most important topics. It is shown that using an overarching approach, e.g. by combining operation with train control, better results in terms of capacity usage and energy efficiency can be reached.

1.2.3 Prevailing Literature under Systemic Criteria As already mentioned, most of the currently available literature focuses quite detailed on subsystems of the railway system; publications dealing with more than one subsystem or even the entire system are rather rare. One of the systemic approaches can be found in the EU Railenergy project (2006–2010), which is discussed more in detail in section 4.7.1. Despite its systemic claim according to the project description (Railenergy), the (accessible) results are more on a subsystem—or even component—level. A more prospective approach is presented by De Martinis, Weidmann, and Gallo (2014). Their framework combines speed profile optimisation with railway traffic simulation, evaluating the impacts on the system as e.g. delays or conflicts. Thus, the optimisation of one train at the expense of others is avoided. Within the same context of integrating traffic and train control, the works of Weidmann, Laumanns, et al. (2015) and Rao (2015) are to be pointed out. González-Gil, Palacín, et al. (2014) state that the energy consumption of urban rail—as special case of railway systems—is defined by many interdependent factors, requiring a systemic analysis “rather than focusing on energy savings at subsystem level”. Limiting their research to DC systems and combining all available approaches to energy saving, the authors show by “a hypothetical example of application” that up to 35 % of energy might be saved.

– 4 – Chapter 1: Introduction

Stephan and Körner (2014) outline, based on a VDE1 study (Dube, Fraas, et al. 2013), some systemic questions: Which parameters have which influence on energy demand, and to what extent? What is the real result of changing a parameter? Which additional measures are suitable for further improvements? Analysing the energy flow of the entire railway system, they identify traction system, ancillary systems, driving dynamics, operation, and stationary equipment as groups of influencing factors—basically confirming the before- mentioned groups of approaches. Applying a value benefit analysis on a rather generic level, they state that despite the already high efficiency of railways, the fields of reducing losses, avoiding energy waste, and enforcing optimisation are most promising. The final catalogue of measures resulting from that project is presented by Stockhausen, Weem, et al. (2017)—as a very important result, the fact that the most promising measures strongly depend on the operational- technical type of the system—long-distance, regional, metro, freight, AC, DC, etc.—was obtained. Also to be mentioned is the publication by Tian, Weston, et al. (2017), who investigate a metro system including the power supply between substation and vehicle. Thus, this work can be seen as one of the first publications since quite some time that raises a (kind of) systemic claim.

1.2.4 System Modelling and Analysis in Literature When searching for literature concerning railway system modelling, the list of results is rather short. Handbooks deliver some equations that are in most cases somehow based on experimental determination of parameters. Some of these books are by Filipovic´ (2005), Hay (1982), Janicki (2011), Janicki, Reinhard, and Rüffer (2013), Müller (1940), Pachl (2011), Sachs (1973a,b), and Wende (2003)—part of them obviously rather old. Even the publication on methods to determine train resistances by Rochard and Schmid (2000) is based on these (and other) publications from between 1837 and 1995. As discussed more in detail in section 2.2, a majority of the equations from literature are based on rather uncertain parameters; consequently, the determination of forces and thus the energy demand will show a certain error. Nonethe- less, it is—if even revealed—exactly these equations and models that today’s simulation-based research is built on, finally presenting energy and time savings in percent with up to two decimal places (cf. chapter 4). Actually, in a late phase of this work, a publication by Roch-Dupré, Cucala, et al. (2018) proved that in the context of reversible substation studies, too many simplifications are taken in traffic models—which might be portable to system models as well. In terms of system analysis, which could allow to calibrate the equations’ parameters, few publications exists. While a study on ancillary systems’ demand has been published by Isenschmid, Menth, and Oelhafen (2013) and another on

1Verband der Elektrotechnik Elektronik Informationstechnik e.V.—Association of Electrical Engineer- ing, Electronics, Information Technology

– 5 – Energy Saving Potentials in Railway Operations under Systemic Perspectives driving dynamics by Schranil and Lavanchy (2016), most of the knowledge is only available from railway operators’ specialists, e.g. Mr. Tuchschmid (2017) from Swiss Federal Railways (Schweizerische Bundesbahnen—SBB). In addition, simulation tools that combine railway trafﬁc with energy supply simulation have to be mentioned. These are commercial industrial products, which makes them state of the art on the one hand’s side, while ﬁnding any information about these tools is hard on the other. For some of them, publications are available (Aeberhard and Basler 2016; Aeberhard, Basler, et al. 2015; Bagnall, Imrie, and Jacob 2012), where their usual application is discussed: Di- mensioning of railway power supply. Due to the required degree of detail, the energy demand of the investigated scenario can be obtained as “side product”. These tools are discussed slightly more in detail in section 2.2.4.4.

1.3 Research Question and Hypotheses

As to be seen from the previously presented literature review, there is a strong focus on operations research—in most cases fully quantifiable and/or quanti- fied investigations—found in current scientific literature. Different subsystems of the railway system are investigated into the deepest detail, while there are few systemic approaches. On the other hand, complex technical systems cannot be fully described by means of mathematics, they require an engineering approach: systems engineering. The main reason for this is a fact that has been identified and named by González-Gil, Palacín, et al. (2014): The energy demand of—in case of the cited study—urban rail is defined by “a wide range of interdependent factors”. A fact not only true for urban rail, but (at least) for all kinds of railway systems. Somehow striking the core questions mentioned by Stephan and Körner (2014), this study delivers an interesting entry point to systemic studies. Taking the approach of combined investigation of different subsystems, as presented by Weidmann, Laumanns, et al. (2015), and—in contrast to González-Gil, Palacín, et al. (2014)—not (a priori) limiting the study to a specific type of railways, the following research question is formulated:

Which energy-oriented optimisations in subsystems show positive effects considering the entire railway system— and which additional saving potentials disclose by holistic analysis?

Addressing this research question, the following hypotheses are formulated:

1. A functional-qualitative, energy oriented, and energy carrier independent system description (model) exists. 2. This model can be built hierarchically from sub- and sub-subsystems.

– 6 – Chapter 1: Introduction

3. From this system description, a quantifiable model can be derived. 4. System properties, system states, state-changes, or external influences translate into model parameters and/or input variables. 5. Improvement potentials for the sub- and sub-subsystems are largely known and well-documented in literature. 6. Today’s state of analysis for potentials in a systemic perspective including measures’ interactions is rather weak. 7. There are different improvement objectives whose application is mainly depending on network properties2, actual operating conditions, and involved actors. 8. Applying a system-wide approach allows to find system-optimal solutions. 9. The effectiveness of existing energy saving approaches can be classified.

In order to ensure a proper wording and understanding, the two—with regard to research question and hypotheses very central—terms of “energy saving potential” and “systemic potential” are deﬁned as follows:

An ENERGY SAVING POTENTIAL is successfully addressed by a measure, if the positive effect of this measure is traceable at primary energy level.

To address a SYSTEMIC POTENTIAL, the measures to be taken will have to affect at least two subsystems.

1.4 Research Scope

This research focusing on energy saving potentials in railway operations deals with the system in its entirety. This implies that basically, the system from primary energy carrier down to a vehicle’s wheel is within the scope of this work. Obviously, a that broad scope makes a detailed description of single system parts impossible; large parts of the work are based on existing literature. Moreover, the individual subsystems (domains) have to be—more or less—simplified in order to obtain a manageable complexity of the final model, which is intended to depict the entire system from primary energy to wheel. In the first parts of the thesis, the spectrum is kept as broad as possible, investigating the possibilities of railway system modelling (chapter 2); the same is valid for the literature analysis and system investigation for energy saving potentials (chapter 4). However, during the final model formulation (section 2.4) and this model’s implementation (chapter 3), stronger limitations had

2e.g. topography, topology, usage

– 7 – Energy Saving Potentials in Railway Operations under Systemic Perspectives to be set. For instance, the simulation program is prepared for use with different energy supply systems—but only one system is fully implemented and used due to necessary delimitations in modelling: The important mainline supply system 15 kV,16.7 Hz. Moreover, the energy system from primary energy carrier to the vehicle’s energy input—i.e., the pantograph in electric supply systems—had to be regarded in a simplified manner. For all levels “above” sub- or fuel station—i.e., closer to the primary energy—efficiencies have been set. These rather complex systems are topic in other research disciplines and thus simplified in order to keep the scope of this work manageable. Even for the electric energy supply below substation level, i.e. between substation and vehicle, a simplified modelling approach based on the method of power flow analysis had to be used. The case studies (chapter 6) finally focus on the evaluation of selected energy saving approaches in mainline applications (15 kV,16.7 Hz) under systemic aspects, which can be done using the developed model.

Thus, the reader is kindly requested to take note of the following, explicit limitations of this thesis’ scope: – There is no own optimisation of the system or its subsystems performed: The evaluations are based on pre-defined cases rather than on the application of optimisation methods. – Energy saving potentials are evaluated and discussed in a systemic context, but not detailed on their own. – The modelling is based on literature; only few additional subsystem models are developed within this thesis. – The precision of the electric supply model is limited: Efficiencies are used for all layers above the substation; a simplified modification of the power flow analysis between substation and vehicle. – This theoretical part of this thesis deals with all relevant supply systems. However, the empirical evaluations are focused on mainline railways with 15 kV,16.7 Hz supply, as model delimitations require this on the one hand’s side, while they lag presence in today’s research on the other. – This research aims for recognising and evaluating potentials in a systemic context, but does not want to analyse their usability/exploitability or quantify them in an universally valid context. Altogether, this thesis builds upon a broad spectrum of existing knowledge, methods, and models. However, these methods and models are mostly existing for one domain, as e.g. driving dynamics or energy supply. Only in rare cases, two of these domains are modelled and/or investigated jointly—and if, usually not more than two domains are included; in many cases, these works focus on different aims than energy demand. At this point, this thesis starts: Taken the existing knowledge, an additional scientific value is created by extending the

– 8 – Chapter 1: Introduction range of one single system model, integrating the entire railway system from power plant—or even primary energy—down to a vehicle’s wheel, describing all of the involved domains. Of course, this requires to reduce the degree of detail within the modelling of the individual domains in order to enable the formulation of a comprehensive model within the scope of a Ph.D. research.

1.5 Research Structure

Aiming to answer the research question formulated beforehand in the synthesis (chapter 7), three main pillars were built to support the result:

– A comprehensive model of the railway system from primary energy to wheel, developed in chapter 2; – an overview of existing approaches to energy saving in railway systems, discussed in chapter 4, as well as saving potentials derived from the model as done in sections 4.9, 5.3, and 5.4; and – a simulation program that implements the model—as described in chapter 3—and allows to evaluate energy saving measures—chapter 6.

Thereby, this thesis applies the following logic structure of presentation:

1. Analyse, understand, and describe the railway system from primary energy to wheel and quantify it where possible from literature, i.e., build a model— chapter 2.

2. Implement, deﬁne its parameters, calibrate, and validate the model of today’s system; additionally, analyse its sensitivity—chapter 3.

3. Perform a literature research on energy saving potentials, approaches, and methods; collect and structure the results and condense the most important approaches—chapter 4.

4. Explicitly investigate systemic approaches to energy saving in railway systems (section 4.9) and deduce from those ﬁndings implications for the systemic potentials and effects to be evaluated empirically—chapter 5.

5. Deﬁne, conduct, evaluate, and describe the case studies derived from the results found beforehand—chapter 6.

6. Summarise all ﬁndings, draw the overall conclusions, and answer the research hypotheses and question from section 1.3; then, evaluate the results concerning perspectives for further research—chapter 7.

For the purpose of illustration, both schemes—the supportive building as well as the consecutive presentation logic—are sketched in Figure 1.1.

– 9 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Synthesis

Case Studies Intermediate Conclusion

Optimisation Railway System Potentials Simulation Program Determination Railway System of Parameters Model Literature Measured Research Equations Equations Data for found in derived from Calibration & Literature Lit. & Data Validation

(a) Three Base Pillars support the Summary and Conclusion found during this Research Project. The shaded elements correspond to the main chapters.

Literature Research

2 Railway System Modelling

Data 3 Implementation

Intermediate 5 Optimisation 4 Conclusion Potentials

6 Case Studies

7 Synthesis

(b) The Procedure, Methods, and Results are presented in the Structure shown above. The numbers in circles placed in the top right corner of the boxes correspond to the respective chapter’s number.

Figure 1.1: Illustration of the Research Structure. In the upper graphics, the thesis’ contents shown as structure bearing the ﬁnal result in form of the synthesis; in the lower, the presentation/work ﬂow throughout the thesis is shown. The colour shades are the same for the corresponding elements of the upper and the lower graphics (own illustration).

– 10 – Chapter 1: Introduction

1.6 Applied Methods

In general, this thesis aims for finding a possibility to describe and analyse all interrelations that occur in a railway system—investigated from primary energy down to the vehicle’s wheel. A this broad scope of course requires a variety of simplifications, which imply some limitations in terms of applicability for certain parts of this thesis, e.g., not all supply systems can be described adequately using the final model. The model itself is necessary due to the fact that the entire investigation is logically based on a driving vehicle. This driving process is discontinuous, as there are instantaneous transitions between different states—e.g., accelerating and cruising. Moreover, these states are discontinuous when described more in detail; various mutual influences and circular references have to be taken into account. Consequently, there is no closed analytical formulation and solution to this problem. Rather, a step based simulation is required, which first determines the driving dynamics and operational influences, then the supply of the vehicle, and finally, the supply of the system supplying the supply system. Given this background and the research question from section 1.3, different methods were applied that are shortly presented in the following.

For the first two hypotheses—existence of a qualitative model that can be built hierarchically from sub- and sub-subsystems—a comprehensive literature research was conducted in section 2.2. For large parts of the system, good descriptions and models were found in technical books, some others in scientific papers. These describing equations were directly taken as system models, also allowing to derive the assumed hierarchical structure of the system by means of systems engineering. This led to a first understanding of the system, analysing the equations for influences on energy demand in section 2.3 as basis for the final model formulation, which is presented in section 2.4.1. However, this model still shows some imprecision, requiring the additional development of new subsystem models as discussed in section 2.4.2. The models presented there are either derived from literature studies of related fields, obtained from curve fitting based on data found in literature, or results of curve fitting based on measured data. Additionally, general knowledge was considered when developing and deriving some parts of the model. Combining these models, a comprehensive set of equations was obtained, describing the entire system as in section 2.4.3. However, in order to reduce complexity to a manageable degree, some simplifications were applied, especially concerning the electric supply system. Con- sequently, not all types of supply systems are described precisely; the empiric studies are thus limited to the 15 kV,16.7 Hz system. With the formulation of the model as set of equations, a part of the third hypothesis—quantifiability of the model—is already answered: If the parameters are definable, the model is quantifiable. For most of the parameters, their quantification was already obtained from the literature studies on the model,

– 11 – Energy Saving Potentials in Railway Operations under Systemic Perspectives others can be read from technical data or similar. A third group of parameters, which are basically unknown, were derived from specifications of other parameters that were provided by the Swiss Federal Railways (SBB), cf. section 3.2. For the fourth hypothesis, a simulation tool based on the system model was implemented in MATLAB and used for system analysis, as described in chapter 3. As programming method, object oriented programming (OOP) was chosen, allowing to implement the subsystems as objects, while a subsystem’s subsystems were implemented as methods (functions); parameter values are stored as object properties. For the simulation itself, sequential programming is used in the main scripts. Given the tool to be working, the hypothesis is tested, simultaneously implementing a new energy demand evaluation method. The fifth hypothesis again requires an extensive literature study. By gaining an overview on the field of energy demand reduction research, a logical classification of the approaches becomes possible in terms of point of evaluation (e.g., the substation’s energy balance) as well as in terms of applied method to reduce the energy demand. Based on the points of evaluation, which are presented in section 4.1, the findings are discussed in sections 4.2 to 4.6. For hypothesis six that assumes a weak state of analysis concerning systemic potentials and interaction of different simultaneously applied measures, the literature study is targeted on these approaches. The findings—as well as some additional, rather rare and special approaches—are discussed in section 4.7. Based on the overall literature approaches’ summary in section 4.8 and the priorly derived system model, the definition, addressing, and conclusion on systemic potentials is given in section 4.9. This also allows to deal with hypothesis seven, which is drawn in the conclusion of chapter 5. For answering hypotheses seven to nine, an empirical evaluation of the railway system and the influences of different measures is performed. As method of choice, five case studies presented in chapter 6 are selected. Thereby, all of these studies are based on existing mainline railway lines that are then varied according to the definition of the respective case study. By comparison to Status Quo, the measures’ influences are derived and the hypotheses tested. Finally, all methods and results are summarised in the synthesis of chapter 7, allowing to draw an overall conclusion, discuss the hypotheses, and formulate the answer to the research question.

– 12 – 2 System Modelling

2.1 Introduction

In thesis, possibilities to describe and analyse interactions that occur in a railway system are searched, ﬁnally aiming for energy saving approaches that are useful on system level—investigated from primary energy down to the vehicle’s wheel. Therefore, the system needs to be properly understood.

Basing the understanding and investigation on the central unit of railway operations, the vehicle, its discontinuous driving process is an important part. This process does not only show instantaneous transitions between its states– accelerating, cruising, coasting, and braking—moreover, these states are discontinuous when described in detail; various mutual influences and dynamic, circular references have to be taken into account. Consequently, there is no closed analytical formulation and solution to this highly complex problem. Rather, a step based simulation is required, which firstly determines the driving dynamics and operational influences, then the supply of the vehicle, and finally, the supply of the system supplying the supply system. The model developed in the following serves as basis for this intended simulation.

First of all, the railway system’s part consisting of the vehicle and its dynamics is investigated and discussed—sections 2.2.2 and 2.2.3. When studying the literature, the age of the different publications strike the reader’s eye—still, also Rochard and Schmid (2000) base their overview publication on train dynamics on this literature from between 1837 and 1995. However, the literature is comprehensive and allows a good description of most subsystems. Then, given the results of calibration and validation— sections 3.3.3 and 3.4.2—the use of this “older” literature is justiﬁed, as the model’s precision is good in terms of speed and power. Nonetheless, the existence of more recent research would have been appreciated.

In a next step, the energy supply is investigated. Discontinuous supply types are discussed in brief, while the focus is set on continuous supply systems— i.e., electric. Also this subsystem shows a high complexity, enforcing simpliﬁ- cations to enable a full-systemic formulation. The supply system is split into “below substation”—catenary supply, section 2.2.4.3—and “above substation”, section 2.2.4.4. The applied simpliﬁcations limit the models validity, especially for DC systems—which is discussed in the respective paragraphs.

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2.2 Literature Based System Analysis

2.2.1 System Overview For a research topic focusing on a system’s energy demand, an energy-oriented analysis of the system under investigation offers itself as an appropriate starting point. Based on the energy flow analysis carried out by Douglas, Roberts, et al. (2015), an approximate Sankey diagram can be provided as anchor for the upcoming considerations, cf. Figure 2.1. Defining the main energy sink of a railway system—the vehicle—as central point of observation, the “vehicle’s energy input” (VEI) results to be a subsystem boundary. For vehicles with continuous energy supply (usually electric), the VEI is the pantograph (or col- lector), for vehicles with discrete energy supply, it is the fuel door (or similar). From Figure 2.1, energy supply, vehicle, and environment (as energy sink for losses) prove to be three relevant subsystems. In terms of energy, the main parts of the railway energy chain can be defined as follows:

Energy Supply from primary energy to the vehicle’s energy input (VEI)

Traction Energy Preparation from VEI to traction energy3

Traction Energy Usage according to Figure 2.1b3

Environment (i.e. nature) as energy sink Thereby, the distinction into Traction Energy Preparation and Traction Energy Usage allows a more precise analysis of the energy ﬂow and energy behaviour within the system; this is discussed later on.

From the flow, subsystems directly linked to energy demand—energy supply, traction system, and environment—have been defined. Obviously, these are important for an energy-oriented analysis, but not sufficient for operation and dynamics. Based on respective literature—Janicki (2011), Pachl (2011), Weid- mann (2011, 2015), and Wende (2003)—the following parts are added:

Vehicles can be motorised or not; naturally, the traction system is a subsystem of motorised vehicles. Obviously, these need to have a controller, be it a driver or automatic train operation (ATO).

Infrastructure comprises all land, buildings, and other facilities that are needed for railway operation (Pachl 2011, p. 11). By this definition, track elements, interlocking, and energy supply are subsystems of the infrastructure. Other authors define, based on the Technical Specifications for Interoperability (TSI), energy supply and signalling and control as own “structural areas”, adding telematics services as “functional area” (Janicki 2011, p. 34). Moreover, the infrastructure influences the train dynamics: switches, radii, and slopes (motion resistances).

3part of the traction system

– 14 – Chapter 2: System Modelling

DIESEL AT TANK (100 %) ELECTRICITY AT PANTOGRAPH (100 %)

TRACTION Engine Losses ENERGY (60 %) (35 %) TRACTION ENERGY (95 %) Generator Losses (5 %) Transformer/Filter Losses (5 %)

(a) Sankey Diagrams from the Vehicle’s Input Energy (VEI, “Drawn Energy”) to Traction Energy for Diesel (left) and Electric (right) Vehicles.

TRACTION ENERGY (100 %)

Regenerated Braking Energy (10...30 %) Ancillary Services (10...30 %) Drive Chain Losses Motion Resistances (10...15 %) (10...30 %) Dissipated Braking Energy (20...40 %)

Motors (~5 %)

Power Electronics (~2 %)

Transmission and others (~8 %)

(b) Sankey Diagram for Railway (Traction) Vehicles Starting from Traction Energy.

Figure 2.1: Energy Flow in Railway Systems, shown as Sankey Diagrams. The preparation of energy up to the VEI is not shown; for simplicity reasons, regenerated energy is only shown as return path in sub-ﬁgure (b) and assumed to be part of the supplied energy in sub-ﬁgure (a). Own illustration based on Douglas, Roberts, et al. (2015, Fig. 2), numbers approximately and according to the same source. Note that the numbers are rough orders of magnitude only but not precise.

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Operation Control is basically the handling (or operation) of the infrastructure in order to allow train runs according to the schedule as well as decision making on actions in case of deviations; moreover, the planning process makes part of operation control. For the operation itself, different structures exist: From the original, peripheral operation up to highly centralised operation control done in operation centres. In some models, also the interlocking is regarded as part of operation control, being in charge for operation and safeguarding of the route (Weidmann 2015, p. 11).

Environment Based on the equations describing train motion and thus railway operation, the environment inﬂuences the railway system; the main inﬂu- ence factors are aerial resistance (motion resistance) and track conditions (force transmission wheel–rail).

Taking the gained knowledge together, there are four main parts of a railway system: vehicles with their elements controller and traction system; infrastructure comprising track, energy supply, and—depending on the point of view—interlocking and signalling equipment; operation control including the timetable, its creation, its realisation, and (partially) interlocking and signalling; and the environment as energy sink as well as inﬂuencing other elements of the railway system. In terms of operation control, Lüthi (2009) shows the interactions of the different parts of the railway system. From his work, the subsystems and their interactions are shown in Figure 2.2. Note that two mostly independent control loops are employed for operation control.

Summarising, the following partitioning of the railway system into five first layer subsystems appears as most suitable: Energy Supply; Vehicles consisting of controller and traction4 (second layer subsystems); Track including all infrastructure parts used to carry and guide the vehicles as well as signalling and automatic train protection (ATP) equipment; Operation Control including dispatching, traffic control, and interlocking; and the Environment. This model is depicted in Figure 2.3.

2.2.2 Motorised Vehicle 2.2.2.1 Vehicle Overview Main task of a transportation system and thus of railways is to carry people or goods from A to B. In railway systems, this is done by self-powered5 vehicles and trains. Consequently, the motorised vehicle is a very important part of the railway system—even more as it is its main energy converter and sink. To fulﬁl the vehicle’s task, the following steps have to be taken:

4as far as the vehicle is motorised 5in contrast to infrastructure-driven as e.g. cable cars

– 16 – Chapter 2: System Modelling

Figure 2.2: Scheme of Today’s Rail Operation (Lüthi 2009, Fig. 3.5), also showing the interactions between different parts of the system.

– Supply the vehicle with energy – Convert this energy into kinetic energy (increase its kinetic energy) – Overcome the resistances (transmit the force) – Decrease the vehicle’s kinetic energy (brake) – Supply auxiliary and comfort systems with energy

From these tasks and regarding the full-system model in Figure 2.3, the vehicle can be modelled as shown in Figure 2.4. This model is still energy-oriented, but includes the technical subsystems in a more speciﬁc manner. According to this model, the vehicle is analysed in the following, where the structure is chosen more technically oriented: Depending on the traction technology, energy supply and conversion to kinetic energy (including traction energy preparation and drive chain losses) are different. Force transmission, i.e. increasing the vehicle’s kinetic energy, follows the same principles for all technologies, the same applies for the motion resistances that have to be overcome. The basic physics of deceleration (braking) are the same for all traction systems, showing slight differences in application. Finally, auxiliary and comfort systems are discussed, before the section is closed with a look on control.

– 17 – Energy Saving Potentials in Railway Operations under Systemic Perspectives Commands, Measures, Information & ) ONTROL ONTROL C CHEDULE C S ISPATCHING LANNING D NTERLOCKING I Control Commands (P RAFFIC T NITIAL RACK I T PERATION O Route (Rails, Signalling, ATP) (Verbal) Occupation Information Data Trigger Signals ONTROLLER ATP (e.g. Driver, ATO) C

State Curr. Losses Chain Drive

Commands YSTEM Braking Dissipative EHICLE S

NERGY E Auxiliary Traction )V

SAGE oinResistances Motion NVIRONMENT U YSTEM E

AILWAY

S R Systems Comfort RACTION T Regen. Braking OTORISED (M . RACTION

N T .E Losses Conversion RAC REPARATION T P I V E YSTEM S ONTROL UPPLY

S Other Vehicles Losses INCL NERGY E General Qualitative Railway System Model. Subsystems are depicted as rounded rectangles. The block arrows represent the energy flow, thick arrows and Sinks Energy Primary Other Sources Figure 2.3: show the information flow, whileenergy dashed flowing arrows through the indicate respective influences parts. (from ATO—Automatic dot Train Operation; to ATP—Automatic Train arrow Protection in (own illustration). both cases). The width of the energy arrows is not scaled to the amount of

– 18 – Chapter 2: System Modelling

Regenerative TRACTION ENERGY PREPARATION Braking Losses

(1) (2) Drive Chain Losses TRACTION AUXILIARY COMFORT SYSTEMS

Motion Resistances

Ekin

(1) – DRIVE SYSTEM RAKE YSTEM (2) – B S Light Sound Braking Electricity Heat/Cold Dissipative (Driver’s HVAC) Compressed Air Cooling Liq. Flow Heat/Cold (HVAC)

Figure 2.4: Energy Flow Diagram of a Motorised Vehicle, organised according to the technical subsystems. Additionally, there are different controllers that are not shown in this scheme. Arrows that are pointing out of the canvas indicate an energy transmission to the environment. Note that drive and brake system partially overlap; not all paths exist for all technologies (own illustration).

2.2.2.2 Traction Technologies

Introduction: Energy Preparation vs. Energy Usage Having a look into the model of Figure 2.3, it is split into two subsystems traction energy preparation and traction energy usage. According to Figure 2.1, preparation addresses all processes taking place between the vehicle’s energy input (VEI, e.g. fuel door or pantograph; “drawn energy”) and traction energy, while usage is the subsequent step in the chain. This splitting allows a more appropriate analysis as the functions—interaction with supply (preparation) vs. traction task (usage)—are properly separated. Given that both subsystems depend on the drive technology, these technologies are discussed based on the main groups distinguished by Janicki, Reinhard, and Rüffer (2013): Steam Electric Diesel Diesel-Electric Hybrid/Multi-Power-Source As steam traction is no longer used in regular operation since quite some time, an investigation of this technology is considered needless. Meanwhile, all of the others are still in use, thus, they are shortly discussed. Most of the knowledge might be considered as “general knowledge” but can also be found in publications by Filipovic´ (2015), Janicki, Reinhard, and Rüffer (2013), Sachs (1973a), and Steimel (2006).

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M M =

Pantograph Transformer/Resistor Motor Pantograph Transformer Rectiﬁer Motor

(a) Direct Drive. AC with step switch trans- (b) Current Rectiﬁer Drive (AC only). Instead of step switch transformer, DC with resistor. Motor: one phase AC former and rectiﬁer, an ordinary transformer and a controlled con- or DC respectively. verter can be used.

∼ = Transformer 4QC = M 3 3 ∼ ∼ Pantograph = DC Link Motor Inverter Motor

Line Filter DC-DC converter*

(c) Converter Drive. Black—Both systems; grey—AC only; light grey—DC only. The DC-DC converter (marked with an asterisk) does not have to be used and is omitted in some realisations of this topology. If no DC-DC converter is used, line ﬁlter capacitor and DC link can be realised using the same elements.

Figure 2.5: Schematic Representation of Different Electric Drive Topologies. The arrow-indicated power ﬂow is shown for the motoring case (energy drawn from the supply system). Own illustration based on Janicki, Reinhard, and Rüffer (2013, pp. 182–185).

Electric Traction Electric drive systems can, according to Janicki, Reinhard, and Rüffer (2013, p. 182), be classiﬁed by means of different criteria:

– Supply System: AC or DC – Type of Engine: DC-motor, pulsating current motor (PCM), or AC-motor – Drive Chain Topology: direct—current rectiﬁer—converter drive – Type of Control: discrete or continuous

Thereby, the combinations are not arbitrary; in contrast, there is usually one combination of engine type, drive chain topology, and type of control that results in one electric drive topology. Thus, three different basic drive topologies result; they are schematically represented in Figure 2.5:

– Direct AC/DC-fed drives with discrete control and AC/DC motor – AC-fed current rectiﬁer drives with continuous control and PCM – AC/DC-fed converter drives with continuous control and AC motor

As there is only one traction energy preparation part in a drive system (Fig- ure 2.3), the subsystem boundary is the device where the energy ﬂow splits to the different sinks. For different systems, the distinction is proposed as follows: In pure AC systems, the ancillary services usually have one or more dedicated secondary transformer windings that feed the respective sinks. Thus, the transformer is to be regarded as boundary between energy preparation

– 20 – Chapter 2: System Modelling and usage, where the primary winding belongs to the preparation, while the secondary makes part of usage. Alternatively, for reasons of applicability, the secondary winding might—and will—be regarded as part of preparation. In pure DC systems, ancillary systems are usually designed as DC-fed systems that can be supplied directly from the catenary or—especially in older vehicles—with lower voltages using rotating converters; in newer (power electronics based converter drive) systems, DC-DC or DC-AC converters are used. In either case, the boundary between energy preparation and usage is located at the DC voltage directly behind the line side ﬁlter (LSF)—when assuming an energy ﬂow direction from catenary to drive system. A multi-system (MS) vehicles’ drive topology usually corresponds to the one shown in Figure 2.5c, which is basically an adapted AC system. As in DC systems no transformer is used, a different solution is necessary. Then, the ancillary services converters can be fed

(a) via high-voltage DC-DC converter that is directly connected to the DC power supply (catenary) or the ﬁltered DC voltage. (b) using a DC-AC converter to feed the respective secondary winding from the propulsion system’s DC link. (c) directly to their DC link from the propulsion system’s DC link.

Depending on this, the boundary between energy preparation and usage is

– the transformer for pure AC systems in case (a) and AC operation, – the DC link after line voltage ﬁltering in case (a) and DC operation, or – the propulsion system’s DC link in cases (b) and (c), with differences in the ancillary path from case (b) to (c).

Diesel Traction Diesel traction, if explicitly distinguished from diesel-electric traction, covers all systems that are driven by a diesel engine and employ any kind of mechanical force transmission. Therefore, mechanical gear boxes, hydrodynamic, or hy- drostatic systems can be used. Ancillary systems are supplied using the same type of force transmission (e.g. cooling, ventilation, air conditioning) and/or applying a small electric generator. For these traction systems, the system boundary between energy preparation and usage is always the shaft of the diesel engine. From there on, the energy ﬂows to propulsion, auxiliary, and comfort system are separated. As all force transmission systems are described as efﬁciency between engine shaft and wheel in order to keep complexity within a practical extend, they are treated as one group in this thesis.

– 21 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Diesel-Electric Traction The diesel-electric propulsion system is similar to the AC converter drive. In contrast to that, the diesel-electric system is supplied by diesel engine, generator, and rectiﬁer instead of pantograph, transformer, and power electronic converter (four quadrant chopper—4QC), cf. Figure 2.6.

3 = Diesel G ∼ M Engine 3 3 ∼ = 3 ∼ ∼ Generator Rectiﬁer DC Link Motor Inverter Motor

Figure 2.6: Schematic Representation of the Diesel-Electric Drive System based on Janicki, Rein- hard, and Rüffer (2013, Abb. 3-61).

For the power supply of ancillary systems, two possibilities exist. Firstly, additional generators can be driven by the diesel engine—a solution that is usually applied for battery charging (Janicki, Reinhard, and Rüffer 2013, Abb. 3- 63). Secondly, the electric energy can be drawn from the DC link. By connection of adequate converters, the necessary power can easily be provided. This is usually done for all other ancillary systems. Based on this, two different boundaries for the distinction of energy preparation and use are thinkable: Either all sinks are taken into account, which would cause the boundary to be the diesel engine’s shaft. Or the DC loads connected to the second generator are neglected (or fed via DC-DC converter that is connected to the DC link), which would allow to cut at the DC link.

Hybrid and Multi-Power-Source Traction Systems Hybrid traction systems consist—according to Janicki, Reinhard, and Rüffer (2013, p. 229)—of at least two energy conversion systems as well as two energy storages. Thus, electric vehicles that are additionally equipped with a diesel engine in order to also operate in non-electrified areas are not hybrid vehicles but multi-power-source vehicles (“Mehrkrafttriebfahrzeuge”, DIN 25003). While the above-mentioned definition basically allows an arbitrary number and combination of power sources, the most common hybrid system is the combination of diesel with electric traction. In this case, the diesel engine acts as main power source, while regenerated braking energy is electrically stored and used in high-load situations. For hybrid systems, two (main) topologies are distinguished: Parallel, where both engines are mechanically coupled to the axles, while for serial, only the electric motor is mechanically connected to the axles. In the latter case, the topology is nearly identical to the diesel-electric traction system shown in Figure 2.6 but extended by an electric energy storage. In either case, a combination of different drive chain topologies is applied. Depending on the supported energy sources—usually diesel, AC, and/or DC— the system configuration is defined. Basically, all energy flow paths from Fig- ures 2.5c and 2.6 have to be provided if operation under all systems is desired.

– 22 – Chapter 2: System Modelling

Concluding Overview According to the model shown in Figure 2.3, the traction system is split into two parts: traction energy preparation and traction energy usage. From the discussion above, this differentiation can be done for the different drive chain topologies as summarised in Table 2.1.

Table 2.1: Subsystem Boundaries for Traction Systems between Energy Preparation and Energy Usage for Different Drive Technologies. Additionally, the main components of the energy preparation subsystem are indicated. LPC—Line Power Converter. Drive System Technology Subsystem Boundary Preparation AC Transformed Voltage Transformer DC Filtered Line Voltage Line Side Filter (LSF) Electric Multi-System AC Transformed Voltage Transformer DC Filtered Line Voltage LSF / Trafo Diesel Diesel Engine Shaft Diesel Engine Diesel-Electric Diesel Engine Shaft Diesel Engine Hybrid Diesel Engine Shaft Diesel Engine (+Battery) Multi-Power-Source DC Link Trafo and LPC (AC); Hybrid / LSF and LPC (DC); MPS Diesel Engine, Genera- tor, and Rectiﬁer (Diesel)

From the above table, subsystem boundaries can be deﬁned such that they are independent from the drive system technology:

– In AC systems, the transformed voltage is the boundary, the transformer making part of the energy preparation. – In DC systems, the filtered line voltage is the boundary. While the line side filter is part of the energy preparation, all energy usage chains connect to the filtered line voltage. – For diesel—or any other discrete—supply systems, the boundary is the shaft of the engine. The engine itself is part of energy preparation; all devices connected to the shaft are energy users. – In most multi-power-source systems, the DC link voltage is the boundary. For the preparation part, the description of the respective supply system in use (AC, DC, or diesel) applies, extended by the power converter that provides the DC voltage.

Of course, one could argue that for reasons of consistence, a subsystem boundary at DC link voltage should be used in the AC case as well. Admittedly, this is advantageous in case of modern converter-driven vehicles. However, as long as there are tap-switch locomotives in operation, there are vehicles operating that do not have a DC link voltage. For this reason, it is considered to be better deﬁning the boundary at the transformed voltage in AC systems.

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2.2.2.3 Energy Preparation Depending on the actual traction technology, the energy preparation subsystem consists of different elements, as discussed before:

– AC System: Transformer – DC System: Line Side Filter (transformer winding often used in multi-system vehicles) – Diesel or Hybrid System: Diesel Motor – Multi-Power-Source System: Trafo/LSF and LPC in electric operation; Diesel Engine, Generator, and Rectiﬁer in diesel operation

Thus, the properties of the following “preparing” elements are investigated briefly in order to be able to build a model: Transformer, Line Side Filter, Diesel Engine, Generator, and Rectifier. For the sake of manageability, no full electrical/mechanical analysis is conducted; instead, a characteristic description in terms of an efficiency is taken or derived from literature. Note that with this granularity, transformer and line side filter can basically be treated as real inductance, while both line power converter and rectifier can be regarded as power electronic converter. The latter as well as the generator—which is basically a “reverse turning” electric machine—are discussed in section 2.2.2.4, as they are typically associated to the drive chain. For the line side filter (LSF), the basic model of a real (lossy) inductance is applied. The losses are modelled by a series (ohmic) resistance (Betz, Beuth, et al. 1978, pp. 100 sq.), through which the entire current has to flow. Given locomotive powers of up to 6.4 MW (Vectron MS) for all supplying systems (Siemens AG 2010), currents of up to 250...430...2.1E3...4.2E3 A are possible for the different feeding voltages; the TSI allow for conventional lines up to 800...900...4E3...5E3 A per train (Kießling, Puschmann, and Schmieder 2014, Tab. 5.17 p. 347). Usual coils in LSF applications show an inductance of 1...40 mH, bearing around 800 A at 3 kVDC and weighing 2...4 t (Bocchetti, Carpita, and Giannini 1993; Siemens Transfomers LLC 2013). For transformer integrated LSF, a filter winding power of 50 kVA is stated for a rated transformer power of 1650 kVA and 1.3 MW vehicle power (Siemens Transfomers LLC 2013, p. 5). For the equivalent series resistance, Jongeling (2017, p. 58) assumes a value of 28 mΩ, which results—with a phase current of 1 kA—in a power loss of 28 kW, which is about 0.5 % with regard to a nominal power of 6 MW. Thus, a maximum efficiency of 99 % is assumed. LSF, transformer, and electric machine are based on the same magnetic effects, thus, a decrease in efficiency is expected for partial-load operation, which is discussed in the following section for the asynchronous traction machine (ASM). For transformers, some efficiency values are found in literature. For 16.7 Hz systems, Claessens, Dujic, et al. (2012, p. 17) state 90...92 % for a classic transformer; Isenschmid, Menth, and Oelhafen (2013) determine 96.3 %. Addition- ally, transformer efficiency decreases in partial-load operation; the best value

– 24 – Chapter 2: System Modelling is reached for nominal load. As a transformer basically behaves as an induction machine (ASM)—or, more precise, vice versa—the efficiency as function of load will show a similar behaviour.6 For this reason, a similar (scaled) efficiency curve as for the ASM is used for the transformer. For diesel engines, the efficiency is normally heavily non-linear with respect to torque and rotational speed. In some applications—especially with mechanical transmission—different gear ratios are used, thus, the situation becomes even more complex. For diesel engines, efficiencies of 20...54 % are usual (N.U. 2012); based on Douglas, Roberts, et al. (2015, p. 1153), an efficiency of about 40 % can be taken as general value. For specified diesel engines with a given characteristic diagram, from the gram diesel per kWh value x, an efficiency can be calculated for a specific operating point (Andersson 2016; N.U. 2012):

1 kWh 1 = η · 43.3 MJ/kg ⇒ η = 83.14x (2.1) x g −

A more detailed analysis would require getting into thermodynamics, which is, regarding the topic of this thesis, far out of scope.

2.2.2.4 Drive Chain The drive chain covers the energy conversion from “prepared traction energy” to the axle. Of course, there are different models as there are different traction technologies. For a precise description and determination of the drive chain efﬁciency and thus the losses, each drive chain topology would have to be investigated from an electrical or mechanical systems engineering point of view. However, regarding the fact that in this thesis the entire system is the main matter of interest, this detailed kind of analysis is out of scope. Thus, a simpli- ﬁed consideration for different sets of traction technologies is preferred.

For each motorised vehicle, the most important source of information on its tractive capabilities is the so-called ZV-diagram, which shows the available traction (or brake) force at wheel for a certain vehicle speed. Most important quantities limiting the traction forces are starting tractive effort FTr,S, the 1 2 power hyperbola FTr ∝ vt− , the engine’s tip-over limit FTr ∝ vt− , and the maximum speed. If the ZV-diagram is not given, it can usually be derived from starting tractive effort FTr,S, nominal power, and max speed (Meyer 2012). As the ZV-diagram indicates force and thus energy at wheel, the transmission has to be taken into account when looking for the energy demand at VEI. In the following, this is discussed for the most common traction technologies.

6Note that this model is simpliﬁed, as in reality, cross-impacts between all windings of the traction transformer occur. These interactions have been investigated by the author in his master thesis on traction transformer modelling (Bomhauer-Beins 2014), but are far out of scope of this research.

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The most important technology is AC-fed with (three-phase) AC engine. The transformer being part of the energy preparation, the elements with η < 1 are

– Converter: line power converter (LPC), DC link, motor converter (MC), and their power transmission wiring – Three-Phase AC Motor (asynchronous or synchronous) – Gearbox

Starting with the converter, between others, conduction, switching, and blocking losses occur—cf. Heumann (1991, pp. 272 sq.). For typical converters with more than 100 kW, he states 0.97...0.98 being typical efﬁciencies—only

0.5 % losses occur due to the semiconductors (for a minimum voltage of 600 VDC, which is usually fulfilled in railway applications). Generally, a decrease in efficiency is observed for partial load operation, as some of the losses are constant and load-independent. Note that in an usual AC railway application, two converters are employed (LPC and MC). For the DC link, metal-paper capacitors are employed in most cases. In terms of losses, the loss factor is the determining quantity. For pulse frequencies below 1 kHz—usually fulfilled for railway traction drives—the loss factor of metal-paper capacitors is around 0.005 (Kemnitz, Steffen, et al. 2007, p. 80). The power loss can be determined as

2 PL = U · ω · C · tan δ, (2.2) tan δ denoting the power factor, C the capacitance, and ω the angular frequency of the voltage U. Given the values of SBB’s Re 460 series locomotive—1800 V DC link voltage pulsating with 331/3 Hz over 4×30 capacitors with 4×27.6 mF in total (Gerber, Drabek, and Müller 1991, p. 30)—a power loss of roughly 500 W results.7 Given that the relevant ripple is much smaller than the nominal DC link voltage and a nominal power of about 5 MW in railway traction applications, the DC link efﬁciency results to be above 99.99 %. As industrial details are usually not published, there is no concrete information found on wiring losses. But given the dimensions of a vehicle, rated power of traction engines (around 1.5 MW), resistance per unit length, and the approximate wiring length, the losses can be estimated. Assuming a copper wiring with maximum phase current of about 500 A with 1.5 A/mm2 max and a cable length of 10 m per phase, the resistance per phase results to be around 10 m R = % · l/A = 0.0175 × 10 6 Ωm · = 500 mΩ, (2.3) − 350 mm2 which delivers with the phase current a maximum power loss of 125 W per phase, thus 375 W per engine. A typical rated power being 0.8...1.5 MW, the losses account for 0.025...0.045 %, resulting in an efﬁciency of 99.955...99.975 %.

7Assuming the capacitor battery is built up of 30 capacitors in series with 920 nF and U/30 each.

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For three-phase asynchronous motors (ASM), Gerber, Drabek, and Müller (1991, p. 36) give an interesting insight for the 460 series. Starting their diagram with η ≈ 55 % at 7 km/h, the efﬁciency increases to reach its maximum of 94 % for the speed range 90...160 km/h and decays again to about 92 % towards the speed maximum of 230 km/h. Based on this, the following equation is derived for x ∈ [0,1] ⊂ R being the “relative load”: ( −13.45x2 + 5.33x + 0.41 if x ≤ 0.2 η(x) = ∈ [0, 1] ⊂ R (2.4) −0.021x + 0.942 otherwise

As illustration, the function of Equation 2.4 is shown in Figure 2.7. For synchronous three-phase motors (SM), η a higher efficiency and power factor is ex- 1.0 pected. Unfortunately, especially due to increasing competition within the rolling 0.8 stock sector, this newer technology is worse 0.6 documented than the older one. As rule of 0.4 v thumb, η = 95...99 % is known in electri- 25 % 50 % 75 % cal engineering, being undoubtedly higher 100 % than the efficiency of ASM. Note that in ei- Figure 2.7: Efficiency of the Induction ther case, a power factor smaller than one Machine (ASM) over the Vehicle’s Speed indicates reactive losses heating the ma- Range. Own plot from Equation 2.4. chine. However, the reactive power oscil- lates between the phases of the engine, not influencing the power balance at the DC link—which makes it thermally relevant, but not energetically. Concerning the gearbox efficiency, Filipovic´ (2015, p. 57) states some numbers. For dual transmission in full-load operation, 95 % are achieved; up to 99 % for well-engineered single transmission gearboxes. As mean values, 97...98 % can be used, decaying in partial-load operation. Figures given by Förster (1991, p. 436) indicate a linear decrease but do not allow a quantification. Summing up, a total drive chain efficiency (excluding transformer) of at maximum 91 % for an ASM (95 % for a SM) results, lower for part-load operation.

In DC-fed systems with three-phase AC machines, there is usually no LPC but only an inductor as LSF—in MS vehicles, the transformer inductance is often used as filter (Meyer 2013). The rest of the drive chain—DC link, MC, wiring, motor, gearbox—is equivalent to the AC case. As the LSF is regarded as part of the energy preparation, it is not considered here. The efficiency of the drive chain in DC case is thus higher than for AC, up to 93 % (ASM) or 97 % (SM). Another wide-spread traction technology is diesel-electric. Depending on the exact topology of the traction system, it could be discussed whether the rectifier and DC link make part of drive chain or energy preparation; here, they are regarded as part of the energy preparation for the sake of flexibility. Then, the drive chain only comprises motor converter, wiring, and machine, showing a maximum efficiency of 93 % (ASM) to 97 % (SM).

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For the “classic” diesel traction system, hydraulic force transmission is used. Then, the drive chain starts at the engine shaft, where either a hydraulic transmission with gearbox or torque converter are attached (Beyer, Degenkolbe, et al. 2005; Janicki, Reinhard, and Rüffer 2013). In terms of efﬁciency, values of 75...82 % for torque converters and 84...89 % for gearboxes are given (Beyer, Degenkolbe, et al. 2005, p. 34). Even though these are rather extensive intervals, a more detailed analysis would be out of scope of this thesis.

Some older rolling stock—built before the introduction of power electronics— still uses discontinuous control (e.g. tap switches under AC, resistors under DC). As this technology has started to fade out with the world’s ﬁrst series converter locomotive delivered in the early 1980s, it will mostly vanish until 2020/30. Therefore, dealing with these technologies is considered pointless.

2.2.2.5 Force Transmission to Rail Given the kinetic energy of engines and wheel sets, the underlying force has to be transmitted to the rail. This transmission depends on the wheel-rail friction, usually denoted using the friction (or adhesion) coefﬁcient µ. From physics, the adhesion condition for a transmittable force FR from wheel to ground with given friction coefﬁcient µ between them is known as

FR ≤ µ · m · g, (2.5) assuming a horizontal ground plane; m the mass on the wheel. To determine µ, Wende (2003, p. 158) introduces physical and statistical friction theory—the former explaining µ from a physic’s point of view, the latter regarding µ as stochastic parameter determinable by means of mathematics. Not going into the backgrounds—the interested reader might refer to Wende (2003, pp. 159 sqq.) and Sachs (1973a, pp. 53 sqq.)—the common model for traction, introduced by Curtius and Kniffler (1950), is discussed here shortly. From measurements, they propose the equation (converted from kg/t to unit 1) 7.5 km µTr = + 0.161 vt train speed in /h. (2.6) vt + 44 As they state, this curve has to be seen as “tentative mean value” of the measurements. In cases of wet and slippery rails, all measured values are found below this curve; in case of ideally conditioned rails, nearly all values are located above. Figure 2.8 illustrates the measurements and the derived curve. The variation of values, which is mainly depending on the rail conditions (Fil- ipovic´ 2015, p. 39), can be included using an additional addend. The Curtius-Kniffler-Equation is the mostly used equation to determine the tractive friction coefficient µTr, sometimes also for braking. Deviating, SNCF distinguishes between straight track and curves (Wende 2003, pp. 176 sq.):

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0.5

0.4

0.3

0.2

0.1

v (km/h) 20 40 60 80 120 140 160 (a) Measurement Values Illustration, taken from the 100 original publication of Curtius and Knifﬂer (1950). (b) Illustration of Curtius-Knifﬂer-Curves. Black the Speed ranges from 0 to 160 km/h, µ from 0 to 400 kg/t. curve according to original Equation 2.6, green +0.1 (well-conditioned rails), red –0.1 (wet and slippery rails) (own illustration).

Figure 2.8: Friction Coefficient according to Curtius and Kniffler (1950). Note that the µ-axes of both sub-figures are horizontally aligned (0 kg/t = 0, 100 kg/t = 0.1, etc.).

8 km/h + 0.1 · v µ = µ · t , (2.7a) Tr 0 km 8 /h + 0.2 · vt

km vt in /h; µ0 = 0.330 without and µ0 = 0.400 with sand

250 m + 1.55 · rc µTr,c = µTr · in curves, rc the curve radius (2.7b) 500 m + 1.10 · rc

Also inﬂuencing is the wheel-rail-slip, the slip speed vs being the difference between rotational wheel speed and vehicle’s ground speed. µTr being zero for m vs = 0, it increases parabolically until vs = vsC ≈ 0.5 /s, then decaying exponen- tially towards sliding friction µSl. Wende (2003, p. 164) proposes the following:

vs vs µ = 2 · µC · · 1 − for 0 ≤ vs ≤ vsW (2.8a) vsC 2 · vsC

ω(vsW vs) ωvs µ = (µW − µSl) · e − · e− + µSl for vs > vsW (2.8b)

The parameters are obtained from measurements. In modern systems, this effect can be neglected as the control always targets the maximum friction.

For braking, different approaches are used. Sachs (1973a, pp. 75 sq.) and Wende (2003, p. 177) cite Metzkow, stating that for speeds up to 120 km/h, the friction coefﬁcient for braking can be regarded as speed-independent—Sachs (1973a, p. 76) even claims that this behaviour can be expected to persist for speeds up to 200 km/h, at least for straight tracks. Based on Metzkow’s results, the following values are thus used in practice: 0.15 for block brakes, 0.12 for disc brakes in modes P or G, 0.15 for disc brakes in mode R, and 0.18 for dynamic brakes. Also these values show a certain deviation—Table 2.2.

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Table 2.2: Friction Coefﬁcients for Braking; data from Wende (2003, p. 177).

Track Condition µBr (rolling) µSl (sliding) dry and clean 0.18 to 0.26 0.05 to 0.06 wet and clean 0.18 to 0.22 0.05 to 0.06 wet and slippery 0.10 to 0.16 0.04 to 0.05 dry and sanded 0.28 to 0.46 0.06 to 0.10

Given µ, the transmitted force for acceleration, deceleration, and cruising is limited to the value µ · m · g. Note that friction and slip are producing losses; nonetheless, these are usually neglected—mostly not even mentioned.

2.2.2.6 Acceleration Resistances When accelerating a vehicle, different resistances have to be overcome. Some of them are effective for all operating states—for these, cf. section 2.2.2.7— others only while accelerating (or decelerating) the vehicle. When calculating train motion dynamics, the acceleration resistances are usually split into two groups: the linear acceleration of a mass point,

FR = m · a, (2.9) and the rotational masses acceleration, which results from wheel sets, gearboxes, and engines. For their inclusion, a rotational mass factor ζ is usually used (Müller 1940; Weidmann 2011):

FR = m · ζ · a. (2.10)

As discussed in a prior study (Bomhauer-Beins 2017) based on the works of Müller (1940), the physical background are the moments of inertia, basically to be included via mechanical energy analysis:

mv2 Jω2 Z E = E + E = + , J = r2dm (2.11) tot kin rot 2 2 E ζ = tot (2.12) Ekin

For simplification, all rotating masses mrot are assumed to be concentrated in their outer ring; thus, the following simplification becomes possible, with mt the train’s mass: m + m ζ = t rot (2.13) mt The resulting error is usually below 5 %. Additionally, the wear of rotational masses—especially wheels—has an influence on the exact value of ζ, which is thus varying in “rather large boundaries” (Müller 1940, p. 3). Nonetheless, Müller considers the usual mean value of 1.06 for carriages as valid; applying Equation 2.13, ζ = 1.09 results for modern EMUs (Bomhauer-Beins 2017).

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Note that despite the usual wording “acceleration resistance”, acceleration itself does not cause losses. Rather, acceleration and deceleration are reversible changes in kinetic energy of the vehicle—with some losses occurring in the conversion process. This fact is represented in the formulas discussed, therefore, this note is added for physical correctness only.

2.2.2.7 Motion Resistances

Introduction and Overview Motion resistance is the major topic if dealing with energy in railway—or generally transportation—systems (Wende 2003, p. 19): If there were no resistances to the desired movement, no forces had to be overcome, resulting in a zero-energy balance for travelling from A to B. Many books can be found dealing—more or less focused—with this topic of driving dynamics; the following considerations are based on the books by Filipovic´ (2015), Hay (1982), Müller (1940), Sachs (1973a,b), Weidmann (2011), and Wende (2003). Notably, the research that all these publications rely on dates back some time. More- over, it is frequent practice to use “rules of thumb” for certain values: rolling resistances, breakaway resistance, curve resistances, rotational mass coefficient, and aerodynamic coefficients (Filipovic´ 2015; Weidmann 2011). Research on aerodynamics is today usually (only) done for safety reasons (running stability and derailment safety under crosswind), as presented by Baker, Jones, et al. (2004), Biadgo, Simonovic, et al. (2014), Guo, Xia, et al. (2015), Orellano and Schober (2006), Suzuki, Tanemoto, and Maeda (2003), and Weber, Benker, and Naupert (2007). Also, very detailed descriptions in order to optimise aerodynamic forces can be found (Yang, Song, and Yang 2016). This focus has been confirmed by aerodynamics professional Mr. Steiling (2017). Nonetheless, the referenced literature presents different equations that allow a description. These are collected in the following, ordered by the type of resistance they describe. The before-mentioned challenge in terms of accuracy is discussed within the respective paragraphs—in some cases, an improved or extended model has been derived and is presented later on. As a help for orientation, the motion resistances and their classification are given in Figure 2.9. Note that acceleration and deceleration are not regarded as resistance; physi- cally, it is a conversion process to or from kinetic energy. Thus, it is treated in the respective sections.

Rolling Resistance The rolling resistance is detailed described by Wende (2003, pp. 110–116) as “basic resistance”. This does not only include the rolling resistance in a stricter sense, but also sliding, ﬂexing, bearing, dynamic, and acoustic resistance. The rolling resistance (stricter sense) results from the deformation of track and wheel at the contact spot, which forms an ellipsis; this continuous generation and regression of the contact spot is its cause. As a result of the contact

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Point Resistanceg

Track Resistancesg Slope Resistance

Curve Resistanceg Motion Resistancesg Rolling Resistance

Running Resistances Aerial Resistanceg

Breakaway Resistance

Figure 2.9: Motion Resistances and Their Classification. Illustration according to Weidmann (2011, Abb. 2 on p. 4 in ch. 6) ellipsis, the contact point is shifted out of the vertical line through the axis. Thus, a momentum results, which finally leads to the resistive force. As defining equation for the rolling resistance (stricter sense), the following is proposed:

" 1 1 1 1 2! rt− − rw− rt− − rw− FRR,strict(v) = mt · g· 0.0687 · 1 + 0.5784 · 1 1 + 0.7776 1 1 rt− + rw− rt− + rw− s # Fw 1 v 2 · 3 · + 0.0006 · (2.14) 2 1 m Esteel · rw rwrt− + 1 27.7 /s

Thereby, rt denotes the head radius of the track, rw the wheel radius at running kN 2 tread, Fw the wheel force, and Esteel = 2.2E8 /m the elastic modulus of steel. An additional resistive force, resulting as reaction to a torque at the vertical axis, is the gliding resistance. Sine run of the wheel set, transmission of lateral guiding force from track to vehicle, differences in radii of a wheel set, and im- perfections resulting from mounting tolerances are included herein. This coefficient is not mathematically determinable but has to be measured for each vehicle. For carriages, the gliding friction coefficient is in the range of 0.5...1.0 . For locomotives, it ranges from 1.5 for DB’s BR 143 over 2.0...2.5 (diesel-h hydraulic locomotives with group drive)h up to 3...4 with couplingh rods; the gliding resistance coefficient resulting from tractionh forces is proportional to the static friction coefficient µ, f = 0.02µ (Wende 2003, p. 113). Summarising: ( [0.0005, 0.0010] · m · g in case of carriages FGR = (2.15a) [0.0015, 0.0040] · m · g in case of locomotives

FGR,T = 0.02 · µ · m · g (at driven axles only) (2.15b)

Note that in this case not the entire train mass mt has to be taken into account but only the respective axle load.

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Another component of the rolling (or basic) resistance results from the bearings. Wende (2003, p. 113) derives the following equation:

mCB rBP FBR = m · g · · · µB (2.16) mC rw

rBP For the radius ratio /rw (bearing pin radius to wheel radius at running tread),

1 mCB 5 /6 is proposed, for the ratio carriage body mass to carriage mass— /mC— /6.

The bearing friction coefﬁcient µB is 0.0050 for slide bearings and 0.0017 for roller bearings. Assuming this, the bearing resistance can be approximated as

( 6 694 × 10− · m · g for slide bearings FBR ≈ (2.17) 6 236 × 10− · m · g for roller bearings

These values increase for decreasing outside air temperatures. A last component to investigate are dynamic, acoustic, and ﬂexing resistance. Dynamic resistances result from steadily occurring and damped oscillations, acoustic resistances from sound radiation of the vehicles, and ﬂexing resistances from rubber suspension. Equations are given by Wende (2003, p. 114): v FR,dynamic = m · g · kdyn · (2.18) 27.7 m/s  0.085...0.10 for high speed lines  0.150...0.20 h for good track systems kdyn = 0.250...0.30 h for normal track systems  0.500...1.00 h for poor track systems h FR,acoustic = 0.0005 · m · g (2.19)

FR,ﬂex = [0.0005, 0.001] · m · g (2.20)

From these parts, the rolling (or basic) resistance is obtained by summing up all components. This resistance is a stochastic variable that shows relatively large deviations, which are not only depending on the concrete vehicle. For calculations, a mean value is usually used (Wende 2003, p. 114). Based on this, a rolling resistance coefﬁcient wl,r is introduced, allowing a simpliﬁed formulation of the rolling resistance that is used in the following:

FRR = wl,r · m · g (2.21)

More precisely, the slope angle β would have to be included, as resistive forces are determined by the normal force—and not the gravitational. However, for typical slopes in railway applications of 40 or less, this inﬂuence usually negligible when investigating the entire system.h

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Aerial Resistance Analysing aerial resistances, Wende (2003) distinguishes inner and outer aerial resistance. While the latter can be seen as the “commonly known” resistance, he deﬁnes the inner aerial resistance as to result from air movements within the vehicle that are necessary for ventilation, combustion, and air conditioning.

For the inner aerial resistance, he presents two formulas. The ﬁrst describes the aerial resistance resulting from the air ﬂow through the brake discs. The second is based on the fact that air, which has been drawn in for, e.g., cooling purposes, has to be accelerated to the vehicle’s speed—this resistance is called air pulse resistance. The respective formulas are given in the following:

v v 2 FR,BD = nBD · c1,BD · + c2,BD · (2.22a) 27.7 m/s 27.7 m/s

FR,air pulse = ρn · Qtot · (v + ∆v) (2.22b)

Herein, nBD denotes the number of brakes discs, c1,BD and c2,BD resistance coef- m3 ﬁcients (4.33 N and 3.16 N for TGV), and Qtot the train’s air ﬂow ( /s). For the latter, the following “rule-of-thumb”-values are proposed (Wende 2003, p. 142):

3 –8 m /s per MW nominal locomotive power for electric locomotives

3 – 16 m /s per MW nominal engine power for diesel locomotives

3 –3 m /s for a carriage with pressure ventilation – Negligible for carriages without pressure ventilation

The outer aerial resistance consists of many different parts that are applicable for a single vehicle, for an entire train, or in tunnels. A listing of the factors is given in Table 2.3.

Table 2.3: Classiﬁcation of Outer Aerial Resistances (Wende 2003, p. 119).

Single Vehicle Entire Train Tunnel – Compression Force at Nose – Compression Force at First – Compression Force at First – Wake Force at Tail Vehicle Vehicle – Wake Force at Last Vehicle – Surface Friction – Wake Force at Last Vehicle – Turbulence Forces – Surface Friction of All – Air Gap Forces of All Vehicles Vehicles – Cross Wind Friction – Turbulence Forces of All Vehicles – Inter-space Forces of All Vehicles – Compression Force in case of Cross Section Enlarge- ment of the Following Vehicle

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NORTH (0°) y Heading ϕ x Vehicle Speed vt

Wind Source Direction ψ2 Wind Direction ψ1 Wind SpeedYaw Angle α

v w

Figure 2.10: Schematic Illustration of the Deﬁned Quantities. The vectors are labelled with their absolute values (own illustration).

For the analysis of the outer aerial resistances, some definitions are necessary. These are not unique in literature; thus, the definitions used in the context of this research are fixed in the following. For illustration, the defined quantities are schematically shown in Figure 2.10.

Vehicle-Oriented x-y-z-Coordinate System For the analysis of train runs, a vehicle-oriented coordinate system is commonly used. The x-axis is pointing into the direction of movement, while the y-axis is deﬁned as lateral axis; z represents the vertical axis. If the train is modelled as mass point, the origin of the coordinate system is congruent with the mass point. Otherwise—i.e. if the length of the train is larger than zero—the origin is (usually) deﬁned at the nose of the train.

Vehicle Speed vt The current speed of the vehicle in its direction of movement, i.e., along the x-axis.

Wind Speed vw The current speed of the wind in its direction. Note that the wind speed is, depending on the current weather situation, more or less varying. Consequently, the use of mean values might become necessary.

Heading ϕ Angle between north (0°) and the x-axis, measured clockwise.

Wind Direction ψ1 Angle between north (0°) and current wind direction, measured clockwise; 180° shifted towards ψ2.

Wind Source Direction ψ2 Angle between north (0°) and direction of wind origin, measured clockwise. Keep in mind that a wind is usually named according to its origin (e.g. north-easterly trades).

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Yaw Angle α The angle difference between heading ϕ and wind source direction ψ2, measuring the angle that is less or equal to 180°: 0° ≤ α ≤ 180°.

Longitudinal Wind Speed vw,x x-component of the wind speed: vw,x = vw·cos(α). With this deﬁnition, the sign of the longitudinal wind speed results such that a direct addition with the vehicle speed is possible to obtain the relative speed between air and vehicle.

Lateral Wind Speed vw,y y-component of the wind speed: vw,y = vw · sin(α).

In literature, additional definitions for the yaw angle can be found; accordingly, a resulting wind speed is defined. These values can easily be obtained by vector operations on the previously defined quantities; therefore, they are not included in the set of definitions here. A fundamental equation for the (outer) aerial resistance can be found in Weidmann (2011, p. 8 of ch. 6), Sachs (1973a, p. 15), and Wende (2003, p. 125):

1 2 FAR = /2 · cw · Ab · ρ · v . (2.23)

Thereby, cw denotes the aerial resistance coefficient, Ab the reference area (air- flow-exposed area), ρ the air density, and v the relative velocity between vehicle and air flow. For cw, Sachs (1973a, p. 31) provides some values determined in experiments by the Japanese Railways, which are depending on the shape of the train’s nose. Wende (2003, p. 123) proposes the formulation

Asurf cw = cform + cF · , (2.24) Arib cform being the form factor of the surface under investigation, cF the friction factor, Asurf its surface area, and Arib the rib area (rectangular in respect to the direction of the air ﬂow; “seen” area). For the form factor, the equation

cform = 1 + ∆p (2.25) is given, ∆p being the pressure difference between upwind and downwind side.

The friction factor cF is depending on the surface texture, the kind of air ﬂow (laminar/turbulent), and the Reynolds number. Further following Wende (2003) in his line of argumentation, the reference area Ab is equal to the rib area Arib, which would require to include the yaw angle α into the calculation. According to this argumentation, the rib area Arib would have to be used as effective area: !! vw sin α Ab = Arib = Asurf · cos arcsin p 2 2 (vt − vw cos α) + (vw sin α) s v2 sin2(α) = A · 1 − w (2.26) surf 2 2 vt − 2vtvw cos α + vw

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vt1 vt2

x cos γ = w w x = w cos γ ⇒ · y ~vres sin α = y ; sin γ = y γ vw vres ~v α γ = arcsin vres sin α t ⇒ vw · γ ~vw v cos α = t2 ; sin α = y vw vw x v = v2 + y2 = ⇒ res t1 q (v v cos α)2 + (v sin α)2 t − w w q

Figure 2.11: Geometric Situation to Derive Arib (Projection), seen from the top. The height of the vehicle is assumed to be h = const., w the width of the front; thus, A = h w. Vectorial addition surf · of train speed ~vt and wind speed ~vw delivers the resulting (air) speed ~vres. The effective area Arib is deﬁned as x h, x being the rib length (own illustration). ·

Figure 2.11 illustrates the geometric situation from which the above equation has been derived. Note that the train length is not taken into account.

Sachs (1973a, p. 16) defines Ab as “largest cross section in the direction of air flow” as long as the object under investigation is prism-shaped. Therefore, the length l of the carriage has to be taken into account: From the resulting air flow angle γ, an additional length l · sin γ is seen, which enlarges the effective surface by a factor of sin γ (Sachs 1973a, p. 26). For the gaps between the carriages, the enlargement per inter-carriage gap is min(|d·tan γ|, w) for γ 6= 90° and zero otherwise (Wende 2003, p. 128), w the carriage body width and d the gap length. Additionally, the considerations shown in Figure 2.11 are valid and also result in an enlargement of the effective surface. According to Sachs (1973a, p. 16), for non-prism-shaped vehicles as locomo- tives8 and empty open freight carriages as well as for vehicles with rough surfaces, a so-called “equivalent surface” Ab + ∆Ab has to be used. For simplification and “as usual in aerodynamics”, he proposes the use of the geometric surface, which is taken with a factor c—depending on the shape of carriages— into an “equivalent surface”

Aeq = c · Ab. (2.27)

The equivalent surfaces Aeq can be experimentally determined; the results of some early measurements are shown below in Table 2.4.

8While today’s locomotives can be regarded as prism shaped—apart from some aerodynamics—in 1973, different designs were used; moreover, steam locomotives were still in operation.

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Table 2.4: Equivalent Surfaces of Different Trains, obtained from DRG measurements in 1929 and presented by Sachs (1973a, p. 17). The train length is given as additional information and shows that Aeq is not only depending on the train length, but also on the type of carriage.

2 Vehicles Aeq (m ) Train Length 6 Bü-type carriages 13.1 10 Bü-type carriages 19.3 12 Bü-type carriages 20.8 ~261 m 18 B2i-type carriages 24.0 ~251 m 21 B3-type carriages 27.7 ~246 m

Obviously, not only the train length but also the number of carriages in-

fluences the equivalent surface. The non-linear increase of Aeq with linear increase of the carriage number results from higher resistances at first (pressure) and last carriage (suction). Sachs (1973a, p. 17) cites Goß, who assumed a resistance of 10 for the first carriage and obtained 0.8 for the second, 1.0 for an arbitrary middle, and 2.6 for the last, while a single carriage shows a value of 12. Moreover, a reduction of the equivalent surface is observed for small inter-carriage gaps or if there is no body width reduction (Sachs 1973a, p. 18). Additionally, note that the quantitative results of Goß (1895/96) are already declared deprecated by Sachs; the same can be assumed for the numbers given in the respective book itself (1973). Therefore, these results are not discussed. As shown before, different definitions of the surface area are applicable. A pragmatical solution is proposed by Weidmann (2011, p. 10 of ch. 6) and Wende

(2003, p. 125): As the aerial resistance coefﬁcient cw is only valid for a given 2 surface area, a consistent reference surface of Ab = 10 m should be used. Contrariwise, a more detailed analysis is possible, e.g. further subdividing the sources of aerial resistance as presented by Sachs (1973a, pp. 30 sqq.)—but this would exceed the scope of this thesis.

Focusing on the next parameter in Equation 2.23, the air density ρ, Wende (2003, p. 123) presents the following equation:

p p (T ) ρ = a · 1 − 0.377 · φ · S (2.28) J 287 /kg K · T (in K) pa

The actual temperature T , absolute air pressure pa, and relative humidity φ are given by the respective weather conditions; the temperature-depending steam pressure of water pS can be found in corresponding tables. Note that the for norm values ρn,0 and ρn, a relative humidity of φ = 0 is assumed; the corresponding steam pressures of water are 6.1 mbar and 17 mbar (Grimm 2016). Weidmann (2011, p. 11 of ch. 6) presents a comparatively simple formula, taking only actual absolute air pressure pa and temperature T into account: ρ · p · 273.16 ρ = n,0 a (2.29) 1013 mbar · (273.16 + T (in °C))

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He states that in normal operation, a deviation in the range of +12 % (high air pressure in winter) to –5.6 % (low pressure in summer) is possible. Comparing the formulas, a maximum deviation of about 2 % occurs for high temperature, high humidity conditions (30°C, φ = 100 %), where Equation 2.29 overestimates the air density. The difference decays with lower values and gets close to zero for temperatures below 0°C or a relative humidity below 20 %.

In case of a tunnel, different conditions arise (e.g., vw = 0). Nonetheless, an additional inﬂuence is only to be considered for tunnel lengths of 500 m or more (Wende 2003, p. 138). The effects to be taken into account are

– acceleration of the air in front of the train to the train’s speed vt and “push- ing” it towards the end of the tunnel. – overcoming the pressure difference between nose and tail, which results in a force along the train. – “pulling” the air behind the train to restore the air volume of the tunnel.

According to Wende (2003, pp. 139 sq.), two parameters are used to describe these phenomena: a different aerial resistance coefficient, which replaces cw in Equation 2.23, and an “obstruction coefficient” representing the ratio between effective (rib) surface area and tunnel cross section. The former is usually determined experimentally, while the latter can be calculated—typically using 2 the norm surface area of Ab = 10 m . Note that the obstruction coefficient tends to increase with the train length. Weidmann (2011, p. 12 of ch. 6) introduces “for simple problems” a so-called tunnel factor kt into Equation 2.23,

1 2 FAR = /2 · cw · Ab · ρ · kt · v , (2.30) basically corresponding to the principles of Sutter (1930) and Pellis (1979, p. 975); kt is sometimes considered valid for all train speeds vt. On the other hand, different factors inﬂuence its value: the tunnel’s length, cross section, and wall, and the nose shape of the train. The tunnel factor is usually determined from measurements; some examples in Table 2.5. As estimation,

Weidmann proposes kt = 3.4 for elderly single, 1.8 for elderly double, and 1.4 for modern double track tunnels. Note that, despite this formulation, not the tunnel’s age but its wall structure is determining: New tunnels are built with smooth surfaces producing less resistance than traditional quarry stone walls. Independent of the modelling approach, it is important to note that the aerial resistance within a tunnel is not constant when passing it, even not if the train speed is constant over the entire observation period. Rather, the resistance decays towards the middle of the tunnel and then re-increases towards its end, as Sachs (1973a, p. 33) presents from SBB measurements.

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Table 2.5: Some Tunnel Factors kt; collection inspired by Weidmann (2011). Mostly, the given tunnel factors are mean values from passenger and freight trains. Length Cross Section k Tunnel t Source (in m) (in m2) (–) Albis CH 3360 24.0* 3.48 Sutter (1930) Gotthard CH 15 002 41.0 2.81 Gaillard (1973) Gotthard Base CH 57 104 41.0‡ 1.55 Schranil and Lavanchy (2016) Hauenstein CH 8134 44.8 2.61 Gaillard (1973) Kerenzerberg CH 3955 49.5 2.43 Gaillard (1973) Massico IT 5378 approx. 42 2.23 Pellis (1979) Mühlberg I D 5513 85.5 1.43 Peters (1990) Ricken CH 8603 25.0* 6.64 Gaillard (1973) * Single Track Tunnel ‡ Double Track Line in Two Single Track Tunnel Tubes

Additionally, Sachs (1973a, pp. 28 sq.) shows an influence of the yaw angle α on the (outer) aerial resistance: Measurements of the Dutch Railways (NS) for two- and three-carriage diesel-electric trains showed maximum values of aerial resistance at α ∈ [8°,16°], increasing the value of cw by about 25 %. In contrast, experiments of the erstwhile LMS9 show the maximum for α ≈ 60° in case of a normal and α ≈ 30° for an “ideally streamlined” train; the developing of values is significantly varying, cf. Figure 2.12. For wind speeds above 22 mph (~35 km/h), Hay (1982, p. 86) confirms 60° for a maximum aerial resistance.

(a) Normal Construction (b) Streamlined “As Possible” (c) Ideally Streamlined

Figure 2.12: Wind Influence on the Total Resistance as shown by Sachs (1973a, p. 29). Note the deviating definition of the yaw angle α (cf. Figure 2.10). m is defined as ratio of wind speed AB = ~vw and train speed CB = ~vt: m = vw/vt. The subcaptions of graphs (a) to (c) indicate the type of train used; each train consisted of six carriages.

In a discussion with aerodynamics professional Mr. Steiling (2017), he confirmed that the largest values of aerial resistance result—for modern vehicles—at a yaw angle of around 60°, mostly caused by downwind side air flow changes at around 40° and 80°. The latter can also be seen in the experimental flow study of Chiu and Squire (1992, p. 50). Further, the maximum of

9London, Midland and Scottish Railway

– 40 – Chapter 2: System Modelling the pressure coefficient at 60° results from this study (Chiu and Squire 1992, pp. 69, 72). Weber, Benker, and Naupert (2007, p. 121) show in their publication that both rolling momentum coefficient cm,x and lateral air resistance coefficient cw,y show the maximum at 60° if measuring an end vehicle; for middle carriages, the maxima are reached at 90°. A comprehensive study on the angle dependence of different force coefficients has been conducted by Orellano and Schober (2006), who confirm the priorly presented findings. In contrast, Wende (2003, p. 125) includes the “indirect influence” of cross winds using lateral wind speed vw,y and resulting lateral force Fy. According to his argumentation, Fy is to a smaller part compensated by a skew in the rail-wheel contact and to a major part by wheel flange contact of the downwind side wheel. The resulting wheel flange resistance can be obtained as

∆Fx = 0.5 · µ · Fy. (2.31)

A similar explanation is given by Hay (1982, p. 86), but he argues that a minimum wind speed—equivalent to a minimum side force Fy—must occur in order to reach wheel ﬂange contact at the downwind side.

To conclude the observations on outer aerial resistances, a few words shall be spent on non-stationary phenomena. These occur as reaction to abrupt changes in the outer conditions—e.g. meeting an opposite train, entering or leaving a tunnel—and are usually not relevant in terms of driving dynamics. On the other hand, when investigating safety issues, especially in terms of derailment risk in high speed applications, they may be of crucial importance (Wende 2003, p. 141). On this topic, often co-investigated with cross-wind in- ﬂuences, different publications can be found, e.g. by Deeg and Bolten (2006), Guo, Xia, et al. (2015), Matschke, Grab, and Bergander (2002), Orellano and Schober (2006), and Weber, Benker, and Naupert (2007); the topic’s importance has been conﬁrmed by Mr. Steiling (2017).

Breakaway Resistance During stand-still, the bearing friction increases as axle and bearing pin are pressed together, displacing the lubricant. When the axle starts to turn, the lubricant is distributed again, the friction decays to its normal value. While the friction is increased by a factor of ten and decaying over a distance of 5 m in case of slide bearings, the increase by a factor of two decays over less than 2 m in case of—nowadays common—roller bearings. In either case, the friction increases with decreasing temperatures (Wende 2003, p. 117). In terms of numbers, Wende states some 20 N/kN for slide and 6 N/kN for roller bearings; some more differentiated values are listed in Table 2.6. In practice, the breakaway resistance is rarely calculated; a so-called maximum starting mass is regarded as more relevant quantity (Pachl 2011; Wende 2003). Indications on the calculation of the resistance itself were not found.

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Table 2.6: Breakaway Resistance Factors: Weidmann (2011, ch. 6 p. 14), Rotter (1966, pp. 233 sq.).

Bearings Type Full Train Locomotive Carriage

Slide 10...20 N/kN 20...25 N/kN 12...20 N/kN Roller 7...18 N/kN 4...7 N/kN 2 N/kN

Total Running Resistance In practical applications, the running resistance is often not split into the different parts as presented before. Instead, formulas with experimentally determined coefﬁcients for constant, speed proportional, and speed quadratic term are used. Root of all these formulas it the Davis Equation, which is

2 (2.32) FR = A + B · vt + C · vt , as it can e.g. be found in Schranil and Lavanchy (2016). The coefﬁcients A, B, and C usually have to be determined empirically (Pachl 2011, p. 25). Pachl (2011, pp. 25 sq.) presents two formulas that are special formulations of the Davis Equation, introduced by Strahl (Equation 2.33) and Sauthoff (Equation 2.34) respectively:

v 2 f = c + (0.007 + c ) · t (2.33) R 1 2 10 2 1 fR = 1.9 + cb · vt + 0.0048 · (n + 2.7) · Aeq · (vt + 15) · (2.34) mt

N Both result in a resistive force coefﬁcient fR (in /kN) and use the vehicle speed km vt—measured in /h—as input quantity. c1 depends on the bearing type; it becomes 2.0 for slide bearings, 1.4 for roller bearings, and 1.2 for fully loaded block trains. c2 depends on the type of train and ranges from 0.013 for fully loaded block trains to 0.1 for empty freight trains of open carriages. While Strahl’s formula is nowadays used at DB for freight trains only, Sauthoff’s formula is applied for passenger trains. Therein, cb is the rolling resistance factor, which is 0.0025 for modern, four-axle carriages; n denoting the number of car- 2 riages; Aeq the equivalent cross section (set to be 1.45 m ). The train mass mt has to be applied in unit tons. Note that Strahl’s formula was used for passenger trains as well; a slightly different notation with corresponding coefﬁcients can be found in Wende (2003, p. 153). Wende (2003, pp. 152–155) introduces different formulas that are either used for certain applications or as “standard formulas” by state railways. For high speed trains, DMUs, or EMUs, the Rappenglück formula is proposed, s 100 kN v v + ∆v 2 F = 0.001223 · · m g · +0.00102 · t · m g + F · t , R 1 t m t WZ2 m mtgnx− 27.7 /s 27.7 /s

kN 2 FWZ2 = 0.21 kN · np + 0.1321 kN · nb + 0.001419 /m · Crib lt + (nc − 1) · dicgl kN 2 (2.35) + 0.09 /m · Arib; Crib the contour length of the rib surface Arib,

– 42 – Chapter 2: System Modelling while as alternative for freight trains, the equation of Jentsch and Preysing is presented, which is similar to the formula of Rappenglück. Having a look at the formulas used by the state railways, in most cases an equation of the form

vt vt 2 fR = c1 + c2 · + c3 · (2.36) 27.7 m/s 27.7 m/s can be found (i.e., the Davis Equation), where the parameters c1, c2, and c3 are determined slightly different; e.g., in Italy, c2 = 0 in all cases. French state railways SNCF additionally use the Rappenglück formula (2.35) for high speed, DMU, EMU, and locomotive applications.

Curve Resistance The curve resistance has many different causes and influencing factors, e.g. wheel flange contact of the outer wheels, different distances for outer and inner wheel while having a stiff connection, curve radius rc, friction coefficient µ, axle-base la, gauge, and gauge widening. Consequently, most of the formulas have been derived empirically (Sachs 1973a, pp. 44 sq.). The detailed interrelations can be found in Wende (2003), if the reader is interested. Instead, different models are shortly discussed.10 Wende (2003, p. 107) presents three equations that are used for vehicle development, in DB’s regulations, and for tramway applications (grooved rail); cf. Equations 2.372.39.

0.153 · drt + 0.1 · la fc = (2.37) rc k fc = , k a length constant, ∆r a radius constant (2.38) rc − ∆r 0.158 · drt + 0.033 · la fc = (2.39) rc

N Hereby, fc denotes the force coefficient (in /kN), drt the distance of the running treads, la the (mean) distance of the axles that are mounted in a fix frame (wheelbase), and rc the curve radius. According to Sachs (1973a, p. 46), the formula of v.Röckel has been modified by C. Mutzner taking the decaying curve resistance for large radii into account:

c1 − c2rc fc = , (2.40) rc − c3 with c1 = 800, c2 = 0.4, and c3 = 40 for standard gauge. Thus, the resistance tends towards inﬁnity for radii below 40 m, while it becomes practically zero for radii of 2000 m and above.

10This review is not comprehensive, e.g., irrelevant models as for two-axle carriages are omitted.

– 43 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Adding another inﬂuence factor, the wheelbase la, the formula of A. Frank is given (Sachs 1973a, p. 46):

2 la la Z0 fc = 180 · − 15 000 · · for worn wheel rims, (2.41a) rc rc Q0 2 la la Z0 fc = 148 · − 12 000 · · for new wheel rims, (2.41b) rc rc Q0 where Z0 denotes the drag force of the rearward carriages, with mean values Q Q Z0 = 0/15 for passenger, Z0 = 0/7.5 for freight trains, and Q0 the weight. Another formula that delivers more precise values has been presented by Parodi and Tétrel and used by Protopapadakis in his study:

µ · (0.72 · drt + 0.47 · la) fc = (2.42) rc

Depending on the gauge, the formula is valid for different intervals of la: 1.75...8.25 m for standard gauge, 1.25...5 m for meter gauge, 1...3 m for 75 cm gauge, and 0.9...2.5 m for 60 cm gauge (Sachs 1973a, p. 47). drt is 1.5 m for standard gauge, 1.05 m for meter gauge, and gauge + 50 mm for smaller gauges (Bundesamt für Verkehr 2014, AB 48.1). Additionally, Sachs (1973a, pp. 49–52) presents a study by G. Schramm that aimed for generally valid formulas based on the principles of track guiding. Skipping the derivation, the curve resistance coefﬁcient is ﬁnally calculated as

500µ f = · (g + g ). (2.43) c b 1 3 Thereby, b denotes a small feed (in the direction of movement), resulting in slide distances g1 and g3 the for front and rear axle. The latter two are depending on the exact position of the point of inﬂexion, i.e., its distance p from the front axle. In general, i.e. p < la, the curve resistance coefﬁcient can be written as s 500 l2 r · σ d 2 f = µ · · a + r · σ · c + 1 + rt c  c 2 rc 4 la 2 s  l2 r · σ d 2 + a + r · σ · c − 1 + rt · (1 + µ · tan γ), (2.44) c 2  4 la 2

γ denoting the angle of the wheel ﬂange (at least 67° according to TSI), which results in tan γ = 2.36. For the track play, σ = 0.015+s is proposed, with s = 0.005 m for 172 m < rc ≤ 200 m and s = 0 m for rc ≥ 200 m; µ can be assumed to be 0.25 or determined according to the model(s) presented in section 2.2.2.5.

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Slope Resistance Resulting from the downhill force occurring for any slope not equal to zero, the slope resistance may be positive or negative. Assuming β to be the slope angle, the resulting force is obtained from physics and trigonometry as

FR = mt · g · sin β. (2.45)

For small values of β, sin β = tan β holds.11 Taking into account the trigonomet- ∆h ric relation tan β = 1000 m = i0, the slope resistance can be rewritten as

FR = mt · g · i0. (2.46)

Wende (2003, pp. 93 sqq.) states that usually, the distance/elevation profiles contain a narrow grid of values, which are not suitable for calculations. There- fore, quantities as mean inclination and mean corrected inclination are introduced. As this reduction of complexity also decreases the accuracy of the calculations and is—thanks to increased computational power—non-essential, these approaches are not discussed in this thesis. Rather, the train is not modelled as mass point—which would be the adequate modelling when using the mean values—but as mass band. This approach is more precise but requires the actual values of inclination to be used. Note that in a strict sense of physics, the slope leads to a change in height (above sea level) of the entire train. Thereby, the energy used to move the vehicle uphill (or gained from downhill movement) results in a change of potential energy—which is a lossy energy conversion. On the other hand, the vehicle mass may change during an investigated cycle, e.g. by loading or unloading. Moreover, it is unlikely that the vehicle reaches the same height above sea level at beginning and end of the cycle, resulting in different levels of potential energy. Thus, the investigation is significantly simplified by regarding the slope as resistance and declaring the respective energy demand accordingly.

Point Resistance When a train passes a point (switch), additional forces arise that result e.g. from wheel flange guidance. The respective force coefficient ranges from 0.5 to 1.0 N/kN with a mean value of 0.75 N/kN. For movement calculations, this influence is usually neglected but of certain importance when investigating hump yard operations (Weidmann 2011, p. 21 of ch. 6).

11For angles β 10° = 176 , the difference is below 3 . For adhesion based railways with a ≤ maximum inclination of abouth 70 , the approximation ish thus justiﬁed. h – 45 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Brake Systems in Railway Operation

Adhesion-Dependent Adhesion-Independent Systems Systems

Friction Brakes Dynamic Brakes Rail Brakes

– Disc Brakes – Electrodynamic Brake – Electromagnetic Rail Brake – Block Brakes – Hydrodynamic Brake – Eddy Current Brake – Drum Brakes

Figure 2.13: Classiﬁcation of Brake Systems (Janicki, Reinhard, and Rüffer 2013, Abb. 5-8). This scheme only covers vehicle brakes used in operation. Parking brakes or infrastructure-side brakes, e.g. track brakes, are not included.

2.2.2.8 Deceleration (Braking)

Introduction and Overview Basically, deceleration is a change in the kinetic energy of the train. While its increase (acceleration) is always a conversion from input energy (usually electricity or diesel) to kinetic energy, its decrease can be of different kinds, see also Figure 2.4: Usually, the conversion is either into heat (dissipative) or electric energy (regenerative), the latter as long as the drive system is designed accordingly. This means that for the increase of kinetic energy, the drive chain losses (cf. section 2.2.2.4) are occurring solely, while for its decrease, the brake systems of trains have to be investigated more in detail. Janicki, Reinhard, and Rüffer (2013) give a comprehensive overview on braking technologies; they distinguish air brake, dynamic brake, electromagnetic rail brake, and eddy current brake. Moreover, additional functions and installations are treated, e.g. electro-pneumatic brake (ep-brake), emergency brake equipment, spring-loaded brake, and anti-skid system. Concisely focusing on brake systems for rail vehicles, they classify the existing ones as shown in Fig- ure 2.13. Except for the electrodynamic brake, all systems are dissipative. Of course, there are additional systems, e.g. to secure the vehicle against escape. These systems are separate from the ones used in operation and thus irrelevant for operational considerations, so they are not discussed here.

Friction Brakes: Principle of the Air Brake The basic principle of friction brakes consists in dissipating the kinetic energy of the train into heat. Therefore, additional friction is built up, either between

– brake shoe and wheel (block brake), – brake shoe and brake disc (disc brake), or – brake shoe and brake drum (drum brake).

– 46 – Chapter 2: System Modelling

The physical functional chain when applying a friction brake consists of the steps application of brake shoe force, deceleration of the axle, and transmission to the rail. Thereby, two different friction coefﬁcients appear: One between brake shoe and wheel (or disc, or drum) and the other between wheel and rail (Janicki, Reinhard, and Rüffer 2013, p. 310). The standard friction brake system is the indirect air brake: With decreasing pressure of the main brake pipe, an increasing brake force is applied through the brake cylinder (Wende 2003, p. 221). The braking process is described by different quantities and interactions, e.g., by Wende (2003, pp. 225–250).

As ﬁrst reaction to a decreasing pressure of the main brake pipe, a force is built up within the brake cylinder (piston force):

Fpiston = ft · fst · Apiston · pcyl,max, (2.47)

Apiston the piston area and pcyl,max the maximum cylinder pressure, being 3.6 bar for normal passenger (P) or freight (G) brake mode and 3.8 bar for the rapid brake (R). ft and fst denote time and brake step factor; the latter is

zB fst = , (2.48) zB,max with zB the current brake step and zB,max the number of brake steps. Assuming a fully released brake at 5 bar main brake pipe pressure and a fully applied at

3.5 bar with a step size of 0.05 bar, zB,max = 29 results. The time factor ft covers the reaction time of the system, which is needed to react and build up the brake force before the state of developed braking is reached. The first cylinder will react after 1.5 s. This time increases with increasing distance to the locomotive if no electro-pneumatic brake is used,12 as the main brake pipe inserts a non-negligible delay. Given a propagation speed 13 m of the command of vDu = 100...150 /s—according to Sachs (1973a, p. 758), m vDu ≥ 250 /s for modern brake systems—the time needed until the signal reaches a certain distance from the train’s nose can be calculated easily. Finally, for filling and venting the brake cylinder, an exponential approach is used. Important parameters are filling and venting time tF and tV, which are depending on the brake mode (P, G, or R). For modes P and R, the values are 4 s and 18 s; in mode G, the respective values are 24 s and 52 s:

0.556,0.877 t { } ft = ﬁlling (2.49a) tF 0.356,0.466 t { } ft = 1 − venting (2.49b) tV

12In case of an electro-pneumatic brake, the brake signal is not transmitted via the main brake pipe but using an electric cable. The electric signal travels with the speed of light, thus, all brake cylinders receive the command at the same time, therefore reacting synchronously. 13“Durchschlagsgeschwindigkeit”

– 47 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Special situations may arise when multiple vehicles are actively controlling the air brake system. However, these cases are neglected here, as they are of rather low importance in today’s railway operation. Altogether, the piston force can be written as function of brake step, brake mode, piston area, and maximum cylinder pressure:

 0.556 t  t P or R brake, ﬁlling  F  0.877 z  t G brake, ﬁlling B tF (2.50) Fpiston = Apiston · pcyl,max · · 0.356 z t B,max  1 − P or R brake, venting  tV  0.466  1 − t G brake, venting tV

Note that for t ≥ tF, Fpiston takes its maximum value Apiston · pcyl,max · zB/zB,max for the respective brake step, while for t ≥ tV, it becomes zero.

The piston force is transmitted via brake linkage to brake shoe and wheel or disc. Given that each brake cylinder supplies nBS brake shoes (nBS ≤ 4), moreover gear ratio i, brake linkage efﬁciency ηBL, linkage friction force FR, and FC the counter force per brake shoe, the contact force per brake shoe results to be

i · ηBL FBS = (Fpiston − FR) · − nBS · FC. (2.51) nBS Especially in freight transportation, load-proportional brake settings are used. The adjustment is either done manually switching between two different transmission ratios, or automatically adjusting the brake cylinder pressure. Gen- erally, not the entire range of possible masses can be covered (Wende 2003, p. 228). Additionally, the brake linkage efﬁciency ηBL is depending on the maintenance state of the brakes, reducing it by up to 0.01 per month without maintenance; best values are ηBL = 0.91...0.94 (Wende 2003, pp. 227 sq.). Brake force per brake shoe FBS being known, the number of brake shoes per axle nBS given, the brake force applied to this axle is—for block brakes—

FB,x = µBW · nBS · FBS. (2.52)

In case of disc brakes, the brake disc radius rBD has to be taken into account:

rBD FB,x = µBD · nBS · · FBS, (2.53) rw

µBW and µBD the friction coefﬁcient brake shoe–wheel and brake shoe–brake disc respectively. Note that due to adhesion, this force has to be smaller than the maximum transmittable force µmxg, mx the axle load (gliding criterion). In order to determine the friction coefﬁcients µBW or µBD, Wende (2003, p. 232) introduces Karwatzki’s formula as method of choice:

FB,x + k2 vt + k4 µBW = µBD = k1 · · , (2.54) FB,x + k3 vt + k5

– 48 – Chapter 2: System Modelling

km vt in /h, k1 to k5 from Tab. 5.7 in Wende (2003, p. 234). The validity of this formula is limited (max. values) to 40 kN and 120 km/h (grey iron shoes), 40 kN and 160 km/h (composite shoes), or 15.2 kN and 160 km/h (disc brakes).

In practice, the maximum applicable brake force FB,max per vehicle is related to the empty or a typical mass of the vehicle, let it generally be FG,typ = mtyp · g. The resulting ratio is called retardation (“Abbremsung”): F ϑ = B,max (2.55) FG,typ

Despite these rather detailed descriptions of physical interrelations, a so-called braking weight (“Bremsgewicht”) was established in practice and is still used as main (and sole) source of information on the braking properties of a train. In spite of a historically well-deﬁned derivation of this quantity—to be found in Sachs (1973a, pp. 759 sqq.)—today, the values are determined on an experimental basis (Adler 1990).

Dynamic Brakes For dynamic braking, the brake forces are generated by the drive system, by electric motor or hydraulic gear (Janicki, Reinhard, and Rüffer 2013, p. 354). In case of electric traction and for modern (converter driven) vehicles, the brake force is produced “reversing” the energy ﬂow, showing the same characteristics as for driving.14 The generated energy can be either fed back to the grid or dissipated using a brake resistor; combinations are also possible. Note that the possibility of feeding back the energy is depending on the supply system; not all systems are always receptive (Janicki, Reinhard, and Rüffer 2013; Meyer 2012). In AC systems, especially if centrally fed and interconnected with the national grid, energy feedback is usually possible as there are enough sinks and controllable sources within the grid. Vice versa, in DC systems, the receptivity is only given if another train within the same feeding section requires energy, as there is usually no bidirectional link between railway and national grid (cf. section 2.2.4), at least in traditional systems. This allows to use the same model as for traction cases, only distinguishing how to account the regenerated energy for the energy balance depending on the supply system’s receptivity. Some further limitations are implemented within the vehicle control (Meyer 2012); these can easily be included if they are known. For older—pre-converter—electric vehicles, the characteristics of the dynamic brake are often completely different from the driving characteristics. But also in this case, a ZV-diagram that contains the braking part would allow to easily determine the maximum brake force and thus the regenerated energy for a

14For low speeds—below 1...10 km/h—the effectiveness of the electric brake decays; moreover, the maximum brake force is usually around 25 % lower than the starting tractive force (Glasl 2017; Mazzone and Hohenbichler 2017).

– 49 – Energy Saving Potentials in Railway Operations under Systemic Perspectives given vehicle speed (Meyer 2012; Meyer and Nerlich 2017). On the other hand, a determination without this information is almost impossible. Also for diesel-electric vehicles an application of the electric brake is possible. However, the regenerated energy cannot be used outside the vehicle, thus, it has either to be dissipated or—if designed accordingly—used for auxiliary or comfort purposes. The latter is also applied for electric locomotives, sometimes on purpose while passing neutral sections using the so-called “Stützbremsbetrieb”—supportive braking (Meyer 2012). For conventional diesel traction, a retarder can be used in the hydraulic system. This acts as an additional gear, which implicates that the brake force will not regenerate energy but additionally consume energy as the applied braking force has to be generated by the diesel engine. Therefore, the dynamically applied braking force has to be included as additional energy demand.

Rail Brakes The group of rail brakes contains two different systems: magnetic rail brake and eddy current brake. While for the magnetic brake, the force results from magnetically produced friction between brake shoe and rail, an electro magnetic force is applied for the eddy current brake (wear-free). Both are activated in case of very low main brake pipe pressures (below 3 bar); they only have the states on–off and are not controllable. In normal operation, their importance is usually low except for lines with high speeds and short distances between distant and main signal (Janicki, Reinhard, and Rüffer 2013; Meyer 2012). In fact, the eddy current brake is used in normal operation in some rare cases (with ICE 3 vehicles in high speed operation); however, the magnetic rail brake is applied exceptionally only.

2.2.2.9 Traction Auxiliary Systems and Their Control

Systems Overview Under the term of traction auxiliary systems, all traction-related energy sinks on-board the vehicle are subsumed—i.e., all systems that allow the vehicle to fulﬁl its traction task. These are (Filipovic´ 2015, pp. 179 sq.)

– battery charger, – compressor (providing compressed air), – air fans (for air cooled devices), – oil pumps (for oil cooled devices), – vacuum pumps (if vacuum brake existing), and – driver’s HVAC.

Naturally, these systems are depending on surrounding conditions: the usage of the air brake deﬁnes the required compressor energy, the warming of the ma- chinery the cooling and thus the fan’s / oil pump’s energy demand, the weather

– 50 – Chapter 2: System Modelling the HVAC demand etc. However (and except for HVAC), these values can be taken as mostly constant for a given route. For a derivation of the detailed interrelations, detailed thermodynamic and brake–battery–HVAC use analysis would be necessary—of which none has been found in literature; carrying it out in context of this research is out of scope. Nonetheless, some approach for modelling the auxiliary demand is necessary. While for the driver’s HVAC the same basic principles as for the passengers’ HVAC are valid and thus covered there (section 2.2.2.10), for the other values, the literature is consulted. For example, Filipovic´ (2015, p. 180) states some power intervals that can be seen as typical values: compressor 6...20 kW, motor ventilators 5...40 kW, other ventilators 1...20 kW, oil pumps 1...3 kW, vacuum pump 4...10 kW; summing up to 17...93 kW in total. More numbers are given for SBB’s Re 460 by Gerber, Drabek, and Müller (1991, p. 30) in their overview on the main electric apparatuses:

– Four board net converters, 50 kW each (basically comfort) 15 – Compressor MSR 63 DT with 3800 l/min max (estimated ∼25 kW)

3 – Four motor ventilators, 7.5 kW each (max 3016 rpm and 2.1 m /s)

3 – Two oil cooler ventilators, 25 kW each (max 3016 rpm and 3.2 m /s) – 2×2 oil pumps (transformer and converter), 5.5 kW each

Thus, an installed auxiliary power of about 100 kW results for this 4.8-MW- locomotive (6.1 MW peak power); 200 kW are installed for board net (basically comfort) services. As no information on loading conditions has been found, assumptions based on this data become necessary later on.

Control and Its Modelling For all mentioned auxiliary systems, the control is demand driven: if the battery had to deliver energy, it is recharged accordingly; if compressed air has been used, the compressor is activated, and so on. The battery is used to supply devices that need continuous power supply, also when passing neutral sections or similar. In case of traction auxiliary, this is vehicle control technology, driver’s cab (control units), and lighting (Janicki, Reinhard, and Rüffer 2013, p. 200). As these loads are usually constant, the same applies for the actual battery load. Consequently, the battery charger control can be neglected, assuming that the battery charger delivers constant power. As the battery charger is usually a power electronic converter, its efﬁ- ciency is in the range of 95...99 %; the efﬁciency of the battery itself is neglected as it is—in normal operation—only used during short and rare neutral sections. The compressor is activated when pressurised air has been used—mainly for air brakes, door, and closed toilet systems (Janicki, Reinhard, and Rüffer 2013, p. 301). As it is desirable to always have pressurised air available, the

15Industrial compressors for 7 bar, 1800 l/min have around 11 kW (Atlas Copco AB 2014, pp. 23 sqq.).

– 51 – Energy Saving Potentials in Railway Operations under Systemic Perspectives compressor is usually acting at full load as soon as the pressure decayed and until the nominal pressure is reached again (on-off-control). This allows to determine a deﬁnite energy demand necessary for a certain operating section, which can be used without need for any compressor control model. Apparatus cooling is depending on the actual apparatus temperature, as certain limits must not be exceeded. This indicates a dependency on the actual power, the power history (temperature development until now), and the environmental conditions. These interactions are—as already stated for machine heating—too complex to be included in a systemic context. Unfortunately, there has also no literature been found on demand estimations or modelling for cooling energy. Therefore, an estimation model had to be built. The driver’s HVAC is basically the same as the passengers’ HVAC, applied for only one person and a smaller air volume. For this reason, the driver’s HVAC is treated together with the passengers’ in the comfort systems section.

2.2.2.10 Comfort Systems and Their Control

Systems Overview Talking about comfort systems, one usually addresses all installations of a passenger train that are not related to the transportation task but used for the passengers’ comfort. Also in freight transportation, some installations can be classiﬁed as comfort systems, ensuring the “comfort” of the transported goods and/or loading and unloading, as e.g. cooling for frozen goods. The most important energy source for passenger comfort systems is the train bus, which provides electric energy; it supplies HVAC, battery charger, lighting, control systems, and passenger information systems (PIS). Power outlets for passengers, e.g. to charge their mobile devices, are also connected to the train bus. Door closing, mechanical brakes, and the lavatories usually employ compressed air as energy source. The train bus itself is fed from a dedicated secondary winding of the traction transformer or, in DC systems, directly from the ﬁltered line voltage (Janicki, Reinhard, and Rüffer 2013, pp. 457 sqq.). In case of freight transportation and for so-called “carriages with temperature manipulation”, either passive systems are used (i.e. ice reservoir and wind-driven ventilators) or the necessary energy for active operation is generated using axle generators and batteries (Janicki, Reinhard, and Rüffer 2013, pp. 408 sq.), increasing the running resistance of the train. Also some loading/unloading systems require energy for their operation that is gained from kinetic energy of the carriage itself; after a distance of 5 km that has been travelled with at least 10 km/h, hydraulic systems are fully charged. Alternatively, pneumatic systems can be used that are either supplied by the locomotive or stationary (Janicki, Reinhard, and Rüffer 2013, p. 395). If the system is powered by the locomotive, directly (pressurised air) or indirectly (resistance by axle generators), an integration into the energy analysis is required.

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Having a look on the energy demand of the different systems, some of them are determined by external factors (HVAC, freight cooling). Other systems show a more constant behaviour, i.e., control and PIS. The behaviour of lighting depends on its control: If—as usual—constantly lighted and in case of an on-off-control, lighting will have a constant energy demand. In case of a more ﬁne-tuned control (e.g. with modern LED lighting systems), there will be some variations depending on the outside light intensity and the exact control scheme. For the power outlets, a mainly stochastic behaviour is to be expected—nonetheless, assuming an equal distribution, the expected demand should be more or less constant over time. The battery charger itself only shows some approximately constant losses, as the battery is used to power the ﬁnal loads but does not act—apart from its losses—as sink itself. Unfortunately, no evaluations of comfort systems were found; the presented studies are usually before-after-comparative (Beusen, Degraeuwe, and Debeuf 2013; Isenschmid, Menth, and Oelhafen 2013; Stephan and Körner 2014). Therefore, an own model is developed and discussed in section 2.4.2.

Comfort Systems Control Similar as for the auxiliary systems, the comfort systems control is basically demand-oriented. Regarding the fact that the battery ensures non-disruptive operation of lighting, control, and PIS, only the latter three have to be discussed, neglecting the battery charger itself. The demand of control systems, lighting, and passenger information is basically constant: These systems are steadily running as long as the vehicle is in operation; based on considerations from an earlier project, an uncontrolled and negligible constant load can be assumed (Bomhauer-Beins 2017; Bomhauer- Beins, Schranil, and Weidmann 2018b). Concerning HVAC, the usual control mode consists in a temperature based cooling/heating power with constant ventilation, the energy demand thus being a function of the actual temperature inside the carriage, inﬂuenced by outside temperature and possibly other factors as e.g. solar radiation (Isenschmid, Menth, and Oelhafen 2013; Tuchschmid 2017). Newer approaches propose to introduce a CO2-concentration-based ventilation control, which would allow to adjust the ventilation level to the number of passengers within the respective carriage—also reducing the energy demand (Beusen, Degraeuwe, and Debeuf 2013; SBB AG 2016). On the other hand, a modelling approach was not found in literature. Therefore and given the fact that the HVAC energy demand is not negligible (SBB AG 2016), a model was developed and is presented later.

– 53 – Energy Saving Potentials in Railway Operations under Systemic Perspectives Tr,set set q,set F T

v × I set + PI dq,set

- I ÷ + PI + dq Uabc × - PI abc ÷ ϕΨ

Ψ d,set

| | I mach. abc PLL model dq feed fwd ω1

- U=,set + s s PWM PWM U I ϕ ω

line motor T force trans- Ucatenary U U= vact converter converter M mission ω

Figure 2.14: Motion Control Scheme: Electric Traction Chain (black/grey) including Control (blue)— simpliﬁed. PI a proportional-integral controller, PWM a pulse-width modulation controller for converters, PLL a phase locked loop. ϕ the electric angle of the rotor; s the switching signals for a converter. Own illustration based on information provided by Kolar (2010) and Omlin, Ronner, and Steimer (2011).

2.2.2.11 Motion Control

General Control Structure The motion control tasks comprise all actions related to changing the kinetic energy of the vehicle or train. As top-level controller, usually a driver or an automatic train operation (ATO) system is employed, depending on the grade of automation. Providing a torque or a desired speed as input for the automatic controller, the latter based on current and ﬂux, the vehicle dynamics are controlled by delivering voltage set values for the converter output (Omlin, Ronner, and Steimer 2011, part 2, ch. 8 and 11). These set values serve as input for the converter control, ﬁnally delivering a switching sequence for the semiconductors, usually by application of pulse-width modulation—PWM (Kolar 2010). A schematic overview of this typical structure is given in Figure 2.14.

Simplified Control Model Obviously, the complexity of this control is too high for inclusion in a systemic model; instead, a focus on the functionality is applied. From a system’s point of view, most relevant is speed control respecting the limitations of the drive chain. In contrast, the engine’s exact behaviour is not relevant as long as the influences on energy demand are quantifiable. For the latter, an approach was presented in section 2.2.2.4, Equation 2.4. Concerning control, the following assumptions are made (Filipovic´ 2015; Hecht, Jänsch, et al. 2008):

– Manipulated variable is the vehicle speed, i.e. speed limits and/or recommendations/targets are known or a speed proﬁle is given.

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– Changes in speed occur with the maximum available forces, readable from ZV-diagram or calculation of the maximum transmittable force. – Comfort criteria are included by limitation of the maximum allowable acceleration or deceleration. – Reaction times or driver’s behaviour can be included using dead times and additional acceleration limits.

This approach only requires the knowledge of the ZV-diagram (or its determining values, cf. section 2.2.2.4), the comfort/safety limits—e.g. |a| ≤ 1.3 m/s2 (Weidmann 2011, p. 6 ch. 5)—some information or assumptions on the driver’s behaviour, and the speed limitations or profile. For the driving style, Capasso, Lamedica, et al. (2016) consider five different driver behaviours for their simulation studies; this approach—i.e. defining a set of assumptions on behaviour— seems to be useful, as the entire variety of styles is impossible to capture. This functional approach—summarisingly consisting of set speed, vehicle power constraints, and additional limitations—allows a traction technology independent application: As input quantity, a desired speed is given; the traction system reacts—within the limits—with maximum applicable force; the energy demand results from forces and efficiencies as described priorly. Applying the equations, a new vehicle speed results that is sent back to the control’s input, allowing to exactly reach the set speed. All in all, this implements the speed controller—which is finally relevant to operations—and “assumes” the subor- dinate control levels “just to work properly”.

Overload Capacity of Electric Machines A special topic when dealing with electric traction is the overload capability of electric machines—or, more precisely, their time-dependency in terms of deliverable maximum power without causing damages: As their permanent power is usually determined by thermal limits, the output power may exceed the nominal (permanent) power for limited periods of time. Depending on the applied power level—e.g. permanent, hourly, or 5-Min-power—different traction (and electric braking) forces are available. Thereby, the basic condition for the load limit is simple: As long as the temperature limit is not exceeded, an “arbitrary” amount of power can be delivered. As soon as the maximum permissible temperature is reached, the output is limited to permanent power. The cooling (as auxiliary service) then reduces the machine temperature, thus re-establishing the overload capability. Consequently, temperature, heating, and cooling of the machine are determining for the permissible power and thus maximum available traction force (Bikle, Colotti, and Küng 2012; Weidmann 2011). The thermal limit is between 90°C and 250°C, depending on the thermal class of the engine (Bikle, Colotti, and Küng 2012, p. 1 of ch. 6). In railway applications, the regular coolant starts to boil at 108°C, the surrounding temperature is usually deﬁned to be between –30°C and +40°C (Binder, Koch, and Jöckel 2003)—a thermal limit of 100°C (housing temperature) has to be assumed.

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Rth

Ith

Qth Cm Cenv

Figure 2.15: Simpliﬁed Heat Model of an Electric Machine as presented by W-tech (2017). The environmental heat capacity Cenv can be assumed to be inﬁnite; Rth the thermal transition resistance; Cm the machine’s heat capacity.

In a (rather) complete thermal model, many parameters have to be known or determined; more than ten are shown in the model presented by Bikle, Colotti, and Küng (2012, Abb. 6.7). In practice, a simpler analysis is used for thermal dimensioning: The model shown in Figure 2.15 consists of heat source, heat capacitance of the machine, heat transfer resistance to the environment, and inﬁnite heat capacity of the environment (W-tech 2017). The heat source Qth is equivalent to the power loss of the engine, the heat resistance Rth depending on the cooling power. The environmental temperature translates to the voltage

Uenv at Cenv, the same applied for the machine temperature Um and its heat capacity Cm. Given an efﬁciency η for a corresponding power P deliverable during time T , assuming a maximum allowable machine temperature Um,max and applying the thermal-electric analogy, an estimation on Cm can be made:

5 T · Ith 4 + e− Um,max Cm = · 1 − (2.56a) Um,max 5 Um,max − Uenv T · Ith 0.8 · Um,max ≈ · 1 − with Ith = (1 − η) · P (2.56b) Um,max Um,max − Uenv

Thus, the machine temperature Um(t) could be derived: Z (2.57a) Um(t) = Cm · Im(t) · dt Ith(t) = (1 − η)P (t) (2.57c)

Um,max − Uenv Im(t) = Ith(t) − Ienv (2.57b) Ienv = (2.57d) Rth

These equations show the dynamics of the machine temperature, being dependent on environmental conditions as well as on its own history. An inclusion of this behaviour would increase the complexity above the bounds of reason— additionally, in many cases, system and timetable design allow to neglect these effects. Therefore, the machine temperature is neglected.

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2.2.3 Non-Motorised Vehicle Basically, non-motorised vehicles are simpliﬁcations of motorised vehicles: The components comfort systems, acceleration resistances, motion resistances, dissipative braking and parts of the ancillary system remain, while all other parts or subsystems of a motorised vehicle do not exist. Thus, the considerations of the respective sections can be used as they are; further discussions for the non- motorised vehicle are not necessary. In fact, it seems to be more appropriate for concrete model implementation not to distinguish between motorised and non-motorised but rather to focus on entire trains—i.e., on operational units.

2.2.4 Energy Supply System 2.2.4.1 System Structures As already discussed in prior sections, there are two basic energy supply concepts: Either discrete—the vehicle carrying the fuel—or continuous. Nowa- days, this distinction is almost synonymous to diesel or electric traction, some experimental concepts as hybrid and hydrogen being part of the discontinuous supply group. Multi-power-source vehicles make part of both groups and have to be handled according to the actually used power source. In either case, the energy supply starts with primary energy—be it fossil oil, coal, uranium, water, wind, or anything else.

In case of discontinuous supply, the first stage of energy supply consists in converting the primary energy carrier into a form that is suitable for traction purposes. This process needs energy as well; for diesel traction, it is the refining of fossil oil to diesel fuel (Andersson 2016). Then, the fuel is transported to fuel stations, i.e., to the vehicles. Depending on refining method, mode of transport, and transport distance, the amount of energy used here varies. The energy being transported to the fuel station, it basically arrived at the vehicle’s energy input (VEI), from where on the vehicle model discussed in section 2.2.2 may be applied. Nonetheless, the energy used to provide the fuel— starting from primary energy—has to be taken into account. In case of diesel, 120 % of the actually fuelled amount of energy has typically been used in terms of primary energy. Within this difference, the production (i.e., refining processes) accounts for 85 %, while transport and distribution account for the remaining 15 % (Andersson 2016). For thermally produced hydrogen, up to 155...284 % of the fuelled amount of energy are used in terms of primary energy, depending on the exact product, process, and input materials. For an electrolytic hydrogen production, the amount of primary energy may even rise to 181...661 % of the finally fuelled amount of energy (Edwards, Hass, et al. 2014, pp. 9, 27, 29). As hydrogen based traction systems are rather rare, these ranges serve to give an impression but are not investigated further. For diesel traction, the factor 1.2 can be taken as basis for rough estimations; more

– 57 – Energy Saving Potentials in Railway Operations under Systemic Perspectives detailed analyses are out of scope of this thesis—especially when facing the fact that at least in Europe, most of the traction services are provided using electric power supply (Rees and Stephan 2017).

For continuous supply, different topologies exist. An overview and detailed explanations are given by Biesenack, Braun, et al. (2006), introducing four different topologies: DC, standard frequency AC, centralised special frequency AC, and decentralised special frequency AC; cf. Figure 2.16. All of these systems are (co-)fed from the national power grid, consisting of multiple stages of transformation and transmission, each being lossy. These losses are depending on type or distance respectively, introducing a dependency on power path and geographic location. Taking additionally into account that at public energy supply level many different sources and sinks interact over a complex network, an analysis of these processes is well-suited for an electrical engineering research, but out of scope of a railway system analysis. A similar consideration has to be made for the railway power grid (if existing; cf. Figure 2.16). Thus, a simplification—e.g. assuming a mean efficiency for the transmission—has to be made in order to enable a systemic analysis. For transformers, new requirements have to be fulfilled since 2015: the peak efficiency of power transformers (6 MVA rated power and above) has to be above 99.5 %—or 99.3 % in case of dry-type transformers (Siemens AG 2015, p. 3). Given this peak efficiency, the approach from section 2.2.2.4—i.e. Equation 2.4 (p. 27)—can be applied. The IEC (2007, p. 8) proposes a typical range of efficiency from 90 % to 98 % depending on the load situation, revealing a slight increase in transformer efficiency in the past years. For transmission lines, the losses are around 2.5 %; ranges of 3...5 % for power plant to step-down substation and 8...15 % for power plant to user losses are given (IEC 2007, p. 8). Finally, the “production” of electric energy from a primary energy carrier has to be taken into account. Depending on the generation—or more precise, energy transformation—method, different efficiencies (primary to electric energy) are reached. Orders of magnitude are 35 % for nuclear power plants, 45 % for steam power plants, 65 % for combined power plants, and 80 % for hydro power plants (Biesenack, Braun, et al. 2006, p. 64). Behmann (2008) states a well-to- plant efficiency of 99 % for hydro power plants16 and 94 % for steam based electricity generation; for well-to-pantograph (well-to-VEI) efficiencies, 83 % and 30 % are given. Assuming the priorly stated transmission efficiency of about 5 %, generation efficiencies of 88 % and 34 % result—which fits the numbers by Biesenack, Braun, et al. (2006). Note that sometimes, electric energy generation from renewable sources, i.e. water, solar, and wind—is regarded to have an efficiency of 100 % as the power source is unlimited (Andersson 2016).

16Most likely, this addresses run-of-river power plants and not pumped hydro power stations—but this is not clearly stated.

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Figure 2.16: Structure of Railway Energy Supply Systems. Figure from Biesenack, Braun, et al. (2006, Bild 1.2) but translated from German and slightly modiﬁed. Light grey the public power grid, dark grey the railway power supply system.

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Generally, the comparison of fossil energy carriers to renewable sources causes controversial discussions: While fossil energy carriers are limited and additionally, energy must be spent to gain energy from these carriers, renewables can be regarded as being unlimited and “just there”. Thus, the input energy is—strictly mathematically speaking—infinite, which results in any efficiency related to this primary energy being undefined. A possible approach to compare them anyhow might consist in defining another base quantity, e.g. the kinetic energy of the water when investigating a run-of-river power plant.

As another possible scale, the CO2 emission per generated amount of electric energy might be thought of. However, this side note is only intended to point out the difficulties when comparing different primary energy sources—but is not within this thesis’ scope and to be resolved by other research disciplines. Altogether, the overall efficiency is highly dependent on the composition of the finally delivered electric energy. In 2008 in Germany, 26 % of the energy were generated by nuclear power plants, 47 % from coal, 9 % from gas, and 16 % from renewables (N.U. 2010), whereas the share of renewables increased to 42 % in 2016 (Köstler 2017). SBB currently gain 90 % of their energy from renewables, aiming to reach a 100 % share by 2020 (SBB AG 2017). Note that there are—of course—different levels of control within the energy supply network. But focusing the research presented here on railway operations, the electric supply and its control are assumed to be perfectly working.

2.2.4.2 Railway Power Grid As railway power grid, all elements of electric power transmission between connection point to public grid and catenary feeding point are understood— see dark grey part in Figure 2.16. Thus, the railway power grid consists of different elements, depending on the concrete supply system:

– Transmission Lines, two- or three-wire, 10...380 kV – Central Transformer/Converter Substations (if in a centralised special frequency AC system) – Catenary-Feeding Substations, either rectiﬁer, transformer, or transformer/converter substations

The transmission lines are investigated more detailed in the following section on catenary supply. In short, losses are caused by the serial resistance of the wire being 24...236 mΩ/km; for the reactance, values of 61...250 mΩ/km are given for 110 kV, 16.7 Hz (Biesenack, Braun, et al. 2006, Tab. 4.4 p. 114), the latter being equivalent to 0.6...2.4 mH/km of inductance. The other line parameters are omitted here for the sake of simplicity.

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For substations, different types have to be distinguished: Transformer Substa- tions, Converter Substations, and Rectifying Substations, where transformer and converter substation may be combined. Even though all of them show marginally different properties, e.g. a slight dependency of the efficiency on the voltage levels, they are discussed “per type” in order to limit complexity to a practicable level. For (frequency) converting substations, either rotating (usually SM to ASM) or stationary (power electronic) converters are used for special frequency AC systems. The elastic grid coupling, which is reached by SM-ASM pairing, is always connected to losses by the (control medium) slip of the ASM. According to the already discussed differences between ASM and SM, the efficiency of the SM-ASM converter will be slightly lower than for the SM-SM one—in the range of 80 % to 85 %. For stationary converters, the advantage of high efficiency— even in partial-load operation—persists (Biesenack, Braun, et al. 2006, p. 86). Assuming a similar efficiency—as the application is similar—as for the DC rectifier feeder, their efficiency is in the range of 92 % to 98 % (cf. further down). In decentrally fed special frequency AC systems, SM-SM converters are used; their efficiency ranges load-dependent over a year from 86 % to 90 % with a peak efficiency of 95 % (Biesenack, Braun, et al. 2006, pp. 76 sq.). Concerning transformer substations, the same applies as for transformers within the public power grid (cf. section 2.2.4.1). Given the maximum feeding currents to be 12 kA in DC and 3 kA in AC systems, the power is in the range of 36 MW to 75 MW (Biesenack, Braun, et al. 2006, p. 123). This requires the transformers being dimensioned accordingly, demanding the peak efficiency to be above 99.5 % since 2015 (Siemens AG 2015, p. 3). Furthermore, the already discussed load dependency applies (cf. sections 2.2.2.4, 2.2.4.1). Rectifier substations are only used for feeding DC systems, converting the energy from the public grid (usually 50 Hz at some 10 kV) into 1.5 kV or 3 kV DC. Mostly, these are built as passive diode rectifiers showing forward (“loss”) voltages in the order of 1 V per path (Biesenack, Braun, et al. 2006, p. 159), i.e. 0.6 % or 1.2 % of the nominal output voltage. Controllable thyristors show a higher forward voltage, up to 4 V per path (Biesenack, Braun, et al. 2006, p. 160), resulting in 2.4 % to 4.8 % losses. Adding some filter and wiring losses— analogous to the vehicle’s drive chain—an efficiency of about 92 % to 98 % results for rectifier substations. However, the overall infrastructure losses from power plant to pantograph may account for 22 %, 18 %, 10 %, or 6 % in 600 V, 750 V, 1.5 kV, and 3 kV DC systems (González-Gil, Palacín, et al. 2014).

Concerning railway power plants—which are only used in centralised, special frequency AC systems—the same applies as for public power plants. They have been discussed in the prior section mentioning their efﬁciencies of 35 % to 100 % with regard to the primary energy.

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Given this high degree of complexity, the use of an inclusion into the full system model has to be questioned. Rather, it is considered useful to cut at the substation’s bus bar allowing the energy exchange between different feeding sections of the same substation but not analysing the energy exchange with the railway power grid. Only for very speciﬁc situations, the inclusion of the latter might allow some additional insights, making it a question of scenario design whether or not to include the railway power grid.

2.2.4.3 Catenary Supply For the energy supply from feeding point (at substation) to pantograph, the catenary is the only element used. For modelling electric transmission lines, an ideal conductor is used, adding a series resistance, a series inductance, and a wire-ground capacitance with conductance in parallel; cf. Figure 2.17a. The shunt admittance is usually small and thus often neglected (Andersson and Franck 2010, p. 63). A simpler model consists in only using a series impedance (i.e. resistance and inductance, cf. Figure 2.17b), as applied in practical dimensioning (Kießling, Puschmann, and Schmieder 2014, p. 308). If reactive phenomena are neglected or do not exist (e.g. in ideal DC cases)—i.e., a purely active analysis is conducted—a simple series resistance is sufﬁcient to model the line losses; cf. Figure 2.17c.

R0l L0l R0l

U1 UTr U1 UTr

l l

(a) Complete Catenary Model by (b) Practical Catenary Model ac- (c) Simpliﬁed Active Power Cate- Andersson and Franck (2010, Fig- cording to Kießling, Puschmann, nary Model (own illustration). ure 5.3). and Schmieder (2014, Bild 5.1).

Figure 2.17: Different Electric Models to describe the Catenary. Dashed quantities denote per- length-values for resistance R, inductance L, conductance G, and capacitance C; U1 the feeding point voltage, UTr the traction voltage at pantograph; l the distance between feeding point and vehicle.

The per-length parameters depend on the implementation of the overhead line, its condition, and the outside temperature; they vary from 119 mΩ/km (new 2 catenary with a 150 mm cross section at 20°C) to 300 mΩ/km (20 % wear at 2 80 mm and 40°C) for R0 (Kießling, Puschmann, and Schmieder 2014, p. 310). For the inductance, 0.05 mH/km can be assumed for overhead lines; values of 0.0512...0.0640 mH/km are given for further conductors (Kießling, Puschmann, and Schmieder 2014, p. 314). More information can be found in Tab. 5.1 sqq. presented by Kießling, Puschmann, and Schmieder (2014, pp. 310 sqq.).

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Note that also the current return path via rail and earth faces resistances; Ωmm2 1 values of %20 = 0.222 /m and αR = 0.0047 K− are given for use with

Ωmm2 %(T ) = %20 · (1 + αR · (T − 20)) [%] = /m, (2.58) T being the rail temperature in °C. If the rails are not continuously welded, 2.5 m length have to be added per joint (Kießling, Puschmann, and Schmieder 2014, pp. 310 sq.). For the reactance, 0.15...0.22 Ω/km and 0.07...0.11 Ω/km are given for 50 Hz and 16.7 Hz systems respectively (Kießling, Puschmann, and Schmieder 2014, p. 315), resulting in 477...700 µH/km and 667...1050 µH/km. At the same source, resistances of 0.12...0.25 Ω/km and 0.06...0.13 Ω/km are proposed for the respective frequencies. Summarising the practical model of the catenary, the total resistance in 16.7 Hz systems is 180...430 mΩ/km and 240...550 mΩ/km in 50 Hz systems; for the inductance, the ranges are 720...1100 µH/km and 530...750 µH/km.

2.2.4.4 Energy System Modelling For methods on modelling electrical systems, a publication by Hardel, Körner, and Stephan (2014) gives a good overview. Using the basic procedure of [1] generating the electrical network, [2] formulating the equation system, [3] application of a solver method, and [4] evaluation, they present the standard procedures Power Flow Analysis, Classical Branch Current Method, and Modified Branch Current Method. All of them implement the above-mentioned process and share the characteristic that steps [1] to [3] may have to be done repeatedly for one time step, while step [1] has to be done by the user. However, each method has its specific properties. For power flow analysis, a symmetric power net with fixed source-sink-structure is assumed. Power flow, current, and voltage are determined based on the network nodes’ power balances; main quantities are active power P , reactive power Q, voltage U, and phase angle δ. Substations, ESS, and recuperating vehicles act as power sources, while motoring vehicles, ESS, and additional end users are represented as sinks. Impedances of transmission lines are incorporated using a single-element-single-phase representation. Resulting—nonetheless—in a non-linear system of equations, the numerical solvers may take a considerable amount of time to converge; some special cases and applications cannot be modelled adequately (e.g. element potentials, auto or boost transformers). The classical Branch Current Method cannot implement voltage sources, which have to be converted to current sources applying the Norton theorem, requiring a reference node for potential analysis. Having the resulting node voltages defined, the branch currents are expressed as branch voltages and, finally, node voltages. After all, the equations can be formulated based on input current vector and node admittance matrix. Thus, the model allows to include all nodes and branches of the network, resulting in a realistic representation of the physical system; however, non-linear elements as voltage sources, transformers et al. cannot be modelled using the classical method. Some of this

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Figure 2.18: DC Traction Power Network Equivalent Circuit (Tian, Weston, et al. 2017, Fig. 4), shown here to illustrate the electric energy supply system’s complexity. Note that the powers and positions of trains are varying in time; consequently, also the distances and thus the resistances are variable in time. Thus, the system complexity is further increased by one dimension. method’s drawbacks are overcome with its modified version, where prohibited elements of the classical method can be treated using additional equations. The remaining procedure is very similar and, in case of a network limited to ideal current sources and linear admittances, actually the same. Both methods allow a rather comprehensive analysis of the electrical phenomena occurring in electric networks. However, they require the—usually user driven—generation of the network’s impedance or admittance matrix. While for a power or even energy oriented analysis as intended in this thesis the power flow analysis is the preferable method, the necessity of the impedance matrix causes some conflicts with the model structure and its implementation. The publication by Tian, Weston, et al. (2017) also deals with this topic of modelling the electric energy supply system and its high degree of complexity; Figure 2.18 is taken from that publication in order to illustrate that difficulty. For the reason of high complexity, a simplified version of the power flow analysis is employed in the following; cf. sections 2.4.3.3 and 3.1.4.

In industry, there are actually tools available that can co-simulate trains runs together with the electric supply network. Usually, these tools are used to dimension the railway energy supply and investigate, for instance, fault and protection cases; however, due to the complexity of the topic, the energy demand can be obtained from those tools as “side product” (Aeberhard, Basler, et al. 2015). As state of the art, OpenTrack/OpenPowerNet (Bagnall, Im- rie, and Jacob 2012; Institut für Bahntechnik GmbH 2019), µPAS (Aeberhard

– 64 – Chapter 2: System Modelling and Basler 2016; Aeberhard, Basler, et al. 2015), FABEL (Enotrac AG 2017), SINAnet/WEBAnet (SIGNON Deutschland GmbH 2016, 2018), and Sidytrac (Siemens AG 2016) shall be mentioned; an existing prEN17 formulates requirements for such simulation tools used in traction power supply system design. However, for all these tools, an immense effort in terms of time and money was invested due to the high complexity of both systems and their mutual interactions. Consequently, an own implementation of a similar tool—or even an application of an existing one—as part of a holistic system model would be far out of scope of a Ph.D. research. Therefore, the existence of these tools is acknowledged as state of the art, but not included into this research. Thereby, reaching the goal of formulating a holistic model of the entire system—from primary energy to wheel—is safeguarded.

2.2.5 Track The track fulfils the tasks of supporting and guiding the train. Within the interactions between track and vehicle that result from these tasks, resistances against the motion of the train arise. They result from slopes, curves, and points; cf. Figure 2.9 (p. 32). The possibilities of modelling these resistances have been discussed in context of the motorised vehicle (section 2.2.2). The description of track properties is usually done in a simplified manner: transition curves of all forms are neglected or indicated simplified, e.g., as curve band indication for radii; typically, main radius and main slope are given— Figure 2.19 shows an exemplary route profile. Depending on the source used, points are neglected as well; this is for example the case in the digital SBB data. Consequently, a certain deviation might result when using the indicated values while being in a transition curve. But, assuming the change in radius or slope being indicated at the middle of the transition curve, the error of the first half is approximately compensated by the error of the second half. Taking for example Figure 2.20, the “braking” force is overestimated for the first part, while the “accelerating” one is overestimated for the second. Similarly, the radius is overestimated in transition curves, but on the other hand, the curve resistance—being somehow inversely proportional to the radius, cf. section 2.2.2.7 on pp. 43 sqq.—is underestimated. Altogether, it can be assumed that the errors cancel themselves mutually, which allows to neglect them in modelling.

17prEN 50641:2017 Railway applications – Fixed installations – Requirements for the validation of simulation tools used for the design of traction power supply systems

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Figure 2.19: Example of a Route Proﬁle, SBB high-speed route 450 Olten–Bern (Wägli 2010, p. 57).

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Figure 2.20: Change of Slope with Transition Curve (Freystein, Muncke, and Schollmeier 2008, Bild 5.61 p. 220). While the resistive downhill force is overestimated for the uphill section (left A to ta), the accelerating downhill force is overestimated for the second part when using s1 and s2.

2.2.6 Operation Control In the context of this thesis, the term of operation control covers all steps from planning down to traffic control and interlocking, cf. Figure 2.3 on p. 18. As generally known, the (initial) schedule defines for each train on the network its position for a given time—mostly time- and route-continuous, as path- time-diagrams are usually used. Alternatively, the schedule can be regarded as set of time-location-pairs for operation points as e.g. stations—which is how a schedule is usually created (Pachl 2011, p. 166). This interpretation allows to more freely handle the reserves, enabling driving strategy optimisations in order to reduce energy demand, as it is often applied in literature and discussed in sections 4.3 sqq. Thus, the schedule can—and is in this thesis—with good accuracy be regarded as one table per train containing for each operation point a scheduled time. The other main components of a schedule that are listed by Pachl (2011, p. 166) are either implicitly included when investigating a certain train run—i.e. days of service, itinerary, and maximum permissible speeds—or of negligible importance with regard to the model (i.e., the train number). The second level of operation control—dispatching—can be regarded as “real- time rescheduling”, which is described by Pachl (2011, chapter 8). If the operation happens to be exactly as planned in the schedule, there is no need for dispatching. But as soon as there are deviations, the risk of conflicts with other trains—possibly operating as planned—might develop. Then, a new schedule is required: Starting from the given situation, an—ideally conflict-free—solution is searched. This solution is communicated using the signalling equipment. The according signal aspects can be regarded as influencing or modifying the speed limitations: A green light aspect allows to drive with the maximum speed permissible for the respective section, a warning (or restricted speed) aspect reduces the maximum speed to the indicated value or in a way that stopping in front of a red light aspect remains possible (depending on the braking capabilities), while the latter is—de facto—a zero-speed limit (Bundesamt für Verkehr 2015, pp. 109 sqq.). Consequently, signal aspects can be modelled as time-

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v v v v

s s s s

(a) (b) (c) (d)

Figure 2.21: Interpretation of Signal Aspects as Modifications of Speed Limits. Green bullets denote green aspect signals, green circles the announcement of a speed restriction, yellow bullets its execution, orange bullets a warning aspect, and red bullets a stop aspect. The dashed grey line indicates the maximum speed limit of the route, the black line the actual speed limit including signal aspects, and the cyan curve a possible speed profile followed by the driver (own illustration). dependent (or more precise: infrastructure-state- and time-dependent) variations of the speed limitations. Figure 2.21 illustrates the proposed principle. The third level of operation control is traffic control. As to be seen in today’s operation centres, this level basically covers the execution of the—initial or dispatched—schedule: Executing the operation commands at infrastructure (track) level and forwarding the infrastructure feedback to the dispatching in order to keep the information up-to-date. Thus, the traffic control part acts as the highly important interface between operation control and infrastructure but is of no operational importance as long as there are no disturbances. As infrastructural failures—as outages of switches or interlocking stations— are out of scope of this research, this and all lower control levels are omitted in the following. In other words, the relevant parts of the “control pyramid” as shown in Figure 2.22 are levels one and two, while the bottom-levels traffic control, interlocking, and infrastructure are assumed to be “perfectly working”.

Figure 2.22: Levels of Operation Control (own illustration).

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2.3 Inﬂuences on Energy Demand

2.3.1 Introduction and Overview Reviewing the findings of section 2.2, the major parameters influencing the energy demand within the railway system can be identified. They can be allocated to certain subsystems and are (usually) defined by one main factor. Table 2.7 gives an overview of the relevant parameters. Taking again the functionally oriented, systemic point of view, the resulting influences can be classified into three different groups: technical factors that are mainly given by the laws of physics; operational interactions that result from arbitrary processes during operations; and external influences, which are basically not linked to the railway system but influence its energy demand. According to this classification, the derived influences are briefly described in the following sections. Note that some influences of comfort systems—lighting, passenger information, and passenger power supply—have been shown to be irrelevant in Bomhauer-Beins (2017) and Bomhauer-Beins, Schranil, and Weid- mann (2018b); thus, they are neglected. Altogether, the influences on energy demand crystallise as rather complex network of interactions, influences, and dependencies between different parameters and subsystems—e.g., the number and behaviour of passengers might influence the train mass (parameter) and decisions of operation control (subsystem). A complete description seems—from today’s point of view and within the scope of this thesis—to be impossible. Nonetheless, for a detailed understanding of energy demand, it would be highly interesting to follow this line of research and develop a complete model of energy-relevant interactions including a quantification.

2.3.2 Technical Factors Whenever a technical system is built—be it e.g. a vehicle or an infrastructure— a specification should exist. This specification is usually determined by the task of the system-to-be-built. Of course, the sets of given and free parameters vary, depending on the exact case of application. Nonetheless, specifications define or influence many of the parameters of group II from Table 2.7, as moment of inertia, specific resistances, inner air flow, and vehicle cross section. Some of them—vehicle cross section, running treads distance, wheel base—are (additionally) given/limited by norms and standards. The same for parameters of group III: As soon as the specifications are defined, the variability of curve radii, inclination, and route heading is strongly shortened, the former two also limited by standards or legislation. The tunnel factor may be influenced to a certain degree (cross section, texture, exact form), but is also determined by the specific requirements.

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Table 2.7: Relevant System Model Parameters Influencing the Energy Demand. For each parameter, its name as well as its symbol are indicated. Column P classifies how easily an optimisation would be possible; – = no possibilities, +++ = rather broad spectrum of possibilities. Additionally, the most important determining factor is indicated, as well as the process step in which an adjustment could be possible. The first column G indicates groups of influencing factors: I—energy supply, II—vehicle, III— infrastructure, IV—operations, V—environmental influences. In subgroup IIa, rather “free” components in vehicle design are united, while subgroup IIb is rather strictly defined by norms, legislation, or similar. G Parameter P Determined by Adjust in I Efficiency η ++ physics dimensioning Electric Resistance R + physics dimensioning/design

IIa Vehicle Mass mt ++ physics dimensioning/design Moment of Inertia* J + physics dimensioning

Aerial Resistance Coef. cw ++ physics design Speciﬁc Resistances w + physics dimensioning/design

IIb Vehicle Cross Section Ab (+) norm dimensioning/design Train’s Inner Air Flow Qtot + requirements dimensioning/control

Running Treads Distance drt – norm –

Wheel Base la + norm dimensioning

III Tunnel Factor kt + req./physics dimensioning/design

Curve Radius rc + nature/req. planning Track Slope (Inclination) i0 + nature/req. planning Line Heading** ϕ + nature/req. planning IV Ac-/Deceleration a +++ directive directive/operation ‡ Vehicle Speed vt +++ deﬁnition directive/operation Wheel-Rail Slip ς + physics dimensioning/control ‡ V Wind Speed vw – nature – Wind Direction** ψ – nature – Friction Coefﬁcient µ + nature operation Solar Radiation Ψ – nature – Outside Air Temperature Ta – nature – Air Density ρ – nature –

Outside Air Pressure pa – nature – Relative Humidity φ – nature –

* More commonly known for its consequence, the rotational mass factor ζ ** Line heading and wind direction together result in parameter yaw angle α ‡ Vehicle speed and wind speed are important for parameter relative air speed

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Two parameters that can be handled slightly more freely are vehicle mass and aerial resistance coefficient cw, even though their optimisation is sometimes contradictory. In terms of vehicle mass, improved structures or lightweight materials are possible measures. Aerodynamic design tools allow a “better” shaping of the vehicle, while covering discontinuities of the vehicle skin reduces turbulences—both finally reducing aerodynamic resistances. Nonetheless, the laws of physics limit the remaining possibilities. Note that e.g. the stability of a carriage body must be given at all times, as well as a certain mass is required for a desired engine power, resulting in a corresponding moment of inertia and so on. Similarly, effects of efficiency and (electric) resistances are imposed, only leaving limited range for improvement in terms of e.g. optimising the design of apparatuses or usage of improved materials— more lightweight, less electric resistance, etc. Another technical influencing factor is the wheel-rail-slip ς. As discussed in section 2.2.2.5, the force transmission from wheel to rail is—amongst others— depending on this quantity. In modern traction systems, ς is automatically controlled with command variable maximum adhesion, making the correlated losses a matter of control quality—which is usually quite high, allowing to neglect the losses occurring here. Without intending to follow it further, it shall be mentioned that in earlier times, the driver was responsible for slip control.

Additionally, it has to be mentioned that most parameters are subject to tolerances, be it in production, operation, or somewhere else. One—in railway systems quite famous—example is the vehicle’s tare mass. Given a series 500 EMU (ICN), the tare mass according to speciﬁcation is 355 t; it has been determined with new wheels having a diameter of 820 mm (Stolz 2007, p. 304). Assuming a maximum allowable wear of 40 mm and taking standard wheel dimensions as calculation basis, this wear may result in about 1...4 t of possible weight difference (cf. appendix B); the lower value for wear only at driven wheels, the higher for all wheels worn. Consequently, technical tolerances and properties of the system limit the possible precision of the model. Even though these are rather generic considerations—which is not reasonably possible in a different manner due to the high number of thinkable variations—they are quite important. Basically, they show that the energy demand of a railway system is already deﬁned to a rather high degree long time before it is even put into operation.

2.3.3 Operational Interactions 2.3.3.1 Operation and Dispatching Recapitulating the definition of operation control used in the context of this thesis, initial planning of the schedule, dispatching, and traffic control/interlocking are covered. Considering these, especially the former two turn out to have considerable influences on the energy demand.

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In the phase of initial planning (scheduling), each individual train run is defined by which route it takes and how much time is foreseen. As the infrastructure is usually given at that moment, this planning process determines the requirements in terms of speed and acceleration. The tighter the schedule, the higher the necessary values of both—which directly influences the energy demand, primarily via aerial resistance (cf. Equation 2.30). Moreover, the integrated reserves have a non-negligible influence in two ways: First, the more reserves, the more possibilities exist for the application of energy saving methods as reduced top-speed or coasting. Second, low reserves will, in case of delays, not allow to reduce the delay within one section—in worst case, the delay is increased, which results in an even higher energy demand for the next section, as all reserves need to be used in terms of maximum speed and acceleration. In combination with adverse weather conditions, especially when increasing aerial resistance (section 2.3.4.3) and/or decreasing adhesion (section 2.3.4.4), these effects may mutually intensify. Regarding dispatching, decisions taken may significantly influence train runs and their energy demand. The functional chain consists of deviation from schedule, decision/execution, and effects on train runs. The consequences taking effect on energy demand are signal aspects—e.g., a train encounters a warning or reduced speed aspect even though it should be a green aspect according to schedule. As each acceleration/deceleration increases the energy demand, mostly due to limited efficiencies and braking losses (cf. sections 2.2.2.4 and 2.2.2.8), all unnecessarily restricting signal aspects18 increase the energy demand. Note that not intervening is a decision with its own consequences.

2.3.3.2 Driving Strategy The term of driving strategy comprises all decisions that are up to the driver within the limits of regulations (e.g. speed limits) and planning (schedule). Depending on the latter as well as on the actual operational situation, there are different thinkable driving styles. The most important decisions a driver has to make within this context are:

– Driving at maximum allowable speed or using the reserves? – How to use the brakes? How much of speed reduction by electric, how much by mechanical brake? – Choice of deceleration strategy: Cruising–Braking or rather Cruising– Coasting–Braking?

Of course, these decisions are inﬂuenced by different factors. One of them are the actually available reserves: When being on time, there is usually much more room for energy efﬁcient driving as the reserves can be used, while in case of delay, the reserves are smaller, zero, or even “negative”. Moreover, the

18Of course, restrictions due to safety reasons—for instance, passing a switch using the diverting path—are never unnecessary.

– 72 – Chapter 2: System Modelling driver’s experience—what is more than just knowledge of the route—influences the choice of strategy. If for example a certain operational situation occurs rather often, the driver will improve his strategy each single time, nearly reaching the energy optimal trajectory after some iterations. Similar considerations are valid for following or being followed by trains. Nowadays, it is possible to support drivers in making optimal decisions by providing relevant information on the current operational situation and e.g. transmitting proposed speeds—this is often done by so-called Driver Advisory Systems (DAS), which are discussed as possible energy saving approach in chapter 4. Another important influence factor on driving style are the planned reserves. While for small—or, theoretically, even no—reserves, the driving style is implicitly given, the variety of options for the driver increases with the reserves.19 Also for the usage of the different brake systems, there are many influencing factors. On the one hand’s side—as always—there are personal preferences; on the other, there are technical and external factors. For instance, a train composed of locomotive and carriages usually allows the driver to choose the brake system, while modern EMUs normally calculate the so-called “brake blending” from required brake force and current system state. Apart from that, the behaviour is completely different for locomotives (four driven/electrically braked axles at front or at the end of the train) when compared to EMU, where multiple distributed driven axles are standard. For coasting, not only the original schedule and the actual operational situation are relevant. Additional—rather deciding—factors are topology and topography of the route. Depending on those, it is not always possible to apply coasting for longer distances, as it has been confirmed by SBB professionals.

2.3.3.3 Passengers Passengers may directly inﬂuence operations with their actions. Thus, an extraordinary high number of passengers might result in an excess of dwell time, inserting a delay into the system with the respective consequences. The same occurs if passengers block doors either intentionally when trying to force the train to wait for delayed colleagues or unintentionally by carelessly placing their luggage. Of course, rather rare cases as e.g. application of the emergency brake and passenger does not disturb the operation are thinkable as well and somehow inﬂuence the operational processes.

19Very large reserves actually bear the risk of being “wasted” at the beginning of the line, endangering the punctual operation of following trains and/or reducing the range of possible reactions when experiencing a disturbance later on the line.

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2.3.4 External Influences 2.3.4.1 Topography The influences of topography somehow correlate with the aspects mentioned in section 2.3.2 on technical factors. Its main influence takes effect on the infrastructure development: Possible line routings are defined by the topography, making implications on many other parameters as curve radii, slopes, necessity of tunnels and/or bridges. Through theses parameters, the topography— together with the decisions taken by the planners—influences further parameters and properties, as e.g. the speed limit of the line, the required power of the locomotives/EMUs/DMUs in use, and the extent of influences treated in the following sections. Together with these indirect influences, the topography also defines the respective apparatus heating: The higher the power that is required to fulfil a given traction task, e.g. due to a steep slope, the higher the heating of the engines. This induces a cooling process and thus an increased auxiliary energy demand. Of course, the outside temperature has an influence on cooling as well, as the surrounding air acts as heat sink.

2.3.4.2 Loading and Passengers The influence factors passengers and loading are somehow interconnected for passenger services, while the prior is irrelevant in freight transportation. The loading of a train directly influences its mass: the more load, the higher the mass. As it can be seen from the system analysis performed in section 2.2, a higher mass implicates an increased energy demand. In case of freight, the mass is primarily determined by the load (payload usually higher than empty weight), while in passenger services, the number of passengers influences the total mass but to a less significant extend. In addition, the number of passenger influences the energy demand of HVAC systems, as investigated by Giebel (2018). Therein, it turns out that each passenger should be regarded as emitting heat and humidity, thereby changing the air condition within the carriage.

2.3.4.3 Wind, Air Pressure, and Air Density

Another influencing factor is the wind—more precise: its speed vw and direction ψ1, as the relative speed between vehicle and air influences the aerial resistance, cf. Equation 2.23. Moreover, the wind direction determines the yaw angle α between wind and train, which is relevant for additional aerodynamic effects. Of course, both are depending on vehicle speed vt and heading ϕ. Wind speed and direction can be obtained from weather stations and/or mete- orological services; the air density ρ is taken into account, requiring the knowledge of the absolute air pressure pa. However, the relative humidity φ can be neglected (Equations 2.28, 2.29). Also the temperature T has a certain influence on the aerial resistance, as to be seen in the same equations.

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2.3.4.4 Precipitation, Humidity, and Dust Another factor being influenced by the environment—in particular by precipitation, humidity, and dust—is the friction coefficient µ between rail and wheel. µ is parameter of the force transmission from wheel to rail (Equation 2.5) limiting the possible acceleration/deceleration, of the wheel flange resistance (Equation 2.31), and—in some formulations—of the curve resistance (Equa- tions 2.422.44). Filipovic´ (2015, p. 39) gives an overview of the variance of coefficient µ, which is summarised in Table 2.8.

Table 2.8: Rail Conditions and Corresponding Friction Coefﬁcient µTr following Filipovic´ (2015, p. 39)—translated and shortened.

Rail Condition µTr perfectly clean, surrounded by vacuum (laboratory only) 0.8 extremely good conditions 0.5 very good adhesion conditions 0.4 dry rails and average conditions: Equation 2.6 0.19...0.33 bad conditions 0.10 very bad conditions 0.05

From practice, the environmental inﬂuences causing a certain quality of adhesion and force transmission are known:

Good Conditions are reached with dry and clean rails or with “washed” rails, i.e. after a longer period of rain with a certain minimal intensity. Average Conditions are what we mostly see in daily operation. Some dust on the rails, maybe cleaned by an intense rain some time ago—basically: neither clean nor dirty rails. Bad Conditions arise with moist and dirty rails. Most popular cases are slight rain (or high humidity) wetting deposited dust or moist foliage on rails. The listing shows that friction between rail and wheel is depending on actual and past environmental conditions as air pollution; point of time, intensity, and amount of precipitation; relative air humidity; and also on the usage of sand. Up to now, there is no model that describes this interaction or allows to derive µ from the named quantities. Its complexity being too high to develop it here, the friction coefﬁcient is directly used as input quantity; its determination according to the argumentation above.

2.3.4.5 Outside Temperature and Solar Radiation For certain “applications”, the temperature should be kept within given limits: Passengers mostly demand a temperature of around 20°C, and also for the apparatuses, operating temperatures are deﬁned. These are in either case in- ﬂuenced by outside air temperature and solar radiation. While the prior may

– 75 – Energy Saving Potentials in Railway Operations under Systemic Perspectives act cooling (e.g. in winter) or heating, the latter is always heating—but less relevant, especially for the apparatuses that are usually not directly exposed. Therefore, the outside air temperature has to be included into the model: On the one hand, it influences the HVAC demand—the corresponding evaluations are usually done temperature-only based (Tuchschmid 2017)—on the other, the apparatus heating/cooling depends on it, influencing the auxiliary systems demand. Thus, the temperature is regarded as an input quantity, being determined for each point in time and space. For solar radiation, no data basis or publications have been found, which forces the neglect of this factor. Moreover, there are some influences on the electric energy supply system, as the transmission resistance—and thus the transmission losses—are temperature-dependent (Equation 2.58). But, as the wires are usually heated by the current, this influence is rather small. Taking additionally into account that the proposed modelling of the electric energy supply system is rather ap- proximative, the neglect of influences on the system is considered acceptable.

2.4 System Model Formulation

2.4.1 Intermediate Summary of Literature and Influences From previous sections, two highly energy-relevant parts of the railway system are identified: vehicle (motorised or non-motorised) and energy supply system. For either system, the occurring phenomena can be described using various approaches showing different levels of detail, complexity, and precision. More- over, the relevance of track and operation control have been discussed, as well as external influences. Altogether, the following elements are the most important to build a comprehensive railway system model based on literature:

– The Vehicle · Energy Preparation, Drive Chain, and Motion Control · Acceleration Resistances · Motion Resistances · Traction Auxiliary · Comfort Systems – The Energy Supply System · Power Generation and High Voltage Power Grid · Railway Power Grid · Substation and Catenary – Track – Operation Control – External (Environmental) Inﬂuences

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PROPULSION DRIVER CONTROL

ENERGY DRIVE CHAIN PREPARATION

Electric Epot = Converter Gearbox MOTION Machine mgh RESISTANCES mv2 E = t Transformer (1) kin 2 PROPULSION DISSIPATIVE SYSTEM BRAKE

TRACTION AUXINCL CONTROL (1) BRAKE CONTROL, part of PROPULSION CONTROL

COMFORT SYSTEMSINCL CTRL

HVAC

Figure 2.23: Schematic Overview of the Vehicle Model, depicted for an AC traction system. Dark grey boxes represent subsystems introduced in section 2.2.2, light grey indicates sub-subsystems (partly not named but illustratively indicated). Subsystems labelled in green are to be regarded as energy sinks, orange boxes represent (physical) storages, blue boxes the motion control units. Black block arrows indicate the energy ﬂow, blue arrows the information ﬂow of motion control (own illustration).

For the vehicle, different subsystems have been identified and introduced; a schematic overview is shown in Figure 2.23; the most important parameters are collected in Table 2.9. Following the energy flow, the first subsystem is the energy preparation, highly depending on the traction technology: its concrete implementation are diesel motor, transformer, line side filter, rectifier/converter, or combinations of them. As a precise description would be too complex in a systemic context, the usage of efficiencies is considered the most useful approach. The energy preparation subsystem is followed by the drive chain, whose structure shows a certain dependency on the traction technology under investigation but is always composable using a selection of the following elements: – line power converter, DC link, motor converter, transmission wiring – DC motor, single-phase AC motor, three-phase AC motor – mechanical force transmission, hydraulic force transmission

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Table 2.9: Most Important Parameters of the Vehicle Model, collected from the literature study presented in sections 2.2.2.3 to 2.2.2.7. The “cf. page” column indicates on which page of this thesis the respective parameter is discussed. Subsystem Parameter Value cf. page LSF Efficiency 99 % 24 Energy Preparation Trafo Efficiency 90...96.3 % 24 Diesel Engine Efficiency 20...54 % 25 Converter Efficiency 97...98 % 26 DC Link Efficiency 99.99 % 26 Wiring Efficiency 99.955...99.975 % 26 ASM Efficiency* 55...94 % 27 SM Efficiency* 60...99 % 27 Drive Chain Gearbox Efficiency 95...99 % 27 Diesel Torque Converter Efficiency 75...82 % 28 Diesel Gearbox Efficiency 84...89 % 28 Total Efficiency with ASM (AC)* 91 % 27 ≤ Total Efficiency with SM (AC)* 95 % 27 ≤ Adhesion Coefficient (Traction) 0.10...0.40 29 Adhesion Coefficient (Braking) 0.10...0.46 30 Physics of Motion Rotational Mass Factor 1.09 30 Breakaway Resistance 2...25 N/kN 42 * This value is significantly depending on the actual operating point

Together, these elements result in a drive chain efficiency on the one hand’s side, defining the ZV-diagram on the other. Taking these two together, a quite precise description of the drive chain results; the usage of speed-dependent efficiencies for one or more elements may further increase the model precision. The exclusive use of three-phase AC engines in modern vehicles allows to forgo the deprecated technologies of DC and single-phase AC engines; moreover, hydraulic and multi-stage mechanical gearings are rather rare applications that are for this reason out of scope. Nonetheless, the (speed-dependent) modelling as an efficiency would be applicable for these as well. Next in line, the physics of motion apply; various sets of equations have been presented for the different tasks within the propulsion of a vehicle: The limits of force transmission from wheel to rail, the laws of lossless acceleration and deceleration—i.e. reversibly changing the kinetic energy—including technically given losses (especially brake usage), and different types of motion resistances. For most of these resistances, various formulas exists, all of them more or less based on empirically determined or even estimated parameters. These are often only valid for a certain vehicle, defined situations, or otherwise limited in validity. The slope resistance has to be treated as special case: Basi- cally only changing the potential energy, it is not a resistance in a strict sense but rather a reversible energy conversion. On the other hand, the mass of the vehicle may vary during the change of potential energy; additionally, the cycle

– 78 – Chapter 2: System Modelling of increasing and decreasing potential energy might exceed the observed span in time or space, which results in a visible effect on the energy demand as with resistances. For the influence of wind yaw angle and tunnels, i.e. the determination of the tunnel factor, no satisfying description has been found. As both of them are considered non-negligible, possibilities of modelling are proposed in the following section; in contrast, point resistances have been shown to be negligible for train run investigations.20 Motion control turns out to be a complex topic that goes deep into the area of electrical engineering when conducting a detailed analysis. For this reason, a simplified control model is proposed. In this approach, a simple speed controller is applied that tries to reach the set speed as fast as possible, taking some boundary conditions into account. Some modifications of this control approach allow the inclusion of different driving styles, which—together with the physics of motion and the knowledge of the drive chain—allow to quite precisely model the train dynamics and determine the corresponding energy demand. The finally used model is presented in a later section. Concerning auxiliary systems, their components have been identified— battery charger, compressor, and different kinds of pumps (air, oil, vacuum); the driver’s HVAC was taken out and classified as comfort system. Some values of installed power were taken from literature, as well as strong indications of demand driven control. On the other hand, concrete correlations between activator and system reaction have not been found. As thermodynamic vehicle modelling—which would be required in order to derive a precise model—is out of scope, the assumptions used are presented later on. The range of comfort systems is rather broad and applicable for freight and passenger transportation. Yet, for most of the systems, their energy demand can be estimated or determined with an acceptable precision, finally allowing an inclusion into the system model. Some of the comfort systems mentioned are HVAC, lighting, information, and power outlets in passenger operation, or cooling in freight transportation. Not focusing on freight in this thesis, the associated systems can be disregarded—which does affect the general validity of the model, as they could be easily included. In terms of passenger comfort, it has already been discussed that lighting, information, and power outlets are negligible (Bomhauer-Beins 2017; Bomhauer-Beins, Schranil, and Weidmann 2018a,b). For HVAC, some data is available that allows the derivation of a calculation model, which is presented in section 2.4.2.

Also for the energy supply, different subsystems with varied modelling approaches exist. However, there are two main tasks that have to be dealt with: conversion and transmission—irrespective of the type of supply system. Its most important parameters are collected in Table 2.10.

20Note that point resistances are not to be considered negligible for shunting; but this is out of scope of this research.

– 79 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Table 2.10: Most Important Parameters of the Energy Supply System, from section 2.2.4. The “cf. page” column indicates on which page of this thesis the respective parameter is discussed. Level Parameter Value cf. page Nuclear Power Plant Efficiency 35 % 58 Electric Energy Steam Power Plant Efficiency 45 % 58 Generation Combined Power Plant Efficiency 65 % 58 Hydro Power Plant Efficiency 80 % 58 Diesel Refining & Transport Efficiency ~83 % 57 Above Substation Hydrogen Preparation Efficiency 17...64 % 57 Power Plant to Substation Efficiency 95...97 % 58 Substation Efficiency 80...98 % 61 Substation to Catenary Resistance 119...300 mΩ/km 62 Vehicle Total Feeding Resistance 180...430 mΩ/km 63

The process of conversion covers all actions that change the form of energy. For discrete supply, the conversion from primary energy (e.g. crude oil) to fuel (e.g. diesel) has to be taken into account. As discussed, a factor of 1.17 with regard to the fuel’s energy amount can be taken for primary demand in diesel systems; up to a factor of 6 for certain hydrogen production methods. These numbers can be converted into efficiencies, resulting in values of 17...85 %. In electric systems, the conversion from primary energy takes places in power plants; depending on their type, the efficiency ranges from 35 % (nuclear power plants) to 100 % (renewable energy sources). Additionally, the electricity is transformed repeatedly in terms of voltage and/or frequency, each transformation showing losses. For usually used high-power transformers, peak efficiency values of 99.3...99.5 % have been stated, additionally taking into account the decay according to Equation 2.4—to be applied for each stage of voltage transformation. A similar approach can be used for frequency transformation, with peak efficiencies of 93 % for rotating and 97 % for static transformers. Transmission, or—in case of discrete supply—transportation has to be included as well. For the diesel case, an increase by 1.03 based on the delivered fuel is usually caused by transportation, interpretable as a transportation efficiency of 97 %. Together with the conversion efficiency of 85 %, the already stated factor of 1.2 from tank to primary energy (i.e., 83 % efficiency) results. A more detailed investigation on discrete supply is considered pointless, as the focus of this research is set on electric energy supply. In case of the latter, transmission lines are used for energy distribution. These lines can be described using models of different precision—one to four elements per unit length; their efficiency depends linearly on the transmission distance. If reactive phenomena are neglected, as intended in this thesis, the simplest—one element—model can be applied. Alternatively and especially for higher, more complex grid levels (e.g. the high voltage national grid), the usage of average efficiency values as presented by the IEC appears as most feasible.

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In terms of modelling electrical systems, power flow analysis and branch current method have been discussed. For a systemic investigation as intended here, the power flow analysis is the preferable method out of those two. How- ever, due to its requirement of handling the network’s impedance matrix on the one hand side and the systemic claim to be able to treat discrete supply systems within the same framework on the other, a simplification of this method is necessary, which is discussed later on—cf. sections 2.4.3.3 and 3.1.4.

All other subsystems do not appear as (dynamic) models; rather, they define boundary conditions in terms of parameter values. Thus, the subsystem of track and signalling defines curve and slope resistance; furthermore, it influences the actual speed profile when interpreting signal aspects as speed limitations. The same applies for operation control: the decisions taken here influence timetable and speed profile, be it due to a delayed clearance to depart, a speed recommendation, or a restricting signal aspect. Additionally, the influences on energy demand that have been derived in section 2.3 translate into parameters of the equations describing vehicle and energy supply system. For instance, the topography can be mentioned as (partially) defining radii and slopes. Wind speed and direction influence aerial resistance; humidity, dust, and precipitation the adhesion. An overview was given in Table 2.7 (p. 70).

Drawing—in brief—an overall conclusion about the descriptions of the railway system and the inﬂuences on its energy demand, the following can be said:

– Values of energy demand result from investigation of vehicle(s) and energy supply; those are the core subsystems in railway energy analysis.

– Not all necessary sub-subsystems or phenomena are sufﬁciently described; this is dealt with in the next section.

– All other subsystems—apart from vehicle and energy supply—basically translate into parameters of the system description (model).

– These other subsystems’ parameters can be constant or varying in time.

2.4.2 Model Specification for Additional Phenomena 2.4.2.1 Wind Speed and Direction In the previous sections, it has been discussed that the usual model for aerial resistances does not take all relevant effects into account. In a related study (Bomhauer-Beins 2017), significant influences of wind angle and speed have been shown. Thus, a model has been developed in the context of this thesis and pre-published (Bomhauer-Beins and Weidmann 2018); an English translation of the article is given in appendix D.

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The resulting model is based on the parameters wind speed vw, train speed vt, wind (source) direction ψ2, track heading ϕ, and some numerically determined coefﬁcients. The base equation for the aerial resistance was derived from the models presented before to be

1 2 FAR = /2 · cw · kt · Ab · ρ · (vt + vw cos α) · (1 + fα(α)) . (2.59)

On open track—where the wind is relevant—the tunnel factor kt equals 1 and can be omitted. As the yaw angle α is the deciding parameter, it needs to be determined from ψ2 and ϕ, the latter two usually available from datasets: |ϕ − ψ | α = |ϕ − ψ | − 2 · 2 · (|ϕ − ψ | − 180°) (2.60) 2 180° 2

Given that and α in degree, a ﬁrst helper function kα,1(α) is calculated as 1.511 1.687 vw 2 vw kα,1(α) = −0.00177 · · α + 0.333 · · α + 1. (2.61) vt + vw vt + vw

! Deﬁning α0 by kα,1(α0) = 1 and

d1 = kα,1(α0) (2.62a) d d = k (α ) (2.62b) 2 dα α,1 0 2 (vt − vw) (2.62c) d3 = 2 , (vt + vw) the second helper function kα,2(α) for the area kα,1(α) < 0 can be written as d − (180° − α )d − d k (α) = 3 0 2 1 ·α2 + (d − 2aα ) · α + aα2 − d d α . (2.63) α,2 ° 2 ° 2 2 0 0 1 2 0 (180 ) − 2α0 · 180 + α0 | {z } =:a

The ﬁnal helper function kα(α0) is now combined as follows: ( kα,1(α) if 0° ≤ α ≤ α0 kα(α) = (2.64) kα,2(α) if α0 ≤ α ≤ 180°

Additionally, two special cases—vw = 0 and vt < vw—have to be discussed. For logical reasons, vw = 0 implicates the aerial resistance being the same for all angles α, i.e., fα = 0 and (1 + fα) = 1 for all α. Similarly, it can be seen that for vt = 0, the yaw angle α dominates the situation and the factor (1 + fα) becomes 1 for all values of α. Then, a smooth transition towards the factor determined 2 ! by Equation 2.64 is assumed. Deﬁning α2 by (vt +vw cos α2) = 0, the following applies for all cases with α2 < 180°, vt > 0, and vt < vw:

( vt/vw kα,1(α) for α ≤ α kα,1(α) 2 kα(α) = (2.65) −1 otherwise

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1.5 kα,2 1.5 1.5

1.0 1.0 1.0

0.5 0.5 0.5 kα,1 0 α 0 α 0 α 0° 0° 0° 30° 60° 90° 30° 60° 90° 30° 60° 90° 120° 150° 180° 120° 150° 180° 120° 150° 180°

(a) Cosine Model (red, eq. 2.23) (b) Extended Model for different (c) Extended Model for different and presented Extended Model train speeds and given wind speed wind speeds and given train speed km m (blue, eq. 2.59) for vt = 120 /h and of vw = 5 /s of vt = 120 km/h m vw = 5 /s

Figure 2.24: Illustration of the presented Extended Model of Aerial Resistance; resistance nor- malised to be 1.0 for α = 0°. In (a) comparison to the usual (cosine) model, in (b) and (c) sample application for different combinations of wind and train speeds. The colours in (b) and (c) are assigned to the following train/wind speeds: red, green, blue, cyan, orange, magenta for 0, 50, 80, 120, 160, 200 km/h and 0, 2, 4, 6, 8, 10 m/s respectively (own illustration).

Finally, the corrective factor’s function fα of Equation 2.59 is calculated as

fα(α) = kα(α) − 1. (2.66)

The complete model’s behaviour is illustratively shown in Figure 2.24.

2.4.2.2 Tunnel Influence In the literature study of section 2.2.2.7 (pp. 39 sqq.), a tunnel’s influences on the aerial resistance and thus the energy demand has been discussed. It is generally agreed that tunnels have an influence on the aerial resistance. Nonethe- less, the applied tunnel factor kt (cf. Equations 2.23 and 2.59, pp. 36 and 82) is usually determined experimentally. However, Gaillard (1973) presents in his work a formula that is based on the obstruction coefficient αw, calculated from the tunnel’s (Atun) and the vehicle’s (Ab) cross section: 3 2.21 αw Atun kt(αw) = 1 + with αw = (2.67) αw αw − 1 Ab While the neglect of the tunnel’s surface structure seems to be acceptable at least for conventional tunnels (i.e., not for optimised surfaces as in Lötschberg and Gotthard base tunnel), the missing influence of length results in non- acceptable deviations especially for tunnels with small values of αw. Based on the curves presented by Gackenholz (1974) that correlate length and tunnel resistance, a logarithmic correlation seems to be a possible approach. However, Equation 2.67 delivers correct values for “longer” tunnels, which would require to adjust this formula for “shorter” tunnels like

3 2.21 αw kt(αw, l) = 1 + · f(l) (2.68) αw αw − 1

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Given some tunnel’s values (Table 2.5 on p. 40, Figure 2.25a), a hyperbola A f(l) = + D (2.69) (l − C)B turns out to be a suitable approach. With curve ﬁtting, the parameters A, B, C, and D can be determined as

A = −0.77665 B = 1.83 C = 2 D = 1 (2.70)

Finally, the tunnel factor can be described as

2.21 α 3 −0.77665 k (α , l) = 1 + w · + 1 (2.71) t w 1.83 αw αw − 1 (l − 2)

Equation 2.71 is basically applicable for tunnel lengths of at least 3 km and with an obstruction coefﬁcient of 2.5 or more. In some rare cases (short tunnels with large tunnel cross sections, e.g. l = 3 km and αw > 3), the formula delivers values kt < 1. This is of course for physical reasons not possible; it is proposed to set kt = 1 in that case. Figure 2.25 shows the function’s graph and presents a comparison of measured and calculated tunnel factors.

2.4.2.3 Auxiliary Systems In section 2.2.2.9, the auxiliary systems of a railway vehicle have been introduced and discussed, without ﬁnding a model in literature to determine its power or energy demand. Based on the data presented there (Filipovic´ 2015; Gerber, Drabek, and Müller 1991), Table 2.11 was condensed as basis for a power estimation of the traction auxiliary. In order to derive power and energy, the operation of the different auxiliary systems is thus investigated. For more details concerning on-board energy management, the interested reader is kindly asked to refer to the thesis of Giebel (2018), as in a systemic context, these details are out of scope.

Table 2.11: Condensed Overview on Auxiliary Powers. The Usual Range is based on the data provided by Filipovic´ (2015), while the Re 460 values are taken from Gerber, Drabek, and Müller (1991). The per-MW-values are calculated using the hourly power rating of 6.1 MW. Usual Range Re 460

Compressor 6–20 kW 25 kW 4.0 kW/MW Ventilation 6–60 kW 55 kW 9.0 kW/MW Pumps 5–13 kW 22 kW 3.5 kW/MW Total 17–93 kW 102 kW 16.5 kW/MW

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k l (km) α t dev. w meas. calc. Albis 3.360 2.68 3.48 3.95 14 % Kerenzer 3.955 5.53 2.43 1.91 –21 % Mühlberg 5.513 9.55 1.43 1.55 9 % Hauenstein 8.134 5.01 2.61 2.65 2 % Ricken 8.603 2.79 6.64 6.34 –4 % Gotthard 15.002 4.58 2.81 2.99 6 %

(a) Comparative Data Table

6 (–) t

k 4

15 2

10 2.5 4 6 l (km) 8 5 αw (–)

(b) Function’s Graph

Figure 2.25: Illustration of the Tunnel Factor Estimation. Used information, measured (meas.) and calculated (calc.) tunnel factor kt as well as the deviation (dev.) on top (a); graph of the function kt(αw, l) as in Equation 2.71 below (b) (own illustration).

Compressor For all services operated with pressurised air, the compressor “generates” this air under usage of (mostly electric) input energy. These services are listed by Janicki, Reinhard, and Rüffer (2013, p. 301); most of them are considered out of scope (e.g. parking brake), part of another subsystem (e.g. HVAC), or negligible (e.g. main switch). Thus, only the indirect air brake remains as relevant system; the addition of “a percentage” for losses and other systems might be thought of. In order to analyse the demand of the brake system, its operation has to be investigated. Based on the system description (Bundesamt für Verkehr 2015), the air ﬂowing from auxiliary air tank to brake cylinder will be lost. Moreover, the volume of the brake cylinder is smaller than the air tank’s volume; the

– 85 – Energy Saving Potentials in Railway Operations under Systemic Perspectives latter being at max 100 l per braking system.21 For a usual four-axle carriage, two brake systems (one per bogie) can be assumed, resulting in an auxiliary air volume of 200 l per carriage. In addition, the main brake pipe—diameter at least 25 mm for passenger and 35 mm for freight wagons according to UIC—has to be ﬁlled after a braking process. Assuming a typical passenger train with a maximum length of 400 m, it might consist of one locomotive and up to 14 wagons, resulting in a maximum auxiliary air volume of 15×200 l = 3000 l. The brake pipe volume of about 200 l for a normal service brake—pressure reduction by 1 bar—is neglected. Thus, the compressor will have to “produce” at maximum 3000 l at a pressure of 5 bar after a braking process. Given the compressor capacity of 3800 l/min (at 1 bar), this requires operation for about 240 s at 25 kW, resulting in 1.7 kWh of electric energy. Taking into account that this kind of braking—using the full air volume—will practically never occur while due to electric braking capabilities, the pneumatic brake is in most cases only used once between two stations, this amount of energy is considered negligible (a train run between two stations usually requires some 100 kWh of electric energy). Even taking some 10...20 % of additional demand for losses and other systems, the amount stays small compared to the overall demand, allowing to neglect the entire pressurised air system including the compressor.

Ventilation The ventilators’ task is to create air motion for cooling purposes, the control is usually stepwise with steps 0, 1/3, 2/3, and 1. The choice of the step depends on the actual temperature of the apparatus to be cooled. As modelling this would require a complex thermodynamic model (cf. p. 55 sq.), a more simple approach is chosen. Based on the fact that apparatus heating is a consequence of losses that are usually proportional to the actual power PTr, a power-based ventilation control is presented here. Assuming that for low vehicle powers no cooling is required, the following approach is proposed: 1 |PTr| 1 Pvent = · 3 · + · 0.009 · Ph (2.72) 3 Ph 6

Pumps are used to keep cooling liquids—often oil—in motion, especially in transformers and converters. The pumps are usually moving the coolant with constant speed, while the ventilator power deﬁnes, via air ﬂow, the cooling power. For this reason, the pumps are assumed to be working constantly with nominal power, resulting in the description

Ppumps = 0.003 · Ph (2.73)

21for Knorr Bremse systems

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Altogether, the description for the auxiliary system’s power demand consists of the parts for ventilation and pumps:

Paux = Pvent + Ppumps 1 |PTr| 1 = · 3 · + · 0.009 + 0.003 · Ph (2.74) 3 Ph 6

2.4.2.4 Heating, Ventilation, and Air Conditioning (HVAC) It has been discussed that HVAC has a non-negligible share concerning the energy demand of a train run; nonetheless, the literature does not provide models that allow to estimate or calculate the respective demand. For this reason, data provided by SBB has been evaluated to ﬁnd a description of the phenomena. Figure 2.26 depicts two datasets used for model derivation. Part of the variation seen for the same outside temperature is most likely resulting from differences in passenger numbers per coach, as each passenger brings in a certain heating power and acts as humidity source. A detailed investigation was conducted by Giebel (2018); however, this level of detail would result in a too high model complexity when aiming for a system model.

°C 0 5 10 15 20 25 (a) Heating Power of a class EW IV coach as func- (b) HVAC Powers of a class 500 (ICN) coach as function of the outside temperature. Illustration by SBB tion of the mean (!) daily outside temperature. Graph- (M. Tuchschmid). ics from Bomhauer-Beins (2017) but simpliﬁed.

Figure 2.26: Illustration of HVAC Power Demands; plots from different measurements of SBB.

Based on these datasets and discussions with experts on the topic, as e.g. Mr. Tuchschmid (2017), the following assumptions are made:

– For each of the functions, heating, cooling, and ventilation, the power is linearly dependent on the outside temperature.

– The required HVAC power scales proportionally to the volume of air onto which the HVAC is applied.

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Moreover, the following equations can be derived from the ﬁgures, T the current outside air temperature in °C:

kW PH(T ) = −1.14 /°C · T + 26.4 kW for EW IV (2.75a) kW PH(T ) = −0.79 /°C · T + 18.2 kW for series 500 (2.75b)

PV(T ) = 3 kW for series 500 (2.75c) kW PAC(T ) = 0.5 /°C · T − 7.3 kW for series 500 (2.75d)

It has to be taken into account that for the series 500 EMU, only a mean outside temperature is given, which most likely results in an offset. Moreover, there is no information on the actual load, while the band width of the EW IV data exactly corresponds to the expected difference from empty to fully loaded carriages assuming 100 W heating power per person—which will be neglected. For a relation to the air volume, the technical drawings for the EW IV coaches (Generalsekretariat SBB 1988, p. 30) are used. From this, an inner air volume of approx. 220 m3 is derived.22 For the series 500, a slightly lower height (3.95 m instead of 4.05 m) is indicated; additionally and due to the tilting technique, the width is reduced towards the top of the train. On the other hand’s side, the carriages are slightly longer than the EW IV ones (26.8 m instead of 26.4 m outer body length). Altogether, it is to be assumed that the speciﬁc heating power per cubic meter is approximately the same as for the EW IV.23 Using Equations 2.75a and 2.75b, the air volume of the series 500 coach can be estimated based on the assumption that both vehicles are similarly isolated, i.e., show the same heating power demand per cubic meter of air. Thus, a value of around 150 m3 results. Even though this seems to be comparably low, it is classiﬁed as likely: Assuming the interior length to be—similar to the EW IV— 0.4 m shorter than the outer body length (i.e., 26.4 m) and the passenger area having a trapezoidal cross section with (estimated) side lengths of 2.6 m and 2.4 m, an inner height of 2.3 m results—which is realistic. Taking this together, the following equations result for an HVAC model:

−0.005 P (T ) = · T + 0.120 kW/m3 (2.76a) H °C kW 3 PV(T ) = 0.020 /m (2.76b) 0.033 P (T ) = · T − 0.049 kW/m3 (2.76c) AC °C

Of course, these models have to be applied depending on the outside air temperature. From Figure 2.26, a heating operation up to 22°C can be read, where the cooling starts at 10°C mean (!) outside temperature—which is, according

22The exact value is 218.116 m3, which results from the interior measures of 26.1×2.655×2.105 m (length×width×height) and an additional half cylindric roof with a radius of 1.3325 m 23It could be argued that there have been advances in isolation between 1981 (EW IV) and 1999 (series 500); on the other hand, the spatial conditions in railway carriage design are restrictive and do not allow seminal improvements.

– 88 – Chapter 2: System Modelling to experts, likely to be for current outside temperatures of 15...17°C. Assuming alternation to occur between 17°C and 22°C, a range that has also been found by Isenschmid, Menth, and Oelhafen (2013), the following set of equations describes the proposed model:  0.0050 kW 3  − · T + 0.1400 /m for T < 17°C  °C P (T ) = 0.0036 kW 3 (2.77) HVAC − °C · T + 0.1162 /m for 17°C ≤ T ≤ 22°C   0.0033 kW 3  °C · T − 0.0287 /m for T > 22°C Already having mentioned the publication by Isenschmid, Menth, and Oel- hafen, an interesting discovery shall be discussed here: While the formulas obtained from Figure 2.26 lead to heating and cooling slopes of –0.79 kW/K to –1.14 kW/K and around 0.5 kW/K respectively (Equations 2.75), Isenschmid, Menth, and Oelhafen (2013, p. 401) obtain for the series 525 a signiﬁcantly steeper heating slope of –2.53 kW/K while the cooling slope’s value of 0.67 kW/K is in a similar range. The difference results from differing power demands at –10°C: 73.2 kW (series 525, built 1998–2005), around 38 kW (EW IV, built 1981–1990), and about 25 kW (series 500, built 1999–2005)—this implicates signiﬁcant variations between different kinds of rolling stock. The reason for this difference could, based on the available information, only be guessed.

2.4.3 Final Formulation 2.4.3.1 Granularity and Structure The investigations presented in chapters 2.2 and 2.3 show extent and complexity of the railway system, even though the analysis has mostly been limited to static, energy-only considerations. Indeed, a more detailed analysis would be possible, including also time-dynamic behaviour of voltage, current, and other internal quantities. However, energy optimisation is only possible if the system is operating stable as otherwise, the priorities had to be set differently. Thus, especially electrodynamic phenomena as transients, instabilities, or self-excitations are far out of scope of this thesis. Moreover, these effects are relevant for a systemic stability analysis—but when investigating quasi-static operations, as intended here, static and dynamic analysis deliver nearly the same results. Therefore, the model is built using the power P as base quantity, which additionally allows to include non-electric drive systems if desired. The electro-technical analysis is limited to a rather small extent for the sake of simplicity; if necessary, voltage U or current I are derived using the relation

P = U · I, (2.78) which is—neglecting time-dependencies—valid for all electric systems. In terms of structure, a hierarchic one was shown in the previous sections. The five main subsystems (or first layer subsystems) were identified to be En- ergy Supply, Vehicle, Track, Operation Control, and Environment. While the

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first two are mainly defined by physical correlations (equations), the latter three define those equations’ parameters. An illustration of this model’s structure for one vehicle is shown in Figure 2.27. Naturally, the model can be extended by adding respective blocks, e.g., another vehicle.

2.4.3.2 Vehicle Model As main energy sink, the vehicle is the most important subsystem to model. Its energy input (VEI)—the pantograph in case of an electric supply system— is the interface where power and energy are drawn from the supply system and fed back if applicable. The vehicle itself consists of an energy preparation, a comfort, an auxiliary, a drive chain, and a motion resistances sub-subsystem. Each of these has its own task, resulting in a bigger or smaller share of the drawn energy being forwarded to the environment. Additionally, the vehicle can be regarded as containing—or even being—a “storage” for potential and kinetic energy. The behaviour of all of the mentioned sub-subsystems can be described by physical correlations and thus equations; these are shortly discussed and given in the following. Note that the inductive behaviour of the drive chain might cause voltage-current-phase shift (in AC systems), resulting in an occurrence of reactive power. In order to keep the model complexity at a level that remains manageable, it has been decided to neglect reactive power. Especially from a railway operator’s point of view, this is justiﬁed as, according to information obtained from SBB specialists, only active power and energy is evaluated in operations and billing.

Physics of Motion. The basic law of the equilibrium of forces can also be formulated for railway applications. Deﬁning mtot = mt = mtare + mload to be the total vehicle mass, ζ the rotational mass factor, FR the sum of all resistances, and FTr the tractive force, the equilibrium is

FTr − FR − mt · ζ · a = 0 (2.79a)

Applied to the main cases—either realising a desired acceleration or applying a given tractive force—this allows to determine the required tractive force FTr for a given acceleration a as

FTr = FR + mt · ζ · a (2.79b) and the resulting acceleration a from a given tractive force FTr as F − F a = Tr R (2.79c) mt · ζ For a numerical calculation model, discrete values have to be used. Being either distance (∆s) or time (∆t) based, the following equations of motion serve as model basis here:

– 90 – Chapter 2: System Modelling

ENERGY SUPPLY OPERATION CONTROL

– May reduce vmax by tempo- Electricity Generation rary speed restriction

– May reduce vmax due to conﬂict via signalling Transmission System – May advise vrec

– Indirectly requires vmin and a via timetable Substation min

Catenary

VEHICLE Energy Preparation

Comfort

AUX

Drive Chain

Epot

Resistances Ekin

ENVIRONMENT TRACK

– Defines track condition (µ) – Defines speed limit vmax – Has wind speed v ; wind – May have curve radius r < w c ∞ (source) direction ψ1, ψ2; – May have slope i0 = 0 air temperature T ; air 6 – May be in tunnel, i.e. kt > 1 pressure p a – Defines track heading ϕ – Defines load mload – Energy source and sink

Figure 2.27: Architecture of the Railway System Model for Electric Systems. Energy Supply and Vehicle subsystem are the only subsystems that are part of the energy flow (black block arrows); the environment could be regarded as both primary energy source and final energy sink. The subsystems Track, Environment, and Operation Control define the parameters of the equations that describe the energy flow defining subsystems. Note that this model can be used for every other type of system (e.g. diesel) by using an according model of the energy supply. Energy demand of operation control and infrastructure are neglected as they are not within the scope of research (own illustration).

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∆s = v · ∆t (2.80a) 1 2 = /2 · a · (∆t) + v0 · ∆t (2.80b) Thereby, v and a are mean values of speed and acceleration that are assumed to be constant for an interval ∆s and ∆t; v0 represents the vehicle’s speed at the beginning of the respective interval. The same equations apply for braking, but with FTr and a being negative. For a track section with constant parameters, the train can be modelled as mass point, while for changes in speed limit, slope, and radius, the train length becomes relevant. Therefore, the train is described as mass band with equally distributed mass. Even though this assumption is not fully true, the resulting error is negligible as the mass differences between the vehicles are usually rather small (maximum axle load). In case of freight trafﬁc, especially single wagon load, there might occur an inﬂuence; but an investigation of those relations is considered out of scope of this thesis.

Motion Resistances. According to section 2.2.2.7, rolling, aerial (outer/inner), curve, and slope resistance are considered relevant motion resistances:

FR = FRR + FAR + FAR,i + FCR + FSR (2.81)

Even though the slope resistance FSR corresponds to a reversible change in potential energy, it is regarded as resistance: In contrast to kinetic energy, the potential energy will change in most cases, visible as an energy demand or generation for the respective journey. Deﬁning the different addends, formulas for various sources of the rolling resistance have been shown. All of them basically consist of an empirical factor multiplied with train mass and earth’s gravitational acceleration, mostly speed-independent. Regarding this fact and aiming for a manageable system model, a simpliﬁed formulation is used:

FRR = wl,r · mt · g (2.82) with wl,r being an empirically determined rolling resistance coefﬁcient. This factor usually ranges from 1.5 N/kN to 5.0 N/kN (Weidmann 2011, p. 6 of ch. 6) and can be estimated from coefﬁcients of the Davis Equation (Bomhauer-Beins 2017; Bomhauer-Beins, Schranil, and Weidmann 2018a). An extended model for the outer aerial resistance has been presented in section 2.4.2.1 and is applied in the following:

1 2 FAR = /2 · cw · kt · Ab · ρ · (vt + vw cos α) · (1 + fα(α)) (2.59)

This model is valid for all practically relevant wind speeds vw and yaw angles α on open track; in tunnels, α becomes 0° or 180°, the wind speed vw given by the tunnel’s natural convection. kt is 1 on open track (inﬁnite tunnel cross section Atun), while for tunnels, it has been discussed in section 2.4.2.2. Accordingly, the following model is used:

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 1 Atun = ∞ kt(αw, l) = 3 (2.83) 2.21 αw 0.77665 1 + · · − 1.83 + 1 otherwise  αw αw 1 (l 2) − −

Due to the simplified modelling, values of kt < 1 may result for the case Atun < ∞. In that case, kt is defined to be 1. For the air density ρ, the following equation that only requires the outside temperature Ta (to be inserted in °C but without unit), is applied: 273.16 ρ = 1.293 kg/m3 · (2.84) 273.16 + Ta Of course, an application of Equation 2.28 or 2.29 would be possible as well. However, these equations would require the knowledge of absolute air pressure pa, relative humidity φ, and steam pressure of water pS. While the latter can be found in tables, the former two would have to be measured. On the other hand, the deviation due to this simplification is below 2 % (cf. section 2.2.2.7, p. 38), thus not justifying the application of the more complicate model. The inner aerial resistance according to Equations 2.22 (p. 34) might sum up to an additional 40 % considering an electric locomotive running at 100 km/h, which is non-negligible. Therefore, it is approximatively included: vt vt 2 FAR,i = nBD · 4.3 N · + 3.1 N · +ρ·Qtot ·(vt +vw cos α) (2.85) 27.7 m/s 27.7 m/s

Thereby, nBD denotes the number of brake discs, ρ the air density, and Qtot the 3 inner air ﬂow of the train. For the latter, 8 m /s per MW nominal power are 3 used for electric and 16 m /s for diesel locomotives; for carriages with pressure 3 ventilation, 3 m /s are included. For the curve resistance, the formula of Parodi and Tétrel (Equation 2.42, p. 44) offers a good compromise between complexity and precision:

µ · (0.72 · drt + 0.47 · la) FCR = · mt · g (2.86) rc drt denotes the distance of the running treads (1.5 m for normal gauge), la the ﬁxed frame wheelbase (e.g. bogie), and rc the curve radius. The slope resistance is fully covered by Equation 2.46 (p. 45) and does not need any further explanation:

FSR = mt · g · i0 (2.46)

Limitation of the Tractive Force. The applicable tractive force FTr that ac- celerates or decelerates the vehicle is basically limited by three parameters: starting tractive force FTr,S, available power, and transmittable force. While the starting tractive effort FTr,S ≥ FTr is speciﬁed and known for most traction vehicles, the available power has to be treated more carefully. In case of discrete energy supply, the rated power Pn is usually the one of the on-board

– 93 – Energy Saving Potentials in Railway Operations under Systemic Perspectives generator. This generator does not only have to feed the drive chain but also auxiliary and comfort systems. Therefore, the actually available traction power depends on the current power demand of these systems, leaving a variable amount of power PTr ≤ Pn for traction purposes. With continuous (electric) energy supply, the power capacity can—in general—be regarded as unlimited. Thus, the power demand of ancillary systems does not reduce the available traction power, as it would be the case in discretely supplied systems, as e.g. diesel. However, whenever an electric engine is used, the question on how to handle its overload capacity remains. The rated power Pn of an electric vehicle is usually the power that can be delivered for an arbitrary long time without overheating the engine. Yet, electric machines can deliver higher power for a certain amount of time, e.g. an hour or ﬁve minutes. For instance, the hourly power rating Ph is usually around 20...25 % higher than the nominal power (Bomhauer-Beins 2006). In normal operations, the force (power) demand decays drastically as soon as the desired speed is reached, allowing the engine to

“cool down”. For this reason, the hourly rating Ph is used for determining the maximum available traction force without automatic control of the engine temperature (cf. section 2.2.2.11), leaving a plausibility check as to be performed manually. Even though the usage of Ph seems to be pessimistic, there are two reasons for it: First, this value is usually obtainable with passable effort from data sheets—in contrast to power values for shorter periods in time. Second, even the hourly power rating is rarely fully used, as it can be seen from data and simulations presented later on.

Thus, Ph is used as deciding power limitation; taking a basic relation from physics, the power-depending tractive force limit is determined as

Ph FTr ≤ (2.87) vt

These limitations, FTr,S and Ph, correspond to the ones discussed in section 2.2.2.4. The engine’s tip-over limit (power hyperbola) is neglected. Concerning adhesion, the formula of Curtius and Knifﬂer (Equation 2.6, p. 28) is applied, extended by an addend fRC ∈ [–0.1,+0.1] in order to model different rail conditions, as discussed in section 2.2.2.5: 7.5 µ = + 0.161 + fRC (2.88) vt + 44 In the same section, the speed-independence of the adhesion coefﬁcient for mechanical braking has been reviewed. Being in a range of 0.10...0.16 for “bad” and 0.18...0.26 for “good” rail conditions (cf. Table 2.2 on p. 30), the formula

µ = 0.8 · fRC + 0.18 (2.89) is used. In case of sliding, µ = 0.05 holds for all speeds and rail conditions.

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From Force to Power and Energy. The model presented is meant to evaluate power and energy of the railway system and its parts. The power P being the base quantity (cf. section 2.4.3.1), it is determined from force F :

P = F · v (2.90)

The energy E is obtained either from force F or power P using distance s or duration t of application: E = F · s (2.91a) E = P · t (2.91b)

Consequently, the (electric) tractive power at wheel, PTr, results from mechanical traction or braking force Fel applied by the electric engines and the vehicle’s actual speed vt: PTr = Fel · vt. (2.92)

Mechanical braking forces Fmech convert the kinetic energy into (unusable) heat; therefore, mechanical braking powers are regarded as losses.

Drive Chain. Having determined the required traction power PTr at wheel, it has to be provided by the drive chain. Its task consists in converting the input energy (i.e., electric energy) to mechanical energy and vice versa, if possible and designed accordingly. Thus, the drive chain is described as an efﬁciency

ηD. Assuming a complete set-up consisting of LPC, DC link, MC, wiring, engine, and gearbox, the following equation is used to determine ηD, based on the discussion in section 2.2.2.4 and, in particular, Equation 2.4:

( 2 2 −13.45x + 5.33x + 0.41 if x ≤ 0.2 ηD,ASM(x) = 0.9750 · 0.9999 · 0.9996 · · 0.975 | {z } −0.021x + 0.946 otherwise | {z } LPC & MC, DC link, wiring gearbox | {z } engine ( −13.45x2 + 5.33x + 0.41 if x ≤ 0.2 = 0.9264 · (2.93a) −0.021x + 0.942 otherwise ( −13.45x2 + 5.33x + 0.452 if x ≤ 0.2 ηD,SM(x) = 0.9264 · (2.93b) −0.021x + 0.952 otherwise

Thereby, Equation 2.93a is valid for the asynchronous machine (ηmax = 0.94), while Equation 2.93b is to be applied for synchronous machine drives

(ηmax = 0.97). Variable x ∈ [0,1] describes the system load in relation to the vehicle’s technical top speed. Thus, the drive chain power PD at the drive chain’s connection to the transformer results to be

( 1 PTr · ηD− if PTr > 0 PD = (2.94) PTr · ηD otherwise

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Comfort Systems. As discussed in section 2.2.2.10, the only relevant comfort sub-subsystem is HVAC. An applicable model has been developed in section 2.4.2.4 and delivered Equation 2.95. By multiplication with the vehicle’s inner air volume V , the comfort systems’ power Pcomf is determined:

Pcomf = PHVAC · V (2.95a)  0.0050 kW 3  − · Ta + 0.1400 /m for Ta < 17°C  °C P (T ) = 0.0036 kW 3 (2.95b) HVAC a − °C · Ta + 0.1162 /m for 17°C ≤ Ta ≤ 22°C   0.0033 kW 3  °C · Ta − 0.0287 /m for Ta > 22°C

Traction Auxiliary. For the traction auxiliary systems, a description based on compressor, ventilation, and pump usage was presented in section 2.4.2.3. There, Equation 2.74 was introduced as auxiliary system model: 1 |PTr| 1 Paux = · 3 · + · 0.009 + 0.003 · Ph (2.74) 3 Ph 6

Braking Model. As long as an electric machine is used as motoring device, the drive chain is assumed to show the same behaviour for braking as for acceleration, which is—as discussed—true for modern converter vehicles. The regenerated energy can be used for different purposes:

– Supply comfort and/or auxiliary systems (Pcomf, Paux)

– Feed back to catenary (only in receptive electric systems)

– Convert to heat (via brake resistor, PBr)

The choice depends on the situation, where internal use (comfort or auxiliary) is preferred over feedback, while feedback is preferred over conversion to heat. For the electric brake, the ZV-diagram delivers the necessary information on the maximum available braking force. The brake forces of electric and pneumatic brake sum up to the actually applied braking force. Time constants are neglected for reasons of the model’s complexity, further properties of the brake system are considered negligible for quasi-static analyses. The same applies for additional brake systems as the magnetic rail brake, which are not applied in normal operation. Sliding is considered irrelevant, as most of today’s vehicles are equipped with slip-slide control.

Energy Preparation. At the drive chain’s side of the energy preparation, different powers sum up: drive chain, comfort, auxiliary, and braking resistor power. The basic considerations on the energy preparation sub-subsystem have been presented in section 2.2.2.3. Three models for the different traction systems were introduced: A line side ﬁlter for DC systems, a transformer for AC

– 96 – Chapter 2: System Modelling systems, and a diesel engine for diesel systems. For the latter, the characteristic diagram has to be given, which allows to include the diesel engine by application of Equation 2.1 (p. 25) as efﬁciency ηEP. For electric systems, the following descriptions are used, based on Equation 2.4 (p. 27): ( −13.45x2 + 5.33x + 0.462 if x ≤ 0.2 line side ﬁlter ηEP(x) = (2.96a) −0.021x + 0.995 otherwise ( −13.45x2 + 5.33x + 0.432 if x ≤ 0.2 transformer ηEP(x) = (2.96b) −0.021x + 0.962 otherwise

The variable x describes the current load of the traction system, x ∈ [0,1] ⊂ R, in relation to the hourly power rating Ph. With these equations, a maximum efficiency of 99 % is assumed for the line side filter; for the transformer, the peak efficiency is 96 %. The description as efficiency delivers all necessary information for a power-/energy-oriented analysis and moreover ensures model compatibility for a later inclusion of the diesel traction system. Going back to the power that has to be delivered at the output of the energy preparation sub-subsystem, the vehicle’s input power PVEI can be determined:

( 1 (PD + Pcomf + Paux + PBr) · ηEP− PD + Pcomf + Paux + PBr > 0 PVEI = (2.97) (PD + Pcomf + Paux + PBr) · ηEP otherwise

Kinetic and Potential Energy. As already mentioned, acceleration and slope resistance are—more precisely spoken—energy conversions from input energy (e.g. electricity, diesel) to kinetic or potential energy. Consequently, both forms of energy have to be converted back at some instance in time. While for the kinetic energy, the zero-level is reached at every stop, the potential energy is more complex as there is no intrinsic zero-level; moreover, changes in mass are possible. For these reasons, the conversion to kinetic energy (Ekin) is not regarded as resistance; in contrast, the potential energy (Epot) is—slope resistance. However, both can be calculated for every instance in time: 1 2 (2.98a) (2.98b) Ekin = /2 · mt · vt Epot = mt · g · h

Motion Control, starting from the driver’s (or ATO’s) behaviour in following allowances and going down to the engine’s torque and current control, is very complex. However, it can be simpliﬁed for energy analysis, as the determining parameters are speed and acceleration. Therefore, it is deﬁned as follows:

– Controlled quantity is vehicle speed vt, for which a set value is given. – No controller implementation. It is assumed that the set speed has to be reached as fast as possible while respecting the physics of motion. – Additional restrictions can be deﬁned as part of either the vehicle’s (driver’s) or the route’s properties.

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Vehicle Properties. The equations presented in this section contain many different parameters; a major part of them deﬁned by properties of the respective vehicle under investigation: Empty weight mtare, payload mpayload, adhesion mass madh, and retardation ϑ can often be read from vehicle signage or technical data, the latter also containing information on available power Pn, Ph, starting tractive force FTr,S, and top speed vmax. The number of brake discs nBD can be counted or estimated from the number of axles—usually, there are two to three brake discs per axle, depending on the maximum speed. Simi- larly, the inner air ﬂow Qtot can be estimated. The running treads distance drt is given by the gauge, the vehicle cross section Ab usually by the clearance gauge and readable from technical drawings. Aerial and rolling resistance coef-

ﬁcients cw and wl,r can be estimated based on the Davis coefﬁcients, which have been determined experimentally by SBB for some vehicles—see section 3.2. For modern EMUs (SBB’s 500 and 511.0 series), the rotational mass factor has been determined according to Equation 2.13 as ζ = 1.09 (Bomhauer-Beins 2017; Bomhauer-Beins, Schranil, and Weidmann 2018b).

2.4.3.3 Energy Supply System In the context of this thesis and based on the discussion presented in section 2.2.4, the (electric) energy supply subsystem is regarded as consisting of four sub-subsystems: catenary, substation, transmission, and generation.

The Catenary delivers electric energy from substation to vehicle. Stray and reactive effects are neglected, which allows to use an equivalent series resistance per length Rcat0 as description, resulting in a power loss of |P | 2 VEI (2.99) PL = Rcat0 · l · Icat = Rcat0 · l · , Ucat with l the catenary length between vehicle and substation. Thus, the power fed into (or taken back from) catenary (feeding point power PFP) results to be

|PVEI| (2.100) PFP = PVEI + Rcat0 · l · Ucat

Note that with the usage of a constant catenary voltage Ucat, the model loses its validity for DC systems: While in AC systems, especially 15 kV,16.7 Hz, the voltage Ucat is high and quite stable due to a strongly interconnected supply system, slight fluctuations do not significantly affect the line current (I) and its resulting losses (I2R). This situation changes completely in DC, where the voltage is significantly lower (3 kV or less), and the grid interconnections are generally much weaker. Then, line current and losses strongly depend on the actual line voltage. However, the model application in this thesis’ context is limited to 15 kV,16.7 Hz systems for this reason. For the resistance per unit length and including the current return path via m rail, values of R0 = 180...430 Ω/km have been found for 16.7 Hz systems; for

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m 50 Hz systems, the values are in the range of R0 = 240...550 Ω/km. The effective value depends on the type of wire and rail (the larger the cross section, the lower the resistance), their wear (increases the resistance), and the temperature (resistance increases with temperature). It is assumed that the rails are continuously welded; otherwise, current forward (120...300 mΩ/km) and return path (60...250 mΩ/km) would have to be investigated separately, adding 2.5 m of path length per rail joint in the return path.

The Substation transforms electric energy from transport to catenary voltage, mostly using a high power transformer (AC systems) or a power electronic rectifier (DC systems). Both elements can be described in terms of their efficiency. For the rectifier, values of 92...98 % are proposed; for the transformer, the peak efficiency has to reach 99.5 % in EU since 2015 but was typically in the range of 90...98 %. Additionally, the transformer’s efficiency shows a load- dependency. But, as substations allow various energy flows of different routes to overlap, this is neglected here as an estimation of the actual transformer load is practically impossible. Thus, efficiency ηUW is used as substation model. Finally, its input power PUW is calculated as

1 X (2.101) PUW = ηUW− · PFP FP ∀

The Transmission System covers all network elements between substation and generator. A more detailed description might be used in electrical network analysis but is not suitable for railway systemic investigations due to the complexity. Rather, the rule-of-thumb values given by IEC are used, modelling the transmission system as efﬁciency ηgrid: The losses from power plant to step- down substation—which quite exactly covers the part discussed here—account for 3...5 %, resulting in an efﬁciency of 95...97 %. Its power losses are thus

PL,grid = (1 − ηgrid) · PUW (2.102)

The Generation of electric energy takes place in power plants, where it is converted from primary energy carriers. The efﬁciency ηgen is highly depending on the primary energy source and the conversion method, ranging from 35 % to 100 %. From the numbers presented in section 2.2.4.1, the generation efﬁcien- cies (for the year 2008) can be estimated: Germany around 50 %, Switzerland about 80 %. Then, the generation losses are expressed as

PL,gen = (1 − ηgen) · (PUW + PL,grid), (2.103)

ﬁnally resulting in a power demand at “plant’s gate” of

(2.104a) Pprim0 = PUW + (1 − ηgrid) · PUW + (1 − ηgen) · (PUW + (1 − ηgrid) · PUW) 1 1 (2.104b) = ηgen− · ηgrid− · PUW

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Note that in this formulation, the well-to-plant efﬁciency ηw2p is not yet included. Doing so, the ﬁnal primary power demand results to be

1 1 1 (2.105) Pprim = ηw2p− · ηgen− · ηgrid− · PUW

For diesel and other systems, similar models could be built, e.g., interpreting the transmission efﬁciency ηgrid as transportation energy. Nonetheless, this is omitted as the focus lies on electric railways covering the vast majority of the system load and therefore main target of energy saving efforts.

Energy Supply System Properties. In the proposed modelling approach, the energy supply system is mainly parametrised as efﬁciencies; these have been discussed in the prior paragraph and in section 2.2.4. While this usage of “general values” reduces the model precision, it is a deciding simpliﬁcation in order to enable full-system analysis: Due to the complexity of especially an electric supply system and countless other actors, it is nearly impossible to determine the energy path and exact losses with limited model complexity. For the path substation–vehicle–substation, a more precise model is chosen, describing the electric path as equivalent series resistance per length Rcat0 for catenary and rail. Depending on catenary cross section and wear, this parameter shows a certain variety; in most cases, its determination will be based on estimation.

Chosen Modelling Approach. Two of the requirements formulated for the model—[1] being dynamically composable from subsystems and sub- subsystems as well as [2] being universally applicable for different supply system types—implicate some restrictions for the energy system modelling, respecting the given scope and time frame of this research project. Hence, the formerly selected power flow analysis has to be simplified: For all stages above railway substations, only efficiencies—that are directly applicable to instantaneous power values—are used. For the catenary, only active power is taken into account—the vehicle model does not consider the reactive part—allowing to describe the catenary as length-dependent resistance. Furthermore, all nodes—e.g., substations—of the network are described as subsystem each, being connected to a set of lines or line sections. However, these requirements significantly impede the network description as (impedance) matrix. As a consequence, the network complexity that can be represented is limited. In particular, one and two sided feeding can be implemented with an arbitrary number of vehicles on the respective line; furthermore, a limitation of feedback capability in terms of recuperated power is possible. However, connections between parallel catenaries of double track lines, stand-alone track-side ESS (ABS), or power limitations in feeding power cannot be represented at the moment. A later extension of the model towards these applications seems possible; also, a change of system structure within the energy supply system representation might be thought of if subsequent research is conducted.

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For the research presented here, these limitations are accepted. Moreover, their influence on the results is classified defensible: The non-implementation of parallel catenary connections (called “Fünferschalter” in Germany) will slightly enlarge the losses of the catenary, as the power will be “rerouted” via the next substation, which might be a longer path, i.e., having a higher resistance. For stand-alone track-side ESS, the same is valid—effects on voltage stabilisation cannot be shown anyhow already by choosing power as base quantity (see section 2.2.4.4). The non-limitation of feeding power has two effects: First, there is no information on the network’s reaction on possible overload of one substation or node. However, for the case studies intended here—assuming normal operating conditions—this case will never occur. Second, there is no possibility to implement a load-dependent efficiency of the substation. Even though this implicates an accuracy loss at substation level, the effort to include this feature would not pay off in terms of result precision increase.

2.4.3.4 Operation Control The tasks of operation control as discussed in sections 2.2.6 and 2.3.3.1 comprise planning (timetabling), real-time operation control (dispatching), and execution of dispatcher’s decisions (interlocking, partly signalling). From these tasks, two to three parameters can be identified that are influenced by operation control: First, the speed limit vmax may be reduced due to temporary speed restrictions or via signal aspects. Second, a recommended or target speed vrec can be transmitted to the driver or ATO, influencing the speed of the vehicle. The timetable does not really define a third parameter but still somehow influences operations and energy demand: With the time given to travel from A to B, certain requirements in terms of minimum speed and acceleration are defined. Consequently, this does not really belong to the model but might influence the driving style. Thus, it has to be taken into account when creating evaluation scenarios but can be neglected for the model. The operational influence of passengers as discussed in section 2.3.3.3 is neglected as an inclusion would only increase the model’s complexity significantly without adding any value with respect to the research question.

2.4.3.5 Track This subsystem consists of track, interlocking, and signals (including cab signalling). As the latter two basically execute operation control’s commands, they are not regarded as inﬂuencing the parameters in any way. However, the track inﬂuences parameters collected in sections 2.4.3.2 and 2.4.3.3.

Most of them can be determined easily: speed limit vmax, slope i0, and curve radius rc are given in digital infrastructure descriptions or can be obtained from geographic information systems (GIS). As soon as the route is known in terms of coordinates—as available in digital infrastructure descriptions as well—the heading ϕ can be calculated from two coordinate pairs.

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The determination of the tunnel factor kt is a bit more complicated, as usually, only tunnel cross section and length can be read from infrastructure information. For this reason, the tunnel factor has been treated in section 2.4.2.2. Accordingly, Equation 2.83 is used in order to take tunnel effects into account.

2.4.3.6 Environment There are different parameters in the railway system model’s equations that are determined by the environment. Most of them can be measured without dif-

ficulty: wind speed vw, wind source direction ψ2 (or wind direction ψ1), air temperature Ta, and absolute air pressure pa. Having a look on the data provided for this research, all parameters except the last—pa—are available. Therefore, the simplified model of Equation 2.84 is used for air density ρ: 273.16 ρ = 1.293 kg/m3 · (2.84) 273.16 + Ta Furthermore, the environment influences the friction coefficient µ (sections 2.2.2.5, 2.3.4.4). Therefore, the Curtius-Kniffler-equation is extended by a “rail condition factor” fRC (Equation 2.88). Without known correlation between µ, fRC, and environmental conditions (precipitation, dust), fRC is estimated for given situations (calibration) or “arbitrarily” set in case studies.

Additionally, the vehicle load and thus parameter mload are considered as determined by the environment: The number of passengers or amount of freight to be carried is deﬁned outside the railway system itself (e.g. passenger’s de- sires, shipper’s demand). Consequently, it makes part of the environment.

2.4.3.7 Model Summary, Strengths, and Weaknesses

Summary. The model describes energy-related phenomena of the entire railway system, from primary energy to wheel. It consists of five subsystems: Energy Supply and Vehicle—which are described by a set of formulas—as well as Operation Control, Track, and Environment, who “only” define or influence some of the formulas’ parameters. In the previous sections, Equations 2.80 2.98 describe the vehicle, while Equations 2.992.105 define the energy supply system. The parameters used in these equations are listed in Table 2.12, sorted by the subsystem that defines the respective parameter. The model is based on physical laws, assigning certain amounts of energy to dedicated phenomena, as specific resistance components, auxiliary, or comfort systems. By variation of parameters defined by operations or environment, their influences can be investigated. Being power based, the model can easily be split into subsystems, which have a certain input and/or output power. For all of them, a modelling has been proposed here—moreover, by the chosen mod- ularity of the model, each can be revised at any time, e.g. for implementation of a more precise description. In addition, the choice of power as base quantity enables a simple derivation of the corresponding energy demand.

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Table 2.12: Parameters Used in the Railway System Model. The first column indicates which subsystem defines the respective parameter. This table only comprises parameters used in the final model; omitted ones are not listed. Also, mathematically defined parameters (e.g. drive chain efficiency ηD) or state variables (e.g. current speed vt) are not mentioned. OC—Operation Control. Symbol Parameter Name Remarks

mtare Empty (Tare) Weight

mpayload Maximum Payload secondary

madh Adhesion Mass ζ Rotational Mass Factor 1.06...1.09 for modern EMUs

Pn Nominal (Continuous) Power

Ph Hourly Power Rating usually used

FTr,S Starting Tractive Force vmax Top Speed technical or commercial Vehicle ϑ Retardation for brake force limitation

wl,r Rolling Resistance Coefﬁcient cw Aerial Resistance Coefﬁcient

Ab Vehicle Cross Section Qtot Inner Air Flow

drt Running Treads’ Distance 1.5 m for normal gauge

nBD Number of Brake Discs mΩ Rcat0 Resistance per Length catenary + rail, 180...550 /km ηUW Substation Efﬁciency 90...98 %

ηgrid Transmission Efﬁciency 95...97 %

En. Supply ηgen “Generation” Efﬁciency 35...100 %

vmax Speed Limit route property, may depend on vehicle type ϕ Line Heading necessary for yaw angle α i0 Track Slope

rc Curve Radius

kt Tunnel Factor

fRC Track Condition dust, humidity, precipitation (µ) vw Wind Speed ψ2 Wind Source Direction necessary for yaw angle α Ta Air Temperature

mload Current Vehicle Payload passengers/freight

Environment ρ Air Density

pa Absolute Air Pressure using norm value 1013 mbar

vmax Speed Limitation temp. restrictions, signal aspects

OC vrec Recommended Speed

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Innovations. Apart from being—to the author’s best knowledge—the first model that describes the entire system from primary energy to a vehicle’s wheel, the wind and its influence are taken into account for the first time. The model including these factors has been developed for this thesis and presented in section 2.4.2.1. Additionally, a novel, simple, vehicle-state-dependent model for the auxiliary power demand has been derived in section 2.4.2.3, as there was no literature found covering this topic. Last but not least, the model is applicable to arbitrary railway systems (e.g., for all relevant energy supply systems) by only changing few parameters or sub-models of sub-subsystems.

Strengths. The model shows some major advantages and improvements compared to the also systemically oriented—and in terms of standards the next- in-line—metro system model presented by Tian, Weston, et al. (2017): Cause- based analysis of running resistances instead of the generic Davis Equation, more precise and load-dependent description of the drive chain efﬁciency, explicit auxiliary and comfort system inclusion, consideration of environmental impacts (wind, rain), and applicability to different system types.

Weaknesses. Due to its degree of detail, the model requires a rather large set of parameters—see Table 2.12—even though many simplifications have been applied to enable the desired systemic approach. Moreover, most of the parameters are not precisely known or determinable. Thus, they are subject to parameter calibration (cf. section 3.3)—finally impeding the exact calibration of each of them due to their high number. The neglect of electro-technical phenomena—current and voltage dynamics— tends more to stabilise the model than reducing its precision, as its purpose are quasi-static analyses of systems in the state of normal operation rather than transients or instabilities. However, there is precision lost at this point. For reasons of applicability and versatility, a simplified description of the power supply system was chosen, which disables the description of certain (electrical) elements and supply types (DC); a full implementation of power analysis was not possible. Here, a development towards the approach of Tian, Weston, et al. (2017) and a fully dynamical impedance matrix generation (Hardel, Körner, and Stephan 2014) remains as promising option for further research—especially following the approach of co-simulation.

Conclusion. Altogether, the model is considered to be a well-ﬁtting approach to systemic analyses of a railway system’s energy demand. Even though the model seems complex, it is an extremely simple representation of a highly complicated real system. The proposed simpliﬁcations might be reduced in further works, making this model also a good starting point for future system- oriented analyses of energy demand and saving measures in arbitrary railway systems—as claimed by Dube, Fraas, et al. (2013).

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3.1 Program Implementation

3.1.1 Concept and Structure The basic structure of the program is derived from the system model as shown in Figure 2.27 (p. 91); its concept implements the principle of small and easily changeable subroutines in order to enable frequent and consistent reuse of functionalities while leaving the possibility to change only small parts of the program if the model shall be adjusted or extended. The most suitable approach to implement clearly structured systems that consist of multiple functional units is object oriented programming (OOP). MATLAB being a proven programming language especially developed for engineering problems while supporting OOP, it offers as well-ﬁtting solution to the implementation problem. Based on the subsystems derived and presented before (esp. section 2.4.3), the following program structure appears as most logic and targeted approach:

– Implementation of the vehicle as own object (class), allowing to give vehicles a set of general functionalities (methods) and properties while adjusting one speciﬁc vehicle’s behaviour by parameter values.

– Implementation of the environment as class with properties and methods, accessed by relating subsystems (e.g. vehicles) by reference; ensures that all subsystems use the same environment description.

– Implementation of the entire energy supply system—from primary energy to the vehicle’s energy input (VEI), be it electricity or fuel—as one class using the same principles as for vehicle and environment. The energy supply system interacts with all vehicles, which are “known” to the energy system as references (handles), stored in class properties.

– The track’s inﬂuences are included where their effects become visible. For

example, speed maximum vmax, curve radius rc, track slope i0, and tunnel cross section are stored as vehicle class properties (infrastructure data ta-

ble), which allows to calculate necessary information (e.g. tunnel factor kt at a given position of the vehicle).

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– Coordination of all mentioned subsystems (i.e. operation control) is done by each calculation’s/scenario’s main script. Public class functions are used to provide the necessary functionalities and interfaces—while the actual calculations are done by protected methods.

– Evaluation services are provided by each calculation’s main script after the execution; alternatively, they might be implemented in an own evaluation script. Either way, the evaluation can be adjusted to each scenario’s need.

– Additional service functions provide basic operations, as e.g. unit or coordinate system conversions. These are implemented as functions outside the classes and can be used wherever they are needed.

This programming approach allows to easily interact with the different objects using the specified public methods (documented in appendix E), while the actual model calculations are done in protected, kind-of-atomic methods24 that can be adjusted (e.g. to implement a more sophisticated model) rather easily. These different “privacies”—i.e., access rights—for methods and properties are a major advantage of the OOP approach. In general, the attributes public and protected can be used for both methods and properties, while for the latter, different attributes can be used for reading and writing the property’s value. In general, public methods (and properties) can be accessed from “anywhere” in the program, e.g. from the main script to start a calculation step. In contrast, protected methods can only be accessed by functions of the same class but not from any other script. Thereby, it is ensured that only defined interfaces are provided in order to start and conduct the calculation—while “quick and dirty” solutions by directly accessing subroutines are prevented. This allows to properly control the calculation process, which is important to assure the program’s correct functionality. In terms of calculation base, two choices are possible: distance and time. The distance based approach seems to be more suitable for the vehicle, especially as the information on track properties is provided on a distance base (e.g., a set of parameters every ten meters of distance). However, the universally valid quantity for the entire system is time—be it a real time (for instance, Nov 26, 2016, 6.23 pm) or be it a calculation time (seconds since first step). Therefore, the vehicle class has been built up on a distance basis first but extended to and enhanced on time base, as all other program components. For illustrative purposes, the process of a general time step calculation is given as flow chart in Figure 3.1.

24In programming, methods executing a deﬁned (minimal) set of operations forming a logic operation that either fails or succeeds as entity are called atomic operations. In a wider sense, the aim of breaking complex procedures in their smallest logic parts is often also referenced as atomic programming.

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Main Script

Create Environment Object

Create Vehicle Object(s)

doStep() method Create Energy System Object (time based)

Calculation Step of Vehicle 1 Determine motion resistances

Determine set speed vset

Calculation Step of Vehicle n Determine necessary acceleration and force Energy System Calculation Step

Apply physical limitations

All vehicle calcula- Determine new speed v = at + v0 no tions completed?

yes Determine new position (km)

Determine force and power values

Calculate auxiliary and comfort powers

Determine total power at pantograph

Update time and check break condition

Figure 3.1: Flow Chart Representation of one Time Step of the Railway System Model. The vehicle step routine is also shown (right hand side); implicitly, a time based calculation is assumed (decision omitted). For the objects, “create” always includes “conﬁgure”. For more details on the energy supply system routine, see Figure 3.2 (own illustration).

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3.1.2 The Vehicle Class Purpose of the vehicle class is to store all information and provide all methods that are necessary to calculate a train run for a given route. The class supports—due to its development history—basic operations and testing possibilities on distance and time base as well. However, the full system works on time base, thus, the distance based functions are omitted in this report. The class itself has an extensive set of properties. Some of them are vehicle parameters (e.g. maximum tractive force, hourly power rating), while a second group of properties defines the class’ (e.g. step width, result parameters to be stored) and vehicle’s (e.g. acceleration limits, speed control mode) behaviour. Where default values for these properties are specified within the class, they are defined as constant25 class properties. A third group of properties covers system states: those of the vehicle (e.g. current speed and acceleration, applied forces, powers) as well as information on surroundings (e.g. infrastructure data table, reference to the environment class). Public methods are only offered for the absolutely necessary functionalities: apart from the constructor to (a) adjust some properties, (b) initiate the calculation of a step, (c) receive certain information on the vehicle’s state, and (d) in order to enable interactions with other subsystems (e.g., classes). For the mentioned groups, the following is implemented:

(a) Deﬁning load (in %, kg, or t), acceleration limits, name, schedule, speed and brake controller mode, speed recommendations, and time step width

(b) One general function doStep (calculation base independent) as well as the function calcVehicle for time based calculations and a given target time (implicit override of the step width)

(c) Speciﬁed functions deliver current power and position (track km); additionally, it can be checked whether the vehicle operates on a given line

(d) If the energy supply system cannot absorb (all) regenerated power, the vehicle can be forced to dissipate power using its brake resistor

The protected methods implement the equations presented in section 2.4.3.2. Thereby, they provide as speciﬁc functions as possible. For instance, there is a method calcMotionResistances that is only used for calling motion resistance component speciﬁc methods as for e.g. rolling and aerial resistance. For the latter, calcAerialResistances uses functions of the environment class to determine, for instance, wind speed and direction while using external functions for subtasks as determining the angle between wind and train direction. With each step, a row is added to the table of results runTable (distance based) or runTableT (time based). In either case and depending on the value of the resultMode property, different calculation results are stored:

25Constant is, besides public and protected, another accessibility attribute that prohibits any change of the value, i.e., the default value is used throughout the entire calculation.

– 108 – Chapter 3: Implementation, Calibration, and Validation resultMode reduced Time, position, actual speed and acceleration, recommended and set speed, timetable values (arrival, departure, and dwell time), power at VEI resultMode default In addition to the reduced set: different powers (at drive chain input, at wheel; HVAC, auxiliary, and brake resistor power) as well as electric and mechanic (braking) force resultMode all In addition to the default set: tunnel factor kt as well as all values of the resistive forces

In order to enable proper interaction with other system components—e.g., the energy system that needs information on vehicle position and power while being able to reject regenerated power—and, in particular, for ensuring value consistency, the vehicle class is derived from MATLAB’s handle class. Thus, only one instance of the object is created while each copy or pass of this object behaves equal to pass-by-reference-calls of other programming languages. For a full reference of public methods, please refer to appendix E.1.

3.1.3 The Environment Class

Using the same concept, the environment class is derived from MATLAB’s handle class and provides services to determine the environment’s inﬂuences. The class provides two different operation modes: generic and norm.

In generic mode, the class properties—rail condition correction factor fRC, outside air temperature Ta, wind source direction ψ2, and wind speed vw—are defined as generic values. These are stored as numeric (double) type, therefore, this mode allows short execution times.26 In norm mode, the class collects the information from weather data tables that contain—in this case—one measurement each five minutes for about 850 points (specified by latitude and lon- gitude) in Switzerland. The following relevant information is thereby provided:

– Air Temperature (°C)

– Wind Speed (km/h)

– Wind Source Direction (°)

For each request, the vehicle’s coordinates as well as a real-world time have to be speciﬁed. From this information, the respective value is determined from linear interpolation between the four closest points in space as well as between the two next instances in time. Determining relevant rows from time and coordinates requires much computing time as no logical indexing can be used; instead, MATLAB’s find function has to be employed.

26A test run for one vehicle between Olten and Solothurn took 23 s in generic and 515 s in norm mode—which is a factor of more than 20 caused by the environment class operation mode.

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The methods (functions) are based on the demand of the other systems; they allow to provide different publicly accessible services: Determination of air density ρ, rail condition correction factor fRC, adhesion coefficient µ, air temperature Ta, wind direction ψ2, and wind speed vw. In terms of configuration, the values for the generic mode can be set from outside; additionally, the fRC value as well as a start time stamp can be specified. The latter allows to call methods with the calculation time of the entire model (seconds since start), leaving the conversion to real-world time to the called method. The public methods of the environment class are documented in the appendix, section E.2.

3.1.4 The Energy Supply Class Based on section 2.4.3.3, this class provides the computation of the energy supply system—with a focus on continuous (electric) supply systems.27 The system type as well as a start time can be specified when constructing the energySupply object; a reference to environment is necessary as preparation for a possible inclusion of environmental influences on the energy supply system (e.g., temperature-depending resistances of the overhead lines). Given the object oriented structure, energy system nodes—e.g., substations— are represented as class properties of type struct having different properties themselves. These are, among others, connected lines and their resistances as well as a time-based result table. The evaluation is done vehicle based—cf. the flow charts in Figure 3.2—causing the modelling limitations introduced in sections 2.2.4.4 and 2.4.3.3. Due to scope and time limitations of this research work, no possibility to translate from an object oriented structure into an impedance matrix form as described in section 2.2.4.4 was found. However, the figure nicely illustrates the complexity of a sequential electric supply net calculation and thereby the advantages of an impedance matrix description. In order to allow a proper interaction of the train run simulation on the one hand with the electric net simulator on the other hand, a separate implementation for each system would be advantageous. Enabling a communication using interfaces, each simulator could be tailored to the respective subsystem’s exact needs. Consequently, the concept of co-simulation seems to be the most promising for implementing multi-domain models covering different subsystems. Within the scope of a Ph.D. research, such a model is not feasible, however, it remains as interesting challenge for further research.

The accessible functions, i.e. the public methods, comprise system set-up— addition of nodes (substations) and vehicles—a function to select a predeﬁned energy supply system (AC 15 kV, AC 25 kV, DC 1.5 kV, DC 3 kV, diesel) or deﬁne a manual system, and a function to calculate a step in time.

27Discrete, i.e. fuel-based, supply systems have not been fully implemented due to reasons of scope; however, the class architecture allows an easy inclusion in a possible later extension.

– 110 – Chapter 3: Implementation, Calibration, and Validation n y calcEnergySystem to calculate? More vehicles —no (own illustration). Determine feeding n power acc. to distances n —yes; y . y n Vehicle di- More nodes to evaluate? Reject exceeding regenerated power rectly at node? calcNodes2 Method. n n y method calcNodes2 y y private Feeding? Two Sided at node? More lines Redistribute power to other substation, then reject to vehicle n y Flow Chart of a Time Step for the (b) Current vehice in this section? Find recuperating Select next node; Store node proper- vehicles at this node find connected lines ties for later evaluation Select next line of current node y n node y n n n one to evaluate? for this line? Another node Max feedback Another vehicle node and feeder Determine feeder Exactly power exceeded? for current vehicle within the system? Select next vehicle Determine vehicle’s determine its position max. feedback power within energy system; and node input power Calculate feeding power Select next node and get y y Method. Flow Chart of an Energy Supply Class Calculation Step (simplified). On the left hand side, a time step using the general function Reset Values Store Results calcNodes2() Calculate Grid Power Calculate Node Powers Calculate Primary Power calcEnergySystem Calculate Generator Power Flow Chart of a Time Step for the (a) Energy Supply Systemusing Class called Figure 3.2: from the main script is shown; the right hand side shows a (severely) simplified representation of the

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Due to the model’s, the feeder power computation requires a higher effort compared to substation, grid, generator, and primary, which are determined from the sum of feeder powers and element efﬁciencies. The feeder powers are calculated for each step according to the following procedure (Figure 3.2b):

1. For each node, ﬁnd the vehicles that are fed by this node using route name and vehicle position. 2. Determine for each vehicle to which lines an electric connection exists. 3. Determine for each node feeder powers and total node power. 4. In case of power regeneration, check if the power can be taken back. If not, reject the power to the vehicle and recalculate the node. 5. Determine the node’s input power from its efﬁciency. 6. Write the results into the corresponding class properties.

The input power of all nodes being determined by a protected method allows to calculate the higher level powers (grid, generator, primary) using the specified efficiencies. The efficiency from primary power to generator input power (i.e., refining and transportation losses) is neglected for the moment: Due to model structure and available data, a determination would be speculative. For a description of the energy supply system class’ public methods, please refer to appendix E.3.

3.1.5 System Coordination: Operation Control In contrast to the other subsystems presented so far and due to the scope of the research question, operation control is not implemented as an own class. Instead, public functions of the priorly presented classes are used to determine or inﬂuence the behaviour of the other system elements from the scenario’s main script directly. The following possibilities are offered to the coordination:

– Change of the vehicle load specifying a value in % of payload, kg, or t – Limitation of the vehicle’s acceleration/deceleration – Definition of a vehicle’s schedule by indicating a location (station or route kilometre), arrival time, dwell time, and departure time – Definition of scheduled speed by specification of a speed for given route kilometres; dwell times can be added where the scheduled speed is zero – Definition of a target speed for a certain area as set of route kilometres – Definition of stops and dwell times for route kilometres or station names – Energy supply system configuration by node property specification: efficiencies, max powers, storage capacity, initial amount of stored energy

These adjustments can be made prior to or at any instance during the computation. In the latter case, they will become effective for the next time step.

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(a) Result table of the vehicle class (runTableT) (b) Result table for substation, (c) Result table for a node grid, and generator level; en- (feeder level) of the energy supply system class ergy supply system class

Figure 3.3: Examples for Result Tables Generated by the Calculation Program. On these tables, mathematical as well as graphical evaluations can be based (own illustration).

The full system computation itself is done using a while loop, employing the vehicle class’ return parameter lastRun as termination condition. In order to ensure a proper calculation including all mutual inﬂuences, the sequence of

1. calculate all vehicles; store return values including actual time tact 2. determine the current time from all tact returned by the vehicles28 3. start the energy system calculation with determined tact value is maintained for every computed scenario.

3.1.6 Evaluation of Results The evaluation of the results is based on each vehicle’s runTableT table as well as on the result tables of the energy system: resultTable for the system above substation level, and each node’s table with feeder, total output, and input power, which is stored in the obj.nodes.(node_name) struct. These variables deliver information on different parameter values of each train and the energy supply system—for each time step. Based on the time difference between time stamps (∆t) and the power values, the corresponding amount of energy can be integrated (cf. Equation 2.91b). Based on these tables—examples for reduced result mode are given in Figure 3.3— mathematical as well as graphical evaluations can be performed. The result evaluation is usually embedded as third part—after initialisation and main calculation—into a scenario’s main script, but can be performed separately as well. The latter is to be preferred whenever result tables are stored locally, intending to repeat the evaluation without recalculation at a later time.

28The vehicle class’ internal time stops as soon as the vehicle has reached its termination condition

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3.2 Determination of Parameters

3.2.1 Parameters of the Vehicle Types of Parameters. In the vehicle class, 30 parameters are used. Most of them are defined during design and construction, meaning that they can be read from technical data, data sheets, or publications about the vehicle. Others have been determined by measurements and are thus known, while a third category of parameters needs to be estimated. While the determination for the first two categories is straight forward and listing them in Table 3.1 is considered sufficient, the third category needs some more attention.

Defining Unknown Parameters. Unknown parameters need to be estimated or derived from known information. As basis, general knowledge, orders of magnitude, or data provided by railway operating companies can be used. The efficiency values of drive chain (ηD) and energy preparation (ηEP)—see section 2.4.3.2, Equations 2.93, 2.96—are defined using general knowledge.

For some other parameters—aerial resistance coefficient cw, inner air flow Qtot, and specific rolling resistance wl,r—measurements exists that can be used as base: For many of their vehicles, SBB has determined the parameters A, B, and C of the Davis Equation (cf. p. 42)

2 FR = A + B · v + C · v (2.32) which allow, by comparison of coefﬁcients, to derive values for model’s parameters as follows. Given the model introduced in section 2.4.3, the comparison of coefﬁcients for its parameters wl,r, Qtot, and cw results to be A wl,r = (3.1) mt · g 1 4.3 N · nBD Qtot = ρ− · B − (3.2) 27.7 m/s ! 1 6.2 N (3.3) cw = · 2C − 2 ρ · Ab (27.7 m/s)

These values serve as starting point for parameter calibration as described in section 3.3. A comparison to “standard values” is used as—after unit consistency proof—a second veriﬁcation step for the above equations.

3.2.2 Parameters of the Environment In the environment class, the number of parameters is signiﬁcantly lower. Leaving the properties required by programming aside, seven constants and, in generic mode, four parameters are used. Physical constants are obtained from literature—cf. Table 3.2 for an overview—inﬂuences of the open air rail conditions on the condition in tunnels are estimated.

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Table 3.1: Parameters of the Vehicle Class and Their Determination. Given are the parameters with their symbols and units, the method of their determination (“source”), and annotations if necessary.

Parameter Source Annot. 2 Vehicle Cross Section Ab m Technical Drawings (1) Breakaway Resistance 1 Estimation (2) Brake Allowance – Speciﬁcation

Aerial Resistance Coefficient cw 1 Derivation + Tuning (3) Constant Davis Coefficient A N Measurements (4) s Linear Davis Coefficient B N /m Measurements (4) 2 Quadratic Davis Coefficient C N s /m2 Measurements (4)

Running Treads’ Distance drt m Speciﬁcation Energy Feedback Capability – Speciﬁcation

Drive Chain Efﬁciency ηD 1 Estimation (5)

Energy Preparation Efﬁciency ηEP 1 Estimation (5)

Starting Tractive Effort FTr,S N Technical Data

Max Electric Brake Force FB,el,max N Technical Data (1) HVAC Model W/m3 Derivation (6)

Fixed Frame Wheel Base la m Technical Data (7) Vehicle Length l m Technical Data (7)

Adhesion Mass madh kg Technical Data (1)

Maximum Payload mpayload kg Technical Data (7)

Tare Weight mtare kg Technical Data (7)

Number of Axles nx 1 Technical Data

Number of Brake Discs nBD 1 Technical Data

Hourly Power Rating Ph W Technical Data (8) m3 Inner Air Flow Qtot /s Estimation/Derivation Supply Type – Speciﬁcation Retardation ϑ 1 Technical Data (7)

Spec. Rolling Resistance wl,r 1 Derivation + Tuning (3) m El. Brake Min Speed vEB,min /s Technical Data (1) m Operational Top Speed vmax /s Technical Data 3 Inner Air Volume Vair m Estimation Rotational Mass Factor ζ 1 Technical Data (1),(9)

ANNOTATIONS (1) For newer vehicles, the exact determination is often difficult (knowledge protection) (2) The breakaway resistance is almost negligible; estimation based on Weidmann (2011) (3) Value can be derived from measured Davis coefficients but should be fine-tuned (4) For details, cf. Schranil and Lavanchy (2016) (5) For details on the estimation, see section 2.4.3.2, Equations 2.93, 2.96 (6) Detailed derivation in section 2.4.2.4 (7) Usually indicated at the vehicle’s outside (8) Sometimes, the power rating is not properly declared (constant/hourly/short-time) (9) As these values are usually not accessible, an estimated value has to be used

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Table 3.2: Parameters of the Environment (physical constants only) and their values.

Parameter Value Celsius-Kelvin-Difference CKd 273.16 m Earth’s Gravitational Acceleration g 9.81 /s2 kg 3 Standard Norm Air Density ρn,0 1.293 /m Days per Second 1/24/60/60

For the latter, three parameters are used: one to specify the standard rail condition factor fRC for tunnels, which is set to be zero, assuming average rail conditions in tunnels. A second parameter fRCf is used to deﬁne the level of inﬂuence of the outdoor fRC on the tunnel’s fRC, included as

fRC = fRC,tunnel-default + fRCf · fRC,outdoor (3.4)

The third parameter serves as consideration boundary: The tunnel is assumed not to change the value of fRC if its length is below a threshold. Based on the literature discussed in chapter 2, a value of 500 m is considered useful. In generic mode, four parameter values are deﬁned as ﬁxed properties: rail condition correction factor fRC (default: 0), wind source direction ψ2 (0°), out- m side air temperature Ta (20°C), and wind speed vw (0 /s). These values can be set before or during a calculation using a public method. In norm mode, these parameters are—with exception of the rail condition correction factor fRC— obtained from weather data tables for each simulation step.

3.2.3 Parameters of the Energy Supply The parameter-relevant properties of the energy supply consist of efficiencies (well-to-plant process, generator, and grid), system frequency, and voltage values (min, typ, max). Moreover, connected lines and nodes are stored. The values of the energy supply system can be chosen selecting a pre-defined system (cf. Table 3.3) or specifying the parameters manually. The values for the pre-defined systems have been taken from section 2.4.3.3 and the lecture notes of Meyer (2012). Note that for the implementation presented here, only the typical values are used, voltage fluctuations are neglected—which is why the model is no longer valid for DC systems, cf. section 2.4.3.3. Nodes can represent substations, energy storage systems (ESS), or combined stations. Their specification comprises up to 13 parameters, storing

– information on input/output efﬁciencies, – a list of connected lines and their resistance-per-length values, – information on the node’s position and fed sections, – the node’s maximum power capabilities (grid and storage), – the actual power values at all observation points (e.g. feeder, substation),

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Table 3.3: Energy Supply System Parameters and Their Values for different pre-defined systems. The generation efficiency is valid for the Swiss rail energy mix; the determination of well-to-plant efficiencies would exceed the scope of this thesis, which leads to a neglect of this factor and to an application of an efficiency of 1. The diesel system is prepared but not studied in the following; also, the voltage range is given for information purposes only. In the model, the typical voltage is used.

AC 15 kV AC 25 kV DC 1.5 kV DC 3 kV diesel System Frequency Hz 16.7 50 0 0 NaN Minimal Voltage V 11 000 17 500 1 000 2 000 NaN Typical Voltage V 16 000 27 000 1 750 3 500 NaN Maximum Voltage V 17 250 27 500 1 800 3 600 NaN Generation Efficiency 1 0.80 0.80 0.80 0.80 0.86 Grid Efficiency 1 0.95 0.96 0.96 0.96 0.97 Well-to-Plant Efficiency 1 1.00 1.00 1.00 1.00 1.00

– the maximum storable amount of energy in case of ESS availability, and – the current amount of stored energy.

Most of the information is defined by the investigated network, which makes the corresponding parameter’s value mainly a function of the case to be studied. The physical values—efficiencies and per-length resistances—have been discussed in section 2.4.3.3 and are selected within the specified ranges, according to the assumed properties of the node or its connected catenary.

3.3 Model Calibration

3.3.1 Calibration Method and Measures 3.3.1.1 Real World Data For calibration, real-world data provided by SBB is used. One part of this data originates from energy meters installed in some of the series 500 ﬂeet (ICN) vehicles, recording each second time stamp, line voltage, line current, active and reactive power, GPS position, and speed. As each ICN EMU is composed of two independent “half trains” with one drive chain each, two devices are used per train. For powers, the values are added to obtain the full train’s power, while for speed and voltage, the mean value is used. During data preparation from raw data, column names have been changed; additionally, a position has been calculated from the GPS positions as one-dimensional information on distance travelled from route start and projected to another set of data, the digital track map. An extract from a data table is given in Figure 3.4a. The digital track maps have been provided by SBB. These tables contain, for a given route, information on radius, slope, tunnel cross section, permissible speeds, signal and station locations, coordinates (in Swiss LV03 system), and height above sea level—discretised with a step width of about 10 m. The GPS

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(a) Energy Meter Data Table of a series 500 (ICN) train.

(b) Digital Track Map.

Figure 3.4: Exemplary Real World Data used for Calibration. The original data has been provided by Swiss Federal Railways (SBB) and slightly reshaped for data format consistency (own illustration). position in WGS84 format has been added by conversion from LV03 system; additionally, the heading has been determined based on two position indications. As illustration, some rows of such a table are shown in Figure 3.4b. Combining these two data sets allows to obtain detailed information on the characteristics of a given, recorded train run. More precisely spoken, speed and active power can be associated with a track segment of which all details are known. However, there are two main challenges when intending to use these two data sources as calibration base:

1. The digital track map is discretised on a distance base (in general 10 m), while the energy meter data is discretised in time (1 s). 2. Location and speed given in the energy meter data are based on GPS measurements that are not available in and unreliable close to tunnels. Thus, this information is lacking in tunnel areas.

The ﬁrst of these challenges requires to decide whether the information shall be stored on a distance or a time base. Based on the fact that an equal step with for both bases is reached at 10 m/s (36 km/h) and in most cases, trains run at faster speeds, the smaller loss of accuracy occurs when selecting distance as base. Then, the energy meter data are projected and added to the digital track map, resulting in a precise description of the respective unique train run. For the projection, the following scheme was used: If there is more than one energy meter measurement per 10 m, the closest one to the position listed in the digital

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10 m/s

Figure 3.5: Merging of Distance and Time Based Data. Cross-marks represent the locations where (time-based) on-board energy meter measurements are taken, circles represent entries of the digital track map. For an accelerating train (exemplary speed proﬁle given in the upper graph), the on-board measurements occur frequently for low speeds, while the distance increases with speed. Thus, for low speeds, multiple on-board measurements would be projected to one infrastructure point, whereas for higher speeds, one on-board measurement is projected to multiple track map entries (grey arrows). The track map discretisation step width being 10 m and on-board measurements being taken each second, the mode changes at 10 m/s, i.e. 36 km/h—dotted line (own illustration).

Figure 3.6: Exemplary Data Table of a Train Run. This table comprises all relevant information on track properties as well as speed and power measurements (own illustration). track map is taken. If there are less energy meter measurements than track points, the values of the (in terms of kilometrage) prior energy meter point are used until a new measurement is available—the procedure is illustrated in Figure 3.5. An exemplary result table is given in Figure 3.6. Of course, this procedure may cause steps and plateaus in speed and power curve. However, an interpolation would require a significantly higher effort due to the distance- variability of energy meter measurements—without adding a significant gain in accuracy. For this reason, the presented (simpler) approach has been used. The second challenge has been addressed in a separate research project at IVT. Using algorithmic support as well as track and power information in tunnels, the missing sections of speed profiles have been reconstructed (Sessa, De Martinis, et al. 2018).

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3.3.1.2 Calibration Method The idea of calibration consists in two major points: First, ﬁnding possible mistakes in the program’s code; second, adjusting its parameters in a way that the model’s results are as close to the real world measurements as possible. Remembering the program’s task being the determination of energy demand from a given speed proﬁle or speed recommendation, the following procedure was chosen to reach these calibration goals:

1. Load a measured train run as described above and perform a calculation for as similar conditions as possible (track, environment, load). 2. Apply the measured speed trajectory as recommended speed. Additionally, neglect interactions as between train length and speed restrictions: The program shall follow the given speed proﬁle as closely as possible. 3. Compare the calculation results in terms of speed and (active) power to the measurements; perform adjustments that bring the calculation results closer to the measured values.

Ensuring Similar Conditions. While train speciﬁcation and track data can be regarded as constant over time, allowing to simply load the default data for vehicle and route under investigation, there may obviously arise different (exterior) conditions concerning other parameters: the load will vary, and so the weather does. As the model takes these quantities into account, the corresponding parameters (i.e. current payload mload, wind speed vw, wind source 29 direction ψ2, and air temperature Ta) have to be set accordingly. For the application of similar environmental conditions (vw, ψ2, Ta), the environment class’ norm mode is used, using the departure time of the train run under investigation as indicator for the weather table selection. These weather tables30 contain in a ﬁve-minutes-resolution for 855 geographic locations in Switzerland information on the required (and more) weather parameters. Given the current position of the vehicle, the closest four points of measurements are detected and their values interpolated according to their distance to the vehicle. These are then used for the environmental parameters. Concerning the load, which could be derived from passenger numbers, no data could be obtained from SBB it is regarded as highly business relevant. Thus, the usual load curve of the respective kind of service is used. Based on the data provided by Weidmann (2011), a MATLAB function has been implemented that determines the relative load from departure time. Like this, the conditions for the calculated run are as close as possible to the ones that were met by the measured run. However, some uncertainties remain:

29As discussed earlier, it is not (yet) possible to determine the adhesion coefﬁcient µ—or the correction term fRC—from weather data. For this reason, that particular inﬂuence had to be neglected. 30These tables were created by SBB and have been delivered and used by courtesy of Meteomat- ics AG, St. Gallen

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Effects like deviations within the demand, production tolerances that may result in different tare weight, and differently worn wheels cannot be included, simply because there is no information available—except the estimation of up to 150 kg wheel wear per axle.31 Consequently, the precision of the calculation is limited; an accordance of 100 % is, by principle, not to be expected.

Application of Measured Speed as Set Speed. As mentioned above, for the model’s purpose, its calculation of power (and energy) demand should lead to similar values as measured when following the measured speed proﬁle on the same route. Therefore, the measured speeds are written as target speed values into the train model; a special calculation mode for calibration (and validation) is introduced. In this mode, the regulatory speed limits are neglected, which is necessary due to the unknown GPS sensor position that delivers the position data of the measured train: As in railway operations, speed limits have to be kept for the entire train, deviations of one train length may occur due to the sensor position when taking these limitations into account (for instance, the calculation is done for the nose but the sensor was at tail). However, all other functions remain without change. Speed controller and target speed handling are set to automatic mode (i.e., application of friction brakes allowed). The inclusion of the Davis B coefﬁcient is set to tuning (calibration) mode, i.e., Bv is calculated but for the power calculation, the speed proportional result of inner aerial resistance is used. All result values are recorded in order to enable a comprehensive analysis and understanding.

Start and End Speed Tolerance. The program is, at its current state that is intended for this thesis, written for calculations from standstill to standstill; the end of calculation is detected by the vehicle’s speed falling below a min- m imum speed limit (1E–3 /s) when being close to the ﬁnal kilometrage (tolerance: 100 m) of the route deﬁnition. For measurements that start and end with speeds “close enough” to zero (applied tolerance: 0.2 m/s), these speeds are set to zero, the calculation is performed. Runs that start and/or end with a higher speed are excluded from the set. Thus, 23 runs on the Olten–Solothurn line remain that are used for calibrating the model with a series 500 (ICN) train.

Comparison and Adjustments. Based on measured values and calculation results, two kinds of comparison have been performed. Optical comparison of the result curves to the measured ones allows to easily recognise systematic errors or signiﬁcant deviations; a mathematical evaluation is introduced in section 3.3.1.3. Based on this, parameters are adjusted; afterwards, a new run is done checking for the consequences on the quality indicators. This process is iterated until no further improvement is reached. In case that systematic

31Resulting in around 4 t weight difference due to wheel wear per train, which is about 10 % of the maximum payload of an ICN! For the estimation/derivation, please refer to appendix B.

– 121 – Energy Saving Potentials in Railway Operations under Systemic Perspectives problems or programming mistakes become obvious, these are investigated and corrected. A systematic problem that has been found is the base unit problem, discussed later in section 3.3.2. Results and adjustments made during the calibration process are presented in section 3.3.3.

3.3.1.3 Model Quality Indicators The main interest of a model’s calibration and validation consists in minimising the error. Regarding the fact that speed and power are calculated in order to ﬁnally determine the energy—being the integral of the power over time— (discrete) integral ratio IR, mean error ME, and the error’s standard deviation SE offer themselves as indicators for the model quality. The formulas are: 1 P 1 n C(n0) · ∆n0 X (3.5b) IR = N 0 0 (3.5a) ME = [C0(n) − M(n)] 1 P∀ N N n M(n) · ∆n n ∀ ∀ s 1 X SE = [C (n) − M(n) − ME]2 (3.5c) N 0 n ∀ Thereby, the symbols are deﬁned as follows:

N Number of measured points ∆n0 Distance between two N 0 Number of calculated points calculated points n Index for measured points

n0 Index for calculated points C Calculated values ∆n Distance between two C0 C interpolated to n measured points M Measured values

Note that N and N 0 are not equal (distance based measurements vs. time based calculation steps); usually, N 0 < N. Consequently, the calculated values C have to be interpolated to the measurement points n (resulting in C0) in order to be able to determine ME and SE according to the above Equations 3.5. Given that the comparison is done on a distance base for reasons discussed above, the integral does not deliver an energy—however, if the integral over distance shows a high quality, also the integral over time will, as the values are linked to each other over the speed—whose quality is investigated as well. The (ideal) values aimed for are obvious: IR = 1, ME = 0, and SE = 0. IR and ME additionally allow statements on the kind of deviation, as shown in Table 3.4.

Table 3.4: Implications of the Model Quality Indicators on the type of deviation. Columns below target and above target are to be understood as implication if the indicator’s value is below given target value and implication if the indicator’s value is above given target value respectively. target value below target above target integral ratio IR 1 calculated value too large calculated value too small mean error ME 0

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3.3.2 Base Unit Problem As mentioned beforehand, there are two different base units during calibration and validation: distance (route information) and time (energy meters and calculation program). Consequently, the step lengths are not equal for the different data sets—a fact causing certain effects to be taken into account.

One concerns the deﬁnition of set speed vset. While target speed vrec, which is derived from the measured speed, is known for a kilometrage, the exact kilometrage—and thus the correct vrec—are known after calculating the step. To overcome this phenomenon, complex prognostics would be needed—which exceed the scope of this thesis. Instead, the last kilometrage’s vrec is used as next step’s vset. Consequently, the set speed might be delayed in comparison to the measured speed that is used as recommended speed, cf. Figure 3.7a. Moreover, the set speed may show plateaus and steep slopes instead of a constant change when the speed is low (time step smaller than distance step, cf.

Figure 3.7b) or “skip” short-period or high-frequent changes in vrec when the current speed is high (time step larger than distance step, cf. Figure 3.7c).

vrec vset v v → v

vcalc,n vset,n+1 s s s prior measurement v space space → rec (a) Basic Principle: The prior speed (b) A high frequency of calcu- (c) At high speeds, the frequency measurement in relation to position lated points (low speed) results in of calculated points is lower than n is taken as vset for step n+1; a plateau-step-shape of the vset the one of measured points. In this the set speed lags the measured curve, while the measured speed in case, speed changes of short dura- speed. fact shows a “smooth” behaviour. tion might get lost.

Figure 3.7: Schematic Illustration of the Base Unit Problem. Dotted lines with black diamonds represent speed measurements, dashed red lines with circles the set speeds, and green crosses results of the speed calculation (own illustration).

Another effect that might occur are oscillations in the power curve, which result from the method of measured data preparation: The measured speed and power values have been assigned to a kilometrage. In case there are more measurements than distance steps, the (in terms of position) closest measurement has been selected and added, spare measurements were disregarded. In the opposite case—less measurements than distance steps—the last known measured value was added to the kilometrages as long as no new measurement (ﬁt- ting the kilometrage) has been found. This may result in a plateau-step-shape of the recommended speed curve, while the speed actually showed a steady change. Depending on the step size ratio between route information and calculation, this may result in oscillations in the power curve, cf. Figure 3.8.

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v P

Figure 3.8: Origin of Power Oscillations in Calibration and Validation (schematic). All speed curves dotted, power curves solid; diamond: measured; x-mark: recommended (ﬁtted to route data step width); red circles: set/calculated. The different step widths result in an oscillation of the power curve while in reality, a constant braking power is observed (own illustration).

By applying an interpolation from measurement to route information step width, this phenomenon could be avoided. However, this multi-step interpolation would cause high effort, as the distance between the measurement steps is variable. Moreover, the phenomenon only occurs in calibration and validation, as these are the only contexts where a high-precision speed recommendation— i.e., a change every 10 m—is used. In addition, the oscillation of power occurs around the measured (“real”) value, ending up at quite the same mean value and thus quite the same amount of energy. Consequently, the higher effort for data preparation is regarded as unjustiﬁable.

3.3.3 Calibration Result Discussion Already the ﬁrst run of the iterative tuning procedure with all parameters as calculated (cf. Table B.1) showed a good quality: For speed, the integral ratio resulted to be 0.997 with a mean error of –0.559 km/h (full scale: 200 km/h); for the power, values of 1.219 and 0.397 MW resulted (full scale: 6.6 MW). For the speed proportional resistance—i.e., parameter Qtot—larger errors with integral ratio 1.431 and mean error 1.147 kN were obtained (full scale: 4 kN). From result investigation, the retrieved value of aerial resistance resulted to be overestimated: The target speed is not reached (ME < 0), especially for higher speeds with the necessary amount of power being to too high (ME > 0),

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32 indicates this conclusion. Consequently, cw was reduced. Multiple runs— 0.90, 0.70, 0.50, 0.55—finally led to a best-fitting value of cw = 0.59. Also, air flow Qtot turned out to be imprecise. Due to the fact that only the Davis Equation’s B coefficient is connected to this parameter (Equations 2.32, 3.2), a direct calibration—several runs with iterative correction—was conducted. Finally, the following adjustments are identified as best values:

– Aerial resistance coefﬁcient set to be cw = 0.59 (was 0.95) 1 – Qtot to be scaled by /1.6

With these two adaptations, of which the former ﬁts the literature (Sachs 1973a, Abb. 1.26, p. 31) better then the value derived from the values of SBB, the calibration quality presented in Table 3.5 is reached.

Table 3.5: Final Calibration Quality Overview. Given are for 23 runs over the calibration route Olten– Solothurn with a series 500 EMU: mean value of the quality indicator as well as its smallest, best (i.e., closest to target), and largest value. IR—integral ratio; ME—mean error; SE—error standard deviation. Full scale speed is 200 km/h, full scale power 6.6 MW; relative values referring to these full scale values.

absolute values relative values mean Min best max unit mean best worst IR 0.999 0.998 1.000 1.000 v ME –0.157 –0.337 –0.028 –0.028 km/h –0.001 –0.000 –0.002 SE 0.820 0.501 1.230 km/h 0.002 0.003 0.006 IR 0.998 0.917 1.004 1.192 P ME –0.012 –0.153 0.002 0.257 MW 0.002 0.000 0.039 SE 0.692 0.631 0.801 MW 0.105 0.096 0.121

With a mean integral ratio (IR) of 0.999, i.e. 99.9 %, and an IR range of 0.998 to 1.000, the area under the calculated speed trajectory almost perfectly ﬁts the area under the measured one. With a mean error (ME) of –0.157 km/h for a speed range of 0...200 km/h (–0.1 % full scale, below 0.2 % in absolute values and all cases) and the error standard deviation of 0.820 km/h, it results that 68.27 % of the errors are in the range of –0.977...0.663 km/h (–0.5...+0.3 % full scale). This is a well and easily acceptable value, especially when taking into account that different parameters (as e.g. the weight) are insecure. Thus, the program can be said to almost perfectly following the given speed trajectory. Also, the power values are satisfactory: With an IR mean value of 0.998, almost the same energy demand is obtained by calculation as from the measurements (deviation 0.2 %)—despite the different calculation bases. Given that background, the IR range of 0.917 to 1.192 can be accepted, especially with ME being below 0.4 % and an SE of 12 % or less for all investigated runs. For illustrative purposes, an exemplary result plot of measured and calculated speed and power curves is shown in Figure 3.9.

32The speed proportional resistance is excluded as possible reason due to the signiﬁcantly lower values compared to the aerial resistance.

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v (km/h)

200

150

100

5 10 15 20 25 30 35 s (km)

P (MW)

0 5 10 15 20 25 30 35 s (km)

-2

-4

-6

Figure 3.9: Speed and Power (at pantograph) Results obtained in Calibration, Run No. 25 on the Olten–Solothurn line. The black curves show the input (measured) values, grey curves represent the values calculated by the model developed and presented in this thesis. Status at the end of calibration as in Table 3.5 (own illustration).

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However, one deviation that—still—can be observed repeatedly is a difference in the maximum electric braking power, where the model reaches less power than measured. For this phenomenon, two possible reasons were identified: 1. The overload capacity of the electric engine is used in the measured run, i.e., the short-term braking power is above the parametrised power maximum (vehicle’s hourly power rating). The overload capacity is, due to reasons of scope, neglected in this model (cf. section 2.2.2.11). 2. The drive chain efficiency fro braking might be underestimated. Based on the available data, this can neither be proven nor falsified. Consequently, a final conclusion on the reason cannot be drawn; however, the usage of the overload capacity seems to be the most likely reason. As this deviation cannot be corrected in the scope of this work, the resulting error has to be accepted. This is tolerable, as it will occur for all runs equally, results in a slight offset in power. Finally, a slightly higher energy demand than it would have been measured in reality will be seen for all runs.

3.4 Validation

3.4.1 Programming Style Generally, validation is the “confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled” (DIN Deutsches Institut für Normung e.V. 2015, p. 50), i.e., that the products fulfils its specification. In case of this research, also the software is comprised herein, as is shall correctly implement the model from section 2.4.3. Thus, programming style is an important part: By proper code authoring, it can be ensured that the correct calculations are performed. The program developed here follows this principle. Functionalities and properties are linked to an object, thus making sure that all relevant information is stored in this place only. Moreover, the class methods are restricted to a manageable extent, ensuring that for one functionality (e.g. the aerial resistance) always the same method is used. Figure 3.10 illustratively shows a (pseudo) code segments for a calculation step in time. Moreover, public and protected functions are distinguished. This ensures that all calculations follow the intended procedures, avoiding “weird” combinations of function calls. A documentation of public functions, as given in appendix E, provides clear interfaces and usage guidelines. As a third validation measure, proper and detailed code documentation— comments—is used. This allows to easily understand the sections’ ideas and purposes. Thus, finding possibly contained mistakes is significantly simplified. A sample of this code documentation is given in Figure 3.11. Taking all this together, the programming style provides a solid base for a properly functioning, valid calculation tool.

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%%MAINCALCULATIONSCRIPT

% vehicle object creation VEH = vehicle(vehType,'time',resultMode,infraTable,environmentObj);

% one timestep calculation [VEH,tact,lastStep] = VEH.doStep();

%%VEHICLECLASS function [obj,tact,lastStep] = doStep(obj,varargin) [obj,lastStep] = doStepInTime();%SF! end function [obj,lastStep] = doStepInTime(obj,varargin) obj.F_res = obj.calcMotionResistances();%SF! obj.F_res_tot = sum(obj.F_res);

obj = obj.calcTractionInTime();%SF! obj = obj.calcAuxPower();%SF! obj = obj.calcHVACpower();%SF! obj = obj.calcPowers();%SF! end function [res_vector,kt] = calcMotionResistances(obj,varargin) res_vector = NaN(1,7); res_vector(1) = obj.calcRollingResistance();%SF! [res_vector(2),kt] = obj.calcAerialResistance(vact,idx);%SF! res_vector(3) = obj.calcInnerAerialResistance(vact,idx);%SF! res_vector(4) = obj.calcCurveResistance(vact);%SF! res_vector(5) = obj.calcSlopeResistance(idx);%SF! %... end function [res,k_t] = calcAerialResistance(vact,idx) coord = obj.getWGS84();%SF!

if obj.infraTable.tcs(idx) == inf k_t = 1; vw = obj.env.getWindSpeed(coord,obj.t_act);%SF! psi2 = obj.env.getWindDir(coord,obj.t_act);%SF! alpha = calc_wind_angle_alpha(obj.infraTable.hdg(idx),psi2);%SF! windAngleFactor = ...%SF! aerial_resistance_scaling_factor(vact,vw,obj.infraTable.hdg(idx),psi2); else [tlen,tidxs] = obj.detTunnelLength(idx);%SF! alpha_w = mean(obj.infraTable.tcs(tidxs)) / obj.A_b; k_t = (1 + 2.21/alpha_w) * (alpha_w / (alpha_w-1))^3; vw = 0; alpha = 0; windAngleFactor = 1; end

rho = obj.env.getAirDensity(coord,obj.t_act);%SF! res = 0.5 * obj.c_w * k_t * obj.A_b * rho * ... ( vact + vw * cos(deg2rad(alpha)))^2 * windAngleFactor;%SF! end

Figure 3.10: (Pseudo) Code Snippet for Aerial Resistance Calculation. In all lines that are marked with %SF!, subfunctions are called that realise an even smaller and thus easier veriﬁable part of the calculation task (own illustration).

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% Method to calculate the next step % @paramOBJ obj the object itself % @paramINT Dt[opt] time step width(s) % @uses vehicle.brakeCurves % @uses vehicle.t_act % @uses vehicle.doStepInTime() % @uses vehicle.doStepInSpace() % @uses vehicle.estBrakeCurves() % @returnOBJ the modified object % @returnDBL current time(s) % @returnBOOL1 if last row reached function [obj,tact,lastStep] = doStep(obj,varargin)

%MAKESURETHATBRAKECURVESAREESTIMATED if obj.brakeCurves == 0 obj = obj.estBrakeCurves(); end

%SELECTFUNCTIONFORTIME/DISTANCEBASED if obj.timeBased == 1

if nargin == 1 [obj,lastStep] = obj.doStepInTime(); else [obj,lastStep] = obj.doStepInTime(varargin{1}); end

else

[obj,lastStep] = obj.doStepInSpace();

end

%ASSIGNTIMERETURNVALUE tact = obj.t_act; end

Figure 3.11: Code Documentation Example. Shown is method doStep, the main calculation function that is called by the calculation’s main script for doing one step (in time or distance) of the vehicle class. The code is copied directly from the program’s source code (own illustration).

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3.4.2 Comparison to Real World Data As second part of the validation, a comparison to measured data is performed. While the SBB data on the Olten–Solothurn route with a series 500 train (ICN) have been used for calibration, the opposite direction—i.e., Solothurn–Olten— was used for validation. An additional calculation-measurement comparison was done for the longer Biel–Neuchâtel–Yverdon route that includes one intermediate stop. Also this route is operated with series 500 trains. The author is aware of the fact that a comparison to real world data where one case is the opposite direction of the calibration line and the other still uses the same rolling stock is a rather weak validation. However, given the data collected and provided by SBB—the data collection on-board the vehicles is a rather new project, see also Bomhauer-Beins, Schranil, and Weidmann (2018a)—there is no possibility given to provide a more extensive validation. Moreover, the Biel–Neuchâtel–Yverdon line shows quite different characteristics compared to the Olten–Solothurn line, which at least ensures model applicability for different line types. More detailed information on both lines is provided in appendix C.

The method and quality indicators used for validation are the same as for calibration: The measured speed is applied as recommended speed that the train should follow; the resulting, calculated speed and power curves are compared to the measurements. On the one hand’s side, an optical comparison is done, on the other, the already introduced quality indicators—integral ratio (IR), mean error (ME), and error standard deviation (SE)—are investigated. The optical comparison of the 81 (14 + 67) investigated runs did not disclose any signiﬁcant deviations—an example of the Biel–Neuchâtel–Yverdon line is given in Figure 3.12. The quality indicators listed in Table 3.6 show nearly as good values as for calibration.

Table 3.6: Validation Quality Indicators. Given are for 14 runs on the Solothurn–Olten and 67 runs on the Biel–Neuchâtel—Yverdon line with a series 500 EMU: mean, best, and worst value of the respective quality indicator. IR–integral ratio; ME–mean error; SE–error standard deviation. Full scale speed 200 km/h, full scale power 6.6 MW. Solothurn–Olten Biel–Yverdon mean best worst unit mean best worst unit IR 0.999 1.000 0.998 0.999 1.000 0.992 v ME –0.101 –0.014 –0.266 km/h –0.050 0.000 –0.193 km/h SE 0.697 0.496 0.964 km/h 0.655 0.187 1.313 km/h IR 1.028 1.004 1.243 1.058 1.000 1.384 P ME 0.021 0.003 0.287 MW 0.028 0.001 0.274 MW SE 0.672 0.539 0.805 MW 0.601 0.202 0.883 MW

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v (km/h)

150

100

10 20 30 40 50 60 s (km)

P (MW)

0 10 20 30 40 50 60 s (km)

-2

-4

-6

Figure 3.12: Speed and Power (at pantograph) Results obtained in Validation, Run No. 100 on the Biel–Neuchâtel–Yverdon line. The black curves show the input (measured) values, grey curves represent the values calculated by the program (own illustration).

Given that speed can be regarded as input quantity, power as output quantity and based on the discussion conducted beforehand, for accepting the model and its implementation as validated,

– 1 % deviation in speed’s full scale IR mean, – 10 % deviation in power’s full scale IR mean, – 0.1 % full scale mean ME in speed, and – 1 % full scale mean ME in power can be allowed—which aligns with the usual acceptance range of 5...10 % error (Douglas, Weston, et al. 2017). With IR mean errors of 0.1 % for speed and 6 % for power while the ME mean values are 0.1 km/h (0.05 %) and 0.03 MW (0.5 %), the model and its implementation can be accepted as validated.

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3.5 Sensitivity Analysis

3.5.1 General Idea and Procedure As already mentioned in prior sections, different model parameters cannot be defined precisely but will show uncertainties. This plays a role in calibration and validation, as discussed in these contexts. In the following, those parameters with the most significant uncertainty/variability are investigated concerning their influences on a train run’s energy demand. For evaluation, the results are compared to a “standard run”, whose parameters are given in Table 3.7; the remaining (vehicle) values as given in Table B.1 (p. 229). Generally, the parameters are varied in a rather broad range and partly for unrealistic values. This allows to more easily recognise the kind of relation between parameter variation and energy difference, e.g., linear, sub-linear, etc. thus giving a more complete picture.

Table 3.7: Parameter Conﬁguration for the Standard Train Run. Parameter Symbol Value

Aerial Resistance Coefﬁcient cw 0.59 m3 Inner Air Flow Qtot 7.7 /s m Maximum Acceleration 1.2 /s2 m Maximum Deceleration –1.2 /s2

Rail Condition Correction fRC 0.00 Air Temperature Ta 20°C

Wind (Source) Direction ψ2 0° km Wind Speed vw 0 /h Vehicle Load 33 % Dwell Time at Departure/Destination 0 s Davis B Factor excluded

Note that for the line Biel–Neuchâtel–Yverdon, a departure time for Neuchâtel of 1080 s (18 Min.) after start in Biel has been set; for the opposite direction, 1200 s (20 Min.) after departure in Yverdon—the values are taken from the ofﬁcial timetable published by SBB and accessible via sbb.ch. For the standard train runs, the results shown in Table 3.8 have been obtained.

Table 3.8: Travel Times and Energy Demands of the Standard Train Runs. Route Short Name No. Duration Energy Demand Olten–NBS–Solothurn OL–SO 1 876 s 486.2 kWh Solothurn–NBS–Olten SO–OL 2 881 s 394.3 kWh Biel–Neuchâtel–Yverdon BI–YV 3 2031 s 673.1 kWh Yverdon–Neuchâtel–Biel YV–BI 4 2043 s 664.4 kWh

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The sensitivity analysis was conducted for the following parameters and values, of which the results are presented hereafter:

– Aerial Resistance Coefﬁcient cw; cw = 0.2 ... 0.8; ∆cw = 0.1 km km – Wind Speed vw and (Source) Direction ψ2; vw = 0 ... 30 /h; ∆vw = 5 /h for each wind direction ψ2 = 0 ... 360°; ∆ψ2 = 10°

– Vehicle Mass mt applying a load mload; mload = –6 ...+44 t; ∆mload = 2 t

– Rail Condition Correction Factor fRC; fRC = –0.1 ... +0.1; ∆fRC = 0.05

– Drive Chain Efﬁciency ηD; shifting Equation 2.93a (p. 95) by ∆η = –0.35 ... +0.10; ∆ηD = 0.05

– Adhesion Mass madh; madh = 90 ... 120 t; ∆madh = 5 t

– Outside Air Temperature Ta; Ta = –20 ... +30°C; ∆Ta = 5°C

3.5.2 Aerial Resistance Coefﬁcient

This coefficient—cw—is usually determined experimentally; in this thesis, it has been derived from the measured C coefficient of the Davis Equation, see Equation 3.3 on p. 114. Due to the fact that it may vary in a range of 0.2...0.8 (Sachs 1973a, p. 31), which is –67...+33 % compared to the obtained value of cw = 0.59, its influence is investigated here. Evaluated for both lines, Olten–Solothurn and Biel–Neuchâtel–Yverdon, and cw = 0.0...1.0, the detailed results are given in the appendix in Table F.1. For the relevant range of cw = 0.2...0.8, a variation in energy demand of – 15...+7 % resulted for the “slower” line from Biel to Yverdon, while for the high speed line Olten–Solothurn, a variation of about –25...+15 % was found.

Changing cw by 0.1 (ca. 15 %), the energy demand changes by about 7 %. Figure 3.13 illustrates the results of the four calculations. As to be expected from the formula, a linear dependency occurs. Consequently, minor mistakes in determining cw or Ab do not signiﬁcantly inﬂuence the overall result in terms of energy demand.

∆E (%) +30

+20

–0.5 –0.4 –0.3 –0.2 –0.1 +10 (–83 %) (–67 %) (–50 %) (–33 %) (–17 %) ∆c +0.1 +0.2 +0.3 +0.4 w (+17 %) (+33 %) (+50 %) (+67 %) –10

–20

–30

Figure 3.13: Inﬂuence of Variations in the Aerial Resistance Coefﬁcient cw on the Energy Demand of a Train Run. Original Value of cw is 0.59; Black for Olten–Solothurn, grey for Solothurn–Olten, light grey for Biel–Yverdon, and white for Yverdon–Biel (own illustration).

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3.5.3 Wind Speed and Direction In today’s state-of-the-art analysis of energy demand in railway systems, wind speed and direction are usually neglected. However, it has been discussed that for single train run investigations, this approach is most likely incorrect (cf. sections 2.2.2.7, 2.3.4.3). For this reason, an extended model was developed; cf. section 2.4.2.1 and appendix D. Based on this model, different test runs for all four lines have been calcu- km lated applying the variations mentioned above (vw = 0...30 /h, ψ2 = 0...360°). It resulted that the variance in energy demand caused by wind speed and direction may reach from –6 % up to +15 %. Thus, results of a prior study have been confirmed, also highlighting the importance of taking wind effects into account when investigating single train runs. As an illustration, the results of the Yverdon–Biel line are shown in Fig- ure 3.14; for the complete set of results, see appendix F, Tables F.6F.9. Even though wind speed of more than 20 km/h are rather rare (Bomhauer-Beins, Schranil, and Weidmann 2018b), they occur and may—especially when calibrating models—significantly influence the quality of the results.

750

700 (kWh)

E 650 30

600 10 km vw ( /h) 270 180 90 0 ψ2 (° )

Figure 3.14: Wind Inﬂuence on Energy Demand for the Yverdon–Biel Line. Shown are wind speed vw, wind (source) direction ψ2, and total energy demand per run E (own illustration).

3.5.4 Vehicle Mass The vehicle mass is a parameter that may vary in a rather broad range. As reasons, manufacturing tolerances as well as varying wear—up to 150 kg per axle concerning the wheels, cf. section 3.3.1.2—on the one hand’s side, as well as actual payload on the other hand’s side, are to be mentioned.

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Altogether, a mass variation of up to –6...+44 t (–12/3...+12 % in relation to tare mass) might occur for a series 500 (ICN) train, which has been investigated; the results are given in the appendix, Table F.2. For this variation, a difference in energy demand of ±5 % was observed for the two lines times two directions, where the relation between mass and energy variation is linear.

3.5.5 Rail Condition The adhesion coefficient µ may directly (Equations 2.5, 2.86, pp. 28, 93) or indirectly—higher speeds and/or acceleration for delay compensation— influence the energy demand of a train run. Mainly, µ depends on past and current weather conditions, as these define the condition of the rail. According to section 2.2.2.5 (p. 28), this phenomenon is included using the rail condition correction factor fRC, which may vary in the range of –0.1...+0.1. Evaluating the effects, two interesting observations are made: First, the energy demand goes with fRC, ranging from –5 % (fRC = –0.1) to about +1 % (fRC = +0.1). Second, the train run duration increases for low values of fRC by up to 2 %. As fRC reduces in these cases the value of µ, the (traction) force transmission is declined, reducing the ability to ac- and decelerate. Figure 3.15 illustrates these findings.

∆E (%), ∆t (%) –0.10 +2 –0.05 fRC +0.05 +0.10 –2

–4

–6

Figure 3.15: Inﬂuence of the Rail Condition on Energy Demand and Travel Duration. Black, grey, light grey and white show the energy (E) variations for the lines Olten–Solothurn, Solothurn–Olten, Biel–Yverdon, and Yverdon–Biel. Variations of the travel time t are indicated in aquamarine colour from dark to light shade for the same routes (own illustration).

However, if the scheduled run time is met exactly—which means that all reserves are used—the influence of the rail condition is negligible: For the investigated routes, the influence stays in the range of ±2 %—with one exception, 1 where fRC = –0.10 resulted in a change in energy demand of –2 /2 %. Conse- quently, the rail condition does not significantly influence the energy demand of a train run as long as sufficient reserves are placed within the schedule in order to compensate the reduced transferability of traction force. All results—energy demands and run times—of the rail condition investigation are given in the appendix’ Table F.5.

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3.5.6 Drive Chain Efficiency As discussed in chapter 2, the drive chain—between transformer output voltage and wheel—shows an efficiency ηD < 1, some elements’ efficiency depending on the current load. Thus, a load-dependent model has been developed in section 2.2.2.4; the maximum efficiency (series 500 EMU—ICN) resulted to be 0.87—i.e., 87 %. Without changing the model from Equations 2.93, the maximum efficiency was varied around the nominal maximum point by –0.35...+0.10. The results being completely listed in the appendix (Table F.3), the change in energy demand turned out between +90 % (with –40 % in efficiency) and –20 % (with +11 % in efficiency), with an efficiency reference value of 0.87. The results are illustrated in Figure 3.16, where a super-linear influence of the drive chain efficiency on the total energy demand can be seen.

∆E (%)

+80

+60

+40

+20 +0.05 +0.10 (+6 %) (+11 %) ∆ηD –0.35 –0.30 –0.25 –0.20 –0.15 –0.10 –0.05 (–40 %) (–34 %) (–29 %) (–23 %) (–17 %) (–11 %) (–6 %)

–20

Figure 3.16: Change in Energy Demand as Function of Drive Chain Efﬁciency. For each investigated route—Olten–Solothurn (black), Solothurn–Olten (grey), Biel–Yverdon (light grey), and Yverdon–Biel (white)—the relative energy demand for a varied drive chain efﬁciency (offset) is shown. Additionally, the trend line connecting the mean values of energy demand change is indicated as dashed black curve (own illustration).

For the energy preparation’s efficiency, i.e. the transformer in AC systems, a similar behaviour is expected from the subsystem’s properties. However, energy preparation additionally supplies auxiliary and comfort systems, which implicates that a change in efficiency will have larger effects. Especially with the fact that per 1 % change in the overall drive chain efficiency ηD the change in energy is above 1 %, the significance of the efficiency becomes obvious. Even though this behaviour might seem unexpected, it is quite logical. Remembering that the efficiency of the engines is depending on the current operating point—cf. Figure 2.7, p. 27—and additionally taking into account that an engine is rarely operated in its optimal operating point, it becomes clear that a reduced peak efficiency will have a more-than-linear influence on the drive chain’s overall efficiency.

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This justiﬁes on the one hand’s side the detailed analysis performed in section 2.2.2.4, while on the other hand’s side, more precise information on the exact behaviour would enhance the model quality. However, the effect starts to become severe (super-linear) only for unrealistic efﬁciency values of below

77 % peak efﬁciency (∆ηD ≤ –0.15). Moreover, this precise information on drive chain behaviour as well as exact values are quite difﬁcult to obtain; thus and for reasons of scope, this work is continued with the values discussed so far.

3.5.7 Adhesion Mass The adhesion mass determines—together with adhesion coefficient µ—the maximum force that can be transmitted from wheels to rail. The series 500 EMUs (ICN) having a nominal adhesion mass of 105 t (Stolz 2007, p. 304), variations of 90...120 t (i.e. approx. ±15 %) are calculated. For all four investigated routes, no significant changes in energy (≤ 0.1 %) or (minimum) run time have been observed. Consequently, the influence of adhesion mass variations—that might also be caused by estimating this quantity if necessary—are negligible.

3.5.8 Outside Air Temperature In the model, HVAC is the only energy demand affected by the outside air temperature. This allows to derive an HVAC energy demand as function of temperature Ta and duration of operation (duration of journey) t by evaluating Equation 2.95b for a given temperature Ta and multiplying the resulting power with the duration of operation t. Like this, Figure 3.17 is derived, illus- trating the HVAC energy demand depending on these two quantities.

250

200

150

(kWh) 100 E 50 60 0 45 30 15 − 5 − 5 15 15 t (Min) 25 Ta (°C)

Figure 3.17: HVAC Energy Demand as Function of Air Temperature and Operation Duration. Basi- cally, the ﬁgure represents Equation 2.95b, p. 96 (own illustration).

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∆E (%)

+30

+20

Ta (°C ) –20 –15 –10 –5 0 +5 +10 +15 +20 +25 +30

Figure 3.18: Relative Energy Demand for Different Air Temperatures for all four investigates lines: black—Olten–Solothurn, grey—Solothurn–Olten, light grey—Biel–Yverdon, white—Yverdon–Biel. For the standard run, an outside air temperature Ta of 20°C was taken, which is the reference for the relative energy amounts (own illustration).

Additionally, the temperature variation has been included into a set of calculations. However, the investigation of a relative change in temperature (in per cent) does not make sense due to the underlying formula being discontinuous for three different temperature ranges—cf. Equation 2.95b, p. 96. The result is depicted in Figure 3.18, where the shape of Equation 2.95b as well as the inﬂuence of the duration—the run time Olten–Solothurn is about half the run time Biel–Yverdon—can be seen.

3.5.9 Energy Supply System For the energy supply system, a meaningful sensitivity analysis is not possible due to the model’s structure: Above substation level, efficiencies are used for describing the losses. Consequently, there is always a direct linear correlation between the value of the parameter and the resulting amount of energy loss. A similar statement is true for the catenary model used between substation and pantograph: The energy loss of the catenary being proportional to distance between vehicle and substation on the one hand’s side and the catenary per- length-resistance parameter value on the other hand’s side, the correlation is quite obvious. However, a deviation in the resistance estimation will influence the energy demand result in a more significant extent the larger the distances between vehicle and substation—and thus between the substations—are.

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4.1 Overview and Classiﬁcation

Energy saving in railway systems is a frequently treated topic in literature. Especially in subsystems, where the direct application of some evolutionary algorithm, genetic algorithm, linear programming, or neural network is possible, an immense number of publications is available. However, publications which take the systemic context into account are rather rare. Due to the ex- tensiveness of the existing literature, a complete review is not possible—thus, this claim is not raised for this chapter. Especially in the context of timetable optimisation, which is presented in section 4.3.1, the number of available publications makes it impossible to discuss all of them.

Starting the overview with publications including systemic aspects, one is by Meyer and Aeberhard (1997), based on the physics of train motion. The identified energy saving measures are classified into the groups measures concerning the train—mass reduction, reduction of aerial and rolling resistance— maximum usage of regenerative brakes, and measures concerning the traction vehicle—e.g. reduction of copper losses, improvement of converter efficiency, optimised control. Also, catenary, substations, and HVAC are identified as energy sinks that could be used as starting point for optimisation. Focusing on train operation, Albrecht (2008) describes the railway system as control loop, allowing the identification of some interactions within the system. Touching the topics of speed profile optimisation and conflict avoidance, he gives an introduction to an intensively investigated field. Similarly, Hill- mansen (2012) analyses the railway system and identifies different energy saving strategies: regenerative braking, energy storage, and train control. Feng, Zhang, et al. (2013) identify “energy cost factors”, proposing approaches to reduce cost: coasting control, track alignment, train attributes, and system operation. Bosch (2014) confirms that precise, punctual operation and energy consumption are linked: the more precise the operation, the less conflicts occur— confirming that conflict avoidance is a possible approach to energy saving. Stephan and Körner (2014) investigate the energy flow in electric railway systems and identify possible measures to reduce the energy consumption. Drive chain, on-board power supply, driving dynamics, operations, and stationary installations are the five groups of influences that are formed, overarching 33 influencing factors in 13 subgroups. These results are presented more in detail by Stockhausen, Weem, et al. (2017), where the summary of results rec-

– 139 – Energy Saving Potentials in Railway Operations under Systemic Perspectives ommends to reduce losses, avoid wasting energy (i.e. in driving style, HVAC, and on-board power net), and promote energy optimisation (i.e. improve supply grid structures, introduce driver assistance system—DAS, and segregate different types of traffic)—all based on a study by Dube, Fraas, et al. (2013). “An assessment of available measures” for traction energy reduction is presented by Douglas, Roberts, et al. (2015). Distinguishing urban, high speed, and inter-city line types and taking the energy flow into account, different approaches to energy saving are classified along two dimensions: service, rolling stock, and infrastructure on the first, procedures and technology on the second. Apart from these quite generic publications, there are some on a more specific but still quite general level. In this area, the span ranges from improvement possibilities in urban systems (De Sousa and Pereira 2015; López-López, Pecharromán, et al. 2014; Oettich, Albrecht, and Scholz 2004; Yang, Li, et al. 2015; Zemek 2015) over the analysis of timetabling and control approaches (Lüthi 2008; Novak 2016; Scheepmaker, Goverde, and Kroon 2017) to solutions by automation (Pelz and Griem 2015).

Moreover, there are publications from industry showing activities there. For instance, Köbel (2008) presents technologies offered by a rolling stock supplier: among others, aerodynamically optimised vehicle shaping, thermo-efficient HVAC, driver advisory systems (DAS), and energy storage systems (ESS). From operator side, Aeberhard (2011) present current and future challenges in energy supply. Load fluctuations (load management) are more dominating the field but being linked to and often handled like energy demand challenges. Also, in production (Schranil and Grossenbacher 2016) and service planning (Schranil and Anders 2017), energy efficiency is considered. For the first, different spheres of activity are defined: energy-optimised train run (supported by driver training and advisory systems), node and route capacity management (including prognosis and optimisation tools), optimised dwell times, and powerful production (i.e., by centralisation and automation). For service planning, some changes are expected as well: Improved planning priorities, which prefer scheduled services instead of regular interval services; improved line network (avoiding parallel guidance of inter-city services); homogenisation of speed limits (to avoid unnecessary accelerations and decelerations); and improved vehicle scheduling, i.e., better fitting the vehicle capacity to demand.

Taking together all these investigations, propositions, and classiﬁcations, a variety of approaches opens up. Having the main focus of the presented research in mind, which is overarching and systemic, a systemic approach is most suitable. Thus, the conclusion results that for a system, not the concrete action or measure is relevant but only its functionality within the system. Therefore, the detailed literature review on energy saving approaches—which is presented in the following—is structured by this systemic functionality. The following groups of measures were found:

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Energy Demand of a Vehicle. Being the systems main energy sink, the vehicle’s demand (excluding regeneration, cf. appendix A) can be reduced. All measures targeting this aim are included in this group.

Energy Balance of a Vehicle. The energy balance also including the regenerated energy, measures of this group aim to reduce the net energy consumption of a vehicle. In most cases, this is done by operational methods.

Energy Balance of a Substation. With usually not only one vehicle operating within a substation’s supply area, interactions of the vehicles can be considered and improved—done by measures in this group.

Usage of Braking Energy. Most electric vehicles can generate electric energy while braking. In some cases—especially in DC-powered, rectiﬁer fed systems—this energy cannot be used and is dissipated over a brake resistor. Approaches of this group address a better usage of this potential.

Supply System Measures. Also, the supply system—transmitting energy to the vehicle—could be improved. Also in this group, technical (statical) as well as operational measures are applicable. Given the fact that both topics are core ﬁelds of research in electrical engineering, these approaches are only considered in brief but excluded from detailed analysis.

Additionally, there are approaches that do not really ﬁt into one of these groups—either because they show characteristics of more than one group or, because of their speciality, do not ﬁt into any group. These are treated separately; for orientation, the group structure is illustrated in Figure 4.1.

Energy Saving in Railway Applications

Vehicles’ Vehicles’ Substations’ Braking Supply Further Energy Energy Energy Energy Use System Approaches Demand Balance Balance Measures

– Drive Chain Impro- – Timetable Optimi- – Timetable & Speed – Timetable & Speed – Technical Measures vement sation Profile Optimisation Profile Optimisation – Management / Smart – Ancillary Services – Conflict Avoidance – Coordinated Control – On-Board Usage Grid Approaches – Speed Profile – Track-Side ESS – Supply Network Optimisation Adjustments

Figure 4.1: Functional Classiﬁcation of Energy Saving Approaches in Literature. The overarching goal of energy saving is approached by six groups having different functions when analysed in systemic context. For each group, some of the concrete measures are indicated (own illustration).

Note that the chosen structure by functionality partially leads to the same approach appearing in different groups, depending on the point of view. For instance, this happens for track-side energy storages, applied to improve the substation’s energy balance—section 4.4.3—or when investigating technical approaches concerning the energy system—section 4.6.1.

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In all of the literature, the amount of energy saved applying the presented approach is indicated. Mostly in per cent, which is of course highly depending on the definition of 100 %. Sometimes, this base is not indicated; in most cases, the scope of investigation is limited to a rather short section of track in DC systems—often with significant simplifications in modelling. Consequently, these numbers are not directly comparable to each other; moreover, the exact value has to be interpreted with care for the before mentioned reasons. How- ever, the values given in the literature are cited in the following “as written” without analysing and commenting them.

4.2 Targeting the Vehicles’ Energy Demand

4.2.1 Technical Aspects of a Vehicle in Terms of Energy Having in mind that the main energy demand of the railway system raises from trains, an obvious approach is the reduction of their energy demand. Ba- sically, the entire vehicle—from pantograph to wheel—is thereby “targeted”. Improvements can be reached by component improvement, use of innovative components, or improved control of (sub)systems. A fundamental analysis of these saving potentials is presented by Meyer and Aeberhard (1997). Apart from the drive system and its different losses, reduction of train mass and aerodynamic resistances are identified as possible approaches to (technical) energy reduction. For lowering the aerial resistance, a smaller vehicle cross section, improved head shape, reduced number of bo- gies, and avoiding skin discontinuities are proposed. Additionally, improved bearings reduce the rolling resistance and lower the energy demand. A comprehensive study on a vehicle’s energy demand has been conducted by Isenschmid, Menth, and Oelhafen (2013) for the BLS’ regional train “Nina” (series 525). With a total of 13 measurement points, input energy (at pantograph), energy flow of each of the two traction converters, and auxiliary as well as comfort systems have been monitored over the period of one year. Based on this, the following partitioning resulted: traction 56.5 %, traction auxiliary 4.6 %, HVAC 33.1 %, and DC loads 5.8 %. A first approach to reduce the energy demand is the high share of HVAC, which can be reduced quite simply by a complete switch-off during non-operational phases (e.g., at night). Another approach could be a load-oriented control of the HVAC system, reducing HVAC power if only few passengers are aboard. While the numbers from above do not motivate improvements in traction auxiliary systems or DC loads (lighting, passenger information), the traction system itself shows a high share in energy. Optimising the drive chain would consequently be an additional approach to reduce a train’s energy demand. These two approaches—drive chain and comfort systems improvement—show some appearances in scientific literature and are briefly dealt with.

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4.2.2 Drive Chain The probably most important part of the drive chain is the traction motor, which finally converts the (mostly) electric energy into a movement. The possibilities to improve its efficiency are reviewed by Matsuoka and Kondo (2010). First of all, major losses in asynchronous electric traction motors (ASM) are identified: stator copper/iron and rotor copper, mechanical, and stray losses. While the use of permanent magnet synchronous machines (PMSM) eliminates the rotor copper losses whereas the machines themselves are more lightweight, other losses have to be approached differently. In most cases, the use of alternative materials or the application of modifications in machine construction are proposed in order to reach the identified improvements. The consequences of introducing PMSM are investigated by Douglas, Schmid, et al. (2016) by means of a cost benefit analysis. As most important advantages of the PMSM, the authors claim that “PMSMs are not only able to reduce traction losses but also increase regenerative braking capability and save energy through mass reduction” (Douglas, Schmid, et al. 2016, p. 123); evaluations in metro and high speed systems delivered savings of 5 % to 10 %. It is shown that there is no significant difference if the energy supply system is not receptive. With a receptive supply system, the savings result to be 3 % to 11 %, with the higher savings for urban transport systems. Economically, there are good reasons to use PMSM in high-speed and urban services, but also in inter-city services, their application is stated to be “not unreasonable”. Another approach concerns the traction transformer that is necessary for all AC systems. Especially in 15 kV,16.7 Hz systems, the transformer is a large and heavy device with limited efficiency, increasing the weight of a train. An approach—which is no longer actively followed due availability problems and economic reasons—was the Power Electronic Traction Transformer, PETT (Claessens, Dujic, et al. 2012, 2013; Zhao, Dujic, et al. 2014). Instead of feeding the catenary voltage to a transformer followed by a power electronic converter, a cascade of power electronic converters is directly connected to the catenary voltage. The cascade is necessary as nowadays, there are no semiconductor devices that can handle voltages as high as at the catenary. The AC current flowing in from the catenary through the converter cascade is transformed such that the galvanic separation by transformers is realised on mid-frequency level (some kHz), where the laws of physics allow smaller transformer sizes. Thus, the system’s power density can be increased from 200...350 VA/kg in today’s systems to 500...750 VA/kg, while the overall efficiency (AC in to DC out) increases from 88...90 % to more than 95 %. An illustration is given in Figure 4.2. Moreover, the usage of dry-type transformers is investigated, as reported by Bahnonline.ch (2013). A regional train in Switzerland was equipped with this new kind of transformer, which is more lightweight and efficient than the traditional ones. In first tests, demand reductions up to about 10 % were reached.

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Figure 4.2: Power Electronic Traction Transformer Approach (right) in comparison to a modern AC traction system topology (left). Illustration by Claessens, Dujic, et al. (2012, p. 13).

A third approach in order to improve the vehicle’s drive chain consists of improving the control software. Meyer, Heck, et al. (2016) apply such optimisations for the “Allegra” EMUs and Ge 4/4III series locomotives of Rhätische Bahn (RhB) in Switzerland, which will reduce RhB’s total traction energy demand by 2 % (i.e. 1.9 GWh per year). The realised measures concern engine ﬂux and DC link voltage control as well as engine cut-offs in partial load situations.

4.2.3 Comfort Systems HVAC systems are responsible the most significant share of the ancillary system’s energy demand. Thus, optimisation research is conducted; for example, Amri, Hofstädter, and Kozek (2011) present a thermal simulation of its power consumption. They propose a demand-driven control, applying the CO2- concentration based approach of Shi, Sheng, and Hu (2010) from the heavy- rail to metro systems. By simulation, a saving potential of 25...60 % is shown, simultaneously reducing the CO2 emissions by 150 t per year. Beusen, De- graeuwe, and Debeuf (2013) present a monitoring campaign that confirms “interesting potentials” in the HVAC area, being approved by the results of Isenschmid, Menth, and Oelhafen (2013). Also, the SCCER (Swiss Compe- tence Centre on Energy Research) Mobility conducts some research on HVAC (Raubal, Jonietz, et al. 2017). Nonetheless, the demand is only influenceable within the physical-technical limits of the used systems, cf. section 2.3.4.

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4.3 Targeting the Vehicles’ Energy Balance

4.3.1 Timetable Optimisation In railway systems, the timetable is the basis for all operations. Tradition- ally, timetables are designed from calculated minimal run times and some reserves—the latter often based on experience or estimations. However, the quality as well as the reserves do not only determine the operational stability but also inﬂuence the energy balance of the train running according to the respective timetable. One major criterion are the operationally remaining reserves, ﬁnally determining the driver’s range of applicable eco-drive methods. The number of publications on this topic is extensive; therefore, only an overview on selected works is given in the following.

Cucala, Fernández-Cardador, et al. (2012) propose a joint optimisation of timetable and eco-driving. Therefore, they apply a fuzzy linear optimisation model for the timetable, including uncertain delays as fuzzy influences. The slack time is determined according to UIC Code 451-1 and distributed by the fuzzy optimisation model according to the objective function and its constraints. One of the model’s inputs is the result of the eco-driving optimisation, which is obtained employing a genetic algorithm in combination with a train run simulator. For the Madrid–Barcelona high speed line, energy savings of around 6 % resulted from simulations of the presented solution. A similar approach—calculating alternative running times for timetabling based on speed profiling—is followed by Chevrier, Pellegrini, and Rodriguez (2013). In their research, a train journey is divided into sections from one stop to another. Prohibiting the “absurd” sequence of braking–acceleration, an “Indicator-Based Evolutionary Algorithm” is used to perform the bi-objective optimisation in terms of running time and energy demand for one section. While the four regimes of acceleration, cruising, coasting, and braking are included, the method of regenerative braking is omitted as the authors consider it irrelevant due to the fact that an energy sink—i.e. a synchronised train— would be needed, which is out of the scope of their research. Apart from that, they allow for two different speed levels in each section’s part (with a constant maximum speed), sometimes resulting in “bumpy” speed profiles. Therefore, a smoothing post-processing is included. In their results, an energy saving of 48 % to 54 % is obtained by extension of the run time by 10 % and 15 % respectively. For “the other direction” that has a positive gradient, they achieve energy savings around 20 % by stretching the run time by 2 % to 8 %. Defining the “energy-efficient operation technique” as including the two levels of an optimised timetable as well as the speed profiles, Su, Li, et al. (2013) apply this approach to the Beijing Yizhuang subway line. The solving process basically consists of five steps: [1] obtain the energy-efficient driving strategy using the Pontryagin maximum principle, [2] calculate minimum run and re-

– 145 – Energy Saving Potentials in Railway Operations under Systemic Perspectives serve times, [3] distribute reserve times and determine the run time for each section, [4] calculate the energy-efficient speed profile for this section, [5] extend this to the entire route. Thus, a complete integrated timetable is con- structed, which reduces the energy consumption between stations on average by about 10 % and by 14.5 % for the entire route. Moreover, the authors claim that the computation time is below 0.2 s, which makes the algorithm not only suitable for planning but also fast enough for real-time application in automatic train operation (ATO) systems. Mineyoshi, Kobayashi, et al. (2016) apply the same approach but use a more detailed model. In contrast to other works, vehicle load variations as well as losses of the traction circuit are included in their system description. Addition- ally, regenerative brakes are considered in their work. Similar is the procedure of Xu, Li, and Li (2016), where the passenger time (consisting of waiting and travelling time) is included as additional objective, called “Time-Energy- Efficient model”. The problem is solved using a genetic algorithm, resulting in a reduction of energy by about 9 % and passenger time by 1.39 %; additionally, the authors claim that the approach could be developed into a decision support system for dispatchers. Also, Montrone, Pellegrini, et al. (2016) follow the approach to calculate running times taking energy demand into account.

A slightly different approach is proposed by Schöbel, Rüger, et al. (2009) for commuter lines: More precisely, real-time calculated and reduced dwell times would enable to save energy. In their approach, the number of passengers is estimated for the next station, delivering a required dwell time. As dwell times are usually determined for highest load, a lower dwell time is expected to result in most cases. The excess time is used as additional travel time, allowing e.g. to travel with a lower top speed thus reducing the energy demand.

4.3.2 Conflict Avoidance As soon as a train cannot follow its planned speed profile due to a signal aspect—be it warning or be it stop—one speaks about a conflict. Conflicts do not only cause the train to loose speed and thus time—which will lead to delays, network capacity loss, and passenger dissatisfaction—but also increase the energy balance of the train run. Even assuming a fully regenerative, electric deceleration, there is still the efficiency of the drive chain causing losses. Thus, there are different motivations to avoid conflicts.

In this context, Lüthi, Weidmann, et al. (2007) present a rail traffic management systems that combines real-time rescheduling with advanced train control, aiming for each train always having an up-to-date and conflict free schedule with an accuracy of seconds—cf. Figure 4.3 for illustration. It is proven by simulation that delays in the network are significantly decreased. Further- more, Lüthi (2008) analyses energy saving possibilities; in a simulation case

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Figure 4.3: Integrated Real-Time Rescheduling as presented by Lüthi (2008, Fig. 3). Avoiding the conflict, not only energy is saved but also time. study, savings of up to 50 % resulted applying speed adjustments instead of full-stop and re-acceleration. Rao (2015, pp. 75 sq.) proves that by application of an integrated traffic management, the energy demand always decreases. Caimi, Fuchsberger, et al. (2009) deal with train scheduling in compensation zones (areas of less dense traffic allowing delay reduction). Assuming entering and leaving times to be given, energy-minimal speed profiles with adequate placement of reserve times are aimed for. Considering all trains simultaneously as an integer linear programming problem, the feasibility is shown. Another approach to avoid conflicts is the so-called “Green Wave Policy”. Cor- man, D’Ariano, et al. (2009) propose to hold trains at the station to avoid speed profile modifications in open corridor. Applying alternative graphs, they search conflict free paths that no warning or stop aspect will occur. The approach is found to be effective on mainline railways—being computed for two dispatching areas of the Dutch railway network, it allows energy savings of 7...13 %. In the Lötschberg base tunnel in Switzerland, two thirds of its length of roughly 35 km are single track, while at both ends of the single track section the line consists of two tracks. Obviously, this may lead to many conflicts, whereof especially the high-speed point within the tunnel is of high interest. Mehta, Rößiger, and Montigel (2010) present a train control system that has

– 147 – Energy Saving Potentials in Railway Operations under Systemic Perspectives been implemented for this reason. Evaluating its data, the amount of energy saved discloses to be around 12 %, reached through conﬂict avoidance. In a report for the Swiss federal authorities, Meyer, Lerjen, et al. (2009) show that with conﬂict-avoiding train control systems—like the Lötschberg one—there are saving potentials in the order of 5 % for long-distance services and around 2 % for regional services offered by the Swiss Federal Railways (SBB).

4.3.3 Speed Profile Optimisation 4.3.3.1 Overview on the Field As energy saving always has to happen at the energy sink, an energy-oriented optimisation of a single vehicle’s speed profile offers itself as appropriate approach to energy saving. Early publications on this topic are by Ichikawa (1968), who is analytically solving the optimal train operation problem, and Talukdar and Koo (1979), who generate a set of non-inferior (Pareto efficient) train trajectories. Nowadays, the topic is still of high interest in research, which leads to a high number of publications on this topic, including as well some reviews and surveys.

According to their own claim, Wang, Ning, et al. (2011) provide “an integrated survey” on the field of speed profile optimisation, distinguishing the groups of analytical solutions and numerical optimisations. For the group of analytical solutions, the discrete input subgroup is investigated first. There are only few diesel locomotives in this group that have discrete traction and braking force settings. The research started with Howlett (1990) and was solved for a non-zero track gradient and different speed limits (Howlett and Cheng 1997; Pudney and Howlett 1994). For continuous-input locomotives—which is today’s standard—Khmelnitsky (2000) is cited for giving a comprehensive analysis of this field including varying gradient and speed restrictions, where the existence of a unique solution was proven. Liu and Golovitcher (2003) present a complete solution and claim that the approach presented by Golovitcher (2001) is more effective than Khmelnitsky’s. A more detailed model, which not only consists of the control commands maximum acceleration, cruising, coasting, and maximum deceleration but also includes the non-constant efficiency of the propulsion system and regenerative braking, is considered by Franke, Meyer, and Terwiesch (2002). Moreover, Wang, Ning, et al. state that analytical methods “often meet difficulties [...] if more realistic conditions are considered that introduce complex non-linear terms”. In terms of numerical optimisation, the authors distinguish two different approaches: Fuzzy and evolutionary algorithms on the one hand, dynamic programming on the other. The former is already proposed by Yasunobu, Miyamoto, and Ihara (1984) for a fuzzy ATO controller implementation and followed by many other publications. The same applies for evolutionary—often genetic—algorithms, which are e.g. used by Chang and Sim (1997) for coasting

– 148 – Chapter 4: Optimisation Potentials control optimisation. Moreover, Han, Byen, et al. (1999) use a genetic algorithm to generate the optimal reference speed proﬁle, which is—according to the claim of the authors—better than analytic solutions obtained by others. Dynamic programming needs a considerable amount of computation power. Franke, Meyer, and Terwiesch (2002) make use of this now available power, proposing a more detailed, non-linear model that is solved by non-linear and dynamic programming. Bellman’s dynamic programming is applied by Ko, Koseki, and Miyatake (2004) in order to optimise the reference speed trajectory, transforming the original problem into a multi-state decision process. Also, multi-parametric programming is used by some researchers—e.g. Vasak, Baoti, et al. (2009)—but turns out to be too slow for real time applications, as the computation time for the optimal control law is 12 hours.

These research lines have been followed further since the publication of Wang, Ning, et al. For example, Rodrigo, Tapia, et al. (2013) apply a semi-analytical solution, which uses discretisation as well as the Lagrange multipliers method. In their tests—simulation of a Madrid’s Metro line—savings of above 20 % were reached when allowing regenerative braking. Not using regenerative braking, the savings were up to 60 % but with the overall energy balance being higher.

4.3.3.2 Nature Inspired Algorithms For the application with a newly available moving block signalling (MBS) system, the Communication Based Train Control (CBTC), Carvajal-Carreño, Cu- cala, and Fernández-Cardador (2014) propose the application of a NSGA-II-F algorithm out of the genetic algorithms’ family. Their simulator is suitable for fixed and moving block signalling systems and includes the uncertainty of passenger load as fuzzy number. Testing the algorithm on an intersection between two stations of Metro de Madrid, an energy saving of around 7 % was reached. In the same context of CBTC, Fernández-Rodríguez, Fernández-Cardador, et al. (2015) propose a Multi Objective Particle Swarm Optimisation (MOPSO) algorithm for designing ATO speed profiles. In contrast to previous studies—as the authors claim—the main operational uncertainties are considered, which are train load and delays. Like this, the authors achieve energy savings in the range of 3 % to 14 %, where the study from 2015 investigates two stations with 1500 m distance, while in 2014, the two stations were only 500 m apart. The key principles of optimal train control—speed profile optimisation—are extensively treated by Albrecht, Howlett, et al. (2016a,b). The conditions for the existence of an optimal strategy are discussed, where of the sufficient length of the time span planned for the journey is of critical importance; different strategies are considered. It is proven that an optimal strategy always exists, and that this optimal strategy is unique. A simulative application of the findings shows a reduction in energy balance by around 20 % for the TGV between Lyon and Valence.

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A recent publication by Keskin and Karamancioglu (2017) shows the lasting importance of and research community’s interest in the topic. To solve the non- linear optimisation formulation, the authors apply different “nature-inspired evolutionary search methods”—Genetic Simulated Annealing, Fireﬂy, and Big Bang-Big Crunch—to solve the problem. Thereby, quite many boundary factors are now integrated in the considerations: speed limitation, various track alignments, maximum trip time, changes in train mass, and punctuality.

4.3.3.3 Alternative Algorithms and Dynamic Programming A slightly different approach is proposed by Zhao, Roberts, et al. (2014). In this work, a multi-train simulator including some power network parts is implemented in order to test the described Enhanced Brute Force Algorithm. The basic principle consists of the idea, that “each potential movement sequence is assumed as a candidate solution”. To speed up the calculation, an estimated trajectory based on practical train movements is created. Only candidate solutions close to this estimated trajectory are taken into account for further evaluation. The presented simulation, which was carried out for a Beijing Metro line, proves the algorithm to save up to 16 % of energy while using an un- changed timetable. Following a slightly different approach, Liu, Cao, et al. (2015) propose an application of a Tabu Search algorithm in two different variations. In two case studies, which are carried out on a Beijing Metro line, an energy saving of about 2 % to 9 % is obtained. Other approaches that appear but are not that widely spread in literature are problem solving based on kinematic variables (Simonelli, Gallo, and Marzano 2015), and the application of a multiple-optimisation-model based operation method employing non-linear programming in MBS urban rail transit systems for multiple trains (Gu, Tang, and Ma 2016). Thus, more than 30 % of energy savings have been reached comparing the non-optimised simulation results to the optimised ones. Moreover, Su, Tang, et al. (2015) as well as Su, Tang, and Wang (2016) propose a numerical algorithm in low-level (tractive force) ATO control. In addition, an important fact is pointed out: Following target speed proﬁles without taking into account the low-level control of tractive force might result in a frequent change between acceleration and braking. Another special approach is the idea of partial train speed optimisation; a mathematical model proposed by Lu, Wang, et al. (2016) is solvable by applying a mixed-integer linear programming algorithm. Also, the approach of combining different eco-driving strategies—maximum speed, coasting point, and deceleration—(Kumazawa, Sato, and Ogawa 2015) as well as the T-S fuzzy bilinear model approach for high speed trains reaching an energy saving of about 8 % (Yang, Zhang, and Liu 2016) shall be mentioned here.

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4.3.3.4 Extended Observation of Constraints There are few recent publications that do not only consider the single vehicle but also take some operational constraints into account. Especially in applications for Driver Advisory Systems (DAS), the constraints play a significant role as the driver shall not be diverted more than necessary. Taking this background, Albrecht, Gassel, et al. (2010) develop an algorithm in order to deliver the energy-optimal regime sequence with a minimal number of changes. Another approach is the optimisation framework proposed by De Mar- tinis and Weidmann (2015). In their supply-design-modelling-based two-level framework, the first level generates energy efficient speed profiles regarding timetable and infrastructure constraints as well as rolling stock properties, while in the second level, these speed profiles are simulated on a network with given traffic conditions. Wang and Goverde (2016a) explicitly include operational (time and speed restrictions) and signalling (signal aspects as well as automatic train protection) conditions. The formulation is done as multi-phase optimal control model, which is solved by a pseudo-spectral method. Also, the special case of two trains following each other on the same track—the problem of energy-efficient train separation—has been investigated and described (Al- brecht, Howlett, et al. 2015; Zhao, Roberts, et al. 2015); the problem of opposite trains on a single-track line was investigated by Wang and Goverde (2016b).

While the before-mentioned research publications are more or less directly targeting fully automated (ATO) systems, there are also some publications that explicitly deal with an intermediate stage of automation, which is highly relevant for mainline railways: Driver Advisory Systems (DAS). DAS technology has already reached the rolling stock industry. For example, Bombardier Transportation AG (2008) promotes a system that proposes speed, acceleration, and deceleration values. Based on the train’s properties, timetable, and track data, the system is claimed to save up to 15 % of energy while reducing wear to wheel sets, engines, brakes, and track. A special application of DAS arises if the traction/brake force cannot be controlled continuously. This topic is addressed by Howlett, Pudney, and Vu (2009), who calculate “critical switching points for a globally optimal strategy” using a local energy minimisation principle. This is applied in practice—in Australia, long-haul freight trains are equipped with the developed system. A follow-up publication by Albrecht, Howlett, et al. (2013) presents a similar device that is used on freight and passenger trains in Australia and the UK. Zhu, Sun, et al. (2016) propose a DAS using a PC-based central unit and an on-board smartphone. With bidirectional communication, real-time information can be used when optimising train run and energy demand. A similar system is presented by Weidmann, Laumanns, et al. (2015), with main focus on operational optimisation, also enabling energy savings. A detailed analysis is conducted by Rao (2015), explicitly investigating the inﬂuence on energy.

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4.3.3.5 Synopsis Even though the section on speed proﬁle optimisation is rather extensive, mostly due to slight differences between the publications, the basic methods may be grouped quite clearly. This is done in Table 4.1, where for each group some publications are indicated as example.

Table 4.1: Speed Proﬁle Optimisation: Synoptic Table. It is meant to give an overview of the most important research areas and name some typical publications—it does not claim to be comprehensive.

Solution Analytical Semi- Numerical Approach Analytical Applied Lagrange Evolutionary Non- Dynamic Method(s) Multipliers Algorithms Evolutionary Programming Algorithms Exemplary – Howlett – Rodrigo, – Han, Byen, – Yasunobu, – Franke, Publications (1990) Tapia, et al. et al. (1999) Miyamoto, Meyer, and – Howlett, (2013) – Carvajal- and Ihara Terwiesch Milroy, and Carreño, (1984) (2002) Pudney Cucala, and – Zhao, – Ko, Koseki, (1994) Fernández- Roberts, et al. and Miyatake – Howlett and Cardador (2015) (2004) Cheng (1997) (2014) – Liu, Cao, – Gu, Tang, and – Khmelnitsky – Fernández- et al. (2015) Ma (2016) (2000) Rodríguez, – Lu, Wang, – Golovitcher Fernández- et al. (2016) (2001) Cardador, et al. (2015) – Albrecht, Howlett, et al. (2016a,b) – Keskin and Karaman- cioglu (2017) Popularity moderate low very high moderate high in Science

4.3.4 Combinatory Approaches The creation of a conflict-free timetable in real-time and the optimisation of the train’s speed profile may be regarded as two cascaded control loops (Rao 2015; Weidmann, Laumanns, et al. 2015). Thus, combining these two approaches in energy saving promises to result in even larger saving potentials. This was recognised and analysed for the Dutch railway network by D’Ariano and Albrecht (2006). Modelling the train scheduling problem as alternative graph, a system is built to support dispatchers in resolving conflicts that result from delays and disturbances. Additionally, a constructive heuristic algorithm is proposed for the computation of optimal speed profiles, taking automatic train protection (ATP) systems into account. In a case study, the system achieves significant energy savings of up to 45 %, simultaneously decreasing the overall delay. Yang, Li, et al. (2012) investigate in a similar context a mathematical model for finding optimal train movements considering opera-

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Figure 4.4: Moving Horizon Approach (Yan, Cai, et al. 2015, Fig. 4). In each step, the limit of the considered area—the horizon—is moved one step forward. tional interactions, taking energy balance and travel time into account. Thus, the optimal control of multiple trains in a network can be considered being the main focus of the work. For metro systems, Gong, Zhang, et al. (2014) present an “Energy-Efficient Operation Methodology”, modifying dwell times by use of a genetic algorithm in order to generate an energy-efficient timetable. Then, a so-called “Compen- sational Driving Strategy Algorithm” generates a suitable driving strategy. In their pilot system, approx. 5 % of energy savings are realised in ideal situations, while 2 % are reached in case of disturbances. Applying a moving horizon approach—the optimisation is not done from station to station but for a defined distance in front of the train, cf. Figure 4.4— Yan, Cai, et al. (2015) combine real-time traffic information with speed profile optimisation, additionally proposing a “novel dynamic optimisation model” for speed profile planning. To solve the proposed optimisation model, an immune differential evolutionary algorithm is applied. Focusing the high-speed rail sector, numerical examples for the Beijing–Shanghai high-speed line are calculated, reducing the energy balance by 6.3 % and increasing punctuality by 31.2 %. Wang and Goverde (2016c) combine the green wave policy approach (cf. conflict avoidance) with speed profile optimisation. Thereby, they focus on the problem of two trains succeeding each other on the same line and search for algorithms to solve the problem. A slightly different approach is proposed by Ghaviha, Bohlin, and Dahlquist (2016). They combine speed profile optimisation with an enhancement of the drive chain by adding an energy storage system (ESS). Moreover, they include the ability of catenary-less operation on inter-station sections; the control variable is changed from tractive effort to speed change. Applying discrete dynamic programming, a solution is found that is also suitable as basis for DAS. From practical application, Schranil and Keiser (2017) present the “Adaptive Lenkung” (ADL) of SBB. It computes the ideal speed for a certain train (speed profile modification) in order to avoid a stop in front of a red light aspect (conflict avoidance). Thereby, 5 GWh have been saved in 2016, which is around 2 % of the yearly demand, additionally decreasing waste of infrastructure capacity (SBB AG 2015; Schranil and Keiser 2017). Figure 4.5 shows an example.

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Figure 4.5: Exemplary Application of the ADL System (Schranil and Keiser 2017, Bild 6): Regional train (S-Bahn) 30337 is running in front of freight train 69225, which—thanks to ADL—only needs to vary its speed but is not forced to stop.

4.4 Improving the Substations’ Energy Balance

4.4.1 Timetable and Speed Profile Optimisation Timetabling and speed profile optimisation can be used to improve the energy balance of a single vehicle, as it has been discussed in the prior section. More- over, not only one single train but all trains within a feeding area of a substation may be considered when evaluating energy saving potentials. In that case, the energy demand measured as the substation’s bus bar is taken as “demand to reduce”. However, the same approaches as for single train optimisation may be applied: optimise the timetable only, the speed profile only, or both together—as presented in the following.

As part of the EU project MERLIN, the complex system of railways is optimised in terms of energy demand. For the day-ahead optimisation, a methodology is proposed by Fleck, Khayyam, and Monti (2017). Thereby, the entire railway system “with all its participants”, including e.g. buildings, is taken into account. The system is split into multiple zones and parts, which are mathematically described and optimised. Due to the fact that the system is considered “complex”, only a qualitative formulation of the approach is given; genetic algorithms are proposed to solve the optimisation problem.

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Bocharnikov, Tobias, and Roberts (2010) present a speed-profile-based approach. They state that—especially in DC systems—the optimal combination of operating modes (i.e. the speed profile) does not necessarily lead to all possible energy savings if there are other trains within the same electrical section. To overcome this, each speed profile is optimised first using a genetic algorithm; then, the combination of profiles for trains within the same section is investigated. Presenting examples for a DC system, the approach is claimed to be applicable for AC as well. A similar aim—finding speed profiles for all trains minimising the total energy balance—is followed by Tuyttens, Fei, et al. (2013). A genetic algorithm is proposed; testing is performed with different strategies. By application to scenario cases in Belgium, a high braking energy usage rate is achieved; they conclude that an “energy-efficient traffic control solution” can be generated. Following the same basic idea but applying a new genetic algorithm, Good- win, Fletcher, and Harrison (2015) claim to “increase the quality of solutions [...] by an average of 27.6 %”, overall network punctuality and energy consumption being the optimisation objectives. Shashaj, Bohlin, and Ghaviha (2016) also consider the “joint optimization of multiple train speed profiles” for trains operating within the same power supply section. Proposing a Markov Decision Process formulation for the model, the solution is claimed to result in 5...43 % less energy injection by the substations than required by the trains, depending on the weights in the cost function. The minimisation of net energy at substations by usage of braking energy is investigated by Domínguez, Fernández-Cardador, et al. (2012). A train model including on-board ESS as well as a network model are established; energy- optimal ATO speed profiles are designed under different receptivity conditions. For the examples at Metro de Madrid, 6...11 % savings of energy would be possible applying the new ATO speed profiles—without decreasing the quality of service. Especially in low-density traffic situations, the system demand could be reduced by 5...6 % adding ESS—but could increase for high traffic density. Zhao, Roberts, et al. (2017) propose an “integrated metro operation”, considering single train speed profiles and the interactions of multiple trains within a supply section—especially in terms of braking—to optimise the substation’s energy balance. They improve the operation in terms of synchronised braking, while the substation’s energy balance is relieved by 23 %.

4.4.2 Coordinated Control The term of “coordinated control” of substations is proposed by Gonzalez and Manzanedo (2010). Main idea of the research is to adjust the DC output voltage of a feeding substation in order to “allow to minimize the global energy consumption”. Thereby, the EN 50163, which deﬁnes railway voltages and ad- missible deviations, has to be respected. As modern traction systems usually have a high power demand, large currents and thus losses are to be expected.

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Consequently, raising the voltage as much as permissible would lower these losses and increase energy efﬁciency. Moreover, the use of inverter substations having the ability to feed back power to the network is proposed. It is shown that extensions of 5 % in run time can result in energy savings up to 20 %, while the method also allows to optimise for minimal power losses (savings of approx. 5 %) or “Economic Dispatch” (savings of roughly 12 %).

4.4.3 Track-Side Energy Storage When aiming to balance a substation’s energy, i.e. the difference between drawn and delivered energy, an energy storage system (ESS) could be added to the respective substation’s feeding section, absorbing the power fluctuations. A starting point for ESS application, especially based on power electronics, can be seen in the “fundamental study on energy storage system for DC” presented by Hase, Konishi, et al. (2002), motivated by developments in energy storage technology. The presented prototype, consisting of capacitor, diode bridge rectifiers, and converters is tested. Basic charge-discharge-tests are successful, while detailed studies and practical application tests are left open. Gao, Yang, et al. (2014) investigate a super-capacitor (SC) based ESS for metro systems, focusing on braking energy. In the paper, a model used to design a required ESS is presented—in the given case for Beijing Metro Line 5. By application of the resulting ESS, maximum energy savings of roughly 19...22 %, depending on the headway, are obtained; over a day, about 12 % resulted. Not using SC but flywheel based ESS, Gee and Dunn (2015) analyse potential benefits of the ESS introduction in DC light rail networks. In their paper, they perform single and multi train analysis, consider system receptivity, and simulate a substation outage. Altogether, they obtain an energy saving potential of around 22 %, while also voltage drop and power peak can be reduced. Calderaro, Galdi, et al. (2015b) mutually address the problems of siting and sizing SC-based ESS in a metro system. A simulation tool for power flow estimation is built; the optimisation problem solved using a particle swarm algorithm minimising the energy supplied by the substation. In the case study, the energy supplied by the substation is reduced by 10...15 %; in addition, the peak current is reduced by 15 %. Moreover, direct connection of ESS to existing substations is investigated (Clerici and Tironi 2016; Sirmelis 2015), in the latter study resulting in a reduction of energy consumption by 6 % to 8 %. In the broader context of a DC systems including trains, ESS, and traction substations, Yang, Yang, et al. (2017) develop a control strategy for SC-based ESS. Having verified their findings experimentally, the authors claim their solution to be a good way to relieve traction substations. Further broadening the context of ESS application, a supply network wide dimension is reached. The approaches in this context are discussed in sections 4.5.4 (braking energy usage) and 4.6.1 (application from a supply network’s point of view).

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4.5 Improved Usage of Braking Energy

4.5.1 General Considerations Electric machines, energy converters of nearly each traction system, can be used in two ways: Converting electric energy into kinetic energy, or the other way around. If used as brake, i.e. as generator and thus generating electric energy, the (re-)generated energy needs a sink where it can be delivered to. Basically, there are three options: Convert the recovered energy (irreversibly) into heat using a so-called braking resistor, use the energy on-board (i.e. for ancillary services), or feed the energy back to the catenary. If the latter is applied, there is an energy sink needed that is connected to the catenary. While in centralised AC systems—where usually a bidirectional link to the national power grid exists—the energy can be fed back into the national network and will find a sink, DC systems are often fed unidirectionally, enforcing the usage of the energy within at least the same substation’s feeding section. However, if the substations are connected through, as recommended by Dube, Fraas, et al. (2013), regenerated energy might be fed back over more than one feeding section—provided that the vehicle has enough brake voltage reserves. In either case, both the energy balance of the vehicle as well as the energy balance of the substation can be focused on; moreover, in both cases an optimisation of the usage of braking energy can be aimed for. In the context of (usually DC fed) urban rail systems, González-Gil, Palacín, and Batty (2013) evaluate strategies and technologies for an optimal braking energy management. As approaches, timetable optimisation, on-board or track-side energy storage systems (ESS), and reversible substations are mentioned. Timetable optimisation is classified as “preferential measure”; ESS to be “viable”, especially when based on electrochemical double layer capacitors. Kumagai, Fujita, et al. (2016) investigate regenerative braking for a Tokyo suburban line, stating that the reuse and thus the transfer of regenerated energy from one train to another is important. They also identify the already- mentioned possibilities of handling regenerated energy: Transferring it to another (currently demanding) train, limiting/dissipating it over a braking resistor, store it on-board or track-side, or feeding it to the national power grid. These proposed measures can be clustered as follows. For direct reuse within the same electric section, timetable and speed profile optimisation can be applied. Focusing the vehicle, the energy can be used on-board either for ancillary services or—when being stored—for re-acceleration purposes; as a third possibility, an adjustment of the supply network is thinkable, i.e. installing track-side ESS, bidirectional substations or creating a grid with stronger interconnections (connecting substations, for instance).

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4.5.2 Timetable and Speed Profile Optimisation By application of each of the approaches, timetable and speed profile optimisation, the main aim is to synchronise the movements of trains within one electric supply section. Thus, braking phases of one train will happen at the same instance in time as acceleration phases of another one. Consequently, a direct energy transfer is possible while shortening the transmission distance and thus reducing the losses. This is a popular approach in literature. The core problem is addressed by Le, Li, et al. (2015), who formulate the model, which comprises multiple non-interconnected feeding sections of a substation between catenary to rail, as non-linear integer programming problem. Solving it using a simulated annealing algorithm and applying the system exemplary to the Island Line of the Hong Kong mass transit system, the usage of regenerative energy is improved by 4 % to 12 %. From the same starting point, Yang, Chen, et al. (2015) integrate dwell time control and solve their integer programming model using a genetic and an allocation algorithm. For the Beijing Yizhuang metro line, the authors claim their approach to reduce the energy consumption by roughly 7 %. Jamili (2016) proposes a Branch and Bound algorithm for the same problem, reaching a 17.5 % improvement “in time synchronisation” for the case study of “an Iranian metro”. Yang, Ning, et al. (2014) formulate an integer programming model and apply a genetic algorithm for its solution, which does not only include energy demand but also passenger waiting time as objectives. The procedure being alike to the priorly presented, the authors reach a decrease in energy balance by roughly 9 % while passenger waiting time decreases by about 3 % when simulating the Beijing Yizhuang metro line. Yin, Yang, et al. (2017) propose an integrated approach to the same problem. Using a space-time network representation, two optimisation models—an integer and a mixed-integer linear programming model—are derived. As solution, a heuristic algorithm based on a Lagrangian relaxation is applied. In their real-world example of the Beijing Yizhuang subway line, they claim to obtain “good solutions” using the proposed algorithm. Focusing on speed profiles, Watanabe and Koseki (2014) compare two different braking methods—low notch-off speed and strong brake vs. fully regenerative with ordinary notch-off speed—also investigating possibilities for a driver assistance system. The latter results to be better in many cases, but requiring an improved train group control to ensure usage of regenerated energy. Lu, Weston, et al. (2014) also study the braking part of the speed profile to increase the amount of regenerated energy. In their work, they propose a Bellman-Ford algorithm for searching the optimal trajectory, increasing the amount of regenerated energy by over 17 %. Given the availability of regenerated energy, Sun, Cai, et al. (2014) propose multi train cooperation in order to reasonably use it. The main idea consists in adjusting the speed profiles of the trains that are selected to absorb the regenerated energy—Figure 4.6 shows an example. A simulation is carried

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(a) Modiﬁcation for an originally cruising upper train (b) Modiﬁcation for an originally coasting upper train (Sun, Cai, et al. 2014, Fig. 2) (Sun, Cai, et al. 2014, Fig. 3)

Figure 4.6: Examples for Train Cooperation as presented by Sun, Cai, et al. (2014). In either case, the lower train is braking; the upper train’s speed proﬁle (solid) is modiﬁed to absorb the energy (dashed). out for the Beijing Yizhuang subway line, showing that the regenerated energy can be distributed to neighbouring trains. The same approach is followed by Liu, Xun, and Bin (2016), applying a genetic algorithm as solution method. Also simulating an example for the Beijing Yizhuang subway line, they reach a decrease in energy demand by about 3 %.

4.5.3 On-Board Energy Management As the regenerated energy accrues on board a train, a nearby approach consists of using it there. While in most converter driven vehicles the energy can be fed to the ancillary services, the ancillary power is usually not as high as the braking power. Consequently, not the entire amount of energy can be used on board—the rest to be fed back to catenary and/or dissipated. Alternatively, an energy storage system (ESS) can be installed on-board the train, such that the energy is not dissipated, as Barrero, Mierlo, and Tackoen (2008) state: Up to 40 % of energy could be regained using regenerative braking, as they quote. Storing it using ESS, they identify an additional beneﬁt in terms of power peak shaping and line voltage stabilisation. Using this approach in their own simulation studies, they obtain saving potentials of about 25 % for DC light rail applications. Ogasa (2008) proposes a “hybrid energy source”, which is basically a combination of conventional energy supply and on-board ESS, for tram application. Comparing different technologies, a maximum regeneration rate of 44 % is obtained.

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Based on the same approach, Allègre, Bouscayrol, et al. (2010) do not only aim to reuse braking energy by addition of an ESS. Adding external recharg- ing facilities in the stations, they target unsupplied operation on the route. Building a model and applying the proposed system including control, the applicability is proven; positive effects on the energy demand have been observed. Ciccarelli, Iannuzzi, and Tricoli (2012) propose a control strategy for a conventional system that has been extended by an on-board energy device. Thus, the energy demand of a train can be reduced by 12 %; moreover, a positive effect on power peaks (reduction) is observed. Industrial publications, as one by Bombardier Transportation AG (2014), show that the approach has basically reached practice for metro and light rail systems (DC powered). Storing energy when braking and releasing it when accelerating, the system does not only decrease the energy balance—up to 30 %, according to the manufacturer—but also reduces the vehicle’s inﬂuences on the power supply; catenary-free operation is enabled as well.

4.5.4 Supply Network Adjustment Another possible use of regenerated braking energy can be searched outside the single vehicle in motion or without sophisticated timetable or speed profile optimisation. One approach is—among others—presented by Teymourfar, Ne- jati Fard, et al. (2011): Instead of placing the ESS on-board the vehicles, they are placed track-side next to the stations. Thus, the regenerated energy can be stored close to its origin; re-injection into the catenary is possible for the same or any other train demanding energy in the respective section. For the case study (first ten stations of line 3 of the Tehran metro), regenerative energy amounts of about 130 MWh up to more than 2200 MWh per year and station are obtained, equalling an energy saving of 4 % to 31 %. The same idea is followed by Killer, Armstorfer, et al. (2012) who dimension ultra-capacitor (UC) modules and develop a control system. In a case study for Metro de Medellín, it is classified being interesting and feasible both technically and economically. The additional aspect of improvements concerning the voltage drop—linked to line losses—is included by Ciccarelli, Iannuzzi, and Lauria (2012). In this publication, a new methodological approach for the design of track-side ESS is proposed, considering the task as optimisation problem. Employing non-linear programming, the authors succeed in determining an ESS that “guarantees to achieve energy saving and to improve the voltage profile”. De La Torre, Sánchez-Racero, et al. (2015) do not only use UCs but also include batteries in their ESS, thus resulting in the task to optimally size the “hybrid energy storage system”. The advantage of such kind of system is seen in the combination of the high capacity of batteries with high power density and flexibility of UCs. Testing the system for a Spanish high-speed railway line with a length of 169 km, energy and cost savings of 1.96 % and 0.15 % are obtained—the latter already including the costs for the ESS.

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Ratniyomchai, Hillmansen, and Tricoli (2014) present a criterion for capacity and location selection of ESS in light rail systems. As objectives, minimal difference nominal–real pantograph voltage and reduced energy consumption are aimed for; finally, a loss reduction of up to 70 % is reached, while the energy consumption is presented to be decreased by more than 60 %. Also, flywheel ESS are proposed (Gee and Dunn 2015; Jandura, Richter, and Ferkova 2016; Mousavi G, Faraji, et al. 2017); different control strategies for all kinds of ESS are investigated (Iannuzzi 2008; Lin, Li, et al. 2016). Even for high speed applications, the application of ESS is investigated and found to provide significant energy saving potentials (Frilli, Meli, et al. 2017). In practice, ESS are partly implemented. Solis, Pham, et al. (2015) present the LA Metro, where a flywheel-based ESS is used. With a capacity of 8.33 kWh, the system saves 10 % to 18 % of the daily traction energy since 2014.

Additionally, the improvement of network interconnection is an approach to a better usage of braking energy. For an 1.5 kV DC system, Bae, Jang, et al. (2007) determine the saving potential of bidirectional substations compared to the existing unidirectional ones. For the two investigated lines of Seoul metro, the results show that up to 39 % of the delivered energy could be fed back. Also in Europe—i.e., the 15 kV,16.7 Hz network of Germany, Austria, and Switzerland—bidirectional feeding and interconnections are used. Since 2012, a permanent coupling between Swiss and Austrian network is in operation, showing significant benefits not only for energy efficiency but also for network stability and reliability, as Bosch and Obkircher (2015) state.

A kind of mixed approach consists of the idea of using track-side ESS but not necessarily reusing the energy for traction purposes. For example, Nasr, Ior- dache, and Petit (2014) propose to use braking energy by integration of a “smart DC micro-grid”. As in previously presented approaches, the braking energy is stored track-side but reused for loads in the station or its proximity. Even the possibility of charging buses is discussed and considered reasonable. The same approach is followed by Caracciolo, Berrera, and Brenna (2015) for the Ital- ian 3 kV DC system, presenting a respective prototype. Also, Galaï-Dol, De Bernardinis, et al. (2016) propose the usage of such a micro-grid system, where braking energy is reused for electric and thermal consumers in a station.

4.6 Supply System Measures

4.6.1 Technical Approaches As technical approaches, all measures that primarily search for adjustments in the power supply system are subsumed. These measures support one or more of the before-mentioned, functional approaches. Nonetheless, they are presented more technically; thus, a separate discussion is considered useful.

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One possible point of improvement is the placement of substations. Especially in DC systems, with lower voltages and thus higher currents and losses, the placement may influence the system’s behaviour as well as its energy demand. Pereira, Pires, and Nabeta (2014) deal with this problem, applying a genetic algorithm for its solution. They aim for evenly loaded substations, optimising for peak power and consumed energy. Three other approaches from Japan—that have been realised—are described by Hayashiya, Kikuchi, et al. (2013). The first measure is the installation of a lithium ion battery in a DC substation, allowing the reuse of regenerative energy—a quite widely spread approach in Japan, as Hayashiya, Kikuchi, et al. mention 13 of these systems being in operation. Secondly, a power conversion application for AC traction supply is used: As the one-phase traction current is asymmetrically fed back to the three-phase power system, it might cause circular currents not being effectively used. To avoid this, the authors present the idea of connecting two traction circuits to one three-phase transformer, reducing the imbalance of the system. Moreover, a “zero emission station” is realised, mainly by supplying its energy using a system consisting of solar panels and energy storage. The first of these approaches is a very popular in literature: Installing an ESS, be it “somewhere” wayside or be it within existing substations. Optimal location and sizing is addressed by different authors, e.g. Ratniyomchai, Hill- mansen, and Tricoli (2014). The usual procedure can be summarised as finding a mathematical description of the system under investigation, developing a solution method (mostly an evolutionary algorithm or a neural network), and applying the method in a simulation study. In most cases, the results are— in slightly varying formulations—able to “show that certain preferable and compromised schemes of ESS’ location and size can be obtained, acting as a compromise between [...] energy savings, voltage profile and lower installation cost” (Wang, Yang, et al. 2014). The publication of Calderaro, Galdi, et al. (2015a) is more technical. For areas without power supply, they propose the installation of so-called Aux- iliary Battery Substations (ABS) to lower the long-distance peak-power current. Especially in high-speed DC networks—as in Italy—this question is of importance. In a case study, the proposed system and its control prove to fulfil the expectations, allowing high-speed trains to pass electrically weak sections. Similar research is presented by Lamedica, Ruvio, et al. (2015), showing that ABS have positive effects in terms of reducing peak currents and voltage drops.

Another approach consists in inverting substations in DC systems, improving the exchange capabilities with the supply system. An early study of Mellitt, Mouneimne, and Goodman (1984) deals with the questions of inverter capacity, location, current commutation limits, and control. In the comprehensive simulation study, different insights are gained, e.g. how many substations should be inverting and which consequences on the other substations result.

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Zhang, Qian, and Zhang (2017) propose a traction supply scheme consisting of a reversible converter and two 12-pulse diode rectifiers. The control scheme allows to distribute the load between passive rectifiers and converter when feeding power into the catenary, while the converter will take the task to feed back braking energy to the supplying AC grid. Applying the presented topology to Beijing Metro Line 10, energy savings of about 11 % are reached. A laboratory model of such a “system for active filtering and regeneration”, which allows to convert passive DC substations into active—i.e. having the capability to feed back regenerative power—is built, evaluated, and presented by Bitoleanu, Popescu, and Suru (2017). However, Roch-Dupré, Cucala, et al. (2018) prove that the common traffic models employed to determine the usability of inverting substations often contain too many simplifications, which leads them to present a new model. Even though they confirm the usefulness, they find for their case study that the potential is overestimated by 30 %, while the error margin is above ±45 %. In their model, the error margin is reduced to ±2 %—raising fundamental questions about modelling in railway energy research.

A topic related to AC systems is reactive power. Even though there is no active power transfer, i.e., no actual power is transmitted, energy is sent forth and back, thus destabilising the voltage and heating the catenary—which will increase active losses as well. For this reason, there are efforts ongoing to compensate reactive power, as Aeberhard and Gruber (2015) present for the network of Swiss Federal Railways (SBB). Although, the topic of reactive power is highly important for a stable and reliable power supply network operation, its inﬂuences on energy demand are rather small. Furthermore, AC power supply system optimisations are investigated. For example, Soler, López, et al. (2015) describe a methodology for multi-objective optimisation based on a genetic algorithm. Their result is a number of necessary substations as well as their positioning; neutral zones are included. This technology might be interesting for systems that are newly built or completely revised; however, the effort for corresponding modiﬁcations in existing systems is considered to be too high.

In order to increase power system efﬁciency, Tomita, Suzuki, et al. (2017) propose the use of superconducting materials for feeder cables. As positive effects among others, higher regeneration efﬁciency and reduced power loss are mentioned. In a test set-up, 5 % of energy saving were obtained, which is in the same order of magnitude as the current line losses—cooling energy for the su- perconductor included, as claimed in the paper.

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Figure 4.7: Future Railway Energy Supply Grid and its Control as presented by Pilo de la Fuente, Mazumder, and González Franco (2014, Fig. 5). ESS—Energy Storage System; RDG—Railway-Side Distributed Generation (Railway Power Plants); RSO—Railway System Operator. The feedback path to the RDG is given in the publication but makes only sense in case of pumped hydro power plants.

4.6.2 Management and Smart Grid Approaches Management approaches for railway supply systems appear in recent literature. Mostly, the focus is set on load management, but also the effect on energy consumption is considered. Bosch and Aniceto (2013) for example state that a load management system is (for SBB) not only technically feasible, but also economically interesting—basically not influencing the energy consumption. In DC systems, currently unused (and dissipated) regenerative energy might be used to reduce peak power, propose Jung, Lee, et al. (2013). The approach of integrated operation of multiple subsystems is applied for two lines of the Seoul Metro system, allowing energy interchange between these subsystems. By means of simulation it has been shown that peak power is reduced using regenerated energy, also reducing the overall energy demand of the system. Pilo de la Fuente, Mazumder, and González Franco (2014) explicitly introduce the term of “Railway Electrical Smart Grids”, presenting “next-generation railway power systems”—see Figure 4.7. They propose to integrate the railway’s mission with the electrification’s mission, all under coordination of a railway system operator that controls power and traffic flow. Also, a better interconnection to non-railway power systems is proposed, opening the possibility to exchange power and mutually support the different networks. The smart grid approach is also brought up by Khayyam, Ponci, et al. (2015), who describe the railway energy supply system as consisting of distributed loads, sources, and storages. Driven by the idea that (only) dynamic coordination achieves optimal energy usage, a “hybrid centralized-decentralized concept” is proposed. This consists of day-ahead planning carried out by centralised energy management system (entire network), while local sub-networks do their management every 15 minutes. Likewise, Pankovits, Pouget, et al. (2015) propose the implementation of smart grid technologies in terms of hybrid energy management but for substations. Fuzzy logic supervision of the system is introduced for an optimisation model, solved by a genetic algorithm.

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Including also ancillary consumers, Helwig (2016) promotes the usage of ESS. According to her publication, this would allow to shift the load proﬁles (and power peaks), ﬁnally relieving the entire supply network. The effects show mainly in load management but also have positive effects on energy demand, assuming that smart grid management technologies are implemented. For a similarly structured system, Novak, Vasak, and Lesic (2016) propose a hierarchical energy management. Thereby, each train (low level) is controlled to reach minimum travel costs while maintaining requirements in terms of travel time and passenger comfort. The transport coordination (high level) controls the system with regard to systemic aspects and context.

4.7 Further Studies and Proposals

4.7.1 The EU Railenergy Project Apart from the above introduced, in terms of functionality mostly clearly clas- sifiable, there are some rather rare approaches in literature that do not really fit the used classification—be it because of their kind-of systemic approach, or the extraordinariness of the underlying idea. One of these is the EU Railenergy project, which has been conducted from 2006 to 2010 under a kind of systemic perspective: It was “an Integrated Project co-funded by the European Commission” to “address energy efficiency of the integrated railway system and to investigate and validate solutions ranging from the introduction of innovative traction technologies, components and layouts to the development of rolling stock, operation and infrastructure management strategies.” Moreover, “Railenergy [...] will develop a fully integrated approach as the only way to achieve true energy savings. [...]” (Railenergy). However, regarding the project structure, the well-known approaches show up: Modelling—even though a global model was theoretically aimed for—eco- driving and timetabling, decision support tools, energy storage, reuse of waste heat, supply system improvements, hybrid traction, and auxiliary and comfort system demand reduction (Railenergy: Project Structure). As results, a set of short intermediate and final presentations is available. Moreover, it shall be mentioned that one of the declared main outcomes, the

Railenergy Calculator that allows to predict energy savings, CO2 emissions, and—simplified—life cycle costs (Bergendorff 2010) reachable by technological measures, efficient driving, and management of parked trains, could not be accessed.33 The presentations introduce a new technical recommendation for rolling stock and signalling (Wiebe 2010) and the definition of a harmonised procedure of specification and verification of railway rolling stock’s energy consumption including various aspects (Meyer 2010). More on a component level, Accardo (2010) proposes an improved management of motor flux and auxiliary

33Error Message when trying to access railenergy.eu: ERR_CONNECTION_RESET

– 165 – Energy Saving Potentials in Railway Operations under Systemic Perspectives operation; moreover, a saving potential of 1.5 % is discovered in “active ﬁlter- ing”. Other topics cover market and operation analyses (Nolte and Lauszat 2010a,b; Spalvieri 2010), technical enhancements of DC substations (Cornic and Lechelle 2010), rolling stock refurbishment (Donnelly 2010), and medium frequency traction transformer application (Marchetti and Orazi 2010; Weigel 2010). Also, the topic of load management is touched (Eriksson 2009). Altogether, the outcome of the EU Railenergy project can be seen as basis for—or in line with—the approaches presented in sections 4.2–4.6.

4.7.2 VDE Energieoptimaler Bahnverkehr In this study—Energy Optimal Railway Operation—by VDE34 (Dube, Fraas, et al. 2013), the railway system is analysed in terms of energy saving potentials. Based on an introductory description of the system, its history, its importance, and its strengths and weaknesses, the potentials of single (subsystem) measures are discussed, as well as the necessary efforts to realise them. Starting from the energy flow through the system and distinguished for different types of rail services—long distance, freight, regional (AC), metro (DC), and tramway—the system is analysed based on a simulation study. 33 different influences have been expert-rated on a one-to-three scale according to their realistic improvement potential and weighted by relevance. Special importance has been given to the criteria of realisation effort and energy saving potential. The authors conclude that single subsystems are well-investigated, while today’s knowledge on systemic level is stated to be insufficient. However, three main routes to energy demand reduction are identified:

Reduce Losses. Increased usage of regenerative energies instead of thermal power plants; efﬁciency improvements within the drive chain, especially by enhanced control; reduction of (aerodynamic) motion resistances.

Avoid Waste. Behavioural change in terms of driving style and adapted use of comfort systems; maximisation of regenerative braking; improvements concerning the on-board power net.

Promote Optimisation. Especially for DC systems adaptations concerning the network structure, e.g., introduction of two-sided feeding or bidirectional substations; (operational) avoidance of disturbances (signals, especially in mixed-trafﬁc conditions as for tramways).

Altogether, the study conﬁrms the usefulness of the subsystem approaches discussed beforehand while highlighting that a new research approach on systemic level—as followed in this thesis—is necessary in order to reach further improvements; the main outcomes can also be found in related journal publications (Stephan and Körner 2014; Stockhausen, Weem, et al. 2017).

34Verband der Elektrotechnik Elektronik Informationstechnik e.V.—Association of Electrical Engineer- ing, Electronics, Information Technology

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Moreover, a very important result of this study has to be mentioned: In fact, the most promising measure in terms of energy demand is highly depending on the actual technical and operational system properties. Thus, there are different energy saving approaches applicable for an AC fed long-distance line— energy-optimal driving and top speed reduction as most promising measures— compared to those of a DC tram service—two-sided feeding.

4.7.3 Potentials in Rarely Considered Subsystems Huang, Zhong, and Huo (2015) detect a considerable energy saving potential within the cellular railway network. Based on handover procedures and a mealy-type finite state machine, they propose a switching between energy- saving and full running mode. Thus, an energy saving of up to 33 % is reached, which basically results from a mean power reduction in the cellular network. Another idea consists in gaining energy from the railway system itself. Zhang, Zhang, et al. (2016) present a system that converts railway track vibrations into electric energy. As usage, safety facilities and standby power supply units in remote areas are proposed. Experiments with a prototype—amplitude 6 mm, frequency 1...2 Hz—prove the basic applicability of the concept, showing an efficiency of about Figure 4.8: Rail Energy Harvester (Gao, 55 % from mechanical vibration energy Wang, et al. 2017, Fig. 4d). to electric output. A similar system—cf. Figure 4.8—is proposed by Gao, Wang, et al. (2017), who include a DC-DC boost converter and reach a 10 mA output at 5 V—which is 50 mW of power—at 6 Hz vibration frequency and a rail displacement of 2 mm. Similarly, energy could be harvested from bogie vibrations, as Mi, Xu, et al. (2017) propose. By simulation, the mechanical effectiveness of the concept is proven, while “good potential to recycle vibratory energy” is maintained. Kendra, Skrúcaný, et al. (2018) investigate the influence of the railway infrastructure on energy demand and greenhouse gas emissions. Based on a real track with length 60.8 km, they theoretically eliminate all “unnecessary” curves and slopes. Thereby reducing the route length to 56.2 km, the energy demand decreases by 8.6 % in terms of diesel volume and by 1.1 % when measured in MJ/km. Concludingly, they propose considering the energy demand—for each single train run—when planning a railway infrastructure despite possibly higher, one-time investment costs. Interestingly, this contradicts the results of the study presented in section 4.7.2, where slope and curve resistance are classified as negligible (Dube, Fraas, et al. 2013, p. 57).

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4.8 Literature Approaches’ Summary

When reviewing scientiﬁc literature on energy saving in railway applications, an immense predominance of topics interpretable as optimisation problem strikes the reader. In a similar order of magnitude, its classic solutions can be found: Different kinds of algorithms, mostly genetic ones, and dynamic programming. Thus, the questions that are addressable using this approach are highly present, while technical and systemic approaches are rather rare.

Speed Profile and Timetable Optimisation. The idea of optimising speed profiles arises in different characteristics: For a single train, for multi-train cooperation, and for timetable optimisation. In either case, the speed profile(s) of one or more trains is (are) optimised under certain constraints and objectives, the latter usually being travel time (given by the timetable) and energy demand. Applications are proposed for ATO systems, DAS, or (automatised) timetabling. For the latter, the usual approach consists in adjusting the scheduled run times between two stations within certain limits. The procedure is normally the same for both functionalities, optimising the energy balance of a train as well as of a substation; the difference in scope usually only becomes visible in constraints and—sometimes—in additional or more complex model equations. Similarly, the adjustment of dwell times is approached—in both ways, real-time and antecedently.

Conflict Avoidance. Real-time applications of timetable and speed profile optimisation are closely linked to the approach of conflict avoidance. In this case, the same procedures as explained above—under the additional constraint that they have to be fast—are applied to an actual operational situation. From train run estimations and conflict detection, a new, optimised, and conflict-free timetable is created. This timetable generally defines for certain points in time the place where the train has to be at this instance, usually including some reserve times in comparison to the minimal possible (technical) run time. Based on these discrete time-locations-points, a speed profile optimisation is used to generate the energy-optimal profile that fulfils the timetable’s requirements.

Energy Storage Systems. Another popular topic in literature are energy storage systems (ESS), auxiliary battery systems (ABS) here being regarded as special case of ESS. Installation is proposed either track-side—where also the determination of the placement is treated as optimisation problem in some publications—at existing substations, or on board the train. In most cases, the main idea consists in storing regenerative braking energy close to its origin and releasing it when needed, thus avoiding its dissipation and long transmission distances (i.e., losses) on the other. Positive effects on voltage stability are reached as well, even though they are usually not in focus.

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Substation Improvement. The propositions in terms of substation improvement exceed the addition of ESS, even though this is a popular approach. One possibility—which is mainly proposed for DC systems—is the use of bidirectional substations, i.e. using a bidirectional power electronic converter instead of an unidirectional rectifier. Realising this approach, regenerated energy can be fed back to the national power grid and find its sink somewhere outside the railway system.35 This would allow to forgo ESS or train run coordination. Another approach, also mainly for DC systems, consists in adjusting substation control, called “coordinated control”. Thereby, the output voltage is determined such that the pantograph voltage at (each) train is close to the upper limit. In low-density traffic cases and with significant distance between substation and nearest train, the output voltage could be raised above the limit, being reduced by catenary resistance between substation and train. Thus, the necessary current is reduced, simultaneously reducing the line losses.

Vehicle Optimisation. On the rolling stock side, there are different measures that are suitable to increase the efficiency of the drive chain. One of them is the replacement of ASM by PMSM, resulting in higher motoring and braking efficiency. Also, improved control can have significant effects, e.g. by changing magnetic flux control or cutting off motor groups in partial load situations. Of course, the reduction of resistances is a possible approach to energy demand reduction as well. An aerodynamically optimised design reduces the aerial resistance, a lower train mass—e.g. replacing ASM by PMSM, alu- minium vehicle body instead of steel, or mid-frequency instead of low-frequency transformers—results in less rolling and acceleration resistance. Then, ancillary systems—e.g. HVAC—can be addressed, be it by implementing better control or by using more efficient components.

Transmission Loss Reduction. There are some propositions in literature to reduce losses in the transmission system. In this context, the usage of high- temperature superconductors for feeder lines is proposed. Additionally, the application of load management schemes could allow to reduce peak power, which directly affects the peak current and thus peak losses. For systems being planned, an optimisation in terms of substation placement is thinkable.

Extraordinary Ideas. Somehow extraordinary approaches can be found as well. For instance, a more sophisticated control of the railway cellular phones is proposed, allowing a reduction of 33 % in energy demand of the GSM-R network. Other researchers try to gain energy from track or bogie vibrations, also presenting prototypes. By delivering a power in the range of Milliwatt at the time being, this might be an interesting approach in the future, but

35For AC systems, this approach is not discussed in literature as it is usually implemented by the intrinsic system structure.

– 169 – Energy Saving Potentials in Railway Operations under Systemic Perspectives less for immediate application. A controversial approach consists in including the infrastructure design into energy considerations: While some researchers claim it worth considering energy topics—especially in terms of curvature and slopes—when building a line, other classify the inﬂuence as negligible.

Systemic Approaches. In terms of systemic approaches, not much literature exists. Only in some cases, more than one subsystem—e.g., usage of regenerated braking energy for local station purposes, interaction of two vehicles, or interaction of vehicle and operations control—are investigated. However, there have been studies that make a systemic claim (Railenergy) and/or highlight the importance of systemic approaches (Dube, Fraas, et al. 2013).

Saving Potentials—According to the Literature. Concerning saving potentials of the measures proposed and investigated, the ranges given in literature are quite spread and mostly not related to the same base, the latter often not even indicated. Nonetheless, they are summarised here very brieﬂy:

– Replacing ASM by PMSM—5...10 %

– Improved Traction Control—2 %

– Improved HVAC Control—25...60 % (most likely referred to the ancillary energy demand)

– Speed Proﬁle/Timetable Optimisation—2...30 % (up to 55 % when stretching the run time by 15 %)

– Conﬂict Avoidance—2...5 %

– Combinatory Approaches—up to 45 %

– Coordinated control—5...12 % (up to 20 % with run time extension by 5 %)

– Energy Storage Systems—2...30 %

– Train Run Synchronisation—3...17 %

– Inverting Substations—4...39 %

Note that all these relative saving potentials are highly depending on the definition of 100 %. Sometimes, this base is not indicated; in most cases, the scope of investigation is limited to a rather short section of track in DC systems— often with significant simplifications in modelling. Consequently, the above numbers are not directly comparable to each other; moreover, the exact value has to be interpreted with care for the before mentioned reasons. Moreover, it is not possible to sum up these numbers: On the one hand’s side, due to their different bases; on the other, as some approaches rival each other. Thus, with the exploitation of one potential, another potential might vanish.

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Overall Summary. From the above considerations, the following overview on short- to mid-term relevant approaches to energy saving can be given:

– An operational approach is speed proﬁle optimisation for a single train; it can be extended for multiple trains in the same electric supply section, such that regenerative energy is better used (especially in DC systems). The result usually consists of a sequence of the operational states of a traction system: accelerating, cruising, coasting, and braking.

– Based on (energy) optimal speed proﬁles, an energy optimal timetable can be created; the main adjustment parameters are run and dwell times. Thereby, the timetable is heavily depending on boundary/quality conditions, primarily concerning desired speed of travel for the run times.

– Applying speed profile and timetable optimisation in real-time, the possibility of conflict avoidance opens up. Thereby, a prognosis of all train runs within the system is created and checked for conflicts. These are then resolved using timetable optimisation; in a next step, optimal speed profiles for the new timetable are generated.

– Energy storage systems (ESS) are mainly used to store recovered braking energy that could not be used without ESS, as it is often the case in DC systems. For track-side ESS, locations [a] within existing substations, [b] close to stations, and [c] optimised by algorithms are proposed; in the special case of ABS, the placement is given by the structure of the electric supply network. Moreover, the idea of on-board ESS is presented.

– A slightly different ESS approach consists in storing energy but not reusing it for traction. In case of track-side ESS, the supply of station loads is proposed; for on-board ESS, usage in ancillary systems is an option.

– On substation level, the usage of bidirectional feeding systems (being able to feed back braking energy) as well as improved line voltage control (“coordinated control”) to reduce line losses are proposed. The investigations are usually done for DC systems, but especially the latter might be suitable for low-density trafﬁc, AC powered lines as well.

– For the vehicle, efﬁciency of the traction chain, reduction of resistances, and ancillary demand can be addressed. Different measures in soft- and hardware enable these approaches, the latter either by component improvement or replacement.

– The reduction of transmission losses is discussed as well, either by using improved components (e.g. high-temperature superconductors) or load- management schemes reducing peak power, current, and losses.

Altogether, the following nine different approaches to energy saving in railways can be condensed from literature:

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1. Speed Proﬁle Optimisation 2. Timetable Adjustment 3. Conﬂict Avoidance 4. Application of Energy Storage Systems 5. Bidirectional Feeding Systems 6. Traction Chain Improvement (soft- or hardware) 7. Mechanical Vehicle Improvements 8. Ancillary Demand Reduction 9. Reduction of Transmission Losses

4.9 Systemic Potentials

4.9.1 Identification of Systemic Potentials Talking about energy saving potentials, these basically exist at every point in the system where losses—unintentional conversions of energy into heat, e.g. in a machine—occur or an irreversible conversion to another form of energy takes place (for instance, electricity into heat or light). Based on the system model developed in chapter 2, especially referring to Table 2.7, Figure 2.27, and Table 2.12, the potentials can be identified. Many of them can be addressed by rather well-known technical (e.g. increase of efficiency, reduction of vehicle mass, aerodynamic optimisation, improved control) or operational measures (e.g. optimised timetable, driver training), which do not need to be discussed but have been shown to be known in literature in the previous sections. However, many of these approaches from literature do not consider the impact on the entire system—at least not fully—and moreover do not include systemic effects that might occur—positively or negatively. Thus, the aspect of energy saving in a systemic context needs to be discussed, referring to the definition given in section 1.3.

In order to identify systemic potentials, two questions have to be answered:

– Where in the system is available energy “wasted” and how could this be avoided regarding the entire system? – Where in the system occur losses or unnecessarily high demands that can be reduced when regarding the entire system?

For the ﬁrst of the two questions, the system’s energy sources have to be analysed as well as the paths the generated energy may take. In this context, energy sources refer to energy conversions from an arbitrary form of energy into a transmittable form, e.g. electric energy or diesel fuel. With respect to

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Figure 2.27 (p. 91), generation stage (power plant or diesel refinery) and recuperation of kinetic or potential energy in the vehicle are the only energy sources within the system. As the generation stage is either controllable to exactly cover the demand (in electric networks) or the resulting form of energy can be stored reasonably (chemical energy in diesel fuel), there is no waste of energy to be expected here—explicitly excluding conversion losses, as these are to be addressed either technically or methodologically, but not systemically. A different situation arises with (electric) energy regenerated from kinetic or potential energy on board of a vehicle. If there is no possibility to use (in auxiliary or comfort systems) or store (battery, ESS) this energy directly on board, the system is necessary in order to provide a suitable energy sink— which makes the use of regenerated energy a systemic potential. For the second question, lossy system parts have to be identified. As to be seen from Table 2.7 (p. 70) and Figure 2.27 (p. 91), losses occur in each subsystem that makes part of the energy flow: generation, transmission, substation, catenary, energy preparation, auxiliary and comfort systems, drive chain, and through motion resistances. Many of these losses are caused by technical or physical reasons that can—to a certain amount—be addressed within and only within the subsystem itself; these are non-systemic potentials. Note that of course each reduction of energy demand in a “lower level” subsystem—if generation is seen as top of the system—automatically reduces the losses in “higher levels” of the system, as the losses usually grow relatively with the load (efficiency on a percentage basis). However, these effects are not to be regarded as systemic potentials as the corresponding measure only affects one subsystem or even sub-subsystem (e.g. HVAC).

Summarising, there are three systemic potentials of interest: transmission loss reduction, the reduction of losses caused by drive chain and/or motion resistances, and the improved usage of regenerated energy.

4.9.2 Addressing Systemic Potentials 4.9.2.1 Reduction of Transmission Losses Reducing transmission losses can be approached by the following measures:

– The efficiency can be addressed in every part of the energy transmission/- transportation chain of discrete and continuous supply systems. However, this is to be regarded as non-systemic measure as only one subsystem—e.g. refinery, generator, or transmission—is affected. – It can be aimed for a reduction of transportation distance in transmission systems. For discretely supplied systems, this affects locations of, e.g., refinery and fuel station, thus being a pre-operational problem and not within the scope of this thesis. For continuously supplied—i.e., electric—systems, this can be addressed using source-sink-coordination. Then, three subsystems

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are involved in this measure: source, sink, and superordinate control. Con- sequently, transmission distance reduction can be addressed by a systemic measure in continuously supplied systems. Additionally, an installation of an ESS may contribute in reducing energy transportation distances. However, both kinds of ESS address, technically speaking, only one subsystem each: the on-board ESS is an improvement of the vehicle, i.e. reduces its energy balance at VEI, while a track-side ESS is a technical adjustment of the energy supply subsystem.

Consequently, only source-sink-coordination remains as systemic measure in order to reduce transmission losses within the energy supply system.

4.9.2.2 Reduction of Losses from Drive Chain and Motion Resistances The reasons for losses caused by drive chain and motion resistances are mani- fold: limited efﬁciency of the drive chain, technical and/or designed properties of the vehicle, and its interaction with the environment are the major groups. Each can be addressed in order to reduce the energy demand:

– Technical improvements of the traction system may increase the drive chain’s efﬁciency and thus reduce losses. Elements to be considered are e.g. engine, converter, and wiring, but also vehicle control. Howsoever, these measures only affect the vehicle itself and are thus non-systemic.

– Adaptations in terms of vehicle design and component selection or construction might reduce motion resistances, e.g. air (shape) or rolling resistance (bearings, vehicle mass). However, these measures only affect the vehicle and are thus to be classiﬁed as non-systemic.

– An optimisation of the speed profile possibly also allows to reduce the influences of drive chain losses and motion resistances: The more aspects are taken into account by operation control—e.g., the vehicle’s properties and its current state, other vehicles and possible conflicts, delays and reserves, weather conditions—the energetically better the final speed profile will be. In extreme, operation control, the environment, and multiple vehicles will be involved, making this a clearly systemic approach.

Altogether, the idea of optimising speed proﬁles including as much information from other subsystems as possible crystallises as only but interesting systemic method for reducing losses caused by drive chain and motion resistances.

4.9.2.3 Improved Usage of Regenerated Energy Regenerated energy results from using the electric machines to slow down the vehicle. In that case, the machines are operated as generators, producing electric energy, which is then available at the energy preparation subsystem’s level. If no suitable sink exists somewhere within the system, the regenerated energy

– 174 – Chapter 4: Optimisation Potentials would have to be converted into heat using the brake resistor—being a considerable waste of energy. To avoid this, different approaches are thinkable:

– Vehicle control software can be adapted such that the other subsystems, i.e. auxiliary or comfort systems, increase their demand while regenerated energy is available. For at least a share of the regenerated energy, an electrically close sink is offered this way. However, this is a non-systemic measure as it only affects the vehicle. – Another approach consists in installing ESS, either on-board or track-side (continuous supply only). However, the installation of an ESS is a single- subsystem measure, being non-systemic in either case. – Using interconnections in continuously supplied energy systems, the coordination of multiple vehicles in terms of braking and accelerating becomes possible. Requiring a concurrence of at least three subsystems—operation control and two vehicles—this is a clearly systemic approach. – With the same approach of using existing interconnections, the usage of regenerated braking energy in other subsystems—e.g. station lighting or feeding it back to the national grid—can be thought of, which is of course a systemic approach as well.

Summarising, a better usage of regenerated energy is—only regarding systemic measures—possible by coordination of braking and acceleration phases of different vehicles as well as by using the regenerated energy in other subsystems within or outside the railway system.

4.9.2.4 Synopsis As it has been discussed, there are not many approaches and measures that can be classified as being systemic; cf. Table 4.2. As readable from that table, there are three different identified potentials, which have already been discussed in a prior subsection each. One of the measures consists in using (and controlling) the energy supply system such that braking energy is redirected to any other subsystem, e.g. station supply. But note that the part outside railway operations is out of scope of this thesis—not at all implicating that it might not be interesting. A second approach can be classified as a kind of speed profile adjustment but with different boundary conditions and optimisation objectives. Even though this seems to be only one measure, it actually involves the approaches of speed profile optimisation, timetable adjustment, and conflict avoidance as well as the new aspect of including additional information on “everything” (e.g., the environment) that might influence the train run.

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Table 4.2: Identified Potentials, Affected Subsystems, and Systemic Measures (Approaches) suitable to address the potentials. Identified Potential Affected Subsystem Systemic Measure (Approach) Reduction of Transmission System Reduction of electric transmission transmission losses (Energy Supply) distance through vehicle speed profile coordination (braking and acceleration) Reduction of electric transmission distance through addition of substations and/or energy storage systems. Reduction of losses Vehicles (Drive Chain, Speed profile optimisation such from drive chain and Motion Resistances) that conflicts with other trains are motion resistances avoided. Speed profile optimisation under inclusion of additional factors as actual reserves, weather conditions, etc. Improved usage of Vehicles + Coordinating vehicles’ speed pro- braking energy Energy Supply files in order to synchronise braking and acceleration phases, resulting in shorter electric transmission distances and/or less waste of braking energy (same as the first approach). Vehicle + Usage of regenerated braking en- Energy Supply + ergy in another subsystem instead Another Subsystem of converting it into heat over the brake resistor or storing it in an energy storage system.

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5.1 Reason and Purpose

Up to this point, the theoretical backgrounds—literature study in terms of railway system modelling and energy saving as well as an implementation of the system model—have been presented. Thereby building the basis for the following empirical part of this thesis, an intermediate summary seems appropriate.

5.2 Modelling the Railway System

In chapter 2, the railway system has been investigated extensively, based on literature. Using primary energy and irreversible energy losses as system boundaries and including all influences, the final model can be built hierarchically. Five first layer subsystems—vehicle, energy supply, operation control, track, and environment—form the railway system, while these subsystems can be structured using sub-subsystems, and so on—cf. Figure 2.27, p. 91. Two subsystems make part of the traction energy flow and are therefore directly relevant concerning energy investigations: the energy supply system and the vehicle(s). For their (sub-)subsystems, descriptive models and equations can be found in literature; sometimes, different models and approaches exist in order to describe a certain phenomenon. However and especially for the vehicle’s subsystems, the application of rule-of-thumb values, mean values from measurements conducted a couple of decades ago, or empirical values appear to be inevitable and accepted as common practice. Moreover, some of the vehicle subsystems’ investigations and descriptions appear to be incomplete. Given this fact, additional models have been derived from data and/or information from literature for the respective system parts: influence of wind speed and direction on aerial resistance; determination of the tunnel factor from tunnel cross section, vehicle cross section, and tunnel length; the power demand of auxiliary systems as function of the current traction power; and a model of HVAC power given the outside air temperature. The energy supply system, especially above substation or fuel station level,36 is included as efficiencies. This allows modelling these system parts without adding an unmanageable complexity that would arise with electrical models: not only the more complex description of the elements, but also—and

36deﬁning primary energy as top of the energy cycle

– 177 – Energy Saving Potentials in Railway Operations under Systemic Perspectives more important—their usually complex and redundant interconnection at every transmission level makes this a research area for itself. As it is addressed and steadily investigated in electrical engineering, especially in power generation and/or power transmission research groups, this field can be omitted in this thesis without neglecting relevant parts—especially as even the IEC rec- ommends the use of efficiencies for electricity transmission (cf. section 2.2.4.1, p. 57 sqq.). A similar argumentation holds for discretely supplied railway systems for production/refining and transportation of the fuel, e.g. diesel. All five introduced subsystems interact mutually as well as with the traction energy flow, either directly or indirectly. The already discussed subsystems of energy supply and vehicle define the equations that finally deliver the system’s energy demand, while the other subsystems define or influence the parameters of those equations. As examples, the following shall be mentioned:

– Operation Control defines the maximum speed of a route and, via timetable, also required acceleration, rolling stock (mass, length, ...), etc. – Precipitation, dust, and others influence adhesion coefficient µ—factors that are given by the environment. – Properties of the track may have significant influence on the energy demand of a train run: slopes and tunnels to mention two of them.

Given this model, many different influence factors on energy demand can be identified (cf. esp. Table 2.7 on p. 70 and Table 2.12 on p. 103). Some of them are commonly known—e.g. components’ efficiencies, comfort systems, driving style—some of them less, as wind direction, humidity, and precipitation. The model shows some uncertainties, as many of its parameters cannot be determined with desired accuracy. In chapter 3.5 it has been shown that wind effects may be important; moreover, the influence of the drive chain efficiency— and with that, the energy preparation efficiency—might play a significant role. Obviously, this also affects the precision of every single energy study—more or less, depending on the actual system’s properties and the chosen system boundaries. These influences will affect all investigations in a similar manner, resulting in an absolute deviation between model and calculation. However, for comparing different calculated runs—which is what is actually done for the case studies—no significant distortion is expected.

5.3 Energy Saving in Railway Systems

Saving energy in railway systems is frequently addressed and well-covered in literature, cf. chapter 4. The measures from literature can be classiﬁed into two major groups: Approaches that only address one component or subsystem, and approaches that will only show any effect when analysed under a systemic perspective, i.e., including more than one subsystem.

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For the ﬁrst group—measures affecting only one component or subsystem—the following are the major topics and approaches: – Reduction of the vehicle mass, which will reduce the amount of energy necessary to accelerate, decelerate, and overcome rolling and slope resistance. – Reduction of the vehicle’s aerial resistance, i.e., improving its nose shape as well as reducing cross section area and discontinuities along its surface. – Demand reduction in comfort (HVAC) and auxiliary systems, which can be

done by improved control (as CO2-based HVAC control), use of more efficient components (e.g. less waste heat in drive chain), and similar methods. – Generally, improvements concerning the drive chain. This starts with possible transformer weight reductions, for instance by using Dry-Type or Power Electronic Traction Transformers (PETT) and includes the improvement of converters and their components. Moreover, improvements in machine design and construction as well as machine type replacements—e.g., permanent magnet synchronous machines instead of induction machines—are thought of. Also, an improved control that more efficiently controls engine flux and voltage and may switch of single engines in partial load operation reduces the energy demand of the drive chain. – Improvements within the energy supply network, which is a topic of special interest in (often unidirectionally fed) DC systems. Here, an optimised placement of substations—when keeping the supplying national grid in mind—is a possible approach, as well as a general strengthening of network interconnection, for instance employing bidirectional substations that may feed back regenerated braking energy into the national grid. In this case and especially for AC, there are special one-to-three-phase connections investigated in order to reduced imbalances within the national grid, which might be caused by power feedback from the one-phase railway grid. When done properly and carefully, the above presented measures will most likely not have any increasing influences on the energy demand, even though this could be possible—for example in case of covering discontinuities of the vehicle body and thereby significantly increasing the vehicle’s mass. An approach that is also often found and (mostly) belongs to the above group of non-systemics are energy storage systems (ESS). For these, applications on- board a vehicle as well as track-side in substations or stand-alone are investigated. However, these systems might have a negative effect, which is often neglected: – Generally, ESS show a charge/discharge efficiency of clearly below 100 %. In literature, efficiencies of 89 % to 95 % are stated for charging and discharging each (Perin, Walker, and Ledwich 2018), resulting in a mean overall process efficiency of around 85 %. Compared to the losses of electric transmission, which are around 5 % in AC and below 10 % for DC systems of 1.5 kV and above (cf. section 2.2.4), the ESS charge/discharge losses are rather high.

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– When used on-board a vehicle, the ESS adds mass while requiring some space. With energy densities of 10...160 Wh/kg, the additional mass brought by a 2...100 kWh ESS can be signiﬁcant (Ceraolo and Lutzemberger 2014; De La Torre, Sánchez-Racero, et al. 2015)—increasing the energy demand to move the train. In addition, the space required might get lost for payload.

Consequently, ESS are probably a good alternative instead of “burning” regenerated braking energy if feeding it back to a higher-level grid is not possible, as in discontinuously and most DC supplied systems. However, in AC, where the grids are usually interconnected, the application of ESS, especially on-board, might actually be disadvantageous. Nonetheless, the application of track-side ESS can be seen as a systemic approach—part of the second major group—if its application is considered in the context of supply system adjustments that are driven by vehicle-including analysis. This approach basically consists of locating and dimensioning substations according to the energy demand on the respective line, as well as including the application of ESS.

The second major group of approaches can be classiﬁed as systemic, as more than one subsystem needs to be involved to access the potential:

– The application of track-side energy storage systems if considered together with the vehicles as described above. – The joint optimisation of timetable and speed profile allows to determine run time reserves and their placements in a way that they may be used in a more efficient way by speed profile optimisation. For this approach, ideal speed profiles are usually generated first, before the timetable is created based on these profiles. Thus, a more accurate timetabling (operation planning) is reached than by calculating minimum run times and adding rule-of-thumb reserves, while accurate and possible real-time speed profile optimisation (operation control; vehicle control) allows an energy-ideal execution. The additional synchronisation of braking and acceleration phases (vehicle synchronisation) could be regarded as even more advanced approach in this context, as an additional constraint is taken into account. – Conflict avoidance consists in collecting detailed information on the expected trajectory of different (or even all) vehicles on the network at operation control level. Based on this, a new and conflict-free timetable can be generated in real-time and sent to the vehicles, e.g. as a target speed profile. Thus avoiding stops and/or unnecessary accelerations or decelerations, a considerable amount of energy can be saved. – Generally, optimising speed profiles under different targets is a systemic approach as well, as long as not only one vehicle is considered. As targets, the better use of braking energy (vehicle synchronisation), conflict avoidance (see above), or the substation’s energy balance are used—amongst others.

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– Coordinating substation control and vehicle position within the network, transmission losses might be reduced. Given that all vehicle positions are known, the output voltage of the feeding substations might be adjusted (usually increased), such that the losses are reduced by a higher transmission voltage while still keeping the voltage within the limits allowed at pantograph (voltage drop along the catenary). Even though this approach is only discussed for DC systems, an application in AC systems might be thought of for long, single-side fed lines with low trafﬁc density. – Adding smart grid approaches, some researchers propose to consider even larger parts of the electric supply systems for energy demand reduction. In particular, regarding public and railway grid as one interconnected entity is proposed. The advantage would be that larger systems usually include more source and sinks, resulting in more possible connections and, most likely, shorter transmission distances. Altogether, the energy demand would be reduced.

As possible additional systemic saving potential, the inclusion of environmental conditions was identified. As already shown during system modelling, there are different (volatile) influences from the environment on the energy demand, as wind (cf. section 2.3.4.3) and precipitation or dust (section 2.3.4.4)—which can show significant influences (see section 3.5.3). Including according information into (real-time) dispatching and speed profile generation might allow to more efficiently use the reserves in terms of energy demand reduction.

Summarising, there are many different approaches to energy saving in railway systems, most of them present in literature. Many of the measures affecting one subsystem only can be accepted as constructive without further investigation. For the application of ESS, there are some questions left, especially when discussing interconnected, bidirectionally fed continuous supply systems. When considering systemic approaches, there is always some kind of coordination involved: Coordinating vehicles, coordinating supply system levels, or coordinating energy supply and vehicles. Note that one fact is striking: Most of the literature available focuses on urban metro services. These systems are for sure important, but show all similar properties: Usually DC fed, simple infrastructure—mostly double track lines with no interconnections, protected environment (tunnels or separate tracks), homogeneous vehicles, structured timetable. However, mainline railways also face the challenge to reduce their energy demand—a motivation to investigate the applicability of the discovered approaches for that kind of operation.

Another important conclusion from the literature study shall be repeated here brieﬂy: In all of the literature, the amount of energy saved applying the presented approach is indicated. Mostly relatively in per cent, which is of course highly depending on the deﬁnition of 100 %. Sometimes, this base is not in-

– 181 – Energy Saving Potentials in Railway Operations under Systemic Perspectives dicated; in most cases, the scope of investigation is limited to a rather short section of track—often with signiﬁcant simpliﬁcations in modelling. Conse- quently, these numbers are not directly comparable to each other; moreover, the exact value has to be interpreted with care for the before mentioned reasons. In addition, different potentials do not sum up when exploiting them; rather, the exploitation of one potential might blight another one.

5.4 Implications for the Empirical Evaluation

Based on literature and the model, a set of possible approaches to energy saving in railway systems and operations has been shown and discussed. Many of the approaches, especially the ones focusing on one subsystem or component, are well investigated. The concepts are proven, and in many cases, no signiﬁcant drawbacks or negative side-effects have to be expected. One exception is the application of on-board energy storage systems (ESS), especially in AC systems. Basically, the addition of an on-board ESS only affects one subsystem—the vehicle itself. However, as discussed above, the energy balance of this particular vehicle might be improved for the investigated section, while the primary (systemic) energy demand increases. Consequently, the application of ESS is a subsystem optimisation that might show negative effects considering the entire railway system, especially for AC power supply. Thus and according to the research question, a more detailed investigation seems to be appropriate for this particular approach. Also of interest in terms of the research question are saving potentials that disclose by holistic analysis, i.e., when considering two or more subsystems simultaneously. Focusing on operations in AC fed systems and based on the summary in section 5.3, two approaches offer as interesting study objects: The inclusion of environmental factors into dispatching, and the synchronisation of vehicles in terms of acceleration and braking phases. For the prior, especially the investigation of aerial resistance due to wind and consequences from adapted target speed determination rises as fascinating question. For the second, the application of the vehicle synchronisation idea in a realistic mainline railway promises interesting results and insights. During literature analysis, only few approaches appeared that proposed adjustments within the power supply system. However, there might be some potentials: Shortening transmission distances by alternatively placing the substations or even adding more substations might be thought of—which is done in some rare cases for DC fed metro systems. Additionally, the application of ESS within or as standalone substations is thinkable, each idea investigated in few publications about DC fed railway systems. Consequently, the question of applicability in AC fed mainline railway rises—where, however, the use of standalone ESS does not seem useful as in most cases, a connection to the national or even the centralised railway grid is possible. Finally, an investiga-

– 182 – Chapter 5: Intermediate Conclusion tion of power supply structure in terms of alternative placement of substations, installation of additional substations, and the application of stationary ESS inside existing or newly built substations is considered appropriate, as this can be seen as additional systemic approach according to the research question. Shortly summarising the above discussion, there are four interesting approaches that seem appropriate to be investigated in case studies, after skipping the approach of conﬂict avoidance from Table 4.2 for being well- investigated: the impact of on-board ESS on the system, the inclusion of environmental factors into operational decisions, application of vehicle synchronisation in mainline railways, and the evaluation of different power supply system modiﬁcations including the installation of stationary ESS at existing substations. Re-sorting these mentioned approaches slightly, the following scenarios are condensed as case studies for a model-based empirical evaluation:

– Speed proﬁle optimisation under inclusion of additional factors—i.e., environmental conditions, especially wind speed and direction. Moreover, the inclusion of precipitation effects, e.g. rail conditions, would be interesting but cannot be conducted due to lacking information on the exact correlation. – Synchronise braking and acceleration phases of different trains in a realistic mainline railway section. Of course, having a look at the full timetable including all services is necessary, as usage limits the scope of adjustments. – Checking the systemic impacts of on-board ESS in mainline railway vehicles, just adding an energy storage and its according weight to a train (from the idea of reducing transmission losses). – Evaluating the effects of power supply system adjustments, as different placement or varied number of substations, as well as installation of ESS at existing substations (from the idea of reducing transmission losses). More- over, changes in the feedback capability of the substations—i.e., the ability to feed back energy from catenary to railway or national grid—appear to provide interesting results.

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6 Empirical Evaluation

6.1 Introduction to the Case Studies

Up to this point, the railway system was investigated and analysed (chapter 2), a corresponding model implemented as MATLAB program (chapter 3), and energy saving potentials and approaches have been discussed (chapter 4). Thereby, it was seen that measures affecting only one subsystem—e.g., component improvements as, for instance, better engine efﬁciencies—are usually easier to evaluate and will show positive effects on system level, as long as they are properly engineered. Many publications can be found on these approaches, which are—mainly—already applied in industry in one or another way. For this thesis, systemic approaches—i.e., approaches that affect more than one subsystem in order to reduce the total energy demand—are in the focus of interest. These have been outlined in chapter 5, especially in section 5.4. To evaluate the effects of these approaches, simulation using the implemented system model is the preferable method, as the complexity to be dealt with is high. In addition, the application in terms of simulation is used to prove the program’s applicability. The approaches to be investigated are outlined: The inclusion of environmental conditions—wind—when deﬁning operational speed recommendations or target speeds, the synchronisation of acceleration and braking phases of different trains, and the effects of changes within the supply system as installing on-board energy storage, track-side energy storages, or additional substations. The case studies’ investigations, which are focusing on these approaches, are based on existing lines operated by SBB: Olten–Solothurn (via high-speed line/NBS) and Biel–Neuchâtel–Yverdon, for which SBB provided track data and some additional information. Standard values of the model parameters are collected in appendix B; the information on the lines is illustrated in appendix C. Due to the very special properties of the Olten–Solothurn line—high- speed line without intermediate stops and numerous tunnel sections—the case studies focus on the Biel–Neuchâtel–Yverdon line.

The ﬁve case studies conducted are shortly introduced in the following; case studies 1 to 4 are direct implementations of the indications from section 5.4, while the ﬁfth case study results from the others.

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In the first, the usage of time reserves is defined according to the current wind situation. Based on the fact that the wind angle influences the aerial resistance, it is expected to enable energy saving by including this information into the determination of the operational target speed. Secondly, the synchronisation of braking and acceleration phases of different trains is investigated. This approach is often used in literature for DC-fed metro systems; here, its applicability to mainline railways—including its systemic effects—is analysed, based on the total amount of energy delivered to the substations along the line under investigation. The third case study investigates the effect of on-board energy storage systems in mainline railways. While in DC-fed systems, potentials are proven due to limited possibilities of feeding back regenerated braking energy, AC systems usually have a (practically) unlimited feedback capability. Moreover, the energy storage systems are adding weight to the train and have limited charge- discharge efficiencies. The effects of these facts are investigated. For the forth case, the mostly historically grown structures of power supply systems are questioned. By adding energy storage systems and discussing the influences of inverting substations, i.e., the possibility to bidirectionally exchange energy between different levels of the power supply, the possibilities of modern (high) power electronics are used. Case study No. 5 was—additionally—derived from the results of case studies 3 and 4. In these, the significantly negative influence of not using regenerated braking energy was highlighted. Thus, an analysis of [a] disabling the feedback capacity of the system and [b] the applicability of on-board and track-side energy storages was conducted on a purely theoretical basis.

6.2 Case Study 1: Considering Environmental Conditions in Operations

Test Question Is it possible to reduce the energy demand of a train run by considering the wind situation (speed and direction), especially if there are signiﬁcant changes in line heading—and if so, by how much? Test Case For a run of one train, the energy demand is determined for different wind speeds and angles as well as for different percentages of time reserves used. For each combination, the speed maximum is reduced for different sections of the line, so that the run time target is met. Involved Systems The environment as cause for the wind; operation control (OPC) as information combining and target speed deﬁning entity; and one vehicle that runs on the line, thereby affected by the wind conditions and operational guidelines.

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Background. It was shown beforehand that environmental conditions (wind, precipitation) may influence the energy demand of a train run. Coevally, these conditions are—or could be—known to OPC. Thus, the possibility to include this information might be used in real-time operation. Especially in cases where lines have clearly different headings in certain sections, an inclusion of the current wind situation might be beneficial: Recapitulating the investigations on influences (section 2.4.2.1), it has been shown that there is a significant difference in aerial resistance—and thus energy demand—depending on the yaw angle between route (train) heading and wind direction.

Study Description. The SBB line between Yverdon-les-Bains and Neuchâtel shows the before-mentioned property: Leaving Yverdon, the track heading is 336°, but after a roughly 11/2 kilometres, the line turns towards its new (mean) heading of 48°. This leads to an interesting situation: The worst-case yaw angle of 60° occurs for the first section at wind (source) direction ψ2 = 276°— being a slightly beneficial wind direction (α = 132°) for the second section. For the section towards Neuchâtel, the contrary is the case: With α = 60° here, ψ2 results to be 108°—a rather beneficial value for the section towards Yverdon (α = 132°). An overview on the geographic situation is given in Figure 6.1.

Neuchâtel (NE)

48°

336°

276° 108° N Yverdon-les-Bains (YV) 5 km

Figure 6.1: Geographic Overview on the Yverdon–Neuchâtel Line for Case Study 1. Indicated are the determining route directions—dash-dotted arrows—for sections Yverdon–Curve and Curve–Neuchâtel as well as the wind directions—dashed grey arrows. 0° = 360° = North (own illustration).

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Consequently, it is expected that an inclusion of the information on the wind situation allows to save energy. In particular, instead of using the reserves equally distributed by reducing the maximum speed for the entire route (strategy 0—S0), it is proposed to reduce the speed maximum for the negatively affected section ﬁrst, before using reserves on the other (strategy 1—S1). Thus, the aerial resistance is reduced where the wind is adverse. From the above considerations, the case study on inclusion of environmental conditions is deﬁned as given in the following:

– Route between Yverdon and Neuchâtel, operated with an ICN (series 500) – Sectioning of the line: km 65.152–63.693 and km 61.896–29.198 based on the Biel–Neuchâtel–Yverdon kilometrage – Mean headings for the two sections: 336° and 48° – Exemplary wind directions: 108° and 276°

– Wind speeds under investigation: 10 km/h, 20 km/h, 30 km/h – Run times based on the 2018 timetable, accessed via sbb.ch; minimum (technical) run times determined using the model; reserves used for energy saving: 25 %, 50 %, 75 % – Power supply: Substations at Neuchâtel and Yverdon as given in Fig- ure B.1; two-sided feeding

– Additional constraints: Target speed vrec, given by OPC for the wind-affected section, shall not be below 50 km/h

Results. The simulation of this case study delivered the set of results given in Table 6.1. For all investigated variations, the wind-oriented speed reduction (S1) shows a higher demand in energy, on vehicle as well as on substation level. Even though this contradicts the expectation, the result is explainable: The aerial resistance being a quadratic function of the relative speed between air and train, it increases more significantly for higher train speeds. Thus, the reduction of aerial resistance in the area of adverse wind conditions cannot compensate for the higher resistance due to higher driving speeds in the other section. Consequently, the increase in energy demand is more significant for the 276° wind than for the 108° wind: In the first case, the section with higher driving speed is longer than in the second, resulting in a significantly higher aerial resistance.

Additional Analysis. Based on these results, a different investigation approach was defined. In this modified case study, the speed reduction for the section with adverse wind conditions is defined directly. Exemplary, only the case where ψ2 = 276°, i.e. the affected section being between km 63.693 and 65.152, with a wind speed of 20 km/h is simulated. As additional simplification, only the reserve usage of 50 % is tested, omitting the cases of 25 % and 75 % of

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Table 6.1: Energy Demand for Different Operational Strategies and Wind Situations. S0 is the “default strategy”, i.e., reducing the speed maximum for the entire route in order to fulfil the reserve usage (Res. used); in case S1, the speed maximum is reduced for the section with adverse wind conditions first. Res. Wind Vehicle Energy ∆E Substation Energy ∆E used speed from S0 (kWh) S1 (kWh) (%) S0 (kWh) S1 (kWh) (%) 108° 403.2 416.2 +3.2 449.5 465.5 +3.6 10 km/h 276° 396.4 430.9 +8.7 434.2 474.9 +9.4 108° 427.1 441.8 +3.4 466.7 483.5 +3.6 25 % 20 km/h 276° 396.4 434.1 +9.5 434.2 478.0 +10.1 108° 450.7 466.1 +3.4 491.7 509.1 +3.5 30 km/h 276° 398.1 436.1 +9.5 436.2 480.3 +10.1 108° 392.5 417.6 +6.4 428.8 457.1 +6.6 10 km/h 276° 378.0 424.1 +12.2 413.4 467.0 +13.0 108° 408.6 433.4 +6.1 445.8 473.9 +6.3 50 % 20 km/h 276° 376.6 419.4 +11.4 412.1 462.4 +12.2 108° 430.7 455.1 +5.7 469.2 496.8 +5.9 30 km/h 276° 376.8 425.2 +12.8 412.5 468.6 +13.6 108° 377.3 406.1 +7.6 411.7 444.4 +7.9 10 km/h 276° 363.3 389.2 +7.1 396.9 427.1 +7.6 108° 393.0 422.0 +7.4 428.3 461.3 +7.7 75 % 20 km/h 276° 357.7 388.4 +8.6 391.1 426.6 +9.1 108° 414.5 443.5 +7.0 451.0 484.0 +7.3 30 km/h 276° 361.4 390.2 +8.0 395.2 428.5 +8.4 reserve usage for energy saving purposes. Given this set-up, the target speed definitions as well as the energy demands given in Table 6.2 results. Thereby, only the values around a speed maximum of 132 km/h for the wind-affected section are shown, as a simulation for 80...160 km/h showed the minimum to be around this value.

Discussion. All obtained results support the conclusion that the overall speed reduction has significantly more influence on the total energy demand of a train run than an additional speed reduction made due to adverse wind conditions. In Table 6.2, it can even be seen that the resulting differences do not correlate with the wind but with the mean speed limit: for all variations, nearly the same speed limit (±0.3 km/h) results for the longer, non-affected section for all defined speed limits of the wind-affected section. Consequently, the (nearly negligible) difference in energy demand (±0.2 %) has to be assigned rather to the differences in the maximum speed of the wind-affected section between km 63.693 and 65.152 than to wind-related aerial resistance effects. This also shows that the run time tolerance’s (±3 s) influence is larger than the wind’s influence on the energy demand.

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Table 6.2: Case Study 1 Variation: Results. vmax,1 the speed maximum (deﬁned by operations) for the wind-affected section between km 63.693 and 65.152, vmax,2 for the other section between km 29.198 and 61.896. For the ﬁrst section, the speed limit is set; for the second, the speed limit is determined so that the given run time is met ( 3 s). E and E denote the energy delivered to the vehicle and the ± VEH UW substations, respectively. Var. 0 1 2 3 4 5 km km km km km km vmax,1 160.0 /h 136.0 /h 134.0 /h 132.0 /h 130.0 /h 128.0 /h km km km km km km vmax,2 140.2 /h 140.2 /h 140.2 /h 140.2 /h 140.2 /h 140.5 /h

EVEH 374.7 kWh 374.7 kWh 374.8 kWh 374.2 kWh 374.3 kWh 375.5 kWh

EUW 410.1 kWh 410.1 kWh 410.2 kWh 409.7 kWh 409.8 kWh 410.9 kWh

However, a different situation might arise for other lines. For instance, the following is assumed: A first part of the line shows a higher speed limit, e.g. 160 km/h, while a second with significantly different heading has a significantly lower speed limit, e.g. at 80 km/h. If first section is affected by adverse wind while these are neutral or advantageous for the second, the effects accumulate differently. In fact, the speed would then be reduced in the first section, thus reducing the aerial resistance from the relative air speed (quadratic) and the influence of the wind angle (linear). Simultaneously, the speed profile of the second section would see no change, as both effects show less negative influence on the energy demand than in the first. Nonetheless, the sections’ length still has an influence on the energy demand, as it is the power integral over time. Thus, it is not fully excluded that there are—for very specific cases— energy saving potentials to be found using this approach. However, finding these seems to be a rather complex task exceeding the scope of this thesis. Moreover, this strategy’s applicability is limited by timetable constraints as following trains that might be scheduled for another service and/or line but use the same tracks, needing higher speeds to fulfil the requirements. Then, a speed reduction would cause delays inducing higher energy demands in other sections, as delays should be compensated to ensure stable operation.

Conclusion. In a cross-comparison to the wind angle model as presented in sections 2.4.2.1 and 3.5.3, these results lead to a differentiated conclusion. Given the fact that the wind angle may have a non-negligible influence on the individual train run in terms of aerial resistance, it should be used during modelling to obtain precise parameter values. Contrariwise, its influence in operational situations—especially with the usually occurring low wind speeds— remains small, so that the inclusion of wind influences into operation control does not offer worthwhile benefits in terms of energy demand. Altogether, the inclusion of environmental factors as the wind might be an academically interesting approach in certain, very specific situations; in today’s practice, however, there is—based on these results—no possibility seen to decrease the energy demand by using this case study’s approach.

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6.3 Case Study 2: Vehicle Synchronisation

Test Question Is there an energy saving potential in synchronising acceleration and braking phases of different trains in AC mainline systems? Test Case For the inter-city services between Biel and Yverdon, the braking and acceleration phases of the two directions’ trains are separated by a few minutes only. By shifting the timetable of one of the trains, these phases are synchronised, evaluating the difference in energy demand. Involved Systems By changing the timetable, operation control (OPC) is involved; additionally the affected and all other investigated vehicles; moreover, the energy supply system that is used for energy exchange.

Background. As discussed in chapter 4, the synchronisation of braking and acceleration phases of two or more trains is a present topic in current research on energy saving in railway applications. Mostly, the approach is applied in (DC-fed) metro systems where usually no possibility of feeding back regenerated braking energy is given. The question rises whether there is also an energy saving potential in this approach for centrally fed AC systems—in which, typically, the energy can be fed back with high transmission efﬁciency.

Study Description. In order to exemplary determine the energy saving potential of synchronising multiple trains’ braking and acceleration phases, this case study—based on a real-world-situation—is deﬁned as follows:

– Route Biel–Neuchâtel–Yverdon with stops at these three stations – Operated with four series 500 EMUs (ICN) – Comparison of the timetable “as is” (from sbb.ch for 2018) versus synchronised braking and accelerating at Neuchâtel – Power supply: Substations at Biel, Neuchâtel, and Yverdon as given in Fig- ure B.1; two-sided feeding – Run times according to the 2018 timetable, i.e., usage of all route-bound reserves for energy saving purposes; consequently, on-time arrival – Investigation of two trains per direction, i.e., all departures within one hour (on this inter-city service)

The timetables given to the vehicles are summarised in Table 6.3; the speed proﬁles around Neuchâtel station are shown in Figure 6.2.

Results. For evaluation, the overall amount of energy fed to all substations as well as the energy delivered to Neuchâtel substation—which should be relieved most due to better inter-train exchange of energy—are taken into account. However, the effect reached by the synchronisation is rather small:

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Table 6.3: Timetables of Case Study 2. Original (“timetable”) and adjusted (“synchronised”) version for the stations of Biel (BI), Neuchâtel (NE), and Yverdon-les-Bains (YV). Given are the speciﬁed arrival (Arr) and departure (Dep) times in minutes from simulation start; start time being Minute 0 of an arbitrary normal operating hour. timetable synchronised Train BI NE YV BI NE YV Arr Dep Arr Dep Arr Dep Arr Dep Arr Dep Arr Dep 1 13.0 16.0 32.0 34.0 51.0 53.0 13.0 16.0 32.0 34.0 51.0 53.0 2 43.0 45.0 61.0 63.0 80.0 82.0 43.0 45.0 61.0 63.0 80.0 82.0 3 43.0 46.0 24.0 26.0 5.0 6.0 48.5 51.5 29.5 31.5 10.5 11.5 4 75.0 77.0 57.0 59.0 37.0 39.0 76.5 78.5 58.5 60.5 38.5 40.5

v v

t (Min) t (Min) 25 30 35 55 60 65

(a) First Train Crossing, Minutes 23 to 35 (b) Second Train Crossing, Minutes 55 to 66

Figure 6.2: Speed Profiles around Neuchâtel Stop. Black the train travelling from Biel to Yverdon, grey the opposite direction. The solid lines show the synchronised speed profiles (synchronisation highlighted in light grey); the dotted line show the original speed profile according to timetable of the Yverdon–Biel train (own illustration).

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– The total energy demand of all substations for all four trains decreases from 2586.9 kWh to 2564.9 kWh, which is –0.9 %. – The Neuchâtel substation energy input decreases from 1229.8 kWh to 1227.3 kWh, which is –0.2 %.

With two departures per direction per hour and the line being operated 181/2 hours a day, a daily overall energy demand reduction from 47.858 MWh to 47.451 MWh would result; with 365 operating days, the possible savings sum up to 148 MWh. Given that an average Swiss household uses 4 MWh a year (SBB AG 2015), the savings still correspond to about 37 households.

Discussion. The results show that also in AC-fed mainline railways, there is a certain potential in synchronising acceleration and braking phases of different trains. This effect is caused by a reduction in transmission distance, simultaneously reducing the transmission losses. Over a year, a non-negligible potential resulted for only one line. However, it should be noted that additional effects might “accidentally” contribute to an energy demand reduction. For instance, shifting trains may lead to additional braking-acceleration synchro- nisations outside the Neuchâtel station area—which actually happens in this scenario. An effect that might, for other lines and/or different boundary conditions, have the adverse result, thus increasing the total energy demand. Con- sequently, the final impact of timetable adjustments can only be determined considering the entire railway energy supply system. For a centralised AC system, this would require to consider all railway power plants as well as all grid interconnections with the public grid and neighbouring railway systems. Altogether, there is a certain advantageous effect in synchronising braking and acceleration phases, which might—even though it seems small—sum up to considerable amounts (37 households equivalent per year on the investigated line). However, compared to the 600 GWh (150 000 households) of energy that SBB wants to save until 2020 (SBB AG 2015), it is practically negligible. The main reason is to be found in the high transmission efficiency of interconnected, centralised AC supply systems. With the model presented here, the energy demand reduction is caused by not having the substation efficiency applied in case of synchronised trains. Nonetheless, the losses of the catenary still occur. If there is a direct electric connection between the different catenaries of the Neuchâtel station, the losses—and thus the overall energy demand—will decrease further. However, effects outside the area or line under investigation might de- or increase the overall energy demand of the system. Moreover, effectively synchronising braking and acceleration phases of different trains—i.e., adjusting the timetable—is only possible as long as there are no conflicts with other trains. For the investigated double-track line between Biel and Yverdon, not only the analysed long-distance trains use the existing infrastructure. In between, many regional and commuter services use the same tracks, especially in the Neuchâtel area—see Figure 6.3.

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Figure 6.3: Excerpt from the Graphic Timetable 2018 around Neuchâtel, 8 to 9 am. Each line represents a train, showing a dense trafﬁc around Neuchâtel station (NE). By SBB AG (2018).

Given a that dense traffic, there is basically no possibility to synchronise trains. However, the “cancellation effect” might occur “automatically”, as there are always trains in operation, braking, and accelerating. Especially with an interconnected supply system, a highly efficient operation is ensured just by the system’s architecture. Consequently, an (active) application of vehicle synchronisation appears as non-practicable approach in dense traffic. However, there are circumstances that might allow an easy application of this strategy. If the infrastructure fulfils the requirements—at least, sufficient capacity and possibility to simultaneously accelerate and brake (i.e., double- track lines)—and a sufficient operational precision is reached, an energy demand reduction might be possible with low efforts. Still, there are further systemic aspects to be kept in mind, as e.g. connecting services—also road-bound.

Conclusion. Altogether, synchronising braking and acceleration phases of different trains will usually have an advantageous effect on the energy demand of a railway system. However, in centralised AC systems—and all other electric systems with feedback capability—this effect is, due to high transmission efficiency, rather small. Of course, a different situation arises for systems without feedback capability—as, for instance, traditional rectifier-fed DC systems—where regenerated braking energy would have to be dissipated if no accelerating or—at least—motoring train is available. Taking traffic density and network complexity into account, the synchronising strategy becomes more challenging the more dense the traffic and the more complex the network is. Moreover, additional energy interchange effects might then occur. Nevertheless, for low density traffic areas as well as for simple systems without feedback capability, synchronisation might promising.

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Going back to the initially formulated question whether there is a saving potential in this approach when applied to mainline railways, the answer is as follows. Yes, there is a saving potential in synchronising braking and accelerating trains also in AC mainline systems—but the effect is rather small and subject to strong limitations in terms of realisability as soon as there is dense and/or inhomogeneous trafﬁc. However, there might be a signiﬁcant potential for systems with limited feedback capability, as traditional DC systems.

6.4 Case Study 3: On-Board Energy Storage

Test Question Assuming realistic on-board energy storage systems (ESS) including their weight and charge/discharge efﬁciency, is there a reduction in energy demand reached by using them in AC mainline railways? Test Case For a real line and a real schedule of a service on this line, the effect of different on-board ESS on the train run’s energy demand is evaluated and compared to the case without any ESS. Involved Systems Primarily, the vehicles equipped with ESS are affected by this measure. However, the energy ﬂow is changed by using on-board ESS, which is why also the energy supply system is involved in this approach to energy saving.

Background. On-board ESS are often discussed and presented as possible approach to energy and power optimisation problems in literature, where energy savings of up to 30 % are claimed; see also chapter 4. Mostly, these investigations have been done for DC-fed metro systems; often, it is not clearly stated which of the ESS’ properties are implemented and which are neglected (e.g., efficiency, mass). This implies, on the one hand, the question whether there is a significant potential for mainline railways; on the other hand, a side-focus on the influence of the ESS’ efficiency and mass is kept (systemic aspects).

Study Description. In order to gain an insight into the inﬂuences of on-board ESS in mainline railways on energy demand, different realistic—but partly non-existing—ESS were deﬁned, based on data from literature (Ceraolo and Lutzemberger 2014; De La Torre, Sánchez-Racero, et al. 2015; Fröhlich, Klohr, and Pagiela 2008; Perin, Walker, and Ledwich 2018; Yang, Yang, et al. 2017):

1. 2.5 kWh and 700 kW, 1.5 t 4. 10 kWh and 700 kW, 2 t 2. 1.0 kWh and 300 kW, 500 kg 5. 100 kWh and 700 kW, 3 t 3. 10 kWh and 30 kW, 1.3 t 6. 15 kWh and 4200 kW, 9 t, i.e. 6×1.

For all ESS, a (mean) efﬁciency of 92 % for charging and discharging each is set, as the typical efﬁciency range is 89...95 % (Perin, Walker, and Ledwich 2018).

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Electrically, the ESS is assumed to be connected to the energy preparation part directly—i.e., to the transformer—being usable by traction, auxiliary, and comfort systems. Additionally, control is deﬁned to prefer ESS charge over feedback to catenary when braking; for the motoring case, it starts discharging the ESS (i.e., supplying energy to the traction chain) as soon as the actual traction power at wheel exceeds 50 % of the hourly power rating Ph. In this case study, the (theoretically possible) supply of auxiliary and/or comfort systems by ESS is neglected. To avoid deep discharge as well as over-charging, the ESS charging state is kept between 20 % and 80 % of the ESS’ capacity. The remaining deﬁnitions of the case study were chosen in close accordance with the other studies presented beforehand:

– Inter-city service on the Biel–Neuchâtel–Yverdon line with stops at these three stations – Operated with one series 500 EMU (ICN) – Timetable “as is” from sbb.ch for 2018; shifted by –13 Min to accelerate the simulation (that starts at 0 Min) – Power supply: Substations at Biel, Neuchâtel, and Yverdon as given in Fig- ure B.1; two-sided feeding – Run times according to timetable, i.e., usage of all route-bound reserves by speed reduction (and thus, for energy saving); consequently, on-time arrival at all stations – Investigation of a single train run for each variation

Results. For all trains runs, the speed profile is the same, as to be expected from the case study definition. Some results in terms of ESS power, ESS charging state, and power at pantograph are depicted in Figure 6.4. In order to rate the ESS addition’s impact in terms of a systemic energy demand, the energy delivered to the vehicle as well as to all substations (by the grid) is evaluated for each variation; the results are collected in Table 6.4. Summarising, none of the ESS contributes to a reduction of the energy demand when analysing the values measured at pantograph or at substation—in best case, there is no significant negative influence. However, the amount of energy stored within the ESS is not the same at beginning and end of the analysed train run, as recognisable in the bottom graph of Figure 6.4. Therefore, the additional column EVEI,corr in Table 6.4 delivers the more accurate picture of what is actually happening in terms of energy: Here, the initial charge of the ESS has been added to the amount of energy that has been delivered to the vehicle, while the amount of energy stored at the end has been subtracted. Still, the energy demand does not decrease compared to the case without ESS, even though there is no significant increase.

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v (km/h)

150

100

s (km)

PVEI (MW) 6

s (km)

-3

-6

PESS (kW) 600

300

s (km)

Note: P (blue) for presentation -300 ESS,6 reasons cut at 800 kW; ± max value would be 4.2 MW -600

EESS (%)

s (km) 10 20 30 40 50 60

Figure 6.4: Selected Power and Energy Proﬁles of On-Board Storages. Shown are—from top to bottom—the speed proﬁles of the trains (identical for all variations), the power at pantograph PVEI, the ESS charging power PESS, and the ESS charging state in per cent. Red—ESS 1; green—ESS 3; cyan—ESS 5; blue—ESS 6 (own illustration). – 197 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Table 6.4: Energy Balances for the Different ESS. The cases are numbered according to the list in text, where in case 0, the standard vehicle without ESS is used. EVEI the energy demand at pantograph; EUW the energy delivered to the substations. In column EVEI,corr, the change of stored energy within the ESS is explicitly considered. Different tables are given for various initial charge states of the ESS.

Case EVEI EUW EVEI,corr 0 584.7 kWh 639.6 kWh 584.7 kWh 1 589.9 kWh +0.9 % 643.5 kWh +0.6 % 588.4 kWh +0.6 % 2 585.5 kWh +0.1 % 639.9 kWh +0.0 % 584.9 kWh +0.0 % 3 590.6 kWh +1.0 % 645.3 kWh +0.9 % 588.5 kWh +0.7 % 4 594.5 kWh +1.7 % 646.7 kWh +1.1 % 588.5 kWh +0.7 % 5 625.9 kWh +7.0 % 676.4 kWh +5.8 % 588.3 kWh +0.6 % 6 603.6 kWh +3.2 % 653.5 kWh +2.2 % 594.6 kWh +1.7 %

(a) With an Initial ESS Charge of 20 % of the Nominal ESS Storage Capacity.

Case EVEI EUW EVEI,corr 0 584.7 kWh 639.6 kWh 584.7 kWh 1 589.2 kWh +0.8 % 642.7 kWh +0.5 % 588.4 kWh +0.6 % 2 585.2 kWh +0.1 % 639.6 kWh 0.0 % 584.9 kWh +0.0 % ± 3 590.1 kWh +0.9 % 644.7 kWh +0.8 % 588.5 kWh +0.7 % 4 591.6 kWh +1.2 % 643.6 kWh +0.6 % 588.6 kWh +0.7 % 5 604.5 kWh +3.4 % 652.9 kWh +2.1 % 588.8 kWh +0.6 % 6 599.4 kWh +2.5 % 649.1 kWh +1.5 % 594.9 kWh +1.7 %

(b) With an Initial ESS Charge of 50 % of the Nominal ESS Storage Capacity.

Case EVEI EUW EVEI,corr 0 584.7 kWh 639.6 kWh 584.7 kWh 1 588.5 kWh +0.7 % 641.9 kWh +0.4 % 588.5 kWh +0.7 % 2 584.9 kWh +0.0 % 639.3 kWh –0.5 % 584.9 kWh +0.0 % 3 588.6 kWh +0.7 % 643.3 kWh +0.6 % 588.6 kWh +0.7 % 4 588.7 kWh +0.7 % 640.4 kWh +0.1 % 588.7 kWh +0.7 % 5 588.9 kWh +0.7 % 638.3 kWh –0.2 % 588.9 kWh +0.7 % 6 595.1 kWh +1.8 % 644.7 kWh +0.8 % 595.1 kWh +1.8 %

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Additional Analysis. Given these results, a more detailed analysis of the energy shares is conducted for the ESS specifications 1 (existing system), 3 (low- power system), 5 (high-power, high-capacity system), and 6 (six ESS 1, high- power, increased capacity). Different points of the system have been evaluated separately: Traction, drive chain input, auxiliary and comfort systems, pantograph (VEI), substation output and input, and grid power. For all these points, the energy balances are evaluated, as presented in Table 6.5. Also here, the application of ESS is always worse than feeding back the energy to the next higher layer of the supply system; an expected result having the different efficiencies in mind that have been discussed beforehand. Apart from the fact that the efficiency of charging and discharging an ESS is lower than transmitting the energy via grid, the amount of energy required to move the train increases due to the additional of the ESS. Actually, this is the reason for the increased “traction” energy demand that can be observed in Table 6.5. While there is—as expected—no change in the comfort system’s energy demand, the auxiliary energy demand increases in two cases where the mass of the ESS requires higher power and thus more cooling. With the efficiencies of drive chain, energy preparation, catenary, and substation being below 100 %, these effects expand when looking at higher levels of the energy flow path.

Discussion. It is observed that for all applied, realistic ESS, the total energy demand increases if an ESS is used. This is mainly due to two facts: The ESS charge-discharge efﬁciency, which is below the grid transmission efﬁciency on the one hand’s side—around 85 % compared to over 90 % for the grid, cf. section 2.2.4.1—and the additional weight of the ESS that has to be carried around on-board the vehicle on the other hand’s side. Notably, even though expected, the energy values corrected according to the

ESS’ energy balance—EVEI,corr in Table 6.4—are practically independent of the ESS’ initial charge state, while the other values—EVEI, EUW—vary. This shows that for a given ESS, the total energy demand is independent on its initial state; however, it can influence a case study’s results if the energy change within the ESS is not considered carefully. Apart from energy concerns, influencing the power demand at a vehicle’s pantograph is possible, allowing to relieve the supply networks from power peaks—see also Figure 6.4. If discussed in that context, the ESS used need to have a sufficient capacity and charge/discharge capability, as for railway vehicles, powers of several MW are usual. Moreover, an intelligent charge- discharge control would support an as-ideal-as-possible grid relieve. But, as this is a load management topic, it is out of scope of this thesis. Nevertheless, it should be kept in mind that if the efficiencies of ESS can be increased above grid transmission efficiency, the application of on-board ESS might become a relevant topic in terms of energy saving, especially, if an additional increase in energy density allows to reduce weight and amount of required space for installation.

– 199 – Energy Saving Potentials in Railway Operations under Systemic Perspectives 0.0 (%) ), their ± E E ∆ Case 6 15.630.9 2.3 4.5 +0.1 361.6465.4 52.6 67.7603.6 +1.8 613.9 +1.8 653.5 87.7687.9 89.2 +3.2 594.6 95.0 100.0 +2.8 +2.2 86.4 +2.2 +1.7 (kWh) Share (%) E 0.0 (%) ± E ∆ Case 5 15.530.9 2.2 4.3 +0.1 358.0460.6 50.3 64.7625.9 +1.0 637.0 +0.8 676.4 87.9712.0 86.5 +7.0 588.3 95.0 100.0 +6.7 +5.8 82.6 +5.8 +0.6 (kWh) Share (%) E ) referred to case 0, where no ESS is used. The separated row E 0.0 0.0 (%) ∆ ± ± E ∆ Case 3 15.430.9 2.3 4.5 358.7460.8 52.8 67.8590.6 +1.2 602.8 +0.9 645.3 87.0679.2 88.8 +1.0 588.5 95.0 100.0 +1.0 +0.9 86.6 +0.9 +0.7 (kWh) Share (%) E 0.0 0.0 (%) ± ± E ∆ Case 1 15.430.9 2.3 4.6 358.7460.9 53.0 68.0589.9 +1.2 601.6 +0.9 643.5 87.1677.3 88.8 +0.9 588.4 95.0 100.0 +0.8 +0.6 86.9 +0.6 +0.6 (kWh) Share (%) E Case 0 15.430.9 2.3 4.6 354.6456.8 52.7 67.9 584.7596.9639.6 86.9 673.2 88.7 584.7 95.0 100.0 86.9 (kWh) Share (%) E Detailed Energy Demands with Different ESS. Indicated are the energy demands of different subsystems or measurement points respectively ( takes initial and ﬁnal charge state of the ESS into account, i.e., the initial energy is added, the ﬁnally stored energy subtracted from the directly measured VEI related to the energy delivered to the grid (generator output), and the relative change ( Subsystem Traction Drive Chain Auxiliary HVAC VEI Feeder Substations Grid VEI, corr Share VEI,corr energy. HVAC—Heating, Ventilation, Air Conditioning; VEI—Vehicle’s Energy Input, i.e., the pantograph. Table 6.5:

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Conclusion. Given the results obtained and the discussion conducted above, there is—at the time being—no use in applying on-board ESS in AC-fed mainline railways. However, if there is no (or a limited) feedback capability into the grid, an application of ESS is to be preferred over converting braking energy into heat. The same applies for non-electrified lines, where with diesel-electric vehicles, the braking energy might be used. Moreover, the usage of electrically- only driven trains may become possible by battery-based ESS (see below). If, in future, higher energy densities and/or higher charge-discharge efficiencies can be reached, the application of ESS might become interesting in more cases, possibly also for AC-fed mainline railways. Anyhow, regarding load management, on-board ESS might be beneficial, as they allow to relieve the feeding grid from power peaks—but this discussion would be out of scope of this thesis. To be mentioned is the striking difference between the results of this case study and the findings presented in various publications. While the prevailing literature claims saving potentials of up to 30 % (see section 4.8), this study concludes the application of ESS to be disadvantageous. The reason for this is seen in the fact that most scientific research focuses on (traditional) DC systems with no or strongly limited feedback capability. Then, storing the energy is advantageous over converting it into heat. Moreover, the AC system investigated here is interconnected, thus basically always allowing to feed back energy to the grid. As its transmission efficiency is above the ESS’ charge- discharge efficiency, the prior method is advantageous. Given that, it is considered likely that in a comparison of inverting substations to ESS application in traditional DC systems, the inverting substation and thus the interconnected transmission system results to be preferable; cf. also section 6.6.

Additional Note. As an interesting “side note”, the battery train presented by Mach, Buschbeck, et al. (2018) shall be mentioned here. For this vehicle, a storage capacity of 300 kWh is installed that lasts for about 35...40 km; an increase to 400 kWh is intended. Given the results presented priorly, the proposed use of this vehicle for non- or partially electrified lines appears as useful application in order to replace diesel driven vehicles—especially in terms of “green mobility” and usage of regenerated braking energy. But, from an energy demand perspective, an application on electrified lines does not make sense— except the service is extended into non-electrified areas. However, if there is a significant increase in charge/discharge efficiency in the future, the application of ESS in mainline applications has to be rethought. Even more, if an increase in energy density and thus, a reduction in weight and volume can be reached.

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6.5 Case Study 4: Power Supply Modiﬁcations

Test Question Which changes in energy demand result from changing the power supply structures—i.e., adding substations, disabling feedback capabilities, and adding track-side energy storage systems (ESS)—and what are the implications for energy saving? Test Case For an existing line (Biel–Neuchâtel–Yverdon) with given power supply infrastructure, different modiﬁcations as mentioned above are simulated and compared to the Status Quo in terms of energy demand. Involved Systems Mainly, the electric energy supply is affected. However, in cases of rejected braking energy, also the vehicle is involved.

Background. The energy supply of railway systems is usually historically grown, resulting in today’s system structures. New technologies as power electronics and energy storages allow to consider changes in this structure in order to improve the system—also in terms of energy demand. Additionally, ideas as shortening transmission distances by adding substations are tested. Facing the fact that ESS and inverting substations are discussed in literature—but mainly in the context of DC-fed metro systems—their inﬂu- ence and applicability in AC-fed mainline railways is tested here. To give a more complete picture, the usually available feedback capability of AC systems is partially disabled in order to investigate this inﬂuence as well.

Study Description. To analyse track-side ESS, a slight extension of the program from chapter 3 was necessary: The ESS’ specification (i.e., capacity, initial charge, min/max charge, charge/discharge efficiencies, maximum power) was included, additional result columns for power and charge were introduced. Moreover, the substation evaluation function was extended so that any power is directed to/from ESS first before the substation’s grid interconnection is used. These additional lines of code are shown in Figure 6.5. For the evaluations in the context of this thesis, the charge/discharge efficiencies are defined to be 92 % each as default values of the respective properties (ESS_eta_c, ESS_eta_d); a time step width of 1 s is assumed for reasons of simpler implementation. However, both can be adjusted with some effort in terms of time, but with no change in logic or structure of the program. As study object, the Biel–Neuchâtel–Yverdon line is selected again. The geographic situation together with the electric supply feeding this double track line is depicted in Figure 6.6. It is assumed that in the original configuration, each section—between two substations—is fed from both sides (two-sided feeding). This situation is chosen as reference case; the following variations— (sub-)cases—were performed in addition to the default (“as is”) case 0:

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1 % IfESS can be used, use it first(with priority above 2 % grid connection) 3 %SIGN convention: ESSpower>0 => charging theESS 4 %(deliver energy fromESS) 5 if sum(obj.nodes.(curr_node).Pout) > 0 && ESS_can_deliver 6 7 if sum(obj.nodes.(curr_node).Pout) > obj.nodes.(curr_node).Pmax(3) 8 P_ESS = obj.nodes.(curr_node).Pmax(3); 9 else 10 P_ESS = - sum(obj.nodes.(curr_node).Pout); 11 end 12 13 elseif sum(obj.nodes.(curr_node).Pout) < 0 && ESS_can_take 14 15 if sum(obj.nodes.(curr_node).Pout) < obj.nodes.(curr_node).Pmax(4) 16 P_ESS = obj.nodes.(curr_node).Pmax(4); 17 else 18 P_ESS = - sum(obj.nodes.(curr_node).Pout); 19 end 20 21 else 22 23 P_ESS = 0; 24 25 end 26 27 % No direct interaction betweenESS and feeding grid! 28 if abs(P_ESS) > abs(sum(obj.nodes.(curr_node).Pout)) 29 P_ESS = - sum(obj.nodes.(curr_node).Pout);%SIGNCONVENTION! 30 end 31 32 % Determine newESS energy 33 obj.nodes.(curr_node).Pstore = P_ESS; 34 E_ESS = obj.nodes.(curr_node).E; 35 % include efficiencies 36 if P_ESS > 0 37 E_ESS = E_ESS + obj.ESS_eta_c * P_ESS * 1;%THE1sSHOULDBEDYNAMIFIED 38 else 39 E_ESS = E_ESS + obj.ESS_eta_d * P_ESS * 1;%THE1sSHOULDBEDYNAMIFIED 40 end 41 obj.nodes.(curr_node).E = E_ESS; 42 43 %DETERMINENODE'SINPUTPOWER 44 % different output powers need to be summed up before 45 % applying the efficiency 46 obj.nodes.(curr_node).Pin = sum(obj.nodes.(curr_node).Pout) - P_ESS; 47 if obj.nodes.(curr_node).Pin >= 0 48 obj.nodes.(curr_node).Pin = ... 49 obj.nodes.(curr_node).Pin / obj.nodes.(curr_node).eta1; 50 else 51 obj.nodes.(curr_node).Pin = ... 52 obj.nodes.(curr_node).Pin * obj.nodes.(curr_node).eta2; 53 end

Figure 6.5: Additional Source Code for ESS Inclusion into Substations, applied in function calcNodes2 of class energySupply: lines 1–41 and term -P_ESS in line 46. Any power delivered from or to a substation is directed to/from ESS ﬁrst before the grid interconnection is used (own illustration).

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Biel (BI) ×UW Biel

Neuchâtel (NE)

×UW Neuchâtel

N Yverdon-les-Bains (YV) 10 km UW Yverdon× mobile, 20 MVA max

Figure 6.6: Geographic Overview on the Biel–Neuchâtel–Yverdon Line; stations represented as black bullets and labelled, existing substations indicated with red crosses and labelled as “UW”.

A Disabling the feedback capability of substation Neuchâtel (mid of the line) B Disabling the feedback capability of substation Yverdon (end of the line) C Disabling the feedback capability of all substations D Adding an ESS to Neuchâtel substation (in reference case) E Adding an ESS to Neuchâtel substation while disabling feedback capability F Adding an ESS to all substations while disabling all feedback capabilities G Adding two substations, in the middle between the substations each

Thereby, each ESS is deﬁned to have a capacity of 100 kWh and a charge/discharge power of 700 kW—based on the ESS presented by Ceraolo and Lutzem- berger (2014) and De La Torre, Sánchez-Racero, et al. (2015)—and as on-board ESS No. 5 from case study 3. The different set-ups from the above listing are schematically illustrated in Figure 6.7. In addition, two initial charging states are used for the ESS where available: 50 % (D, E, F) and 20 % (D2, E2, F2). The run times are based on the ofﬁcial 2018 timetable of the inter-city service Biel–Yverdon. Two—nearly simultaneous— trains of this service are evaluated (arr—Arrival; dep—Departure):

1. Biel arr .13, dep .16—Neuchâtel arr .32, dep. 34—Yverdon arr .51, dep. 53 2. Yverdon arr .05, dep .06—Neuchâtel arr .24, dep .26—Biel arr .43, dep .46

In order to accelerate the simulation, the entire timetable was shifted by –5 Min; the services are operated by two series 500 vehicles with standard settings as given in appendixes B and C. As evaluation quantity, the energy delivered to all substations is chosen.

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20 MW 20 MW ≤ ≤

YV NE BI YV NE BI

(a) Case 0—Situation as it is: Two sections with two (b) Case A—Disabling the feedback capability of the sided feeding each; feedback possible at all substations; Neuchâtel substation, which is in the middle of the line. feedback at Yverdon is limited to 20 MW.

20 MW 20 MW ≤ ≤

YV NE BI YV NE BI

(c) Case B—Disabling the feedback capability of the (d) Case C—Disabling the feedback capability of all of Yverdon substation at the end of the line. the substations along the line; i.e., unidirectional feeding.

20 MW 20 MW ≤ ≤

YV NE BI YV NE BI

(e) Case D—Adding an ESS to Neuchâtel substation (f) Case E—Adding and ESS to Neuchâtel substation without any further changes. while disabling its feedback capability.

20 MW 20 MW ≤ ≤

YV NE BI YV NE BI

(g) Case F—Adding and ESS to all substations and dis- (h) Case G—Adding two substations, each in the middle abling feedback capabilities (unidirectional feeding). of two existing ones, resulting in ﬁve sections.

Figure 6.7: Illustration of the (Sub-)Cases Investigated in Case Study 4. Thick lines represent the catenary and its feeding substations; thin lines grid and ESS—depicted as battery symbol— interconnections. Arrows indicate the possible directions of power ﬂow. YV–Yverdon; NE–Neuchâtel; BI–Biel (own illustration).

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Results. The results obtained from the simulations are given in Table 6.6. Case G shows the lowest total energy demand, which is 99.4 % of reference case 0. According to the fact that by adding substations the transmission distances are shortened and thus the losses reduced, this result is expected. More- over, as the transmission efficiency in electric systems is high (about 97.5 % for transmission lines, cf. section 2.2.4.1), the rather small influence of only 0.6 % seems plausible as well—the savings of 5 kWh summing up to 185 kWh per day and 67.5 MWh per year, calculated according to case study 2 (181/2 operating hours a day, two departures per hour and direction). However, this investigation does not consider the additional losses on the grid side, which may be zero if there is already a transmission line existing, but can be higher if a new line has to be built for feeding the additional substation(s). While most of the other results also meet the expectation, some interesting points are highlighted: For example, case C (no feedback, no ESS—108.2 %) does not show the highest energy demand but instead, case F (no feedback, all substations with ESS, initial charge 50 %—122.0 %) does. Especially in comparison with the lower initial charge case F2 (20 %—104.7 %), this result serves as interesting indicator: While in case F the ESS are discharged first before drawing energy from the grid, which is equivalent to preferring the less efficient source, the ESS have to be charged by regenerated energy first in scenario F2. Thus, the total demand of case F is higher than the one of case F2. Note that disabling the feedback capability of one substation has nearly no effect (case 0 vs A and B): With two-sided feeding, the energy can be fed back using the other substation of the respective section that offers this possibility.

Discussion. Summarising the results, the only measure showing a beneficial tendency in terms of energy demand is the shortening of transmission distances by adding substations. While this is an expectable result, the necessary investment should be taken into account as well. Given that only 0.6 % of energy (67.5 MWh per year on this service, which is roughly 17 households) are saved by adding two new substations, the costs for new substations are probably too high. Of course, recapitulating the dense traffic on this line (cf. Figure 6.3) that exceeds the investigated inter-city service, the savings are probably higher. However, there is no change in the order of magnitude to be expected, which means that there is no incentive to build new substations. On the other hand’s side, there is a high potential in ESS application if the feedback capability of the grid is limited: storing energy instead of converting it into heat via brake resistor is preferable, which can be seen from a reduced energy demand of case F2 when compared to case C. Moreover, it is seen in comparison of cases 0, A, and B that with two sided feeding, only one substation per section needs to be able to feed back—or store—regenerated braking energy in order not to increase the energy balance significantly compared to the no-feedback-no-storage case. This could be reached by making selected substations invertible, which will usually have a higher efficiency than ESS.

– 206 – Chapter 6: Empirical Evaluation 100.0 % the difference of energy stored in the ESS ESS E ∆ . In the rows below the energy demands, the relative energy ESS E ∆ – UW E represents the energy delivered from grid to substation, UW E 100.2 % 100.0 % 108.2 % 104.9 % 99.7 % 108.5 % 103.3 % 122.0 % 104.7 % 99.4 % the total amount of energy delivered to the railway system—i.e., — — — — –25 kWh +5 kWh –25 kWh +5 kWh –85 kWh +5 kWh — in,tot E 0 A B C D D2 E E2 F F2 G 100.0 % 100.6 % 100.7 % 100.6 % 114.0 % 105.5 % 100.2 % 109.2 % 103.9 % 122.7 % 105.3 % Energy Demands of Case Study 4. 1218 kWh 1220 kWh 1218 kWh1218 kWh 1380 kWh 1220 kWh 1253 kWh 1218 kWh 1219 kWh 1380 kWh 1297 kWh 1278 kWh 1263 kWh 1214 kWh 1383 kWh 1322 kWh 1280 kWh 1258 kWh 1211 kWh 1486 kWh 1275 kWh 1211 kWh ESS E UW in,tot E ∆ E Table 6.6: (if available), and demand compared to a selected base case (which is indicated using bold font) is given.

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Whenever ESS are applied, an intelligent control is necessary in order not to increase losses unnecessarily—as seen from comparison of cases F and F2. There, the negative influences of the ESS are caused by control, which prefers the ESS as sink and source all the time. Thus, there might be feeding from (less efficient) ESS only, instead of using the grid as energy source. Rather, supplying trains by discharging the ESS should be regarded as “necessary evil”. Therefore, it is important to make sure that the ESS only acts as supportive source; the grid should remain prioritised. Preferably, when designing ESS control, discharge is primarily regarded as instrument to ensure a maximum absorption of regenerated braking energy, possibly using different sinks—but not as source for trains or other users of electric energy. However, note that parts of this phenomenon result from system boundary effects: The definition of the system’s boundaries as well as its initial state (for instance, “bypassing” of energy via the ESS’ initial charge) are very important in many cases; moreover, the exact position of the trains, the timetable, and even the topography influence the results. To overcome these difficulties, a more comprehensive study covering all trains of a certain line over a longer period, as e.g. an entire operating day, could be used for more precise quantification. Preferably, also the scope in terms of geographic area should be extended. Anyway, the basic interrelation could be shown as discussed above, while an extended analysis—in space and time—would not add as much value to answering the research question as it would cost in time.

Conclusion. Generally, transmission distance shortening is advantageous in terms of energy demand, as long as the alternative method has a higher efficiency than the existing. This is given if substations are added to the supply infrastructure, or if by application of track-side ESS the waste of regenerated braking energy is avoided. However, investment cost should be kept in mind. Nevertheless, at the time being, there is no useful measure identified for application of ESS in AC-fed mainline systems: The costs for new substations are probably too high compared to the energy saving potential; the efficiency of ESS is below the grid transmission efficiency. However, as soon as the feedback capability of the grid is too weak to feed back the entire regenerated braking energy—as typical in traditional DC systems—the application of ESS is highly advantageous. Nonetheless, also in these systems, the application of reversible substations—i.e., implementing the feedback capability—is a valuable option. Especially if two-sided feeding is used, as in that case, only one substation per section would have to be equipped with an inverter. Which of the options is preferable depends on the efficiency of the respective systems. From the literature review, it is to be expected that—at the time being—a reversible substation (inverter with efficiency above 95 %, usually) is to be preferred over the installation of an ESS (charge-discharge efficiency of ~85 %). However, as already seen in CS 3: If higher energy densities and/or charge- discharge efficiencies are reached, ESS might become interesting in more cases.

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6.6 Case Study 5: Energy Storages in Unidirectional Supply Systems

Test Question Which applicability for energy storage systems (ESS)—on- board or track-side—results in supply systems without feedback capability? Is there a signiﬁcant advantage for on-board or track-side ESS? Test Case For the Biel—Yverdon inter-city service, the energy demand of two train runs using ESS—track-side and on-board—is tested, assuming there is no possibility to feed back energy to the grid. The results are compared to the real case, where the feedback capability is given. Involved Systems Depending on the scenario, power supply and/or vehicle are modiﬁed. In each case, both of them are involved into the evaluation of this case study. Applicability Due to its non-realisability and model limitations concerning the supply system, this case study is to be seen as qualitative / indicative only.

Background. From two prior case studies (CS), CS 3 and 4, it can be seen that in systems without energy feedback capability, the total energy demand increases signiﬁcantly—in that case, the application of track-side ESS improves the energy balance (CS 4). Moreover, there is no signiﬁcant change in energy demand when adding on-board ESS in supply systems with feedback capability (CS 3). From these observations, the test question given above results.

Study Description. Besides the “default”/reference case 0—which represents the situation as it is found today—the feedback capability is disabled for the further cases that are deﬁned as follows:

A No use of ESS, neither on-board nor track-side B Use of track-side ESS in all of the substations C Use of on-board ESS in all vehicles D Use of (track-side and on-board) ESS in all entities

The ESS used is the same for all entities, vehicles and substations: Capac- ity of 100 kWh, maximum charge/discharge power 700 kW, installation mass 3 t, charge/discharge efﬁciency 92 % each, (dis)charge limits 20 % and 80 % of the nominal capacity. For reasons of simplicity, all ESS are assumed to be charged to the same level at the beginning of the simulation (50 %); a second run is performed for 20 %. In all cases, the Biel–Yverdon line according to Figure C.2 with substations at Biel, Neuchâtel, and Yverdon is investigated. Two series 500 EMUs are operating the service according to the original 2018 inter-city schedule, cf. also Table 6.3, trains 1 and 3.

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Table 6.7: Results of Case Study 5. For an initial charge of 50 % and 20 % respectively for all ESS within the system, the energy delivered from grid to substations (EUW), the energy change of track- side (∆Ets) and on-board (∆Eob) ESS, and the total energy delivered to the system—below substation 0 level—(Etot) are given. The relative energy demands are related to the “as-is”-case (Erel,0) and the A “no-feedback-at-all”-case (Erel,A). Case 0 A B C D Initial Charge — — 50 % 20 % 50 % 20 % 50 % 20 %

EUW (kWh) 1218.1 1379.7 1382.8 1279.8 1350.6 1388.0 1406.4 1342.0

∆Ets (kWh) — — –84.9 +5.1 —— –85.6 +4.4

∆Eob (kWh) — — —— +45.2 +85.3 +45.2 +85.3

Etot (kWh) 1218.1 1379.7 1467.7 1274.7 1305.4 1302.7 1446.8 1252.3

Erel,0 100 % 113 % 120 % 105 % 107 % 107 % 119 % 103 %

Erel,A 88 % 100 % 106 % 92 % 95 % 94 % 105 % 91 %

Results. The results are collected in Table 6.7. The energy delivered by the grid is used as measure; of course, the change of energy in the ESS is considered as well. As to be expected, the energy demand increases significantly when disabling the feedback capability, as to be seen in the comparison of cases 0 and A. But, as a very interesting result, the total energy demand ends up to be even higher when using track-side ESS and assuming an initial charge of 50 %. In that case, the same effect as already discussed in CS 4 shows up, which means the following. Instead of drawing a significant share of energy from the grid, the vehicle is (nearly) completely fed by the ESS. Regarding the efficiencies of grid and ESS, higher losses occur when preferring the ESS—while the ESS is discharged, thus finally showing a lower charging state. Vice-versa, on-board ESS always reduce the energy demand compared to the no-feedback-no-storage case A. Apart from the fact that in this case, the transmission losses between vehicle and substation are omitted, there are significant differences in control. While for the vehicle, the ESS is charged first before feeding back and supports the movement by discharging at 50 % of the hourly power rating and above, the track-side ESS is implemented to be always first choice. Like this, the (less efficient) feeding by ESS is preferred over the more efficient feeding from the grid, thus increasing the losses. Consequently, a control that uses the ESS supplementary only—e.g. as soon as a certain output power is required—is the preferable solution.

Discussion. Altogether, it can be stated that the usage of (adequately controlled) ESS is always preferable over converting the regenerated braking energy into heat via brake resistor. As long as an adequate control is given, there is no signiﬁcant difference between track-side and on-board ESS in this set- up—see the 20 % pre-charge case. Still, there is an increase in energy demand with both types of ESS when compared to the default case (energy feedback).

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In comparison of on-board and track-side ESS, the track-side version has the advantage of being useful for all vehicles, independent on whether the vehicles have on-board ESS installed or not. Moreover, there is no additional mass added to / space required in the vehicles. On the other hand, on-board ESS additionally eliminate the transmission losses between vehicle and ESS. With this case study, results of the prior CS 3 and 4 are supported: On the one hand, the application of ESS is not useful where feedback into the electric grid is possible, as the transmission losses in these systems are below the losses of an ESS charge-discharge-cycle. On the other hand, the application of ESS is strongly recommended wherever any regenerated braking energy would have to be converted into heat using a brake resistor otherwise. This is not only the case in most DC supplied systems but also and especially for DMUs— supporting the idea of battery or, at least, ESS-supported diesel-electric vehicles as already mentioned in CS 3. Of course, the applicability of ESS in terms of energy demand will increase if the charge-discharge efﬁciency is increased towards AC-fed systems—as it was already discussed in prior case studies. With a weight and/or volume reduction—i.e., increased energy density—ESS will become more interesting for on-board use as well.

Conclusion. The usage of ESS in supply systems without feedback capability is a recommendable measure, as energy can be stored and reused instead of being converted into heat. Thereby, the difference between on-board and track- side ESS is, in this case study, non-significant. However, the fact that track- side ESS do neither add weight nor need space in vehicles nor that they are dedicated to a certain vehicle gives them a set of advantages—while in favour of the on-board ESS, an additional reduction of the transmission distances— and thereby losses—has to be mentioned. However, the most efficient way to reduce energy demand remains—as long as there is no significant increase in ESS efficiency—a bidirectional feeding system that allows to feed back regenerated braking energy to higher layers of the supplying network. Thus, inverting substations are even more recommendable than ESS in terms of energy saving.

6.7 Summary and Conclusion

In the case studies presented beforehand, systemic aspects of different approaches to energy saving in railway applications have been investigated: The inclusion of environmental conditions (wind) into the determination of speed recommendations, the synchronisation of braking and acceleration phases of different vehicles, and system variations in terms of usage of energy storage systems—on-board as well as track-side—and power supply modiﬁcations.

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Overall Results Overview. Overall, the results are mixed in terms of effects on the energy demand in (AC supplied) mainline railway systems; there was no measure identified with a significant positive effect. However, there is some potential in synchronising the braking and acceleration phases of different trains within the same supply section; even though “accidental” synchronisation or asynchronism may strengthen or weaken this effect. Also, measures shortening the transmission distances—e.g., placing additional substations—reduce the energy demand as long as the overall efficiency of the applied measure is above the transmission efficiency. For energy storage systems (ESS), the charge-discharge efficiency is usually below the transmission efficiency of electric supply systems. Thus, their application is not useful in systems where regenerated braking energy can be fed back to higher levels of the supply system. However, if there is no or only a limited possibility to feed back regenerative energy—i.e., parts of the energy would have to be converted into heat using a brake resistor—the application of ESS is highly recommendable as alternative solution. In that context, track- side ESS show the advantage of not adding weight to the vehicles while they can be used by all vehicles within the respective section; vice versa, on-board systems allow to reduce transmission losses between vehicle and ESS. Note that—nonetheless—the usage of inverting substations may have even more significant effects, as power electronic converters are usually more efficient than storage systems. In addition, nearly the full effect of inverting substations is, given a two-sided feeding, already reachable upgrading every second substation. With improvements in terms of efficiency and/or energy density of ESS, their application might, however, become attractive in systems with feedback capability as well. As long as there is no increase in ESS efficiency, the implementation of inverting substations in systems without feedback capability appears as preferable measure. Independently, special care should be taken concerning the ESS control design. Especially in the—priorly used—design of the track-side ESS’ control, where the ESS is preferred as energy source and sink in every situation, a negative effect has been observed: Partially, only the ESS delivered energy to the trains, which is a far less efficient way than drawing energy from the grid—an effect that scales with the initial charge of the ESS. With a control strategy that only supports the energy delivery by discharging the ESS—as for the vehicle, where the ESS comes in at 50 % of the hourly power rating—this effect does not occur that extensively. Altogether, the discharge of ESS is to be regarded as kind of a “necessary evil”—thus, an intelligent control strategy is important for an optimal usage of the potentials of an ESS. However, the control’s influence may be overestimated due to system boundary effects, for instance initial charge. Expanding the scope of investigation in terms of time (e.g., to an entire day) as well as geography (analysing an entire network) and properly evaluating the energy flow including all boundaries to neighbouring systems, this effect’s influence could be reduced to a non-significant level.

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An additional, positive effect of ESS—which is regarded as being out of scope of this thesis—is the possibility of their application in load management. With ESS, on-board and/or track-side, power peaks can be reduced, thus relieving the supply system. Given a sufficient rate of reliability, this might allow to dimension it for smaller powers, thus reducing the installation costs. Also, the evolution of (on-board) ESS allows to operate battery trains, which may run electrically in non-electrified sections. Apart from being able to reuse regenerated braking energy, these vehicles contribute to a better air quality in the surroundings of non-electrified lines and may possibly reduce the environmental strain caused by diesel driven vehicles. Vice versa, the inclusion of environmental conditions into the determination of operational speed recommendations did not deliver any additional energy savings; in contrast, there are negative effects dominating. This occurs mainly due to the quadratic influence of the relative speed between air and vehicle, which exceeds the effect of the wind angle. However, the influence of the wind angle must not be neglected in model calibration, as it still affects the energy demand of a train run in a non-negligible extent.

Limitations of Applicability. For all measures, it is important to mention that they are subject to—partially significant—limitations concerning their applicability. If the measures contain operational changes, be it either concerning the timetable—e.g. for synchronising braking and acceleration phases—or be it real-time—e.g. including environmental influences—there are effects on other services in the network. These effects will be easy to handle in homogeneous systems as, for instance, metro systems. In more complex systems with inhomogeneous traffic as in mainline railways with regional, long-distance, and freight services, there are many mutual influences that might make the application of these measures impossible. As an example, real-time speed reduction for one train may cause the following train to be delayed, which again may have several consequences on the energy demand on this affected as well as of other services, their punctuality, etc. Measures affecting the infrastructure are usually linked to a non-negligible investment, e.g., when building additional substations—possibly even with new transmission lines—or adding energy storages. Moreover, this kind of measures may cause more or less resistance of people considering themselves affected, which might slow down corresponding projects significantly. Alto- gether, infrastructure-side measures usually require high efforts that seem, especially in AC-fed mainline railways as investigated here, as too high— particularly when compared to their saving potential.

Importance of System Boundaries. The necessity of properly and clearly deﬁning the boundaries of the investigated system became obvious. Especially in the case where ESS have been employed, their initial state of charge has to be taken into account carefully. This due to the fact that—assuming the ESS

– 213 – Energy Saving Potentials in Railway Operations under Systemic Perspectives is installed below substation level—via the initial charge, additional energy is brought into the system—energy that is not delivered via grid and substation during simulation! Consequently, this initial energy may falsify the entire result and especially its interpretation if not considered adequately. One possible approach—that has been followed here—is to take the initial charging state into account as energy to be added, while the ﬁnal charging state is taken as energy to be subtracted from the amount of energy.

Significance of Interconnected Supply Systems. From case studies 3 to 5 (sections 6.4 to 6.6), it was seen that with today’s state of the art, the energy feedback of regenerated energy into the supply network is always to be preferred over the application of an energy storage. This is especially valid for AC systems with a high transmission efficiency of more than 95 % but remains still valid for most DC systems: Also these usually show an efficiency of above 85 %, which is the mean efficiency of an energy storage system’s charge-discharge cycle. Thus, it is more recommendable to investigate the application of invertible substations for DC systems than the use of energy storages. Regarding on-board application, the ESS’ disadvantages of additional weight and space requirements at the expense of payload and/or seats have to mentioned. Of course, this conclusion will have to be rethought as soon as significant improvements in storage (battery) technology are reached. First, if the efficiency of energy storage systems reaches the transmission system’s efficiency, the above conclusion becomes invalid and thereby, the use of ESS potentially interesting. Second, concerning on-board applications, the energy density plays an important role, as with a considerably higher energy density, less weight would be added to the vehicles while less space would be required.

Energy Saving as Challenge of Application. Going back to the analysis of the railway system and its energy saving potentials (chapters 2.3, 4, 5.3, and 5.4) and combining those findings with the results of the case studies, it has to be said that at the time being, the energy saving potentials in railway systems are known. Suggested and analysed approaches as the inclusion of environmental factors or the application of different methods—usage of ESS, synchronising of braking and acceleration phases—did not deliver additional potentials. Thus, energy saving topics should be regarded as “problems of application”, which means that known methods have to be applied and fitted to real situations. However, research potentials are seen in terms of energy storage development (charge-discharge efficiency, energy density/weight), and component or subsystem improvement (e.g., more efficient engines). Still, energy saving often conflicts with other aspects that are relevant in railway operations. For example, reducing top speed is well-suited for reducing the energy demand. However, it might significantly decrease the offer’s attractiveness in terms of travel time and connecting services.

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7.1 Overall Summary and Conclusion

General Overview. This thesis deals with the comprehensive topic of energy saving in railway operations. To answer the research question, different steps have been undertaken:

– Based on literature, derivations from related ﬁelds of research, and available data, a comprehensive railway system model was built. It depicts the entire system from primary energy down to the vehicle’s wheel. However, to keep it manageable, simpliﬁcations were necessary.

– The model was implemented as MATLAB simulation tool; calibration and validation showed a high precision of the results when compared to measured real world data. The implementation was done using object oriented programming, allowing to easily adjust—especially, more precisely describe— single subsystems or functionalities of the model in consequent research. – A literature study was performed on energy saving in railway systems, which delivered an extensive list of results. However, most of it focuses on subsystems and (unidirectionally) DC fed systems. Partly, extreme simpliﬁcations are applied. Based on literature and the system model, systemically relevant approaches to energy saving in railway operations were derived. – In case studies, the model’s applicability was proven. Being focused on 15 kV,16.7 Hz systems, the most powerful measure to reduce the energy demand consists in strongly interconnected supply systems. This often outper- forms all others or even overrides their potential.

Main Conclusions. Two major conclusions are drawn from this work. First, the most efficient approaches to energy demand reduction are known in literature; the classification given by Dube, Fraas, et al. (2013) can be confirmed: optimise operations, avoid energy waste, and reduce losses. Second and consequently, the importance of academic research on this field is questionable. Rather, applied research should be preferred, i.e., evaluating the available, known measures for specific cases and possible applications for which approach is the most useful to be followed. However, there is potential for further academic research, but more in terms of systems analysis and modelling as well as materials and component improvement.

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Contradiction to Literature. A special point to be highlighted is the difference between the findings of this work and the ones presented in other publications: While in literature, partly huge saving potentials are obtained, this study shows some possible measures but with rather small effects. This difference is traced back to two causes: First, the literature’s studies usually investigate traditional DC (metro) systems. These are normally fed by unidirectional substations, which significantly limits the possibility to exploit regenerated energy: The energy can only be used when a sink is available within the same supply section. Additionally, supply sections in DC systems are usually shorter than in AC systems due to the lower voltages, which result in higher currents and losses. These disadvantages do not occur in centrally fed AC systems, thus, this factor does not exist in AC systems. A second reason might be seen in the scope of investigation, which is usually strongly limited in literature. But, when only investigating one substation, one train, or one station, the effect within this small area might be significant. However, effects occurring outside this area are not taken into account—effects that might relativise the amount of energy saved, or that even might reduce or compensate the positive effect, as it was discussed in the case studies.

7.2 Answer to the Research Question

7.2.1 Hypotheses Discussion This thesis’ research question and hypotheses were formulated in section 1.3 to guide the work. Based on the results obtained during the study, the hypotheses are shortly discussed in the following before addressing the research question.

1) A functional-qualitative, energy oriented, and energy carrier independent system description (model) exists. From the literature study presented in chapter 2, such a system description was derived, describing the railway system from primary energy down to a vehicle’s wheel. This description is based on the system’s functions—as delivering energy to the vehicle or accelerating/decelerating a vehicle—and is independent of the energy carrier in most of its parts. Thus, this hypothesis can be accepted; the model description can be found in section 2.2.

2) This model can be built hierarchically from sub- and sub-subsystems. The quantitative system model that was derived from the qualitative system description is depicted in Figure 2.3 (p. 18). In this ﬁgure, the hierarchical structure of the system can easily be seen: The railway system itself consists of the ﬁve subsystem vehicle, energy supply system, track, operation control, and environment. These subsystems again can be divided into different sub- subsystems, etc. Therefore, this hypothesis can be accepted.

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3) From this system description, a quantifiable model can be derived. As shown in section 2.4, the subsystems vehicle, energy supply, and environment can be described using a set of equations. The parameters of these equations are either obtainable from literature and/or data sheets; other parameters are defined by the other subsystems, as slope (track) or currently valid speed limit (operations). All of the parameters used in the system equations were determined, the model was implemented as simulation tool in chapter 3. Thus, the entire model is fully quantifiable; the hypothesis is accepted.

4) System properties, system states, state-changes, or external influences translate into model parameters and/or input variables. For the simulation tool developed in chapter 3, the subsystems vehicle, environment, and energy supply were implemented as classes (objects). These objects have methods—implementing the equations and thus the functionalities—as well as properties (parameters). The latter are determined by the (sub-)system itself (e.g., vehicle mass); from external influences, e.g. the HVAC demand of a vehicle from the outside air temperature; as result of a state change (e.g., energy demand for a speed change); etc. Also, the object’s states—e.g., the vehicle’s current speed and power demand—translate into model properties (parameters). As input variables, only few configuration values are required. Thus, the system can be parametrised, allowing to accept this hypothesis.

5) Improvement potentials for the sub- and sub-subsystems are largely known and well-documented in literature. The literature on energy saving in railway applications is quite comprehensive and was discussed in chapter 4. Thereby, many different approaches to energy saving applying a variety of methods was found. Even though the aims of improvement can be grouped as well as the different measures proposed, a comparison to the hierarchical model shows that the possible potentials are covered. Also, exotic approaches and studies on systemic aspects were found— even though the latter are rare. Consequently, this hypothesis is accepted.

6) Today’s state of analysis for potentials in a systemic perspective including measures’ interactions is rather weak. Even though the literature predominantly deals with potentials and measures that are applied and evaluated in sub- or even sub-subsystems (component improvement), there are sporadic investigations on systemic aspects. Some of them starting from the research focused on components, then taking additional aspects into account; others—as presented in section 4.7—directly focusing on systemic aspects. The analysis performed during this work did not deliver sig- niﬁcant additional systemic potentials; thus, this hypothesis has to be rejected.

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7) There are different improvement objectives whose application is mainly depending on network properties, actual operating conditions, and involved actors. Based on the ﬁndings of chapters 4 and 6, it can be said that the applicability of different measures/approaches is highly depending on each kind of actual surrounding conditions. Therefore, the applicability of measures has to be evaluated for each single case individually, considering as much of the system as possible, as some effects are only observable on a systemic level. In addition, different improvement objectives—e.g., trafﬁc density vs. energy saving—may be contradictory. Consequently, this hypothesis is accepted.

8) Applying a system-wide approach allows to find system-optimal solutions. Especially during the case studies of chapter 6 it turned out that systemic effects should not be neglected when aiming for system-optimal solutions; however, the effect obtained is rather small. This mainly due to the fact that local measures may have effects outside the originally modified area—effects, that may support or reduce the desired result. In addition, it was seen that the definition of system boundaries may have an influence on a study’s results. Consequently, these boundaries should be set as broad as possible, using an as detailed model as possible—findings confirming this hypothesis, which is thus accepted, even though it has to be admitted that the impact is rather small.

9) The effectiveness of existing energy saving approaches can be classified. As already discussed for hypothesis 7, there are many influences on the applicability of energy saving measures and approaches, which may heavily depend on the respective surrounding conditions. Thus, a general classification of energy saving approaches is not possible; however, for a certain application, the practicality can be classified using systemic approaches. Therefore, this hypothesis cannot be accepted globally but for specific cases of application.

Altogether, most of the hypotheses could be accepted: It is possible to hierarchically build an energy oriented, energy carrier independent railway system model from primary energy to wheel, which can be quantiﬁed and implemented as simulation tool. Thereby, all necessary information is included via model/input parameters. In documented research, many approaches and even more investigations on railway energy optimisations exist—which are mainly, but not exclusively, focusing on small subsystems or component optimisation. How- ever, there are systemic analyses that cover most of the related potentials. It was also found that the applicability of energy saving measures is highly depending on the concrete situation, resulting in the fact that there is no possibility to globally rank the available measures according to their impact. How- ever, when evaluating measures for a speciﬁc application, a broad and comprehensive system model should be used while special care should be taken on the system boundaries: Otherwise, effects outside the investigated part of the system might support or hinder the measure’s impact without being noticed.

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7.2.2 Research Answer Finally, having the research work conducted and documented as well as having considered the hypotheses formulated in the beginning of this thesis, the overarching research question and its answer can be discussed. For reasons of convenience, the research question is repeated:

Which energy-oriented optimisations in subsystems show positive effects considering the entire railway system— and which additional saving potentials disclose by holistic analysis?

For the first part of the question—subsystem optimisations—it can be seen from the model equations that any subsystem or component optimisation, which is carried out properly and carefully, will have a positive effect on the entire system. As an example, the improvement of an engine’s efficiency is presented: By this measure, the energy demand at its terminals is reduced for a given traction task. Assuming no other changes within the system, this lower energy demand is going to scale through all other system parts, all of them having an efficiency below 100 %. Thus, the primary energy demand is reduced by increasing one single component’s efficiency. However, the condition of “properly and carefully” engineered optimisations should be kept in mind: If there are other changes linked to this efficiency increase, as an increase in weight, the analysis will have to be carried out more in detail, as an increased weight of the vehicle increases the amount of energy required to move the vehicle, i.e., overcoming its motion resistances. Concerning systemic potentials, i.e. the question’s second part, only few approaches were identified in which two or more subsystems need to interact in order to create the saving potential. The influence of environmental conditions showed to be important for correct model calibration but does not suit as broadly applicable measure. Also for the other approaches under investigation it was found that their potentials are rather small in 15 kV,16.7 Hz systems: inclusion of environmental influences, synchronisation of acceleration and braking phases, and the shortening of transmission distances. En- ergy storage systems even proved to be counterproductive. Rather, it could be shown that the potentials are broadly known in literature—but their evaluation is usually done under too strong limitations, resulting in their results mainly being vacuous considering the primary energy demand. Altogether, the most important finding can be summarised as follows: Ap- propriate energy-oriented optimisations—be it in subsystems / components or be it on a systemic level affecting multiple subsystems—show positive effects on the entire system if [a] they are carried out properly and carefully and [b] if they are designed and evaluated on a systemic level considering as many mutual interactions as possible.

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7.2.3 Discussion of Methods In this work, different approaches and methods were applied. Retrospective, some proved to be good, while others leave room for improvement.

As basis for model building and energy saving potentials identification, the literature review proved to be the correct approach. Lots of literature exist on both topics, allowing to draw a significant share of information from this source. Even though many publications on modelling date back some time, the resulting model is quite precise. Missing information could successfully be deduced by studying neighbouring fields of research or derived from data delivered by railway operators. With publications on energy saving, two challenges could not be solved properly: Finding the most relevant literature in a huge amount of papers listed in scientific databases, and getting aware of relevant publications in non-listed journals—especially affecting professional journals in German.

To keep the model’s complexity manageable, simplifications were necessary. Yet, train dynamics are depicted in detail with not much room for improvement; the description of the electric power supply above substation follows the IEC’s recommendations and can be regarded as good model. Vice versa, a more detailed model of the traction system would be preferable, as efficiencies do not properly picture the reality—electro-magneto-mechanical models would be desirable. However, these are complex and out of this work’s scope. Moreover, the railway energy supply between substation and vehicle was massively simplified, resulting in the impossibility to describe DC systems adequately. Even though this was necessary, it shows that a slightly different approach should be preferred, which is co-simulation. Anyhow, this is not a problem of modelling but of connecting various domains, as different levels of energy supply, driving dynamics, and traction technology. In this thesis, a first approach was taken, proving that a model covering the entire system can be built—which was not reached in the Railen- ergy project. As a lesson learnt, the recommendation of co-simulation results. With this approach, each subsystem would be modelled separately, communi- cating with each other using interfaces. Basically, this is prepared using object oriented programming; however, the electric supply would require an own iterative process with multiple loops during one time step of the superordinate simulation loop based on driving dynamics.

As an additional challenge, the intelligent determination of parameters and their variation remains. While in this thesis, values have been set by hand in order to prove the functionality of the underlying concept, the application of evolutionary algorithms or similar should be thought of for future work.

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7.3 Perspectives for Further Research

During this thesis, many fields of technology, engineering, and research were touched. In most of the areas, solid basics were found and used as basis for this research; in others, additional work was required in order to enable answering the research question. From these fields as well as from this study itself, some perspectives for further research open up. The most important perspective is seen in co-simulation: Modelling each of the subsystems introduced in this thesis on itself—in classes, or even as stand- alone simulation tools—and connecting them via interfaces and APIs. Thereby, a proper and adequate modelling of each subsystem (domain) would be ensured, while simultaneously, the important aspect of holistic system analysis comprising all subsystems could be fulfilled. Thus, the author strongly recom- mends to conduct research concerning suitable modelling and simulation. Thereby, the inclusion of all possible factors into the determination of the energy optimal operation of a railway line or network should be taken into account. This comprises on the one hand’s side the application of a precise, comprehensive system model for the calculation, while on the other, influences that have been neglected until now should be included—especially aiming for environmental influences as wind, precipitation, etc. Then, the development of an intelligent, relevant parameter variation method should be searched for; evolutionary algorithms might serve as a sub-problem solution. Another field that offers a significant potential is system modelling: Descrip- tions of the motion physics in railway operations seem to be forgotten since some decades. Dating back that long, their precision is questionable; it is expected that with new methods, better models could be found. These would allow to more precisely determine the energy demand, more clearly showing the potentials of certain measures—and probably even to find new approaches to energy saving. Especially in the field of aerodynamics, a lot of research seems to be going on; however, only few publications are available. Moreover, there are research potentials seen in other fields than traffic engineering. For instance, the availability of improved energy storages—especially with significantly higher energy densities—might essentially change the result of research works as the one being on hand. The same might apply for material sciences (e.g., more lightweight materials). However, the main potential in the field of railway’s energy demand optimisation is seen in applied research. As basically all approaches and measures in order to reduce the system’s energy demand are known, the next step is to apply them in practice. This requires an adapted analysis for each specific situation as there are many interactions and dependencies; a general classification of saving potentials is not possible. Thereby, the findings from academia would find their way into practical application, finally really reducing the energy demand of railway systems.

– 221 –

Appendix

A Wordings and Deﬁnitions

In literature, science, and practice, many different nomenclatures can be found. Thus, the wording—i.e. nomenclature—used in this thesis is deﬁned below for some selected terms in order to avoid misunderstandings.

Ancillary System All parts of a traction system that are not directly related to propulsion; distinguished in auxiliary and comfort system. Auxiliary System All parts of a traction system that are not directly related to propulsion but necessary to fulﬁl the propulsion task, e.g. apparatus cooling/ventilation, battery charging, etc. Subset of the ancillary system.

Comfort System All parts of a traction system that are only used for (passenger) comfort issues, e.g. HVAC and Lighting. Subset of the ancillary system. Conversion System A (sub)system whose (main) task is to convert energy from one from to another, e.g. from fossil to electric or from electric to kinetic energy.

Drawn Energy Energy drawn by the vehicle from its supply system, measured at the vehicle’s energy input (VEI). This might be diesel fuel (at fuel door) or electricity (at pantograph). Drive System cf. Propulsion System

Energy According to Andersson (2016), energy is “a commodity that is traded or exchanged because of its physical or chemical energy content, which can be used to provide different services, converted to other forms, or stored”. Energy Balance Difference between energy demand and energy recovery of a conversion system (or a set of conversion systems). Thus, the energy balance can be improved by reducing the energy demand or by increasing the energy recovery.

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Energy Demand Amount of energy that a conversion system (or a set of conversion systems) draws from the transmission system. Keeping the final output forces constant, the energy demand of a system can only be reduced by improving the system’s efficiency; alternatively, the final output forces can be reduced. Energy Recovery Amount of energy that is delivered to the transmission system by a conversion system (or a set of conversion systems) which usually draws energy from the transmission system.

Power The physical quantity that is deﬁned as energy per time. Propulsion System Part of the traction system that is exclusively dedicated to the propulsion task, i.e. accelerating and braking the train.

Recuperation Ability of an electric drive system to generate electric energy while reducing the kinetic energy of the driven system; may also be used for the activity.

Traction Energy Part of the drawn energy that remains after the ﬁrst on- board conversion stage(s) at the vehicle; the exact point depends on the vehicle’s drive chain topology. Traction System The entire energy conversion system on-board a train including supply for ancillary systems. Transmission A (sub)system whose (main) task consists in transporting System energy from one geographic location to another one.

Vehicle’s Energy The point where the vehicle is supplied with energy. For Input (VEI) discontinuous supply (i.e. Diesel), the VEI is the fuel door; in case of continuous supply (i.e. electricity), the pantograph is considered as VEI.

– 226 – B Numbers and Parameters

Known Values

Parameters that are considered to be generally known will not be derived or cited. This concerns the following parameters and their values:

• Gravitational Acceleration g = 9.81 m/s2

Values from Literature

• Air Density (norm value for railway applications, Ta = 15°C, pa = 1013 mbar) kg 3 ρn = 1.225 /m (Wende 2003, p. 142)

• Air Density (norm value, Ta = 0°C, pa = 1013 mbar) kg 3 ρn,0 = 1.293 /m (Weidmann 2011, p. 10 of ch. 6) • Density of Steel ρ = 7.9 t/m3 (DMK/DPK 2003, p. 168) • Elastic Modulus of Steel kN 2 Esteel = 2.2E8 /m (Wende 2003, p. 110)

Derived Values

The values of wl,r, Qtot, and cw can be derived from measured coefﬁcients of the Davis equation (cf. chapter 3.2), Equations 3.13.3, p. 114.

The wheel wear can be estimated based on the assumption of a wheel being a perfect cylinder with volume (DMK/DPK 2003, p. 59) V = π · r2 · h. (B.1)

Given a new wheel diameter of dnew = 820 mm (Stolz 2007, p. 304) and a usual wear allowance of ∆r = 2 cm in radius—i.e., dworn = 780 mm—as well as a wheel thickness of 140 mm (Polach 2015b, p. 6), the difference in volume results to be 1 2 1 2 1 2 2 (B.2) ∆V = π · h · ( /2 · dnew) − ( /2 · dworn) = /4 · π · h · dnew − dworn 2 2 3 = 1/4 · π · 0.14 m · (0.82 m) − (0.78 m) = 0.0704 m (B.3) per wheel. With two wheels per axle and a steel density of 7900 kg/m3, a wear- induced mass difference of 111.2 kg per axle results for the ICN. With a certain safety margin, 150 kg per axle seems to be an appropriate estimation.

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Vehicle Parameters

In Table B.1, the parameters obtained and used for the series 500 EMU (ICN) are subsumed. The parameters are obtained from different sources.

• Weights/Masses: Either from Stolz (2007) and/or read from vehicle markings

• HVAC Model: Developed from data of EW IV and series 500 (cf. section 2.4.2.4) and applied to all other vehicles as well.

• Air Volume: Calculated from technical drawings for EW IV and Re 450 DPZ (Gen- eralsekretariat SBB 1988), estimated for other vehicles.

Deviating from Table B.1, two values have been adjusted during calibration (cf. section 3.3) and are set as follows:

• Aerial Resistance Coefﬁcient cw = 0.59 1 • Inner Air Flow Qtot scaled by /1.6

– 228 – Appendix B: Numbers and Parameters

Table B.1: Initial (pre-calibration) Parameter Values of the ICN for usage with the vehicle class. Indicated are the parameter, its value including unit, and the source on which it was based. Parameter Value Source 2 Vehicle Cross Section Ab 11.2 m (6) Breakaway Resistance 0.002 (E) Brake Allowance N (1) Aerial Resistance Coefficient cw 0.95 eq. 3.3 Constant Davis Coefficient A 2100 N (1) Linear Davis Coefficient B 68.4 N s/m (1) 2 Quadratic Davis Coefficient C 6.8688 N s /m2 (1) Running Treads’ Distance drt 1.5 m (1) Energy Feedback Capability YES (1) 13.45 5.33 0.410 Drive Chain Efficiency η 0.9246 − eq. 2.93 D · 0 0.021 0.962 − 13.45 5.33 0.432 Energy Preparation Efficiency η − eq. 2.96 EP 0 0.021 0.962 − Starting Tractive Effort FTr,S 210E3 N (2) Max Electric Brake Force FB,el,max 210E3 N (3)  0.0050 0.1400  − HVAC Model  0.0036 0.1162  eq. 2.95 − 0.0033 0.0287 − Fixed Frame Wheel Base la 2.7 m (4) Vehicle Length l 187.6 m (2) Adhesion Mass madh 105E3 kg (2) Maximum Payload mpayload 44E3 kg (1) Tare Weight mtare 355E3 kg (2) Number of Axles nx 28 (2) Number of Brake Discs nBD 56 (5) Power Rating Ph 5.2E6 W (1,2) 3 Inner Air Flow Qtot 46.2 m /s eq. 3.2 Supply Type c (continuous) (1) Retardation ϑ (4) Spec. Rolling Resistance wl,r 0.0006 eq. 3.1 El. Brake Min Speed vEB,min 5 km/h (E) Operational Top Speed vmax 200 km/h (2) 3 Inner Air Volume Vair 1050 m (E) Rotational Mass Factor ζ 1.09 (6)

SOURCES (E) Estimation (1) Data provided by SBB (2) Stolz (2007) (3) For modern vehicles, maximum tractive and braking force are equal (Meyer 2012, 2013) (4) Vehicle Markings (5) Two Brake Discs per Axle (6) Bomhauer-Beins (2017)

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Standard Parameter Values

Table B.2 shows the standard values used for simulation conﬁguration where no other value is indicated for the respective study. In Figure B.1, the parameter values for the different standard substations are given. The parameter values are “simply set” based on the ranges discussed in the modelling chapter 2.

Table B.2: Standard Values of the Major Classes’ Parameters. Object Parameter Value

Rail Condition Correction Factor fRC 0 Air Temperature T 20 °C environment a Wind (Source) Direction ψ2 0 ° m Wind Speed vw 0 /s m Acceleration Limit a 1.2 /s2 lim ± vehicle Payload mpayload 33 % Minimum Station Dwell Time 60 s Minimum ESS Charge 0.20 energySupply Maximum ESS Charge 0.80 ESS Charge/Discharge Efﬁciency 0.92

– 230 – Appendix B: Numbers and Parameters

Figure B.1: Standard Settings of the Energy System’s Substations; code line usage slightly modiﬁed for layout reasons (own illustration).

%SUBSTATIONSETTINGS(alphabetically ordered) SET.ENS.UWBI.type='UW';% Substation Biel SET.ENS.UWBI.Pmax = [inf, -inf, 0, 0]; SET.ENS.UWBI.eta1 = 0.95; SET.ENS.UWBI.eta2 = 0.95; SET.ENS.UWBI.storeC = 0; SET.ENS.UWBI.storeE0 = 0; SET.ENS.UWBI.lines = {'BINE';'NEBI'}; SET.ENS.UWBI.lineR = [ 0.3; 0.3]; SET.ENS.UWBI.posKM = [-2.400; -2.400]; SET.ENS.UWBI.secKM = [-inf,inf; -inf,inf];

SET.ENS.UWDI.type='UW';% Substation Deitingen SET.ENS.UWDI.Pmax = [ 20, -20, 0, 0]; SET.ENS.UWDI.eta1 = 0.95; SET.ENS.UWDI.eta2 = 0.95; SET.ENS.UWDI.storeC = 0; SET.ENS.UWDI.storeE0 = 0; SET.ENS.UWDI.lines = {'WASO';'SOWA'}; SET.ENS.UWDI.lineR = [ 0.3; 0.3]; SET.ENS.UWDI.posKM = [43.295; 43.295]; SET.ENS.UWDI.secKM = [-inf,inf; -inf,inf];

SET.ENS.UWNE.type='UW';% Substation Neuchâtel SET.ENS.UWNE.Pmax = [inf, -inf, 0, 0]; SET.ENS.UWNE.eta1 = 0.95; SET.ENS.UWNE.eta2 = 0.95; SET.ENS.UWNE.storeC = 0; SET.ENS.UWNE.storeE0 = 0; SET.ENS.UWNE.lines = {'BINE';'NEBI';'NEYV';'YVNE'}; SET.ENS.UWNE.lineR = [ 0.3; 0.3; 0.3; 0.3]; SET.ENS.UWNE.posKM = [30.898; 30.898; 30.898; 30.898]; SET.ENS.UWNE.secKM = [-inf,inf; -inf,inf; -inf,inf; -inf,inf];

SET.ENS.UWOL.type='UW';% Substation Olten SET.ENS.UWOL.Pmax = [inf, -inf, 0, 0]; SET.ENS.UWOL.eta1 = 0.95; SET.ENS.UWOL.eta2 = 0.95; SET.ENS.UWOL.storeC = 0; SET.ENS.UWOL.storeE0 = 0; SET.ENS.UWOL.lines = {'OLWA';'WAOL'}; SET.ENS.UWOL.lineR = [ 0.3; 0.3]; SET.ENS.UWOL.posKM = [-1.600; -1.600]; SET.ENS.UWOL.secKM = [-inf,inf; -inf,inf];

SET.ENS.UWYV.type='UW';% Substation Yverdon SET.ENS.UWYV.Pmax = [ 20e6, -20e6, 0, 0]; SET.ENS.UWYV.eta1 = 0.95; SET.ENS.UWYV.eta2 = 0.95; SET.ENS.UWYV.storeC = 0; SET.ENS.UWYV.storeE0 = 0; SET.ENS.UWYV.lines = {'NEYV';'YVNE'}; SET.ENS.UWYV.lineR = [ 0.3; 0.3]; SET.ENS.UWYV.posKM = [67.552; 67.552]; SET.ENS.UWYV.secKM = [-inf,inf; -inf,inf];

SET.ENS.UWWA.type='UW';% Substation Wanzwil SET.ENS.UWWA.Pmax = [inf, -inf, 0, 0]; SET.ENS.UWWA.eta1 = 0.95; SET.ENS.UWWA.eta2 = 0.95; SET.ENS.UWWA.storeC = 0; SET.ENS.UWWA.storeE0 = 0; SET.ENS.UWWA.lines = {'OLWA';'WAOL';'WASO';'SOWA'}; SET.ENS.UWWA.lineR = [ 0.3; 0.3; 0.3; 0.3]; SET.ENS.UWWA.posKM = [25.600; 25.600; 25.600; 25.600]; SET.ENS.UWWA.secKM = [-inf,inf; -inf,inf; -inf,inf; -inf,inf];

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C Line Characteristics

On the following pages, the lines used an investigated in this thesis—Olten– Solothurn and Biel–Neuchâtel–Yverdon—are shortly presented and charac- terised using graphical representations of their descriptive data. The data used to visualise the information have been provided by the Swiss Federal Railways (SBB) as digital track maps.

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Olten

Solothurn N 5 km

km vmax,N ( /h) 200

150

100

s (km)

i0 ( ) 20 h 10 s (km) -10

-20

r (km) 12 8 4 s (km) -4 -8 -12

2 Atun (m ) 75

25 s (km) 5 10 15 20 25 30 35

Figure C.1: Characteristics of the Olten–Solothurn Line, direction Solothurn–Olten. From top to bottom: Geographic overview; permissible speed for vehicles of type N (grey: direction Olten–Solothurn); slope; curve radii; and tunnel cross section area. For the sake of graphical representability, inﬁnite radii and tunnel cross sections are shown as zero-values (own illustration).

– 234 – Appendix C: Line Characteristics

Neuchâtel Biel

N 10 km Yverdon-les-Bains

km vmax,N ( /h) 200

150

100

s (km)

i0 ( ) 20 h

s (km)

-10

-20

r (km) 12

4 s (km) -4

-8

-12

2 Atun (m ) 75

s (km) 10 20 30 40 50 60

Figure C.2: Characteristics of the Biel–Neuchâtel–Yverdon Line, direction Yverdon–Biel. From top to bottom: Geographic overview; permissible speed for vehicles of type N (grey: direction Biel–Yverdon); slope; curve radii; and tunnel cross section area. For the sake of graphical representability, inﬁnite radii and tunnel cross sections are shown as zero-values (own illustration).

– 235 –

D Derivation of the Extended Wind Model

ABSTRACT ten neglected. For a network-wide view, In order to calculate, simulate, and op- this approach—which is widespread in timise the energy demand of train runs, industry and based on the stochastic of the running resistances have to be mod- wind and the mutual extinction of its elled. Mostly, simplified—as the Davis influences—is to a certain extent compre- equation—or approximated formulas for hensible. For a single train run, however, part resistances are used. This also ap- a neglect is not appropriate because the plies for the aerial resistance: for cw influence might be relevant. For this rea- value, effective surface area, air density, son, the derivation of a model that allows and relative air speed, either approxi- the integration of the yaw angle was car- mate or norm values are used in gen- ried out while facing a question of energy eral. Contrariwise, aerodynamic inves- demand analysis. tigations show that the aerial resistance Based on a short literature review in does not decay with the cosine of the in- the following section, the new model with creasing yaw angle as the use of rela- its derivation is presented in section 3, tive air speed indicates; in contrast, the whereby a possible wheel flange contact aerial resistance increases up to a yaw due to crosswind is considered. In the angle of 60°. In this paper, a numerical case studies presented in section 4, the model that includes this phenomenon is model is applied and its necessity proven presented; moreover, wheel flange con- before the findings are recapitulated in tact occurrence as consequence of side the fifth section. wind forces is discussed. The presented case studies confirm that the wind and 2 STATE OF THE ART its yaw angle may have a relevant influ- For modelling the railway system, var- ence on the energy demand. ious approaches can be found in literature, allowing a more or less precise anal- 1 INTRODUCTION ysis of individual phenomena. A gen- Energy saving is a present topic in eral approach considering all running re- the railway sector since some time, not sistances and being frequently applied is least as economic factor. In this con- the so-called Davis equation, text, more and more optimisation meth- 2 FR,l = A + B vt + C v . (D.1) ods are proposed to reduce the energy · · t demand—usually of a train run. Of- FR,l is the total running resistance force ten, the model equations presented by taking effect on a train travelling with Sachs (1973a) are used as basis. In speed vt. The coefficients A, B, and C are particular, with regard to the air re- determined during test runs and depend sistance, the phenomenon of yaw angle on the respective vehicle type. dependency—which is qualitatively described already in Sachs’s book—is of-

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Ab vz be mentioned. However, all equations α are based on one or more empirical pa- vw vw,y rameters (Sachs 1973a; Wende 2003). v w,x Already Sachs shows that a cosinus- Figure D.1: Definition of Yaw Angle α, which oidal shape of the aerial resistance force, is measured so that 0° α 180°. v the train’s as described by equation Equation D.3, ≤ ≤ t speed, vw the wind speed, α the yaw angle (own does not agree with measurements. In- illustration). stead, the maximum air resistance occurs at α 12° or α 60°, depending on ≈ ≈ the experiment (Sachs 1973a). A descrip- However, for a more precise analysis of tive equation, however, is not presented. energy demand that is also meant to pro- The angle of about 60° manifests itself vide information on the different shares, in more recent publications on aerody- this consideration is not sufficiently ac- namics in the railway sector: This maxi- curate: Where does the constant share mum is shown by Chiu and Squire (1992) come from? What is described by the as well, also illuminating its cause: the speed-proportional addend? downwind side flow patterns change sig- In this context, the works of Sachs nificantly in the range of α 40°...60°. (1973a) and Wende (2003) give a detailed ≈ Moreover, the publications of Orellano description of the railway system; also, and Schober (2006) and Weber, Benker, lecture scripts support with some ideas. and Naupert (2007) suggest a similar Based on the latter, the following equa- conclusion. Unfortunately, these inves- tion is assumed for the aerial resistance tigations are usually limited to safety (Weidmann 2011): aspects as, for instance, the derailment 37 1 2 risk, as experts confirm. Fl,l = /2 cw Ab ρ kt v . (D.2) · · · · · As a further influence of the cross- The consideration here is limited to open wind on resistances, a possible wheel routes, which makes tunnel factor kt to flange contact is considered: A lateral become 1. The air density is assumed to displacement of a vehicle might cause have standard norm value ρ = 1.293 kg/m3; such a contact, which would increase the drag coefficient cw and reference sur- rolling resistance. Depending on the au- face Ab are vehicle-dependent, their de- thor, this effect is given a superficial role termination is not discussed in detail (Wende 2003) or an occurrence is con- here. Speed v denotes the relative speed sidered probable only for higher wind between air and vehicle; thus, Equa- speeds of 35 km/h and above (Hay 1982). tion D.1 is rewritten by introducing vehicle speed vt and wind speed vw: Based on these findings, a model is derived using resistance and coefficient 1 2 Fl,l = /2 cw Ab ρ kt (vt+vw cos α) . (D.3) · · · · · · curve shapes as well as mechanical con- The definition of the yaw angle α is siderations of the wheel-rail-system, al- shown in Figure D.1; equation Equa- lowing to include the described effects. tion D.3 is referred as cosine model in the following. In addition, there are a variety of considerations on the influence of the yaw angle—as some keywords, the increase 37According to a statement of Mr. Daniel Steil- in the effective cross section and the in- ing, aerodynamics expert at RUAG AG, in a fluence of the carriage connections shall phone call on April 24, 2017.

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3 EFFECTS OF THE YAW ANGLE model shows that in tailwind case (in 3.1 AERIAL RESISTANCE particular at α = 180°) the air resistance It has been discussed in section 2 that results below plausible values. the cosine model, which only uses the While the turbulence model behaves— wind’s direction-of-travel component for in the range of “smaller” angles (up to calculating the relative air speed, does about 130°)—according to the statements not agree with measurements. Instead, of literature and logically explainable, it in the range of yaw angles up to about has an implausible behaviour in the tail- 90°, air resistance values occur that are wind case. If the wind hits the train higher than those at α = 0°. Turbu- straight from the rear (α = 180°), there lence effects on the vehicle’s skin, at are no significant differences in turbu- wagon transitions, and in the lee are lence effects to be expected compared cited as causing the increased aerial re- to the case where the wind hits from sistance in case of non-frontal flow.37 This the front (α = 0°).38 The minimum air is taken into account by adding a fac- resistance is obtained by using a rele- tor kα into the calculation according to vant relative speed resulting from sub- Equation D.2. Then, the aerial resis- traction of the air speed from the train tance in open field is described as speed. Nevertheless, according to the turbulence model, significantly lower re- 1 2 Fl,l = /2 kα(α) cw Ab ρ (vt +vw) (D.4) · · · · · sistance values result. Consequently, the

Thereby, vt denotes the train speed, and cosine model is preferred in this case. vw the wind speed. The results of aero- This raises the question of how the two dynamic investigations suggest that the models can be combined in order to cover shape of kα(α) can be approximated by all boundary conditions. Since the maxi- a second degree polynomial in α. If one mum symmetry of the turbulence model additionally considers that the relative seems plausible, it is assumed that this speed between air and vehicle is deci- model is valid for the case kα > 1. Fur- sive while its influence seems to be non- thermore, it has already been discussed linear, the following approach is suitable: that the cosine model provides a plausi- x ble value for α = 180°. Assuming that the vw 2 kα(α) = a α (D.5) range for kα < 1 can be approximated by · vt + vw · a second order polynomial too while the y vw + b α + c transition between the models is contin- · vt + vw · uously differentiable, sufficient informa- By means of curve fitting, the parame- tion is available for a model combination. ters a, b, x, and y are derived; c = 1 results The definition of the combination model from kα(α = 0°). Thus, the following equa- is based on the cosine model according to tion for this modelling approach—called equation Equation D.2. In order to in- turbulence model in the following due to clude the results of the prior sections, a the causative air turbulence—results: helper function fα is introduced: 1.511 2 vw 2 1 2 kα(α) = 0.00177 α Fl,l = / cw Ab ρ (vt + vw cos α) − · vt + vw · | · · · · {z · } =:F 1.687 l,l,α vw (D.6) + 0.333 α + 1; (1 + fα) (D.7) · vt + vw · · [α] = °

A comparison of the results for the cases 38In case of symmetric EMUs, the difference 0° and 180° to the results of the cosine should—ideally—be zero.

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The computation of fα is based on Fl,l,α 3.2 WHEEL FLANGE CONTACT and kα according to the following: For an increased rolling resistance caused by wheel flange contact, the lateral dis- 1. Calculate kα for vw, vt, and 0° ≤ α 180° placement due to side wind has to be cal- ≤ culated, determining whether it is suf- 2. Determine Fl,l,α,norm = Fl,l,α / Fl,l,α(α = 0°) 39 ficient to cause this contact. For sim- 3. Obtain α0 via kα 1 and define ≈ plicity, it is assumed that the displace- = d1 kα(α0) ment for each car (and the locomotive) 4. Calculate d2 = d/dα kα(α0) can be considered independently, since

5. Define d3 = Fl,l,α(α = 180°) in general, the entire train is exposed to the same wind and moreover, the typ- Using the now known values d1, d2, and ical European couplings allow the nec- d3 for the range where kα < 1, generally written as essary movement. Thus, the additional rolling resistance for each vehicle can be 2 kα,2 = a α + b α + c, (D.8) · · determined depending on its mass and this model can be defined by the follow- wheel-set geometry; for the whole train, ing set of equations (with α1 = 180°): the additional rolling resistance is computed by summing up the individual ve- d3 (α1 α0)d2 d1 (D.9) a = 2− − − 2 hicles’ resistances. The approximately si- α1 2 α0 α1 + α0 − · · nusoidal shape of the lateral force coeffi- b = d2 2 a α0 (D.10) − · · cient (Orellano and Schober 2006) indi- 2 c = a α d1 d2 α0 (D.11) · 0 − · · cates that only the lateral wind component of w is causing the displacement: Note that the values of d1 to d3 depend v on vw and vt, which complicates an an- vw,y = vw sin α (D.13) alytically closed formulation—therefore, · it shall be omitted here. Combining kα The analysis is based on the usual for 0° α α0 and kα,2 for α0 wheel-rail profile pairing of Switzerland— ≤ ≤ ≤ α 180°, a new kα results. This function standard gauge with wheel profile S1002, ≤ can be determined—without an enor- Vignole rail 60E1 and installed slope of mous effort—only numerically. 1:40 (Polach 2015b)—the wear of both Starting from equation (D.7), only one profiles neglected. Then, with a deflec- last step is necessary to obtain the de- tion ∆y of 8.5 mm and more, contact be- sired function fα: tween wheel flange and rail occurs (Po- lach 2015b, Bild 18, S. 10). Neglecting fα = kα Fl,l, ,norm. (D.12) − α all types of slip, the balance of forces be- Figure D.2 exemplary shows the between wheel and rail can be derived from haviour of the three models according the geometry shown in Figure D.4. For to Equations D.3, D.4, D.7; the re- this purpose, the centering effect on a sulting curves of aerial resistance Fl,l wheel-set according to Polach (2015a) is as obtained from the final combination used as starting point: model—according to Equations D.7 to (D.14) D.12 Fy = Q (tan δl tan δr) —are depicted for some vt-vw-pairs · − Figure D.3 in . Thereby, Q denotes the vertical force on

the wheel, while δl and δr denote the contact angles of left and right wheel.

39 Friction between wheel and rail only oc- In theory, d1 = 1, but due to numeric computation applying a discrete step width of usually curs dynamically and becomes zero in the 1°, d1 1, which requires a generic deﬁnition herein investigated state of equilibrium. ≈

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1.3

1.2

1.1

1.0

0.9

0.8 Aerial Resistance

0.7 Relative 0.6

0.5

0.4 0 20 40 60 80 100 120 140 160 180 Yaw Angle α (in degree)

Cosine Model Turbulence Model Combination Model Figure D.2: Behaviour of the Three Presented Models for an example with train speed vt = 160 km/h m and wind speed vw = 5 /s (own illustration).

Fl,l in kN Fl,l in kN Fl,l in kN 40 40 40

30 30 30

20 20 20

10 10 10

α α 0 0 0 α 0° 0° 0° 60° 30° 90° 30° 90° 60° 30° 60° 90° 120° 180° 150° 120° 180° 150° 150° 180° 120°

(a) vw = 0 m/s (b) vw = 5 m/s (c) vw = 10 m/s

Fl,l in kN Fl,l in kN Fl,l in kN 40 40 40

30 30 30

20 20 20

10 10 10

0 α 0 α 0 α 0° 0° 0° 30° 60° 90° 60° 90° 30° 60° 90° 30° 180° 120° 150° 180° 120° 150° 120° 180° 150°

(d) vt = 50 km/h (e) vt = 150 km/h (f) vt = 250 km/h

Figure D.3: Illustration of the Aerial Resistance Model. In the upper row (a–c), different train speeds for a given wind speed are depicted; in the lower row (d–f) vice versa. Colour code: red, green, blue, cyan, orange, and magenta represent train speeds 50, 80, 100, 120, 160, and 200 km/h in a–c and 0, 2, 4, 6, 8, and 10 m/s (d–f). Depicted the absolute aerial resistance (kN) for different yaw angles α (own illustration).

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7 5 5 4 1.95 10− yk 1.38 10− yk − × 4 −3 × 3 2  3.68 10− yk 4.17 10− yk − × 2 − × 3  2.33 10− y + 5.98 10− = f  − × k × δ1  δ  if –30 mm yk < 0 mm Y  ≤ f (y ) =  δ k   9 5 7 4  8.69 10− yk + 5.77 10− yk − × 5 3 × 4 2 1.12 10− y + 1.96 10− y  k k  − × 3 × 2  3.69 10− y + 3.43 10− = f  k δ2 Q  − × ×  if 0 mm yk < 30 mm  ≤  (D.16) 

With ∆y pointing in the same direc- Figure D.4: Wheel-Rail-Geometry (right tion as the wind (sign(∆y) = sign vw,y), for wheel and rail), contact point highlighted. De- lee side yk,lee = ∆y holds, for windward placement of the wheel not to scale; installed − slope not shown. δ the contact angle between side yk,luv = ∆y. As the carriage displace- wheel and rail, Q the vertical, and Y the lateral ment induces an opposing force, Equa- force on the wheel (own illustration). tion D.15 can be reformulated, using

nRS for the number of wheel-sets:

The equilibrium of forces concerning fδ1( ∆y) fδ2(∆y) = | − − | the lateral displacement ∆y of the car- nRS 2 cw,y Ab,y ρ vw sin α (D.17) riage40 can be formulated: m g · · · · · · This equation can solved for ∆y by itera- Fw,y = Fy = Q (tan δl tan δr); (D.15) · − tion; thus, ∆y can be derived from wind δ = f(∆y) {l,r} speed and direction. In addition, the con- For estimating angle δ and restoring tact angles δ are obtained. As stated earlier, wheel flange con- force Fy, it is assumed that the wheel tact occurs for ∆y 8.5 mm. At the profile S1002 according to EN 13715 has ≥ flange thickness 32.5 mm and height additional point of contact, the differ- 28 mm. This way, the wheel profile can be ence between wind-caused displacement Equa- expressed section-wise as a mathemati- force and opposing centring force ( tion D.14 cal function whose slope at contact point ) is applied, scaled by friction coefficient µ and contact angle δ: yk corresponds to the first derivative of this function in the same point. Assum- 1 2 FR,lee = /2 cw,y Ab,y ρ v (D.18) ing these coordinates, the relevant range · · · · w Q (tan δl tan δr) sin δ µ is –30 mm yk 30 mm (de facto, larger ≤ ≤ − · | − | · · shifts do not occur); for the contact an- Simplifying the approach is possible by gles δ = arctan(fδ), the following holds in an approximation, which is using half function of displacement (to be used in yk of the vehicle’s weight instead of Q,41 millimetres, but without unit): cf. Figure D.4. Note that the contact angle must be taken into account, while the rail is considered as orthogonal plane in the contact point. Then, the additional resistance per wheel follows to be

41Assumption: The vehicle’s weight is equally distributed to the wheels, i.e., each wheel is loaded with exactly that share of weight that 40Reference position is the non-displaced results by dividing the vehicle’s weight by its wheel-set in equilibrium: δl = δr number of wheels.

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( 1 2 mgµ cos δ if ∆y 8.5 mm FR,lee = ≥ Table D.1: Case Studies’ Main Properties. ϕ 0 otherwise short for the route heading, ψ2 for the direction (D.19) of the wind’s origin. Thereby, µ denotes the adhesion coeffi- Case A Case B cient, m the mass on the wheel or wheel- Distance 37.595 km 18.594 km set. The simplification included in Equa- Mean ϕ 239° 118° “Good” ψ2 60° 300° tion D.19 tends to deliver too high val- “Bad” ψ2 270° 118° ues for the additional resistance force. Wind Speeds 0, 5, 10 m/s Vehicle Type Single Deck Double Deck Long Distance Regional 4 INFLUENCE QUANTIFICATION Max Speed 200 km/h 125 km/h In order to estimate the influences according to the presented model, two routes—travelled by a given vehicle— speed and direction are subject to certain were examined as case studies A and B. stochastic and show typical frequency Table D.1 summarises the key data of distributions over a year. For the routes these case studies; Figure D.5 shows considered, a corresponding evaluation the geographical situation. As refer- of recorded weather data has been con- ence, the no-wind case was chosen; var- ducted, with the following results: ious cases were then calculated using – For case study A, wind speeds over a MATLAB program, once with setting all wind directions between East and “always (least) favourable approach an- West (40°...260°) dominate in the range gle” (Variation I), once “constant wind of 2 to 6 km/h. East and west winds direction, real route direction” (Varia- (from 50° and 240°) may reach speeds tion II). In each case energy input Ein, of up to 25 km/h. energy recovery , and energy balance Eout – On route B, south winds (from 180°) = – were evaluated. As ex- Etot Ein Eout dominate in the similar speed range, pected, the energy demand increases sig- wind speeds over 10 km/h do not occur. nificantly with higher wind speeds and less favourable angles. It is also not sur- In a further analysis, it was possible prising that for route B, which almost to determine an average increase of the completely follows its mean direction, the energy demand of a train run due to influences are more clearly recognisable, wind effects by around 3 %, which will whereas route A shows no positive effects not be discussed in more detail here. in the “tailwind” case; Table D.2 gives It is striking, however, that the in- an overview of the results. Calculating fluence of wheel flange contact is al- the relative changes in demand for dif- most negligible, since the required lat- ferent angles and wind speeds, an area eral displacement of 8.5 mm start occur- showing the influence of both variables ring for side wind speeds vw,y (!) of on air resistance and energy demand can 25 km/h (pax coach) or just under 40 km/h be derived. The result obtained from two (four-axle locomotive), which coincides case studies is shown in Figure D.6. with statements from the literature— It should be noted that wind speeds influence from about 35 km/h (Hay 1982). m of 10 /s (36 km/h) virtually do not occur in Switzerland.42 In addition, both wind

42Suisse Eole; METEOTEST: Windenergiedaten der Schweiz, Windgeschwindigkeit in 50 m Höhe über Grund. URL: http://wind-data.ch/windkarte/, accessed on Oct 24, 2017

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N N

10 km 5 km

Figure D.5: Route Traces of the Case Study Routes A (left) and B (right). Black arrows indicate the mean track heading, green arrows the “good” wind direction, and red arrows the “bad” wind direction (own illustration).

Table D.2: Relative Variation of Energy Demand due to Wind Influences. All different cases are referenced to the no-wind scenario; Variation I applying a fixed yaw angle, Variation II using a fixed wind direction. ∆E ∆E ∆E Wind in out tot I II I II I II m good 5 /s –6 % +3 % +4 % –6 % –10 % +8 % m good 10 /s –11 % +10 % +8 % –15 % –29 % +21 % Case A m bad 5 /s +13 % +11 % –9 % –7 % +24 % +20 % m bad 10 /s +42 % +33 % –19 % –15 % +69 % +54 % m good 5 /s –3 % –0 % +1 % –0 % –6 % –0 % m good 10 /s –6 % +1 % +2 % –1 % –11 % +3 % Case B m bad 5 /s +10 % +9 % –3 % –2 % +17 % +16 % m bad 10 /s +33 % +30 % –8 % –6 % +57 % +51 %

Figure D.6: Relative Energy Demand Etot from Case Studies A (left) and B (right), in which only the wind direction and speed were varied compared to the reference (vw = 0). Although the patterns show similarities, signiﬁcant differences are evident, which result from the deviation of the actual route direction from its mean direction. For case study A, these deviations are clearly present, while in case study B, the route mostly follows the mean route direction; see also Figure D.5 (own illustration).

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5 CONCLUSION is not available, approximations can be In this article, an aerodynamic model— made using daily mean values; for the which not only takes wind speed into considering a longer period of time, the account, as it has been the case up to use of weather statistics seems suitable. now, but also the wind direction—was de- Overall, it would be desirable for re- rived from aerodynamic measurements search in the field of railway aerody- documented in literature. Furthermore, namics to increasingly address these ef- based on a mechanical wheel-rail system fects. A more precise and yet easy-to-use analysis, a model for determining the ad- model of wind resistance, taking into ac- ditional, side-wind-induced rolling resis- count the factors mentioned—wind speed tance caused by wheel flange contact of and direction, driving speed, route head- the leeward wheels was derived. ing, and train length—would be of great Based on two case studies, it was value for other research areas. As it has shown that the influence of the wind been shown, in particular for the increas- and its angle on the energy demand of ing number and importance of energy a train run can be significant (average considerations and optimisations, there over several case studies +2...+3.5 %, in is no such model present while the de- some cases up to +10 %) and an a pri- scribed effects should not be neglected. ori neglect, common in many considera- Until an improved model is available, tions, is often inadmissible. In contrast, a the use of a simplified one—such as the wheel flange contact occurs only at rela- one presented here—is recommended. In tively high crosswind speeds (from about particular, if optimisation models are 25 km/h with an angle of 90°) and can thus calibrated with real-world measurement generally be classified as negligible. values, the inclusion of wind influences The model presented here is a first, is of crucial importance: in the unfavor- numerical approach; it was created out able case, neglecting wind influences can of necessity for an energy consideration. result in incorrect values for the parame- The results were based on literature, it is ters, resulting in consequential errors in therefore limited to the consideration of the entire model application. the difference between the absolute values of wind and train speed. The inclusion of the yaw angle takes place using an auxiliary function, which in turn is based on wind and route direction in the current point of investigation. Thus, the effects of train speed and route heading on the resulting (effective) yaw angle are neglected; likewise, the train length is not considered. Furthermore, note that the model is only valid for open routes. In tunnels, it can be simplified assuming a wind speed vw = 0; however, this assumption is prob- This chapter has been used in an external ably erroneous. Thus, this model is ba- research project and published in German as sically applicable to single-trip analyses Bomhauer-Beins, A. und U. Weidmann (2018): in mostly open terrain, where the wind “Grundlagen für ein neues Modell des Luftwider- speeds and directions at each route point stands von Eisenbahnfahrzeugen”. at pass time are known. If this data In: ZEVrail 142 (10), pp. 410–416

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E Program Methods Documentation

E.1 Class vehicle

OBJ obj = vehicle(STR type, STR calc_base, STR result_mode, TBL infra_table, HDL env[, ARR[DBL] a_lim[, DBL vact[, DBL tact[, INT idx]]]) Constructor; creates the vehicle object. Parameters STR type Vehicle type, e.g. 'ICN' STR calc_base Calculation base: 'time' 'dist' STR result_mode Detail degree of results: 'all' 'default' 'reduced' TBL infra_table Infrastructure description in table format HDL env Environment class handle ARR(DBL) a_lim [opt] Max acceleration (> 0) and deceleration (< 0) DBL vact [opt] Initial speed in m/s DBL tact [opt] Initial time in s INT idx [opt] Infrastructure table row to initialize for Return Values OBJ obj A vehicle object (handle)

[OBJ obj, DBL tact, BOOL lastStep] = calcVehicle(OBJ obj, DBL tnew) Computes a step for a given new time (instead of for a time step). Parameters OBJ obj The vehicle object itself DBL tnew New time to compute until, in s Return Values OBJ obj The modiﬁed vehicle object (handle) DBL tact New time in s, equal to tnew BOOL lastStep 1 if the end of infrastructure has been reached; 0 otherwise

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[OBJ obj, DBL tact, BOOL lastStep] = doStep(OBJ obj[, DBL Dt]) Calculates one step in space or time, depending on the calculation base. Parameters OBJ obj The vehicle object itself DBL Dt [opt] timebased only Time step width (s) Return Values OBJ obj The modiﬁed vehicle object DBL tact New time in s BOOL lastStep 1 if the end of infrastructure has been reached; 0 otherwise

DBL PVEI = get_P_VEI(OBJ obj) Reads the current power at the vehicle’s energy input (VEI) and corrects possible errors in extraordinary situations, i.e. empty return values. Parameters OBJ obj The vehicle object itself Return Values DBL PVEI Power at vehicle’s energy input (pantograph) in W

DBL KM = getPositionKM(OBJ obj) Determines the vehicle’s current position as route km from the result table. Available in timebased mode only. Parameters OBJ obj The vehicle object itself Return Values DBL KM Vehicle’s position as km of the route

STR line = getTrackID(OBJ obj) Reads the value of the Track ID (line ID) that is stored as property. Parameters OBJ obj The vehicle object itself Return Values STR line The track’s/line’s name

– 248 – Appendix E: Program Methods Documentation

BOOL onLine = isOnLine(OBJ obj, STR lineID) Checks whether the vehicle operates on a speciﬁed line/track/route Parameters OBJ obj The vehicle object itself STR lineID Name of the line to be checked Return Values BOOL onLine 1 is vehicle’s track id and lineID are equal; 0 otherwise

OBJ obj = loadVehicle(OBJ obj, DBL load, STR type[, BOOL supBrakeEst]) Sets a new (pay)load value, in per cent, kilograms, or tons. Parameters OBJ obj The vehicle object itself DBL load Numeric load value STR type Unit of the load: '%' 'kg' 't' BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = rejectRegeneratedPower(OBJ obj, DBL power) Forces the vehicle to dissipate the speciﬁed amount of (regenerated) power over its braking resistor. Parameters OBJ obj The vehicle object itself DBL power Amount of power rejected by substation (W) Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = resetResults(OBJ obj) Resets the vehicle results in order to enable a new run. Parameters OBJ obj The vehicle object itself Return Values OBJ obj The modiﬁed vehicle object

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OBJ obj = setAccLimits(OBJ obj, ARR(DBL) alim[, BOOL supBrakeEst]) Deﬁnes the limitations of acceleration and deceleration values according to the input parameters. Parameters OBJ obj The vehicle object itself ARR(DBL) alim Max acceleration (> 0) and deceleration (< 0) BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setBrakeMode(OBJ obj, STR mode[, BOOL supBrakeEst]) Adjusts the vehicle’s braking behavior. Parameters OBJ obj The vehicle object itself STR mode New brake mode: 'auto' 'el_only' BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setESS(OBJ obj[, DBL cap, DBL PmaxIN, DBL PmaxOUT, DBL etaIN, DBL etaOUT, DBL E0, DBL mass]) Defines the availability and properties of an on-board ESS. If called with only one argument, i.e. obj = obj.setESS(), an (eventually) existing ESS will be removed from the vehicle. Parameters OBJ obj The vehicle object itself DBL cap ESS storage capacity (Wh) DBL PmaxIN Maximum charging power (W) DBL PmaxOUT Maximum discharging power (W) DBL etaIN Charging efficiency (–) DBL etaOUT Discharging efficiency (–) DBL E0 Initial charge / stored energy (Wh) DBL mass Additional mass (kg) Return Values OBJ obj The modified vehicle object

– 250 – Appendix E: Program Methods Documentation

OBJ obj = setIncludeDB(OBJ obj, INT incDB)

Defines whether the B-coefficient of the Davis equation is taken into account separately or in addition; calibration mode is obtained with incDB set to 2. Parameters OBJ obj The vehicle object itself INT incDB 0—ignore B; 1—use B; 2—calculate Bv but don’t use for motion resistance calculation Return Values OBJ obj The modified vehicle object

OBJ obj = setName(OBJ obj, STR newName) Sets an identifying name for the vehicle object. Parameters OBJ obj The vehicle object itself STR newName The vehicle’s new name Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setParam(OBJ obj, STR param, MIX value) Adjusts a property value for evaluation reasons. Parameters OBJ obj The vehicle object itself STR param Name of the property whose value is to be adjusted MIX value New value of property param Return Values OBJ obj The modiﬁed vehicle object

– 251 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

OBJ obj = setSchedule(OBJ obj, ARR{DBL|STR} loc, ARR(DBL) arr, ARR(DBL) stop, ARR(DBL) dep) Adds values of arrival, dwell, and departure time to speciﬁed rows of the distance based runTable Parameters OBJ obj The vehicle object itself ARR{DBL|STR} loc Locations where to add the values as route km (DBL) or name of station (STR) ARR(DBL) arr Arrival times in s ARR(DBL) stop Dwell times in s ARR(DBL) dep Departure times in s Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setScheduledSpeed(OBJ obj, ARR(DBL) km_vec, ARR(DBL) v_vec[, ARR(DBL) dwell_vec[, BOOL supBrakeEst]]) Adds values for scheduled speed to the speciﬁed rows (route kms) of the distance based runTable. Parameters OBJ obj The vehicle object itself ARR(DBL) km_vec Locations where to add the speed values (km) ARR(DBL) v_vec Speed values to add (km/h) ARR(DBL) dwell_vec [opt] dwell times for v = 0 (s) BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setSCmode(OBJ obj, STR mode[, BOOL supBrakeEst]) Sets the speed controller mode of the vehicle. Parameters OBJ obj The vehicle object itself STR mode Controller mode: 'follow_vbrake' 'step_prognostic' BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

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OBJ obj = setSpeedRecom(OBJ obj, MAT(DBL) spec) Adds a speed recommendation to the route information, which might be sent by operation control. Parameters OBJ obj The vehicle object itself MAT(DBL) spec Matrix of speed recommendation speciﬁcation; per row: start km, end km, speed (km/h) Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setStops(OBJ obj, ARR(DBL) km_vec, ARR(DBL) dwell_vec[, ARR{STR} BPs[, BOOL supBrakeEst]]) Adds stops (timetable) to route information of the vehicle. Parameters OBJ obj The vehicle object itself ARR(DBL) km_vec Route kms to set stops for (may be empty) ARR(DBL) dwell_vec Dwell times for the stops ARR{STR} BPs [opt] stop location spec by station (instead of kms) BOOL supBrakeEst [opt] 1—suppress re-estimation of brake curves Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setTimeStep(OBJ obj, DBL delta_t) Deﬁnes a new default time step width for the vehicle. Parameters OBJ obj The vehicle object itself DBL delta_t New time step width (s) Return Values OBJ obj The modiﬁed vehicle object

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OBJ obj = setTrackID(OBJ obj, STR trackid) Speciﬁes the name for the vehicle’s route. Parameters OBJ obj The vehicle object itself STR trackid Name of the route to be set Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setTuningSpeed(OBJ obj, VEC[DBL] tableCol) Assigns a given table column to the recommended speed column. Parameters OBJ obj The vehicle object itself VEC[DBL] tableCol The speed values to set (m/s) Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setVmax(OBJ obj, MAT[DBL] spec[, STR mode]) Sets new max speed values to property runTable. Parameters OBJ obj The vehicle object itself MAT[DBL] spec New maximum speed in km/h; per row: start_km, end_km, v_max STR mode [opt] 'red' (reduce) or 'inc' (increase) only Return Values OBJ obj The modiﬁed vehicle object

OBJ obj = setVrecMode(OBJ obj, STR mode) Deﬁnes the mode of handling speed recommendations. Parameters OBJ obj The vehicle object itself STR mode Brake mode to be activated: 'el_only' or 'auto' Return Values OBJ obj The modiﬁed vehicle object

– 254 – Appendix E: Program Methods Documentation

E.2 Class environment

OBJ obj = environment([STR mode, [DTT start_time]]) Class Constructor. Parameters STR mode [opt] Class mode 'generic' or 'norm' DTT start_time [opt] Model start time in datetime format Return Values OBJ obj The environment object

DBL rho = getAirDensity(OBJ obj[, VEC[DBL] coord[, DBL time]]) Calculates the air density. If the optional arguments are not given, a mean value will be used. Parameters OBJ obj The object itself VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values DBL rho The resulting air density in kg/m3

DBL fRC = getFRC(OBJ obj, DBL TunLen[, VEC[DBL] coord[, DBL time]]) Delivers the currently relevant rail condition correction factor. If the optional arguments are not given, a mean value will be used. Parameters OBJ obj The object itself DBL TunLen Length of the current tunnel (m) VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values DBL fRC Rail condition correction factor (–)

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DBL mu = getMu(OBJ obj, DBL v_train, STR mode[, DBL TunLen[, VEC[DBL] coord[, DBL time]]]) Calculates the current adhesion coefﬁcient µ. Parameters OBJ obj The object itself DBL v_train Current vehicle speed (m/s) STR mode 'slide', 'brake', 'curve', or 'drive' DBL TunLen Length of the current tunnel (m) VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values DBL mu Friction coefﬁcient µ (–)

DBL T = getTemp(OBJ obj[, VEC[DBL] coord[, DBL time]]) Delivers the currently relevant outside air temperature. Parameters OBJ obj The object itself VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values DBL T Outside air temperature (°C)

DBL psi2 = getWindDir(OBJ obj[, VEC[DBL] coord[, DBL time]]) Delivers the currently relevant wind (source) direction. Parameters OBJ obj The object itself VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values

DBL psi2 Current wind source direction ψ2 (°)

– 256 – Appendix E: Program Methods Documentation

DBL vw = getWindSpeed(OBJ obj[, VEC[DBL] coord[, DBL time]]) Delivers the currently relevant wind speed. Parameters OBJ obj The object itself VEC[DBL] coord [opt] Position in WGS84, [lat lon] DBL time [opt] Time from start in s Return Values DBL vw Wind speed (m/s)

OBJ obj = setGenericValues(OBJ obj, DBL fRC, DBL T, DBL psi2, DBL vw) Sets the weather parameters when in generic mode. Parameters OBJ obj The object itself DBL fRC Rail condition correction factor (–) DBL T Outside air temperature (°C) DBL psi2 Wind source direction (°) DBL vw Wind speed (m/s) Return Values OBJ obj The modiﬁed object

OBJ obj = setFRC(OBJ obj, DBL FRC) Sets the current rail condition correction factor (adhesion). Parameters OBJ obj The object itself DBL FRC Rail condition correction factor (–) Return Values OBJ obj The modiﬁed object

OBJ obj = setModelStartTime(OBJ obj[, DTT dtvalue]) Stores a new model start time as property. Parameters OBJ obj The object itself DTT dtvalue [opt] Model start time in datetime format Return Values OBJ obj The modiﬁed object

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E.3 Class energySupply

OBJ obj = addNodes(OBJ obj, MAT{MIX} props) Adds node(s) (e.g. substations) to the supply system. Parameters OBJ obj The object itself MAT{MIX} props Properties of the node(s): one node per row, each row consists of STR node_id, STR type, DBL eta1, DBL eta2, ARR[DBL] Pmax, DBL storageCap, DBL energyStored, ARRSTR lineList, ARR[DBL] lineR, ARR[DBL] posList, ARR[DBL] secList. Pmax: [Poutmax Poutmin Pstoremax Pstoremin] Return Values OBJ obj The modiﬁed object

OBJ obj = addVehicles(OBJ obj, ARR{HDL} vehArr) Adds handles of vehicles operated within this energy system. Parameters OBJ obj The object itself ARR{HDL} vehArr Handles of vehicles supplied by this energy system Return Values OBJ obj The modiﬁed object

OBJ obj = calcEnergySystem(OBJ obj, DBL tact) Calculates the behaviour of the energy system for a given step in time. Parameters OBJ obj The object itself DBL tact Time lapsed since start (s) Return Values OBJ obj The modiﬁed object

– 258 – Appendix E: Program Methods Documentation

OBJ obj = energySupply(HDL envPointer[, DBL tact[, STR systemType]]) Constructor. Parameters OBJ obj The object itself DBL tact [opt] Initialisation time (s) STR systemType [opt] Supply system type Return Values OBJ obj The energySupply object

OBJ obj = selectSystem(OBJ obj, STR sysType[, ARR{MIX} values]) Sets the system parameters for the selected system. Parameters OBJ obj The object itself STR sysType System to be selected ARR{MIX} values [opt] values to be set for sysType 'manual'. order: DBL freq, STR name, DBL volt_min, DBL volt_typ, DBL volt_max, DBL eta_gen, DBL eta_grid, DBL eta_w2g Return Values OBJ obj The modiﬁed object

OBJ obj = setNodeState(OBJ obj, STR name, STR key, DBL val) Function sets a specified node’s state to a given value. Parameters OBJ obj The object itself STR name Node name of the node to be modified STR key Identifier of the element DBL val New value of the element Return Values OBJ obj The modified object

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E.4 Additional Functions

In the following, the independent helper functions are shortly described. How- ever, not all functions are documented—rudimental functions as deg2rad (degree to radiant conversion) are omitted.

res = aerial_resistance_scaling_factor(DBL vt, DBL vw, DBL phi, DBL psi2)

Calculates factor fa according to Equations 2.592.66. Parameters m DBL vt Train speed vt ( /s) m DBL vw Wind speed vw ( /s) DBL phi Route heading ϕ (°)

DBL psi2 Wind (source) direction ψ2 (°) Return Values DBL res Aerial resistance scaling factor (–)

OBJ veh = applySpeedAdjustment(OBJ veh, MAT[DBL] adjVar) Applies the determined set speed to the given vehicle. Parameters OBJ veh The vehicle to receive new set speed MAT adjVar Variable containing the adjustment information Return Values OBJ veh The modiﬁed vehicle object

VEC[DBL] dist_vect = calc_WGS84_distances(ARR[DBL] coord_arr, VEC[DBL] coord_pair) Calculates distance between two WGS84 coordinates in meters, based on spheric geometry. Parameters ARR[DBL] coord_arr Set of coordinates as lon-lat row vectors VEC[DBL] coord_pair Base point coordinates as row vector Return Values VEC[DBL] dist_vect Coordinate distances to base point (m)

– 260 – Appendix E: Program Methods Documentation

DBL alpha = calc_wind_angle(DBL phi, DBL psi2)

Calculates yaw angle α between train heading and wind direction. Parameters DBL phi Route heading ϕ (°)

DBL psi2 Wind (source) direction ψ2 (°) Return Values DBL alpha Yaw angle α (°)

BOOL RTok, MAT[DBL] adjVar = checkRunTime(OBJ veh, MAT[DBL] adjVar, DBL arr_dev_tol) Checks whether the current run meets the run time requirements and stores related information. Parameters OBJ veh The vehicle object to be checked MAT[DBL] adjVar Variable containing speed adjustment information DBL arr_dev_tol Deviation tolerance for arrival time (s) Return Values BOOL RTok true if the requirement is met, false otherwise MAT[DBL] adjVar Variable containing speed adjustment information

MAT[DBL] new_coord = LV03_to_WGS84(MAT[DBL] coord) Converts coordinates from Swiss LV03 coordinate system into the international WGS84 system. Parameters MAT[DBL] coord LV03 coordinates as row vectors Return Values MAT[DBL] new_coord Corresponding WGS84 coordinates as row vectors (lat/lon)

MAT[DBL] new_coord = WGS84_to_LV03(MAT[DBL] coord) Converts coordinates from the international WGS84 system into Swiss LV03 coordinates. Parameters MAT[DBL] coord WGS84 coordinates as row vectors Return Values MAT[DBL] new_coord Corresponding LV03 coordinates as row vectors (y/x)

– 261 –

F Results of the Sensitivity Analysis

The following tables present an overview on the inﬂuence of parameter variations on the energy demand of a train run. For all routes—Olten–NBS– Solothurn, Solothurn–NBS–Olten, Biel–Neuchâtel–Yverdon, and Yverdon– Neuchâtel–Biel—a series 500 EMU (ICN) is used. Except the parameter explicitly given as the one varying, all parameters are set according to the description of the investigation in chapter 3.5. Note that Tables F.1F.9 contain the raw results of the calculation performed using the speciﬁed parameters; Table F.2 only shows every third calculated mass variation due to reasons of layout. For evaluation and interpretation, please refer to the respective section of the main part of this thesis.

– 263 – Energy Saving Potentials in Railway Operations under Systemic Perspectives 0.05 0.10 0.15 0.92 0.97 1.02 452.4 421.8364.7 393.8 337.7 313.0 621.6 574.5613.1 531.3 566.3 523.2 0.00 0.87 673.1 664.4 394.3 486.2 0.6 0.7 0.8 0.9 1.0 487.7 515.3396.3 539.4 420.1 565.1 443.7666.7 591.2 467.6 691.8 492.7 714.4 738.5 762.7 675.3 697.9 720.8 746.4 770.3 18 24 30 36 42 44 0.59 495.9 495.9395.4 502.1 400.9 507.0 406.2669.0 512.1 410.6 678.0 515.8 412.8 686.3 414.8 693.7 705.4 707.6 679.0 688.2 699.9 708.9 710.7 711.0 673.1 664.4 394.3 486.2 14.5 673.1 664.4 394.3 486.2 on the Energy Demand of a Train Run. The emphasised column indicates the standard run; the run w c on the Energy Demand of a Train Run. The emphasised column indicates the standard run; the run times do D η 0.0 0.1 0.2 0.3 0.4 0.5 0.52 0.57 0.62 0.67 0.72 0.77 0.82 –12 –6 0 6 12 –0.35 –0.30886.7 –0.25740.3 800.6 –0.20 666.3 –0.15 728.2 –0.10 604.1 666.2 –0.05 612.5 550.7 565.4 504.2 523.6 463.3 427.0 338.3 363.1258.2 386.9 280.3536.3 412.4 302.1 561.2518.3 439.0 325.9 585.1 542.1 464.5 349.9 608.8 567.0 373.3 631.9 591.9 653.8 618.0 643.1 460.7 467.5373.9 473.1 378.4629.0 478.8 381.7 641.0619.2 485.8 388.7 649.0 632.5 390.9 661.6 640.9 671.5 652.2 658.6 1269.7 1142.91258.0 1035.8 1131.9 943.8 1025.5 863.7 933.0 792.9 854.2 729.8 783.8 721.0 (t) (–) (–) (–) w η m c ∆ ∆ D,max η Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Influence of the Vehicle Total Mass on the Energy Demand of a Train Run. The emphasised column indicates the standard run; the run times do not Influence of the Aerial Resistance Coefficient Influence of the Drive Chain Efficiency Route OL–SO SO–OL BI–YV YV–BI Route OL–SO SO–OL BI–YV YV–BI Route OL–SO SO–OL BI–YV YV–BI times do not show differences and are therefore not indicated. show differences and are therefore not indicated. Table F.1: Table F.2: Table F.3: not show differences and are therefore not indicated.

– 264 – Appendix F: Results of the Sensitivity Analysis 25 30 486.0 489.5 394.3 396.5 676.1 686.1 668.9 678.4 -curve that is shifted by a rail condition correction µ 20 673.1 664.4 394.3 486.2 Scheduled Run Time 960 960 960 960 960 1021 10212099 1021 2099 10212100 2099 1021 2099 2099 2099 2099 2099 2099 –0.10 –0.05386.9 0.00 386.1 386.8257.9 0.05 388.1 261.6 0.10 389.1 261.1514.7 262.1 526.9 263.1 526.9503.3 530.5 503.7 535.0 510.5 513.9 517.4 881 881 876 876 0.05 0.10 2031 2031 2043 2043 395.0 396.4 676.2 680.0 668.7 672.0 486.7 487.4 881 876 0.00 2031 2043 673.1 664.4 394.3 486.2 on the Energy Demand of a Train Run (via HVAC). The emphasised column indicates the standard run; the run times T Shortest Run Time –20 –15 –10 –5 0 5 10 15 893 877 897 882 2044 2032 2061 2044 575.6 563.5474.9 552.2 464.7848.4 541.0 454.4 825.8845.0 529.9 444.2 803.2 821.8 517.6 434.4 780.6 798.6 506.5 423.0 758.4 775.6 494.5 413.2 736.3 752.3 403.0 713.5 729.7 691.6 707.0 683.3 –0.10 –0.05 474.3 485.5 378.3 392.4 631.8 670.3 627.2 658.7 (–) (°C) a RC f T Duration (s) Duration (s) Duration (s) Duration (s) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Energy (kWh) Influence of the Rail Condition on the Energy Demand of a Train Run. Basis for the calculation is the Influence of the Air Temperature ; these differences might be caused by e.g. wet dust on the rails. Note that for this factor, also the run times may vary—influences on the force transmission! RC f Route OL–SO SO–OL BI–YV YV–BI Route OL–SO SO–OL BI–YV YV–BI do not show differences and are therefore not indicated. The emphasised column indicates the standard run. Table F.4: Table F.5: factor

– 265 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Table F.6: Wind Inﬂuences on the Energy Demand of a Train Run for the Olten–Solothurn Line. The energy demands are given in kWh; vw the wind speed, ψ2 the wind (source) direction. 0° = 360° = North.

v (km/h) w 0 5 10 15 20 25 30 ψ2 (° ) 0 486.2 484.3 483.1 483.2 484.1 485.9 489.6 10 486.2 482.9 480.2 478.4 477.7 479.1 479.7 20 486.2 481.6 477.3 474.1 472.1 471.8 470.7 30 486.2 480.2 474.7 470.1 466.5 465.0 462.5 40 486.2 479.2 472.5 466.9 461.9 459.5 456.0 50 486.2 478.5 471.1 464.8 459.4 454.6 451.8 60 486.2 478.1 470.0 463.6 457.7 453.1 448.9 70 486.2 476.2 470.0 463.4 456.6 452.3 448.2 80 486.2 476.7 468.9 462.6 457.5 453.3 449.9 90 486.2 477.6 470.7 465.6 461.5 457.7 454.9 100 486.2 478.9 473.1 468.5 465.2 462.7 463.4 110 486.2 480.1 475.2 472.1 472.1 471.7 471.7 120 486.2 481.4 477.8 476.2 477.8 480.4 482.1 130 486.2 482.5 480.5 482.3 485.6 488.3 491.5 140 486.2 483.8 483.3 486.8 491.6 495.6 500.7 150 486.2 485.1 487.7 491.2 497.5 503.6 510.0 160 486.2 486.2 490.2 495.3 502.9 510.6 518.8 170 486.2 487.3 492.7 498.8 507.7 516.6 525.7 180 486.2 488.3 494.7 501.8 511.6 521.4 531.5 190 486.2 489.2 496.3 504.0 514.6 524.5 535.8 200 486.2 489.8 497.5 505.5 516.4 526.7 537.9 210 486.2 490.3 498.2 508.3 517.1 528.3 538.9 220 486.2 490.7 498.8 508.8 517.9 528.7 539.1 230 486.2 490.9 499.1 509.2 518.3 528.9 539.2 240 486.2 491.0 499.3 509.5 518.2 528.8 539.3 250 486.2 491.1 499.2 507.5 518.0 528.3 538.6 260 486.2 491.1 497.5 507.3 518.2 528.0 539.1 270 486.2 490.9 497.6 507.0 518.1 528.5 540.8 280 486.2 490.5 496.8 506.2 515.5 527.8 539.0 290 486.2 490.4 496.3 504.0 514.1 524.4 537.4 300 486.2 489.8 495.5 502.3 510.6 523.2 534.0 310 486.2 489.0 494.0 500.7 508.3 518.8 529.0 320 486.2 489.8 493.7 499.6 506.5 516.3 524.9 330 486.2 488.5 491.1 495.7 501.6 510.5 518.3 340 486.2 487.0 488.6 491.9 496.3 503.8 510.1 350 486.2 485.7 486.0 487.6 490.5 494.3 498.7 360 486.2 484.3 483.1 483.2 484.1 485.9 489.6

– 266 – Appendix F: Results of the Sensitivity Analysis

Table F.7: Wind Inﬂuences on the Energy Demand of a Train Run for the Solothurn–Olten Line. The energy demands are given in kWh; vw the wind speed, ψ2 the wind (source) direction. 0° = 360° = North.

v (km/h) w 0 5 10 15 20 25 30 ψ2 (° ) 0 394.3 395.4 396.5 403.4 408.9 416.0 425.5 10 394.3 396.4 400.0 405.2 411.5 419.0 427.9 20 394.3 397.0 401.4 406.7 413.3 420.2 428.4 30 394.3 397.2 402.2 408.3 413.8 421.0 429.0 40 394.3 397.9 402.7 408.8 414.5 420.8 431.3 50 394.3 398.4 404.4 408.8 416.8 422.9 432.3 60 394.3 398.6 403.4 409.4 416.5 423.2 433.6 70 394.3 398.7 405.0 409.8 417.0 424.1 434.3 80 394.3 398.6 405.2 410.6 418.0 425.8 437.4 90 394.3 398.8 404.9 411.2 419.9 429.5 441.9 100 394.3 398.8 404.3 412.4 419.8 431.8 444.0 110 394.3 398.4 403.9 411.2 421.0 431.2 443.4 120 394.3 397.9 403.0 409.2 419.0 429.0 440.3 130 394.3 397.2 401.7 407.7 416.4 425.7 435.9 140 394.3 396.5 399.8 405.5 410.7 420.1 429.1 150 394.3 395.6 398.0 401.2 406.5 413.2 420.5 160 394.3 394.4 396.9 398.2 401.7 405.9 412.5 170 394.3 393.4 393.5 394.5 396.4 400.7 404.8 180 394.3 392.4 391.4 391.7 392.4 394.6 397.5 190 394.3 390.7 389.3 389.5 388.2 390.0 391.9 200 394.3 389.9 386.8 385.3 384.6 384.4 385.3 210 394.3 388.8 385.2 382.1 380.6 379.9 379.2 220 394.3 388.4 383.7 379.8 377.2 375.9 375.1 230 394.3 387.4 380.4 376.9 373.4 373.0 371.3 240 394.3 385.3 380.6 376.1 372.8 370.4 368.8 250 394.3 385.8 380.7 376.4 373.0 369.0 366.8 260 394.3 386.4 381.6 376.4 373.4 371.5 370.5 270 394.3 387.2 383.3 378.5 377.1 375.5 374.4 280 394.3 388.0 384.9 381.2 380.3 379.2 378.8 290 394.3 388.7 386.6 384.2 383.1 383.1 383.8 300 394.3 389.5 388.0 386.7 386.7 387.7 389.5 310 394.3 390.3 389.7 389.5 390.6 392.7 395.1 320 394.3 391.0 391.4 391.4 394.3 397.3 401.0 330 394.3 391.4 392.7 393.7 397.0 401.3 406.4 340 394.3 392.1 394.4 397.7 401.9 406.6 413.4 350 394.3 392.7 395.8 399.4 404.2 409.8 419.7 360 394.3 395.4 396.5 403.4 408.9 416.0 425.5

– 267 – Energy Saving Potentials in Railway Operations under Systemic Perspectives

Table F.8: Wind Inﬂuences on the Energy Demand of a Train Run for the Biel–Yverdon Line. The energy demands are given in kWh; vw the wind speed, ψ2 the wind (source) direction. 0° = 360° = North.

v (km/h) w 0 5 10 15 20 25 30 ψ2 (° ) 0 673.1 665.0 660.7 657.3 655.9 656.3 657.2 10 673.1 662.9 654.9 650.2 646.2 643.5 642.1 20 673.1 659.3 650.5 642.6 637.8 633.0 627.7 30 673.1 657.5 645.5 636.2 628.3 622.0 617.0 40 673.1 656.1 643.4 632.9 622.3 614.7 608.6 50 673.1 655.6 642.4 631.2 620.4 612.3 605.8 60 673.1 655.9 643.1 633.6 624.7 615.8 610.9 70 673.1 657.3 646.8 637.7 630.0 623.6 617.1 80 673.1 659.2 650.6 643.0 637.5 633.2 629.7 90 673.1 661.2 654.8 649.6 646.4 644.2 643.0 100 673.1 664.9 659.4 656.7 655.7 656.3 657.7 110 673.1 666.1 664.2 664.1 665.9 669.1 673.3 120 673.1 668.3 669.2 671.9 676.7 683.2 690.7 130 673.1 670.4 674.2 679.3 687.1 696.8 707.1 140 673.1 672.5 677.4 686.6 697.2 709.9 723.5 150 673.1 674.3 681.6 694.1 707.4 723.0 740.2 160 673.1 677.6 687.2 700.0 715.1 734.7 756.0 170 673.1 679.3 690.6 705.4 723.0 743.9 766.7 180 673.1 680.3 692.6 708.8 726.9 749.8 772.5 190 673.1 681.2 694.9 710.3 729.9 750.9 774.9 200 673.1 681.8 695.2 710.3 729.7 749.2 772.2 210 673.1 682.0 695.1 709.5 728.1 746.4 767.5 220 673.1 682.0 694.7 708.1 725.6 743.3 763.3 230 673.1 681.8 693.9 706.6 721.8 739.9 759.1 240 673.1 682.6 693.8 706.1 721.2 739.9 758.7 250 673.1 682.6 694.3 707.5 723.7 744.1 764.7 260 673.1 682.5 694.7 709.1 726.6 748.1 771.4 270 673.1 682.0 694.2 709.3 729.1 750.0 774.2 280 673.1 680.3 693.0 707.9 727.6 748.0 771.9 290 673.1 679.2 691.0 705.0 724.1 743.4 766.4 300 673.1 677.9 688.0 700.5 718.2 735.3 758.0 310 673.1 675.9 684.1 694.8 708.4 726.1 745.4 320 673.1 673.9 679.2 688.5 699.4 715.0 730.4 330 673.1 671.9 674.7 680.6 689.7 698.1 712.4 340 673.1 669.7 670.1 672.2 677.5 684.7 693.8 350 673.1 667.4 665.3 664.9 666.6 670.2 675.7 360 673.1 665.0 660.7 657.3 655.9 656.3 657.2

– 268 – Appendix F: Results of the Sensitivity Analysis

Table F.9: Wind Inﬂuences on the Energy Demand of a Train Run for the Yverdon–Biel Line. The energy demands are given in kWh; vw the wind speed, ψ2 the wind (source) direction. 0° = 360° = North.

v (km/h) w 0 5 10 15 20 25 30 ψ2 (° ) 0 664.4 672.8 684.5 697.9 715.8 738.5 762.9 10 664.4 673.7 685.7 699.2 717.4 738.9 764.0 20 664.4 673.0 686.2 699.5 716.8 737.0 761.3 30 664.4 673.7 686.4 699.1 715.4 733.3 756.0 40 664.4 674.0 686.7 698.6 712.9 730.9 750.3 50 664.4 673.9 686.5 697.5 710.3 726.6 744.0 60 664.4 674.0 686.3 697.8 710.9 726.6 745.2 70 664.4 673.9 685.9 700.2 714.5 731.2 752.2 80 664.4 673.8 688.1 702.0 718.7 739.1 760.0 90 664.4 673.4 686.9 702.8 720.4 741.9 765.4 100 664.4 672.2 686.0 701.1 719.2 740.8 765.0 110 664.4 671.9 684.0 698.0 715.7 736.5 759.0 120 664.4 670.6 680.9 693.3 708.8 728.3 748.7 130 664.4 668.9 675.9 687.7 699.4 716.1 734.1 140 664.4 667.0 671.5 680.5 690.9 703.3 717.9 150 664.4 663.5 667.0 672.0 680.3 688.8 699.3 160 664.4 661.5 662.2 664.0 668.3 674.8 681.6 170 664.4 659.2 656.0 654.7 657.3 659.3 663.1 180 664.4 656.8 651.1 647.9 645.7 645.4 645.6 190 664.4 654.4 646.0 640.3 636.3 633.4 631.1 200 664.4 652.2 641.9 633.4 625.9 620.9 617.4 210 664.4 650.3 638.0 627.5 619.2 612.9 607.7 220 664.4 648.6 635.3 623.3 614.4 607.6 601.0 230 664.4 647.9 633.1 622.0 612.6 604.7 597.9 240 664.4 648.2 633.7 623.0 613.8 605.9 600.0 250 664.4 649.6 636.8 626.0 617.8 611.6 606.4 260 664.4 651.8 641.8 631.8 624.9 619.8 616.8 270 664.4 653.9 645.9 639.7 633.7 630.8 631.2 280 664.4 655.9 650.4 646.8 644.8 644.8 647.6 290 664.4 658.2 655.5 654.6 655.7 658.6 665.0 300 664.4 661.3 660.3 662.1 667.1 674.1 682.4 310 664.4 663.6 665.1 670.8 678.3 688.0 699.2 320 664.4 665.6 670.6 677.6 688.1 702.2 717.8 330 664.4 668.7 674.5 683.8 698.4 713.7 732.3 340 664.4 670.3 678.0 690.6 705.5 723.4 747.0 350 664.4 671.8 682.2 694.9 711.6 732.2 757.4 360 664.4 672.8 684.5 697.9 715.8 738.5 762.9

– 269 –

Bibliography

Accardo, L. (2010). “Electric Regional Rail Services. Innovative Rolling Stock Tech- nologies”. In: Railenergy Final Conference. Bruxelles.

Adler, G. (1990). Lexikon der Eisenbahn. 8. Auﬂage. Berlin: Transpress Verlag. ISBN: 3-344-00160-4.

Aeberhard, M. (2011). Aktuelle und zukünftige Herausforderungen in der Bahn- stromversorgung der SBB.

Aeberhard, M. and E. Basler (2016). “Railway power supply simulation from small to very large networks”. In: eb - Elektrische Bahnen 114 (INT), pp. 17–23.

Aeberhard, M., E. Basler, P. Deutschmann, M. Holderegger, and W. Kaiser (2015). “Zugfahrtsimulationen in landesweiten Bahnenergieversorgungsnetzen”. In: eb - Elektrische Bahnen 113 (5), pp. 244–257.

Aeberhard, M. and R. Gruber (2015). “Mobile Anlagen für Blindleistungskompen- sation”. In: Elektrische Bahnen 113 (6-7), pp. 336–341.

Albrecht, A. R., P.G. Howlett, P.J. Pudney, and X. Vu (2013). “Energy-efﬁcient train control: From local convexity to global optimization and uniqueness”. In: Auto- matica 49 (10), pp. 3072–3078. ISSN: 0005-1098. DOI: 10.1016/j.automatica. 2013.07.008.

Albrecht, A. R., P. G. Howlett, P. J. Pudney, X. Vu, and P. Zhou (2015). “Energy- efﬁcient train control: The two-train separation problem on level track”. In: Journal of Rail Transport Planning and Management 5 (3), pp. 163–182. ISSN: 2210- 9706. DOI: 10.1016/j.jrtpm.2015.10.002.

– (2016a). “The key principles of optimal train control – Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points”. In: Transportation Research Part B: Methodological 94, pp. 482–508. ISSN: 0191-2615. DOI: 10.1016/j.trb.2015.07.023.

– (2016b). “The key principles of optimal train control – Part 2: Existence of an optimal strategy, the local energy minimization principle, uniqueness, computational techniques”. In: Transportation Research Part B: Methodological 94, pp. 1–30. ISSN: 0191-2615. DOI: 10.1016/j.trb.2015.07.024.

Albrecht, T. (2008). “Energy-Efﬁcient Train Operation”. In: Railway Timetable & Traf- ﬁc: Analysis - Modelling - Simulation. Ed. by I. A. Hansen and J. Pachl. 1st. Hamburg: Eurailpress, pp. 83–105. ISBN: 978-3-7771-0371-6.

– 271 – Bibliography

Albrecht, T., C. Gassel, A. Binder, and J. van Luipen (2010). “Dealing with Oper- ational Constraints in Energy Efﬁcient Driving”. In: IET Conference on Railway Traction Systems. Birmingham, UK: IET.

Allègre, A. L., A. Bouscayrol, et al. (2010). “Energy storage system with supercapacitor for an innovative subway”. In: IEEE Transactions on Industrial Electronics 57 (12), pp. 4001–4012. ISSN: 0278-0046. DOI: 10.1109/TIE.2010.2044124.

Amri, H., R. N. Hofstädter, and M. Kozek (2011). “Energy efﬁcient design and simulation of a demand controlled heating and ventilation unit in a metro vehicle”. In: IEEE Forum on Integrated and Sustainable Transportation Systems. Vienna: IEEE, pp. 7–12. ISBN: 978-1-457-70990-6. DOI: 10.1109/FISTS.2011.5973605.

Andersson, G. (2016). Lecture ‘Energy System Analysis’. Zurich.

Andersson, G. and C. M. Franck (2010). Scriptum ‘Electric Power Systems’. Zurich: ETH Zurich (EEH).

Atlas Copco AB (2014). Gesamtkatalog für Druckluft-, Gas- und Vakuumlösungen. Stockholm, Sweden.

Bae, C.-h., D.-U. Jang, Y.-g. Kim, C. Se-ky, and M. Jai-Kyun (2007). “Calculation of regenerative energy in DC 1500V electric railway substations”. In: Internatonal Conference on Power Electronics, pp. 801–805. DOI: 10 . 1109 / ICPE . 2007 . 4692497.

Bagnall, T., I. Imrie, and M. Jacob (2012). “Queensland Rail’s proof of concept for OpenPowerNet”. In: eb - Elektrische Bahnen 110 (8-9), 3–7 (Sonderdruck).

Bahnonline.ch (2013). SOB FLIRT: Praxistest für Trockentrafos. URL: http://www. bahnonline.ch/wp/67249/sob-flirt-praxistest-fuer-trockentrafos.htm (visited on 04/27/2017).

Baker, C., J. Jones, F. Lopez-Calleja, and J. Munday (2004). “Measurements of the Cross Wind Forces on Trains”. In: Journal of Wind Engineering and Industrial Aerodynamics 92, pp. 547–563. ISSN: 0167-6105. DOI: 10 . 1016 / j . jweia . 2004.03.002.

Barrero, R., J. V. Mierlo, and X. Tackoen (2008). “Enhanced Energy Storage Sys- tems for Improved On-Board Light Rail Vehicle Efﬁciency”. In: IEEE Vehicular Technology Magazine (September), pp. 26–36. ISSN: 1556-6072. DOI: 10.1109/ MVT.2008.927485.

Behmann, U. (2008). “Bahnenergiebedarf der Traktionsarten im Vergleich”. In: Elektrische Bahnen 106 (12), pp. 547–554.

– (2015). “Traktionsenergie sparen als Nebeneffekt”. In: Elektrische Bahnen 113 (11), pp. 586–587.

Bergendorff, M. (2010). “Methodology, Results & Calculator”. In: Railenergy Final Conference. Bruxelles.

– 272 – Bibliography

Betz, F., K. Beuth, J. Lauwaßer, W. Schmidt, and A. Wunderlin (1978). Grundkennt- nisse Elektrotechnik: Elektrotechnik · Elektropraxis · Werkstoffkunde. Ed. by F. Betz and E. Huber. 3. Auﬂage. Hamburg: HT Verlag Handwerk und Technik. ISBN: 3-582-03621-9.

Beusen, B., B. Degraeuwe, and P. Debeuf (2013). “Energy savings in light rail through the optimization of heating and ventilation”. In: Transportation Research Part D: Transport and Environment 23, pp. 50–54. ISSN: 1361-9209. DOI: 10. 1016/j.trd.2013.03.005.

Beyer, K.-H., J. Degenkolbe, et al. (2005). Handbuch Diesellokomotiven. Ed. by VEB Lokomotivbau Karl Marx (Babelsberg 1967). 1. Auﬂage. Stuttgart: Trans- press Verlag. ISBN: 978-3-613-71269-0.

Biadgo, A. M., A. Simonovic, J. Svorcan, and S. Stupar (2014). “Aerodynamic characteristics of high speed train under turbulent cross Winds: A numerical investigation using unsteady-RANS method”. In: FME Transactions 42 (1), pp. 10–18. ISSN: 1451-2092. DOI: 10.5937/fmet1401010B.

Biesenack, H., E. Braun, et al. (2006). Energieversorgung elektrischer Bahnen. Wiesbaden: Teubner. ISBN: 978-3-519-06249-3.

Bikle, U., A. Colotti, and L. Küng (2012). Scriptum ‘Energiewandler der Mecha- tronik’. Zurich: ETH Zurich (PES).

Binder, A., T. Koch, and A. Jöckel (2003). Permanentmagneterregter Direktantrieb für die elektrische Traktion am Beispiel des ICE 3. Karlsruhe.

Bitoleanu, A., M. Popescu, and C. V. Suru (2017). “Conﬁguring and Experimen- tal Evaluation of a Laboratory Model for Filtering and Regeneration in Active DC Traction Substations”. In: International Symposium on Advanced Topcis in Electrical Engineering. Nucharest, Romania: IEEE, pp. 591–596. ISBN: 978-1- 509-05160-1.

Bocchetti, G., M. Carpita, and G. Giannini (1993). “Line ﬁlter for high power inverter locomotive using active circuit for harmonic reduction”. In: European Conference on Power Electronics and Applications. Brighton, UK: IEEE, pp. 267–271.

Bocharnikov, Y. V., A. M. Tobias, and C. Roberts (2010). “Reduction of train and net energy consumption using genetic algorithms for trajectory optimisation”. In: IET Conference on Railway Traction Systems, pp. 32–32. ISBN: 978-1-849-19211-8. DOI: 10.1049/ic.2010.0038.

Bombardier Transportation AG (2008). EBI Drive 50 Driver Assistance System. Stockholm, Sweden.

– (2014). MITRAC Energy Saver. Zurich, Switzerland.

Bomhauer-Beins, A. (2006). Eine Geschichte der Entwicklung der Elektrolokomo- tive in der Schweiz vom Krokodil zur Lok 2000. Maturitätsarbeit an der Kanton- sschule Glattal, Dübendorf.

– 273 – Bibliography

Bomhauer-Beins, A. (2014). “Development of a Time Domain Model for Multi- Winding, Single-Phase Transformers”. Master Thesis. ETH Zurich.

– (2017). Automation zwischen Unterwerk und Schiene: Abschätzung vorhandenen Energiesparpotentials. Tech. rep. Zurich: Institut für Verkehrsplanung und Transportsysteme.

Bomhauer-Beins, A., S. Schranil, and U. A. Weidmann (2018a). “Abschätzung des Energiesparpotentials der Automatisierung im Bahnbetrieb”. In: Elektrische Bah- nen (4-5), pp. 150–156.

– (2018b). “Einﬂüsse auf den Bahnenergiebedarf und diesbezügliche Potentiale der Automatisierung”. In: Schweizer Eisenbahn-Revue (3), pp. 140–144.

Bomhauer-Beins, A. and U. A. Weidmann (2018). “Grundlagen für ein neues Modell des Luftwiderstands von Eisenbahnfahrzeugen”. In: ZEVrail 142 (10), pp. 410–416.

Bosch, J. (2014). “Pünktlichkeit spart Energie – Modellierung von Einﬂussfaktoren auf den Bahnenergiebedarf”. In: Elektrische Bahnen 112 (10), pp. 596–602.

Bosch, J. and J. M. Aniceto (2013). “Potenziale für das Lastmanagement im Bahnenergiesystem”. In: Elektrische Bahnen 111 (2), pp. 98–103.

Bosch, J. and C. Obkircher (2015). “Dauerhafte Netzkupplung ÖBB-SBB”. In: Elek- trische Bahnen 113 (10), pp. 482–486.

Bundesamt für Energie (2014). Schweizerische Gesamtenergiestatistik 2013. Bern.

Bundesamt für Verkehr (2014). Ausführungsbestimmungen zur Eisenbahnverord- nung (AB-EBV). Bern.

– (2015). Schweizerische Fahrdienstvorschriften FDV. Bern.

Caimi, G., M. Fuchsberger, et al. (2009). “Conﬂict-free train scheduling in a compensation zone exploiting the speed proﬁle”. In: International Seminar on Rail- way Operations Research. Zurich: Institute for Operations Research at ETH Zurich.

Calderaro, V., V. Galdi, G. Graber, and A. Piccolo (2015a). “Energy Management of Auxiliary Battery Substation Supporting High-Speed Train on 3 kV DC Systems”. In: International Conference on Renewable Energy Research and Applications. Vol. 4. Palermo: IEEE, pp. 1224–1229. ISBN: 978-1-4799-9982-8. DOI: 10.1109/ ICRERA.2015.7418603.

– (2015b). “Optimal siting and sizing of stationary supercapacitors in a metro network using PSO”. In: Proceedings of the IEEE International Conference on In- dustrial Technology (June), pp. 2680–2685. DOI: 10.1109/ICIT.2015.7125493.

– 274 – Bibliography

Capasso, A., R. Lamedica, et al. (2016). “Individual driving style impact on traction energy consumption in railway lines: a simulation model”. In: International Sym- posium on Power Electronics, Electrical Drives, Automation and Motion. Capri: IEEE, pp. 665–670. ISBN: 978-1-509-02067-6. DOI: 10.1109/SPEEDAM.2016. 7525929.

Caracciolo, M. B., M. Berrera, and M. Brenna (2015). “Conversion systems for braking energy recovery in 3 kVDc railway lines”. In: AEIT International Annual Conference. AEIT. DOI: 10.1109/AEIT.2015.7415256.

Carvajal-Carreño, W., A. P. Cucala, and A. Fernández-Cardador (2014). “Optimal design of energy-efﬁcient ATO CBTC driving for metro lines based on NSGA-II with fuzzy parameters”. In: Engineering Applications of Artiﬁcial Intelligence 36, pp. 164–177. ISSN: 0952-1976. DOI: 10.1016/j.engappai.2014.07.019.

Ceraolo, M. and G. Lutzemberger (2014). “Stationary and on-board storage systems to enhance energy and cost efﬁciency of tramways”. In: Journal of Power Sources 264, pp. 128–139. ISSN: 0378-7753. DOI: 10.1016/j.jpowsour.2014. 04.070.

Chang, C. and S. Sim (1997). “Optimising train movements through coast control using genetic algorithms”. In: IEE Proceedings – Electric Power Applications. Vol. 144. IET, pp. 65–73. DOI: 10.1049/ip-epa:19970797.

Chevrier, R., P. Pellegrini, and J. Rodriguez (2013). “Energy saving in railway timetabling: A bi-objective evolutionary approach for computing alternative running times”. In: Transportation Research Part C 37, pp. 20–41. ISSN: 0968-090X. DOI: 10.1016/j.trc.2013.09.007.

Chiu, T. and L. Squire (1992). “An experimental study of the ﬂow over a train in a crosswind at large yaw angles up to 90°”. In: Journal of Wind Engineering and Industrial Aerodynamics 45, pp. 47–74.

Ciccarelli, F., D. Iannuzzi, and D. Lauria (2012). “Stationary ultracapacitors storage device for improving energy saving and voltage proﬁle of light transportation networks”. In: Transportation Research Part C 21 (1), pp. 321–337. DOI: 10.1016/j.trc.2011.11.002.

Ciccarelli, F., D. Iannuzzi, and P. Tricoli (2012). “Control of metro-trains equipped with onboard supercapacitors for energy saving and reduction of power peak demand”. In: Transportation Research Part C 24, pp. 36–49. DOI: 10.1016/j. trc.2012.02.001.

Claessens, M., D. Dujic, F. Canales, and P. Stefanutti (2012). “Traction transformation”. In: ABB review (1), pp. 11–17.

Claessens, M., D. Dujic, et al. (2013). Kleiner, leichter, efﬁzienter – der Leis- tungselektronische Traktionstransformator (PETT). Genf. URL: http : / / new . abb.com/docs/default- source/automation- power- world- switzerland- docs/kleiner-leichter-effizienter-pett.pdf?sfvrsn=2.

– 275 – Bibliography

Clerici, A. and E. Tironi (2016). “Multiport converters and ESS on 3kV DC railway lines : case study for braking energy savings”. In: IEEE International Conference on Environment and Electrical Engineering. Florence, Italy: IEEE. ISBN: 978-1- 509-02320-2. DOI: 10.1109/EEEIC.2016.7555700.

Corman, F., A. D’Ariano, D. Pacciarelli, and M. Pranzo (2009). “Evaluation of green wave policy in real-time railway trafﬁc management”. In: Transportation Research Part C 17, pp. 607–616.

Cornic, D. and S. Lechelle (2010). “"Best of" Reversible DC Substation”. In: Railen- ergy Final Conference. Bruxelles.

Cucala, A. P.,A. Fernández-Cardador, C. Sicre, and M. Domínguez (2012). “Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and drivers behavioral response”. In: Engineering Appli- cations of Artiﬁcial Intelligence 25 (8), pp. 1548–1557. ISSN: 0952-1976. DOI: 10.1016/j.engappai.2012.02.006.

Curtius, E. and A. Knifﬂer (1950). “Neue Erkenntnisse über die Haftung zwischen Treibrad und Schiene”. In: Elektrische Bahnen 21 (9), pp. 201–210. ISSN: 0013- 5437.

D’Ariano, A. and T. Albrecht (2006). “Running time re-optimization during real- time timetable perturbations”. In: WIT Transactions on the Built Environment 88, pp. 531–540. ISSN: 1743-3509. DOI: 10.2495/CR060531.

De La Torre, S., A. J. Sánchez-Racero, J. A. Aguado, M. Reyes, and O. Martínez (2015). “Optimal Sizing of Energy Storage for Regenerative Braking in Electric Railway Systems”. In: IEEE Transactions on Power Systems 30 (3), pp. 1492– 1500. ISSN: 0885-8950. DOI: 10.1109/TPWRS.2014.2340911.

De Martinis, V. and U. A. Weidmann (2015). “Definition of energy-efficient speed profiles within rail traffic by means of supply design models”. In: Research in Transportation Economics 54, pp. 41–50. ISSN: 0739-8859. DOI: 10.1016/j. retrec.2015.10.024.

De Martinis, V., U. A. Weidmann, and M. Gallo (2014). “Towards a simulation-based framework for evaluating energy-efﬁcient solutions in train operation”. In: WIT Transactions on the Built Environment 135. Ed. by C. A. Brebbia, N Tomii, P Tzieropoulos, and J. M. Mera, pp. 721–732. ISSN: 1743-3509. DOI: 10.2495/ CR140601.

De Sousa, C. A. and S. L. Pereira (2015). “Comparative Study of Existing Methods for Improving the Energy Efﬁciency of the System Traction of Subway”. In: IEEE Brazilian Power Electronic Conference and Southern Power Electronics Confer- ence. Fortaleza, pp. 1–5. ISBN: 978-1-479-98779-5.

Deeg, P. and K. A. Bolten (2006). “Prüfung der aerodynamischen Lasten eines Zuges auf der freien Strecke”. In: Eisenbahningenieur 57 (9), pp. 34–42.

– 276 – Bibliography

DIN Deutsches Institut für Normung e.V. (2015). Qualitätsmanagementsysteme – Grundlagen und Begriffe (ISO 9000:2015); Deutsche und Englische Fassung EN ISO 9000:2015.

DMK/DPK (2003). Formel und Tafeln. Mathematik – Physik. Zürich: Orell Füssli, Zürich. ISBN: 3-280-02162-6.

Domínguez, M., A. Fernández-Cardador, A. P. Cucala, and R. R. Pecharromán (2012). “Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed proﬁles”. In: IEEE Trans- actions on Automation Science and Engineering 9 (3), pp. 496–504. ISSN: 1545- 5955. DOI: 10.1109/TASE.2012.2201148.

Donnelly, B. (2010). “Refurbishment of Rolling Stock”. In: Railenergy Final Confer- ence. Bruxelles.

Douglas, H., C. Roberts, S. Hillmansen, and F. Schmid (2015). “An assessment of available measures to reduce traction energy use in railway networks”. In: Energy Conversion and Management 106, pp. 1149–1165. ISSN: 0196-8904. DOI: 10.1016/j.enconman.2015.10.053.

Douglas, H., F. Schmid, C. Roberts, and S. Hillmansen (2016). “Evaluation of Per- manent Magnet Motor Energy Saving Technology for Different Types of Rail- ways”. In: IEEE International Conference on Intelligent Rail Transportation. Birm- ingham, UK: IEEE, pp. 123–129. ISBN: 978-1-5090155-5-9. DOI: 10 . 1109 / ICIRT.2016.7588721.

Douglas, H., P. Weston, D. Kirkwood, S. Hillmansen, and C. Roberts (2017). “Method for validating the train motion equations used for passenger rail vehicle simulation”. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 231 (4), pp. 455–469. ISSN: 20413017. DOI: 10.1177/0954409716631784.

Dube, N., W. Fraas, et al. (2013). Energieoptimaler Bahnverkehr – auf dem Weg zum 1-Liter-Zug. Tech. rep. Frankfurt am Main: Energietechnische Gesellschaft (ETG) im Verband der Elektrotechnik Elektronik Informationstechnik (VDE) e.V.

Edwards, R., H. Hass, et al. (2014). Well-to-Wheels analysis of future automotive fuels and powertrains in the European context – WELL-TO-TANK (WTT) Re- port, Appendix 2, Version 4a. Tech. rep. Joint Research Centre of the European Commission, Institue for Energy and Transport. DOI: 10.2790/95629.

Enotrac AG (2017). FABEL. URL: https : / / www . enotrac . com / de / software - tools/fabel.php (visited on 02/04/2019).

Eriksson, E. (2009). “EETROP – The Key to Efﬁcient Railways”. In: Energy Efﬁ- ciency Days. Tours.

Feng, X., H. Zhang, et al. (2013). “A Review Study on Traction Energy Saving of Rail Transport”. In: Discrete Dynamics in Nature and Society 2013. ISSN: 1026- 0226. DOI: 10.1155/2013/156548.

– 277 – Bibliography

Fernández-Rodríguez, A., A. Fernández-Cardador, A. P. Cucala, M. Domínguez, and T. Gonsalves (2015). “Design of Robust and Energy-Efﬁcient ATO Speed Proﬁles of Metropolitan Lines Considering Train Load Variations and Delays”. In: IEEE Transactions on Intelligent Transportation Systems 16 (4), pp. 2061–2071. ISSN: 1524-9050. DOI: 10.1109/TITS.2015.2391831.

Filipovic,´ Z. (2005). Elektrische Bahnen. 4. Auﬂage. Berlin Heidelberg: Springer. ISBN: 3-540-21310-4.

– (2015). Elektrische Bahnen. 5. Auﬂage. Berlin Heidelberg: Springer. ISBN: 978- 3-642-45226-0. DOI: 10.1007/978-3-642-45227-7.

Fleck, M., S. Khayyam, and A. Monti (2017). “Day-ahead optimization for railway energy management system”. In: International Conference on Electrical Sys- tems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electriﬁcation Conference, ESARS-ITEC. Toulouse: IEEE, pp. 1– 8. ISBN: 978-1-509-00814-8. DOI: 10.1109/ESARS-ITEC.2016.7841381.

Förster, H. J. (1991). Automatische Fahrzeuggetriebe. Berlin Heidelberg: Springer. ISBN: 978-3-642-84119-4.

Franke, R., M. Meyer, and P. Terwiesch (2002). “Optimal Control of the Driving of Trains”. In: Automatisierungstechnik 50 (12), pp. 606–613.

Freystein, H., M. Muncke, and P. Schollmeier (2008). Entwerfen von Bahnanlagen. 2. Auﬂage. Hamburg: DVV Media Group GmbH | Eurailpress. ISBN: 978-3-7771- 0379-2.

Frilli, A., E. Meli, et al. (2017). “Braking Energy Recovery in High Speed Trains: An Innovative Model”. In: Advances in Italian Mechanism Science. Springer, pp. 327–334. ISBN: 978-3-319-48374-0. DOI: 10.1007/978-3-319-48375-7_35.

Fröhlich, M., M. Klohr, and S. Pagiela (2008). “Energy Storage Systems with Ultra- Cpas on Board of Railway Vehicles”. URL: https://uic.org/cdrom/2008/11_ wcrr2008/pdf/R.3.4.3.2.pdf.

Gackenholz, L. (1974). “Der Luftwiderstand der Züge im Tunnel”. In: ZEV-Glasers Annalen 98 (3), pp. 79–84.

Gaillard, M. A. (1973). “Zur Aerodynamik der Zugbegegnung im Tunnel und auf offener Strecke”. Dissertation. ETH Zürich. ISBN: 3-260-03592-3.

Galaï-Dol, L., A. De Bernardinis, A. Nassiopoulos, A. Peny, and F. Bourquin (2016). “On the Use of Train Braking Energy Regarding the Electrical Consumption Opti- mization in Railway Station”. In: Transportation Research Procedia 14, pp. 655– 664. ISSN: 2352-1465. DOI: 10.1016/j.trpro.2016.05.321.

Gao, M., P. Wang, Y. Cao, R. Chen, and D. Cai (2017). “Design and Veriﬁcation of a Rail-Borne Energy Harvester for Powering Wireless Sensor Networks in the Railway Industry”. In: IEEE Transactions on Intelligent Transportation Systems 18 (6), pp. 1596–1609. ISSN: 15249050. DOI: 10.1109/TITS.2016.2611647.

– 278 – Bibliography

Gao, Z., Z. Yang, K. Zhao, F. Lin, and B. Wang (2014). “Control strategy research of urban rail transit for equipped with wayside-based supercapacitor energy saving and reduction of power peak demand”. In: ICIC Express Letters 8 (9), pp. 2477– 2484. ISSN: 1881-803X. DOI: 10.1016/j.ijepes.2014.11.019.

Gee, A. M. and R. W. Dunn (2015). “Analysis of Trackside Flywheel Energy Storage in Light Rail Systems”. In: IEEE Transactions on Vehicular Technology 64 (9), pp. 3858–3869. DOI: 10.1109/TVT.2014.2361865.

Generalsekretariat SBB (1988). SBB Reisezug- und Gepäckwagen. Bern: SBB AG.

Gerber, M., E. Drabek, and R. Müller (1991). “Die Lokomotiven 2000 – Serie 460 – der Schweizerischen Bundesbahnen”. In: Schweizer Eisenbahn-Revue (10), pp. 3–47. ISSN: 0717-6163.

Ghaviha, N., M. Bohlin, and E Dahlquist (2016). “Speed proﬁle optimization of an electric train with on-board energy storage and continuous tractive effort”. In: In- ternational Symposium on Power Electronics, Electrical Drives, Automation and Motion. Capri: IEEE, pp. 639–644. DOI: 10.1109/SPEEDAM.2016.7525913.

Giebel, S. (2018). “Verfahren für ein Energiemanagement in Bordnetzen elektrischer Triebzüge”. Dissertation. Technische Universität Dresden.

Glasl, M. (2017). “Energieefﬁziente Auslegung des Antriebs elektrischer Schienen- fahrzeuge für den Nahverkehr”. In: ZEVrail 141 (Tagungsband SFT Graz), pp. 111–119.

Golovitcher, I. M. (2001). “Energy efﬁcient control of rail vehicles”. In: IEEE Inter- national Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace. Tucson, AZ, USA: IEEE, pp. 658–663. ISBN: 0- 780-37087-2. DOI: 10.1109/ICSMC.2001.969927.

Gong, C., S. Zhang, F. Zhang, J. Jiang, and X. Wang (2014). “An integrated energy- efﬁcient operation methodology for metro systems based on a real case of Shanghai Metro Line One”. In: Energies 7 (11), pp. 7305–7329. ISSN: 1996- 1073. DOI: 10.3390/en7117305.

Gonzalez, D. and F. Manzanedo (2010). “Power losses minimization in D.C. electric railways by means of traction substations coordinated voltage control”. In: IET Conference on Railway Traction Systems.

González-Gil, A., R. Palacín, and P. Batty (2013). “Sustainable urban rail systems: Strategies and technologies for optimal management of regenerative braking energy”. In: Energy Conversion and Management 75, pp. 374–388. ISSN: 0196- 8904. DOI: 10.1016/j.enconman.2013.06.039.

González-Gil, A., R. Palacín, P.Batty, and J. Powell (2014). “A systems approach to reduce urban rail energy consumption”. In: Energy Conversion and Management 80, pp. 509–524. ISSN: 0196-8904. DOI: 10.1016/j.enconman.2014.01.060.

– 279 – Bibliography

Goodwin, J. C., D. I. Fletcher, and R. Harrison (2015). “Multi-train trajectory optimisation to maximise rail network energy efﬁciency under travel-time constraints”. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 230 (4), pp. 1318–1335. ISSN: 0954-4097. DOI: 10.1177/ 0954409715593304.

Grimm, H. (2016). Wasser und Dampfdruck: Dampfdrucktabelle für Wasser und Eis, Zusammenhang mit Sieden und Siedepunkt, Formel zur Berechnung durch Interpolation. URL: http://www.wissenschaft- technik- ethik.de/wasser_ dampfdruck.html (visited on 08/15/2017).

Gu, Q., T. Tang, and F. Ma (2016). “Energy-Efﬁcient Train Tracking Operation Based on Multiple Optimization Models”. In: IEEE Transactions on Intelligent Trans- portation Systems 17 (3), pp. 882–892. ISSN: 1524-9050. DOI: 10.1109/TITS. 2015.2502609.

Guo, W., H. Xia, R. Karoumi, T. Zhang, and X. Li (2015). “Aerodynamic effect of wind barriers and running safety of trains on high-speed railway bridges under cross winds”. In: Wind and Structures 20 (2), pp. 213–236. DOI: 10.12989/was. 2015.20.2.213.

Han, S.-H., Y.-S. Byen, et al. (1999). “An optimal automatic train operation (ATO) control using genetic algorithms (GA)”. In: IEEE TENCON. Proceedings of the IEEE Region 10 Conference 1, pp. 360–362. DOI: 10 . 1109 / TENCON . 1999 . 818425.

Hardel, S., S. Körner, and A. Stephan (2014). “Leistung oder Spannung? – Ko- rrekte elektrische Netzberechnung für Bahnen”. In: Elektrische Bahnen (8-9 (Sonderdruck)), pp. 3–10.

Hase, S.-I., T. Konishi, et al. (2002). Fundamental Study on Energy Storage System for DC Electric Railway System. Osaka.

Hay, W. W. (1982). Railroad Engineering. 2nd Ed. New York-Chichester-Brisbane- Toronto-Singapore: John Wiley & Sons. ISBN: 0-471-36400-2.

Hayashiya, H., S. Kikuchi, et al. (2013). “Possibility of energy saving by introducing energy conversion and energy storage technologies in traction power supply system”. In: European Conference on Power Electronics and Applications. DOI: 10.1109/EPE.2013.6631780.

Hecht, M., E. Jänsch, et al. (2008). Handbuch ‘Das System Bahn’. 1. Auﬂage. Hamburg: DVV Rail Media (Eurailpress). ISBN: 978-3-777-10374-7.

Helwig, E. (2016). “Energiemanagementmethoden bei elektrischen Bahnen und Nebenverbrauchern”. In: Elektrische Bahnen 114 (3), pp. 140–145.

Heumann, K. (1991). Grundlagen der Leistungselektronik. Wiesbaden: Springer. ISBN: 978-3-519-46105-0. DOI: 10.1007/978-3-663-10220-5.

– 280 – Bibliography

Hillmansen, S. (2012). “Electric Railway Traction Systems and Techniques for En- ergy Saving”. English. In: IET Professional Development Course on Electric Trac- tion Systems. London: IET.

Howlett, P. G. (1990). “An optimal strategy for the control of a train”. In: Journal of the Australian Mathematical Society. Series B. Applied Mathematics 31 (4), pp. 454–471. ISSN: 0334-2700. DOI: 10.1017/s0334270000006780.

– (2000). “The optimal control of a train”. In: Annals of Operations Research (98), pp. 65–87. ISSN: 0254-5330. DOI: 10.1023/a:1019235819716. URL: http:// link.springer.com/article/10.1023/A:1019235819716.

Howlett, P. G. and J. Cheng (1997). “Optimal driving strategies for a train on a track with continuously varying gradient”. In: Journal of the Australian Mathematical Society. Series B. Applied Mathematics 38 (3), pp. 388–410. ISSN: 0334-2700. DOI: 10.1017/S0334270000000746.

Howlett, P. G., I. Milroy, and P. J. Pudney (1994). “Energy-Efﬁcient Train Control”. In: Control Engineering Practice 2 (2), pp. 193–200.

Howlett, P. G. and P. J. Pudney (1995). Energy-Efﬁcient Train Control (Series ’Ad- vances in Industrial Control’). 1st. London: Springer. ISBN: 9781447112198.

Howlett, P.G., P.J. Pudney, and X. Vu (2009). “Local energy minimization in optimal train control”. In: Automatica 45, pp. 2692–2698. DOI: 10.1016/j.automatica. 2009.07.028.

Huang, J., Z. Zhong, and H. Huo (2015). “A dynamic energy-saving strategy for green cellular railway communication network”. In: EURASIP Journal on Wire- less Communications and Networking 89. ISSN: 1687-1499. DOI: 10 . 1186 / s13638-015-0317-2.

Iannuzzi, D. (2008). “Improvement of the Energy Recovery of Traction Electrical Drives using Supercapacitors”. In: International Power Electronics and Motion Control Conference. Poznan, Poland: IEEE. ISBN: 978-1-424-41741-4. DOI: 10. 1109/EPEPEMC.2008.4635475.

Ichikawa, K. (1968). “Application of Optimization Theory for Bounded State Vari- able Problems to the Operation of Train”. In: The Japan Society of Mechanical Engineers 11 (47), pp. 857–865.

IEC (2007). Efﬁcient Electrical Energy Transmission and Distribution. Geneva: IEC International Eletrotechnical Commission. URL: http://www.iec.ch/about/ brochures/pdf/technology/transmission.pdf.

Institut für Bahntechnik GmbH (2019). OpenPowerNet. URL: https://openpowernet. de/ (visited on 02/04/2019).

Isenschmid, C., S. Menth, and P.Oelhafen (2013). “Energieverbrauch und Einspar- potential des S-Bahn-Gliederzugs RABe 525 ’Nina’ der BLS AG”. In: Schweizer Eisenbahn-Revue (8-9), pp. 398–403.

– 281 – Bibliography

Jamili, A. (2016). “Robust Stochastic Optimization Model and Branch and Bound Algorithm for Train Scheduling Using Regenerative Braking Energy”. In: IEEE International Conference on Intelligent Transportation Systems (ITSC). Rio de Janeiro, Brazil: IEEE. ISBN: 978-1-509-01889-5. DOI: 10 . 1109 / ITSC . 2016 . 7795948.

Jandura, P., A. Richter, and Z. Ferkova (2016). “Flywheel Energy Storage System for City Railway”. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion. Capri, Italy: IEEE, pp. 1155–1159. ISBN: 978-1- 509-02067-6. DOI: 10.1109/SPEEDAM.2016.7525923.

Janicki, J. (2011). Systemwissen Eisenbahn. Berlin: Bahn Fachverlag. ISBN: 978- 3-9808002-6-6.

Janicki, J., H. Reinhard, and M. Rüffer (2013). Schienenfahrzeugtechnik. 3. Au- ﬂage. Berlin: Bahn Fachverlag. ISBN: 978-3-943214-07-9.

Jongeling, A. (2017). “Optimising the Input Filter of Traction Installations in DC Railway Power Systems”. Master Thesis. Delft University of Technology.

Jung, S., H. Lee, et al. (2013). “A Study on Peak Power Reduction using Regen- erative Energy in Railway Systems through DC Subsystem Interconnection”. In: Journal of Electrical Engineering and Technology 8 (5), pp. 1070–1077.

Kemnitz, A., H. Steffen, et al. (2007). Formeln und Tabellen Elektrotechnik. Ed. by W. Böge and W. Plaßmann. Wiesbaden: Vieweg. ISBN: 978-3-528-03973-8.

Kendra, M., T. Skrúcaný, J. Grencík,ˇ and P. Šulko (2018). “Impact of Railway Track Parameters on Energy Consumption and GHG Production of Passenger Train Operation”. In: Road and Rail Infrastructure V. Ed. by S. Lakušic.´ Zadar, Croatia: Department of Transportation, Faculty of Civil Engineering, University of Zagreb, pp. 647–653. DOI: 10.5592/CO/CETRA.2018.712.

Keskin, K. and A. Karamancioglu (2017). “Energy-Efﬁcient Train Operation Using Nature-Inspired Algorithms”. In: Journal of Advanced Transportation 2017. DOI: 10.1155/2017/6173795.

Khayyam, S., F. Ponci, et al. (2015). “Railway Energy Management System: Centralized-Decentralized Automation Architecture”. In: IEEE Transactions on Smart Grid 7 (2). ISSN: 1949-3053. DOI: 10.1109/TSG.2015.2421644.

Khmelnitsky, E. (2000). “On an optimal control problem of train operation”. In: IEEE Transactions on Automatic Control 45 (7), pp. 1257–1266. ISSN: 0018-9286. DOI: 10.1109/9.867018.

Kießling, F., R. Puschmann, and A. Schmieder (2014). Fahrleitungen elektrischer Bahnen. Ed. by Siemens Aktiengesellschaft Berlin und München. 3. Auﬂage. Erlangen: Publics Publishing. ISBN: 978-3-89578-407-1.

– 282 – Bibliography

Killer, A., A. Armstorfer, A. E. Díez, and H. Biechl (2012). “Ultracapacitor assisted regenerative braking in metropolitan railway systems”. In: IEEE Colombian Intel- ligent Transportation Systems Symposium. DOI: 10.1109/citss.2012.6336687.

Ko, H., T. Koseki, and M. Miyatake (2004). “Application of dynamic programming to optimization of running proﬁle of a train”. In: Computers in Railways IX. Vol. 15. Dresden, Germany: WIT Press, pp. 103–112. ISBN: 1-853-12715-9. DOI: 10 . 2495/CR040111.

Köbel, C. (2008). Bombardier Energy Saving Technologies and Their Applications. Berlin. URL: http : / / www . dmg - berlin . info / page / downloads / vortrag _ koebel.pdf.

Kolar, J. W. (2010). Scriptum ‘Leistungselektronik’. Zurich: ETH Zurich (PES).

Koseki, T. (2010). “Technologies for saving energy in railway operation: General discussion on energy issues concerning railway technology”. In: IEEJ Trans- actions on Electrical and Electronic Engineering 5 (3), pp. 285–290. ISSN: 19314973. DOI: 10.1002/tee.20531.

Köstler, K. (2017). Zahlenübersicht zum Umwelt-Vorreiter 2016. URL: http : / / www.deutschebahn.com/file/de/11874008/WWeghbb3o4KUo4sucbAc2rXrZoo/ 13835694/data/faktenblatt_umwelt.pdf?hl=Bahnstrommix.

Kumagai, K., T. Fujita, M. Nakahira, Y. Mizuguchi, and H. Sonoda (2016). “Com- parative Evaluations of Regenerative and Electro-dynamic Braking and Power Substations Along Graded Section of a Japanese Suburban Rail Line”. In: IEEE Electrical Power and Energy Conference (EPEC). IEEE, pp. 1–6. ISBN: 978-1- 509-01919-9.

Kumazawa, K., K. Sato, and T. Ogawa (2015). “Energy-Efﬁcient Train Speed Pro- ﬁle Generator by Combining Partial Energy-Oriented Driving Approaches”. In: Electrical Engineering in Japan 135 (4), pp. 368–375. ISSN: 1348-8163. DOI: 10.1541/ieejias.135.368.

Lamedica, R., A. Ruvio, et al. (2015). “Application of battery auxiliary substations in 3kV railway systems”. In: AEIT International Annual Conference. Naples: IEEE, pp. 1–6. DOI: 10.1109/AEIT.2015.7415249.

Le, Z., K. Li, J. Ye, and X. Xu (2015). “Optimizing the train timetable for a subway system”. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 229 (8), pp. 852–862. ISSN: 0954-4097. DOI: 10.1177/0954409714524377.

Lin, F., X. Li, Y. Zhao, and Z. Yang (2016). “Control Strategies with Dynamic Thresh- old Adjustment for Supercapacitor Energy Storage System Considering the Train and Substation Characteristics in Urban Rail Transit”. In: Energies 9 (4), p. 257. ISSN: 1996-1073. DOI: 10.3390/en9040257.

Liu, F., J. Xun, and N. Bin (2016). “An Optimization Method for Train Driving Trajec- tory in Urban Rail Systems”. In: Youth Academic Annual Conference of Chinese

– 283 – Bibliography

Association of Automation (YAC). Wuhan, China: IEEE, pp. 5–10. DOI: 10.1109/ YAC.2016.7804929.

Liu, R. R. and I. M. Golovitcher (2003). “Energy-efﬁcient operation of rail vehicles”. In: Transportation Research Part A 37, pp. 917–932. DOI: 10.1016/j.tra.2003. 07.001.

Liu, S., F. Cao, J. Xun, and Y. Wang (2015). “Energy-Efﬁcient Operation of Single Train Based on the Control Strategy of ATO”. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Las Palmas: IEEE, pp. 2580–2586. ISBN: 978-1-467-36595-6. DOI: 10.1109/ITSC.2015.415.

López-López, Á. J., R. R. Pecharromán, A. Fernández-Cardador, and A. P. Cucala (2014). “Assessment of energy-saving techniques in direct-current-electriﬁed mass transit systems”. In: Transportation Research Part C 38, pp. 85–100. ISSN: 0968-090X. DOI: 10.1016/j.trc.2013.10.011.

Lu, S., M. Q. Wang, P. Weston, S. Chen, and J. Yang (2016). “Partial Train Speed Trajectory Optimization Using Mixed-Integer Linear Programming”. In: IEEE Transactions on Intelligent Transportation Systems 17 (10), pp. 2911– 2920. ISSN: 1524-9050. DOI: 10.1109/TITS.2016.2535399.

Lu, S., P. Weston, S. Hillmansen, H. B. Gooi, and C. Roberts (2014). “Increasing the regenerative braking energy for railway vehicles”. In: IEEE Transactions on Intelligent Transportation Systems 15 (6), pp. 2506–2515. ISSN: 1524-9050. DOI: 10.1109/TITS.2014.2319233.

Lüthi, M. (2008). “Evaluation of energy saving strategies in heavily used rail networks by implementing an integrated real-time rescheduling system”. In: Com- puters in Railways XI. Ed. by J Allan, E Arias, et al. Vol. 103. Southampton: WIT Press, pp. 349–358. ISBN: 978-1-845-64126-9. DOI: 10.2495/cr080351.

– (2009). “Improving the efﬁciency of heavily used railway networks through integrated real-time rescheduling”. PhD thesis. Zurich. ISBN: 978-3-905826-11-1.

Lüthi, M., U. A. Weidmann, F. Laube, and G. Medeossi (2007). “Rescheduling and train control: A new framework for railroad trafﬁc control in heavily used networks”. In: Proceedings of the 86th Transportation Research Board, p. 13. DOI: 10.3929/ethz-a-005684094.

Mach, S. von, J. Buschbeck, H. Flerlage, U. Zimmermann, and P. Boev (2018). “Die Entwicklung und Markteinführung des TALENT 3 Batterietriebzuges”. In: Eisenbahntechnische Rundschau (9), pp. 115–119.

Marchetti, P.and F. Orazi (2010). “Electric AC Highspeed Rail Services”. In: Railen- ergy Final Conference. Bruxelles.

Matschke, G., M. Grab, and B. Bergander (2002). “Nachweis der Sicherheit im Schienenverkehr bei extremem Seitenwind”. In: Eisenbahntechnische Rund- schau 51 (4), pp. 200–206.

– 284 – Bibliography

Matsuoka, K. and M. Kondo (2010). “Energy Saving Technologies for Railway Trac- tion Motors”. In: IEEJ Transactions on Electrical and Electronic Engineering 5 (3), pp. 278–284. ISSN: 1931-4973. DOI: 10.1002/tee.20530.

Mazzone, A. and N. Hohenbichler (2017). “Lokomotiven – Trends in der Entwick- lung der Triebfahrzeuge”. In: ZEVrail 141 (Tagungsband SFT Graz), pp. 16–21.

Mehta, F., C. Rößiger, and M. Montigel (2010). “Potenzielle Energieersparnis durch Geschwindigkeitsempfehlungen im Bahnverkehr”. In: Signal + Draht 102 (9), pp. 20–25.

Mellitt, B., Z. Mouneimne, and C. Goodman (1984). “Simulation Study of DC Transit Systems with Inverting Substations”. In: IEE Proceedings B: Electric Power Ap- plications 131 (2), pp. 38–50. ISSN: 0143-7038. DOI: 10.1049/ip-b:19840008.

Meyer, M. (2010). “Speciﬁcation and veriﬁcation of energy consumption”. In: Railenergy Final Conference. Bruxelles.

– (2012). Lecture ‘Eisenbahn-Systemtechnik I’. Zurich.

– (2013). Lecture ‘Eisenbahn-Systemtechnik II’. Zurich.

Meyer, M. and M. Aeberhard (1997). “Vom Gratisstrom zur Energiesparlokomo- tive – Energieverbrauch bei elektrischen Bahnen”. In: Schweizer Eisenbahn- Revue (1-2), pp. 28–39.

Meyer, M., A. Heck, G. Walch, and M. Müri (2016). “Reduktion des Traktionsen- ergiebedarfs der Allegra-Triebwagen der RhB”. In: Schweizer Eisenbahn-Revue (2), pp. 69–71.

Meyer, M., M. Lerjen, S. Menth, M. Lüthi, and M. Tuchschmid (2009). Veriﬁzierung der Stromeinsparung durch energieefﬁzientes Zugsmanagement. Tech. rep.

Meyer, M. and I. Nerlich (2017). “Ermittlung örtlicher Zug-Bremskraftkollektive für das SBB-Netz und verbesserte Prognosen von Rollkontakt-Ermüdungsschäden”. In: ZEVrail 141 (Tagungsband SFT Graz), pp. 166–174.

Mi, J., L. Xu, S. Guo, L. Meng, and M. A. A. Abdelkareem (2017). “Energy Harvest- ing Potential Comparison Study of a Novel Railway Vehicle Bogie System with the Hydraulic-Electromagnetic Energy-Regenerative Shock Absorber”. In: Pro- ceedings of the ASME Joint Rail Conference. Philadelphia, PA, USA: American Society of Mechincal Engineers. ISBN: 978-0-7918-5071-8.

Mineyoshi, T., H. Kobayashi, K. Natori, and K. Kondo (2016). “A Study on an Energy Saving Train Scheduling in Consideration of Vehicle Load Variations and Loss Model of Traction Circuit”. In: International Conference on Electrical Machines and Systems (ICEMS). Chiba: IEEE.

Montrone, T., P. Pellegrini, P. Nobili, and G. Longo (2016). “Energy Consumption Minimization In Railway Planning”. In: IEEE International Conference on Envi-

– 285 – Bibliography

ronment and Electrical Engineering. Florence: IEEE. ISBN: 978-1-509-02320-2. DOI: 10.1109/EEEIC.2016.7555534.

Mousavi G, S., F. Faraji, A. Majazi, and K. Al-Haddad (2017). “A comprehensive review of Flywheel Energy Storage System technology”. In: Renewable and Sus- tainable Energy Reviews 67, pp. 477–490. ISSN: 1364-0321. DOI: 10.1016/j. rser.2016.09.060.

Müller, W. (1940). Die Fahrdynamik der Verkehrsmittel. Berlin: Verlag von Julius Springer. ISBN: 978-3-642-50596-6.

Nasr, S., M. Iordache, and M. Petit (2014). “Smart Micro-grid integration in DC railway systems”. In: IEEE PES Innovative Smart Grid Technologies Conference Europe. Istanbul: IEEE, pp. 1–6. ISBN: 978-1-479-97720-8.

Nolte, R. and C. Lauszat (2010a). “Diesel Rail Services—Introduction”. In: Railen- ergy Final Conference. Bruxelles.

– (2010b). “Diesel Rail Services—Recommendations”. In: Railenergy Final Con- ference. Bruxelles.

Novak, H. (2016). “Energy-efﬁcient optimal control in railway transport systems”. In: Distribution.

Novak, H., M. Vasak, and V. Lesic (2016). “Hierarchical Energy Management of Multi-Train Railway Transport System with Energy Storages”. In: IEEE Inter- national Conference on Intelligent Rail Transportation. Birmingham, UK: IEEE, pp. 130–138. ISBN: 978-1-509-01555-9. DOI: 10.1109/ICIRT.2016.7588722.

N.U. Railenergy. URL: http://www.railenergy.org/ (visited on 06/18/2018).

– Railenergy: Project Structure. URL: http://www.railenergy.org/structure. shtml (visited on 06/18/2018).

– (2010). Deutsche Bahn fährt mit dreckigem Kohlestrom. Ed. by A. Peters. Ham- burg. URL: https://www.greenpeace.de/sites/www.greenpeace.de/files/ fs_100222_BahnDatteln_cv_0.pdf.

– (2012). Pkw-Antriebe im Überblick – Vergangenheit, Gegenwart und Zukunft. URL: https : / / www . springerprofessional . de / motorentechnik / pkw - antriebe-im-ueberblick-vergangenheit-gegenwart-und-zukunft/6561052 (visited on 09/04/2017).

Oettich, S., T. Albrecht, and S. Scholz (2004). “Improvements of energy efﬁciency of urban rapid rail systems”. In: Urban Transport X 16, pp. 573–582. ISSN: 1462- 608X.

Ogasa, M. (2008). “Energy saving and environmental measures in railway technologies: Example with hybrid electric railway vehicles”. In: IEEJ Transactions on Electrical and Electronic Engineering 3 (1), pp. 15–20. ISSN: 1931-4973. DOI: 10.1002/tee.20227.

– 286 – Bibliography

Omlin, A., B. Ronner, and P. K. Steimer (2011). Scriptum ‘Elektrische Antriebssys- teme I’. Zurich: ETH Zurich (PES).

Orellano, A. and M. Schober (2006). “Aerodynamic Performance of a Typical High- Speed Train”. In: WSEAS International Conference on Fluid Mechanics and Aerodynamics, pp. 18–25.

Pachl, J. (2011). Systemtechnik des Schienenverkehrs. 6. Auﬂage. Wiesbaden: Vieweg+Teubner Verlag. ISBN: 978-3-834-81428-9.

Pankovits, P., J. Pouget, B. Robyns, F. Delhaye, and S. Brisset (2015). “Towards railway-smartgrid: Energy management optimization for hybrid railway power substations”. In: IEEE PES Innovative Smart Grid Technologies Conference Eu- rope (January), pp. 1–6. DOI: 10.1109/ISGTEurope.2014.7028816.

Pellis, P. (1979). “Der Luftwiderstand bei Zugfahrten im Tunnel”. In: Schienen der Welt (Dezember), pp. 973–982.

Pelz, M. and T. Griem (2015). “Energieefﬁziente Automatisierungslösungen für den Bahnverkehr”. In: ZEVrail 139 (8), pp. 284–290.

Pereira, F. H., C. L. Pires, and S. I. Nabeta (2014). “Optimal placement of rectiﬁer substations on DC traction systems”. In: IET Electrical Systems in Transportation 4 (3), pp. 62–69. ISSN: 2042-9738. DOI: 10.1049/iet-est.2010.0063.

Perin, I., G. R. Walker, and G. Ledwich (2018). “Load Sharing and Wayside Battery Storage for Improving AC Railway Network Performance, with Generic Model for Capacity Estimation, Part 2”. In: IEEE Transactions on Industrial Electronics 65 (12), pp. 9459–9467. ISSN: 0278-0046. DOI: 10.1109/TIE.2018.2838066.

Peters, J.-L. (1990). “Bestimmung des aerodynamischen Widerstandes des ICE/V im Tunnel und auf freier Strecke durch Auslaufversuche”. In: Eisenbahntechni- sche Rundschau 39 (9), pp. 559–564.

Pilo de la Fuente, E., S. K. Mazumder, and I. González Franco (2014). “Railway Electrical Smart Grids: An introduction to next-generation railway power systems and their operation”. In: IEEE Electriﬁcation Magazine 2 (3), pp. 49–55.

Polach, O. (2015a). “Grundlagen der Spurführung”. In: Vorlesung "Dynamik der Schienenfahrzeuge". Zürich: ETH Zürich.

– (2015b). “Kontakt Rad-Schiene I: Berührgeometrie, Normalkräfte”. In: Vorlesung "Dynamik der Schienenfahrzeuge". Zürich: ETH Zürich.

Pudney, P. J. and P. G. Howlett (1994). “Optimal Driving Strategies for a Train Jour- ney With Speed Limits”. In: Journal of the Australian Mathematical Society Se- ries B-Applied Mathematics 36 (1), pp. 38–49. ISSN: 0334-2700. DOI: 10.1017/ S0334270000010225.

Rao, X. (2015). “Holistic rail network operation by integration of train automation and trafﬁc management”. PhD Thesis. ETH Zürich.

– 287 – Bibliography

Ratniyomchai, T., S. Hillmansen, and P. Tricoli (2014). “Optimal capacity and positioning of stationary supercapacitors for light rail vehicle systems”. In: Inter- national Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 807–812. ISBN: 978-1-479-94749-2. DOI: 10.1109/SPEEDAM.2014. 6872019.

Raubal, M., D. Jonietz, et al. (2017). “Towards an Energy Efﬁcient and Climate Compatible Future Swiss Transportation System”. Zurich.

Rees, D. and A. Stephan (2017). “Die Migration ist das Entscheidende – Inter- view mit Prof. Dr.-Ing. Arnd Stephan”. In: Eisenbahntechnische Rundschau (9), pp. 10–13.

Roch-Dupré, D., A. P. Cucala, R. R. Pecharromán, Á. J. López-López, and A. Fernández-Cardador (2018). “Evaluation of the impact that the trafﬁc model used in railway electrical simulation has on the assessment of the installation of a Re- versible Substation”. In: International Journal of Electrical Power and Energy Systems 102, pp. 201–210. ISSN: 0142-0615. DOI: 10.1016/j.ijepes.2018. 04.030.

Rochard, B. and F. Schmid (2000). “A review of methods to measure and calculate train resistances”. In: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 214 (4), pp. 185–199. ISSN: 0954-4097. DOI: 10.1243/0954409001531306.

Rodrigo, E., S. Tapia, J. Mera, and M. Soler (2013). “Optimizing Electric Rail Energy Consumption Using the Lagrange Multiplier Technique”. In: Journal of Trans- portation Engineering 139 (3), pp. 321–329. ISSN: 0733-947X. DOI: 10.1061/ (ASCE)TE.1943-5436.0000483.

Rotter, R. (1966). “Die Phasen hoher Beanspruchung in der Zugförderung und ihr Einﬂuß auf den Wechselstrombahnmotor”. In: Elektrische Bahnen 37 (10), pp. 233–243.

Sachs, K. (1973a). Elektrische Triebfahrzeuge, Band 1. 2. Auﬂage. Wien: Springer. ISBN: 3-211-81072-2.

– (1973b). Elektrische Triebfahrzeuge, Band 2. 2. Auﬂage. Wien: Springer. ISBN: 3-211-81072-2.

SBB AG (2015). Energiestrategie 2015. Faktenblatt.

– (2016). Energiebilanz ICN.

– (2017). Energiestrategie | SBB. URL: https : / / company . sbb . ch / de / sbb - als-geschaeftspartner/leistungen-evu/energie/energiestrategie.html (visited on 09/18/2017).

– (2018). Graﬁscher Fahrplan 302 – Gorgier-St Aubin – Biel/Bienne – Biel/Bienne RB. URL: http://www.fahrplanfelder.ch/fileadmin/fap_pdf_graphic_tt/ 2018/G302.pdf (visited on 08/31/2018).

– 288 – Bibliography

Scheepmaker, G. M., R. M. Goverde, and L. G. Kroon (2017). “Review of energy- efﬁcient train control and timetabling”. In: European Journal of Operational Re- search 257 (2), pp. 355–376. ISSN: 0377-2217. DOI: 10.1016/j.ejor.2016.09. 044.

Schmid, F. and C. Goodman (2014). “Overview of Electric Railway Systems”. In: IET Professional Development Course on Electric Traction Systems. London: IET, pp. 1–15.

Schöbel, A., B. Rüger, A. Nash, and M. Turk (2009). “The potential for saving energy by more precisely calculating station dwell times on commuter rail service”. In: Distribution, pp. 1–8.

Schranil, S. and N. Anders (2017). “Energieefﬁzienz in der strategischen Angebot- splanung der SBB”. In: Elektrische Bahnen 115 (2-3), pp. 100–105.

Schranil, S. and P. Grossenbacher (2016). “Energieefﬁzienz in der Bahnproduk- tion”. In: Eisenbahntechnische Rundschau (9), pp. 152–157.

Schranil, S. and P. Keiser (2017). “Anwendung der Adaptiven Zuglenkung (ADL) bei den Schweizerischen Bundesbahnen (SBB)”. In: Signal + Draht 109 (7+8), pp. 6–14.

Schranil, S. and V. Lavanchy (2016). “Fahrdynamische Messfahrten im Gotthard- Basistunnel”. In: Elektrische Bahnen 114 (7), pp. 388–393.

Schweizer Bundesrat (2018). Anreize fürs Energiesparen im Bahnverkehr. URL: https : / / www . admin . ch / gov / de / start / dokumentation / medienmitteilungen.msg-id-72029.html (visited on 09/07/2018).

Sessa, P. G., V. De Martinis, A. Bomhauer-Beins, U. A. Weidmann, and F. Corman (2018). “A hybrid stochastic approach for train trajectory reconstruction”. In: Con- ference on Advanced Systems in Public Transport and TransitData. Brisbane.

Shashaj, A., M. Bohlin, and N. Ghaviha (2016). “Joint optimization of multiple train speed proﬁles”. In: International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG. Bydgoszcz, Poland: IEEE, pp. 478– 483. ISBN: 978-1-467-37293-0. DOI: 10.1109/CPE.2016.7544235.

Shi, Z., L. Sheng, and S. Hu (2010). “A novel nonlinear control method of carbon dioxide CO2-demand controlled ventilation in air-conditioned train”. In: Interna- tional Conference on Computer, Mechatronics, Control and Electronic Engineer- ing (CMCE). Changchun, China: IEEE, pp. 161–164. ISBN: 978-1-424-47956-6. DOI: 10.1109/CMCE.2010.5609873.

Siemens AG (2010). Siemens Vectron – Datenblatt. Erlangen.

– (2015). New EU requirements for transformers Ecodesign Directive from the European Commission. Erlangen: Siemens AG Energy Management Division. URL: http://m.energy.siemens.com/US/pool/hq/power- transmission/

– 289 – Bibliography

Transformers/inserts/insert_new-eu-requirements-for-transformers_ ecodesign-directive_EN.pdf.

Siemens AG (2016). System design with Sitras Sidytrac and Sitras EMF. Munich · Erlangen.

Siemens Transfomers LLC (2013). Transforming Energy into Speed: "Siemens Transformers" LLC, Russia—Traction Transformers. Voronoezh, Russian Fed- eration.

SIGNON Deutschland GmbH (2016). Signon Suite.

– (2018). SINAnet and WEBAnet. URL: http://www.elbas.ch/sinanetwebanet_ e.html (visited on 02/04/2019).

Simonelli, F., M. Gallo, and V. Marzano (2015). “Kinematic formulation of energy- efﬁcient train speed proﬁles”. In: AEIT International Annual Conference. AEIT. DOI: 10.1109/AEIT.2015.7415255.

Sirmelis, U. (2015). “Direct Connection of SC Battery to Traction Substation”. In: International Scientiﬁc Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). Riga: IEEE, pp. 5–9. DOI: 10.1109/RTUCON. 2015.7343126.

Soler, M., J. López, J. M. Mera Sánchez De Pedro, and J. Maroto (2015). “Method- ology for Multiobjective Optimization of the AC Railway Power Supply System”. In: IEEE Transactions on Intelligent Transportation Systems 16 (5), pp. 2531– 2542. ISSN: 1524-9050. DOI: 10.1109/TITS.2015.2412460.

Solis, O., K. Pham, et al. (2015). “Saving Money Every Day: LA Metro Subway Wayside Energy Storage Substation”. In: Proceedings of the 2015 Joint Rail Conference (JRC2015). San Jose, USA: ASME.

Spalvieri, C. (2010). “Overview about the market segment "Electric Regional Rail Services"”. In: Railenergy Final Conference. Bruxelles.

Steiling, D. (2017). Telefonat bzgl. Aerodynamik im Bahnbereich.

Steimel, A. (2006). Elektrische Triebfahrzeuge und ihre Energieversorgung – Grundlagen und Praxis. 2. Auﬂage. München: Oldenbourg Industrieverlag. ISBN: 978-3-8356-3090-1.

Stephan, A. and S. Körner (2014). “Energieoptimierung in elektrischen Bahnnet- zen”. In: Verkehr und Technik (9), pp. 351–354.

Stockhausen, U. von, J. van der Weem, F. Steinhorst, and A. Stephan (2017). “Wie sich Energieverbrauch reduzieren lässt”. In: Der Nahverkehr (4), pp. 48–56.

Stolz, T. (2007). Triebfahrzeuge der Schweiz. Luzern: Minirex. ISBN: 978-3-907- 01431-8.

– 290 – Bibliography

Su, S., X. Li, T. Tang, and Z. Gao (2013). “A subway train timetable optimization approach based on energy-efﬁcient operation strategy”. In: IEEE Transactions on Intelligent Transportation Systems 14 (2), pp. 883–893. ISSN: 1524-9050. DOI: 10.1109/TITS.2013.2244885.

Su, S., T. Tang, and Y. Wang (2016). “Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Sim- ulation Model”. In: Energies 9 (2), p. 105. ISSN: 1996-1073. DOI: 10 . 3390 / en9020105.

Su, S., T. Tang, L. Chen, and B. B. Liu (2015). “Energy-efﬁcient train control in urban rail transit systems”. In: Proceedings of the Institution of Mechanical En- gineers, Part F: Journal of Rail and Rapid Transit 229 (4), pp. 446–454. ISSN: 0954-4097. DOI: 10.1177/0954409713515648.

Sun, X., H. Cai, X. Hou, M. Zhang, and H. Dong (2014). “Regenerative braking energy utilization by multi train cooperation”. In: IEEE International Conference on Intelligent Transportation Systems, pp. 139–144. DOI: 10.1109/itsc.2014. 6957680.

Sutter, K. (1930). “Der Luftwiderstand auf Eisenbahnzüge in Tunneln”. Disserta- tionsschrift. ETH Zurich.

Suzuki, M., K. Tanemoto, and T. Maeda (2003). “Aerodynamic characteristics of train / vehicles under cross winds”. In: Journal of Wind Engineering and Industrial Aerodynamics 91, pp. 209–218.

Talukdar, S. N. and R. L. Koo (1979). “Multiobjective Trajectory Optimization for Electric Trains”. In: IEEE Transactions on Automatic Control AC-24 (6), pp. 888– 893. ISSN: 1558-2523. DOI: 10.1109/TAC.1979.1102180.

Teymourfar, R., R. Nejati Fard, B. Asaei, and H. Iman-Eini (2011). “Energy recovery in a metro network using stationary supercapacitors”. In: Power Electronics, Drive Systems and Technologies Conference, pp. 324–329. ISBN: 978-1-612- 84421-3. DOI: 10.1109/PEDSTC.2011.5742440.

Tian, Z., P.Weston, et al. (2017). “System energy optimisation strategies for metros with regeneration”. In: Transportation Research Part C: Emerging Technologies 75, pp. 120–135. ISSN: 0968090X. DOI: 10.1016/j.trc.2016.12.004.

Tomita, M., K. Suzuki, et al. (2017). “Energy-saving railway systems based on superconducting power transmission”. In: Energy 122, pp. 579–587. ISSN: 0360- 5442. DOI: 10.1016/j.energy.2017.01.099.

Tuchschmid, M. (2017). Fachkonversation zum Thema Heizung, Lüftung und Kli- matisierung.

Tuyttens, D., H. Fei, M. Mezmaz, and J. Jalwan (2013). “Simulation-based genetic algorithm towards an energy-efﬁcient railway trafﬁc control”. In: Mathematical Problems in Engineering. ISSN: 1024-123X. DOI: 10.1155/2013/805410.

– 291 – Bibliography

Vasak, M., M. Baoti, P. Nedjeljko, and M. Bago (2009). “Optimal Rail Route Energy Management under Constraints and Fixed Arrival Time”. In: Proceedings of the European Control Conference. Budapest, Hungary: IEEE, pp. 2972–2977. ISBN: 978-3-952-41739-3.

W-tech (2017). Auslegung elektrischer Antriebe: Thermische Motorauslegung. URL: http://www.servotechnik.de/fachwissen/auslegung/f_beitr_00_ 706.htm (visited on 09/13/2017).

Wägli, H. G. (2010). “Bahnproﬁl Schweiz”. In: Schienennetz Schweiz. Ziegel- brücke: AS Verlag & Buchkonzept. ISBN: 978-3-909111-74-9.

Wang, B., Z. Yang, F. Lin, and W. Zhao (2014). “An improved genetic algorithm for optimal stationary energy storage system locating and sizing”. In: Energies 7 (10), pp. 6434–6458. ISSN: 1996-1073. DOI: 10.3390/en7106434.

Wang, P. and R. M. Goverde (2016a). “Multiple-phase train trajectory optimization with signalling and operational constraints”. In: Transportation Research Part C: Emerging Technologies 69, pp. 255–275. ISSN: 0968-090X. DOI: 10.1016/j. trc.2016.06.008.

– (2016b). “Train Trajectory Optimization of Opposite Trains on Single-Track Rail- way Lines”. In: IEEE International Conference on Intelligent Rail Transportation. Birmingham, UK: IEEE, pp. 29–37. ISBN: 978-1-509-01555-9. DOI: 10.1109/ ICIRT.2016.7588546.

– (2016c). “Two-Train Trajectory Optimization with a Green-Wave Policy”. In: Trans- portation Research Record (2546), pp. 112–120. DOI: 10.3141/2546-14.

Wang, Y., B. Ning, F.Cao, B. De Schutter, and T. J. van den Boom (2011). “A Survey on Optimal Trajectory Planning for Train Operations”. In: Distribution, pp. 589– 594.

Watanabe, S. and T. Koseki (2014). “Train group control for energy-saving DC- electric railway operation”. In: International Power Electronics Conference, pp. 1334–1341. ISBN: 978-1-479-92705-0. DOI: 10.1109/IPEC.2014.6869759.

Weber, T., T. Benker, and H.-F. Naupert (2007). “Aspekte zur Verbesserung der Seitenwindstabilität von Schienenfahrzeugen”. In: Eisenbahntechnische Rund- schau (03), pp. 119–125.

Weidmann, U. A. (2011). Systemdimensionierung und Kapazität (Band 2.2). Zürich: ETH Zurich (IVT).

– (2015). Bahninfrastrukturen (Verkehr II). September. Zurich: ETH Zurich (IVT).

Weidmann, U. A., M. Laumanns, M. Montigel, and X. Rao (2015). “Dynamic capacity optimisation by fully automated rail operation”. In: Railway Update (3-4), pp. 58–63.

– 292 – Bibliography

Weigel, J. (2010). “Medium-Frequency Traction Transformer—Outcome of Railen- ergy Basic Concept”. In: Railenergy Final Conference. Bruxelles.

Wende, D. (2003). Fahrdynamik des Schienenverkehrs. Wiesbaden: B. G. Teubner Verlag. ISBN: 9-519-00419-4.

Wiebe, E. (2010). “Introduction to the UIC / UNIFE Technical Recommendations”. In: Railenergy Final Conference. Bruxelles.

Xu, X., K. Li, and X. Li (2016). “A multi-objective subway timetable optimization approach with minimum passenger time and energy consumption”. In: Journal of Advanced Transportation 50, pp. 69–95. ISSN: 0197-6729. DOI: 10.1002/atr. 1317.

Yan, X. H., B. G. Cai, B. Ning, and W. ShangGuan (2015). “Moving Horizon Op- timization of Dynamic Trajectory Planning for High-Speed Train Operation”. In: IEEE Transactions on Intelligent Transportation Systems 17 (5), pp. 1258–1270. ISSN: 1524-9050. DOI: 10.1109/TITS.2015.2499254.

Yang, H., K.-p. Zhang, and H.-e. Liu (2016). “Online Regulation of High Speed Train Trajectory Control Based on T-S Fuzzy Bilinear Model”. In: IEEE Transactions on Intelligent Transportation Systems 17 (6), pp. 1496–1508.

Yang, L., K. Li, Z. Gao, and X. Li (2012). “Optimizing trains movement on a railway network”. In: Omega 40 (5), pp. 619–633. ISSN: 0305-0483. DOI: 10.1016/j. omega.2011.12.001.

Yang, Q.-S., J.-H. Song, and G.-W. Yang (2016). “A moving model rig with a scale ratio of 1/8 for high speed train aerodynamics”. In: Journal of Wind Engineering and Industrial Aerodynamics 152, pp. 50–58. ISSN: 0167-6105. DOI: 10.1016/ j.jweia.2016.03.002.

Yang, X., A. Chen, X. Li, B. Ning, and T. Tang (2015). “An energy-efﬁcient scheduling approach to improve the utilization of regenerative energy for metro systems”. In: Transportation Research Part C: Emerging Technologies 57, pp. 13–29. ISSN: 0968-090X. DOI: 10.1016/j.trc.2015.05.002.

Yang, X., X. Li, B. Ning, and T. Tang (2015). “A Survey on Energy-Efﬁcient Train Op- eration for Urban Rail Transit”. In: IEEE Transactions on Intelligent Transportation Systems 17 (1), pp. 2–13. ISSN: 1524-9050. DOI: 10.1109/TITS.2015.2447507.

Yang, X., B. Ning, X. Li, and T. Tang (2014). “A Two-Objective Timetable Opti- mization Model in Subway Systems”. In: IEEE Transactions on Intelligent Trans- portation Systems 15 (5), pp. 1913–1921. ISSN: 0218-4885. DOI: 10 . 1142 / S0218488513400011.

Yang, Z., Z. Yang, H. Xia, F. Lin, and F. Zhu (2017). “Supercapacitor State Based Control and Optimization for Multiple Energy Storage Devices Considering Cur- rent Balance in Urban Rail Transit”. In: Energies 10 (4), p. 520. ISSN: 1996-1073. DOI: 10.3390/en10040520.

– 293 – Bibliography

Yasunobu, S., S. Miyamoto, and H. Ihara (1984). “Fuzzy control for automatic train operation system”. In: Control in Transportation Systems. Proceedings of the 4th IFAC/IFIP/IFORS Conference. Baden-Baden, Germany. ISBN: 0-080-29365-4.

Yin, J., L. Yang, T. Tang, Z. Gao, and B. Ran (2017). “Dynamic passenger demand oriented metro train scheduling with energy-efﬁciency and waiting time minimization: Mixed-integer linear programming approaches”. In: Transportation Research Part B: Methodological 97, pp. 182–213. ISSN: 0191-2615. DOI: 10. 1016/j.trb.2017.01.001.

Zemek, K. (2015). “A systems approach to energy efﬁciency”. In: Metro Report International (June), pp. 56–57.

Zhang, G., J. Qian, and X. Zhang (2017). “Application of a High-Power Reversible Converter in a Hybrid Traction Power Supply System”. In: Applied Sciences 7 (3), p. 282. ISSN: 2076-3417. DOI: 10.3390/app7030282.

Zhang, X., Z. Zhang, et al. (2016). “A portable high-efﬁciency electromagnetic energy harvesting system using supercapacitors for renewable energy applications in railroads”. In: Energy Conversion and Management 118, pp. 287–294. ISSN: 0196-8904. DOI: 10.1016/j.enconman.2016.04.012.

Zhao, C., D. Dujic, et al. (2014). “Power Electronic Traction Transformer—Low Volt- age Prototype”. In: IEEE Transactions on Industrial Electronics 61 (7), pp. 3257– 3268. ISSN: 0885-8993. DOI: 10.1109/TPEL.2013.2248756.

Zhao, N., C. Roberts, et al. (2014). “Train trajectory optimisation of ATO systems for metro lines”. In: International IEEE Conference on Intelligent Transportation Systems, pp. 1796–1801. ISBN: 978-1-479-96078-1. DOI: 10.1109/ITSC.2014. 6957953.

Zhao, N., C. Roberts, S. Hillmansen, and G. Nicholson (2015). “A Multiple Train Trajectory Optimization to Minimize Energy Consumption and Delay”. In: IEEE Transactions on Intelligent Transportation Systems 16 (5), pp. 2363–2372. DOI: 10.1109/TITS.2014.2388356.

Zhao, N., C. Roberts, et al. (2017). “An integrated metro operation optimization to minimize energy consumption”. In: Transportation Research Part C: Emerging Technologies 75, pp. 168–182. ISSN: 0968-090X. DOI: 10.1016/j.trc.2016. 12.013.

Zhu, H., X. Sun, L. Chen, S. Gao, and H. Dong (2016). “Analysis and design of Driver Advisory System (DAS) for energy-efﬁcient train operation with real-time information”. In: IEEE International Conference on Intelligent Rail Transportation. Birmingham, UK: IEEE, pp. 99–104. ISBN: 978-1-509-01555-9. DOI: 10.1109/ ICIRT.2016.7588717.

– 294 – Declaration of Pre-Publications

Some parts of this dissertation have been pre-published or used in other projects. These usages and publications are declared in the following.

Chapters 2.2 and 2.3 Parts of these chapters’ ﬁndings have been published in Bomhauer-Beins, A., S. Schranil und U. Weidmann: “Einﬂüsse auf den Bahn- energiebedarf und diesbezügliche Potentiale der Automation” In: Schweizer Eisenbahn-Revue 3 (2018), pp. 140–144

Chapters 2.2, 2.3; Section 2.4.2 Parts of these ﬁndings have been published in Bomhauer-Beins, A., S. Schranil und U. Weidmann: “Abschätzung des Energie- sparpotentials der Automatisierung im Bahnbetrieb” In: Elektrische Bahnen 4-5 (2018), pp. 150–156

Sections 2.3.3 and 2.3.4.3–2.3.4.5 Some of the ﬁndings have been used in Bomhauer-Beins, A.: Automation zwischen Unterwerk und Schiene: Abschät- zung vorhandenen Energiesparpotentials – Eine Forschungsarbeit im Rahmen von SBBFORSCHUNGSFONDS und SBBHUB. Forschungsbericht, IVT, ETH Zürich. Zurich 2017

Section 2.4.2.1 is based on and extends the ﬁndings of publication Bomhauer-Beins, A. und U. Weidmann: “Grundlagen für ein neues Modell des Luftwiderstands von Eisenbahnfahrzeugen” In: ZEVrail 142 (10), pp. 410–416

Appendix D The ﬁndings of this chapter were used in Bomhauer-Beins, A. und U. Weidmann: Automation zwischen Unterwerk und Schiene: Abschätzung vorhandenen Energiesparpotentials – Eine Forschungs- arbeit im Rahmen von SBBFORSCHUNGSFONDS und SBBHUB. Forschungsbe- richt, IVT, ETH Zürich. Zurich 2017 and have been published as Bomhauer-Beins, A. und U. Weidmann: “Grundlagen für ein neues Modell des Luftwiderstands von Eisenbahnfahrzeugen” In: ZEVrail 142 (10), pp. 410–416

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Author’s Curriculum Vitae

Axel Bomhauer-Beins MSc & BSc ETH EEIT · born on May 9, 1989, in Rostock (Germany) Citizen of Germany and Uster ZH

07.15–06.19 Ph.D. Student Civil Engineering ETH Zurich, Institute for Transport Planning and Systems (IVT) Chair for Transport Systems (VS), Prof. Dr. U. Weidmann

09.14–03.19 Scientiﬁc Assistant ETH Zurich, Institute for Transport Planning and Systems (IVT) Chair for Transport Systems (VS), Prof. Dr. U. Weidmann

02.12–06.14 MSc ETH in Electrical Engineering and Information Technology Focus on Electric Energy Systems & Mechatronics (Power Electronics) ETH Zurich

10.11–01.12 Internship Bombardier Transportation (Switzerland) AG, Zurich Business Unit Propulsion and Controls, Dept. Converter Control

09.07–09.11 BSc ETH in Electrical Engineering and Information Technology Focus on Electric Energy Systems & Mechatronics (Power Electronics) ETH Zurich

08.01–09.07 University Entrance Diploma and Certiﬁcation in Latin Focus on Mathematics and Natural Sciences Kantonsschule Glattal, Dübendorf ZH

08.95–07.01 Primary School 11.98–07.01 Primarschule Pünt, Uster ZH, Switzerland 08.95–11.98 Städt.-Kath. Grundschule Zülpich, Germany

as of April 2019

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ISBN 978-3-905826-49-4