Examining the load peaks in high-speed railway transport

Similar documents
The Modeling and Simulation of DC Traction Power Supply Network for Urban Rail Transit Based on Simulink

Train Group Control for Energy-Saving DC-Electric Railway Operation

Simulation of Collective Load Data for Integrated Design and Testing of Vehicle Transmissions. Andreas Schmidt, Audi AG, May 22, 2014

Development of an energy efficient train traffic control system for saving electricity

Applications for energy storage flywheels in vehicles of Deutsche Bahn AG

Development of Motor-Assisted Hybrid Traction System

Featured Articles Utilization of AI in the Railway Sector Case Study of Energy Efficiency in Railway Operations

Testing Items During Gearbox Developement

Development of a High Efficiency Induction Motor and the Estimation of Energy Conservation Effect

Study on Braking Energy Recovery of Four Wheel Drive Electric Vehicle Based on Driving Intention Recognition

Charging Electric Vehicles in the Hanover Region: Toolbased Scenario Analyses. Bachelorarbeit

HS2 Traction Energy Modelling

Strength Analysis of Seat Belt Anchorage According to ECE R14 and FMVSS

AECC Clean Diesel Euro 6 Real Driving Emissions Project. AECC Technical Seminar on Real-Driving Emissions Brussels, 29 April 2015

Inductive Power Supply (IPS ) for the Transrapid

This document is a preview generated by EVS

Holistic Range Prediction for Electric Vehicles

Special edition paper

Development of an actively controlled, acoustically optimised single arm pantograph

Application of claw-back

Improvements of Existing Overhead Lines for 180km/h operation of the Tilting Train

Noise Reduction in Railway Traffic Noise Abatement at Source

A study of the train performance simulation for Korean next Generation high-speed train. high-speed train.

Energy Management for Regenerative Brakes on a DC Feeding System

Methodologies and Examples for Efficient Short and Long Duration Integrated Occupant-Vehicle Crash Simulation

Wayside Energy Storage System Modeling

MODELING, VALIDATION AND ANALYSIS OF HMMWV XM1124 HYBRID POWERTRAIN

WET GRIP TEST METHOD IMPROVEMENT for Passenger Car Tyres (C1) GRBP 68 th session

Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control

K. Shiokawa & R. Takagi Department of Electrical Engineering, Kogakuin University, Japan. Abstract

TESTING OF AUTOMOBILE VW GOLF OPERATING ON THREE DIFFERENT FUELS

The Generator-Electric Vehicle- A New Approach for Sustainable and Affordable Mobility

Development of a Mobile Application for Android to Support Energy-Efficient Driving of Electric Vehicles

Applicability for Green ITS of Heavy Vehicles by using automatic route selection system

REAL TIME TRACTION POWER SYSTEM SIMULATOR

TABLE OF CONTENTS. Table of contents. Page ABSTRACT ACKNOWLEDGEMENTS TABLE OF TABLES TABLE OF FIGURES

Explanation and Validation of the Flat Belt Method ENTWURF Fahrzeugtechnik Fahrleistung und Verbrauch EGNT/2

Approach for determining WLTPbased targets for the EU CO 2 Regulation for Light Duty Vehicles

Multi-body Dynamical Modeling and Co-simulation of Active front Steering Vehicle

Test Based Optimization and Evaluation of Energy Efficient Driving Behavior for Electric Vehicles

Skid against Curb simulation using Abaqus/Explicit

Chapter 4. HS2 Route Capacity and Reliability. Prepared by Christopher Stokes

EFFECT OF SURFACE ROUGHNESS ON PERFORMANCE OF WIND TURBINE

POWER DISTRIBUTION SYSTEM ANALYSIS OF URBAN ELECTRIFIED RAILWAYS

PUBLICATION NEW TRENDS IN ELEVATORING SOLUTIONS FOR MEDIUM TO MEDIUM-HIGH BUILDINGS TO IMPROVE FLEXIBILITY

Braking Performance Improvement Method for V2V Communication-Based Autonomous Emergency Braking at Intersections

Level of Service Analysis for Urban Public Transportation of Dumlupinar University Evliya Celebi Campus in Kutahya, Turkey

GENERAL OVERVIEW OF HIGH-SPEED IN EUROPE

The evaluation of endurance running tests of the fuel cells and battery hybrid test railway train

Driving dynamics and hybrid combined in the torque vectoring

INVESTIGATION OF ICING EFFECTS ON AERODYNAMIC CHARACTERISTICS OF AIRCRAFT AT TSAGI

Driving techniques and strategies for freight trains

The DLR Project Next Generation Train (NGT)

Energy Management and Hybrid Energy Storage in Metro Railcar

NUMERICAL ANALYSIS OF IMPACT BETWEEN SHUNTING LOCOMOTIVE AND SELECTED ROAD VEHICLE

Is Low Friction Efficient?

Planetary Roller Type Traction Drive Unit for Printing Machine

Analysis of minimum train headway on a moving block system by genetic algorithm Hideo Nakamura. Nihon University, Narashinodai , Funabashi city,

Electric Vehicle Battery Swapping Stations, Calculating Batteries and Chargers to Satisfy Demand

Vehicle Turn Simulation Using FE Tire model

ENMOVER. Ronny Petersohn Armin Raupbach Kevin Krebs Alexander Bunzel

VALIDATION OF A HUMAN-AND-HARDWARE-IN-THE- LOOP CONTROL ALGORITHM

WITHOUT MUCH OF A STIR

CONSULTATION DOCUMENT

Development of Catenary and Batterypowered

Electric traction energy metering on German Railways and the impact of European standardisation on the energy billing process in Germany

Use of Flow Network Modeling for the Design of an Intricate Cooling Manifold

Appendix 4. HS2 Route Capacity and Reliability. Prepared by Christopher Stokes

ENERGY STORAGE SOLUTIONS FOR IMPROVING THE ENERGY EFFICIENCY OF PUBLIC TRANSPORT VEHICLES

LEVER OPTIMIZATION FOR TORQUE STANDARD MACHINES

Development of Bi-Fuel Systems for Satisfying CNG Fuel Properties

Modal Analysis of Automobile Brake Drum Based on ANSYS Workbench Dan Yang1, 2,Zhen Yu1, 2, Leilei Zhang1, a * and Wentao Cheng2

A Large Modern High Speed Reciprocating Compressor

Munich - Augsburg Scenario Pack 01

COMPUTER BASED COMPARISON OF TRAIN PERFORMANCE BEHAVIOUR ON A CERTAİN ROUTE

FLUID DYNAMICS TRANSIENT RESPONSE SIMULATION OF A VEHICLE EQUIPPED WITH A TURBOCHARGED DIESEL ENGINE USING GT-POWER

Racing Tires in Formula SAE Suspension Development

Collaborative vehicle steering and braking control system research Jiuchao Li, Yu Cui, Guohua Zang

Special edition paper Development of an NE train

Analysis of modelling electric transportation networks

INFLUENCE OF CROSS FORCES AND BENDING MOMENTS ON REFERENCE TORQUE SENSORS FOR TORQUE WRENCH CALIBRATION

Pantograph and catenary system with double pantographs for high-speed trains at 350 km/h or higher

Hydro Plant Risk Assessment Guide

Innovative Power Supply System for Regenerative Trains

COMPANY MAIN TOPIC [ PRODUCT ] TracFeed OSS. English. Overhead Conductor Rail. Made in Germany

Porsche Engineering driving technologies

How do we make city buses cleaner and more comfortable?

Special edition paper

A dream? Dr. Jürgen Bredenbeck Tire Technology Expo, February 2012 Cologne

inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE

Kolbenschmidt Pierburg Group

Specifications and schedule of a fuel cell test railway vehicle. T. Yoneyama, K. Ogawa, T. Furuya, K. Kondo, T. Yamamoto

WET GRIP TEST METHOD IMPROVEMENT for Passenger Car Tyres (C1) Overview of Tyre Industry / ISO activities. Ottawa

Tariff Design Issues: Approaches for Recovering Grid and System Costs

1. Introduction. Vahid Navadad 1+

The research on gearshift control strategies of a plug-in parallel hybrid electric vehicle equipped with EMT

Power Interchange System for Reuse of Regenerative Electric Power

Improvement of Train Operation and Passenger Flow Simulator for Detailed Estimation of Train Movement on High Frequency Railway Lines

inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE

THE FKFS 0D/1D-SIMULATION. Concepts studies, engineering services and consulting

Transcription:

Examining the load peaks in high-speed railway transport Yigit Fidansoy, M.Sc. Technische Universität Darmstadt, Germany fidansoy@verkehr.tu-darmstadt.de Paper prepared for DEMAND Centre Conference, Lancaster, 13-15 April 2016 Only to be quoted and/or cited with permission of the author(s). Copyright held by the author(s). 1

1 Abstract Due to the increasing costs, transport demand and the climate change, the importance of energy efficiency is increasing. The highest proportion of energy consumed in the railway is the so called traction energy. This energy is required for the train run between two stops. Railway traffic is managed by timetables. Consequently, as long as the punctuality is assured the timetable design plays a decisive role on the energy consumption and its allocation over time. In railway transport, the energy consumption will be for periods of 15 minutes by energy supplier measured. Load peaks may occur in these periods due to the changes in train numbers or other factors. These peaks cause high costs for the railway operators because of the price policy of energy suppliers. Energy consumption peaks are partially inevitable due to the timetable design. The most significant factor is naturally the number of trains in a period of 15 minutes, which is based on the transport demand and cannot be affected without changing the already existing and valid timetable. On the contrary, the train speed and acceleration, which are also significant factors especially on high-speed railway transport, can be adjusted so that the load peaks due to the train runs can be avoided. Hence, this study focuses on the influence of these factors on the energy consumption and gives recommendations about the speed profiles. The objective is to verify the significance of factor train speed and acceleration. Keywords: traction energy, optimization, railway operation 2

2 Introduction The importance of the energy efficiency is becoming more significant. Energy efficiency in railway traffic can be measured by the energy consumption per train unit km. To achieve higher energy efficiency less energy per train unit per km should be consumed. Energy efficiency is not the only factor that determines if a rail transport company operates efficiently. Analysis of the measured data from Deutsche Bahn Fernverkehr AG (german high-speed train operating company, abbr. DB Fernverkehr AG) shows that the energy consumption in certain time periods is higher than other time periods. Figure 1 shows that i.a. between 17:30 and 17:45 the highest energy consumption occurs. This situation has many consequences. Firstly, energy pricing is higher at peaks which results in higher costs for train operating companies. Since DB Fernverkehr AG uses exclusively green energy the storage of the energy is a problem. In this respect the objective of this study is to examine the causes of the energy peaks and make recommendations on how to reduce energy peaks for DB Fernverkehr AG. Energy providers charge for the peak hours higher, because of the increase in supply price. This can have a significant impact on traction costs of a train operating company. Quick energy, generated by peaking power plants (for example natural gas fueled or pumped storage power plants) has its higher costs. Figure 1: Energy consumption peaks on 28.05.2014 Railway traffic can be regarded as a production process. In this respect, railway operation research defines three phases of a train run: network planning, timetable planning and operation (see Figure 2). Although the traction energy will only be consumed in phase train operation, the amount of the consumed energy also depends on the decisions in other phases. For example the output of network planning are train lines, destinations and stops. Depending on this information and other factors (e.g. other train operating companies or the capacity of the train routes) a timetable will be generated. Then again the timetable defines when the trains will run with which maximum speed and allowances for an energy efficient driving-style. As a result, timetable might be a reason why these peak hours occur. 3

Figure 2: Phases of a train run Terminology A travel time between two stop points consists of pure running time and allowances. Pure running time is defined in [1] as the time in which a train is taking advantage of the traction unit, observes the speed limits and adopted dynamic driving conditions (inertia coefficient, friction coefficient, braking conditions, air resistance, etc.) can be performed. For non-uniform traffic demand, the waiting for delayed connections but also for the unexpected effects like weather or temporary speed restrictions must be taken into account, so that minor delays can be reduced. For these reasons, allowances should be planned. Energy consumption in this paper signifies only the tractive energy consumption which is required for the train run between two stops. Other energy consumers in a train like air-conditioning or lightening are not considered. Mentioned high-speed section in this paper includes only train speeds over 250 km/h. Deutsche Bahn Fernverkehr DB Fernverkehr AG is a rail transport company which operates regular national and international long-distance passanger trains. According to [4] in Germany over 700 daily high-speed connections are frequented. In addition to that around 250 international connections to 80 European cities are provided. Thus daily around 340000 passengers travel with InterCityExpress (ICE), InterCity (IC), and EuroCity (EC) trains of DB Fernverkehr AG. Energy efficiency belongs to the guiding principle of DB Fernverkehr AG next to the safety and punctuality. The results of this paper are a product of research cooperation between DB Fernverkehr AG and Technische Universität Darmstadt about energy efficiency. Influence factors on energy consumption According to [2] the most important influence factors on energy consumption are temperature, vehicles, punctuality and mode of operation which can be altered by different driving strategies. The results of a sensitivity analysis in [3] show that also train mass and top speed are significant influence factors. Figure 1 shows the results of the complete train runs of DB Fernverkehr AG in one day. Due to the fact that the number of trains and punctuality around peak times are not varying much the hypothesis is that the reasons of the energy peaks are mode of operation and top speed. Trains use most of their energy to compensate resistance which are caused by different conditions. These are structural like tunnel or curve resistances, but mainly speed related resistances. As can be seen in function of Keßler/Junker (1) the air resistances are proportional to the square of the velocity. With higher speeds 4

resistances are higher and as a consequence the energy consumption is also much higher. In this respect, speed has a major impact on energy consumption. w l = a 0 + a 1 V + a 2 V 2 (1) a 0 a 1 V describes the roll resistance describes the pulse resistance moving air masses a 2 V 2 describes the air resistance Definition of study area for analysis of timetable In this paper, only new high-speed lines will be considered: Hannover-Würzburg, Frankfurt-Köln, Mannheim- Stuttgart, Nürnberg-Ingolstadt, Hannover-Berlin and Karlsruhe-Basel. The reason for that is the maximum permissible speed. Only these lines contain high-speed sections, where trains achieve speeds over 250 km/h. The study will be conducted for the peak days 28.05.2014 and 25.07.2014. These days were with the highest energy consumption. There are many peaks on these days that occur regularly. In this paper only 25.07.2014 will be considered. The methodology and results do not vary. For further research only the peak between 14:00-15:00 will be studied. This peak is selected due to the constant number of trains. In order to get correct results it is necessary to extend the period of investigation at least one hour, otherwise trains that are already driving could not be included. In this respect the period of investigation is between 13:00-15:00. Only ICE trains can achieve high-speed sections. Insofar only this class of train will be considered. 5

3 Methodology Principles At first, the energy consumption dependent on target speed was calculated in order to prove the hypothesis that acceleration in the high-speed section is high energy consuming. The calculations are computed for an ICE3 train on a slopeless line and are based on the basic formulas of train dynamics which may be found in [3]. Speed section [km/h] Energy consumption [kwh] Energy consumption per speed difference [kwh/km/h] 0-250 604,94 2,42 0-300 869,95 2,90 250-300 265,02 5,30 Table 1: Energy consumption dependent on target speed Table 1 shows that energy consumption for 1 km/h acceleration in the high-speed section is more than the double energy consumption for 1 km/h acceleration in lower speeds. Although the high-speed section contains only a small span of speed it has relatively higher importance on energy consumption. Analysis of Timetable For the purpose of time allocation of ICE trains in the high-speed section the timetable and the actual train runs of DB Fernverkehr AG was analyzed. The data contains information about departure, arrival and thoroughfare times with six seconds accuracy. Times are differentiated between timetable and actual times. Hence, it is also possible to access delay times. Furthermore, trains may be identified with train and line number. First, the required time to accelerate in the high-speed section is determined for each operation control post (train station) on new high-speed lines by observations or if available by measured data. For example for the line Nürnberg-Ingolstadt in direction to Ingolstadt from operation control post Nürnberg Reichswald 5 minutes are required to accelerate in the high-speed section. This time was added to departure or thoroughfare times in order to acquire the actual times when trains arrive the high-speed section. By doing so all ICE trains on these lines are allocated to time intervals of 15 minutes to determine the number of trains which are accelerating in the high-speed section. These train numbers are the basis of the comparison. If the numbers are higher in peak intervals, it indicates that high energy consumptions are resulted from these trains. Calculation of energy consumption Total tractive energy consumption consists of three driving regimes: e ttttt,i = e aaaaaaaaaaaa,j + e ccccccc,k e bbbbbbb,k,l (2) where i is the driving style, j is the speed, k is the target speed and l is the braking mode. The difference to the running time calculation is the negative sign of the braking regime. This sign describes the regenerative braking energy. The energy consumption is calculated in kwh. Energy-saving driving styles may contain also the driving 6

regime coasting. Due to the very low friction between wheel and rail it is possible to coast long distances without traction power. The coasting distance is dependent on many conditions such as slopes, current speed, weight and weather. In this driving regime, there will be no energy consumed since there is no traction power required. Input The basis of this study is the timetable of DB Fernverkehr AG. The timetable contains information about departure, arrival and thoroughfare times with six seconds accuracy. Trains may be identified with train and line number. Timetable was localized in order to access only the required information on the lines of study area. As input the number of trains that are accelerating in the high-speed section is conducted. Input for the energy consumption calculations are train and infrastructure properties. 7

4 Results With the aid of the Microsoft Excel program, the timetable data was analyzed to determine the train numbers in time intervals. The results are given separately for each peak days. 28.05.2014 Figure 3 shows the results for peak day 28.05.2014 in peak hour 17:00-18:00 with intervals of 15 minutes. The blue bar shows the actual number of trains that are driving in the high-speed section and the red bar shows the number of trains according to the timetable that should be driving in the high-speed section. 25 Number of trains 20 15 10 5 0 17:00-17:15 17:15-17:30 17:30-17:45 17:45-18:00 Time Figure 3: Results of 28.05.2014 The data of the energy consumption analysis of DB Fernverkehr AG (see Table 2) shows that the total energy consumption between 17:30-17:45 is highest. Even though in next time interval only one more train is driving in system there is a difference of 59869.49 kwh. This difference results from the high number of trains that are driving in the high-speed section. As seen in figure 3 the actual number of trains between 17:30-17:45 is the double of the number between 17:45-18:00. Time Total number of trains Energy consumption [kwh] Regenerative energy [kwh] Total energy consumption [kwh] 17:00-17:15 245 463306.559 51539.067 411767.492 17:15-17:30 254 461033.644 55052.318 405981.326 17:30-17:45 258 519533.067 47842.416 471690.651 17:45-18:00 259 467258.59 55437.437 411821.153 25.07.2014 Table 2: Energy consumption on 28.05.2014 The results for peak day 25.07.2014 can be seen in figure 4 and table 3. These results confirm the results of peak day 28.05.2014. Again the time interval with the highest energy consumption is the time interval with the highest number of trains that are driving in the high-speed section. 8

20 Number of trains 15 10 5 0 14:00-14:15 14:15-14:30 14:30-14:45 14:45-15:00 Time Figure 4: Results of 25.07.2014 Time Total number of trains Energy consumption [kwh] Regenerative energy [kwh] Total energy consumption [kwh] 14:00-14:15 251 464805.749 59468.356 405337.393 14:15-14:30 254 500683.71 58122.596 442561.114 14:30-14:45 251 453553.689 54255.279 399298.41 14:45-15:00 251 455390.004 54125.831 401264.173 Relation to Energy Consumption Table 3: Energy consumption on 25.07.2014 Figure 5 shows the results of calculations for the energy consumption. The red bar shows the energy consumption due to the trains that are driving in the high-speed section. It is clear that maximum speed, a property of timetable, has a high impact on energy consumption and therefore may cause as appropriate energy consumption peaks. total energy consumption[kwh] 500000 450000 400000 350000 300000 250000 200000 150000 100000 50000 0 14:00-14:15 14:15-14:30 14:30-14:45 14:45-15:00 Time Figure 5: Classification of the energy consumption on 25.07.2014 9

5 ACKNOWLEDGMENTS To find the reasons of the peaks on energy consumption theoretical and practical studies were applied. Theoretical calculations of energy consumption show that in high-speed sections more energy is required to accelerate. In order to verify the acknowledgments from theoretical calculations an analysis of timetable from DB Fernverkehr AG was conducted. Study area was restricted to new high-speed lines and ICE trains. The results show that number of trains that accelerate in the high-speed section has an influence on energy consumption that leads to energy peaks. In summary, it can be stated that among the numerous influence factors on energy consumption mode of operation and top speed have a high impact. In order to increase energy efficiency and reduce costs a good timetable design is necessary. It is also conceivable to develop and integrate an energy demand management to driver advisory systems in order to prevent predictable peak demand. For further research, it is recommended to perform a sensitivity analysis of top speed in order to determine the potential of reducing the peaks. In the sensitivity analysis also running times should be considered. Furthermore, it is recommended to investigate the effects of changes in departure times and timetable. For this purpose a timetable simulation tool may be used. 10

List of Figures Figure 1: Energy consumption peaks on 28.05.2014... 3 Figure 2: Results of 28.05.2014... 8 Figure 3: Results of 25.07.2014... 9 Figure 4: Classification of the energy consumption on 25.07.2014... 9 List of Tables Table 1: Energy consumption dependent on target speed... 6 Table 2: Energy consumption on 28.05.2014... 8 Table 3: Energy consumption on 25.07.2014... 9 11

References [01] ROTHE, Irene: DB Konzernrichtlinie 405. DB AG, 2009 [02] Görtz, Anke; Pätzig, Martin: Energieeffiziente Zugfahrt im Fernverkehr. In: Deine Bahn 02, 2015 S. 54-58 [03] Fidansoy, Yigit: Systemkonzept zur energiesparsamen Fahrweise, Darmstadt, 2014, Masterarbeit [04] Deutsche Bahn AG, http://www.deutschebahn.com/de/konzern/geschaeftsfelder/dbbahnfernverkehr/2190874/ dbfernverkehr.html, 23.12.2015 12