Train Energy Bench Marking on the WCML

8/6/2019 Train Energy Bench Marking on the WCML

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Train Energy Benchmarking on the West Coast Main Line

Tarusenga, Kunashe M

Mechanical Engineering and Energy, School of Engineering and Physical Sciences, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS, Scotland

ABSTRACT

An opportunity exists to minimise cost by optimising train energy consumption. Further

efficiencies in energy consumption are crucial for the continued sustainability of business and

more widely the environment. This study considers in-journey energy economy and

underlines a severe fluctuation in this parameter not seen previously. Furthermore, it is noted

that this fluctuation is not random, strongly suggesting an opportunity to model consumption

in an effort to optimise consumption variance. Basic consumption models have been

developed which demonstrate an above 95% accuracy of predicted to measured consumption

for the major routes of the West Coast Main Line.

http://www.data-ai.com/



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1. INTRODUCTION

A significant opportunity exists to minimise cost by optimising train energy consumption.

Managing energy consumption is a priority for train operating companies as this resource

represents a major cost to the industry. By understanding the characteristics of train energy

consumption, steps may be taken to optimise energy use.

The purpose of this study is to inform train energy management practice. Specifically, the aimis to highlight energy consumption characteristics in the course of passenger train journeys.

By building this picture of energy dissipation across the network, a reference pattern of

consumption can be developed and used as a benchmark to manage future consumption.

Particular emphasis centred on energy consumption per given train location on the network.

This was achieved through correlation analysis of consumption and train distance from

reference locations.

A supplementary objective was to outline specific consumption levels (kWh/km) to further

inform energy billing reform in the rail industry. Specific consumption levels were

determined by means of a consumption correlation approach for a sample of 525 journeys.

This study aims to augment the ongoing energy billing reform and efforts to better understand

train energy consumption.

Improved energy management is crucial to long term business sustainability but equally as

important and directly linked are the environmental concerns, particularly emissions

reductions. These issues are underscored by legal requirements on business and industry to a

26 % reduction in CO2 emissions by 2020 against a base line of 1990[4]

. Efficiencies in energy

use by the rail industry may well provide a means to achieving these emissions reductions. In

light of this, it is important that innovation in improved energy management and thus

emissions is continued if rail is to maintain the advantage[6]

over other modes of transport.

Previous studies focused on the effects of station calling pattern and service punctuality on

train energy consumption. Notably, it was found that energy consumption increased

consistently with higher frequency calling patterns[2]

. In addition, service punctuality was

found to neither diminish nor increase train journey consumption totals.

This study builds on these previous findings and outlines energy consumption models for the

major routes of the West Coast Main Line.

2. METHODOLOGY and APPROACH

Consumption data was recorded on Virgin Pendolino train 390049 in the period between

August 2007 and May 2008. Rated at 5.1 MW[1]

, this train class is one of the more powerful

electric train types operating on the UK network. The train is fitted with 9 vehicles and has a

stated maximum speed of 225 km/h – typically, average maximum speeds of 190 km/h were

observed during the study.

Implementation of this study was conducted in 2 main phases as follows:



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i. Selection and classification of journeys

ii. Evaluation of study metrics and analysis

In the sections that follow, details of the processes undertaken in implementing this study are

outlined.

2.1 Selection and classification of journeys

Passenger train services analysed in this study were solely weekday service operations.

Weekend services were known to have been regularly subject to severe interruptions due to

route modernisation work. By excluding weekend operations entirely, results from this study

could better reflect consumption characteristics for normal train operation. This aspect is

important in determining benchmark consumption characteristics.

Classification of train journeys was defined by the route and station calling pattern. For

example, all services both peak and off peak from Manchester Piccadilly to London Euston

calling at Stockport and Macclesfield were classed as 2 stop services. Station calling pattern

was selected as a key study parameter as previous studies indicated station stops as significant

contributors to energy consumption. Different classes of journeys were treated separately in

the analysis to better highlight consumption characteristics of the given service type. In total

525 journeys were selected. Table 2.1 below summarises the routes considered in this study.

The following abbreviations are used to denote major stations on the West Coast Main Line:

EUS – London Euston

GC – Glasgow Central

LIV – Liverpool Lime Street

MAN – Manchester Piccadilly

WVH – Wolverhampton

BHM – Birmingham New Street

Table 2.1 – West Coast Main Line route summary

Route Distance

(km)

Stations

EUS - GC 645.2 Watford Junction, Milton Keynes Central, Rugby, Stafford,

Crewe, Warrington Bank Quay, Wigan North Western,

Preston, Lancaster, Oxenholme Lake District, Penrith North

Lakes, Carlisle, Lockerbie, Motherwell

EUS - LIV 312.1 Watford Junction, Milton Keynes Central, Rugby,

Nuneaton, Stafford, Crewe, Runcorn

EUS - MAN 296.1 Watford Junction, Milton Keynes Central, Rugby,

Nuneaton, Tamworth, Stoke-on-Trent, Macclesfield,

Stockport

EUS - WVH 202.7 Watford Junction, Milton Keynes Central, Rugby,

Coventry, Birmingham International, Birmingham New

Street, Sandwell and Dudley

EUS - BMH 181.8 Watford Junction, Milton Keynes Central, Rugby,

Coventry, Birmingham International



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Variants to the above routes exist where the train is routed through alternative branch lines.

These were excluded from the study in preference for routes with a larger number of repeat

journeys. For example, all services via the Northampton branch line were excluded from the

study.

Figure 2.1 – Main routes of West Coast Main Line

network

Distribution of passenger journeys in the data sample is illustrated in Figure 2.2 below. In

general, the journeys considered had a balanced contribution of north and southbound

journeys. The routes listed above represent the main intercity routes on the West Coast Main

Line and are illustrated on the network map in Figure 2.1.

Northbound Services Southbound Services

Total: 262 Journeys

London Euston toManchester Piccadilly

London Euston to

Liverpool Lime Street

London Euston to

Glasgow Central

London Euston toBirmingham New Street

London Euston to

Wolverhampton

Total: 263 Journeys

Manchester Piccadilly toLondon Euston

Liverpool Lime Street toLondon Euston

Glasgow Central to

London Euston

Birmingham New Street toLondon Euston

Wolverhampton to

London Euston

Figure 2.2 – Distribution of passenger journeys by route



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2.2 Evaluation of study metrics

Data Structure

Consumption data from the onboard meters was recorded at 5 minute intervals – a partial

extract is shown below in Table 2.2. These energy consumption records were merged with the

corresponding train operation schedule for 390049. It is important to note that due to the

frequency of data recording (every 5 minutes); collation of the schedule to the recordedconsumption data was approximate.

Collation of train consumption data was conducted by rounding departure and arrival times to

the nearest 5 minutes. For example, a service scheduled to depart at 11:03 was analysed as a

departure at 11:05 with respect to the consumption records. Similarly, an 11:02 departure was

analysed as an 11:00 departure. This approach has been implemented previously[2]

and

deemed suitable in earlier energy studies on 390049.

Table 2.2 - Extract data from onboard meters

Date TimeEnergy Consumed

(kWh)

Regen.

(kWh)

Net Energy

(kWh)

Easting

(º)

Northing

(º)

07/12/07 11:00 18 0 18 -0.1361 51.5297

07/12/07 11:05 20 0 20 -0.1361 51.5297

07/12/07 11:10 167 0 167 -0.1931 51.5369

07/12/07 11:15 323 0 323 -0.3392 51.5942

07/12/07 11:20 79 88 -9 -0.3969 51.6644

07/12/07 11:25 414 2 412 -0.4886 51.7417

07/12/07 11:30 233 0 233 -0.6550 51.8381

07/12/07 11:35 238 31 207 -0.7267 51.9639

07/12/07 11:40 219 28 191 -0.8356 52.090607/12/07 11:45 236 0 236 -0.9886 52.1972

07/12/07 11:50 239 3 236 -1.1233 52.3056

Table 2.3 – Typical train service data

RouteDistance

(km)Service Type Calling Pattern

London Euston to

Manchester Piccadilly296.1 4-stop

Watford, Stoke-On-Trent,

Macclesfield, Stockport

The high volume of data required a repeatable and scalable approach to data management. By

doing this, scenarios could be accommodated where more than one train was metered or a

more extensive metering period was studied – each of these scenarios would significantly

increase the number of journeys and thus data under analysis. Specifically designed

programming routines were developed and used to automate the handling of consumption

data. Appendix 1 is an example showing the computer code for the regression analysis control

program. Figure 2.3 and Table 2.4 below illustrate the structure of output from the energy

management program for a single passenger train journey.



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Figure 2.3 – Data management example for an individual journey

Table 2.4 – Key analysis data parameters

Passenger Journey

Information

Detail

Interval EnergyFactors i.

Timeii. Distance from London Euston

iii. Interval specific consumption

iv. Cumulative consumption

v. Average interval speed

vi. Net interval consumption

vii. Distance from origin station

Journey ID Reference for journey identification

Timetable ID Reference for automated timetable

selection

No. Of Stops Number of station stops in journey

Stop Sequence ID Used to differentiate between 2 journeys of

the same stop count

Interval Specific Energy

Evaluating the manner in which energy was dissipated during the course a journey was a main

focus of this study. This required interrogation of energy consumption per 5 minute interval.

In order to do this, the interval specific consumption of the train had to be determined.

Specific consumption is a measure of train energy economy. This is defined as the energyconsumption per kilometre travelled in each interval. Consumption totals per interval were

readily available from the onboard meters. However, the distance travelled in each interval

had to be estimated.

Estimation of the distance travelled in each interval was determined by plotting the entire

WCML rail track as a series GPS coordinates/points at an average spacing of 70 metres. By

doing this, the straight-line distance between adjacent coordinates could be evaluated using

geometric formulae. Summation of these elemental straight-line lengths was then performed

to determine the distance of a section of track. Using the train’s recorded GPS coordinates at



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the start and end of an interval, 2 equivalent positions on the GPS map could be identified and

the distance between them calculated.

The geometric formulae used to calculate the interval distance is known as the spherical law

of cosines[5]

. This approach assumes the Earth to be spherical and ignores geographical relief

features such as mountain ranges. Typically, for small distances down to a meter, this method

is known to achieve good accuracies.

A test of this method was conducted on the Norton Bridge North Junction to Stone Junction

branch line – this track section is known to be 5.97 km[3]

. The method outlined above

evaluated a distance of 5.84 km, demonstrating an acceptable 2.2 % error. A possible source

of error may come from the selection of start and end coordinates from the GPS map.

Coordinates were selected by visual inspection of the track and may not be exact in

accordance with the Sectional Appendix reference points. This error diminishes further when

the 2 positions marking the start and end points of an interval traversed by the train are

known.

Figure 2.4 below illustrates the approach used for calculating the distance travelled in each

interval.

:

1

Where

x Distance Interval

n

i

ei r E E N N N N x

)]cos()cos()cos()sin()[sin(cos 1 2 21 211

Position 1: Location of the train at start of

interval

Position 2: Location of the train at end of

interval

To calculate the distance travelled in the

interval, the sum of the elemental distances

between the coordinates of the GPS map was

determined.

Figure 2.4 – Estimation of the distacance travelled in an interval



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Latitude 2Coordinate N

Latitude1Coordinate N

Distance Elemental x

Elements of Number n

2

1

i

:

:

:

:

6371km Radius, Earth r

Longitude 2Coordinate E

Longitude1Coordinate E

e

2

1

:

:

:

Curvature in the track was accommodated by varying the spacing of coordinates mapping the

network. On average coordinates were spaced at 70 metre intervals with the maximum

spacing at approximately 300 meters for straight sections of track. For parts of the network

with a high degree of curvature, GPS coordinates were spaced at 20 metres. It is important to

note that this means of determining interval distances is approximate and has scope for

improvement. However, for the purposes of this study the results from this method are

acceptable. Further improvement may be realised by substitution of the spherical law of

cosines with the Haversine formula. This alternative, models the earth as ellipsoidal which is

a more accurate approximation than that of the spherical law of cosines.

Cumulative Energy Consumption

An additional key study metric was the cumulative energy consumption. Cumulative energy

is a running total of the energy consumption up to a point during the course of the journey. At

the terminal station, the cumulative energy is equivalent to the total consumption for that

particular journey. The specific energy as described above is the interval change in

cumulative energy per kilometre. Specific energy as a metric is particularly important as it is

used in the energy billing tariffs quoted to train operating companies. For the journeys

considered, the interval specific energy and cumulative energy consumption were evaluated.

In all the analysis performed, the distance from the origin station is used to identify the

position of the train on the network. Importantly, this reference distance parameter enables the

consumption level at any location on the rail network to be readily interrogated.

3. CORRELATION ANALYSIS RESULTS

Preliminary analysis highlighted trends approximating to a linear dependency between

cumulative consumption and distance from the origin station. Based on these initial positive

indications, a linear regression model was applied to the complete sample of 525 journeys.

The fundamental regression model assumed cumulative energy consumption varied as

follows:

stationorigin from Distance x

EnergySpecific journey Bulk a

constant nConsumptioa

Where

ref :

:

:

1

0

The overall results from this analysis are shown in Table 3.1 below. This table shows the

different values of the coefficients a0 and a1 in the model described above for a range of

calling patterns on each route. Additionally, the table highlights the levels of uncertainty and

accuracy of these coefficients.

ref xaa EnergyCumulative 10



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For the complete journey sample, the accuracy of predicted consumption to actual measured

values was typically above 95%. Confidence in these models may be drawn from the number

of journeys used to formulate each model. Repetitions of north and southbound journeys

running between London Euston and Glasgow Central were low. No more than 10 journeys

for were recorded for each calling pattern on this route. As a consequence, coefficients for the

London Euston – Glasgow Central route (highlighted in red) have a poor reliability.

In Table 3.1 * denotes an alternative calling pattern for services of the same number of scheduled station stops.



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Table 3.1 – Overall regression analysis results

Route Count Correlation Coefficients Uncertainty

Station

StopsJourneys

Data

Pointsa0 a1 stda0 stda1

Model

Accuracy

(kWh) (kWh/km)

Manchester Piccadilly to

London Euston

2 42 1104 283 16.6 11.6 0.07 98.1%

3 8 210 291 16.2 23.5 0.14 98.4%* 3 3 78 264 17.3 36.1 0.22 98.8%

4 10 292 252 17.5 20.9 0.12 98.6%

* 4 13 348 255 17.0 18.5 0.11 98.6%

5 6 170 249 16.9 29.4 0.18 98.1%

* 5 7 205 234 17.7 27.4 0.17 98.2%

London Euston to

Manchester Piccadilly

2 34 891 12 17.02 15.32 0.08 98.0%

3 18 476 48 16.73 24.04 0.13 97.4%

4 14 399 115 17.59 22.61 0.12 98.1%

* 4 14 394 18 17.53 25.62 0.14 97.7%

* 4 6 163 29 17.06 26.30 0.14 99.0%

6 8 240 68 17.87 19.39 0.10 99.2%

Liverpool Lime Street to

London Euston

1 4 120 151 16.27 29.99 0.17 98.7%

2 4 113 89 16.68 19.75 0.11 99.5%

4 4 122 134 17.83 32.20 0.20 98.6%

5 10 307 100 17.13 20.88 0.12 98.5%

* 5 25 782 127 17.50 15.86 0.09 97.9%

6 5 154 112 17.89 24.91 0.15 98.9%

7 7 244 61 18.25 20.39 0.12 99.0%



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London Euston to LiverpoolLime Street

3 2 57 58 15.05 36.37 0.18 99.2%

4 5 147 145 16.58 22.17 0.11 99.4%

* 4 2 64 93 17.86 27.69 0.14 99.6%

5 29 907 130 17.20 15.02 0.08 98.3%

* 5 19 593 110 17.52 14.45 0.07 99.0%

6 1 34 125 17.59 31.48 0.16 99.8%

Glasgow Central to London

Euston

2 10 567 40 16.10 24.88 0.07 99.0%

8 5 314 -66 16.83 27.88 0.08 99.4%

10 1 61 -150 18.60 46.01 0.13 99.7%

* 10 4 251 -46 17.47 36.84 0.10 99.1%

* 10 2 127 24 17.58 35.66 0.10 99.6%

11 3 191 -72 17.91 38.59 0.11 99.3%

* 11 9 598 -32 17.35 28.42 0.08 98.8%

London Euston to Glasgow

Central

2 2 114 -26 17.17 30.77 0.08 99.8%

4 5 286 -31 17.24 34.95 0.09 99.2%

8 3 184 99 17.18 38.58 0.10 99.4%

* 8 3 184 49 17.46 34.92 0.09 99.5%

7 6 359 -23 16.86 37.13 0.10 98.9%

10 5 316 137 17.98 36.14 0.10 99.1%

* 10 5 310 -10 18.07 44.38 0.12 98.8%

11 6 378 -5 17.83 33.01 0.09 99.1%



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Birmingham New Street toLondon Euston

1 2 35 183 15.48 40.14 0.39 98.0%

2 3 54 186 16.54 26.78 0.27 98.7%

3 25 476 180 16.40 12.27 0.12 97.5%

London Euston to

Birmingham New Street

2 2 36 151 15.15 26.78 0.22 99.3%

3 21 391 166 16.57 16.51 0.14 97.4%

* 3 5 114 140 17.13 17.21 0.16 99.0%

* 3 8 155 117 16.42 19.34 0.16 98.6%

5 1 24 138 16.14 33.03 0.29 99.3%

Wolverhampton to LondonEuston

4 3 71 134 16.44 24.53 0.23 98.6%

5 2 48 131 16.70 28.58 0.26 98.9%

* 5 30 718 119 17.24 9.47 0.09 98.0%

6 9 224 117 17.39 12.19 0.11 99.1%

* 6 2 216 2682 22.88 232.59 4.27 11.8%

7 4 97 72 17.77 23.30 0.22 98.6%

London Euston toWolverhampton

4 1 25 42 15.05 55.39 0.39 98.5%

* 4 1 25 123 16.74 25.85 0.18 99.7%

5 3 74 137 16.00 29.82 0.21 98.7%

* 5 32 784 93 17.05 11.66 0.08 98.2%

7 1 30 87 18.55 24.25 0.18 99.7%



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4. DISCUSSION

4.1 General consumption characteristics

High degrees of fluctuation in interval specific consumption were observed consistently for

all journeys in the study. From the specific consumption chart illustrated in Figure 4.1 it can

be seen that the energy economy of the train varies markedly from one interval to the next. Inaddition, the specific consumption appears to follow a distinct pattern. This pattern suggests

that given locations on the rail network may be associated with predictable levels of

consumption.

Previous studies determined bulk specific consumption (complete journeys) values in the

order of 17 kWh/km. It was therefore expected that interval specific consumption would

closely vary around the bulk value. In actuality, the fluctuation was significant as shown in

Figure 4.1. From this chart, it can be seen that specific consumption repeatedly peaked above

40 kWh/km as well as falling to 5 kWh/km. The corresponding bulk specific consumption

was 17.9 kWh/km for the journey set shown.

To achieve savings in energy consumption, the pattern or characteristic shown in Figure 4.1

needs to be investigated further. In particular, an in depth appreciation is required for the

extent to which the pattern can be optimised without adverse effects on normal train

operation.

Figure 4.1 – Typical interval specific energy consumption characteristics

Cumulative energy consumption values demonstrated a tendency to diverge on approach to

terminal stations. As a result variances of overall journey consumption as noted in previous

studies were observed. Crucially, this study shows the progressive nature of the variance

through the course of train journeys. In addition, it appeared that marked changes in the

interval specific energy (gradient of cumulative consumption trace) may have contributed to



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this variance. Figure 4.2 illustrates typical cumulative consumption characteristics. Note that

Figure 4.1 and 4.2 show the interval specific and cumulative consumption for the same set of

5-stop Liverpool Lime Street to London Euston journeys.

These marked changes in interval specific energy were noted to consistently occur following

scheduled station stops. This is consistent with the expected train acceleration following a

station stop. Further discussion of this after station stop consumption is detailed in Section

4.3.

Figure 4.2 – Typical cumulative energy consumption characteristics

4.2 Cumulative consumption models

In total 60 consumption models encompassing the following scope were evaluated:

i. Service route

ii. Train direction – northbound and southbound

iii. Station calling pattern – e.g. 2-stop or 4-stop journeys

Due to the poor reliability of London Euston – Glasgow Central models as mentioned in

Section 3, this route will not be included in the following discussion.

Consumption models resulting from the regression analysis as detailed in Section 3 were

categorised by station calling pattern and route variations. All consumption models were of a

linear form a0+a1 xref and comprised of these two correlation coefficients a0 and a1.

Interpretation of the coefficient a1 is straight forward – this quantity represents the bulk

specific consumption of the train for the given calling pattern and route. However, the

consumption coefficient a0 has not been explicitly encountered in previous energy studies on

train 390049. It is thought this parameter may represent consumption in terms of service

characteristics, in particular station calling pattern.



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A small number of repeat journeys for each variant of station calling pattern limited

interrogation of this consumption constant. Indications from the data suggested the

distribution of station stops from the origin as a potential contributing factor to this

consumption constant a0. Table 4.1 outlines values of a0 and the proximity of the 1st

scheduled

stop for southbound journeys. The data suggests that if indeed a0 is station distribution

dependent, it may also be route specific. Northbound journeys showed no simple correlation

between consumption constant and station distribution.

Unscheduled stops in addition to planned stops may potentially contribute to this consumption

constant. Additional work is required to identify and assess the impact of unscheduled stops

on the consumption constant.

Table 4.1 – Average consumption constant values and proximity of 1st

scheduled stop

Route 1st Station Stop1st Stop Proximity

to Origin

Avg. Consumption

Constant, a0

(km) (kWh)

MAN - EUS Stockport 9.4 261

WVH - EUS Sandwell and Dudley 12.1 115

BHM - EUS Birmingham International 13.0 183

LIV - EUS Runcorn 21.0 111

Statistical assessment of reliability showed typically above 95% accuracy of the linear

consumption models to actual measured values. For consumption models based on the largest

number of journeys per route, reliability analysis was positive – deviation of bulk specific

consumption about a statistical mean ranged from a minimum 0.07 kWh/km to a maximum

0.14 kWh/km. Range of deviation from the mean for the consumption constant was from a

minimum of 9.47 kWh to 16.51 kWh. Table 4.2 below outlines the consumption models

based on the largest sample of journeys per route.

Table 4.2 – Consumption models for service types with the largest volume of journeys

Cumulative Consumption Models

Route Northbound Southbound

EUS - LIV[5-stop]: [5-stop]:

EUS - MAN[2-stop]: [2-stop]:

EUS - WVH[5-stop]: [5-stop]:

EUS - BHM[3-stop]: [3-stop]:

EUS x EnergyCumulative 217 130 .



EUS x EnergyCumulative 6 16 165 .

LIV x EnergyCumulative 517 127 .

MAN x EnergyCumulative 6 16 283 .

WVH x EnergyCumulative 217 119 .

BHM x EnergyCumulative 416 179 .



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4.3 After station consumption

As briefly mentioned above, cumulative consumption showed a marked increase following

station stops - possibly as a direct result of train acceleration. In the context of diverging

cumulative consumption totals, this variable increase in specific consumption may be a

significant factor.

In the scope of this study it was not feasible to rigorously investigate correlations in afterstation consumption. This was due to the 5-minute metering frequency which resulted in very

few data points in the proximity of stations. However, some coarse calculations showed for

example, that for northbound 2 stop services between London Euston and Manchester

Piccadilly, consumption after station stops was 13% higher than the bulk consumption value

of 17.0 kWh/km. Southbound, after station consumption was 26.9% higher than the bulk

consumption level of 16.6 kWh/km.

An opportunity exists to significantly reduce energy consumption if it is found that after

station stop consumption is typical of that for ordinary unplanned train stops. By limiting

unplanned train halts, consumption on parts of the network may be reduced by up to 13% as

outlined above. The impact of unplanned stops may be illustrated by peaks in specific

consumption at Litchfield Trent Valley Junction, approximately 177 km from Liverpool Lime

Street – see Figure 4.1. These peaks are thought to be as a result of unplanned train halts

arising from train regulation in the vicinity of this route junction.

Improved assessment of the impact of station stops on journey consumption total variance is

required and may be achieved by enabling higher metering frequencies. In doing this,

consumption levels in the vicinity of halted train events may be detected thus enabling

consumption correlations to be determined. Furthermore, interrogation of data from the

onboard Train Management System (TMS) may indicate points where the train is stationary.

4.4 Average consumption levels

Average consumption summaries were determined to assess the typical consumption levels on

the various routes. Figures 4.3 and 4.4 show the average journey consumption totals. Notably,

northbound journeys almost consistently consume more energy than equivalent southbound

journeys. This difference between north and southbound journeys is consistent with previous

findings[1]

. It is noted that station calling pattern is a significant factor affecting consumption

levels, further suggesting halts in general may also be a factor. Additional work may be

required to identify unscheduled halts and their impact on north and southbound journeys.



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Northbound ServicesLondon Euston to

Glasgow Central

London Euston toLiverpool Lime Street

London Euston to

Manchester Piccadilly

London Euston to

Wolverhampton

London Euston toBirmingham New Street

Figure 4.3 – Average northbound journey consumption totals

Southbound Services

Glasgow Central toLondon Euston

Liverpool Lime Street to

London Euston

Manchester Piccadilly toLondon Euston

Wolverhampton to

London Euston

Birmingham New Street

to London Euston

Figure 4.4 – Average southbound journey consumption totals

Specific energy consumption of 390049 from this study agrees with results from earlier

metering studies. Typically, route averages for net specific consumption varied between 1.7%

and 7.1% of values determined previously. It is important to note the exclusion of regenerated

energy in the correlation analysis. Previous experience showed that train traction packages

were prone to partial availability leading to inconsistent regeneration and consumption

characteristics. This was done to minimise potential distortion of correlations by the

regeneration characteristics.

Regeneration percentage levels across the main routes were applied to the calculated bulk

specific consumption values. This enabled the comparison of known[1]

specific net to values

from this study. Table 4.3 summarises the two sets of results.

Table 4.3 – Net specific consumption (kWh/km) comparison

Route

Avg. Specific Net (kWh/km)

% Regen.Dec 2007 Metering

Report

390 049 Energy

Benchmarking Report

EUS - GC 13.7 14.4 17.4%

EUS - LIV 13.9 14.1 17.8%

EUS - MAN 13.7 14.1 17.8%

EUS - WVH 13.7 14.5 16.9%

EUS - BMH 14.4 13.4 17.4%



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5. CONCLUSIONS

Findings from this study show that simple linear models may be used to predict the energy

consumption of Virgin train 390049 – in the majority of cases, accuracies of above 95% were

recorded. Of particular interest was the emergence of a consumption constant in each variant

of the linear models. Although this study did not fully explore the interpretation of this

constant, it is thought the station calling pattern, specifically the distance between stations is a

significant factor. Contribution of this consumption constant although small, 6% being thehighest observation, it may be important in the context of variance in consumption totals.

It can be seen that bulk specific energy values from this study reflect closely those determined

from previous studies up to a maximum discrepancy of 7 %. This lends confidence to the

adopted method of assessing correlations of cumulative consumption and interval specific

consumption with distance from the origin station. Crucially, this method allows consumption

characteristics in the course of train journeys to be identified.

Through this approach it was observed that fluctuation of interval specific energy (energy

economy) was significant but importantly this fluctuation appeared to follow a pattern related

to the train position on the rail network. Indeed the linear consumption models found and

those already established for complete journeys may be appropriate for energy billing, but to

manage consumption, full characterisation of the fluctuating interval specific energy is

required. From this full characterisation, it may be possible to identify opportunities to save

energy.

These energy saving opportunities may be realised by understanding and limiting variability

in consumption, particularly at locations immediately following station stops. These regions

were characterised by significant magnitudes of specific consumption – possibly as a result of

train acceleration. Further study may seek to assess the extent to which this consumption can

be optimised without impinging normal train operation. Ultimately such a study wouldrequire the inclusion of variables such as the driving characteristic of the train in addition to a

higher metering frequency as recommended by previous studies[1]

.

Findings from this study strongly suggest that to manage train energy consumption,

fluctuation in energy economy as discussed in Section 4 needs to be better understood.

Importantly, this fluctuation appears to indicate a general pattern from which a consumption

benchmark for the major routes can be developed. From this, future train energy consumption

can then be compared and managed.

Investigation of the impact of train halts presents an opportunity for further study of train

energy consumption. This study highlighted the marked difference in consumption followingscheduled station stops – future work may seek to investigate correlations in this consumption

with train acceleration. Recommendations arising from this work may then inform the

strategy for minimising consumption for planned and unplanned station stops.



19

6. ACKNOWLEDGEMENTS

Valuable direction and supervision of this study was provided by Dr WG Fruh of Heriot-Watt

University. Acknowledgement is also due to the Engineering Department at the Association

of Train Operating Companies and Harry Bird at Virgin Trains for providing the data used in

this study.

7. REFERENCES

[1] Hinde, P “Trial of Energy Metering Equipment on Virgin Pendolino Train 390049 –

Intermediate Report of Trial December 2007” Association of Train Operating

Companies (2007)

[2] Hinde, P and Tarusenga, K “Trial of Energy Metering Equipment on Virgin Pendolino

Train 390049 – Supplementary Report June 2008” Association of Train Operating

Companies (2008)

[3] Network Rail, Sectional Appendix – London North Western (2008)

[4] Lord Nicholas Stern, “Stern Review: The Economics of Climate Change,” CambridgeUniversity Press ISBN: 0-521-70080-9, (2006)

[5] Calculating Distances Between Coordinates, Spherical Law of Cosines:

http://www.movable-type.co.uk/scripts/latlong.html (Accessed 25/04/2010)

[6] Association of Train Operating Companies (ATOC), “Baseline Energy Statement –

Energy consumption and carbon dioxide emissions on the railway,” ATOC (2007)

8. APPENDICES

Appendix 1 - Regression Analysis Control Program

%------------------------------------------ % Energy Data Management Utility % Control program for performing regression analysis

load route_ops

for j = 1:10

rt_name_e = route_ops{j,1};

rt_name_stn = route_ops{j,2};

eval(['load e_factors ' rt_name_e ';']);eval(['load wcml_stations ' rt_name_stn ';']);

eval(['serv = ' rt_name_e ';']);eval (['stations = ' rt_name_stn ';']);

[M,N] = size(stations);

vertical = zeros(M,3);

http://www.movable-type.co.uk/scripts/latlong.html





20

seq_id_vec = route_ops{j,3}(:,1);

halts_vec = route_ops{j,3}(:,2);

[V,W] = size(seq_id_vec);

correl = [];

for u = 1:V

seq_id = seq_id_vec(u,1);

halts = halts_vec(u,1);

e = []; e_c = []; a = []; Vel = []; refX = []; dx = []; ah_binary=[]; e_c1 = []; e_c2 = []; dur = [];

ind = find([serv{:,4}] == halts and [serv{:,5}] == seq_id)';

row = 1;

[P,Q] = size(ind);

while row <= P

i = ind(row,1);

%-------------------------- %section returns the distance travelled between data point

%intervals (dx)

dx_tmp = zeros(length(serv{i,1}),1);

dx_tmp(2:end,1) = abs(diff(serv{i,1}(:,2)))*(1000);

dx = [dx; dx_tmp];

%-------------------------- %section returns the average interval speed (Vel)

Vel = [Vel; serv{i,1}(:,5)];

%--------------------------

%section returns the average interval acceleration (a)

a1 = accl(serv{i,1});a = [a;a1];

%-------------------------- %section returns the distance from the origin station

%(refX)

dir = direction( serv{i,1}(1,2), serv{i,1}(end,2));

switch dir

case 'sb'

x_var = route_ops{j,6} - serv{i,1}(:,2);



21

refX = [refX; x_var];

case 'nb'

refX = [refX; serv{i,1}(:,2)];

end

%-------------------------- %section retunrs the duration of the service (dur)

dur = [dur; serv{i,1}(end,1) -serv{i,1}(1,1),serv{i,1}(end,4)];

%--------------------------

%section returns the markers [1 or 0] for data pointsimmediately following station stops

seq = stn_calls(serv{i,1},serv{i,3});

seq = seq';

sequence = cell2mat(stations(seq,5));

binary_var = afterhalt(serv{i,1},sequence);

ah_binary = [ah_binary; binary_var];

%-------------------------- %section returns the cumulative energy consumption up to a %point in the course of a journey (e_c)

e_c = [e_c; serv{i,1}(:,4)];

%-------------------------- %section returns the origin and terminal cumulative energy %records (e_c1 and e_c2)

e_c1 = [e_c1; serv{i,1}(1,2) serv{i,1}(1,4)];

e_c2 = [e_c2; serv{i,1}(end,2) serv{i,1}(end,4)];

%-------------------------- %section returns specific energy (e)

e = [e; serv{i,1}(:,3)];

%--------------------------

row = row +1;

end

[N,Nd,coeff,stda,residual,r2] =regression(e_c,a,Vel,dx,refX,ah_binary,e_c2,P);



correl_var = [serv{i,4} num2cell(N) num2cell(Nd)

num2cell(coeff(1,1)) num2cell(coeff(2,1)) num2cell(stda(1,1))num2cell(stda(2,1)) num2cell(residual) num2cell(r2)];

correl = [correl; correl_var];end

var_name = strrep(rt_name_e,'e_','correl_');

eval([var_name ' = correl;']);

end

clear correl_var

clearvars -except correl_* dur

Documents

Train Energy Bench Marking on the WCML