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Sar
a K
hay
yam
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entr
aliz
ed-d
ecen
tral
ized
En
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Rai
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Centralized-decentralizedEnergy Management in Railway System
This dissertation develops Railway Energy Management System (REM-S) which for the first time integrates on-board, wayside and coordination services. REM-S is driven by the idea that rege-nerated energy, loads, storages and volatile DERs should be coordinated dynamically to achieve optimal energy usage. REM-S implements two automation architecture standpoints: centralized and decentralized. In the hybrid centralized-decentralized REM-S, the global energy management is executed in control center considering the whole railway network for the following day, the local energy management is done in local control areas during every 15 minutes. The design of the REM-S architecture is based on the representation of the railway system as a smart grid. Besides, it accommodates different time horizons: day ahead, minute ahead and real time. Two level optimi-zation algorithms for energy management are explored in this dissertation to demonstrate REM-S architecture: centralized day-ahead and decentralized minute-ahead algorithms. The optimization is done regarding three different objectives: cost, energy consumption or power demand optimi-zation. The validity of the algorithms and analyses the simulation results in offline and online real case studies are demonstrated in this dissertation. The contribution of this dissertation is not only defining new energy management concept in railway system and designing the REM-S architec-ture but also developing REM-S tool and test the tool as final step in real life.
ISBN 978-3-942789-65-3
Sara KhayyamimInstitute for Automation of Complex Power Systems
Centralized-decentralized Energy Management
in Railway System
Von der Fakultät für Elektrotechnik und Informationstechnik
der Rheinisch-Westfälischen Technischen Hochschule Aachen
zur Erlangung des akademischen Grades eines Doktors der
Ingenieurwissenschaften genehmigte Dissertation
vorgelegt von
Sara Khayyamim, Master of Science
aus
Esfahan, Iran
Berichter:
Univ.-Prof. Antonello Monti, Ph. D.
Univ.-Prof. Eduardo Pilo de la Fuente, Ph. D
Tag der mündlichen Prüfung: 26. November 2018
Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online
verfügbar.
Bibliographische Information der Deutschen Nationalbibliothek
Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb-nb.de abrufbar.
D 82 (Diss. RWTH Aachen University, 2018)
Herausgeber: Univ.-Prof. Dr.ir. Dr. h. c. Rik W. De Doncker Direktor E.ON Energy Research Center
Institute for Automation of Complex Power Systems (ACS) E.ON Energy Research Center
Mathieustraße 10
52074 Aachen
E.ON Energy Research Center I 66. Ausgabe der Serie ACS I Automation of Complex Power Systems
Copyright Sara Khayyamim Alle Rechte, auch das des auszugsweisen Nachdrucks, der auszugsweisen oder vollständigen Wiedergabe, der Speicherung in Datenverarbeitungsanlagen und der Übersetzung, vorbehalten.
Printed in Germany
ISBN: 978-3-942789-65-3 1. Auflage 2019
Verlag: E.ON Energy Research Center, RWTH Aachen University Mathieustraße 10 52074 Aachen Internet: www.eonerc.rwth-aachen.de E-Mail: [email protected]
ABSTRACT
In recent years, environmental concerns and energy price increase are two big motivations to
find solutions for reducing emissions and energy consumption and consequently better
management of energy flow in different field of industry. Reduction of emissions are expected
to be achieved mostly through increase of Distributed Energy Resources (DER) penetration,
net reduction of consumption, management of energy flows for optimal usage of the available
energy, creation and exploitation of flexibility. Railway sector, as a huge connected and
controllable system with ability to transport massive number of passengers and freight, can
have special share in reducing emissions and energy consumption. As an example of
effectiveness of this sector, it can be remarked the energy saving of Swiss Federal Railways
(SBB) in 2017 which was equal to energy consumption of 18450 households in one year.
The modern railway system is a massive, grid connected complex system, with distributed
active loads (trains), sources (particularly DERs) and storages (wayside or on-board storage
systems). Accordingly, the new REM-S (Railway Energy Management System) which is
developed in this dissertation for the first time integrates on-board, wayside and coordination
services. REM-S is driven by the idea that regenerated energy, loads, storages and volatile
DERs should be coordinated dynamically to achieve optimal energy usage.
REM-S implements two automation architecture standpoints: centralized and decentralized.
According to railway system specifications, there is possibility to partition the system to local
areas and also there is possibility to define local or global targets for system optimization.
These two main factors along with other factors like level of system complexity, size of the
system, Information and Communication Technology (ICT) structure and dependability of
different system layers influenced the choice of a hybrid centralized-decentralized concept for
REM-S. In the hybrid centralized-decentralized REM-S architecture, while the global energy
management system is executed in control center considering the whole railway network for
the following day, the local energy management system will be done in local control areas
during every 15 minutes.
The existing smart grid standards, communication protocols and ICT technologies are suitable
for designing centralized-decentralized automation architecture. Hence, the design of the
REM-S architecture is based on the representation of the railway distribution system as a
smart grid. Besides, it accommodates different time horizons. This architecture must be able
to deal with different stakeholders, applications and networks, and it must be interoperable
with the public grid and electricity markets. The development of this architecture requires
harmonization of the standards, protocols and data models of different domains.
Two level optimization algorithms are explored in this dissertation to demonstrate REM-S
architecture: centralized day-ahead and decentralized minute-ahead algorithms for energy
management. All energy players like trains, infrastructure facilities, wayside storages and
distributed energy resources are considered in the simulation. The validity of the algorithms
and analyses the simulation results in offline and online real case studies are demonstrated. In
the online case study, the developed system for Minute Ahead Optimization and Real Time
Operation was tested on the Malaga-Fuengirola line (Spanish railway) for few hours. The
optimization is done regarding three different objectives: cost or energy consumption or
power demand optimization.
The contribution of this dissertation is not only defining energy management concept in
railway system and designing the REM-S architecture but also developing REM-S tool and
test the tool as final step in real life.
ACKNOWLEDGMENT
This dissertation was completed at the Institute for Automation of Complex Power Systems
of the E.ON Energy Research Center at RWTH Aachen University, and this was made
possible only with the support of many people.
Foremost, I must express my sincere gratitude to my doctoral supervisor, Professor Antonello
Monti, for giving me the chance to do my doctoral studies under his guidance, for his
outstanding support and encouragement, and for being a role model for me in both scientific
and non-scientific areas. I would also like to thank Professor Ferdinanda Ponci for all the
valuable discussions, her critical reviews, and excellent suggestions on improving my
academic work.
I would also like to express my gratitude to Professor Eduardo Pilo de la Fuente for his
thorough review of my dissertation and his fruitful comments, and for being the co-examiner
of my doctoral examination.
I am also thankful to Professor Christoph Jungemann and Professor Peter Jax for serving as
my doctoral examination committee members.
Many thanks go to all my colleagues at the ACS institute for the good times during my
doctoral studies, Bettina Schaefer, Nicolas Berr, Lukas Razik, Stefan Lankes, Marlon Fleck,
Mohsen Ferdowsi, Fei Ni, Kanali Togawa, Ivelina Stoyanova, Asimenia Korompili, Robert
Uhl, Marco and Lisette Cupelli, and Milica Bogdanovic for all the great moments of working
and having fun together. Special thanks to our secretaries, Ursual Huppertz, Nicole and Robert
Bielders, and Sylvia Meurers for their constant support and readiness to help.
Finally I would like to express all my gratitude and my love to my family, my supportive and
loving parents for their endless love and motivation, my two lovely sons: Saam for
collaborating with busy mom and giving me strength to go on and Toos for not born before
my defense and accompanying inside me while writing my dissertation and last but not least,
all my love and my gratitude to my kind, supportive and lovely husband, Behnam, without his
encouragement and support, this work would not have been possible.
Aachen, March 2019
Sara Khayyamim
TABLE OF CONTENTS
Nomenclature ......................................................................................................... v
1 Introduction ......................................................................................................... 1
1.1 Background ......................................................................................................... 3
1.1.1 Distributed energy management systems ............................................................ 3
1.1.2 Energy management in microgrids ..................................................................... 4
1.1.3 Energy Efficiency Optimization in Railway ....................................................... 5
1.1.4 Recent railway projects for improving energy efficiency ................................. 11
1.2 Objectives and Contribution ............................................................................. 17
1.3 Dissertation Outline .......................................................................................... 19
2 Centralized-decentralized Architecture [7] .................................................... 21
2.1 Concept ............................................................................................................. 22
2.2 Analysis Phase .................................................................................................. 27
2.2.1 Use Case Analysis............................................................................................. 27
2.2.2 Function Layer .................................................................................................. 31
2.2.2.1 Market Zone ...................................................................................................... 31
2.2.2.2 Enterprise Zone ................................................................................................. 33
2.2.2.3 Operation Zone ................................................................................................. 34
2.2.2.4 Station Zone ...................................................................................................... 37
2.2.2.5 Field Zone ......................................................................................................... 38
2.2.3 Business Layer .................................................................................................. 38
2.2.3.1 Business Actors [53] ......................................................................................... 39
2.2.3.2 Business Processes ............................................................................................ 43
ii
2.3 Architecture ....................................................................................................... 46
2.3.1 Component Layer .............................................................................................. 46
2.3.2 Information Layer ............................................................................................. 50
2.3.3 Communication Layer ....................................................................................... 53
2.4 Evaluation of architecture ................................................................................. 59
2.4.1 Key features characterization ............................................................................ 59
2.4.2 Robustness ........................................................................................................ 60
2.4.3 Hosting capacity ................................................................................................ 60
2.4.4 Architecutre Cost .............................................................................................. 61
2.4.5 Scalability.......................................................................................................... 65
2.4.6 Degraded mode of Operation ............................................................................ 66
2.5 Summary ........................................................................................................... 67
3 Centralized-Decentralized Optimization Approaches ................................... 69
3.1 Modelling Railway System ............................................................................... 69
3.1.1 Train .................................................................................................................. 69
3.1.2 External Consumer (EC) ................................................................................... 70
3.1.3 Distributed Energy Resource (DER) ................................................................. 70
3.1.4 Electrcial Storage Systems (ESS) ...................................................................... 70
3.1.5 Power flow ........................................................................................................ 71
3.2 Centralized Optimization Formulation [64] ...................................................... 72
3.2.1 Energy/Cost Optimization ................................................................................. 75
3.2.1.1 First step- Train, DER and EC .......................................................................... 75
3.2.1.2 Second step- ESS .............................................................................................. 76
3.2.2 Power demand Optimization ............................................................................. 78
3.2.2.1 First step- Train, DER and EC .......................................................................... 78
3.2.2.2 Second step- ESS .............................................................................................. 79
3.3 Decentralized Optimization Formulation [66] ................................................... 82
Table of Contents iii
3.3.1 MAO Negotiations ............................................................................................ 84
3.3.1.1 Agents Identification ......................................................................................... 84
3.3.1.2 Inteoperability among agents ............................................................................ 85
3.3.2 Deviation minimization..................................................................................... 88
4 REM-S Software Suites [67] ............................................................................. 91
4.1 REM-S Offline Suite......................................................................................... 92
4.1.1 REM-S GUI ...................................................................................................... 93
4.1.2 DAO Application .............................................................................................. 94
4.1.3 MAO Application ............................................................................................. 95
4.2 REM-S Online Suite ......................................................................................... 97
4.2.1 Ground’s LOS Application ............................................................................... 98
4.2.2 Ground’s AoERST Application ...................................................................... 101
4.2.3 Ground’s TCCS Application ........................................................................... 101
4.2.4 Train-to-Ground (T2G) API ............................................................................ 102
4.2.5 Train’s DOEM Application ............................................................................ 102
4.2.6 Train’s DAS Application ................................................................................ 103
5 Simulations and Results [72] .......................................................................... 105
5.1 Validation Case ............................................................................................... 105
5.1.1 Introduction ..................................................................................................... 105
5.1.2 DAO Results ................................................................................................... 108
5.2 Offline Case .................................................................................................... 111
5.2.1 Introduction ..................................................................................................... 111
5.2.2 GA parameters setting ..................................................................................... 112
5.2.2.1 Diversity Mechanism ...................................................................................... 112
5.2.2.2 Population Size ............................................................................................... 113
5.2.3 DAO Results ................................................................................................... 115
5.2.4 MAO results .................................................................................................... 116
iv
5.3 Online Case ..................................................................................................... 121
5.3.1 Introduction ..................................................................................................... 121
5.3.2 MAO Results ................................................................................................... 123
6 Conclusion and Future work ......................................................................... 127
6.1 Conclusion ...................................................................................................... 127
6.2 Future work ..................................................................................................... 128
Appendix ............................................................................................................. 130
Related Publications ...................................................................................................... 130
Bibliography ....................................................................................................... 133
List of Figures..................................................................................................... 143
List of Tables ...................................................................................................... 147
Curriculum Vitae ............................................................................................... 149
NOMENCLATURE
3G 3rd Generation of wireless mobile telecommunications technology
AAA Authentication, Authorization and Accounting
AoERST Adapted online Existing Railway Simulation Tool
API Application Programming Interface
CIM Common Information Model
DAO Day Ahead Optimization
DAS Driver Advisory System
DER Distributed Energy Resource
DER EVS DER EMS and VPP system
DMI Data Manager Interface
DMS Distribution Management System
DNS Domain Name System
DOEM Dynamic On-board Energy Manager
DSO Distribution System Operator
EA Enterprise Architect
EBDM Energy Buyer Decision Maker
ECN Ethernet Consist Network
EMO Electricity Market Operator
EMS Energy Management System
EPP Electricity Procurement Planner
ERST Existing Railway Simulation Tool
ESS Energy Storage System
EV Electrical Vehicles
GOSET Genetic Optimization System Engineering Tool
vi
GOS Global Optimization Software
GPRS General Packet Radio Service
GUI Graphical User Interface
HLUC high level use cases
HMI Human Machine Interface
ICT Information and Communication Technology
IEC International Electrotechnical Commission
IED Intelligent Electronic Device
IM Infrastructure Manager
ISST Intelligent Substation
JADE Java Agent Development Framework
KPI Key Performance Indicator
L2G LOS-to-GOS
LOS Local Optimization Software
LTE Long-Term Evolution
MAO Minute Ahead Optimization
MAS Multi- Agent Systems
MDC Meter Data Concentrator
MDMS Meter Data Management System
MERLIN Sustainable and intelligent Management of Energy for smarter
RaiLway systems in Europe: an INtegrated optimization approach
MID Measuring Instrument Device
MODENERGY MODURBAN- Energy savings related aspects
MODURBAN Modular urban guided rail systems
MVB Multifunctional Vehicle Bus
ON-TIME Optimal Networks for Train Integration Management across Europe
OSIRIS Optimal Strategy to Innovate and Reduce Energy Consumption In
Urban Rail Systems
PCC Point of Common Coupling
Nomenclature vii
RailEnergy Innovative Integrated Energy Efficiency Solutions for Railway Rolling
Stock, Rail Infrastructure and Train Operation
RailML Railway Modelling Language
REM-S Railway Energy Management System
RO Railway Operator
RSST reversible substation
RTO Real Time Operation
RTU Remote Terminal Unit
RU Railway Undertaking
SCADA Supervisory Control and Data Acquisition
SGAM Smart Grid Architecture Model
SOAP Simple Object Access protocol
SST non-reversible substation
T2G Train-to-Ground
TCCS Traffic Control Centre Simulator
TDS Track data server
TECREC Technical Recommendation
TMS Traffic Management System
TSO Transmission System Operator
UML Unified Modeling Language
VPN Virtual Private Network
VPP Virtual Power Plant
WIFI Wireless Fidelity
XML Extensible Markup Language
1 INTRODUCTION
In recent years, environmental concerns and energy price increase are big motivations to find
solutions for reducing emissions and energy consumption and consequently, better
management of energy flow in different field of industry. Reduction of emissions, according
to 2020 and 2050 EU energy targets [1] [2], are expected to be achieved mostly through
increase of Distributed Energy Resources (DER) penetration, net reduction of consumption,
management of energy flows for maximum usage of the available renewable energy, creation
and exploitation of flexibility.
Transportation is one of the most polluting and energy consuming sectors in the industry.
From transportation sectors, railway sector as huge connected and controllable system with
ability to transport massive number of passengers and freight can have special share in
reducing emissions and energy consumption. As an example, it is good to take a notice to the
energy saving of Swiss Federal Railways (SBB) in 2017. Their energy saving in one year is
equal to energy consumption of 18,450 households in one year (73.8 GWh/year) [3]. In this
context, European railways have committed to reduce their own emissions by 30% by 2020.
Considering that in European railways the share of electricity from distributed resources is
dramatically increasing [4], updating energy management methods consistently is of great
importance.
In railway system for finding better way to manage energy flow in the system, solutions to
optimize the usage of available energy from public grid, renewable resources, wayside or
onboard energy storage systems and regenerated from trains should be explored. There are
challenges in railway system that makes energy management in the system harder. Challenges
like huge dimension of system (e.g. Deutsche Bahn Company has 19,800 km electrified
railway system in Germany with 20,000 journeys per day [5]), nonlinearity, complex
constraints and time varying characteristics.
The railway constraints like traffic management and technological constraints like maximum
capacity of substations make creation and exploitation of flexibility in the optimization
problem a challenge. For example, trains are one of the main actors in energy flow of railway
system. Because of constraints like punctual arriving time, passenger comfort, speed limits
and geographical constraints, it is hard to find flexibility in their power demand profile to be
applied in energy management optimization problem. On the other hand, the optimized
solution should have a balance between accuracy in optimization solution and computation
time. In railway system, regarding to the size of system, computation time in selecting
optimization algorithm is a challenge and should be considered as an important factor. Most
2 Chapter 1
of theoretical models find exact solutions but with long computation time, on the other hand
the heuristic solutions reach suboptimal solutions in short time.
All researches in recent years for energy management in railway system lack a global system
approach to the problem. They mostly focus on optimizing energy efficiency of trains
operation (consisting timetable optimization and energy efficient driving optimization) while
infrastructural loads are ignored in the problem although they can have significant effect in
the optimal solution. To show the importance of infrastructural load, it is good to mention that
in urban railway system around 50% of loads belong to infrastructural loads [6]. In previous
researches which will be reviewed in the following section, the lack of an integrated energy
management system solution considering all tractional, non-tractional and infrastructural
loads along with distributed energy resources and electrical storage systems is recognized.
The example of energy flow in a European electric mainline railway system shown in
Figure 1.1 indicates that the amount of energy regenerated by the train is comparable with the
energy consumption of “non-tractional loads” (signaling, switches, etc.) or “other loads”
(infrastructural loads like stations, workshops, EV charge stations, etc.). Regenerated energy
on trains together with wayside and on-board train storages, bring notable flexibility to
railway system and supports it in being an important actor in the electricity market and
consequently a backbone in future smart cities.
Public Grid To Traction Grid Substation/Converter Station
TRACTIONSUBSTATION
PUBLIC DISTRIBUTION AND TRANSMISSION GRID
ENERGY MEASUREMENT
Braking energy
Input TRACTIONSUBSTATION
GWh/year
GWh/yearGWh/year
Regenerated Energy
Consumed Energy
CATENARY
RAILS
Traction Consumption
Ancillary services consumption
GWh/year
GWh/year
Regenerated energy GWh/year
Rheostatic energy
Returned to public grid Used by other trains
ENERGYMEASUREMENT
NON TRACTIONLOADS
(Signalling,switches, etc.)
GWh/year
TRACTION DISTRIBUTION AND TRANSMISSION GRID
G
ENERGY
Output Energy Public Gneretors
ENERGY MEASUREMENT
Returned to public network
GWh/year
ENERGY MEASUREMENT
OTHER LOADS
(Stations,Workshops, etc.)
Energy intaken at pantograph level
GWh/yearGWh/year
GWh/year
Spain Electric Generation Mix (2011)
21,2 % Nuclear
18,7% Solar
18,6% Natural Gas
16% Coal
15,3% Wind
10,2% Hydroelectric
GWh/year
2,456.6
2,363.2 62.6
235
300
16364.8
1,968.8 GWh/year
22836
264
148.41,820.4 [Image developed by Fundación Ferrocarriles Españoles (FFE) for MERLIN]
Figure 1.1: Energy flow of a European country case [7]
Introduction 3
1.1 BACKGROUND
Applying smart grid in railway system energy management framework is attracting more
attention these days. Harmonizing standards, protocols and data models of different actors in
railway system in one hand and on the other hand using the modern technologies for
information exchange and smart metering are some reasons for this attention. [8] is one of the
beginning efforts for integrating smart grid in the railway system. It describes the functions
that the transport systems can perform in a demand response vision as a function of interfacing
device in smart grid. By considering the smart grid as special integration of complementary
devices (like measurement units), subsystems (like advanced metering systems) and functions
(like automated meter reading) under the extensive control of a highly intelligent and
distributed command-and-control system, it is expected to configure smart grid as an
interconnected network of microgrids with distributed control. Therefore, distributed energy
management systems and microgrids are two fundamental applications of smart grids in
power system. These applications can be also applied in railway energy management system
and are briefly reviewed in this section.
The baseline for managing optimally the energy is improving the energy efficiency. In railway
system, this occurs by developing an optimal energy flow in rolling stocks, distributed energy
sources, wayside storage systems and all infrastructural loads. In this section by categorizing
the methods for improving the energy efficiency to technological and operational, the most
important operational methods (timetable optimization and energy efficient driving
optimization), which are the mostly related to the target of this dissertation, are reviewed.
1.1.1 DISTRIBUTED ENERGY MANAGEMENT SYSTEMS
Given the size and the value of utility assets, the emergence of the smart grid will be more
likely to follow an evolutionary trajectory than to involve a dramatical change. The smart grid
will therefore materialize through strategic implants of distributed control and monitoring
systems within and alongside the existing electricity grid. The functional and technological
growth of the distributed control and monitoring systems help to create large pockets of
distributed intelligent systems across diverse geographies which allow utilities to shift more
of the old grid’s load and functions onto the new grid with distributed control and
consequently to improve and enhance their critical services [9].
That’s the reason why distributed energy management systems are one of the fundamental
applications of smart grid solutions. Home Energy Management Systems [10] and [11], smart
4 Chapter 1
city quarters and smart cities are examples of distributed control systems trying to achieve
optimal energy scheduling. [12] considers smart buldings as distributed systems and proposes
system architecture for energy managementin them. The focus of [13] is optimal energy
management of smart grids as distributed systems considering unpredictable load demands
and DERs. Considering railway systems as massive distributed systems with possibility to
partition them into local control areas, gives the possibility of using the advantages of
distributed energy management in smart grids.
1.1.2 ENERGY MANAGEMENT IN MICROGRIDS
The microgrid concept has been proposed as an organizing principle for managing
information and power flows for networks with distributed sources [14]. Microgrids are
defined as interconnected networks of distributed energy systems (loads, energy storages and
resources) that are coordinated to achieve autonomous operation [15]. This configuration, by
separating railway system to different control areas, makes similarities between energy
management in microgrids and different railway control areas as interconnected networks.
The traditional hierarchical microgrid control model did not consider sources with electrical
storage capacity. The lack of appropriate control and management strategies has been
identified as a limiting factor for integrating distributed electrical storage systems into
microgrids [16].Control strategies for microgrids with distributed electrical storage systems
can be broadly divided into three categories, based on their architecture: (a) decentralized, (b)
centralized and (c) distributed multi-agent. Distributed multi-agent control offer a desirable
middle ground between the two fully centralized and fully decentralized extremes [15]. Below
some techniques for control and management of microgrids are reviewed:
In [17] and [18], a multi-agent approach is applied for demand side management and demand
response for energy management in microgrids. A priority-based and index-based mechanism
is used in these methods for encouraging customers’ participation in demand side
management and demand response. In [19] a mixed linear integer problem is formulated for
optimal sizing of energy storages in microgrids. As it is presented in [17] and [18], the agent-
based approach shows its capability for spreading the control among different players of
system. In [20], a semi-centralized decision making methodology uses a multi-agent system
for energy efficiency optimization and cost reduction in an energy management system for
buildings. A residential energy management system is also similar to railway energy
management systems with regards to various energy actors being part of the optimization
problem. Reference [21] uses a two level framework for residential energy management. At
the first level, each customer minimizes his own payment cost and at the second level, a multi-
Introduction 5
objective optimization problem is used to optimize technical characteristics of distribution
systems like minimizing the load demand deviation of the whole system. This two level
optimization framework is already applied in this dissertation for Railway Energy
Management System (REM-S) optimization approach as well. In the agent-based control
methodology, which is used in REM-S, each agent is responsible for optimizing its own power
profile. The second level of optimization in REM-S occurs at the system level by considering
all agents as active players in the system.
In recent years there are researches in the railway section that model railway system as micro
grids like [22], [23] and [24]. The similarity in these researches is the application of energy
storage systems for increasing energy efficiency of system. The objective of [22] is to analyze
the stability of DC microgrid integrated in urban railway system by controlling a hybrid
storage system to use regenerated braking energy in non-railway applications like auxiliary
loads in stations or electrical vehicles in neighborhood. In [23], railway microgrid is proposed
to balance energy flows between train, storage system and power utility network. Artificial
Bee Colony algorithm is applied in this paper for optimizing operational cost and energy flow.
In [24], the DC railway electrification grid is modelled as dynamic DC microgrids to
investigate the effect of applying energy storage system in recovering regenerated barking
energy. It compares applying wayside and on-board energy storages and concludes that the
saving of wayside energy storage is more than on-board storage, especially considering the
additional energy consumption for carrying on-board energy storage.
1.1.3 ENERGY EFFICIENCY OPTIMIZATION IN RAILWAY
There are many studies related to methods for optimizing energy efficiency for trains as
individual agents in railway systems. References [25] and [26] reviewed these papers since
1980s. These methods are categorized in operational methods and technological methods.
While the technological methods require higher investment costs to make major
improvements in the system equipment, the operational methods try to find methods to
achieve more efficiency in current system. Figure 1.2 shows the main solutions in urban
railway by categorizing the operational and technological methods in different sections of
system (rolling stock, infrastructure and whole system). [6] after comparing different
technological and operational methods, concludes that there is lack of system approach-global
perspective in this area.
The smart energy management (which is depicted in Figure 1.2 by red box), is the solution
which is explored in this dissertation but only from the operational view. Most of the
operational and technological solutions that are displayed in Figure 1.2, supports smart energy
6 Chapter 1
management. For example the energy metering is the basis for implementing smart energy
management in any system or wayside Electrical Storage Systems (ESS) or Renewable
Energy Generation bring significant flexibility for managing the energy flow of system, ATO
(Automatic Train Operation) or DAS (Driving Advisory System) give the ability to train to
act as smart actor in system and be able respond to smart energy management solutions and
reversible substation provide this opportunity for railway system to send energy to public
grid and act as important actor in electricity market. In Figure 1.2, two solutions are
mentioned specifically in operational column which are the main solutions for energy efficient
train operation: a) timetable optimization b) energy efficient driving optimization
OPERATION TECHNOLOGIES
WH
OLE
SY
STEM
INFR
AST
RU
CTU
RE
RO
LLIN
G S
TOC
K
Figure 1.2: Main solutions for saving energy in urban rail
Timetable optimization synchronizes the actions of trains to maximize the utilization of
regenerative energy based on accelerating and braking time provided by speed profiles [25].
While the energy-efficient driving optimizes the speed profiles at sections to minimize the
tractive energy consumption under timetable constraints. In the following these two methods
are reviewed in literature to find the similarities and differences of these method to the method
which explored in this dissertation.
Timetable Optimization
Optimized Traffic Management
Renewable Energy Generation
Smart Energy Management
Energy Metering
Passenger Movement in Stations
Reduced Power Supply Losses
Lighting & HVAC in stations
Reversible Substations
Low-Energy Tunnel Cooling
Wayside ESS
Lighting & HVAC in Parked Mode
Lighting & HVAC in Service
Efficient Driving optimization
Mass Reduction Thermal Insulation
On-board ESS
Perermanent Magnet Synchronous Motor
DAS ATO
Introduction 7
Timetable optimization
One of the first references related to timetable optimization is [27] targeting not only safe
short headway operation but also improving coordination of train control and energy
management. By coordinating the control of multiple trains in Bay Area Rapid Transit system,
they recovered regenerated braking energy. Their developed control algorithm targeted
reducing peak power consumption, avoiding oscillations and limiting needle peaks at
substations. References [28], [29] and [30] are examples of recent papers focused on timetable
optimization. [28] proposes a multi-objective optimization model in order to optimize the
timetable in subway systems, where the objective of overlapping time is the measure of the
utilization of regenerative braking energy and the objective of total travel time is the measure
of satisfaction of the passengers. [29] proposes a timetable optimization model at the same
station, again aiming to improve the utilization of regenerated energy and the passenger
waiting time. [30] proposes a mathematical model for finding optimal train movements
considering operational interactions. It optimizes energy consumption and travel time by
coasting control. [28] uses simulated annealing for optimization while [29] and [30] use
Genetic Algorithm. Actually the timetable optimization mostly apply Genetic Algorithm and
simulation techniques as approximation techniques for their optimization instead of exact
methods like Pontryagin’s Maximum Principle or Hamiltonian analysis [26]. The
performance indicators which mostly use in timetable optimization references are overlapping
time, peak power consumption and the rate of utilization of regenerated energy.
In [31] the performance indicator for timetable optimization is maximizing utilization of
regenerated energy. A mathematical programming optimization model has been designed to
synchronize the braking of trains arriving at station with the acceleration of trains exiting from
the same or another station. In addition, a power flow model of the electrical network has been
developed to calculate the power-saving factor for each synchronization event in order to
encourage better synchro-nizations, particularly those which have fewer energy losses. By
testing the algorithms in a line of Madrid underground system, it is concluded that a
modification in the published timetables would result in energy savings, with no effect on the
quality of service for passengers and low associated investment costs.
In [32] a modular three-level performance-based railway timetabling framework is proposed.
Each performance indicator is optimized or evaluated at the appropriate level. The
performance indicators are:
Scheduled travel time
Infrastructure occupation and stability
Time required for a given timetable pattern on a given infrastructure
Sufficient time allowance to settle delays
Feasibility
8 Chapter 1
All processes realizable within their scheduled process times
Scheduled train paths are conflict free
Robustness
Delay propagation behavior kept within bounds
Energy efficiency
Timetable allows energy efficient train operations
The three-level timetabling framework are defined as:
Microscopic (track section level) computes reliable process times at a highly-
detailed local level.
Macroscopic (network level) optimizes a timetable at aggregated network level.
Fine-tuning (corridor level) computes energy-efficient speed profiles and optimizes
the schedules of local trains at corridor level taking into account stochastic dwell
times.
The fine-tuning level of timetable optimization has the most similarities with energy
management system proposed in this dissertation (REM-S). Since although in REM-S the
timetable is the hard constraint that is not possible to play with, there is a margin called
running time supplement that in each journey is considered and it is the flexibility that
timetable bring to REM-S optimization problem. The other similarity of timetable
optimization method and REM-S is the holistic approach that they have to the system
optimization, although in timetable optimization the only actors are trains and distributed
energy resources, wayside electrical storage systems and other loads are neglected. Most of
references in timetable optimization focus on regional and metro trains while
REM-S explore the energy management in mainline with long distances. One reason for
explaining this focus is achieving better energy saving with timetable optimization in short
sections comparing to long sections.
Energy efficient driving optimization
One of the first researches for energy efficient driving is [33] in 1968. It intended to find a
train operation which minimizes energy consumption to lead the train from one station to the
next station at the specific time and stop it there. The problem was considered as a bounded
state variable problem and proposed optimal control model to determine the optimal speed
profile.
The optimal speed profile of trains at each section consist of acceleration, cruising, coasting
and braking phase. [34] is one of the first researches that shows in urban line with short
Introduction 9
distance between sections, the energy-efficient speed profile won’t have cruising phase. That
means cruising can have more influence and play more important role, when the average
distance between stations increases, specifically in mainline railway system.
In the recent years, several researches have been done for finding optimal speed profiles of
trains. One of the main focuses of these researches is reducing the running time of algorithms
to enable applying them online. This is one of the main reasons that in recent years, numerical
methods getting more attention than analytical method for solving efficient driving problem.
[35] is one example of these researches which use MAX-MIN ant system algorithm to reach
optimal profile. The running time of the algorithms is reasonable to enable applying it in
online optimization runs.
Most of researches on energy efficient driving focus on optimizing speed profile of one train
between two stations and ignore using regenerated energy. [36] is one of the papers which
consider storage system at substation to use regenerated energy and find minimum energy
consumption by using Genetic Algorithm for selecting the best speed profile. In [37], based
on the energy-saving strategy of a single train, the optimization of multiple train’s trajectories
(actually two trains) is studied. A cooperative control model is formulated with the utilization
of the regenerated energy, which is used to calculate the total energy consumption of an
electric subway system under various energy-saving control strategies.
Three different optimization algorithms are compared in [38]. The authors avoid nonlinear
complexity to simplify optimal control calculations and reduce computation time of
algorithms. The objective function is minimizing total energy and the constraints of timetable,
traction equipment characteristics, speed limits and gradients are taken into account. In this
research, Ant Colony optimization and Genetic Algorithm as heuristic algorithms are
compared with Dynamic Programming as exact method in different example with fixed total
distance but various scheduled journey time. The lowest total energy consumption is achieved
by Dynamic Programing but with longest computational time.
[39] is based on the exact method of Pontryagin’s Maximum Principle to derive the optimal
control and establish a fundamental local energy minimization principal. The authors in this
two research consider the regenerative braking in their model. They concluded that the
regenerative braking is used in a cruising phase to maintain a certain cruising speed during
steep downhill section. This research is one of the few researches that test their method in
high speed train case study.
Energy efficient train control methods struggle between developing accurate advanced models
on one hand and faster algorithms on the other. The algorithms in the existing onboard tools
like DAS (Driver Advisory System) rely on some simplifications to be able to compute
10 Chapter 1
suboptimal driving advice in real time or compute large set of scenarios offline. The systems
like DAS often settles for suboptimal solutions using heuristics, while the theoretical models
try to find the global optimal strategy of course with more computation time. The researches
show that even suboptimal solutions can lead to significant energy savings [26].
In REM-S the calculated optimal speed profiles with energy efficient method can be used as
input for finding the best profiles of the whole system. Although it can happen that the optimal
speed profile of one trains doesn’t selected by REM-S, because of not fitting to optimization
of whole system.
Integrated Optimization
Energy-efficient driving focuses on optimizing the speed profile between adjacent stations for
a single train. It often ignores the regenerative energy transmitting among multiple trains.
Hence the obtained energy-efficient speed profile is only optimal for a single train but not for
multiple trains. Timetable optimization synchronizes the actions of multiple trains to
maximize the utilization of regenerated energy, but it usually assumes the speed profile as a
constant parameter. The tractive energy consumption is not reduced by the obtained optimal
timetable. Therefore, in recent years, a few researchers studied the integrated optimization
methods [25]. The references [40], [41], [42] and [43] are recent papers which chose an
integrated optimization approach considering both timetable and energy efficient driving style
optimization.
[40] proposes to optimize the integrated timetable, which includes both the timetable and the
speed profiles. First, an analytical formulation is provided to calculate the optimal speed
profile with fixed trip time for each section. Second, a numerical algorithm is designed to
distribute the total trip time among different sections and prove the optimality of the
distribution algorithm. Furthermore, the algorithm is extended to generate the integrated
timetable. The simulation of their case study show that energy reduction for the entire route
is 14.5%. The computation time for finding the optimal solution is 0.15 s, which is acceptable
to be applied in real-time control.
[41] examines the energy-efficient operation problem of two trains operating along the same
line consecutively in urban trail transit. Then a moving block signaling system is utilized in
which the following train's tracking target point is moving forward continuously with the
leading train's running. In this case, the following train may be influenced by the leading
train's exceptional situations, leading to energy wasting and arrival time delay. In order to
reduce energy consumption and arrival delay for the following train, a multiple-optimization-
model-based energy-efficient operation method is proposed. In this framework, the following
train can arrive at the station on time when the leading train’s exceptional situation does not
Introduction 11
affect the following train’s arrival time. Moreover, the following train is able to arrive at the
next station by the fastest arrival time with lower energy consumption even when the arrival
time is delayed by the leading train.
In [42] the urban train operation with specific run-time to minimize energy consumption is
formulated as a two-level hierarchical problem. On the first leve1, an optimization model is
designed to decide the appropriate coasting point(s) and number(s) of inter-station run for
energy-efficient urban train operation. On the second leve1, an optimization model of
arranging the train travel time of inter-station run is presented for minimal energy
consumption. Algorithms for solving the two-level optimization model are developed based
on Genetic Algorithm.
[43] focused on the possibilities to better incorporate energy-efficient train operation into the
railway timetable. This paper describes the developed energy-efficient operation model based
on optimal control theory and an algorithm that determines the joint optimal cruising speed
and coasting point for individual train trips; taking into account a desired robustness, the
possibilities for energy-efficient operation, and the desired punctuality during operations. The
results in regional train line simulation, show that it is better to distribute the running time
supplements evenly than concentrating it near the main stations.
Attention to incorporating energy-efficiency in timetable design is increasing in recent years.
As an example Swiss Federal Railways (SBB) uniformly distribute the running time
supplements over the trajectories, use flexible arrival time and by applying adaptive control
in their daily operation, they save 73.8 GWh in 2017 [3].
The energy management system solution in this dissertation has the most similarities to the
integrated optimization approach. Because on one hand it looks for the best driving style of
multiple trains that cause best energy flow in the system and on the other hand it uses the
running time supplement to propose more flexibilities to the system optimization problem.
1.1.4 RECENT RAILWAY PROJECTS FOR IMPROVING
ENERGY EFFICIENCY
In the field of optimizing energy efficiency in the railway system, several European projects
have been carried out since 2005. Below some of the most important projects in this field are
reviewed.
12 Chapter 1
MODURBAN (2005-2009)
MODURBAN focused on urban railway systems. It intended to develop common functional
specifications for operators and a common technical architecture for manufacturers.
MODURBAN had six subprojects. The subproject MODENERGY intended to assess energy
savings-related subsystems [44]. The most focus of this subproject was finding solutions for
better energy consumption of train like applying onboard storage or lighter materials or
optimizing the infrastructure (like tunnels and stations) situation.
Railenergy1 (2006-2009)
This project had more technological emphasis on energy efficiency improvement. The
Railenergy project, targeted to increase the energy efficiency of integrated railway system by
investigating and validating solutions ranging from the introduction of innovative traction
technologies, components and layouts to the development of rolling stock, operation and
infrastructure management strategies. [45].
Railenergy set the target to reduce by 6% the specific energy consumption of the rail system
by 2020 compared to 2005 by addressing different systems, subsystems and components of
the railways with a holistic approach. The following technologies presented in Table 1.1 had
been investigated in terms of their energy saving potentials from the technical and economic
perspective [45].
ON-TIME2 (2011-2014)
ON-TIME intended to improve railway capacity by reducing delays and improve traffic
fluidity. The timetabling is the main focus of this project. The functional objective of
timetabling in this project is to develop a scheduled train path assignment application, with
automatic conflict detection capabilities, that builds on the concept of robust and resilient
timetables, has a unified network coverage, is microscopic at selected parts of the control area,
is scalable, and pluggable to Traffic Management Systems, with user-friendly interfaces and
execution states that correspond to the Infrastructure Manager (IM) timetabling management
milestones [46]. In [46], the timetabling is combined with energy efficient train operation.
1 http://www.railenergy.org/ 2 http://www.ontime-project.eu/
Introduction 13
The optimization objectives are: minimization of energy consumption, minimization of the
expected arrival delay at the main station at the end of corridor and minimization of the
expected delay at the intermediate stations.
Table 1.1: The Railenergy technologies investigated [45]
OSIRIS3 (2011-2014)
OSIRIS intended to assess and compare the overall energy saving potential by applying new
technologies or operational modes and implement them over existing and new equipment. In
this project Key Performance Indicators (KPI) and Standard Duty Cycles to measure energy
consumption in urban rail systems were identified [47]. Table 1.2 from OSIRIS results
presents energy saving potential at different clusters of technologies and operational measures
[6].
3 http://www.osirisrail.eu/
Railway Domain Technologies and measures
Operation Eco-driving (Level 1): Driver training
Eco-driving (Level 2): Driver advice system
Eco-driving (Level 3): Fluid traffic management
Infrastructure Reversible DC substation
Real time power management
2×1.5 kV DC traction system
Asymmetrical auto-transformer (AT) system
Parallel substation (2×25 kV AC)
Reduced line impedance
Increased line voltage (i.e., more than 4 kV DC)
Trackside energy storage unit (electric double layer capacitor/ supercaps)
Onboard components On-board energy storage (electric double layer capacitor/ supercaps)
Use of waste heat (for cooling)
Onboard traction Superconducting transformers and inductances
Medium frequency energy distribution
Innovative hybrid diesel electric propulsion
Onboard optimization Converter control technology (applied during vehicle coasting)
Active filtering technology to reduce input passive filter (reactors) losses
Reuse of converters’ energy losses
Medium voltage loads management
14 Chapter 1
Table 1.2: General evaluation of energy efficiency measures in urban rail systems [6]
Measures Energy saving
potential (%)*
Suitability
for existing
systems
Investment
cost
Cluster Category Solution
Regenerative braking
Timetable
optimization 1-10 High Low
ESS On-board 5-25
Medium High
Stationary High High
Reversible
substations 5-20 High High
Energy-
efficient driving
Eco-driving
techniques
Coasting, optimized speed profile,
use of track gradients 5-10 High Low
Eco-driving tools DAS 5-15 High Medium
ATO 5-15 Medium High
Traction efficiency
Power supply
network
Higher line voltage 1-5 Low High
Lower resistance conductor 1-5 Low High
Traction
equipment
PMSM 5-10 High High
Software optimization 1-5 High Low
Mass reduction Materials substitution 1-10 High Medium
Comfort
functions
Vehicles Thermal insulation 1-5 High Medium
Heat pump 1-5 Medium Medium
LEDs 1-5 High Medium
HVAC and lighting control in
service 1-5 High Low
HVAC and lighting control in
parked mode 1-5 High Low
Infrastructure Low energy tunnel cooling 1-5 Low High
Geothermal heat pumps 1-5 Medium Medium
Control of HVAC, lighting and
passenger conveyor systems 1-5 High Low
LEDs 1-5 High Medium
* Estimated energy savings at system level for a standard case of application.
MERLIN4 (2012-2015)
However, the findings of previous projects lack an integrated approach and they cannot tackle
the energy management for the entire rail network. Hence, the MERLIN project (Sustainable
and intelligent Management of Energy for smarter RaiLway systems in Europe: an INtegrated
optimization approach) was defined to investigate and demonstrate the viability of an
4 http://www.merlin-rail.eu/
Introduction 15
integrated energy management system and to achieve a more sustainable and optimized
energy usage in European electric mainline railway systems. This implies that energy
consumers, producers, and storages are not isolated elements, but players of the global energy
game. A smart and coordinated contribution of each of them brings more savings and provides
more flexibility for the system to manage the energy flow more efficiently. MERLIN aimed
to:
Develop cost-effective intelligent management of energy
Improve cost effectiveness of overall railway system
Improve design of existing and new railway distribution network electrical system
and its interface to public grid
Identify technologies able to optimize energy usage
Identify interface protocol and architecture of railway energy management system
Apply efficient traction supply by optimized use of energy resources
Understand the cross dependency of technological solutions to find optimum
combination of optimized energy usage
Contribute to European standardization (TecRec)
Develop new business model for active interacting of railway system and electricity
market
For following above targets in MERLIN at first the system requirements were identified. Then
the reference architecture for smart energy usage for both planning and operation of system
was developed. Based on the defined architecture, four tools were implemented in MERLIN:
Strategic Decision Making Tool (SDMT), Railway Energy Management Software Suites
(REM-S), Dynamic Onboard Energy Manager (DOEM) and Electricity Buyer Decision
Maker (EBDM). These tools are displayed at Figure 1.3 with different areas of applicability:
planning; operation and electricity market.
Figure 1.3: MERLIN developed tools in different areas
Pla
nn
ing
Strategic Decision Making Tool (SDMT)
Op
era
tio
n
Railway Energy Management Software
Suites (REM-S)
Dynamic Onboard Energy Manager (DOEM)
Ele
ctri
city
Mar
ket
Electrcity Buyer Decsion Maker (EBDM)
16 Chapter 1
For verifying the correct applicability of architecture, the tools were tested both offline and
online in five different scenarios:
1. French High speed 25kV 50Hz AC network
2. Swedish intercity service on 15kV 16.7Hz AC network
3. Spanish suburban service on 3kV DC network
4. British mixed passenger and freight traffic on 25kV 50 Hz AC network
5. British regional traffic on 25kV 50 Hz AC network
Finally, technical recommendations for European standardization was developed from the
results achieved in the projects and the next steps identified.
The key specification of MERLIN is the integrated approach of energy management in whole
railway system for the first time. For this approach the automation architecture was designed
by using smart grid architecture model to apply mainly the ICT features proposed in the model
and to map clearly the interaction between different layers of model (function, business,
information, communication and component). Also, the tools which developed in MERLIN
had the compatibility to be applied in different railway electrification system as it was defined
for the five scenarios. Last but not least, in MERLIN the energy management system was
developed from fundamentals (concept and architecture) and finally was tested in real-time
case which was one of the biggest achievements of MERLIN.
Introduction 17
1.2 OBJECTIVES AND CONTRIBUTION
The objective of this dissertation is to propose a railway energy management system to control
the energy flow in the whole system more efficiently. For optimizing the energy efficiency
three different goals were defined:
Optimize energy consumption in operating the railway system while ensuring the
fulfilment of the applicable performance requirements. The consumption
optimization is driven by the idea that energy regenerated or spared by some actors
in the railway system can be distributed to other actors thus leading to a net decrease
in the energy demand to the public grid.
Optimize power demand of operating the railway system ensuring the fulfilment of
the applicable performance requirements. The reduction of power demand, and
especially of power peaks, can free the electricity network capacity which has a
direct effect on investment strategies for network development; also, it may reduce
the global energy bill and limit or avoid financial penalties.
Optimize costs relevant to energy consumption needed to operate the railway
system while ensuring the fulfilment of the applicable performance requirements.
The energy costs can be lowered through a more rational purchasing strategy, for
example buying energy at a low price (during off-peak hours), storing it and using
it when the price is higher (peak hours). By this approach; a cost reduction can be
achieved even without reducing the total energy consumption.
In order to achieve these goals, the developed Railway Energy Management System
(REM-S), integrates on-board, wayside and coordination services by developing a system that
monitors the energy consumption of different railway subsystems and their components, and
then suggests a “smart” solution for coordinating optimal energy usage in the different parts
of the system.
The most important contribution of this dissertation is not only designing the REM-S
architecture and demonstrating that the smart grid concept and the SGAM (Smart Grid
Architecture Model) framework are applicable in railway system but also developing
REM-S offline and online software suites and as a final step try it in real life.
In this work, the significant differences of this research are compared to previous researches
reviewed:
18 Chapter 1
Here the energy management is done for the whole railway system, including
optimization of train operation, infrastructure facilities (e.g. stations, depots, ground
water pumps, etc.), generators and storages.
Although there are similarities between urban rail transit and mainline railways,
regarding energy-efficient operation, researchers have achieved more advanced
methods and better results in urban rail transit [25], while the scope of this
dissertation is optimizing energy efficiency in mainline railway systems. As main
differences between urban and mainline railway systems it can be pointed out that
mainlines often contain multiple parallel tracks and are typically operated at higher
speeds with much longer running times. Mainlines include substations with bigger
capacity and therefore more interaction with the public grid. Distributed energy
resources or wayside storage systems are more applicable at mainlines. Also in
mainlines, because of bigger distance between stations, cruising become more
important which highlight the effect of optimization of energy efficient driving.
In order to implement REM-S architecture, centralized and decentralized
optimization algorithms are developed and applied at offline and online case
studies. As mentioned in [20] and [21], most of the train operation optimizations are
proved by numerical examples without real world system testing.
Applying the centralized and decentralized approaches enable performance of
energy management on three different time scales: day ahead, minute ahead and
real time, which is the other achievement of this dissertation.
It should be highlighted that neither timetable optimization nor train driving style
optimization are the scope of this research, because the REM-S optimization is done
from system point of view at substation level, therefore the optimization is done by
utilizing the flexibilities from several energy players.
In this research, REM-S divides the railway system spatially to distributed control
areas and temporally to different time scales. The system level centralized
optimization approach is formulated by Day-Ahead Optimization (DAO) and the
decentralized optimization level is formulated by Minute Ahead Optimization
(MAO) and Real-time Operation (RTO).
Introduction 19
1.3 DISSERTATION OUTLINE
The dissertation is structured as follows:
Chapter 1 serves as introduction by presenting a background, motivation and challenges,
objective and contribution of this dissertation.
Chapter 2 introduces the REM-S architecture, which is based on a hybrid centralized-
decentralized concept and developed according to SGAM (Smart Grid Architecture Model)
framework.
Chapter 3 describes the method for modelling integrated railway system in Day Ahead
Optimization (DAO) and Minute Ahead Optimization (MAO) and the optimization
formulation of these two level optimization.
Chapter 4 focuses on the prototype implementation of REM-S. Here, REM-S Offline Suite
and Online Suite with a detailed look at distributed optimization in real-time in the area of
smart grids is presented.
Chapter 5 shows different case studies that are simulated by REM-S offline and online
software suites and presents the simulation results and analysis of the results.
Chapter 6 presents a conclusion of this dissertation and a future outlook of improving the
proposed energy management architecture.
2 CENTRALIZED-DECENTRALIZED
ARCHITECTURE [7]
The modern railway system is a massive, grid connected complex system, with dynamic and
distributed active loads (trains), sources (particularly DERs) and storages (wayside or on-
board storage systems). That’s one of the most important reasons that during these years the
solutions for energy management in the railway system focus on some part of the system like
trains or storage systems and avoid looking the whole system as a connected system that
effects on each other. In the traditional view, the optimizations can improve the energy
efficiency of one train, but at the same time it can cause problem for the whole system for
example by creating power peaks at the feeding substation. On the other hand the finding the
optimal energy flow in the presence of electrical storage systems, distributed energy
resources, regenerated energy and infrastructural load were not explored. In this dissertation,
the holistic approach to the railway system is explored. Therefore a centralized architecture is
necessary to control the whole system by integrating on-board, wayside and coordination
services.
On the other hand, designing the architecture of railway energy management system by
centralized approach should deal with a huge system that is not possible to control it in minute
ahead and real time horizons all together. So the architecture should configure by the
distributed control for different aspects: physic of the system, time frame of operation and
optimization strategy.
For implementing a distributed control on the physics of railway system, the railway system
is separated in to several control areas which can negotiate with each other and also to the
control center. The operation is done in three different time frames: day ahead, minute ahead
and real time. So the control center is managing the centralized day ahead operation while
because it won’t be possible that all the actors follow their day ahead plans in operation, in
each control area the minute ahead and real time operations are controlled by decentralized
control area manager. The day ahead optimization and minute ahead optimization strategies
are considered for creating and exploiting flexibility in control center and decentralized
control areas and proposing optimal operation scheduling to the system actors.
Correct correlation of all actors in the railway system need harmonization of standards,
protocols and data models on one hand and on the other hand applying smart metering and
information exchange technologies. That’s the reason that the developed architecture
configured based on the smart grid architecture model.
22 Chapter 2
This chapter presents the centralized-decentralized architecture of Railway Energy
Management System (REM-S) which is developed according to SGAM (Smart Grid
Architecture Model) framework.
2.1 CONCEPT
According to railway system specifications, there is the possibility to partition the system in
control areas and there is the possibility to define local or global targets for system
optimization. These two main factors along with some other factors such as level of system
complexity, size of the system, dynamic and moving nature of the loads, Information and
Communication Technology (ICT), structure and dependability of different system layers
influenced the choice of a hybrid centralized-decentralized concept for REM-S. In the hybrid
centralized-decentralized REM-S architecture, while the global Energy Management System
(EMS) is executed in Control Center considering the whole railway network for the following
day, the local EMS will be done in local control areas during each timeslot (e.g. 15 minutes).
The existing smart grid standards, communication protocols and ICT technologies are suitable
for designing centralized-decentralized automation architecture. Hence, the design of the
architecture presented here is based on the representation of the railway distribution system
as a smart grid. Besides, it accommodates different time horizons. With reference to the latter
feature, in [48] the model for data management in Control Center of smart grid is studied at
three time window modes. This architecture must be able to deal with different stakeholders,
applications and networks, and it must be interoperable with the public grid and electricity
markets. The development of this architecture requires harmonization of the standards,
protocols and data models of different domains. Furthermore, the communication
requirements for the ICT infrastructure and new business objectives must be defined. To this
aim, the SGAM framework [49] was applied as common language.
The SGAM originally developed in support of the smart grid standardization process, can be
used as aid in designing smart grid architectures in a structured manner according to its five
interoperable layers (Business, Function, Information, Communication and Component),
Zones and Domains [49]. Figure 2.1 illustrates the SGAM Layers, Zones and Domains.
In the proposed partitioned automation architecture, each control area is in contact with the
Control Center and through it with the electricity market via one intelligent interface
substation. The control areas receive the global optimization plan from the Control Center and
implement it locally in their own area, locally accommodating unanticipated mismatches.
Centralized-decentralized Architecture [7] 23
Each control area is in contact with neighboring control areas, in order to coordinate and
resolve issues about balancing energy and power demand. Figure 2.2 shows how the
distributed control architecture can be mapped on railway electrification system. This figure
tries to show a general schematic of railway electrification system that can be represent both
DC and AC electrification systems. As it is showed in Figure 2.2, the neutral sections for AC
electrification system and switching stations in DC electrification system can be the borders
for separating different control areas.
Figure 2.1: SGAM Framework [49]
Multi- Agent Systems (MAS) technology is employed for developing automation and control
system in control areas [50]. Each control area consists of the following active entities:
Intelligent Substation (ISST): an ISST is in communication with all energy related
components within the control area, to send energy consumption/generation
suggestions to them. Each energy related component equipped with an intelligent
entity, called agent, and has the ability to communicate and the intelligence to make
decisions whether to follow the suggestions coming from ISST. ISST is the manager
of control area and acts as main agent in its own area.
24 Chapter 2
Figure 2.2: mapping distributed control on railway electrification system
Other substations: several reversible substations (RSST) and non-reversible
substations (SST) connected to public grid as fixed agents in negotiation with the
main agent of the control area.
Wayside Energy Storage Systems (ESS): the ESS is located in wayside of railway
and considered as fixed agent able to store energy and feed the grid in the
appropriate time that is defined by main agent.
Distributed Energy Resource (DER): renewable sources which belong to the
railway system and are located in control area and are considered as fixed agents as
well.
Dynamic On-board Energy Manager (DOEM): DOEMs are installed on the trains.
They are responsible for energy management inside the train and are in contact with
the main agent to follow its recommendations. This actor is a moving agent which
passes through control areas and is in contact with main agent of each control area.
The trains that are not equipped with DOEM and are not able to communicate with
main agent are called Grey Trains.
External Consumers (EC): ECs are workshops, stations or any other loads such as
Electrical Vehicles (EV) charging stations inside the railway system. They are not
moving like trains so they are considered as fixed agents but they can be passive or
active loads (for example with solar cells they can act as active loads). Hence they
can play their own role as EC agent in the REM-S by bringing flexibility to the
energy management problem.
Centralized-decentralized Architecture [7] 25
Given that the generic load “railway system” interacts with the larger power system (public
grid) and its market (electricity market), it makes sense to adopt a similar time structure for
the energy management, yielding three operational modes:
Day Ahead Optimization (DAO) calculates the optimum behavior of the network, including
power profiles, energy and power purchase, power sell and so on, for the next day time horizon
(24 hrs.).
Minutes Ahead Optimization (MAO) locally predicts and optimizes the following timeslot
(e.g. 15 minutes) of control area status. Following the DAO profile, MAO covers the
interaction with all control area agents, considering power restrictions in the control area as
well as the surpluses and needs of the adjacent control areas and according to them suggests
actions to control area agents such as SSTs, RSSTs, DERs, ESSs or DOEMs of the trains
passing through the control area in the next timeslot.
Real Time Operation (RTO) fulfils the calculated MAO profiles for the control area at each
timeslot, taking into account the real time status and behavior of all the components of the
control area.
Figure 2.3: REM-S Automation Architecture Concept
26 Chapter 2
Figure 2.3 depicts the hybrid centralized-decentralized concept with the three operational
modes related to time and location. In this architecture, by DAO a Global EMS runs for the
whole railway network (limited to one Infrastructure Manager (IM) domain) yielding energy,
power and cost optimization with a top-bottom approach based on train timetables and power
profiles, DERs generation and ECs power demand and ESS status. In Minutes Ahead
timeslots, a Local EMS is executing in the district of each control area with the target of
following the Global EMS plan and minimizing the deviation from DAO plan locally. The
Local EMS is done by coordinating resources to address fast, unanticipated occurrences, such
as some regenerated energy from the passing train, surplus energy stored in ESSs or requests
for more energy for a train which is delayed. This level of optimization is the link between
centralized EMS and the Real Time Operation in all decentralized control area agents. Solving
the optimization problem for all short-term flexibilities in the massive railway system is
unfeasible, while by applying the decentralized automation architecture with main agents
provided for Local EMS, the short term (Minutes Ahead) optimization is achievable.
Centralized-decentralized Architecture [7] 27
2.2 ANALYSIS PHASE
2.2.1 USE CASE ANALYSIS
The starting point for mapping REM-S concept to Smart Grid Reference Architecture is the
use case analysis. Three operational modes defined in the architecture concept based on
different level of optimization and three time scales are considered as Use Case Clusters.
Based on the defined operation of each cluster, high-level use cases (HLUC) were identified
for each of them. The HLUCs are general actions or compliance functionality which are
characterized as generic, i.e. as describing a general concept and not a specific outcome. For
each HLUC, the primary use cases are defined which are applied as functions which should
be developed for implementing the architecture.
The HLUCs defined for executing Day Ahead Operation are: Energy trading, Billing and
Global Optimization.
The main objective of Energy trading is to buy and sell the energy at the best price for the
whole railway network located in the domain of each IM. The Billing calculates the real cost
of the consumed energy and the Global Optimization goal is to optimize the energy/power
consumption and cost of the whole network during next day. This is the high-level
optimization that is done centrally at Control Center.
The Minutes Ahead Operation HLUC is the Local Optimization.
The Local Optimization calculates the optimum power profile of ahead timeslot taking into
account the reference 24 hours power profile. This optimization is done locally in each control
area.
The Real Time Operation HLUCs comprises Real time data acquisition, Estimation,
Operation control and Actions implementation dealing with local area agents.
The Real time data acquisition collects the real time status of each area agent. The Estimation
aggregates the prediction of consumption/generation of each agent in the next 15 minutes,
which is needed for Minutes Ahead Optimization. The Operation control generates
operational suggestions for each agent. Actions implementation get operational suggestions
from the Operation control, calculates the optimum way (i.e. real time actions) to fulfil the
suggestions by each agent.
28 Chapter 2
Table 2.1 shows the relation between Use Case Clusters, HLUCs and primary functions,
which are modeled in the function layer of SGAM.
Table 2.1: Use Case Cluster, HLUC and Primary Use case
Use Case Cluster HLUC Primary Use Case (function)
Day Ahead Operation
Global Optimization
EC forecast
DER forecast
ESS status forecast
Train power profile forecast
Day Ahead Optimization
Audit
Map scheduling to control area demand
Reporting
Deviation Alert_MAO
Energy trading
Energy trading Estimation
Energy trading
Billing Billing
Minute Ahead
Operation Local Optimization
Day ahead profile slicing
Supervision
Minute Ahead Optimization
Power mismatch calculation per control area
Negotiation among neighbour control areas
Deviation Alert_RTO
Real Time Operation
Real Time Data acquisition
Real Time Data acquisition for MAO
Real Time Data acquisition for RTO
Consumption Measurement
Estimation Estimation for MAO
Operation Control Control
Actions Implementation Implementation of the suggestions
Centralized-decentralized Architecture [7] 29
The optimization procedures in all HLUCs are executed using the following set of hard
constraints:
Each train should reach its destination within a maximum window of acceptable
delay agreed with the Railway Operator (RO)
Maximum utilization of internal energy sources (e.g. renewables installed within
the infrastructure) is achieved
Limits of use of the infrastructure (e.g. maximum power of a given SST)
The UML (Unified Modeling Language) Use Case diagrams, based on the Use Case Clusters
and HLUCs definitions are modeled in the SGAM-Toolbox [51] of Enterprise Architect (EA).
The use case steps and information exchange between HLUCs and other actors for supporting
REM-S objectives have been analyzed and are modeled in EA as UML Sequence diagrams.
As a sample, Figure 2.4 displays a Sequence diagram of Minute Ahead normal operation.
30 Chapter 2
Figure 2.4: Minute Ahead normal Operation Sequence diagram
cmp MAO_normal
RTO_15 min
forecast
aggregation
MAO_Power mismatch
calculation per zone
MAO_24h profile
slicing
MAO_local
optimisation
RTO_Real time
Data acquisition
for MAO
MAO_negotiating
among neighbour
zones
RTO_ControlOther zones_negotiating
among neighbour zones
DAO_Mapping
scheduling to
zone consumption
Historic Data
optimized 24h ahead
power profile per zone
Reference 15min power profile
Reference 15min power profile
Previous MAO profile
Forecasted RTO
15min power profile
Power / Energy consumption per train at t=0
Real power generation at t=0
ESS charge status at t=0
External consumers power consumption at t=0
SST status at t=0
RSST status at t=0
Zone 15 min power profile
or the remaining of 15
minutes
Zone 15 min power profile or the remaining of 15 minutes
Zone 15 min power profile or
the remaining of 15 minutes
Total power mismatchForecast of energy to be
provided by other zones
to trains in current zone
Optimized zone 15 min power
profile or the remaining of 15 minutes
Forecast of energy to be provided by
current zone to trains in other zones
31 Chapter 2
2.2.2 FUNCTION LAYER
As shown at Table 2.1, based on the HLUCs, the primary functions are identified to develop
the SGAM function layer. The primary functions are analyzed in detail with specific
information objects as input and output for implementing use case objectives [49]. The
primary functions are detailed enough to be mapped onto a specific architecture. Figure 2.5
displays the function layer of all DAO, MAO and RTO mapped in one SGAM plane. In the
following, it is described that regarding to Figure 2.5, which functions are defined at each
Zone of SGAM and what are their goals.
2.2.2.1 MARKET ZONE
In the Market Zone the Energy trading and Energy trading Estimation functions are defined
to connect the railway business actor to the electricity market at public grid. These functions
are in contact with the electricity market in order to forecast the next day’s energy price and
to buy/sell energy at the best price from/to the electricity market.
Energy trading Estimation
The main aim of this function is to forecast the price of the energy related to a determined
energy demand. This first estimation will enable the first iteration of the Global Optimization
function.
Energy trading
The main aim of this function is to buy and sell the energy for the whole network at the best
price. In order to do that, it will receive the total Power/Energy to buy and sell per Point of
Common Coupling (PCC) organized by blocks of a given likelihood (kWh related to session)
from the Global optimization function and the result of the matching of the bids, which will
give the final energy cost, once the market is closed. So the Energy trading knows about the
energy required at each PCC, the estimated behavior of the market, the constraints from long-
term contracts, the bidding strategies, the electricity open sessions and all related costs [52].
Depending on the deviation between the forecasted and the real cost, this function will decide
whether to relaunch the Global optimization function or not. In case there is a major deviation,
it will also send the real price to the Global Optimization function. Last but not least, it will
send the real price to the Billing function.
32 Chapter 2
* The domain of DAO and MAO functions deponds on the voltage level that railway electrification grid connects to the
public gird that can be Distribution, Transmission or Generation.
Figure 2.5: DAO, MAO and RTO Function Layer in SGAM Plane
DAO
MAO
RTO
Enterprise
«Primary Use Case»
EC forecast
«Primary Use Case»
Deviation
Alert_MAO
«Primary Use Case»
Train power profile
forecast
«Primary Use Case»
ESS Status forecaset
Process
Customer Premise
Station
Field
Generation Transmission Distribution* DER
«Primary Use Case»
Estimation for MAO
«Primary Use Case»
15 min forecast aggregation
«Primary Use Case»
Implemetation of suggestions
«Primary Use Case»
Real Time Data acquisition for MAO
«Primary Use Case»
Real Time Data acquisition for RTO
«Primary Use Case»
Consumption Measurement
«Primary Use Case»
Supervision
«Primary Use Case»
Minute Ahead Optimization
«Primary Use Case»
Power mismatch calculation
«Primary Use Case»
Negotiation among neighbour control areas
«Primary Use Case»
Deviation Alert_RTO
«Primary Use Case»
Control
«Primary Use Case»
Energy trading Estimation
«Primary Use Case»
Energy trading
Market
«Primary Use Case»
Billing
«Primary Use Case»
DER forecast
Operation
«Primary Use Case»
Day Ahead Optimization
«Primary Use Case»
Audit
«Primary Use Case»
Map scheduling to control area demand
«Primary Use Case»
Reporting
«Primary Use Case»
Day Ahead Profile slicing
Centralized-decentralized Architecture [7] 33
The Energy trading and Energy trading Estimation functions are responsible to take the
following decisions in the 1-2 days horizon:
In order to provide the right financial signals to the REM-S, an estimated price of the energy
must be calculated as the weighted mean of: (i) the price of the energy bought/sold by means
of contracts and (ii) the price of the energy bought/sold in each session of the electricity
markets. The latter is known only after the matching procedure is completed and the prices
are published by the Electricity Market Operator (EMO). While these prices are not yet
available, a reasonable estimation is calculated by Energy trading estimation function
according to historical data. The estimated price is used as initial energy price in optimization
procedure.
For each hour, the REM-S determines: 1) what amount of energy is purchased by means of
the long term agreements 2) what amount of the energy has to be offered in each session of
the spot market.
For each hour covered by each session, the REM-S splits the energy into blocks of likelihood
and assigns them a price, according to the bidding strategy and to the contractual agreements
available. The result is called the buy/sell bid for each market session, which has to be sent to
the EMO.
When a major variation occurs in the planned operation, the following decisions are taken:
Determining which sessions of the spot markets can be used to sell/buy the
electricity depending on the hour at which the variation occurs.
Repeating the same process followed in the day time horizon, but restricted to the
actual time horizon.
And, once the above tasks are done, the estimated prices have to be updated.
2.2.2.2 ENTERPRISE ZONE
In the Enterprise Zone, the Billing function is defined. Since the gathered data in REM-S is
integrated from on-board, wayside and coordination services, the Billing function can
calculate the energy consumption for different railway subsystems and their components, and
can consequently send its results to the public grid related actors (such as utilities or energy
suppliers) and railway related actors (IM and RO).
34 Chapter 2
2.2.2.3 OPERATION ZONE
In the Operation Zone, several functions are defined for Day Ahead, Minutes Ahead and Real
Time operation modes.
Day Ahead operation
EC forecast
The main aim of this function is to forecast the power consumption of the ECs. In order to do
that, this function will take into account the weather forecast and internal characteristic values.
DER forecast
The main aim of this function is to forecast the power generation of the DERs. In order to do
that, this function will take into account the weather forecast and internal characteristic values.
ESS status forecast
The main aim of this function is to forecast the stored energy status of ESSs in the next
timeslot (day ahead). In order to do that, this function will take into account the
charge/discharge status of ESSs and their characteristic values.
Train power profile forecast
The main aim of this function is to forecast the power profile of the trains. In order to do that,
this function will take into account the timetable, the track data and the fleet and demand data.
Day Ahead Optimization (DAO)
The main aim of this function is to optimise the power and energy consumption of the whole
network, taking the day ahead power profile forecast received from the RO related to trains,
the Power generation forecast for the next 1-2 day time horizon, the Power demand forecast
for the next 1-2 day time horizon, the Price of the energy forecast related to demand and time
calculated by the Energy trading Estimation function and the updated network data from the
IM into account.
This function will try to reduce the peaks of power and the total amount of consumed energy.
In order to achieve, it will calculate the optimised day ahead power profile. This profile will
be checked by the Audit function and will be recalculated until the Audit function gives an
OK.
Once the Audit function gives an OK, the total energy to buy and sell per PCC will be sent to
the Energy trading function, in order to buy the necessary amount and sell the surplus. There
will only be a surplus when the DAO is relaunched due to a lower than expected consumption
in MAO. This may be due to a failure in a SST, for example.
Centralized-decentralized Architecture [7] 35
Once each market session is closed (2 hours later for example) and the TSO/DSO cost are
published, the Energy trading function will know the real amount of energy bought and the
price related to it and depending on the deviation between the forecast and the real value, it
will send an OK or NOK to the DAO function. This OK/NOK means if the estimation is
accurate enough or not. If it receives a NOK from the Energy trading function, the DAO
function will recalculate the optimization taking into account the real price of the energy.
Whereas, if it receives an OK, it will send the optimised day ahead power profile to the
Mapping function and the forecasted total needed energy per PCC to the Reporting function.
Finally, it receives deviation warning when the MAO cannot solve the deviation between the
next timeslot forecast and the DAO plan and this function shall be relaunched.
Audit
The main aim of this function is to check the optimization results with the constraints received
from the Railway Operator (RO) and the Infrastructure Manager (IM).
Map scheduling to control area demand
The main aim of this function is to divide the optimised day ahead power profile for the whole
network per control area, generating the optimised day ahead power profiles per area.
Reporting
The main aim of this function is to check the performance of the last 24 hours operation
(usually). In order to do that, it will compare the forecasted total P/E needed per PCC,
calculated by the DAO function with the real consumption per PCC, calculated by the
Consumption Measurement function of the Real Time Operational Mode. It will store the
deviation data in order to improve the DAO using statistics.
Minute Ahead operation
Day Ahead Profile slicing
The main aim of this function is to take a fragment containing the next 15 minutes from the
optimised day ahead power profile per control area received from the Mapping function of
the DAO. The Optimised day ahead power profile per area will be received when calculated
by DAO. It must be stored in order to take the 15 minutes ahead just before MAO is launched.
Supervision
The main aim of the Supervision function is to calculate the deviation between the reference
power profile calculated by the DAO for each period and the real energy consumption from
the same period of 15 minutes. The result of this comparison will be used to update the next
period’s MAO optimum power profile. The Supervision function relates each 15 minutes
period with the total amount purchased at the hour section.
36 Chapter 2
Minute Ahead Optimization (MAO)
The main aim of this function is to calculate the optimum 15 minutes ahead power profile for
the current control area minimizing the deviation form DAO plan. This function will ask for
RTO estimation and Real Time status.
In order to do that, it will compare the reference 15 minutes power profile calculated by the
day ahead profile slicing function with the forecast done by DOEMs, ECs and DERs for the
next 15 minutes in order to calculate the 15 min power profile. The comparison is done by
Power mismatch calculation per control area. If the deviation is so big that the optimization
cannot be performed, DAO must be relaunched.
It will also take into account the deviation between the DAO forecast and the real
consumption, calculated by the Supervision function in the previous period, in order to try to
solve excessive consumption or use the surplus of the previous MAO. Last but not least, it
will receive a recalculation trigger from RTO when the RTO cannot solve the deviation
between the MAO and the real time status.
Power mismatch calculation
The main aim of this function is to calculate the deviation between the profile calculated by
the DAO and the profile calculated by the MAO for the same control area. It will obtain the
amount of power needed or power surplus.
Negotiating among neighbour areas
The main aim of this function is to solve the optimum way the power mismatch of each control
area by taking power from or giving power to the neighbour control areas.
Deviation Alert_RTO
The main aim of this function is to relaunch the MAO when a deviation that cannot be solved
in RTO is detected. In order to detect it, this function will receive a deviation warning from
the Control function of the Real Time Operational Mode.
Real Time Operation
Control
The main aim of this function is to generate suggestions for DERs, ESSs, RSSTs, SSTs and
ECs, based on the difference between the optimised 15 min power profile calculated in MAO
and the real time status of each agent.
It will also detect when the deviation between the real time status and the optimum profile is
so big that it cannot be solved by any real time suggestion and MAO shall be relaunched.
Centralized-decentralized Architecture [7] 37
If there is a failure to send the suggestions from Control or if there is a Grey train, this function
will continue sending the information to the rest of the agents.
2.2.2.4 STATION ZONE
The Station Zone is the real aggregation level for Field level data [49], so aggregation of next
15 minutes forecasted situation of agents is prepared here. Furthermore, in this Zone some
real time actions are produced in each agent based on Control commands coming from local
intelligence.
Estimation for MAO
The main aim of this function is to estimate the behavior of the DOEMs, ESSs, DERs and
ECs for the next 15 minutes for the MAO operational mode. This estimation is done every
time the MAO is going to be calculated. It will be done by the DOEMs, DERs and ECs:
DOEM: generates the forecasted power and energy profiles based on the last
received target points and limitations and send the 15 minutes ahead power profile.
It will use the weather forecast to predict the consumption of the onboard auxiliary
loads (kW related to time).
DER: based on the weather forecast predicts the power generation (kW related to
time).
EC: estimate the power consumption (kW related to time).
ESS: estimate the stored energy (kW related to time) based on the last timeslot
charge/discharge status.
15 min forecast aggregation
The main aim of this function is to aggregate the estimations calculated by the previous
function. If the estimation is not received from any of the DOEMs, DERs, ESSs or ECs, this
function will calculate a default forecast taking into account the type of train, EC, ESS or
DER. This will also be done with Grey trains.
When requested by the Real Time Data acquisition function, this function will send the
requested forecast. This may be the real forecast of the requested agent, when the agent has
previously sent it or the calculated default one, when the agent has not sent it.
Implementation of the suggestions from Control
The main aim of this function is to calculate the optimum way to fulfil the suggestions from
the Control function. This function will be executed by each agent:
38 Chapter 2
EC: Fulfil the P/E limitations.
ESS: Follow the charge/discharge orders related to time.
DER: Take the generated energy or not.
SST/RSST: Follow the new suggested operational mode.
2.2.2.5 FIELD ZONE
Real Time Data acquisition
The main aim of this function is to obtain the real time status of each agent of the current area.
If any of the DOEMs, DERs, ESSs or ECs does not send its current status, this function will
request its forecast to the 15 min forecast aggregation function, in order to take the status from
that forecast. For the rest of the agents, this function will send the last received status.
Consumption Measurement
The main aim of this function is to measure the consumption of DOEMs, ESSs, and ECs at
each PCC and store it in order to calculate the total energy consumption.
It will receive from the Reporting function of the DAO Operational Mode the “Total
consumption requirement” message and it will do the following actions (normally every 24
hours):
- Calculate the real energy consumption per PCC since the last DAO (kWh)
- Calculate the real energy consumption per train since the last DAO (kWh)
- Calculate the real energy consumption per EC since the last DAO (kWh)
- Calculate the real stored energy per ESS since the last DAO (kWh)
It will receive from the Supervision function of the MAO the “MAO consumption
requirement” message and it will calculate the MAO Real energy consumption per PCC from
the last MAO consumption requirement.
2.2.3 BUSINESS LAYER
The Business layer indicates which organizations and actors should participate in pursuing
the business objectives (optimizing energy consumption, power demand and cost). In
addition, the business processes that support the business objectives and related functions are
mentioned. The regulatory constraints are taken into account in this layer.
Centralized-decentralized Architecture [7] 39
2.2.3.1 BUSINESS ACTORS [53]
In the conventional railway business interaction with public grid, the railway market structure
simply consists of four main actors: Infrastructure Manager (IM), Railway Operator (RO),
Energy Supplier and Grid Owner. In the novel business model proposed here three main actors
have been added: Electricity Market Operator, Energy Buyer Decision Maker (EBDM) and
Energy Dispatcher. These new actors from REM-S are defined in accordance to SGAM. They
are responsible for arranging the energy trading activities among the Railway Operator, the
Infrastructure Manager and the electricity market, so as to achieve the ideal optimization.
Below these actors are introduced:
Infrastructure Manager (IM)
In the railway industry, the infrastructure refers to a wide range of components, equipment,
installation and facilities. IM means anybody or firm responsible in particular for establishing,
managing and maintaining railway infrastructure, including traffic management and control
command and signalling; the functions of the IM on a network or part of a network may be
allocated to different bodies or firms. IM possesses the access to the railway infrastructure;
meanwhile it is responsible for the reliable and continuous operation of the infrastructure.
According to European Community Regulation 2598/1970 [54], railway infrastructure
comprises the following terms:
Ground area and the line of route;
The track and track bed;
Switches and crossings;
Engineering structures;
Level crossings;
Passenger platforms and goods platforms, and access ways;
Safety, signaling and telecommunications installations;
Electricity power supply;
Lighting installations;
Buildings
Considering an individual IM, it may only possess part of these terms. Here, all kinds of IMs
are categorized into one party. Moreover, the top priority of the IM in the scope of REM-S
definition is stable power supply (reliable energy system) and in-time fault repair.
40 Chapter 2
Railway Operator (RO)/ Railway Undertaking (RU)
Railway Operator means any public or private undertaking licensed according to [55], whose
principal business is to provide services for the transport of goods and/or passengers by rail
with a requirement that the undertaking ensure traction; this also includes undertakings which
provide traction only. It may possess the rolling stocks and it is authorized with the access to
the railway infrastructure.
Energy Supplier
An energy supplier refers to a party that supplies the customers or the market with electricity,
and receives profits from the energy trading activities. The electricity market can be
categorized according to different criteria. Concerning the roles of the energy provider, the
market comprises the following categories [56]:
Wholesale market
Balancing market
Imbalance settlement
Ancillary service market
Retail market
The energy supplier comes from these markets. It is the electricity provider, which trades
either directly with the customers or through Electricity Market Operator. Years ago, the
energy supplier was simply the utility owning the electricity generation. Nowadays, this role
can be more kinds of participants, such as renewable energy generation units and third-party
power plant owners.
In this thesis, concerning the time window for energy dispatch, the electricity market consists
of [52]:
Spot market: timeslot of hours within 24 hours
Shorter-term market: minutes to hours ahead
This classification is determined according to the transaction type of electricity trading.
Grid Owner
The grid owner refers to the concept of Transmission System Operator (TSO) and Distribution
System Operator (DSO).
Centralized-decentralized Architecture [7] 41
TSO is an entity entrusted with transporting energy in the form of electrical power on a
national or regional level, using fixed infrastructure.
DSO is responsible for operating, ensuring the maintenance of and, if necessary, developing
the distribution system in a given area and, where applicable, its interconnections with other
systems and for ensuring the long-term ability of the system to meet reasonable demands for
the distribution of electricity.
Electricity Market Operator
Electricity Market Operator represents any company in charge of all the operations required
for trading with electricity market (receiving the purchasing/selling bids, matching process,
billing….) [57].
Energy Buyer Decision Maker (EBDM)
EBDM is the actor responsible for executing business functions. It determines the optimal
buying and selling procedures, and addresses the short-term procurement. It receives data
from:
Electricity Procurement Planner (EPP): an entity that takes the long-term decisions
related with the buy/sell of the electricity (bidding strategy, long term constrains for
the bidding…).
Forecast Provider: an entity in charge of forecasting the behaviour of future sessions
of the electricity market (prices, energy bought/sold…).
Energy Dispatcher
This actor is named as the overall approach global energy dispatching and local energy
dispatching in REM-S:
Day Ahead Optimization: internal function of REM-S defined in Function Layer
Minute Ahead Optimization: internal function of REM-S defined in Function Layer
42 Chapter 2
Figure 2.6: Interactions of Business actors in the presence of REM-S
Figure 2.6 shows the interactions between the actors in the presence of REM-S. As it is
displayed in this figure in the proposed business model, the interactions consist of transition
of electricity, cash, information, grid access authorization, track access authorization,
reliability, flexibility, efficiency optimization and mobility. A new value, efficiency
optimization, has been added to the interactions. In the conventional business of railway
companies (IMs and ROs), none of the surveyed companies have any independent department
for efficiency improvement. In the proposed model, the optimal efficiency is realized by the
sum of the functions carries out by REM-S.
The values in this model can be both unidirectional and bidirectional. The information flow
between the two partners is always mutual transmitting. The feedback electricity from braking
energy leads to some new information exchange and a new value flow from RO to IM. To
guarantee attaining a sophisticated decision, the RO and the IM offer the detailed energy
planning and the resources´ information. The REM-S actors collect these essential data and
figures out the optimal energy purchase solution. Then they inform RO and IM of the optimal
efficiency approaches. This efficient operation helps RO and IM to save energy costs, so they
pay the REM-S actors for these approaches. Apart from the information communication, the
actors may also provide some prize/ punishment as incentives for global optimal operations.
In the view of railway industry, the satisfaction of the customer demand is always the top
priority. Introducing REM-S architecture, the values transferring with the customers have not
been obviously changed.
Here, the electricity can also flow from the IM back to the energy supplier, as the regenerated
energy flows from trains through railway infrastructure to the supplier or to the electricity
Centralized-decentralized Architecture [7] 43
market. The information exchange between REM-S actors and energy supplier contributes to
the optimal planning of energy purchase. Moreover, the values exchange between energy
supplier and RO is existent; nevertheless, the electricity always flows through the IM, namely
IM is always involved. Besides, the other values, e.g. the information flow and cash flow
between RO and energy supplier, can be both linked through Electricity Market Operator and
direct connection.
2.2.3.2 BUSINESS PROCESSES
The REM-S business functions that introduced in Function Layer are Energy trading
Estimation and Energy trading. Here the business processes defined in Business Layer to
implement the two business functions are described [57].
Energy trading estimation
Three business processes support the Energy trading estimation function. The business
processes are listed at Table 2.2 by showing the information exchange in the processes. A
brief description of processes are presented below:
Market price estimation
It gets the forecasted behavior of electricity market at every hour of each market session and
prepare the stochastic price of energy. This process is an internal process that provides the
input for the Optimization of the energy supply process.
Optimization of energy supply
Optimization of the energy supply process needs the required energy at each PCC, estimated
energy price in electricity market, the contractual constraints, bidding strategy and other
DSO/TSO costs to make optimization on energy required to sell/buy by means of contracts or
at the next sessions. This process is an internal process that prepares the output for the next
process: Calculation of the actual energy price.
Calculation of the price of energy
At calculation of the energy price process as it is shown in Table 2.2, the output of the last
process is the optimization of energy supply for next session or by means of contract. These
inputs by adding other costs coming from DSO/TSO make it possible to calculate the
estimated price of energy that is necessary for starting Day Ahead Optimization function.
44 Chapter 2
Table 2.2: Energy Trading Estimation Processes
SOURCE INPUT PROCESS OUTPUT DESTINATION
Forecast provider
Forecast of the estimated
behaviour of the electricity
markets, for each hour and
each session of the market
Market Price
Estimation
Estimated energy
prices in the
Electricity Market
Optimization of
energy supply
(buy/sell).
Data Storage
Energy required at each PCC
divided in blocks of a given
likelihood, for each hour
Optimization of
energy supply
(buy/sell)
Energy to buy/sell
in next sessions
Calculation the price
of energy
Market Price
Estimation
Estimated energy prices in the
Electricity Market (stochastic)
EPP Contractual arrangement
constraints (1-2 days horizon) Energy to buy/sell
by means of
contracts EPP Bidding strategy
DSO/TSO Other DSO/TSO costs
Optimization of
energy supply
(buy/sell)
Energy to buy/sell in next
sessions Calculation the
price of energy
estimated price of
the energy, for each
hour and each PCC
Day Ahead
Optimization Energy to buy/sell by means of
contracts
Energy trading
Four business processes support the Energy trading function. The business processes are listed
at Table 2.3 by showing the information exchange in the processes. A brief description of
processes is presented below:
Optimization of energy supply
The difference of this process and same process at Energy trading Estimation is in the source
of input information. The optimization of energy supply at the energy trading function uses
the energy required at each PCC calculated by the Day Ahead Optimization function and
calculate the required energy at each session of next day and means of contracts.
Calculation of the price of energy
For calculating the price of energy and the price variation warnings, the results of Electricity
Market matching process are also needed. The price is calculated as a weighted mean of prices
proportional to the energy of each transaction (both the price of the transactions that have been
completed and the ones that are still pending). The prices that have effect on average price
could be a specific price in contract, a market sessions’ price and a price of real time operation
services. The variation is calculated by comparing the price with the price calculated by the
Centralized-decentralized Architecture [7] 45
Energy trading estimation process. Once the day has finished, the real price of the energy can
be calculated and sent to the Billing function.
Detection of EMO open sessions
The Detection of EMO open sessions’ process constantly checks the EMO or the TSO servers
to check if a new session has been opened. In this case, it informs the Day Ahead Optimization
that an open session is existed.
Construction of the bids
Bid Construction process for making bids to send them to each session of Electricity Market
needs bidding strategy that can be prepared by EPP and the amount of Energy for buying or
selling at that session.
Table 2.3: Energy Trading Processes
SOURCE INPUT PROCESS OUTPUT DESTINATION
Day Ahead
Optimization
Energy required at each PCC
divided in blocks of a given
likelihood, for each hour
Optimization of
energy supply
(buy/sell)
Energy to buy/sell
in next sessions
Calculation the price
of energy
Market Price
Estimation
Estimated energy prices in
Electricity Market
EPP
Contractual arrangement
constraints (1-2 days horizon) Energy to buy/sell
by means of
contracts Bidding strategy
DSO/TSO Other DSO/TSO costs
Optimization of
energy supply
(buy/sell)
Energy to buy/sell in next
sessions
Calculation the
price of energy
Real energy price,
for each hour at
each PCC
Billing
Energy to buy/sell by means
of contracts Price variations
and warnings
Day Ahead
Optimization EMO
Results of the Electricity
Market matching process
EMO/TSO Not scheduled electricity
sessions
Detection of EMO
open sessions
Not scheduled
electricity sessions
Day Ahead
Optimization
Optimization of
energy supply
(buy/sell)
Energy to buy/sell in next
sessions Construction of the
bids
Bids for each
session of the
Electricity Market
EMO
EPP Bidding strategy
46 Chapter 2
2.3 ARCHITECTURE
The automation concept, functionalities and business processes of REM-S are defined in the
previous section. In this section the design of component layer, information layer and
communication layer essential to this automation concept is introduced. By identifying these
latter three layers, the defined functionalities are mapped to the physical architecture [49].
2.3.1 COMPONENT LAYER
The new functionalities defined for managing energy in railway operation, should be executed
by some components. The component layer identifies the components, in the form of system,
hardware, software or interface, to implement the intended functionalities, yielding the
physical distribution of all participating components in REM-S architecture. All new
components proposed for REM-S are located on SGAM plane and modelled with SGAM-
Toolbox [51], [58]. Figure 2.7 shows these components in SGAM plane.
The EBDM, which is the business actor of REM-S interfacing with the electricity market,
should be supported by Marketplace system and Energy trading software.
The Billing function needs Billing software to calculate the energy consumption of different
components that are integrated from different railway subsystems in REM-S.
For Global EMS, the EMS/SCADA (Supervisory Control and Data Acquisition) system is
required in order to support operational activities for dispatching energy at higher level of
system in Control Center. Global Optimization Software (GOS) supports intelligent functions
of REM-S in the Control Center and makes an optimum plan for the next day. It must have an
RO server, an IM server and a DER EMS and VPP system to be responsible for gathering the
next day forecasting of timetables, power demands and energy generation.
For Local EMS, the Distribution Management System (DMS)/SCADA supports all operation
activities at each control area to dispatch energy internally or to the neighbor area. The DMS
hosts the ISST of each control area as main agent. It takes care of aggregating forecasted
profiles received from all agents (DER controllers, DOEMs, ECs). This system follows the
controlling suggestions in ISST. Local Optimization Software (LOS) located at ISST supports
intelligent functions of REM-S in each control area and makes an optimum plan for next 15
minutes.
Centralized-decentralized Architecture [7] 47
Figure 2.7. DAO, MAO and RTO Component Layer in SGAM Plane
DAO
MAO
RTO
Market
Marketplace system
Energy trading App.
Enterprise
Billing
Operation
EMS EMS/SCADA DMS/SCADA
GOS LOS
DER EMS and VPP
System
IM Server
RO Server
Station
RTU
Router
HMI
Local SCADA
Field
Process
Customer
PremiseGeneration Transmission Distribution DER
MDMS
MDC
Data Storage
IED MID meter
The components like trains, ECs, DERs, ESSs, transformers, circuit breakers, overheadlines, cables, etc which are part or directly connected to the process
48 Chapter 2
Local SCADA is located at SSTs or RSSTs to implement the actions receiving from ISST.
DER Controller, IED and DOEM have the same role in DERs, ECs or ESSs and trains.
HMI (Human Machine Interface) can be used in Control Center, ISST or other SSTs to
monitor the energy flow and prepare an interface for manual applications. The RTU is the
interface object of the Control Center and ISST components.
The Router at Control Center is needed to realize communication between the different
components (Marketplace system, EMS/SCADA, GOS...) and the control areas, public grid
and electricity market. In ISST, Router is used to communicate with neighborhood areas and
realize communication for data acquisition and sending and receiving control commands. The
MID (Measuring Instrument Device) meter measures the energy consumption (they are
already installed in most of railway system components) and sends them to Meter Data
Concentrator (MDC). MDC performs some preliminary analysis of data, such as bad data
detection and elimination, and then sends data to Meter Data Management System (MDMS)
to gather all data required to calculate and report necessary information (such as energy bill).
Data Storage is needed to save the measured data, optimization results and all other
information that is useful for reporting, supervision, etc.
At Table 2.4, all components applied for implementing each function of DAO, MAO and
RTO are listed with brief description of their applicability.
Table 2.4: Components related to each function at DAO, MAO and RTO
Function Component Application
DAO
Energy trading
Estimation
Energy trading
Application Calculate the next day energy price forecast by estimation algorithms
Marketplace system Get information of sellers and buyers to apply in estimation algorithms
EC forecast IM server Gather historic data of ECs, check weather forecast and apply forecast
algorithms
ESS forecast IM server Gather historic data of ESSs and apply forecast algorithms
DER forecast DER EMS and VPP
system
Gather historic data of DERs, check weather forecast and apply forecast
algorithms
Train power profile
forecast RO server
Gather historic data of train timetables, fleet characteristics, weather
forecast and apply forecast algorithms
Day Ahead
Optimization (DAO)
HMI Prepare a display for the whole system and enable to control the system
manually at Control Centre.
Router Global Optimization communicates internally to the control areas, Energy
trading application, Marketplace system and Public grid RTU
EMS/SCADA Responsible for operational role of the Global EMS (both in transmission
and distribution level). Control and monitor the grid, managing power
quality and security EMS
Centralized-decentralized Architecture [7] 49
GOS Supports the intelligent role of DAO contains optimization algorithm
Audit GOS Check optimization results with IM and RO constraints
Energy trading
Router Communicates internally to GOS and Marketplace system and externally
to Electricity Market RTU
Energy trading
Application
Calculate best price for buying and selling energy, the deviation between
the estimated price and the real price at the closure of the market
Marketplace system Prepare information about the buyers and sellers and negotiate with them
Map scheduling to
control area demand GOS Distribute the next optimal power profile to all control areas
Deviation
Alert_MAO GOS
Re-launch the optimization algorithm based on the triggers received from
MAO
Reporting MDMS
Analysis to develop information from gathered data useful for managerial
report
Data Storage Store information and reports
Billing
Data Storage Send information to Billing Application and store the calculated bills
Router Communicate internally to Data Storage, RO, IM and externally to Grid
owner and Energy Suppliers RTU
Billing Application Calculate the energy bill based on received measured information
MAO
Day ahead Profile
slicing LOS Prepare every 15 minutes power profile from day ahead power profile
Supervision
LOS Calculate the deviation between the DAO reference power profile and the
real energy consumption
MDMS Gather data for calculating deviation between reference power profile and
real energy consumption based on the measured data
Data Storage Store data for comparison and calculate the deviation
Minute Ahead
Optimization (MAO)
Data Storage Store the 15 minutes optimization results
HMI Prepare a display for each control area and enable controlling the system
manually at ISST
Router Communicate with Control Centre and neighbour areas
RTU
LOS Supports the intelligent role of MAO contains optimization algorithm
Power mismatch
calculation LOS Calculate the deviation between DAO and MAO power profile
negotiating among
neighbour control
areas
DMS/SCADA Responsible for energy dispatch issues between neighbourhood areas
HMI Prepare a display for the control area and enable to controlling system
manually at ISST
Router Communicate with neighbour areas
RTU
LOS Find the best solution for the power mismatch problem (surplus/shortage)
Deviation
Alert_RTO LOS
Re-launch the optimization algorithm based on the triggers received from
RTO
RTO
Estimation for MAO
IED Estimate the next 15 minutes behaviour of each entity individually
Router Send estimations of all area entities (like DER controllers, DOEMs, …)
to aggregating function
50 Chapter 2
15 min forecast
aggregation
DMS/SCADA Aggregate estimations calculated at Estimation for MAO
Router Communicate to each entity (Field devices)
RTU
Real Time Data
acquisition
MID meter Measure power consumed/ generated and/or register the last status of the
Process devices
MDC Concentrate measured data and analyse them preliminarily
MDMS Analysis or calculation to obtain necessary information
Router Communicate Field devices to Station devices
RTU
Control
IED Generate suggestions based on the optimal plan and real power
consumption in the control area, led by MAO
DMS/SCADA Follow the created suggestions by controlling actions
HMI Interface to control and monitor the energy dispatching functions by
operator
Router Communicate to all agents such as DOEM, DER, and EC…
RTU
Implementation of
suggestions
DER controller Implement Control suggestions at DER
Local SCADA Implement Control suggestions at Local SCADA in SST and RSST
IED Implement Control suggestions at other agents such as EC, ESS…
DOEM Implement Control suggestions at train
DMS/SCADA Implement Control suggestions at ISST
Consumption
Measurement
MID meter Measure power consumption at PCC
MDC Concentrate measured data and analyse them preliminarily
MDMS Analysis or calculation to obtain necessary information
Data Storage Store the obtained information
Router Communicate Field devices to Station devices
2.3.2 INFORMATION LAYER
Based on the defined components and their functionalities, the information exchange is
detailed here. It is modelled with UML sequence diagrams with SGAM-Toolbox of EA to
show the chronological sequence of information exchange. The information objects and data
models are identified in order to allow an interoperable information exchange via
communication means between components or actors [49].
In REM-S, in some cases the information is exchanged between two business actors, in other
cases it takes place under the umbrella of a unique actor. In the first case, there is a clear need
for standardization (communication and application profiles), while in the second case the
standard could remain as a recommendation. For interoperability, the following data must be
standardized:
Centralized-decentralized Architecture [7] 51
Track data: a centralized track data server distributes the track information (the information
exchange through the rail line) to the ROs using a standardized format.
Timetable data: timetable data to the ROs, related modifications originating from the Traffic
Management System (TMS) in real time (i.e. new arrival or departure times, or passing time
at waypoints).
Forecasted trains power profile: RO must inform the GOS about the forecasted energy
consumption of the following day.
Real time data from DOEM, ESS, ECs, DERs and SST/RSSTs: The exchanged data format
between LOS and GOS and the end components is also a subject of standardization. While
local communication at LOS level comes from the electricity/power field, new REM-S
functions need additional standardized data models.
Estimation for MAO from DOEMs: a consumption profile for the minutes ahead calculated
by train and sent to the LOS.
Suggestions from control to DOEMs: LOS operational suggestions and orders (e.g. temporary
power restrictions, new arrival times, etc.) to all train mounted DOEM.
Having in mind that web services (SOAP- Simple Object Access protocol) are chosen as the
most appropriate way for transferring data and calling function remotely, it makes sense to
use XML (Extensible Markup Language) to define the proposed standard format of the
aforementioned data types.
In that sense, the adoption of RailML (Railway Modelling Language), which is a XML based
solution for simplified data exchange between railway applications, is proposed here.
Although RailML covers many aspects of the railways (e.g. infrastructure, vehicles, etc.), the
novelty of the REM-S concepts requires the extension of its schemas with new tags. The
different schemas of this language are fit for different railway data. The main data structures
defined by RailML are:
Timetable, that contain schedules
Infrastructure, that contains the information about the route, the line characteristics,
stations, etc.
Rolling stock, that contains the rolling stock characteristics
At Table 2.5 detailed information exchange regarding to the defined functions for DAO, MAO
and RTO are listed with mentioning the source and destination of the information exchange.
52 Chapter 2
Table 2.5: Information exchange at DAO, MAO and RTO
SOURCE INPUT FUNCTION OUTPUT DESTINATION
DAO
IM server
network configuration
DAO
EC, DER, ESS, Train
forecast
Audit
Mapping
Reporting
Deviation alert
power required at each
PCC divided in block
of given likelihood,
for each hour
EMS/ SCADA
LOS
timetable
updated status of the network
updated consumption restrictions
per PCC
expected train composition
RO server forecasted power profile of trains
DER EMS
and VPP
system
DER forecast of generation
energy required at
each PCC Data Storage
IM server EC demand forecast
IM server ESS stored energy forecast
MID meter real time status of each equipment
LOS DAO trigger
Energy
trading App.
price variations and warnings
estimated average price of energy
for each hour at PCC
not scheduled electricity open
sessions
Data storage energy required at each PCC
divided for each hour
Energy trading
Energy trading
Estimation
estimated average
price of energy for
each hour at each PCC
Energy trading
App.
GOS
Marketplace
system
forecast of the estimated behavior
of the electricity markets, for each
hour and each session of the
market
price variations and
warnings
Marketplace
system
results of the bidding matching
process, if available
not scheduled
electricity open
sessions
not scheduled electricity open
sessions
energy to buy by
means of contracts, for
each hour
Marketplace
system
Marketplace
system
contractual arrangement
constraints (1-2 days horizon) bids, for each session
Marketplace
system
bidding strategy real energy price Billing App.
DSO/TSO other DSO/TSO costs
Energy
trading App. real energy price
Billing total cost Billing App.
MID meter real consumption measured
IM server contractual arrangement of
energy supplier with IM
IM server contractual arrangement of Grid
owner with IM
RO server contractual arrangement of
energy supplier with RO
Centralized-decentralized Architecture [7] 53
MAO
IED aggregated 15 mins power
profiles
MAO
Profile slicing
Supervision
Mismatch calc.
Negotiation
Deviation Alert
15 mins optimal
power profile per PCC
IED
GOS power required at each PCC for
each hour
15 mins optimal
power profile per SST
and RSST
DMS/
SCADA MAO trigger
15 mins optimal
power profile per ESS
MID meter real time status of each equipment 15 mins optimal
power profile per PCC
RO server transport demand
DAO trigger EMS/ SCADA
GOS IM server
real time consumption restrictions
per feeding sections
real time status of the network
RTO
DOEM train real time status
Real Time Data
acquisition
Measurement
real time status of each
equipment
Local SCADA
IED
DMS/SCADA ESS ESS real time status
EC EC real time status
real consumption
measured Billing App.
RSST RSST real time status
SST SST real time status
DER DER real time status
DER DER estimation of generation Estimation for MAO
forecast aggregation
aggregated 15 mins
power profiles LOS DOEM train profile estimation
EC estimation of consumption
LOS
15 mins optimal power profile per
PCC
Control
Deviation alert
operational
suggestions
DER controller
Local SCADA
IED
DOEM
DMS/SCADA
15 mins optimal power profile per
SST and RSST
15 mins optimal power profile per
ESS
15 mins optimal power profile per
PCC MAO trigger DMS/SCADA
LOS MID meter real time status of each equipment
DMS/
SCADA operational suggestions
Implemetation of
suggestions
real time actions EC
real time actions ESS
real time actions RSST
real time actions SST
real time actions DER
2.3.3 COMMUNICATION LAYER
For REM-S communication layer, mostly the existing communication profiles (represented
as IEC, CENELEC or W3 standards) are used in both the energy and railway fields, although
in some cases new communication profiles are needed to cover some REM-S new
54 Chapter 2
functionalities. Each link is analyzed ensuring interoperability to determine whether the
profiles require standardization or a recommendation is enough.
The REM-S communication layer is based on four main standard families:
IEC 61375: Train Communication Network. It standardizes railway
communications, including train backbone, consist network and train to ground
link. In terms of REM-S four parts are relevant:
o 61375-3-4: Ethernet Consist Network (ECN)
o 61375-2-3: Communication Profile
o 61375-2-4: Application Profile
o 61375-2-6: Train to Ground Communication
IEC 60870-5: Communication profile for sending basic telecontrol messages
between two components permanently connected to an electric power system. Two
relevant parts are:
o 60870-5-101: Transmission Protocols, companion standards especially
for basic telecontrol tasks
o 60870-5-104: Transmission Protocols, Network access
IEC 61850: a flexible, open standard that defines the communication between
devices in transmission, distribution and substation automation systems. To enable
seamless data communications and information exchange between the overall
distribution networks, it is aimed to increase the scope of IEC 61850 to whole
electric network and provide its compatibility with Common Information Model
(CIM) for monitoring, control and protection applications [59]. The REM-S related
parts of this standard is:
o IEC 61850-6: Configuration language
o IEC 61850-7: Basic communication structure for substation and feeder
equipment
o 61850-8: Specific communication service mapping
Centralized-decentralized Architecture [7] 55
SOAP (Simple Object Access protocol): Protocol to exchange object oriented
(structured) data when implementing web services in computer networks. It is based
on the transmission of XML files containing the information over HTTP.
The main standard families are presented at Table 2.6 by showing the necessity of these
standards for exchanging the information needed for implementing each function at DAO,
MAO and RTO.
Table 2.6: Required standards for information exchange at DAO, MAO and RTO
Standard Information Function
DAO
IEC 60870-5-101/104
network configuration
updated status of the network
updated consumption restrictions per feeding sections
updated status of each equipment
measured consumption
DeviationAlert_MAO
Train power profile forecast
Day Ahead Optimization
Audit
Map scheduling to area demand
Reporting
TCP-IP
TIBCO CHANNEL
timetable
expected train composition
Power profile forecast by RO
Day Ahead Optimization
Map scheduling to area demand
Reporting
OpenADR
estimation of consumption
price variations and warnings
estimated average energy price for each hour at PCC
real energy price
EC forecast
Day Ahead Optimization
Map scheduling to area demand
Reporting
Billing
IEC 61850-7-420 DER estimation of generation DER forecast
IEC 60870-6/TASE.2 DAO trigger DeviationAlert_MAO
CIM (IEC-61970)5
energy required at each PCC for each hour
forecast of the estimated behavior of the electricity
markets, for each hour and each session of the market
results of the bidding matching process
not scheduled electricity open sessions
contractual arrangement constraints
bidding strategy
other DSO/TSO costs
estimated average energy price for each hour at PCC
price variations and warnings
energy to buy by means of contracts, for each hour
bids, for each session
real energy price
Energy trading
MAO
IEC 60870-5-101/104
aggregated 15 mins power profiles
real time status of each equipment
real time consumption restrictions per feeding sections
real time status of the network
Profile slicing
Supervision
Minute Ahead Optimization
Power mismatch calculation
Negotiation among neighbor areas
5 In IEC 62325-451-10 ED1, a framework is defined specifically for energy market communications.
The forecasted publication date for this standard is December 2019.
56 Chapter 2
DeviationAlert_RTO
IEC 60870-6/TASE.2
power required at each PCC for each hour
DAO trigger
Profile slicing
Supervision
Minute Ahead Optimization
Power mismatch calculation
Negotiation among neighbor areas
DeviationAlert_MAO
Contract Net
MAO trigger
15 mins optimal power profile per PCC
15 mins optimal power profile per SST and RSST
15 mins optimal power profile per ESS
15 mins reactive power profile per EC
Profile slicing
Supervision
Minute Ahead Optimization
DeviationAlert_MAO
DeviationAlert_RTO HTTP, TCP IP, XML transportation demand
RTO
IEC 60870-5-101/104
real time status of each equipment
operational suggestions
measured consumption
Control
DeviationAlert_RTO
Implementation of Suggestions
Real Time Data acquisition for MAO
Real Time Data acquisition for RTO
Consumption Measurement
ANSI C12.22 EC real time status
real time actions for EC
Real Time Data acquisition for RTO
Consumption Measurement
Implementation of Suggestions
Contract Net
15 mins optimal power profile per PCC
15 mins optimal power profile per SST and RSST
15 mins optimal power profile per ESS
15 mins reactive power profile per EC
MAO trigger
Control
DeviationAlert_RTO
IEC 61375-2-6 real time train status
train profile estimation
Real Time Data acquisition for RTO
Consumption Measurement
Estimation for MAO
IEC 61850-7 (1-4)
IEC 61850-7-420
ESS real time status
RSST real time status
SST real time status
DER real time status
aggregated 15 mins power profiles
real time actions
Real Time Data acquisition for RTO
Consumption Measurement
Estimation for MAO
15 min forecast aggregation
Implementation of Suggestions
OpenADR estimation of consumption Estimation for MAO
15 min forecast aggregation
Figure 2.8 shows the detailed architecture of the communication layer [57]. The REM-S
communication layer comprises different networks:
The train on-board network (green dotted box in Figure 2.8.a) follows the IEC 61375
standard series. In particular, REM-S relies on this communication for the new buses
based on ECN, as these technologies provide great advantages in terms of flexibility,
modularity, cost effectiveness and reusability. Train subsystems, including the DOEM,
are connected to it.
Centralized-decentralized Architecture [7] 57
The IM intranet (Figure 2.8.a) comprises all components and servers required to manage
the rail network. The relevant actors connected to this intranet are: Track data server
(TDS), the DER EMS and VPP system (DER EVS), the GOS and number of LOS units.
The RO intranet (Figure 2.8.a) includes several applications like ticketing, train
maintenance and others. Amongst them, the Energy Forecaster System is a new
component brought by REM-S.
The LOS intranet (Figure 2.8.b) refers to the control area network devoted to control the
power system around an ISST. Different components are connected to this network for
data exchange: Meters, DER, DMS, etc. It provides the pathway to distribute the final
order to the controlled electrical subsystems and to collect low level electrical parameters.
In the REM-S communication layer, the following technologies are defined for exchanging
data between the aformentioed four internal networks.
The train to ground communication links train onboard network and the IM intranet. It is
based on the IEC 61375-2-6 standard that contemplates a multi-technology (GPRS, 3G,
LTE, WIFI...) communication with transparent handovers. In Figure 2.8 both the data
exchange through WIFI hotspots (e.g. at depot, stations...) and through the mobile
telephony network are represented. According to the architecture defined in IEC 61375-
2-6 some components like the DNS (Domain Name System) server and the AAA
(Authentication, Authorization and Accounting) server are needed.
The internet is used to interface third parties from the IM intranet. In particular,
communication from and to the ROs and the electricity market is realized through this
common used network. Obviously security measures should be deployed to avoid
intrusions (e.g. firewalls, VPN...), where the AAA server can play an important role.
When the communication uses the telephony network the internet also serves as the carrier
for the exchanged information.
As the many LOS could be placed in remote areas, different means to provide
communication to the IM network must be provided. Of course, it may happen that the
IM physical network is deployed as far as the LOS facilities, but if it were not the case,
the telephony network should be used. When the mobile telephony network is used, the
same infrastructure deployed for the train to ground communication can be reused, as
shown in Figure 2.8.a.
Currently, the most commonly adopted procedure for informing the train driver about
delays and arrival times, and in general for the communication between the TMS and the
driver is the mobile phone. In this case, the driver has to manually update the DOEM
using the Data Manager Interface (DMI).
58 Chapter 2
(a) Train onboard network, IM intranet, RO intranet
(b) LOS Intranet
Figure 2.8: Detailed REM-S Communication Layer [57]
MCG
Traction
OESS
Aux
DOEMDMI
CCU
IEC 61375-3-4 IEC 61375-3-6 GGSN
IMG
IM Network
DBDB
AAA Server DNS Server
RU NetworkInternet
Energy
ForecasterTrain DB
Track DB
Track Data
Server
SOAP
SOAP
Marketplace
Server
SOAP
GOS
LOS
SOAP
SOAP
SCADAHTTP
SOAP
DSL
LOS
MODEM 3G/4G
LOS
DB
Timetable
ServerTT DB
Internet
IEC 61375-2-3 IEC 61375-2-4
DER EVS
SOAP
LOS
LOS LAN
DER
IED
Meter data
concentrator /
RTU
Smart
meter
Smart
meter
Smart
meter
IEC 60870-5-101
ESS
DMS
IEC 60870-5-104
IEC 60870-5-101
IEC 60870-5-104
IEC 60870-5-101
IEC 60870-5-101
Centralized-decentralized Architecture [7] 59
2.4 EVALUATION OF ARCHITECTURE
The EA UML tool, Key Performance Indicator (KPI) evaluation and analysis of degraded
mode operation are applied for assessing the architecture. EA is used to check the
interoperability of the layer connections between components and functions, communication
protocols, data models, and the feasibility of all use cases, which confirms whether or not the
architecture supports all the use cases. Some KPIs are defined for evaluating the REM-S
architecture conceptually that are addressed at the following points.
2.4.1 KEY FEATURES CHARACTERIZATION
Quantifiable metrics are defined to compare key features of REM-S architecture with fully
centralized architecture [60]:
Ratio between number of new actors and total number of actors
This ratio represents the innovation brought by the new architecture. Nine business actors are
the stakeholders of REM-S:
1. IM
2. RO
3. Energy Supplier
4. Grid owner
5. EMO
6. EBDM EPP
7. EBDM Forecast Provider
8. Energy Dispatcher GOS
9. Energy Dispatcher LOS
The last four actors in the above list are the new actors proposed by REM-S. Therefore, 45%
of actors are new actors defined particularly for REM-S.
The integration of existing standards
Some standard gap or some standard modification can emerge in the proposed architecture
that is described in chapter 2.3.3. Also the adoption of XML for data exchange at railway
system which is called RailML is introduced at chapter 2.3.2.
60 Chapter 2
The hierarchical level of the architecture
For this index it should be specified that how many nodes or how much nominal power is
managed from Control Center or from control areas. As an example considering one of the
scenarios (the line between Paris and Lyon) with 389 km railway is divided to five control
areas. There are 384 trains passing this line daily. Hence, each main agent is dealing with 389
moving loads and two or three ECs and one or two ESSs. In this scenario, the Control Center
is only in contact with five area managers located in five ISSTs. Comparing to centralized
architecture the Control Center in this case should be in contact directly to all 389 moving
loads and ECs and ESSs.
2.4.2 ROBUSTNESS
Here the robustness of architecture to communication loss, communication delay or failure of
hardware and software are analyzed. Since in the hybrid architecture the control nodes (main
agents) are significantly closer to the loads (train, EC…), the communication delays and loss
of data will decrease. Vice versa in the centralized architecture with large data over long
distances between control nodes and loads, channel congestion is more likely to happen and
the number of bottlenecks may rapidly increase which leads to a lower level of architecture
robustness.
2.4.3 HOSTING CAPACITY
The hosting capacity is defined as the maximum amount of new production (e.g. new DERs
in case of REM-S) or consumption (e.g. new trains or new ECs in case of REM-S) that can
be connected without endangering the reliability or quality for other customers [61].
Different performance indices that are related mostly to power quality indices can be selected
for evaluating the hosting capacity of the network. As an example, for phenomena like
network overloading by wind power penetration, a performance index like maximum hourly
value of current through related transformer or the maximum power flow of network [61] can
be considered.
In REM-S architecture, these performance indices are defined as objective functions in DAO
and MAO. In both optimization procedures, hourly and real time power demand optimization
are considered as one of the main goals and the voltage and current standard ranges as hard
Centralized-decentralized Architecture [7] 61
constraints in the optimization procedure; therefore, the optimal solution will take care of
phenomena such as overload, overvoltage or under voltage.
2.4.4 ARCHITECUTRE COST
The architecture cost is a summation of deployment cost and operation cost [62]. In this
formulation, the installation cost is not considered as a deployment cost and it is assumed that
the required hardware and software devices are existing and that it is intended to check the
difference between applying different energy management architectures to the system under
consideration.
Therefore, the deployment cost consists of the costs of deploying data storage, processing and
communication capacity [62]:
CD = CS + CC + CP (2-1)
Where,
CD is the deployment cost,
CS is the storage cost,
CC is the cost of transceivers/receivers,
CP is the cost of processing unit.
Storage cost: the cost of storing data at node k is calculated as [62]:
)()( kSkS SfSkC (2-2)
Where,
fS models the price of storage,
Sk is the total amount of storage capacity, given by the equation (2-3):
Sk = ∆T ×lm
τ (2-3)
62 Chapter 2
Where,
∆T is the time duration,
τ is the sampling interval ,
lm is length of message.
The storage cost for all nodes can be calculated by (2-3). This means that to calculate the
storage cost of whole architecture the cost of the Field Zone nodes (meters), Station Zone
nodes (control area nodes) and Operation Zone nodes (Control Center) should be added.
Equation (2-3) shows that the storage cost has a direct relation to distance. This results more
expensive storage cost in the centralized architecture than in the hybrid architecture due to the
more distance between Field Zone nodes and Control Center. On the other hand, by
considering N as the number of control areas and M as the number of nodes in the Field Zone,
N should be much smaller than M (N<<M); therefore, adding N nodes for developing the
hybrid architecture has no significant effect on the cost of whole architecture. This result is
also proved by simulation in [15] that the storage cost in the centralized architecture is more
expensive than in the hybrid architecture.
Communication cost: the communication cost for each node is calculated as follows [62]:
CC(j) = Tj × fC(Tj) (2-4)
Where,
fC models the price of bandwidth,
Tj is data rate needed to transmit data from one node to the other node (in REM_S, from Field
nodes to Control Center through control area nodes in Station Zone). Tj is calculated as in
(2-5):
𝑇𝑗 = 8𝑙𝑚
𝐹→𝑆
𝑡𝐹→𝑆 (2-5)
Where,
t is the time period for transmitting information form a Field Zone node to a Station Zone
node,
Centralized-decentralized Architecture [7] 63
lm is the length of the message
For calculating the total communication cost for the whole architecture it is necessary to
calculate both the cost of transmitting data from Field Zone nodes to Station Zone nodes and
also from there to the Operation Zone node (Control Center). Hence this relation can be briefly
formulated as (2-6):
CC = ∑ CC(i) + ∑ CC(j)j∈Mi∈N (2-6)
According to (2-5) and (2-6), since the cost scales directly with the length of the message
(which is significantly longer in centralized architecture), and as N, the number of control
areas in the Station Zone, is much smaller than M, the number of nodes at Field Zone,
(N ≪ M), the communication cost in the hybrid architecture is significantly less than in
centralized architecture. This conclusion is in line with the pilot experiment [61] on the
comparison of the communication bandwidth for different architectures.
Processing cost: the processing cost at node k for each query is calculated as (2-7) [62]:
CP(k) = nm × n × fp(k) (2-7)
Where,
nmis number of messages,
n is the number of operations required for each query,
fp(k) is the function that models the processing price.
According to [62] simulation results, the processing operations are significantly fewer in
hybrid architecture compared to centralized architecture. This difference can be interpreted
by the difference between distributing the process effort between the Field, Station and
Operation Zone nodes compared to doing most of the processes centrally in Operation Zone.
The operation cost is defined as the cost of amount of energy consumed in a fixed time period
(e.g. one month) [62]:
CO = Etotal × fE (2-8)
Where,
64 Chapter 2
Etotal is the average energy required by all nodes for operating during the time interval,
fE is the price of energy.
Total energy cost: the energy required at each node for operation is formulated by (2-9):
Ej = Ecj→k
+ Er/wj
+ Epj
(2-9)
The Ecj→k
, Er/wj
and Epj are formulated below [62]:
Ecj→k
= escj→k
lscj→k
+ ercj→k
lrcj→k
(2-10)
Er/wj
= erjlrj
+ ewj
lwj
(2-11)
Epj
= epj
npj
(2-12)
In the formulation above:
Ecj→k
is the energy required to send/receive information between j and k nodes according to the
length of transmitted message (𝑙𝑠𝑐𝑗→𝑘
or𝑙𝑟𝑐𝑗→𝑘
),
Er/wj
is the required energy for reading or writing information from/to storage according to the
length of the message that has been read/written (𝑙𝑟𝑗or𝑙𝑤
𝑗),
Epj is the energy consumption of processing and is calculated by the energy required for
processing a byte of information (𝑒𝑝𝑗) and the number of processed bytes (𝑛𝑝
𝑗) .
The total energy cost is calculated by summing the energy required for all nodes from the
Field Zone to the Operation Zone. It can be seen from equations ((2-10), (2-11) and (2-12))
that the consumed energy has a direct relation to the message length and to the number of
processed bytes. Since in the hybrid architecture regarding to managing minute ahead
operation locally, the length of messages and the number of processed bytes decreases
dramatically, the total energy cost is much less in hybrid architecture than centralized
architecture.
It can be concluded that in the calculation of architecture cost, the larger the number of nodes
in the Field Zone the more beneficial is the hybrid architecture compared to the centralized
architecture.
Centralized-decentralized Architecture [7] 65
2.4.5 SCALABILITY
In this section, another performance metric is defined to evaluate the hybrid architecture
communication cost according to traffic rate, bandwidth size, number of nodes and distance
between nodes, showing how the scalability of architecture would be improved by switching
from a centralized architecture to hybrid architecture.
The total cost of communication in a centralized architecture is calculated as in (2-13) and in
hybrid architecture as in (2-14) [63]:
TotalCostC = βλD̅̅̅̅ M + F0 (2-13)
TotalCostH = (λD̅̅̅̅ M)2
3 (γD̅ (β
γβD̅+F̅)
2
3+ (
γβD̅+F̅
β)
1
3) (2-14)
Where,
is the average traffic rate on Field Zone nodes,
D is the average distance between Field Zone nodes and the area manager,
M is the total number of nodes in the Field Zone,
β is unit cost of bandwidth distance product,
γ is the bandwidth needed for the information exchanged between a distributed server and a
centralized server,
Fj is the cost of deploying MDMS at location j,
F is the average deployment cost of distributed MDMS.
The equation (2-13) shows that the total cost in centralized architecture scales linearly with
the number of nodes, the rate of average data generation of nodes and the average distance
between the nodes and Control Center. In (2-14), the cost scales as x⅔ with the similar
parameters. These two equations show that by increasing the traffic rate to nodes or the
number of nodes, the communication cost increases more rapidly in the centralized
architecture compared to the hybrid architecture, which implies that the hybrid architecture is
more scalable than the centralized architecture.
66 Chapter 2
2.4.6 DEGRADED MODE OF OPERATION
The main structure of REM-S architecture is developed based on normal mode of operation
and then it is modified to ensure that it can support the following degraded mode of operations:
Failure of SST
Failure of ESS
A delayed train tries to catch up on its timetable
Communication with some agents is down
Temporary or Permanent timetable is updated
Temporary or Permanent speed limits are updated
DERs generate more or less energy than forecasted
For fulfilling above degraded modes, the related sequence diagrams were developed to design
the related functions, components and information exchange supporting these cases.
Centralized-decentralized Architecture [7] 67
2.5 SUMMARY
This chapter introduced the new REM-S architecture and mapped to SGAM framework
adopted in Smart Grid applications. The Smart Grid concept in railway systems span the
centralized-decentralized automation architecture, the adoption of different time horizons
(Day ahead, Minutes ahead and Real Time) and the creation of flexibility with DOEM, DER,
EC and ESS. The adoption of the SGAM framework yields the interoperability of different
layers and the interoperability with the rest of the smart grid system.
The standardization analysis identifies which railway or smart grid standards are applicable
in REM-S and which parts need extension.
Regarding functions defined based on the use case and HLUC definitions, the business layer,
component layer, information layer and communication layer are configured.
For implementing REM-S architecture the Global optimization and Local optimization
HLUCs are developed by REM-S online and offline software suites. In this chapter, the REM-
S architecture with some KPIs were evaluated, while in chapter 3 and 4 the implementation
of Global optimization and Local optimization in the frame of the centralized and
decentralized optimization formulations and REM-S software suites are presented. The result
of simulations and a demonstration are presented at chapter 5 to evaluate the architecture in
application and show its performance.
3 CENTRALIZED-DECENTRALIZED
OPTIMIZATION APPROACHES
In the previous chapter, the centralized-decentralized automation architecture mapped to
SGAM framework is introduced. It is described that for verification of Global optimization
and Local optimization HLUCs, REM-S software suites mainly consist of DAO and MAO
functionalities are implemented. As it is specified in the previous chapter, the main function
of Global optimization is DAO and the main function of Local Optimization is MAO. In this
chapter, firstly it is described that how railway system is modeled as integrated system in
DAO and MAO and then the optimization formulation of DAO and MAO is described.
3.1 MODELLING RAILWAY SYSTEM
The actors in the system can be distinguished by their position (moving or fixed) and their
flexibility (full flexibility, data set flexibility or no flexibility). The optimization process uses
these flexibilities to find the best configuration. Here it is described that how the actors are
modeled and how the power flow calculation is done in the railway system.
3.1.1 TRAIN
Since the train, as a prosumer (producer and consumer), may have negative or positive power
profiles, in the formulation of optimization problem the negative and positive parts are
separated, to avoid missing regenerated energy. In other words, each train profile is
formulated as two different profiles: one as a consumer (positive) and the other one as
producer (negative). The power demand and power generation of train is modelled in equation
(3-1):
{(𝑒𝑇𝑛(𝑥, 𝑡))𝑡=1
𝑇 , 𝑒𝑇𝑛 ≥ 0
(𝑔𝑇𝑛(𝑥, 𝑡))𝑡=1𝑇 , 𝑔𝑇𝑛 < 0
(3-1)
where eTn is power demand of train n, gTn is regenerated power of train n, x is the location of
train, t is time in seconds, n is train number and T is duration of timeslot.
70 Chapter 3
Trains constantly change their position and can pass control areas of different substations. The
flexibility is given by prerecorded datasets of train driving styles that vary in their energy
consumption and their position. All datasets suffice the timetable requirements and therefore
guarantee punctual arrival at the train stations.
3.1.2 EXTERNAL CONSUMER (EC)
The infrastructure facilities like workshops, stations or official buildings called External
Consumers are located at a fixed position. Like trains, their flexibilities lie in prerecorded
dataset of energy consumption. The power demand of ECs is modelled in equation (3-2):
(𝑒𝐸𝐶𝑛(𝑥, 𝑡))𝑡=1𝑇 , 𝑒𝐸𝐶𝑛 ≥ 0 (3-2)
where eECn is the power demand of ECn during timeslot T.
3.1.3 DISTRIBUTED ENERGY RESOURCE (DER)
Distributed Energy Resources are located at fixed position. Their energy distribution profile
will be estimated by using the weather forecast and statistical data. By sending different
energy profiles they can propose flexibility to the system and play active role in the
optimization problem. The DERs are modelled as equation (3-3):
(𝑔𝐷𝐸𝑅𝑛(𝑥, 𝑡))𝑡=1𝑇 , 𝑔𝐷𝐸𝑅𝑛 < 0 (3-3)
where gDERn is the power generated by DERn, t is the time in seconds, T is the duration of a
timeslot.
3.1.4 ELECTRCIAL STORAGE SYSTEMS (ESS)
ESSs are located at fixed position. Their charging and discharging strategy can be freely
determined within the physical limits of the storage. The ESS optimization can be done by
deterministic optimization methods which allow to obtain the overall optimum in comparably
low computational times. The ESS charging and discharging status are modelled via its charge
and discharge profile, with limit maximum capacity as equation (3-4):
Centralized-Decentralized Optimization Approaches 71
nn
nnnnnn
C)T(S0
)SS()1T(S)T(S
(3-4)
where Sn+ is per-slot charging profile, Sn
- is per-slot discharging profile, βn+ is charging
efficiency, βn- is discharging efficiency, Sn(T) is charge level of storage system at timeslot T,
Sn(T-1) is charge level of storage system at previous timeslot and Cn is the maximum storage
capacity. In this model the energy level leakage rate is ignored and βn is considering the
conversion losses during the charging and discharging procedure
3.1.5 POWER FLOW
Both centralized and decentralized optimization algorithms (DAO and MAO) require power
flow calculation in optimization loops. Power flow runs to calculate the power demand at each
point of common coupling (PCC).
For DAO, given the massive size of the railway network and the moving nature of most of the
loads (trains), it is critical to use a simple model of electrical network. Here, in the electrical
network model, the substations (ISST, RSST and SST) are modeled as ideal voltage sources
in series with equivalent impedance. In this model the impedance of the overhead line and
catenary are considered in series and are modeled by one series impedance representing the
feeding section. The line is modeled as a Π line model. The loads, except the trains, in this
representation are modeled as constant power loads. Trains are modeled in power flow
calculations as current sources. The required information for modeling a train as a moving
load consists of its power profile and its position, which are assumed as input data. The effect
of train displacement on the electrical model of distribution system is represented by changing
the impedance of feeding sections to correspond to the distance of train to PCCs.
In this model, an actor, who can be moving over time or being installed at a fixed position,
will interact stronger with the substations it is nearest to. Energy flows are allocated to
substations by multiplying the amount of energy with the normalized reciprocal distance of
the participant to the substation. Therefore, the distance 𝑑 of an actor to all 𝑚 surrounding
substations is calculated as equation (3-5):
𝑑𝑖 = |𝑝𝑝 − 𝑝𝑠,𝑖|, ⩝ 𝑖 ⋲ [1, … , 𝑚] (3-5)
Here, 𝑝𝑝 is the position of the actor and 𝑝𝑠,𝑖 is the position of the substation 𝑖 on the track and
m is the number of directly connected substations. Then, the normalized reciprocal distance
𝑟𝑖 of the actor to the substation 𝑖 is calculated by equation (3-6):
72 Chapter 3
𝑟𝑖 =1/𝑑𝑖
∑ 1/𝑑𝑖𝑚𝑖=1
, ⩝ 𝑖 ⋲ [1, … , 𝑚] (3-6)
The energy of each substation 𝐸𝑠𝑢𝑏 results from the multiplication of the normalized
reciprocal distance and the participant’s energy 𝐸 by equation (3-7):
𝐸𝑠𝑢𝑏,𝑖 = 𝑟𝑚,𝑖 . 𝐸 , ∀𝑖 ∈ [1, … , 𝑛] (3-7)
The power peak optimization for ESS is using an energy profile that results from the energy
profiles of the surrounding substations. The storage energy profile (𝐸𝑠𝑡𝑜𝑟𝑎𝑔𝑒) can be calculated
by using the normalized reciprocal distance as formulated in equation (3-8):
𝐸𝑠𝑡𝑜𝑟𝑎𝑔𝑒 = ∑ (𝐸𝑠𝑢𝑏,𝑖 . 𝑟𝑚,𝑖)𝑚𝑖=1 (3-8)
In MAO, since the power flow should run in restricted area and only for one timeslot, the
detail electrical network model is used by railway electrical network simulator.
3.2 CENTRALIZED OPTIMIZATION FORMULATION
[64]
The centralized optimization approach, which is developed for day-ahead optimization,
predicts the best operation strategy for all trains, substations, Electrical Storage Systems,
external consumers and Distributed Energy Resources in the railway system for the upcoming
day for one objective, i.e. optimization of overall energy consumption or power demand or
cost. The result determines an approximated electricity demand for the next day, which can
be purchased at the electricity market in advance. To be compatible with the time step of the
electricity market and to achieve acceptable computational times, the day is divided into time
intervals of predefined length same as electricity market [64].
To find the global optimum in DAO, the best sequence of all solution combinations for all
elements in the system has to be found. DAO can use the flexibility that is defined by railway
energy players for finding the best combination of solutions. That means regarding to railway
system constraints, it is not possible for DAO to propose the railway elements (for example
trains) some demand profile that was not generated by themselves in advance. Therefore, the
DAO optimization approach is developed based on a search algorithm in order to find the best
solution among different solutions that are proposed by railway energy players.
Centralized-Decentralized Optimization Approaches 73
Due to the system complexity, the computational time for the evaluation of one possible
combination is a challenge. Thus, it is necessary to adopt an intelligent approach to find a near
optimal solution so that only a small fraction of possibilities has to be evaluated. Stochastic
optimization procedures, like genetic algorithm, proceed in an intelligent way to predict good
solutions from past evaluations. The use of genetic algorithm is widespread with a broad range
of applications; it allows parallel processing (important to avoid long computational times)
and has proven itself to be robust and fast. Further the genetic algorithm proceeds to better
solutions throughout its run time and will provide the current best solution if stopped.
Therefore, even in cases where the optimum is not found, the current optimum can be used in
further steps. For these reasons, genetic algorithm is used for the DAO process.
The DAO computes optimized power profiles as well as calculating an optimal
charging/discharging strategy of Electrical Storage System for an upcoming day. The power
profile behaviors of the trains as active loads can be changed individually (regarding to
different driving styles and the flexibility which is defined as running time supplement) and
will lead to different power profiles at the substations (point of common coupling). These
different power profiles are the input information for DAO. With these inputs, an
approximated energy demand for the next day can be calculated which allows the purchase of
electricity in advance with better conditions. The general algorithm for identifying the optimal
profiles and the Electrical Storage System charging strategies is illustrated in Figure 3.1.
As it is presented in Figure 3.1, the first step is configuring the grid topology (like line
topology, substations topology and control area borders) and electrical specifications (like
substation capacity or charging/discharging rate of storages). Then the scenario information
and energy player’s data should be considered. Here the flexibilities in the form of different
power profiles are proposed to DAO algorithm. At this step, it will clarify that whether in this
scenario optimizing power demand, energy consumption or cost is targeted. In the next steps,
for each objective its special procedure will follow for optimization that is described in
subchapter 3.2.1 belowfor energy/cost optimization and subchapter 3.2.2 for power demand
optimization. In each subchapter, DAO is implemented in two steps:
First step: finding the best solution for trains, DERs and external consumers
In this step, the complexity of the problem grows exponentially with the size of
problem since the number of combination of solutions grows exponentially with the
number of element who play in energy optimization game. Therefore, genetic
algorithm is applied to select the best combination of solutions [64].
Second step: Finding the best solution for Electrical Storage System
In this step, Electrical Storage charging/discharging strategies are optimized. The
Electrical Storage Systems are beneficial for two optimization objectives: cost
74 Chapter 3
optimization and power peak optimization, which are formulated as convex
optimization by linear and quadratic programs, respectively [64].
Figure 3.1: Generic flowchart of DAO algorithm [64]
Centralized-Decentralized Optimization Approaches 75
3.2.1 ENERGY/COST OPTIMIZATION
3.2.1.1 FIRST STEP- TRAIN, DER AND EC
The overall energy consumption minimization sums up the whole consumed energy. The
objective function is formulated by equation (3-9):
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 ∑ 𝑃𝑖,𝑗 ∙ ∆𝑡𝑖,𝑗∈ℕ (3-9)
where Pi,j is average power demand of substationj at intervali with Δt being the time step size
for DAO calculations. Pi,j should be less than the 𝑃𝑗𝑚𝑎𝑥 which is the maximum tolerable power
of substationj . Pi,j is defined by equation (3-10):
𝑃𝑖,𝑗 = ∑ 𝑇𝑖,𝑗,k 𝑖,𝑗,𝑘∈ℕ + ∑ 𝐸𝐶𝑖,𝑗,𝑙
𝑖,𝑗,𝑙∈ℕ + ∑ 𝐷𝐸𝑅𝑖,𝑗,m
𝑖,𝑗,𝑚∈ℕ + ∑ 𝑆𝑖,𝑗,𝑛
𝑖,𝑗,𝑛∈ℕ (3-10)
Where,
∑ 𝑇𝑖,𝑗,𝑘𝑖,𝑗,𝑘∈ℕ are the train profiles of k trains which at intervali are interacting with substationj.
Each of these profiles should belong to the set of the proposed profiles received from DOEM.
The uncontrollable trains (grey trains) send only one profile to be considered.
∑ 𝐸𝐶𝑖,𝑗,𝑙𝑖,𝑗,𝑙∈ℕ are the power demand of the 𝑙 external consumers which are fed by substationj.
The uncontrollable loads send their one profile as constraint of the problem. The controllable
loads send set of profiles as flexibility, so the DAO can select each profile from the set of
received profiles to solve the minimization problem.
∑ 𝐷𝐸𝑅𝑖,𝑗,𝑚𝑖,𝑗,𝑚∈ℕ are the generated power of m Distributed Energy Resources which can send
energy to substationj. The uncontrollable resources send their one profile as constraint of the
problem. The controllable resources send set of profiles as flexibility, so the DAO can select
each profile from the set of received profiles to solve the minimization problem.
∑ 𝑆𝑖,𝑗,𝑛𝑖,𝑗,𝑛∈ℕ are the charging/discharging profiles of n Electrical Storage Systems which are
interacting with substationj. The charging and discharging efficiency of the storages along
with the maximum storage capacity are the constraints of this actor.
The cost optimization minimizes the total costs for the energy purchase at the electricity
market. Therefore, the time dependent energy cost function is multiplied by the consumed
energy for each interval time. The objective function is formulated by equation (3-11):
76 Chapter 3
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 ∑ 𝑃𝑖,𝑗 ∙ 𝑒𝑛𝑒𝑟𝑔𝑦𝑝𝑟𝑖𝑐𝑒𝑖 ∙ ∆𝑡𝑖,𝑗∈ℕ (3-11)
where energypricei is the price of energy at intervali and Δt is time step size for DAO
calculations.
The trains and external consumers have no influence on each other when cost optimization
and energy consumption optimization are implemented separately, therefore for these
objective functions the optimization algorithm evaluates every train and external consumer
profile individually and finds the best solution [64]. Figure 3.2 shows the main steps of Energy
and Cost optimization for train and external consumer.
Figure 3.2: Generic flow chart Energy/Cost optimization for Train and external consumer
3.2.1.2 SECOND STEP- ESS
The main target for cost optimization in the presence of Electrical Storage System is to buy
energy at low cost and with this energy charge the Electrical Storage Systems in order to
sell/consume it at high cost times and discharge the Electrical Storage Systems. The storage
Centralized-Decentralized Optimization Approaches 77
cost minimization is minimizing the sum of the average storage charging or discharging power
𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 multiplied by the cost function 𝑐𝑖 for each time interval:
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 ∑ 𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 ∙ 𝑐𝑖𝑛𝑖=1 (3-12)
Here, n is the number of time intervals. The storage cost optimization can be formulated as a
linear program.
The cost optimization in the presence of Electrical Storage System has no influence on train
and external consumer optimization. The problem is formulated in the following way [64]:
The storage cost optimization is formulated as a linear program by (3-13) formulation.
𝑚𝑖𝑛𝑥 𝑓𝑇 ∙ 𝑥 𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐴 ∙ 𝑥 ≤ 𝑏
𝐴𝑒𝑞 ∙ 𝑥 = 𝑏𝑒𝑞
𝑙𝑏 ≤ 𝑥 ≤ 𝑢𝑏 (3-13)
Here, 𝑥 represents the charging or discharging power for each time step. In order to allow
different charging and discharging efficiencies, for each of the 𝑛 time steps there are two 𝑥
values, one for charging and one for discharging:
[𝑥1, 𝑥2, … , 𝑥𝑛] are the charging variables;
[𝑥𝑛+1, 𝑥𝑛+2, … , 𝑥2𝑛] are the discharging variables.
The objective function 𝑓 equals the cost function 𝑐 times the ESS charging efficiency 𝜇𝑐ℎ𝑎𝑟𝑔𝑒
or discharging efficiency 𝜇𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒:
𝑓𝑖 = 𝑐𝑖 ∙ 𝜇𝑐ℎ𝑎𝑟𝑔𝑒 , ⩝ i ⋲ [1, … , n]
𝑓𝑖 = 𝑐𝑖 ∙ 𝜇𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒 , ⩝ i ⋲ [n + 1, … ,2n] (3-14)
The inequality constraints are used to make sure that the state of charge of the ESS won’t
exceed the maximum capacity or go below an empty storage. Therefore, the sum of all
previous charged and discharged energy of the storage for each time step has to satisfy these
constraints. The inequality matrix 𝐴 can be written as:
𝑎𝑟𝑜𝑤 𝑖 = [11, … , 1𝑖 , 0𝑖+1, … , 0𝑛] ∙ 24/𝑛, ⩝ i ⋲ [1, … , n − 1]
𝑎𝑟𝑜𝑤 𝑖 = [−11, … , −1𝑖 , 0𝑖+1, … , 0𝑛] ∙ 24/𝑛, ⩝ i ⋲ [n, … ,2n − 2]
78 Chapter 3
𝐴 = [𝑎, 𝑎] (3-15)
The vector 𝑏 of the right hand side of the inequality constraint can be written as:
𝑏𝑖 = 𝑐𝑎𝑝𝑚𝑎𝑥 − 𝑆𝑜𝐶𝑖𝑛𝑖𝑡, ⩝ i ⋲ [1, … , n − 1]
𝑏𝑖 = 𝑆𝑜𝐶𝑖𝑛𝑖𝑡, ⩝ i ⋲ [n, … ,2n − 2] (3-16)
Here, 𝑐𝑎𝑝𝑚𝑎𝑥 describes the maximum storage capacity and 𝑆𝑜𝐶𝑖𝑛𝑖𝑡 describes the initial state
of charge. To ensure that the storage’s state of charge at the end of the day is equal to the
beginning of the day the sum of all charging and discharging has to equal zero, thus the
equality constraint is defined as:
𝐴𝑒𝑞 = [11, … , 12𝑛]
𝑏𝑒𝑞 = 0 (3-17)
The maximum discharging rate is implemented by the lower bound 𝑙𝑏, the maximum charging
rate by the upper bound 𝑢𝑏 of the vector 𝑥:
𝑙𝑏𝑖 = 0, ⩝ i ⋲ [1, … , n]
𝑙𝑏𝑖 = −𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒𝑚𝑎𝑥, ⩝ i ⋲ [n + 1, … ,2n]
𝑢𝑏𝑖 = 𝑐ℎ𝑎𝑟𝑔𝑒𝑚𝑎𝑥 , ⩝ i ⋲ [1, … , n]
𝑢𝑏𝑖 = 0, ⩝ i ⋲ [n + 1, … ,2n] (3-18)
The optimization problem is now defined and the global optimum 𝑥∗ can be calculated by
solving the linear program. The charging and discharging values of the global optimum 𝑥∗ are
added to obtain the actual optimized charging strategy 𝑃𝑐ℎ𝑎𝑟𝑔𝑒:
𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 = 𝑥𝑖∗ + 𝑥𝑛+𝑖
∗ , ⩝ i ⋲ [1, … , n] (3-19)
3.2.2 POWER DEMAND OPTIMIZATION
3.2.2.1 FIRST STEP- TRAIN, DER AND EC
The power demand optimization minimizes the root mean square of all power values for every
substation at every time interval. The objective function is formulated by equation (3-20):
Centralized-Decentralized Optimization Approaches 79
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒 ∑ 𝑃𝑖,𝑗2
𝑖,𝑗∈ℕ (3-20)
This method penalizes peak values and therefore most homogeneous power demand is
achieved. The Genetic Optimization System Engineering Tool (GOSET) [65] is applied to
implement Genetic Algorithm for this part of the optimization. The Genetic algorithm here
can start even with random population or with an existent previous optimization result (for
example the result of one day before) as an initial solution. Starting from initial solution can
speed up the computational time significantly. Figure 3.3 shows the generic flow chart of
power demand optimization for train and EC [64].
Figure 3.3: Generic flow chart of power demand optimization for Train and EC [64]
3.2.2.2 SECOND STEP- ESS
The power demand optimization is using the power profile 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 that results from the
substation power profiles interacting with the Electrical Storage System. The 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 is
dependent on the storage location. Therefore the distance of interacting substations for each
storage is calculated and the 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 is generated by adding the multiplication of the
substation profile times the normalized reciprocal distance. This way substations within a
shorter distance have a bigger influence on the Electrical Storage System. 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖is
formulated by (3-21):
𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 = ∑ (𝑃𝑠𝑢𝑏,𝑗 . 𝑟𝑗)𝑚𝑗=1 (3-21)
Here, 𝑟𝑗 is the normalized reciprocal distance of the storage to the substation 𝑗, 𝑃𝑠𝑢𝑏,𝑗 is power
of 𝑆𝑢𝑏, 𝑗 at interval i and 𝑚 is the number of substations located close to electrical storage
system.
80 Chapter 3
The storage power demand minimization is optimizing the root mean square value of the
average power consumption at the storage location plus the average storage charging or
discharging power 𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 at every time interval. The formulation is stated as below:
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒√∑ (𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 + 𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖)2𝑛𝑖=1 (3-22)
Here, n is the number of time intervals. The storage power peak optimization is formulated as
a quadratic program in MATLAB. The problem is formulated in the following way [64]:
𝑚𝑖𝑛𝑥 1
2𝑥𝑇 ∙ 𝐻 ∙ 𝑥
𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝐴 ∙ 𝑥 ≤ 𝑏
𝐴𝑒𝑞 ∙ 𝑥 = 𝑏𝑒𝑞
𝑙𝑏 ≤ 𝑥 ≤ 𝑢𝑏 (3-23)
While it is possible that the storage is minimizing the power peaks of more than one
substation, according to the storage position, in a first step a virtual power profile 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒 at
the storage location is calculated. Therefore the power profiles of all surrounding substations
𝑃𝑠𝑢𝑏,# multiplied by the normalized reciprocal distance 𝑟𝑖 (see equation (3-6)) of the storage
to the substations are added up:
𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒 = ∑ (𝑃𝑠𝑢𝑏,𝑖𝑛𝑖=1 ∙ 𝑟𝑖) (3-24)
The optimization variables 𝑥 are the sum of the storage power profile and the charging strategy
𝑃𝑐ℎ𝑎𝑟𝑔𝑒 for every time step:
𝑥𝑖 = 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 + 𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 , ⩝ i ⋲ [1, … , n] (3-25)
The matrix 𝐻 is the identity matrix 𝐼𝑛 of the size 𝑛 × 𝑛:
H = In (3-26)
Again, the inequality constraints are used to ensure that the state of charge of the ESS won’t
exceed the maximum capacity or go below zero. Therefore, the sum of all previous charged
and discharged energy of the storage for each time step has to satisfy these constraints. The
inequality matrix 𝐴 can be written as:
𝐴𝑟𝑜𝑤 𝑖 = [11, … , 1𝑖 , 0𝑖+1, … , 0𝑛] ∙ 24/𝑛, ⩝ i ⋲ [1, … , n − 1]
Centralized-Decentralized Optimization Approaches 81
𝐴𝑟𝑜𝑤 𝑖 = [−11, … , −1𝑖 , 0𝑖+1, … , 0𝑛] ∙ 24/𝑛, ⩝ i ⋲ [n, … ,2n − 2] (3-27)
The vector 𝑏 of the right hand side of the inequality constraint has to consider the virtual
power profile for the storage location that is included in the vector 𝑥. It can be written as:
𝑏𝑖 = 𝑐𝑎𝑝𝑚𝑎𝑥 − 𝑆𝑜𝐶𝑖𝑛𝑖𝑡 + ∑ 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑗 ∗ 24/𝑛
𝑖
𝑗=1
, ⩝ i ⋲ [1, … , n − 1]
𝑏𝑖 = 𝑆𝑜𝐶𝑖𝑛𝑖𝑡 − ∑ 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑗 ∗ 24/𝑛𝑖−𝑛+1𝑗=1 , ⩝ i ⋲ [n, … ,2n − 2] (3-28)
To ensure that the storage’s state of charge at the end of the day is equal to the beginning of
the day the equality constraint is defined as:
𝐴𝑒𝑞 = [11, … , 1𝑛]
𝑏𝑒𝑞 = 0 (3-29)
The maximum charging and discharging rates are represented by the upper and lower bound.
While the storage power profile is included in the vector 𝑥, it has to be considered here:
𝑙𝑏𝑖 = 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 − 𝑑𝑖𝑠𝑐ℎ𝑎𝑟𝑔𝑒𝑚𝑎𝑥, ⩝ i ⋲ [1, … , n]
𝑢𝑏𝑖 = 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑖 + 𝑐ℎ𝑎𝑟𝑔𝑒𝑚𝑎𝑥, ⩝ i ⋲ [1, … , n] (3-30)
The optimization problem is now defined and can be solved by a quadratic program. The
virtual power profile at the storage location now has to be subtracted from the global optimum
𝑥∗ in order to obtain the actual optimized charging strategy 𝑃𝑐ℎ𝑎𝑟𝑔𝑒:
𝑃𝑐ℎ𝑎𝑟𝑔𝑒,𝑖 = 𝑥𝑖∗ − 𝑃𝑠𝑡𝑜𝑟𝑎𝑔𝑒 , ⩝ i ⋲ [1, … , n] (3-31)
The Electrical Storage System optimization has influence on the train and external consumer
optimization result, while the Electrical Storage System charging profile is changing the
power profile of the whole system. Another configuration of train and external consumer
profiles might become even better. Thus after computing the Electrical Storage System
storage strategy, the train and external consumer optimization is run again. This iteration loop
continues till the new best fitness value doesn’t vary more than 1% from the best fitness value
of all iterations before. In this case, it is assumed that a configuration near the optimum is
found. The 1% limit is achieved by several sensitivity analysis that compares suboptimal and
global optimal solutions which will present in chapter 5.
82 Chapter 3
3.3 DECENTRALIZED OPTIMIZATION
FORMULATION [66]
Decentralized optimization is done in order to follow the day-ahead centralized optimization
plan. As it is described in chapter 2, the railway system is distributed in different control areas.
Each control area consists of at least one intelligent substation, which is called control area
manager. The control center, which is responsible for executing the DAO, sends the day-ahead
plan of each control area to the control area manager. The control area manager distributes
the day-ahead plan to all substations located in the control area. The target of the MAO is the
fulfillment of the DAO plan by minimizing the deviation from DAO plan that occurs in minute
ahead time window. Here, the optimization is done based on the negotiation between the
control area manager and other agents present in the control area. Each agent tries to modify
its profile in order to follow the control area manager request for minimizing the deviation
between planned power demand of each point of common coupling and the estimated power
demand [66]. The negotiation can continue for some trails to reach the target. That means in
each trial regarding to the profiles received from agents, the area manager provides limitation
request for them. Maximum number of negotiation trails should be defined at the beginning
of execution. The control area manager is also responsible for negotiating with other
neighboring control areas in order to be informed about the trains in the neighboring areas
which will travel to its area in the next timeslot or find probable flexibilities from other areas.
Figure 3.4 shows a brief flowchart of the MAO general steps.
Ahead of the optimization a number of files are being read by the MAO as input information
consisting of:
REM-S and MAO configuration
The REM-S configuration determines in particular the scenario for optimizing a
given timeslot and the number of negotiation steps, since one optimization step can
consist of multiple negotiations between the control area manager and a train during
which the optimal power limitations are. The length of a timeslot is derived from
the MAO configuration.
DAO power profiles of the substations
MAO power profiles of the substations and trains for next timeslot
charging/discharging profiles of ESS
Centralized-Decentralized Optimization Approaches 83
Topology, such as rail network and location of substations on the rail lines,
definition of control areas (line segments) belonging to their so-called Local
Optimization Systems (LOSs). The rail network consists of edges each connecting
two nodes (railway stations). For each line there are distance matrices that define
the distances to connected SSTs. It follows that each SST is matched to a path
distance on a particular line with energy feed-in from the SST.
Gather power profiles from agents who are attended at zonei
Calculate which part of profiles belong to zonei and which part belong to zonei-1 and zonei+1
Send/receive related profiles to/from neighbour zones
Receive power demand at ZN substation
finish
Is it last round of negotiation?
Find period of time which deviation from planned profile occures
Find share of loads power demand in devitation period
Send limitation files to loads
Receive susbtitute profiles from loadsand new profile of ZN substation from ERST
NO
Check susbtation constraint YES
Send limitation files to loads
Receive susbtitute profiles from loadsand new profile of ZN substation from ERST
Figure 3.4: MAO general steps
The MAO is performed within LOSs. Lists of line segments define their control areas. Thus,
all objects (SSTs, trains, etc.) that are within a LOS are associated with the same optimization
84 Chapter 3
system and may influence each other with respect to the optimization. Objects associated with
different LOSs are computationally independent. Hence, the optimization problems of
different LOSs can be solved independently. Having all prerequisites, the MAO begins with
deviation minimization.
A Multi-Agent System (MAS) using Java Agent Development Framework (JADE)6 as
presented in [66] used to specify the intercommunication aspects between the software
components of the Offline Suite, which is presented in chapter 3.3.1. This included figuring
out optimization durations and negotiation timings, defining message headers as well as the
payload, and so forth.
One of the constraints of the field demonstration was the usage of a proprietary
communication API for the REM-S Online Suite to achieve a Train-to-Ground information
exchange as it was not allowed to modify and to interfere with the Train Control / Management
System (TCMS). Therefore, the MAO application used in demonstration designed without
FIPA-ACL based communication protocol. At this point it is important to mention that all
needed functionality which is provided by the Train-to-Ground Library could be achieved by
JADE on the standardized FIPA-ACL, too.
In the following the MAO negotiations, the method for deviation minimization and power
limitation calculation are presented.
3.3.1 MAO NEGOTIATIONS
3.3.1.1 AGENTS IDENTIFICATION
The proposed multi-agent system consists of several instances (number depends on the
physical structure of the railway system) of the following agents: control area manager called
zone agent (ZN agent), train agent (TR agent), external consumer agent (EC agent), wayside
energy storage agent (ESS agent) and DER agent.
ZN agent: this agent as control area manager is responsible for monitoring, control and
negotiation in the domain of its area. It keeps time and is responsible for time synchronization
between agents. It negotiates with different agents, some coming and going (TR agents), some
fixed (e.g. EC, ESS and DER agent) in its area to get their power profile for next timeslot. It
6 http://jade.tilab.com
Centralized-Decentralized Optimization Approaches 85
also negotiates with neighboring area agents to get the information about the trains currently
traveling in the neighboring areas but expected to enter its own area at some time in the
following timeslot. According to the received information, the ZN agent calculates the power
demand of the zone for next timeslot. Comparing this calculated power demand with the
planned power demand of zone (received from DAO), it identifies status (normal or degraded)
of the area in the next timeslot. For each categorized status, a different behavior of ZN agent,
with specific algorithms, is triggered.
TR agent: it is responsible for interfacing and negotiating with the ZN agent and train internal
energy actors. TR agent that is defined in MERLIN as DOEM estimates the power profile of
train (which requires some private information that generally the train manufacturer is not
willing to share) for the next timeslot and sends it to the ZN agent. It gets messages from the
ZN agent and replies. The decision making procedure of TR agent is not in the scope of this
research.
EC agent: the main task of the EC agent is to provide its estimated power profile and
negotiating with the ZN agent. In case of power mismatch occurrence in area, it must be able
to propose new power profile to the ZN agent. Similar to TR agent, the internal energy
management procedure is not in the scope of this research.
ESS agent: the ESS Agent must be able to negotiate with the ZN agent about its
charging/discharging status.
DER agent: this agent provides generation profile in the next timeslot and negotiates with the
ZN agent.
3.3.1.2 INTEOPERABILITY AMONG AGENTS
Beginning of timeslot
At the beginning of each timeslot, all the loads, generators or storage systems (TR agent, EC
agent, DER agent or ESS agent) which are present in the generic area i send a message to ZN
agent with their power profile or charging/discharging status for the next timeslot. Then, it is
the duty of ZN agent to gather the information and analyze them. At first step, it separates the
part of train power profiles which are not related to its area and belong to neighboring control
areas (according to the area border definition). Then it sends those parts of the power profiles
to the neighbor areas.
Now all control areas have their own full input of estimated power profiles, thus each ZN
agent calculates the whole power demand of its area during the next timeslot. If there is no
86 Chapter 3
mismatch between calculated power demand and the planned power demand (received from
DAO), then the status is defined as normal and the related procedure starts, otherwise the
status is defined as degraded and the related procedure starts.
Normal mode
In normal mode, the ZN performs power demand optimization in the seconds time resolution
(while the DAO optimizes the power demand in the 15 minutes time resolution), therefore ZN
agent confirms the actuation of the DAO planned profile to all agents in the area or send some
commands to TR agents for load shifting, in order to avoid peaks created in the seconds time
range. The communications in normal mode are exemplified in Figure 3.5.
Normal Condition Optimization
TR1 and TR2 enter Zone1
TR1 and TR2 leave Zone1 and enter Zone2
Figure 3.5: Communications in normal mode
Degraded mode
In case of degraded mode, the ZN must initiate a negotiation in order to minimize the deviation
between the actual and planned global profiles of the area. To this aim, the ZN agent looks
for flexibility, by sending a call for proposals to all agents in the zone, with the aim of
obtaining some “substitute” power profiles, and to all neighboring zones for obtaining some
flexibility in the format of power availability. Using the offers as input, the ZN agent runs the
degraded mode optimization algorithm, to determine which combination of suggested profiles
is suitable to the optimization objective of minimal deviation. The ZN agent communicates
the acceptance or not of the proposals to the other agents, which update their own power
profiles. The process is completed when the agents inform that the implementation is done. In
Centralized-Decentralized Optimization Approaches 87
case of major deviations that cannot be solved locally, the ZN agent sends a Deviation alert
message to the Control Center, triggering the relaunch the DAO for the whole network
according to new situation. Figure 3.6 shows example of the communication in degraded
mode.
Abnormal Condition Optimization
TR1 and TR2 enter Zone1
Figure 3.6: Communications in degraded mode
Train entering and leaving area
The TR agent informs the ZN agent when the train enters the control area. The ZN agent sends
an acknowledgment. When a train wants to leave the area, it informs the ZN agent again and
the ZN agent sends back an acknowledgment.
Here the proposed dynamic model enables to model a train entering and leaving a control area
during a timeslot. Considering different trains travelling through a control area may change
in a certain timeslot. Since the agents distribution is dependent on the trains location inside a
control area, subintervals is needed in which the duration of trains composition is static.
Deviation of train power profile
If a train changes its power profile in the middle of timeslot, TR agent must send a message
including new power profile to ZN agent. In this case ZN agent runs normal mode
optimization algorithm to do peak shaving and according to optimization results sends
commands to TR agent for load shifting. The compensation of a larger deviation, for which
88 Chapter 3
load shifting is insufficient, must wait for the beginning of the next timeslot and hence for the
execution of the MAO degraded mode procedure.
3.3.2 DEVIATION MINIMIZATION
For each control area the list of included substations is achieved regarding to network topology
file. Based on the DAO profiles of a substation, the average power consumption limit (DAO
limit) that should be enforced is calculated. The MAO allows the application to determine at
what time the DAO limit L was violated. As a result, a list of time intervals with values above
L is obtained (see Figure 3.7). Figure 3.7 identifies the time intervals containing elevated
peaks. LS1 and LS2 are the average values that is planned by DAO for the specific time
interval for Pizarra Substation and Los Prados Substation, respectively.
Figure 3.7: Identification of the time intervals containing elevated peaks
Centralized-Decentralized Optimization Approaches 89
The goal of the optimization is to reduce the elevated peaks by restricting the power
consumption of the trains or other consumers located inside the corresponding control area.
This is achieved by a weighted distribution of limit L across all consumers. This means that
each consumer will get a limitation message to limit its consumption in the next timeslot. This
limit is calculated for each consumer regarding to share of this consumer in creating peak in
the specific time frame (for example in Figure 3.7, t1a-t1b or t2a- t2b etc.) So the weights are
proportional to the power consumption of each energy actor presents at control area at the
specific time that peak will occur. Thus, the total power consumption is guaranteed to be
below the average value.
In case ESS is present, priority for elevating peaks is assigned to the use of the ESS stored
energy. Although it is allowed to use ESS stored energy for compensating deviation from
DAO plan, if based on Electrical Storage System DAO plan, the ESS is in discharging status.
The amount of energy that the ESS can provide, is also specified regarding to its DAO plan.
In the simplest variant (flexible trains only), all trains participate in a decrease of the assigned
power. At first, for each train its power allocation according to MAO is being determined.
Then, on the basis of the sum of all train power allocations, the ratio of power for each trains
within a certain DeviationIssue is computed. Depending on this ratio, a new power distribution
based on the provided power limit is set. Since non-flexible trains cannot be advised to change
their power consumption, their power consumption is subtracted with the result that all
flexible trains together only may reach the new lower value. In case that the given power limit
cannot be reached even if the power of all flexible trains is being limited, a
cannotCompensateProcedure is performed. By running a cannotCompensateProcedure a
Deviation Alert will be sent to DAO, in order to ask re-run DAO for solving the big deviation
problem.
Once it stands form which trains get which limit within a DeviationIssue, it will be stored into
a DeviationResolution list. Finally, all DeviationResolutionentries are sent to train agents
(DOEM).
4 REM-S SOFTWARE SUITES [67]
This chapter presents a prototype implementation of REM-S. REM-S comprehends an Offline
Suite and an Online Suite, providing a detailed look at distributed optimization in real-time in
the area of smart grids. The Offline Suite is a prerequisite for the development of the Online
Suite, as it enables creating and testing the driving profiles before field deployment. Some
software components of the Offline Suite are also part of the Online Suite. The Online Suite
is introduced focusing on its components’ core aspects in order to get a more detailed
understanding of its inner operation consisting the distributed optimization and software
technical design. The Offline Suite was implemented in order to prepare DAO and MAO
power profiles and simulate the MAO offline negotiations. Then the Online Suite was
implemented for the MAO real time negotiations of the distributed optimization during RTO.
The functional and non-functional requirements as the basis for implementing the software
prototype of the REM-S Software Suites that were defined regarding to the defined
architecture in chapter 2 are briefly reviewed here:
Functional requirements:
1) DAO: planning optimal operation of the whole railway system for the next day (regarding
energy management targets) based on forecasted data
2) MAO: a short time optimization based on timeslots to correct deviation from the DAO
planning
3) DOEM: optimization (regarding energy management targets) is performed on rolling stock
(train)
4) MAO system communicates with DER agent to get information about generated amount
of energy
5) MAO system communicates with ESS agent to get information about stored energy and
sends charge/discharge commands
6) MAO system communicates the infrastructural loads to get information about their demand
and send command for increasing/decreasing consumption
7) MAO system communicates the train agent (DOEM) to get information about their
demand/generation profile and send command for limiting consumption
92 Chapter 4
8) In case of electrical substation fail: MAO system calculates the power and energy
consumption needed for next period
9) DAO and MAO system calculate the power and energy consumption needed for next period
10) DAO and MAO system optimize based on maximization of utilization of internal energy
sources (e. g. renewables installed within the infrastructure).
Non-functional requirements:
1) Static information configurable: system configuration of routes, substation location, etc.
2) Dynamic information configurable: speed, start, end, duration, etc.
3) Communications happen in specified timings
4) Each train reaches its destination within a maximum window of acceptable delay agreed
with the Railway Operator (RO)
5) Consideration of infrastructure limits (e. g. maximum power of a given substation)
6) Each train receives the assigned day-ahead optimized profile before departure
7) Each MAO system receives the profiles from the according DAO system for all the trains
expected to go through the corresponding area.
4.1 REM-S OFFLINE SUITE
The REM-S optimizations require information like power profiles of all energy actors in the
system, network topology and train time tables, which must be available in form of well-
defined data of a specific railway network to be fed to the software suite. Hence, such
information must be adapted if the software has to be applied to another railway networks
with their own topology. The Offline Suite was developed for dry runs (i. e. with regard to the
so-called offline scenarios). It consists of two software components also used in the Online
Suite: the DAO application for day-ahead optimization and the MAO application for minute-
ahead optimization. Furthermore, the Offline Suite also includes the REM-S Graphical User
Interface (GUI) which is the front-end for the both mentioned optimization tools implemented
as pure console applications.
REM-S Software Suites [67] 93
The Offline Suite is able to calculate the following cases:
New DAO simulation, using static parameters (scenario topology and electrification
related information) and dynamic parameters (information related to the specific
project-timetable and trains profile).
New MAO simulation based on DAO results and updated new minute ahead train
profiles.
New MAO negotiation based on a previous MAO simulation and new negotiation
phase simulation. Once run previously a MAO simulation which does not fulfil the
requirements, the negotiation process is started. Taking into account the indications
given by the REM-S software for a particular train-service, the user needs to provide
new MAO estimation profiles and the REM-S needs to check the fulfilment of
constraints.
4.1.1 REM-S GUI
The REM-S GUI provides the ability to perform dry runs of the DAO and the MAO
application also in batch execution mode. It can be used by control center’s staff for creating
and testing the driving profiles before field deployment as well as for series of tests (batch
job) to see how well DAO and MAO act in contact each other.
For each batch job, the user can specify the scenario’s name to be optimized, the beginning of
the timeslot to be considered and one of the following three REM-S functionality modes:
1) Functionality 1: run the day-ahead optimization (DAO application);
2) Functionality 2: run one minute-ahead optimization (MAO application) procedure for the
user specified timeslot based on DAO results. This implies the first negotiation step;
3) Functionality 3: run the MAO application for a further negotiation step within the current
optimization procedure.
At Figure 4.1 , the graphic user interface (GUI) of REM-S is displayed.
94 Chapter 4
Figure 4.1: Grafic user interface (GUI) of REM-S
4.1.2 DAO APPLICATION
The day-ahead optimization is performed by the DAO application which predicts the best
operation strategy for all trains, (R)SSTs, ECs, ESSs, and DERs in the railway system for an
upcoming day regarding one objective: overall energy consumption, power demand or cost
optimization [64].
The optimization formulation of DAO application is described in Chapter 3. As described
there, DAO’s input parameters are the number of driving styles, length of time interval, energy
price predictions, timetables, the topology and parameters of the electrical system such as
substations, lines and the location of ESSs, DERs and ECs. The DAO outputs are the optimal
driving styles of each train, the power profiles of ECs and DERs as well as the charging/
discharging status of ESSs.
REM-S Software Suites [67] 95
These outputs are the inputs of the so-called Existing Railway Simulation Tool (ERST) like
railNEOS as used in the case study to calculate the power profile at the related Point of
Common Coupling (PCC).
4.1.3 MAO APPLICATION
The minute-ahead optimization is implemented in the MAO application which computes
power limit files that are sent to the trains (more precisely: DOEMs) for optimized power
consumption. The MAO applications as well as the DAO application can be used for the
Offline and the Online Suite.
At chapter 3 the MAO negotiations and method for deviation minimization and power
limitations were described. Here the method for defining power limits, train composition and
harmonization the intervals is described.
Harmonization process
Harmonization process is developed for potentially overlapping intervals in one LOS.
Figure 4.2 shows a list of overlapping free intervals with minimum limits for each period
generated from different potential overlapping time intervals of all substations associated with
their limits. In Figure 4.2, the pre-harmonization plot shows the overlapping intervals t3a to
t3b, with value LS2 and t1a to t1b with lower value LS1. The result of harmonization process
is shown in the post harmonization plot. It shows that the subintervals t3a to t1a and t1b to
t3b (with values of LS2) are created and the interval t1a and t1b is left untouched, since the
minimal value of LS1 is already given.
Figure 4.2: Pre Harmonization and Post Harmonization time intervals
In order to implement the harmonization process, a RangeMerger class has been developed.
It iteratively transforms several intervals into an overlapping free set of intervals. As such,
96 Chapter 4
each time interval is associated with a merge value and a merge strategy. Here the merge
value is the limit value of the corresponding time interval and the merge strategy is the
minimum operation. The algorithm of RangeMerger is shown later.
Train Compositions
Due to the previously created preconditions, trains with similar composition are located on
the same line segment. Furthermore, the same train within different compositions obviously
can be on different line segments but not simultaneously.
Our model allows trains to migrate between LOS areas during a time interval. Hence, the
exact composition of the trains may change in a certain interval. The weighted power
distribution is dependent on the trains within a given LOS area. Therefore, subintervals with
a static train composition are needed.
In terms to achieve this static composition, the aforementioned RangeMerger class is used.
The trains are labeled uniquely with powers of ’2’ (T0 = 20, T1 = 21, T2 = 22 , … ). Then the
time intervals of those trains passed the given LOS area are determined. For each train the
merge value of the corresponding time interval is set to the unique label (power of ’2’) of the
train. As merge strategy of the RangeMerger class, the sum operation is chosen. This leads to
an overlapping free set of time intervals for which the sum of the powers of two (i. e. the
composition of trains) is constant (see Figure 4.3).
In Figure 4.3 it is shown an example that the interval with merge value 25 contains the trains
T0, T3, and T4 because the sum of 20, 23 and 24 is 25. After this step, the final time intervals
are being collected, summarized and finally written to the wanted train power limit files.
Figure 4.3: Identification of time intervals with a static train composition
REM-S Software Suites [67] 97
4.2 REM-S ONLINE SUITE
The Online Suite is a prototype software implementation of the centralized-decentralized
energy management approach of the REM-S concept (introduced in chapter 2). It allows the
mentioned distributed optimizations within the distributed system (control center, ISSTs,
trains with DOEMs, etc.) during real rail operation (i. e. in real time). The composition of the
Online Suite together with their relation to the field devices and the control center is shown
in Figure 4.4.
Figure 4.4: Software Architecture of REM-S Online Suite
The Global Optimization System (GOS) and the DAO application are located in the control
center. As opposed to the Offline Suite, the DAO application during real time operation does
not rely on the REM-S GUI because it is not directly controlled by a person. Instead, it is
conceptually executed by the GOS application, which orchestrates the Global EMS in an
automated way by sending the optimized operation strategy for the next day to all subordinate
intelligent substations in the form of power profiles, the substations have to comply with.
Similar to the described concept of REM-S, the GOS is connected to one or more LOS
applications through a L2G (LOS-to-GOS) API, which constitute the execution unit of the
Local EMSs. During the online scenario (i. e. field test) on one day only one DAO was
necessary. Thus, the LOS application had access to the DAO data set of only one day, making
98 Chapter 4
the GOS application (shown dashed in Figure 4.4) and the L2G communication unnecessary
for the online scenario. The LOS application running in an ISST is connected with other LOS
applications and trains which themselves execute DOEM applications. In the following
sections, the developed components will be explained in further detail. The DOEM and DAS
(Driver Advisory System) component are not explained in more detail as they are not in the
scope of this research and their design are done by rolling stock company.
4.2.1 GROUND’S LOS APPLICATION
The LOS application locally executes the MAO application and AoERST (Adapted online
Existing Railway Simulation Tool) while maintaining a link to the DAO application through
the GOS application. It coordinates the data and information exchange between MAO and
AoERST application on the one side and, if any, the GOS application on the other side. In
addition, it is conceptually connected to other LOS applications that belong to the same
control center. Each LOS application takes care of the local energy management in its area
thus is connected to all trains having a DOEM onboard. These trains are called flexible trains
while trains without DOEM are called non-flexible trains.
LOS application was written in C++ on the basis of Cygwin which provides a POSIX-API for
Windows [68]. This way, the LOS application is portable without further amendments to
Unix-like operating systems that are compliant with the POSIX standard (IEEE 1003,
ISO/IEC 9945) [69].
In the following, the behavior of the LOS application is described, as it represents the general
approach applied in the MAO. The behavior of the LOS can be implemented as a state
machine and may be abstracted as a flow chart (see Figure 4.5). The states are pictured as bold
ellipses marking those states of the state machine that may serve as entry points from other
states in the flow chart. During runtime, the LOS application starts in the double bordered
ConnectionSetting state and waits for so-called Hello messages dispatched by the trains
inside its area.
As soon as a train has registered, the state switches to GetTimings, where the start time of the
next timeslot to be optimized (tStart) and the next optimization time (tOpt) are determined.
tOpt has to be set ahead of the timeslot to be optimized (tStart) in order to have enough
time for the MAO (e. g. 3 minutes).
REM-S Software Suites [67] 99
Figure 4.5: An excerpt of the LOS application behavior illustrated as a combination of the
states (ellipses) of a state machine and flow charts. The charts between the states show the
control flow within the transitions of the state machine
100 Chapter 4
For 𝑡 being the current time (with 𝑡ℎ the current hour, 𝑡𝑚 the current minute, 𝑡𝑙𝑒𝑎𝑑 the
timespan between tOpt and tStart, and 𝑡𝑠𝑙𝑜𝑡 the length of a timeslot in minutes), the times
tStart and tOpt will be evaluated as shown in Algorithm 3 (Figure 4.6). For example at
t=8:58, with 𝑡𝑠𝑙𝑜𝑡 = 10 and 𝑡𝑙𝑒𝑎𝑑 = 3, the start of the next timeslot to be optimized is
tStart=9:10 where the corresponding MAO needs to start at tOpt=9:07.
Figure 4.6: Algorithm 3- Timings Computations Overview
After the timings have been defined, the state changes to MAO Waiting and waits until tOpt
is reached. Simultaneously, as during each waiting time, the state switches occasionally to
ConnectionChecking in which a heartbeat of a connected DOEM is checked through
T2G_DOEMisAlive(). In a case of a timeout, a connection is reestablished automatically.
As soon as tOpt is reached, the state changes to MAO Opt, where the next optimization step
takes place. It is composed of the following substeps and may be repeated as long as more
negotiation steps are required:
1) The LOS application requests the power profile at the DOEM that corresponds to
the associated flexible train, for the timeslot tStart onwards. If it is the first
negotiation step, no power limits are being sent with the request. Otherwise the ones
from the last negotiation step are used. The DOEM then attempts to generate a train
power profile that fulfills the constraints.
2) As soon as the profile has been received, it is being handed over to the AoERST,
which creates MAO SST power profiles for the involved substations.
REM-S Software Suites [67] 101
3) The power profiles of non-flexible trains for the given timeslot are generated out of
their full day profile.
4) Based on the MAO SST power profiles and the power profiles of all trains, the
MAO application is able to perform the MAO application under the constraints of
the DAO application. This may imply further power limitations for the involved
trains.
5) If the result yields no new power limitations or tStart is already reached, the
current optimization step is aborted and the DOEM attempts to enforce power
limitations on the train based on the last negotiated limitations. If there are new
power limitations, the MAO application goes back to step 1) and continues with the
next negotiation step.
After the whole optimization step is entirely completed or an error has occurred (through
connection loss, data corruption, and so on), the state is changed to GetTimings in which
tStart and tOpt for the next time slot are determined (i. e. for the next optimization step).
At this point, it shall be stated that all timings and retry counts of the LOS application can be
configured completely flexible for different usage scenarios.
4.2.2 GROUND’S AOERST APPLICATION
The Adapted online Existing Railway Simulation Tool (AoERST) calculates the MAO SST
power profiles from train power profiles to be compared with DAO profiles by the MAO
application. During field test, the used AoERST was an online version of railNEOS. The
offline tool is commonly used for energy networks studies; optimum substation location
identification, size and features definition. Its main output is the instantaneous behavior of the
railway system, overhead catenaries voltage and current, substations power demand, and ESSs
energy level. It was adapted for usage as an AoERST in order to calculate only the necessary
information. From train’s driving profile generated by DOEM, AoERST calculates
substations’ power demands (MAO SST power profiles).
4.2.3 GROUND’S TCCS APPLICATION
The Traffic Control Centre Simulator (TCCS) is a GUI application that simulates a traffic
control system which is responsible for updating the timetable as well as showing the current
102 Chapter 4
position of the train, the consumed energy, and the current service number. It automatically
loads so-called RailML files with timetable information corresponding to the service number.
RailML is an open XML based data exchange format for railway applications [70]. Figure 4.7
shows the graphic user interface of TCCS.
Figure 4.7: Graphical user interface of the Traffic Control Centre Simulator (TCCS)
4.2.4 TRAIN-TO-GROUND (T2G) API
A Train-to-Ground API written in C++ was provided by T2G Libraries which are used by the
LOS and TCCS application for communication with the DOEM running on the train.
4.2.5 TRAIN’S DOEM APPLICATION
The DOEM is the mobile part of the Online Suite, installed on-board flexible trains. Its
purpose is to obtain optimized power consumption (balance between traction unit and
auxiliary loads consumption), taking into consideration the timetable and the limitations
imposed by LOS and the infrastructure. DOEM tries to obtain more efficient power and speed
profiles for the traction unit of the train, having the most efficient power profile for the
auxiliary loads and the distribution among them and taking into consideration both the power
limitation for the auxiliary loads and the needs of each one of them. What is more, DOEM
REM-S Software Suites [67] 103
informs the LOS through train power profiles about the behavior of the train, in order to obtain
a real time optimization of the whole system. DOEM also calculates the power consumption
estimation for the next minutes and informs the LOS about it. The negotiation process
concludes with optimized integrated power estimation for the LOS. The TCCS is also
continuously informed about the position of the train and when the TCCS sends a new
timetable, DOEM recalculates the driving profiles [71]. DOEM as a console application
provides information about the current state of the DOEM system and the communication
with LOS and TCCS and so forth. A simple view of DOEM architecture is displayed at
Figure 4.8.
Figure 4.8: DOEM Architecutre [71]
4.2.6 TRAIN’S DAS APPLICATION
The DOEM application is connected to the DAS which provides a GUI as presented in
Figure 4.9 showing all necessary information to the driver. Among others it provides the
following information:
EAT: Expected Arrival Time according to the profile calculated by DOEM
OAT: Official Arrival Time according to the timetable
DEV: Deviation between the Official Arrival Time and the actual arrival time in
seconds
104 Chapter 4
Speedometer: Shows the current speed, the current target speed (red triangle), and
the next target speed (green triangle)
Figure 4.9: Graphical user interface of the Driver Advisory System (DAS)
5 SIMULATIONS AND RESULTS [72]
As it is introduced in section 1.1.5, five different scenarios defined for testing the applicability
of REM-S architecture and REM-S tools. These scenarios had different AC and DC
electrification systems. The Spanish scenario (Malaga-Fuengirola line with a 3kV DC
electrification system) was selected to be tested both in offline simulation and real simulation.
The real simulation was run in Malaga line in December 2015 from 11:00 PM to 3:00 AM in
the attendance of MERLIN partners. The live test was focused on agent-based energy
optimization at MAO. The detail simulation results of this demonstration will be presented in
this chapter.
This chapter starts by analyzing the validation case results, that was defined for checking the
DAO algorithm performance and then followed by analyzes of both offline and online case
studies in Malaga, checking the performance of REM-S offline and online software suites.
5.1 VALIDATION CASE
5.1.1 INTRODUCTION
The validating case study is a simple case study with very limited number of substations and
trains and consequently solutions to enable comparing the results of offline software suit with
manual calculations. The validation case study assumed as a double track line with a 3kV DC
electrification system with 50 km length consisting of two sub1 and sub2 substations. The
topology of the line is displayed in Figure 5.1. For simplifications, the line is neglecting
curves, slopes and speed limitations. In order to limit the number of combinations, an
operation of eight trains from 08:00 to 09:10 is selected for study. During this time, the trains
travel through the line in both A-B and B-A directions. The journey time of each train is 50
minutes. Table 5.2 and Table 5.1 show the energy price and the timetable of trains during this
period.
A B
sub1 sub2
10 km 30 km 10 km
Figure 5.1: Validation case network topology
106 Chapter 5
Table 5.1: Validation case Energy price
Timeslot Price of Energy
(€/MWh)
08:00-09:00 30
09:00-10:00 55
Table 5.2: Validation case timetable
At Service From To
08:00 TR1 A B
08:10 TR2 B A
08:20 TR3 A B
08:30 TR4 B A
08:40 TR5 A B
08:50 TR6 B A
09:00 TR7 A B
09:10 TR8 B A
Each train has three different driving styles as flexibilities, thus 38 = 6561 different
combinations of the train driving profiles are possible. The power profile and speed profile of
different driving styles that are calculated by the trains’ DOEM and proposed to the DAO are
displayed in Figure 5.2.
The driving styles were designed in a way that their differences allow an optimization
potential. For example, the first driving style has no regenerated energy while the two others
have. Alternatively, in opposition to the first and second driving styles, which have smooth
driving patterns, the third driving style has lots of up and downs that represent a volatile
increasing and decreasing of the driving speed. It should be noted that since traffic
management is not in the scope of the REM-S tool, the timetable considered fixed with no
flexibility to change. In addition in this case study, as it is displayed in Figure 5.2, DOEM did
not consider the running time supplement for proposing flexibility to DAO and the flexibility
is only achieved by the different driving strategies.
Simulations and Results [72] 107
108 Chapter 5
Figure 5.2: The power profile and speed profile of three different driving styles that each of
the 8 trains in the validation case can operate in
5.1.2 DAO RESULTS
To validate the DAO results, all 6561 possible combinations of the driving styles for the eight
trains have been calculated in order to find the best result for each objective (energy, cost or
power minimization). The results show that the DAO algorithm completely converged
towards the global optimum. The best energy consumption for the operation of the eight trains
with both methods (GA and manual calculation) is 1148.92 kWh, the lowest cost is 48.135€
and the minimum power peak is 111.92 kW. Therefore, the DAO validation case was verified
successfully.
Table 5.3 compares the global optimum solution of the three objectives with the suboptimal,
average and worst solutions. Although the validation case study has small size and
consequently the optimization algorithm cannot demonstrate its full potential, comparing the
worst or average solutions with the global optimal solution shows significant energy
consumption, cost and power demand savings, based on the optimization objective. On the
other hand, the difference of the global optimal solution and next optimal solution (suboptimal
Simulations and Results [72] 109
solution) is very little (less than 1%), which means if the genetic algorithm, as a heuristic
optimization algorithm, converges to solution close to optimum, this result can be accepted.
Table 5.3: Comparison of different solutions in the validation case
Optimization Objective
Energy Consumption Cost Power Demand
Suboptimal
Solution
Fitness Value 1169.39 kWh 48.87€ 316.77 kW
Difference to global optimal 0.89% 0.17% 0.02%
Average
Solution
Fitness Value 1222.95 kWh 50.91€ 336.97 kW
Difference to global optimal 5.2% 4.1% 6.02%
Worst
Solution
Fitness Value 1267.26 kWh 52.64€ 358.68 kW
Difference to global optimal 8.5% 7.3% 11.7%
In Figure 5.3, the DAO is implemented considering an Electrical Storage System for the power
demand optimization. The upper subplot shows that the storage is charged during the non-
peak time to feed the substation at the peak time. The lower subplot shows that by using the
storage all power peaks are leveled out and the electrical energy at the substation is purchased
with a constant rate over time after using Electrical Storage System.
The specification of the Electrical Storage System considered in the validation case is
presented at Table 5.4.
Table 5.4: Specification of Electrical Storage used in the validation and offline cases
Capacity
(kWh)
Max.
charging rate
(kW)
Max.
discharging rate
(kW)
Energy charging
efficiency (%)
Energy discharging
efficiency (%)
500 2000 1500 98 95
Figure 5.4 shows the results of the Electrical Storage System used for cost optimization for
one day test. The storage stores electricity at the hours with low energy prices and sells it later
at hours with high prices. The upper subplot of Figure 5.4 shows the charging/discharging
power profile of the storage and the electricity price during 24 hours. The lower subplot
illustrates the state of charge of the storage. It can be seen that the storage becomes fully
charged within two hours and remains full charge until high electricity price levels is
encountered. At the high electricity price period, the storage is discharged to feed the
substation.
110 Chapter 5
Figure 5.3: Validation case- Applying an Electrical Storage System for peak shaving in a
traction power substation
Figure 5.4: Validation case- Applying an Electrical Storage System for cost minimization
Optimized Storage power profile
Substation power profile without ESS Substation power profile with ESS
Simulations and Results [72] 111
5.2 OFFLINE CASE
5.2.1 INTRODUCTION
The real offline case study belongs to the Málaga-Fuengirola line with a 3kV DC
electrification system. Figure 5.5 shows the network configuration of this case study.
Figure 5.5: Schematic network of Malaga-Fuengirola case study
Málaga-Fuengirola line is a 30 km long suburban train line with single and double tracks. The
line is supplied by three electrical substations: Los Prados, La Comba and Carvajal. Among
these substations, Carvajal substation is a reversible substation. CIVIA trains circulate through
with a frequency of 20 minutes. The journey lasts 46 minutes and the maximum line speed is
about 135 km/h. The traffic information of this line is presented at Table 5.5.
112 Chapter 5
Table 5.5: Offline case- traffic information
Line Malaga-Fuengirola Fuengirola-Malaga
Circulations 47 46
Start (h) 5:20 6:10
End (h) 23:30 00:20
Frequency (min) 20 20
Journey time (min) 46 46
Maximum speed line 135 km/h
Number of stations for commercial stops
(including start and end point of service) 18
5.2.2 GA PARAMETERS SETTING
5.2.2.1 DIVERSITY MECHANISM
The GOSET algorithm includes 4 different mechanisms for diversity control. Their purpose
is to prevent getting stuck on values near the temporal optimal solutions and to extend the
search space of the train and EC profiles. While diversity control can have influence on the
simulation progress and the simulation time [58], the accuracy and the computation speed of
these 4 mechanisms is evaluated by optimizing this scenario with a population size of 10, the
results are illustrated in the following.
In Figure 5.6, the optimization progress over the generations is displayed. For each
mechanism, 5 different simulations have been executed and the average negative fitness value
is plotted. As it can be seen, the simulation progress is similar for all cases. Below generation
numbers of 1500, mechanism 3 has slightly better fitness values.
Simulations and Results [72] 113
Figure 5.6: Optimization progress of different diversity mechanisms
The computation time of the simulations have been recorded. In order to compare the
computation times, the values have been arithmetically averaged and normalized with the
value for the highest computation time. The result is shown in Table 5.6. As it can be seen, all
computation times are alike. While mechanism 1 and 2 require the most time, mechanism 3
and 4 are 1.5% and 2.6% faster.
Table 5.6: Normalized computation times for the different diversity mechanisms
Diversity Mechnism Normalized time
1 0.992
2 1
3 0.985
4 0.973
The results indicate that the diversity mechanism has not a big influence on the optimization.
No significant differences are resulting neither in the optimization progress nor in the
computational time. Because of the slightly faster progress and little lower run times,
mechanism 3 is used for the DAO.
5.2.2.2 POPULATION SIZE
The population size is next to the number of generations the most important setting for the
GA. There is a direct correlation between the population size and the computational time; a
114 Chapter 5
double of the population size will result in twice the GA run time for the same number of
generations. Thus big populations need faster progress to be considered efficient in
comparison with small populations. Figure 5.7 shows the optimization progress over the
number of generations for different population sizes for the optimization. Each optimization
has been executed 5 times and the average of the negative fitness values are used for the plot.
Figure 5.7: Optimization progress of different population sizes
It can be seen, that the optimization progress is increasing from population size 2 to 5 and
further from 5 to 10. The progress for population size 10 and 20 is almost the same. But
considering that the computational time at least doubles for each increase in population size,
the fastest progress is made by the lowest population size of 2.
A possible explanation for this behavior is, that the near optimal solutions all lay close together
in the parameter space. The diversity mechanism would prohibit that most of the individuals
search at similar locations for big populations. In this case big populations won’t lead to faster
progress. In that case, small populations and a big number of generations should be chosen as
optimization parameters to make fast progress.
It should be noted, that this behavior is problem dependent. Further evaluations of other cases
would allow further insights. Thus no general statement regarding the ideal population size
can be made without further investigation.
Simulations and Results [72] 115
5.2.3 DAO RESULTS
Figure 5.8 compares the DAO results (red line) with a random solution (blue bars) for the
power demand optimization objective. A random solution is used for the comparison while
the train driving styles obtained by drivers are estimated to be random within the driving style
limits. Figure 5.8 demonstrates the significant effect the DAO has on the peak shaving during
most of the time intervals.
Figure 5.8: Offline case- Comparison of substation power demand for a random solution and
the DAO solution
Figure 5.9 shows that the Electrical Storage System can level out the substation power profile
even more, the peaks are reduced around 20%. The storage is charged in the morning when
there are no trains on the way and discharged during the high traffic of network. Thereby the
Electrical Storage System supports the substation in feeding the trains in order to reach a
smoother power profile. The constraint, that the state of charge of the Electrical Storage
System at the end of the day has to be the same as in the beginning of the day, is also satisfied,
as it can be seen in Figure 5.9. The applied Electrical Storage System specification is presented
at Table 5.4.
116 Chapter 5
Figure 5.9: Offline case- Applying Electrical Storage System for peak shaving in traction
power substation
5.2.4 MAO RESULTS
The results shown in this chapter are based on the MAO offline simulation with the power
demand optimization objective for the Málaga-Fuengirola line. All the trains are considered
flexible with the ability to follow the control area manager instructions to recalculate an
optimized MAO estimation. The following shows the settings of this simulation:
MAO timeslot duration: 15 min
Amount of MAO slots: 1
Maximum negotiation trials between MAO and DOEM: 2
Analyzed substations: Carvajal, La Comba (reversible substation) and Los Prados
Limitations received from DAO for each substation:
o Carvajal: 2050 kW
o La Comba: 2125 kW
o Los Prados: 2400 kW
Timetable: Timeslot from 19h00 to 19h15 (See Table 5.7)
Storage power profile
Battery state of charge profile
Simulations and Results [72] 117
Table 5.7: Offline case- timetable of trains pass through the line for 19h00 to 19h15 timeslot
From To Train Departure Arrival
Fuengirola Malaga TR3 18:20 19:06
Malaga Fuengirola TR4 18:30 19:16
Fuengirola Malaga TR5 18:40 19:26
Malaga Fuengirola TR6 18:50 19:36
Fuengirola Malaga TR7 19:00 19:46 +1
Malaga Fuengirola TR8 19:10 19:56
Figure 5.8 shows the comparison between the base substation power profile (without
optimization) and the REM-S contribution profiles for one MAO Slot from 19h00 to 19h15.
P1 shows the original substation power profile and P2 shows the final optimized power profile
(after two trail negotiations).
It can be seen that the maximum power peak (Figure 5.8-M1) at the Los Prados and La Comba
substations could be reduced between 16 % and 18 % only by the contributions of trains in
changing their power profiles based on control area manager indications provided by the
MAO. The power profile changes are achieved by moving the peak period or by removing
peak.
In the Carvajal substation, placed at the end of the line, the reduction of the highest power
peak (Figure 5.8-M1) is approximately 2%. The reason is that the train with the biggest share
of the power peak (TR7) at M1 propose no flexibility and it needs to drive fast (with high
power consumption) in order to arrive on time. However as it can be seen in the Carvajal
power profile in Figure 5.8 M2 and M3 are reduced by 55% and 25% respectively. Table 5.8:
MAO power limitation indications of TR5 and TR7 for peak shaving shows the MAO
indications of two trains in Carvajal and La Comba at the 19h00 to 19h15 timeslots. It can be
seen how the MAO proposes changes to the train’s power profiles in order to get rid of the
peaks at the substations.
118 Chapter 5
P1: Without REM-S P2: With REM-S
Figure 5.10: Offline case- Comparing power profiles of substations before and after using
REM-S
M1
M1 M2 M3
M1 M2
Simulations and Results [72] 119
Table 5.8: MAO power limitation indications of TR5 and TR7 for peak shaving
Peak Time TR5 limit TR7 limit
Carvajal- M1 43-60 443 kW 1320 kW
Carvajal- M2 512-513 41 kW 338 kW
Carvajal- M3 155-157 No limit- in regeneration mode 1880 kW
La Comba- M1 880-881 977 kW 842 kW
Figure 5.11 and Figure 5.12 show how the trains change their speed profile and consequently
their power profile to fulfil the MAO limitations listed in Table 5.8. At Figure 5.11 it can be
seen that in TR5 for fulfilling the MAO target, the gradient of speed is increased at departing
from some stations.
P1, S1: Without REM-S P2, S2: With REM-S
Figure 5.11: TR5 power and speed profile
For TR7, which has no flexibility in its schedule, Figure 5.12 shows that in first round of the
simulation (P2) most of the peak times are shifted. As one of the main restrictions for
optimizing the Carvajal substation’s power profile is the flexibility of TR7, in another round
of simulation its timetable is updated by adding one minute of running time to this train. The
only change applied to the timetable is the arrival time, which means permitting the train to
arrive one minute later than its expected time. The updated power and speed profile of TR7 is
120 Chapter 5
shown in Figure 5.12 as P3. Giving one minute running time supplement to TR7, reduced the
peak power at the Carvajal substation around 20%. Carvajal’s new power profile is shown in
Figure 5.13 as P3.
P1, S1: Without REM-S
P2, S2: With REM-S
P3, S3: With REM-S (considering 1 min running time supplement)
Figure 5.12: TR7 power and speed profile
P1: Without REM-S P3: With REM-S (considering 1 min running time supplement)
Figure 5.13: Carvajal power profile after giving flexibility (one minute running time
supplement) to TR7
M1 M2 M3
Simulations and Results [72] 121
5.3 ONLINE CASE
5.3.1 INTRODUCTION
The following main features were validated during the tests carried out on the Malaga
Fuengirola line in real time operation:
LOS–DOEM communication
MAO Estimation request
Power limitation creation by LOS
MAO Estimation calculation without limitations
MAO Estimation calculation with limitations
Advice generation for the driver
New timetable reception and processing to calculate a new profile until next station
Substation failure detection and corresponding MAO negotiation process
Traction inverter failure detection and driving profile recalculation
Traffic congestion detection and DOEM reaction
Figure 5.14 shows the Malaga demo train and a picture of the MERLIN software suite that is
installed on the tested train.
Figure 5.14: MERLIN software suite installed on the Renfe train (Malaga demo)
122 Chapter 5
Two test cases of online demonstrations that are mainly related to LOS functionality, are
briefly described here: normal operation case and substation failure case
Normal operation: This test aims to describe the usual operation when DOEM is included on
a train and connected to LOS without any disturbances.
At normal operation, every 10 minutes (duration of a timeslots in the online demonstration
case) the LOS requests DOEM for MAO estimation to predict the control area power/energy
consumption as part of the MAO procedure. Meanwhile the driver runs the train according to
the recommendations shown on the screen (Figure 5.14), allowing the train to reach the
stations on time and with an efficient driving style. Without disturbances the system behaves
as expected and fulfils the DAO forecast, therefore no more negotiations between DOEM and
LOS is needed and the first MAO estimation is accepted.
Figure 5.15 shows the negotiation steps between LOS and the onboard DOEM of each train
at normal operation of system.
Figure 5.15: Normal operation steps [71]
Simulations and Results [72] 123
Substation failure: This test aims to describe the operational REM-S behavior under a
substation failure situation. As the REM-S architecture, every timeslot (here: 10 minutes) the
LOS requests DOEM for MAO Estimation to predict the power/energy consumption for the
control area. Due to a failure at the Carvajal substation, La Comba and Los Prados substations
should provide extra power/energy, which is considered as a disturbance and the REM-S
requests with a minimization of the deviation by the DAO. The LOS does not accept the MAO
estimation received from the train and requests power peak reduction by power/energy
limitation indications.
From the architectural point of view, the system cannot work with the remaining substations
if the demanded power from the train is not reduced. In other words, the RTO actions are not
sufficient and the LOS launches a new MAO procedure. As result, the test train receives some
power constraints and generates a new driving profile, trying to fulfil the received limitations
and arriving on time.
5.3.2 MAO RESULTS
In substation failure case, the reaction capacity of the LOS is tested. It shows the feasibility
of having fewer substations (or smaller ones, or ones with lower contracted power) in the line,
if the overall power consumption is managed by the REM-S. It is checked that the resulting
consumed power is compatible with the remaining substations and that the constrained trains
fulfil the indications. Figure 5.16 shows the power and speed profile calculated by the DOEM
after receiving indications from the LOS. In the red square, the decrease in power consumption
and speed is shown due to limiting the traction capacity received from the LOS. The profiles
in Figure 5.16 after second and third negotiations are reaching higher speed values in order to
arrive on time at the Carvajal station without accelerating fast.
Taking into account the profile calculated by the DOEM regarding to the LOS limitations,
Figure 5.17 shows the train’s real behavior (power and speed profiles) measured directly from
the Multifunctional Vehicle Bus (MVB) of the train S/463.
The figure shows that the driver is not following the indications from the Driving Advisory
System (DAS) exactly. While the indication suggests keeping constant speed values, the train
at first is reducing its speed and has to accelerate later on in order to arrive on time. This
acceleration produces unnecessary power peaks that could be avoided by following the
DOEM indications. It is clear that by manual driving the stability and similarity of driving to
the indications is not as good as automatic driving.
124 Chapter 5
1st profile (after 1st negotiation)
2nd profile (after 2nd negotiation)
3rd profile (after 3rd negotiation)
Figure 5.16: Power and speed profile of train S/463 calculated by DOEM for three
negotiations with LOS [71]
Time (hh:mm:ss)
Po
wer
(kW
)
Time (hh:mm:ss)
Spee
d (
km/h
)
Simulations and Results [72] 125
Real profile
Suggested profile
Figure 5.17: Comparing the real power and speed profile of train S/463 with the calculated
profiles of the DOEM [71]
One of the aspects for assessment of correct functionality of the REM-S Online Suite is the
timing of sub-procedures. It is important that the duration of sub-procedures within one MAO
optimization step are short enough for correct communication in realistic timeslots and
optimization deadlines. During the online demonstration test drive, only 2 minutes (usually 3-
126 Chapter 5
5 minutes) were chosen as maximum time for one optimization step that can consist of up to
3 negotiation steps. In Table 5.9 the durations of the sub-procedures of a typical optimization
step measured at online demonstration case are listed.
Table 5.9: Duration of sub-procedures during one minute-ahead optimization step performed
by LOS
Time Sub-Procedure Duration (s)
00:01 Initial MAO request (no limitations) 9
00:10 Execution of AoERST 14
00:24 Execution of MAO 1
00:25 1. MAO request (13 limitations) 18
00:43 Execution of AoERST 13
00:56 Execution of MAO <1
00:56 2. MAO request (25 limitations) 9
01:05 Execution of AoERST 14
01:19 Execution of MAO <1
During sending of a MAO request from LOS and the execution of AoERST there are also
calculations performed by DOEM on the belonging train for a new power estimation profile
of the train which has to be sent to the LOS. Table 5.9 shows that despite longer durations of
this sub-procedure also caused by the used 3G mobile Internet connection between the LOS
application at a station and the DOEM onboard the train, there was enough time for the
executions of the MAO and railNEOS application. Therefore, all needed negotiation steps
fitted within the specified maximum optimization duration of 2 minutes. So, the sub-
procedure timings tested here to ensure the correct communication timings, is fulfilled.
6 CONCLUSION AND FUTURE WORK
6.1 CONCLUSION
A novel railway energy management architecture is presented in this dissertation at chapter 2.
Its development demonstrates achievements in three main domains:
The mapping of the railway system onto the smart grid concept, the first step in the direction
of harmonization of standards of smart grid and railway systems in the area of energy
management and mapping and development of the new architecture for railway systems onto
the reference architecture of the smart grid.
The smart grid concept in railway systems span the centralized-decentralized automation
architecture, the adoption of different time horizons (Day ahead, Minutes ahead and Real-
time) and the creation of flexibility with DOEM, DER, EC and ESS. The adoption of the
SGAM framework yields the interoperability of different layers and the interoperability with
the rest of the smart grid system.
The standardization analysis identifies which railway or smart grid standards are applicable
in REM-S and which parts need extension. In support of this, some recommendations were
identified for TECREC7 especially in the field of rolling stock with the ground
communication and energy related parts of data modelling.
In order to check the applicability of architecture and evaluate its functionalites, the REM-S
offline and online software suites are developed. The REM-S offline and online software
suites are implemented based on the use cases, functions and information exchange.The
centralized and decentralized optimization approaches of these software suites are introduced
in chapter 3 while their specification are presented in chapter 4. The target of these software
suites are implementing an optimal energy management in railway system while integrating
all energy players of the system. The energy management efficiency is evaluated regarding
three different objectives: cost optimization or energy consumption optimization or power
demand optimization.
7 www.tecrec-rail.org/
128 Chapter 6
For the evaluation of REM-S software suites, three different cases are presented at chapter 5:
validation case, offline case and online demonstration in real life. In all cases, the results prove
the effectiveness of REM-S.
In the simple validation case, the DAO results are compared to a manual evaluation of all
possible system behaviors. It is shown that the DAO reaches the global optimum. Analyzing
suboptimal solutions showed that there is less than 1 % difference between the suboptimal
and global optimal results. This indicates that near optimal solutions are sufficiently good and
can be used in the REM-S process.
Running the DAO in the presence of Electrical Storage Systems was studied in the validation
and the real case study. It is shown that the storage can be used efficiently for power peak
shaving (the minimum reduction is 20%) and cost reduction.
The MAO results, in both offline and online cases, showed the reduction of first peaks in all
substations (the percentage depends on the flexibility offered from trains) and second and
third peaks even up to 55%. It is shown that using one minute running time supplement in the
operation of one train, can reduce the power peak around 20% at the related substation. By
using running time supplement, timetable flexibility is used in MAO.
The proposed energy management system in this dissertation also select best driving style of
all trains passing through the whole system from substations point of view in DAO on one
hand and effect on the trains driving style by sending indications in MAO on the other hand.
In the online demonstration for real time run, during the degraded mode (one substation
failure), the LOS negotiates correctly and ask train for minimizing deviation from DAO plan
by sending power limitation. The constrained train fulfilled the indications and arrived on time
to all stations. On the other hand, the sub-procedures timings tested in online case with
reasonable running time of MAO and communication timings between LOS and DOEM.
6.2 FUTURE WORK
To improve the architecture, optimization algorithms and software suites in future, the
following activities are proposed:
A business model is developed in the framework of REM-S architecture [73]. The
proposed business model needs to be expanded more by describing the business
Conclusion and Future work 129
processes and its interaction to liberalize electricity market and to use liberalization
in the railway business model as well.
In this dissertation, three objectives (energy consumption, power demand and cost
optimization) were evaluated separately in three separated problems. It is proposed
to define a unique objective function by merging the three objectives in order to find
the best solutions regarding reduction in energy consumption, power demand and
cost simultaneously.
The focus of this work was designing the REM-S architecture and the algorithms
and softwares developed to demonstrate the architecture, therefore finding better
algorithms for optimization especially in the sector of MAO (like greedy
algorithms) is another place to work.
Because of field demonstration constraints in implementing Train-to-Ground
information exchange between DOEM and REM-S online software suite, JADE on
the standardized FIPA-ACL couldn’t be applied for implementing agent-based
system in MAO. In future it is better to use FIPA-ACL based communication
protocol on both train and ground applications.
The architecture, algorithms and software suites need to be tested in several offline
and online use cases to find out better their weaknesses and strengths. The use cases
like running simulations with different pricing strategies, simulating DAO and
MAO with several control areas, simulating MAO in the presence of ESS, applying
DAO in online demonstration (GOS) and DAO and MAO simulation in the
presence of freight trains and grey trains.
APPENDIX
RELATED PUBLICATIONS
Publications in Scientific Journals
S. Khayyam, F. Ponci, J. Goikoetxea, V. Recagno, V. Bagliano and A. Monti, "Railway
Energy Management System: Centralized-decentralized Automation Architecture",
IEEE Transaction on Smart Grid, vol. 7, no. 2, pp. 1164-1175, March 2016.
S. Khayyam, N. Berr, L. Razik, M. Fleck, F. Ponci and A. Monti, "Railway System
Energy Management Optimization demonstrated at Offline and Online Case studies",
IEEE Transaction on Intelligent Transportation, September 2018.
L. Razik, N. Berr, S. Khayyam, F. Ponci and A. Monti, “REM-S–Railway Energy
Management in Real Rail Operation”, IEEE Transactions on Vehicular Technology,
accepted.
Publications in Scientific Magazines
S. Khayyam, A. Monti, V. Bagliano, I. De Keyzer, “Making energy management in
the railway system smarter”, European Railway Review- Sustainable Rail
Developments”, Issue 3, August 2015.
Publications in Scientific Conferences
S. Khayyam, Z. Huang, E. Pilo de la Fuente, I. Gonzalez, V. Bagliano and A. Monti,
"Evolution of Business Model in Railway Industry in the Presence of Energy
Management System", CIRED Workshop, Helsinki, Finland, June 2016.
Appendix 131
M. Fleck, S. Khayyam and A. Monti, "Day-ahead optimization for railway energy
management system", in International Conference on Electrical Systems for Aircraft,
Railway, Ship Propulsion and Road Vehicles & International Transportation
Electrification Conference (ESARS-ITEC), Toulouse, France , Nov. 2016
S. Khayyam, F. Ponci, H. Lakhdar and A. Monti, "Agent-based energy management
in railways", in International Conference on Electrical Systems for Aircraft, Railway,
Ship Propulsion and Road Vehicles (ESARS), Aachen, Germany, March 2015.
Technical Reports
MERLIN partners, “Reference architecture for Operational REM System Making”,
MERLIN project, Deliverable D2.3, April 2015.
MERLIN partners, “MERLIN Business Models”, MERLIN project, Deliverable D2.4,
July 2015.
MERLIN partners, “Detailed Architecture of REM System”, MERLIN project,
Deliverable D4.1, May 2014.
MERLIN partners, “Preliminary design of the wayside energy dispatcher”, MERLIN
project, Deliverable D4.2, July 2015.
MERLIN partners, "Energy Purchase Decision Maker Algorithm Definition", MERLIN
Project, Deliverable D4.4, 2015.
MERLIN partners, “Operational REM-S assessment report”, MERLIN Project,
Deliverable D6.3, 2015.
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LIST OF FIGURES
Figure 1.1: Energy flow of a European country case [7] ........................................................ 2
Figure 1.2: Main solutions for saving energy in urban rail ..................................................... 6
Figure 1.3: MERLIN developed tools in different areas ...................................................... 15
Figure 2.1: SGAM Framework [49] ..................................................................................... 23
Figure 2.2: mapping distributed control on railway electrification system ........................... 24
Figure 2.3: REM-S Automation Architecture Concept ........................................................ 25
Figure 2.4: Minute Ahead normal Operation Sequence diagram ......................................... 30
Figure 2.5: DAO, MAO and RTO Function Layer in SGAM Plane .................................... 32
Figure 2.6: Interactions of Business actors in the presence of REM-S ................................. 42
Figure 2.7. DAO, MAO and RTO Component Layer in SGAM Plane ................................ 47
Figure 2.8: Detailed REM-S Communication Layer [57].................................................... 58
Figure 3.1: Generic flowchart of DAO algorithm [64] ......................................................... 74
Figure 3.2: Generic flow chart Energy/Cost optimization for Train and external consumer 76
Figure 3.3: Generic flow chart of power demand optimization for Train and EC [64] ......... 79
Figure 3.4: MAO general steps ............................................................................................ 83
Figure 3.5: Communications in normal mode ...................................................................... 86
Figure 3.6: Communications in degraded mode ................................................................... 87
Figure 3.7: Identification of the time intervals containing elevated peaks ............................ 88
Figure 4.1: Grafic user interface (GUI) of REM-S ............................................................... 94
144
Figure 4.2: Pre Harmonization and Post Harmonization time intervals ............................... 95
Figure 4.3: Identification of time intervals with a static train composition .......................... 96
Figure 4.4: Software Architecture of REM-S Online Suite .................................................. 97
Figure 4.5: An excerpt of the LOS application behavior illustrated as a combination of the
states (ellipses) of a state machine and flow charts. The charts between the states show the
control flow within the transitions of the state machine ....................................................... 99
Figure 4.6: Algorithm 3- Timings Computations Overview ............................................... 100
Figure 4.7: Graphical user interface of the Traffic Control Centre Simulator (TCCS) ....... 102
Figure 4.8: DOEM Architecutre [71].................................................................................. 103
Figure 4.9: Graphical user interface of the Driver Advisory System (DAS) ...................... 104
Figure 5.1: Validation case network topology .................................................................... 105
Figure 5.2: The power profile and speed profile of three different driving styles that each of
the 8 trains in the validation case can operate in ................................................................. 108
Figure 5.3: Validation case- Applying an Electrical Storage System for peak shaving in a
traction power substation .................................................................................................... 110
Figure 5.4: Validation case- Applying an Electrical Storage System for cost minimization
............................................................................................................................................ 110
Figure 5.5: Schematic network of Malaga-Fuengirola case study ...................................... 111
Figure 5.6: Optimization progress of different diversity mechanisms ................................ 113
Figure 5.7: Optimization progress of different population sizes ......................................... 114
Figure 5.8: Offline case- Comparison of substation power demand for a random solution and
the DAO solution ................................................................................................................ 115
List of Figures 145
Figure 5.9: Offline case- Applying Electrical Storage System for peak shaving in traction
power substation ................................................................................................................. 116
Figure 5.10: Offline case- Comparing power profiles of substations before and after using
REM-S ............................................................................................................................... 118
Figure 5.11: TR5 power and speed profile ......................................................................... 119
Figure 5.12: TR7 power and speed profile ......................................................................... 120
Figure 5.13: Carvajal power profile after giving flexibility (one minute running time
supplement) to TR7 ............................................................................................................ 120
Figure 5.14: MERLIN software suite installed on the Renfe train (Malaga demo) ............ 121
Figure 5.15: Normal operation steps [71] ........................................................................... 122
Figure 5.16: Power and speed profile of train S/463 calculated by DOEM for three
negotiations with LOS [71] ................................................................................................ 124
Figure 5.17: Comparing the real power and speed profile of train S/463 with the calculated
profiles of the DOEM [71] ................................................................................................. 125
LIST OF TABLES
Table 1.1: The Railenergy technologies investigated [45] .................................................. 13
Table 1.2: General evaluation of energy efficiency measures in urban rail systems [6] ...... 14
Table 2.1: Use Case Cluster, HLUC and Primary Use case ................................................. 28
Table 2.2: Energy Trading Estimation Processes ................................................................. 44
Table 2.3: Energy Trading Processes ................................................................................... 45
Table 2.4: Components related to each function at DAO, MAO and RTO .......................... 48
Table 2.5: Information exchange at DAO, MAO and RTO.................................................. 52
Table 2.6: Required standards for information exchange at DAO, MAO and RTO ............. 55
Table 5.1: Validation case Energy price ............................................................................. 106
Table 5.2: Validation case timetable .................................................................................. 106
Table 5.3: Comparison of different solutions in the validation case ................................... 109
Table 5.4: Specification of Electrical Storage used in the validation and offline cases ...... 109
Table 5.5: Offline case- traffic information ........................................................................ 112
Table 5.6: Normalized computation times for the different diversity mechanisms ............ 113
Table 5.7: Offline case- timetable of trains pass through the line for 19h00 to 19h15 timeslot
........................................................................................................................................... 117
Table 5.8: MAO power limitation indications of TR5 and TR7 for peak shaving ............. 119
Table 5.9: Duration of sub-procedures during one minute-ahead optimization step performed
by LOS ............................................................................................................................... 126
CURRICULUM VITAE
Personal Information
Work Experience
Since 12.2012
Research Associate, RWTH Aachen, Institute for
Automation of Complex Power Systems (ACS), Aachen,
Germany
06.2005 – 12.2012 Research Associate, Niroo Research Institute, Tehran, Iran
09.2003 – 06.2005 Research Expert, Vatan Niroo GmBH, Karaj, Iran
Education
09.2004 – 06.2007 Iran University of Science and Technology, Tehran, Iran
Degree: Master of Science (M.Sc.)
Field of study: Electrical Engineering
09.1998 – 09.2003 K.N. Toosi University of Technology, Tehran, Iran
Degree: Bachelor of Science (B.Sc.)
Field of study: Electrical Engineering
Name: Sara Khayyamim
E-Mail [email protected]
Date of birth: 16 September 1980
Place of birth: Esfahan, Iran
Marital status: Married, 2 Kids
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Optimization of Geothermal
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Modeling Methodologies for
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Measurement System and
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A Medium-Voltage Multi-
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The Diffusion of Selected
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Design considerations and
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Applications of Arbitrary
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The Energiewende in the
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Decision-Making under Multi-
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Design of Novel Control
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System-Level Multi-Physics
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Stochastics-based Methods
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Supply Temperature Control
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Residential City Districts as
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Advanced Control Methods for
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Active Thermal Management
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Development of SiC GTO
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Occupants' Behavior and its
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A Multi-Agent-based
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New Approaches to Dynamic
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The Growing ESCO Market for
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Agentenbasierte
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Analysis of Medium-Voltage
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Upscaling Permeability for
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Data-Driven Approaches for
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Betriebsverhalten freier
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A Digital Hardware Platform
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Predictive Demand Side
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Applications of Paraffin-Water
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Identification of Characteristic
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Experimental evidence of
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Development of Exergy-based
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Quantifying and Aggregating
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Graph Framework for
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Quantifying the Role of Energy
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Parametrierbare
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Virtualization as an Enabler for
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