Analysis, Evaluation and Simulation of Railway Diesel-Electric and Hybrid Units As Distributed Energy Resources

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Article Analysis, Evaluation and Simulation of Railway Diesel-Electric and Hybrid Units as Distributed Energy Resources

Ana García-Garre and Antonio Gabaldón *

Electrical Engineering Area, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain * Correspondence: [email protected]; Tel.: +34-968-338944

Received: 17 July 2019; Accepted: 29 August 2019; Published: 2 September 2019

Abstract: The objective of this paper involves the analysis, identification and evaluation of different possibilities offered by technology for the improvement and the management of the use of energy and hybridization in railways: On board generation, demand response and energy storage, both in traction and auxiliary loads, considering the aggregation of resources and its stochastic nature. The paper takes into account the importance of efficient use of energy in railways, both currently (trains in service, prototypes) and in the future, considering the trends driven by energy policy scenarios (2030–2050) that will affect service and operation of units during their lifetime. A new activity has been considered that will be relevant in the future in the framework of a new electricity supply paradigm: Smart-Grids. According to this paradigm, the interaction of the Electric Power System and the Railway Supply System (somehow embedded in the Power System) will bring new opportunities for the collaboration of these two systems to perform, in a wise economic fashion, a better and more reliable operation of the complete energy system. The paper is focused on a mixed profile with low-medium traffic (passenger and freight): The first part of the route is electrified (3 kV DC catenary) whereas the second part is not electrified. Results justify that complex policies and objectives bring an opportunity to make cost-effective the hybridization of railway units, especially in low/medium traffic lines, which improves their social and economic sustainability.

Keywords: railways; energy storage; regenerative braking; demand response; energy eﬃciency; load modeling; aggregation

1. Introduction Different policies for improving Energy Efficiency (EE) are a cumber stone for sustainability objectives both for the European Union (EU) and for other countries worldwide. Transportation explains a high percent of the final use of energy, and should have considerable potential for improvement. For this reason, in last decade sustainability is also an important issue for the main associations of the railway sector, such as the International Union of Railways (UIC) and the Community of European Railway and Infrastructure Companies (CER) [1,2]. For these associations, and taking 1990 consumption baseline, the objectives are: Fifty percent reduction in the intensity of energy use (energy per passenger and km, i.e., kWh/pkm, or energy per ton and km, i.e., kWh/tkm) in 2030 (0.09 kWh /pkm), and sixty percent in 2050. In terms of environmental objectives, a reduction in emissions of 75% in 2050 is foreseen. To limit the expansion and dominance of road-based transportation, and to increase the market share of the so-called eco-friendly transport modes (railways and maritime transport), different countries are working in different working areas: For instance, Poland through a model of national intermodal transport [3], or the One Belt One Road Initiative (OBOR) of China, and,

Appl. Sci. 2019, 9, 3605; doi:10.3390/app9173605 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3605 2 of 40 especially, the OBOR container freight trains operating since 2011 between Europe and China. Around 13,000 trains have been operated in the last eight years [4]. Railway transportation has several advantages that explain its development in the last decade both in the passenger sector (for instance, the development of 25,000 km of high-speed infrastructures in China [5]), but mainly in the freight sector: Short time (one-third of shipping), no weather influence, high safety, green environmental protection, and its complementarity with shipping transportation (and the development of new harbor infrastructures around the world [5]). An important remark for railways is that passenger transport is cost-effective in some countries, but only for high-speed services (for example in Spain). Freight transport produces profits and predominates in some countries (Canada and the USA), or is operated with high capacity factors (up to 90% in the North Rail Express Freight service, in the north of Europe), in the case of Deutsche Bahn (DB) in Germany (the largest market for freight). For instance, the freight operator DB Cargo will raise its rail traffic volumes in Germany by 70% by 2038–2050 [6]). But it is also known that conventional passenger and freight services very much rely, on a great deal, upon of subsidies from their governments (e.g., China Railway Express [5]). This can be improved through better use of energy (an important cost for the operation of railways) with energy management systems that look for optimal management in railway system, while integrating all energy resources of the system [7,8]. The objectives of different associations and operators in the medium and long term involve the integration of technologies and procedures that have the potential to reduce the impact of the transportation sector on the overall energy demand. Especially, improvements in the following items are considered by international associations (e.g., UIC) and railway operators (e.g., DB), but also by authors in this work: Improvements of efficiency in locomotives with internal combustion engines (ICE): Diesel-electric haulage is a well-known technology, but this alternative has a considerable uncertainty in the future because electric traction has a considerable superiority (more power, efficiency, or lower maintenance costs). Nowadays, diesel locomotives still are in excess in some countries and are used more and more at a minimum level. Nevertheless, they are necessary because railway systems usually have a significant percentage of non-electrified routes. The idea is optimizing efficiency, through storage and regenerative braking, and, besides, to study the possibilities of static and dynamic storage and generation. It is necessary to take into account that locomotives demand the full power of its engine in a limited percent of the time (for instance 10%), and often they work with low efficiency, especially in diesel-electric units. Energy generation and management of “auxiliary” services for vehicles: According to UIC reports [2] around 6% of potential savings can be achieved with these measures. It should be taking into account that train hotel/power loads represent 10–30% of overall demand, and these loads are quite similar to conventional loads, that usually are managed in conventional (public) Power Systems to avoid power peaks and improve capacity factors of industrial and commercial energy systems. The use of more renewable energy: This is a topic related to the sustainability of energy systems that have also been joined by railway operators. For example, DB (Germany) has also advanced toward its goal of using 100% renewable energy and will reach it already in 2038, far ahead of its original goal of 2050 [6]. This objective involves the necessity for greater flexibility in demand, at an aggregated level (significant from the point of view of the energy system), through the use of flexible resources, such as Demand Response and Energy Storage that balance the volatility of renewable resources. These alternatives are developed in more detail through this work. Hybridization: The supply of energy from two or several sources of energy/power offers more possibilities for the operation of the vehicles (electrified and non-electrified routes) and an increased reliability, but also involves a higher capital cost (several energy conversion devices), space’s requirements (e.g., racks for batteries or tanks for storage of the fuel/gas/hydrogen), weight increase, as well as new concerns related to the safety of passengers -in certain options-. For the evaluation of these advantages and drawbacks, several topics will be analyzed. First, existing vehicles and “potential” vehicles with alternative energy supplies (with respect to conventional diesel and electric haulage) will Appl. Sci. 2019, 9, 3605 3 of 40 be considered. Second, it is possible that these hybrid vehicles will be used for the management of energy in railway power networks.

1.1. Energy and Load Indicators For evaluation and comparison purposes, representative indicators (the so-called Key Performance Indicators, KPI [2]) will be considered for discussing the benefits and drawbacks for the different technological solutions discussed in this paper. Moreover, load indicators are of interest because the Railway Power System (RPS) faces similar problems and can apply similar solutions that Public Power System (PES) does. These indicators allow taking decisions on the suitability of each policy and technology in different application fields. To perform this task, these indicators must be representative enough among manufacturers, railway operators and infrastructure operators. The idea of the paper is that conventional indicators sometimes could distort figures of energy use (e.g., reductions in kWh/pkm can be explained by the reduction of service in some lines more than for the improvement of energy figures). For instance, several indicators from the literature [2] and other specifically proposed are considered, such as: Final energy consumed vs. capacity of the vehicle (kWh/seat-km); final energy consumed by occupation (kWh/pkm); final energy consumed in “hotel loads” by occupation; energy consumed by trains in standby/idle/parked; energy recovered through storage (in weight or volume); load factors; flexibility of loads; load aggregation, and ratios of approximate costs of the alternatives being considered (for example capital cost/energy).

1.2. Energy Efficiency in Railways: The Use of Regenerative Braking Regenerative braking is conceptually simple and is a well-known alternative to improve energy efficiency, but involves a very complex problem in practice, since there is not any certitude that two trains are at the same time and location (electrically speaking) with different roles in the Rail Power System (i.e., generator vs. load, a topic to be presented in Section 3.5). This problem is even more complex in conventional railways with low or medium traffic flow because the probability that several trains are in the same track of catenary is especially low. In this case, power cannot be injected in the network because it increases the voltage of the overhead line, or reverse flow is not possible, due to the configuration of substations [9], generation is absorbed and dissipated through the resistive braking bank of locomotives. It is necessary to take into account that the possibilities of regenerative braking are raising due to two factors. First, storage seems now more feasible than a decade ago because of the decrease of capital costs, and the improvements in energy density, power density and lifetime of storage devices (partially driven by the deployment of Electric Vehicles and the Integration of Renewables, RES). Secondly, the capacity of regenerative braking can be increased through converters, so that it is not necessary to use the same rate of friction (pneumatic braking) in blending mode during service braking throughout the entire practical speed range. This is called pure electric braking [10]. As can be shown in different figures, this improves the performance of braking, but only if energy can be stored or utilized in some way. Several solutions have been proposed and reviewed in the literature [11] to increase the potential of regenerative braking. The first alternative is system integration: A path is provided for power flows from trains to Public or Railway Power Systems. This approach involves the use of DC-AC converters (in the case of DC overhead lines, such as the case of this paper), and specifically Voltage or Current Source Inverters (VSI, CSI). Some important considerations should be taken into account, for example: Voltage regulation of DC catenary, power quality, resonance [12], but also capital costs (i.e., it is difficult to foresee a future scenario in where high capital investments are deployed for this integration, that is not necessary for AC overhead lines). An example of this alternative, called HESOP, is presented in Reference [13]. Other options are available such as timetable optimization and traffic management. They can be classified in on-line and off-line algorithms [14], and this problem has also been called the Railway Dispatching Problem. It has been developed to match acceleration and deceleration of trains in a section of the track (especially indicated for routes with high traffic densities and frequent stops). Appl. Sci. 2019, 9, 3605 4 of 40

This policy is more feasible for suburban, metro or tramways because routes are shorter and the density of trains in each section is high, but the premise is a problem for conventional routes, especially for single-track sections and regional and intercity services. Some authors report that up to 14% of energy saving can be achieved through timetable optimization [15]. Another policy is energy storage both on board or off-board [16]. It is important clustering these solutions into AC [17] and DC [18] railway systems because specific problems and solutions are quite different (power flows to the public power system, phase balance, load sharing between feeder stations, reactive voltage support or transformer connections). DC systems are still in use in many railways where the power demand and distance are not critical, but AC systems are preferred for high-speed or heavy haul freight systems. In these cases, energy is transformed and stored in an electric, chemical or kinetic medium [19]. Finally, the change of driver’ patterns is considered in some approaches [20], but this method impacts on timetables and depends on train engineers more than in technical questions. The evaluation of the state of health of storage is also important, and the temperature of these devices (i.e., derating of capacity, for example, in batteries and supercapacitors). This depends on the management of SoC (state of charge); the charge/discharge controller must “foresee” the need to charge and discharge. This seems easier by the use of Physically Based Models (PBLM) for train energy demand, such as the models presented in this work. In this way, the system analyses the requirements of the train to accomplish timetables, the characteristics of the route, and can provide feedback to Energy Storage Systems (ESS) to anticipate control of SoC around desirable levels. This is a complex problem which needs a complete portfolio of mixed alternatives to improve the possibilities of energy recovery.

1.3. Storage Possibilities

1.3.1. On-Board Energy Storage Systems (OESS) In On-board Energy Storage Systems (OESS), the energy from the regenerative braking can be stored and used at any time when the train needs it, for example, if there are discontinuities in the power supply, such as on bridges, on level crossings, in tunnels or for last-mile applications, e.g., TRAXX AC Last Mile Locomotive built by Bombadier [21]. These systems can operate without catenary, and also, they allow reducing the power peaks of the locomotive when it has to accelerate after a stop. In OESS applications, it is usually to install supercapacitors and batteries because of its less weight and volume in comparison with flywheels. Supercapacitors are used when the train needs a high level of power in a short period of time, such as when it is necessary to accelerate after a stop, and batteries are more adequate for supplying energy during larger distances (a few kilometers). Some real applications of OESS have been studied in Reference [11]. Trams circulating in Lisbon and Nice have Ni-MH batteries that allow working in catenary free operation mode and contributing to reducing their emissions and visual impact, as well as increase energy efficiency. Lisbon trams use the Sitras HES system, developed by Siemens [22], while Nice tram was developed by Alstom [23]. In September of 2018, Bombardier transportation [21] presented a hybrid (electric-battery) operated train called “Bombardier Talent 3” planned to passenger services which have an autonomy of around 40 km in battery traction mode. This prototype is currently being tested in Germany, in the Alb-Lake Constance region. There are also implemented some ESS composed by supercapacitors, e.g., in Mannheim, Seville and Saragossa (Europe). Mannheim tram ESS is called “Mitrac Energy Saver”, and was developed by Bombardier [21]. Seville and Saragossa trams (Spain) are developed by CAF, using a system called the RCA system (Rapid Charge Accumulator) [24]. There is also a project in Great Britain, called Parry People Movers, which has developed a light tramway that works with a flywheel system, and it is currently operating in Stourbridge [25]. Appl. Sci. 2019, 9, 3605 5 of 40

1.3.2. Stationary Energy Storage Systems (SESS) The main advantage of Stationary Energy Storage Systems is that there are no space and weight restrictions for the storage system, as they are installed in the trackside. In addition, Stationary Systems allow storing the regenerative energy for various trains at the same time, and the energy recuperated can be used in a different train that initially generates it. This fact helps to reduce the demand power peaks and to increase the number of vehicles that can circulate on the railway path reducing the investments to improve the electrical supply system. However, SESS also have some restrictions. In the first place, the capability of the system is a function of the location of the vehicle, increasing the losses, due to transmission with the distance between the train and the storage system. Furthermore, it is impossible to apply these systems in cases of non-electrified railway paths, or in shorts distances without supply (last-mile, tunnels, etc.). Some real projects of SESS are currently being used in different railway systems. In Philadelphia, ABB has installed a Li-ion batteries system called ENVILINE ESS [26], providing revenue by generating frequency regulation services on the local energy market. This system has also been installed in Warsaw Metro, improving its energy efficiency. Otherwise, Siemens [22,27] has provided supercapacitors SESS (Sitras SES) in Madrid, Cologne, Rotterdam, Toronto, Portland and Beijing metro lines. In respect to flywheels, VYCON installed a system for the Los Angeles Country Metropolitan Transportation Authority (LA Metro) Red line (MRL) called WESS, in order to recover the braking energy from their trains [28]. There is also a flywheel system installed in London underground’s Piccadilly Line [29].

1.4. Case of Study

1.4.1. Railway Route In order to compare the ability of conventional diesel-electric units (Altaria service), hybrid units (Alvia service), and new proposed alternatives, and also to address the sizing of principal components of the power system (storage units, resizing of engine, etc.), a typical itinerary was selected. This route is a radial passenger service from Madrid to Cartagena, in where “Alvia” and “Altaria” services deserve the railway route and located in the southeast of Spain. Specifically, this itinerary has an electrified overhead system (3 kV, DC two-track, overhead catenary) from Madrid to Chinchilla (around 300 km) and a non-electrified one from Chinchilla (Albacete) to Cartagena (single-track, 225 km) without change of the diesel-electric locomotive (Altaria service). At present, there are six trains on service during weekdays (two of them are “Alvia”, and four are “Altaria” type). For simplicity, the results presented will be focused on a section the chosen itinerary: Alcázar de San Juan-Chinchilla-Cartagena. Alcázar de San Juan (150 km away from Madrid) is a XIX century railway node for freight and passenger services to/from the South and South-East of Spain. The track Madrid-Alcázar is more complex (radial and suburban services to other cities, for example, Toledo Cuenca, and some cities in Andalusia) and has been not considered for simplicity. The railway route was selected, due to their speed profiles (160–200 km in some sections), and a rough profile that conditions the haulage and the size of the storage and engine (i.e., energy constraints on the diesel motors are higher, in comparison to a flat line, due to the considerable acceleration power needed to overcome grade resistance and also to the high braking power recovery during down slopes). Obviously, the same simulation method can be applied to analyze other railway routes and diesel or electric locomotives and multiple unit vehicles. The profile of the route (altitude versus distance) from Alcazar de San Juan (ASJ) to Cartagena (CT) is given in Figure1. Appl.Appl. Sci. Sci.2019 2019, 9,, 36059, x FOR PEER REVIEW 6 of6 of 40 41

1000 600 AJ AB CH MU CT AJ CH 800 500

400 600 Single track 300 400 Double track Double track 3 kV-DC 200 3 kV-DC Rail elevation (m) Curve radius (m) 200 100

0 0 200 300 400 500 200 300 400 500 Distance from Madrid (km) Distance from Madrid (km) (a) (b)

FigureFigure 1. Route1. Route characteristics characteristics (main (main stations stations in in red, red, electrified electrified and and non-electrified non-electrified sections, sections, double- double- andand single-track single-track sections sections in orange):in orange): (a )(a Alcazar) Alcazar SJ-Cartagena SJ-Cartagena (sea (sea level) level) track track profile; profile; (b )(b location) location of of curvescurves and and their their radius radius (m). (m).

TheThe route route is used is used both both for freight for freight and passenger and passeng serviceser services and corresponds and corresponds to a track andto a atrack platform and a thatplatform was renovated that was inrenovated 2008. It in has 2008. experienced It has experien a decreaseced a in decrease traﬃc, duein traffic, to the due increasing to the increasing rate of investmentrate of investment in new high-speed in new infrastructures high-speed infrastructures (with EU standard (with rail EU gauge, standard track Madrid-Valencia,rail gauge, track 2010,Madrid-Valencia, and Albacete-Chinchilla-Alicante, 2010, and Albacete-Chinchilla-Alicante, in service since 2013). in service since 2013).

1.4.2. Locomotives, Multiple Units and Rolling Stock 1.4.2. Locomotives, Multiple Units and Rolling Stock For passenger service, rebuilt medium-speed diesel-electric locomotives 334 (with a maximum For passenger service, rebuilt medium-speed diesel-electric locomotives 334 (with a maximum speed of 200 km h, series that is very closed to GEC Class 67 [23] developed for UK operators) have speed of 200 km/h,/ series that is very closed to GEC Class 67 [23] developed for UK operators) have beenbeen used used in in this this work work for for simulation simulation purposes.purposes. Also, Also, new new hybrids hybrids trains trains (hybrid (hybrid diesel-electric diesel-electric and andelectric electric multiple multiple unit, unit, HDEMU HDEMU S-730) S-730) managed managed and boughtbought byby thethe Spanish Spanish Railway Railway Operator Operator (RENFE)(RENFE) during during this this decade decade have have been been considered. considered. The The rolling rolling stock stock considered considered in in simulations simulations were were builtbuilt by: by: Patentes Patentes Talgo, Talgo, Spain Spain (coaches(coaches TalgoTalgo IVIV forfor the diesel-electric locomotive, locomotive, and and coaches coaches for forS-730) S-730) [30], [30], Vossloh/EMD, Vossloh/EMD, Spain Spain (now StadlerStadler RailRail [31[31],], thesethese locomotives locomotives are are based based on on diesel diesel enginesengines 12N710G3B-EC 12N710G3B-EC licensed licensed by Generalby General Motors, Moto 2004).rs, 2004). For freight For freight purposes, purposes, S-253 (TRAXX S-253 (TRAXX series byseries Bombardier, by Bombardier, 2011) [21 ]2011) and S-252[21] and (Eurosprinter S-252 (Eurosprinter pilot series pilot built series by Krauss-Ma built by ﬀKrauss-Maffeiei and Siemens, and 1992)Siemens, [22], are 1992) also [22], used are for also simulation used for purposes. simulation purposes. MainMain characteristics characteristics of theseof these locomotives, locomotives, coaches coaches and and electric electric multiple multiple units units (EMU) (EMU) are given are given in Tablesin Tables1 and 21. and Figure 2. Figure2 depicts 2 depicts some of some these of trains. these trains.

Table 1. Characteristics of locomotives. Table 1. Characteristics of locomotives. Locomotive S-334 S-730 S-253 S-252 Locomotive S-334 S-730 S-253 S-252 Diesel-electric Dual hybrid unit UIC Type Diesel-electric Dual hybridElectric unit Bo’Bo’Electric Electric Bo’Bo’ UIC Type Bo’Bo’ 2*Bo’Bo’ Electric Bo’Bo’ Bo’Bo’ 2*Bo’Bo’ Bo’Bo’ Service Passenger Passenger Freight Passenger/Freight Service Passenger Passenger Freight Passenger/Freight Weight (ton) 90 140 87 86 Weight (ton) 90 140 87 86 Rail gauge (mm) 1668 1435 and 16681 1668 1668 Rail gauge (mm) 1668 1435 and 16681 1668 1668 Max. tractive eﬀort (kN) 92 220 300 300 Max. tractive effort Max. Speed (km/h)92 200 250/220/180220 140300 200 300 (kN) Power (MW) 2.5 4.8/4.0/2.4 5.4 5.6 Max. Speed (km/h) 200 250/220/180 140 200 Voltage (kV) - 25/3/NA 3 3 Power (MW) 2.5 4.8/4.0/2.4 5.4 5.6 1 Voltage (kV)This train (S-730 HDEMU) - allows the change of 25/3/NA gauge. UIC, Union of Railways. 3 3 1 This train (S-730 HDEMU) allows the change of gauge. UIC, Union of Railways. Appl. Sci. 2019, 9, 3605 7 of 40

Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 41

Table 2. Characteristics of coaches. Table 2. Characteristics of coaches. Coaches/Waggons Units Weight (t) Max. Speed (km/h) UIC Type Weight Coaches/WaggonsTalgo IV 9Units 118 Max.180 Speed1 (km/h)1 + 1,1,1,1,1,1,1,1,1,1,1,1 UIC Type+ 1 Talgo (HDEMU) 11 176(t) 250 2,1,1,1,1,1,1,1,1,1,1,2 Flat bogieTalgo wagon IV 9 118 180 1 1 + 1,1,1,1,1,1,1,1,1,1,1,1 + 1 1 87 t 120/100 Sgg-nns Talgo(containers) (HDEMU) 11 176 250 2,1,1,1,1,1,1,1,1,1,1,2 Flat bogie wagon (containers) 1 1 and 200 87 km t /h in some cases. 120/100 Sgg-nns 1 and 200 km/h in some cases.

(a) (b)

FigureFigure 2. 2. LocomotivesLocomotives and EMU used used for for simulation simulation in in the the paper: paper: (a) (HDEMUa) HDEMU series series 730; 730;(b) (b)diesel-electric diesel-electric S-334 S-334 for for passenger passenger services; services; ( (cc)) electric electric series series 253 253 for for freight freight traffic; traﬃc; (d) electricelectric seriesseries 252252 for for passenger passenger and and tra trafficﬃc duties. duties.

1.4.3.1.4.3. Electric Electric Infrastructures: Infrastructures: Substations Substations TheThe capacity capacity of theof electricalthe electrical substations substations during du peakring demands peak demands is another is issueanother to be issue considered. to be Capacityconsidered. and configurationCapacity and limit configuration railway tra ffilimitc and railway the electrical traffic interconnectionand the electrical between interconnection the public Powerbetween System the and public the Railway Power SupplySystem System and (RSS).the Ra Inilway the route Supply Alcazar-Chinchilla, System (RSS). eachIn substationthe route isAlcazar-Chinchilla, conventional non-controlled each substation diode is type conventional non-reversible non-controlled (i.e., rectifier diode substation). type non-reversible The substation (i.e., usuallyrectifier has substation). two power The transformers substation usually (2 3300 has kVA) two power connected transformers to 66 kV (2 sub-transmission × 3300 kVA) connected network. to × These66 kV rectifier sub-transmission substations network. provide 3These kV DC rectifier to the su overheadbstations line.provide With 3 thiskV DC capacity, to the theoverhead number line. of trainsWith is this limited capacity, to 3 inthe the number first and of trains last sections is limited of to the 3 in route the first (notice and that last thissections route of is the a double-trackroute (notice section).that this This route configuration is a double-track of RPS section). is cost-e Thisffective configuration and very of reliable. RPS is cost-effective It can be considered and very as reliable. a good solutionIt can be in denseconsidered traffi cas scenarios, a good solution but has in problems dense traffic in scenarios scenarios, in but which has the problems probability in scenarios of having in trainswhich injecting the probability and demanding of having power trains is injecting low (notice and thatdemanding this is the power case is presented low (notice in thisthat paper: this is Anthe infrastructurecase presented that in has this reduced paper: its An use infrastructure and which fights that withhas reduced sustainability its use concerns). and which Table fights3 shows with thesustainability name of each concerns). substation, Table trains 3 flowshows and the its name coverage of each area. substation, trains flow and its coverage area. Appl. Sci. 2019, 9, x FOR PEER REVIEW 8 of 41

Appl. Sci. 2019, 9, 3605 8 of 40 Table 3. Characteristics of the 66 kV-AC/3 kV-DC substations.

Table 3. CharacteristicsKm from Madrid of the 66 and kV-AC /3 kV-DC substations.Max. Trains Flow 2 Station Substation Substation Distance 1 Acronym Speed Km from Madrid (Weekly, 2010) (km) Max.(km/h) Speed Trains Flow 2 Station Substation and Substation Acronym (km/h) (Weekly, 2010) Alcazar SJ Alcazar SJ Distance 148.5;1 (km)0 AJ 160 110 + 105 + 164 Rio ZáncaraAlcazar SJ Rio Záncara Alcazar SJ 171.9; 148.5; 23.4 0 AJ RZ 160 200 110 69+ 105 + 57+ 164+ 168 SocuéllamosRio Záncara Socuéllamos Rio Záncara 188.1; 171.9; 23.416.2 RZ SO 200 200 69 69+ 57 + +57168 + 168 VillarrobledoSocuéllamos Villarrobledo Socuéllamos 201.8; 188.1; 16.213.7 SO VB 200 160 69 69+ 57 + +57168 + 169 MinayaVillarrobledo Minaya Villarrobledo 223.6; 201.8; 13.721.8 VB MY 160 160 69 69+ 57 + +57169 + 169 Minaya Minaya 223.6; 21.8 MY 160 69 + 57 + 169 La RodaLa Roda La Roda La Roda 240.4; 240.4; 16.816.8 LR LR 160 160 69 69+ 57 + +57169 + 169 La GinetaLa Gineta La Gineta La Gineta 257.7; 257.7; 17.317.3 LG LG 160 160 69 69+ 57 + +57169 + 169 AlbaceteAlbacete Albacete Albacete 276.2; 276.2; 18.518.5 AB AB 200 200 69 69+ 57 + +57180 + 180 Chinchilla Chinchilla 292.2; 16.0 CH 200 69 + 32 + 180 ChinchillaMurcia Chinchilla NA 292.2; 460; NA 16.0 MU CH <160 200 61 69+ +0 +3229 + 180 MurciaCartagena NA NA 460;525; NANA CT MU <160 <160 66 + 6180 ++ 023 + 29 Cartagena1 Distance from NAprevious substation, CH 525; is the NA last substation; 2 Intercity CT + regional <160/commuter + freight. 66 + 80 + 23 1 Distance from previous substation, CH is the last substation; 2 Intercity + regional/commuter + freight.

1.4.4.1.4.4. Timetables for thethe RouteRoute AdditionalAdditional information for simulation and evaluationevaluation purposes is thethe tratrafffificc inin thethe route.route. ThisThis allowsallows determiningdetermining the existenceexistence of criticalcritical pointpoint forfor passengerpassenger andand freightfreight trainstrains andand thethe maximummaximum andand minimumminimum numbernumber ofof trainstrains inin eacheach substationsubstation andand track.track. For the simulation, the tratrafficffic ofof thisthis routeroute isis obtainedobtained forfor realreal tratrafficffic requirementsrequirements andand capacitycapacity ofof thethe single-single- andand double-trackdouble-track sectionssections (Figure(Figure1 1).). Some Some example example of of this this information information is is given given in in Figure Figure3 .3.

Figure 3. Graphic timetable for the railway route Alcázar de San Juan (AJ) to Chinchilla (CH) from Figure 3. Graphic timetable for the railway route Alcázar de San Juan (AJ) to Chinchilla (CH) from 6 6 p.m. to 12 p.m. on weekdays. Passenger trains in green, freight trains in blue, commuter trains in red p.m. to 12 p.m. on weekdays. Passenger trains in green, freight trains in blue, commuter trains in red and regional trains in maroon. and regional trains in maroon. 1.5. Train Simulators 1.5. Train Simulators The simulation of railway systems has been a field of interest for researchers since the early ‘80s.The In 2008 simulation the Technical of railway University systems of has Cartagena been a (UPCT),field of interest Department for researchers of Electrical since Engineering, the early envisaged‘80s. In 2008 the the usefulness Technical of University developing of aCartagen train energya (UPCT), simulation Department software. of Electrical The main Engineering, reason was theenvisaged fast development the usefulness of high-speed of developing services a train in ener Spain,gy simulation and the high software. energy The demand main forreason these was trains the thatfast development affects the whole of high-speed Power Systems services (e.g., in balancingSpain, and of the phases high inenergy 25 kV demand substations for these for high-speed trains that tracks).affects the Moreover, whole otherPower important Systems issues(e.g., balancing being considered of phases were in sustainability 25 kV substations and social for equity:high-speed The Appl. Sci. 2019, 9, 3605 9 of 40 decrease in interest on conventional low traffic non-electrified lines (the case of the route under study) which deserves small/medium cities and rural areas (i.e., social and economic barriers arise for these areas whereas the development and migration to big cities is stimulated). This possibility resulted in the development of simulator software in Matlab [32,33]. The main purpose of the program was to calculate the energy consumption, energy recovery, and running times of trains. Obviously, there are various simulation programs developed both by Universities, Engineering Service Companies and the Railway manufacturing industry. For instance, there are commercially available software packages, such as Trainops ([34] developed by LTK) and Sitras Sidytrac ([35], developed by Siemens). Available programs developed by Universities include: Train Operation Model [36] developed by Carnegie Melon University; OpenTrack by ETH university [37,38], Vehicle Simulation Program (VSP) by Vrije University of Brussels [39] or STEC by KTH (Sweden [20]). The main advantages of this software, and the reason why it is used in this work, is the flexibility that allows for a build-on customization and the integration with other packages of the research team, for example Physically Based Load Models (PBLM) for the evaluation of Energy Storage and Demand Response beyond tractive loads, or the aggregation and management of these resources (from a stochastic point of view). To simulate energy consumption and performance for different train types and categories (freight or passenger), one must first state their properties in the program (tractive effort, braking curves, weight of coaches or wagons, pneumatic braking characteristics, maximum speed, etc., needs to be defined together with number of seats, occupancy, load factor and so forth). Information about timetables and auxiliary systems are other examples of what information is necessary to perform simulations. The railway route also needs to be defined. Line gradients, maximum and target speeds need to be defined along the line, together with information on locations of stops, curve radius, speed limits, as well as dwell time on each station. Once all input data have been included, and a simulation has been performed, the software shows information about total travel time, internal and external forces, speed and acceleration, details about energy consumption and brake characteristics. These data will be discussed in the next section. The main contributions of this paper are the following:

It introduces a simulation tool that integrates different models for the main load (traction), ESS • systems and secondary loads (DR load models for heating and ventilation loads) to evaluate the performance of real railway power systems. Load aggregation procedures used in Public Power Systems are considered; It proposes feasible solutions that allow increasing the energy efficiency of conventional railway • systems by introducing ESS or applying DR policies that enable railways to make greater use of the energy generated through regenerative braking; It takes into account the natural stochastic nature of different events (train delays) to evaluate the • changes in the potential of regenerative braking, and its opportunities. It defines load curves at several aggregation levels (substations, High Voltage feeders, etc.) taking • into account train energy models, timetable for the route, track constraints, and the stochastic nature of some events into the system (train delays); It demonstrates the potential of new DER resources in railways (storage, for example, due to • last-mile capacities in new vehicles and hybrid vehicles) and Load Management to improve the operation of Railway Power System. It also offers a new way to manage new resources to the management of Power Systems, and the improvement of their operation. This potential increases the flexibility of railway demand, operating this system as a smart grid.

The rest of the paper is organized as follows: Section2 describes the materials and methods used, explaining in detail the models applied to simulate the train movement and its energy consumption. Based on this and additional material, Section3 examines the possibilities of using Distributed Energy Resources (DER), such as ESS and DR (for traction and “auxiliary” loads) in order to improve the Appl. Sci. 2019, 9, 3605 10 of 40 performance of the railway system by increasing its energy eﬃciency and reducing power peaks. Finally, some conclusions and future developments are stated in Section4.

2. Materials and Methods

2.1. General Equations of the Train Movement The acceleration, a, of a train in its running direction can be described by a single scalar Newton equation and depends on the external and internal forces produced in the train:

Xn 2 (m + J/R )a = Fk, (1) k=1

Xu Xr m(1 + k)a = Ft F Fc Fg, (2) k− rk − − k=1 k=1 where: n—overall number of eﬀects being considered. M—mass of the train a—acceleration of the train J—moment of inertia of the diﬀerent rotating masses (wheels, cog wheels, motor rotors) in the transmission, which causes an apparent increase of mass. R—wheel radius.

Ftk—propulsive force, tractive eﬀort of locomotive/EMU k of u units in the train. Frk—resistance forces, due to the element r of the train, locomotive, coach, generator, etc. Fc—curve resistance Fg—grade resistance, due to the slope of the track (positive or negative for the acceleration) k—increment of mass, due to rotating inertia, a coeﬃcient in the range (0, 0.30) depending on the type of vehicle. It is in the range (0.06, 0.10) for a complete train.

Resistance forces are the sum of all forces acting on the train at a given time or place. Some of these forces change directly as the axle loading does: For example, journal friction, rolling resistance, or track resistance. Other forces vary with speed and are known as ﬂange resistance. Finally, some forces vary with the square of the speed: Air and wind resistance. These resistance forces are examined in detail with diﬀerent models [40–42], but they are not the objective of this paper. A quadratic formula has been used for decades to approximate rail vehicles resistance:

2 Fr = A + Bv + Cv , (3) where v is the speed of the vehicle (m/s, mph, km/h), and A (N), B (N s/m) and C (Ns2/m2) are regression coefficients obtained by fitting run-down test of modern passenger and freight units to the Davis equation (e.g., different modifications were developed in 1970 by AREA association, or in 1992 by Canadian National [43]). For example, the modified version of 1970:

20 Kv2 F = 0.6 + + 0.01v + , (4) r w wn where w is the weight per axle in tons; n the number of axles, K the air resistance (drag) coefficient, v the speed in miles per hour and F the resistance in pounds per ton. This paper uses different data from manufacturers (EMD, Bombardier, Alstom, Patentes Talgo, Vossloh, Krauss-Maffei, Siemens, etc.), associations (UIC) and railway administrations (SNCF, DB, SBB-CFF, FS, RENFE, etc.) for cars (Eurofima, Corail), locomotives (BoBo, such as Re4/4 III, or CoCo, Appl. Sci. 2019, 9, 3605 11 of 40

Appl. Sci. 2019, 9, x FOR PEER REVIEW 11 of 41 such as Re6/6), and compositions of homogeneous and composite materials (SNCF). For example, Fr (in(in N), N), for for a a high-speed high-speed train train (Train(Train àà GrandeGrande Vitess Vitesse,e, TGV-SE) TGV-SE) from from SNCF SNCF (French (French operator), operator), or oralso also forfor SNCF SNCF locomotives locomotives with with n n axles, axles, bothboth includedincluded in reference reference [42]: [42]: =+ + 2 FvvTGVr 2540 33..; 44 0572 2 Fr = 2540 + 33.44v + 0.572v ; TGV 2 , 0650.65..nn 00360.036 v v2 (5) (5) FFmgr ==+++mg( +1313 + vv +039.);0.39 )locomotive; locomotive r 10001000 mm 10001000 mm wherewhere the the force force is is done done in in N, N, and andv v(speed) (speed) inin kmkm/h./h. TheThe grade grade resistance resistance or or gravitational gravitational forceforce (slope β,, from from which which calculate calculate i =i =sinsinβ):β ): Fmgi= , Fgg = mgi,(6) (6) and curve resistance by means of empirical formulae: and curve resistance by means of empirical formulae: = ke Fc mg , (7) rke Fc = c mg, (7) rc where ke is the gauge coefficient (750 m for 1435 mm gauge), and rc is the curve radius in meters. where kAlle is these the gauge forces coeareffi consideredcient (750 in m simulations, for 1435 mm taking gauge), into and accountrc is the traction curve and radius braking in meters. curves fromAll manufacturers, these forces are and considered also the limits in simulations, and constrai takingnts of the into RPS, account being tractiondescribed and in next braking sections. curves from manufacturers, and also the limits and constraints of the RPS, being described in next sections. 2.2. Traction Effort Curves and Dynamic Braking 2.2. Traction Effort Curves and Dynamic Braking When diesel-electric locomotives and vehicles (e.g., DMU) are compared with electric locomotivesWhen diesel-electric or vehicles locomotives(EMU), it can and be observed vehicles (e.g.,that their DMU) performances are compared in steady with electric state are locomotives higher orfor vehicles a 100% (EMU), electric itlocomotive can be observed than in diesel that their locomotives. performances In the simulations in steady state to be are developed higher for in anext 100% electricparagraph, locomotive electric than locomotives in diesel locomotives.can develop Inup the to simulations5.6 MW (according to be developed to their inTractive next paragraph, Effort electriccharacteristics, locomotives Figure can 4a, develop red curve up toand 5.6 orange MW (accordingin dash-dotted to their line) Tractivewhereas Eaff ortdiesel-electric characteristics, is Figurelimited4a, at red around curve and2.4 MW orange by inthe dash-dotted power of its line) diesel whereas motor a(3300 diesel-electric HP). In addition, is limited anat electric around 2.4locomotive MW by the can power be asked of its for diesel a higher motor power (3300 HP).during In addition,a short time an electric(for instance, locomotive it peaks can power be asked for for a higherabout power10 min). during Consequently, a short time the (for acceleration instance, it is peaks lower power in trains for about with 10 diesel-electric min). Consequently, traction. the accelerationHowever, hybrid is lower units, in trains in diesel with mode diesel-electric of operation, traction. have several However, advantages hybrid to units, be discussed in diesel later mode in of this work. operation, have several advantages to be discussed later in this work.

(a) (b)

FigureFigure 4. 4.E ffEffortort characteristics characteristics ofof didifferentfferent vehicles (null (null values values indicate indicate that that the the unit unit cannot cannot reach reach thisthis speed, speed, see see Table Table1): 1): ( a(a)) Tractive Tractive eeffort;ffort; and ( b) braking braking effort effort for for locomotives, locomotives, DMU DMU and and EMUs EMUs of of diffdifferenterent conception conception and and service service (manufactured (manufactured from 1992 to 2012). 2012).

AtAt the the same same time, time, there there are alsoare also significant significant differences differences in the in braking the braking performance performance of the of di fftheerent locomotives.different locomotives. The maximum The maximum braking powerbraking for power the two for full-electricalthe two full-electrical locomotives locomotives is about is about 5.6 MW; however,5.6 MW; the however, maximum the maximum braking e ffbrakingort is higher effort inis higher S-253 (240in S-253 kN) (240 than kN) S-252 than (168 S-252 kN), (168 and kN), the and curve changesthe curve at low changes speeds. at Inlow the speeds. case of In the the hybrid case locomotiveof the hybrid/vehicle, locomotive/vehicle, the maximum brakingthe maximum power is braking power is limited to 4 MW. Finally, diesel-electric locomotive S-334 only can achieve a Appl. Sci. 2019, 9, 3605 12 of 40 limited to 4 MW. Finally, diesel-electric locomotive S-334 only can achieve a braking power around 1.9 MW (Figure4b). For this reason, diesel and hybrid locomotives have fewer deceleration rates and more time is required to reduce velocity or completely stop a train if only the regenerative braking is applied by railroad engineers.

2.3. Hotel Power (Hotel Loads) In railway systems, the electrical load in the passenger units or coaches (i.e., load of lights, ventilation, heating and air conditioning, information screens, opening and closing of doors, loads in restaurant cars, etc., are referred to as “hotel load” or “hotel power”). UIC estimates that the amount of energy needed for hotel loads range from 10–15% and in some cases (SBB-CFF-FFS, Swiss Railways [44]) up to nearly 40–50% of the total energy consumption of the train [2]. Hotel loads can be directly supplied by the locomotive (e.g., Locomotive S-334) which involves a reduction of traction eﬀort (see Figure5a), can be feed from overhead lines through converters in coaches or, in some cases (e.g., Talgo IV coaches), trains are fed from power cars (1 or 2 cars) placed at the ends of the rake. Each power car is installed with one, or several DG sets generating 3-phase (4 wire) power supply at 3 kV DC or 380 Volts 50 Hz. Power is transmitted to entire rake through two cable feeders, running through the whole length of the train. In “conventional” intercity trains in Spain (mainly in the period 1990–2010), this electric power supply is tapped at each coach (e.g., Euroﬁma standard coaches) through 45 kVA DC/AC converters from 3 kV DC supply. Main characteristics are given in Table4. These characteristics allow foreseeing some ratios of power demand, due to the occupation of passenger trains with respect to average power needed for traction purposes.

Table 4. Hotel loads for 9000/10000 RENFE coaches 1.

Converter (3 Heat Hotel Series HVAC Lighting kVDC-380 VAC) (Resistors) Load/Passenger 9000 30000 kcal/h 45 kVA 11 kW 1400 W 510 W (2nd class, saloon) 22 kW B-10000 30000 kcal/h 45 kVA 14.5 kW 590 W 562 W (couchette) 22 kW 1 Similar to Euroﬁma/UIC standard passenger’s series in Europe.

As it has been stated before, some railway operators report high hotel loads in some trains. For this reason, SBB actually develops a Demand Response (DR) initiative based on the control of head points and heaters in train coaches [45]. This load control, according to SBB pilot reports, happens without affecting the performance of the heating system. This DR policy is well known in conventional power systems in residential and commercial segments [46]. Potential loads of interest are Water Heater (WH) [47] and Heat Ventilation and Air Conditioning Loads (HVAC) [48], due to the thermal inertia (storage) in walls (HVAC) or water tanks (WH), because of the specific heat of building envelope materials or water (WH). This kind of thermal storage covers energy supply to maintain the service (temperature), while power is switched-off. The construction of train vehicles is especially based on steel (c = 477 J/kgK), and aluminum (c = 896 J/kgK) and this represents a valuable capacity for heat storage, with a similar potential to bricks in buildings’ envelope. There are several possibilities to model HVAC systems and their environment (vehicle or building envelope): A white box, grey box, and black box. Some references in the literature classify these models in physics-based, grey box, and data-driven [49]. According to some experiences, the same principles can be applied to thermal models of HVAC systems for rail vehicles [50]. The first alternative is white box models. Thermal design software, such as EnergyPlus, E_Quest [51], or Modelica, are considered as white box approaches, but they have some drawbacks. This approach is too complex (a high cost for computation time and resources) to evaluate demand response (EnergyPlus works with high order Appl. Sci. 2019, 9, 3605 13 of 40 state-space models, e.g., model order around 30–40). Besides, the identification and evaluation of every necessary parameter are time consuming (and consequently, it is difficult to identify them). These models need some simplification to make modeling efforts feasible to evaluate DR policies in short-term (seconds to some hours, time windows necessary for railway loads). An intermediate approach is the software toolbox BRCM that is proposed to simplify the order of these thermal models (i.e., the simplification of models like EnergyPlus [52]). Authors’ research group has previously proposed individual load models for HVAC loads and their envelope for residential dwellings, and the further aggregation by several mechanisms for these kinds of loads in other segments of public power systems [53]. In the literature, these models are called Physically Based Models (PBLM). For residential heating (cooling) devices with and without thermal storage (e.g., ceramic bricks), or for water heaters [53,54]. They involve the development of an equivalent “grey box” model (a lumped RC network, usually called 3R2C, 2R2C, 2R1C or 1R1C depending on the number of lumped RC parameters that have been chosen to model thermal admittances or transmittances of each wall for the overall model—3, 2, or 1 [55]), very close to Appl.References Sci. 2019 [, 519, x,52 FOR], but PEER simplified REVIEW in complexity (the order of state space equations). The reduced14 order of 41 of state-space equations also allows a better estimation of the parameters of each model [49]. Moreover, this approachControl mechanisms makes possible which a further drive aggregationthe demand ofaccording elemental to models load service: [56], a Thermostats necessary condition in heating for loadssmall (m(t) loads in both Figure from 5a), buildings and provid or traine feedback vehicles among/coaches different to make submodels. DR of interest with respect to the overallState management variables: Those demand. usually are temperatures (indoor Xi, and vehicle body Xv).

(a) (b)

Figure 5.5. AnAn exampleexample of of PBLM PBLM model model for for a rail a rail vehicle. vehicle. (a) Model(a) Model 3R2C 3R2C applied applied in this in work; this (work;b) model (b) model2R2C, adapted2R2C, adapted from equations from equations proposed proposed in reference in reference [57]. [57].

TheThese model “grey presented box” models in haveReference been proposed[57] has several recently advantages [57] for diff erentfrom railthe vehiclespoint of (tram,view Metro,of the identificationRegio, Main Line, of parameters, etc.) and their but parameters the aggregation have been of some estimated parameter through into measurements an equivalent performed have some in vehiclesdrawback. into Assuming the Rail Techthat Arsenalthe temperature GmbH (Vienna, variations Austria) in the [rail58]. vehicle The proposed are small models (steady are state), a second the heatorder losses model to (2R2C) the environment and an equivalent are due first to order the over modelall (1R1C).heat transfer They arecoefficient similar to (K) “grey and box” the modelsoverall proposeduncoiled surface for residential of the dwellingsvehicle (A). (5R4C) An estimation in Reference of [K49 ].is Inpossible the case because of dwellings, standards it is necessary(e.g., EN 13129-1)a more complex limits the model, maximum due to exchangesoverall heat between transfer the coefficient space to beof conditionedvehicles (k-value) and other according dwellings to (rooms,climatic floor,zones basement, (I, II or III) etc.) and which vehicle are categories not present (main in the line, case urban, of railway suburban, vehicles. etc.). Other Moreover, differences it is usuallyare: Standards analytically (for example calculated EN13129-1) and known require by the the manufacturer. supply of fresh For air toexample, maintain k COvalues2 levels, range and from the 1.2effect W/m of2 speedK to 2.5 in W/m the exchange2K, and they of heat also between depend theon vehiclethe area and (corridors its environment, or primary due passenger to convection areas, 2 accordingeffects that to changes UIC leaflet significantly 567 [60]). heatThe value losses of coe Cffiv parametercients (up can to 17.8 be basically W/m K accordingattributable to to the specific study heatdeveloped capacity in Referencefor aluminum [59]). and steel, that are used in the manufacturing of vehicle bodies. Table 5 presentsFigure different5 shows physical two “grey values box” to models. evaluate Basic the diparametersfferences areused RC in networks PBLM model (2R1C (Figure model 5). is chosen for the walls, ceiling and ground that integrate the body frame of the vehicle) and the classification and modeling of external sourcesTable and5. Material their e propertiesffects on theused vehicle for thermal (a sole calculations source versus1. several sources and theirZone eff ofects: Only in the indoor or both inThermal indoor and in the body, for instance solar radiation). Both Density Specific Heat Parameter Figurethe5 a,b representMaterial the energy Name balanceConductivity between HVAC appliance and its load (vehicle). The model in Figure5a is made up of several components (sub-models) to(kg/m allow3) an optimal(kJ/kgm flexibility3) in(Figure modelling 5a) Vehicle (W/m2K) and planning (e.g., the evaluation of changes in the efficiency of appliances, due to improvements in Polyurethane foam Indoor 0.025–0.05 35–70 1.68–1.8 (8 mm) ha = 2.49 Indoor Glass 1.17 2529 0.754 kW/K Indoor ABS plastic 2.7 996 1.480 awo = 36 Indoor Air 8.17 1 1.006 kW/K Vehicle Ci = 3 MJ/K Aluminum 237 2700 0.477 body Vehicle Steel 30–40 7850 0.896 body awi = 36 kW/K Vehicle Glass/mineral Cv = 30 MJ/K 0.031–0.041 20–110 0.840 body wool (25–100 mm)

These models will be used for simulation purposes. It must be taken into account that the model represents a coach, and that trains have multiple coaches (see Table 2), i.e., some aggregation process is necessary. These processes are described in detail in Reference [56]. Appl. Sci. 2019, 9, 3605 14 of 40 efficiency, or due to the retrofit of thermal isolation of coaches, planned before periodic refurbishment tasks in older units). These PBLMs usually use thermal-electrical analogies, for example: Dwelling/environment submodels (indoor/vehicle cabin, frame and environment): Parameters that represent heat losses/gains (conduction/convection through vehicle envelope: ha, awo, awi, thermal losses and gains, due to air renovation (HV); as well as heat gains: Solar radiation through windows or the frame (Hs); internal gains, due to passengers (Hr) or appliances (Ha, i.e., lighting, information panels, etc.). Also, the model takes into account heat storage from the specific heat of vehicle body/frame (Cv), and indoor masses (Ci). This last capacity not only represents the capacity of the indoor air, but also the heat capacity of some elements of the passenger cabin that are in thermal equilibrium with the indoor air (e.g., seats) Energy conversion submodel (the appliance): Electrical energy conversion into heat (space heating), “cold” (air conditioning). This is represented by a current source (Hch) and is independent of the dwelling submodels, see Figure5a, where the same vehicle can “host” di fferent appliances with the same or similar service (heating with resistors or heat pumps). Control mechanisms which drive the demand according to load service: Thermostats in heating loads (m(t) in Figure5a), and provide feedback among di fferent submodels. State variables: Those usually are temperatures (indoor Xi, and vehicle body Xv). The model presented in Reference [57] has several advantages from the point of view of the identification of parameters, but the aggregation of some parameter into an equivalent have some drawback. Assuming that the temperature variations in the rail vehicle are small (steady state), the heat losses to the environment are due to the overall heat transfer coefficient (K) and the overall uncoiled surface of the vehicle (A). An estimation of K is possible because standards (e.g., EN 13129-1) limits the maximum overall heat transfer coefficient of vehicles (k-value) according to climatic zones (I, II or III) and vehicle categories (main line, urban, suburban, etc.). Moreover, it is usually analytically calculated and known by the manufacturer. For example, k values range from 1.2 W/m2K to 2.5 W/m2K, and they also depend on the area (corridors or primary passenger areas, according to UIC leaflet 567 [60]). The value of Cv parameter can be basically attributable to specific heat capacity for aluminum and steel, that are used in the manufacturing of vehicle bodies. Table5 presents di fferent physical values to evaluate the parameters used in PBLM model (Figure5).

Table 5. Material properties used for thermal calculations.

Thermal Zone of the Density Speciﬁc Heat Parameter Material Name Conductivity Vehicle (kg/m3) (kJ/kgm3) (Figure 5a) (W/m2K) Polyurethane foam Indoor 0.025–0.05 35–70 1.68–1.8 (8 mm) Indoor Glass 1.17 2529 0.754 ha = 2.49 kW/K awo = 36 kW/K Indoor ABS plastic 2.7 996 1.480 Ci = 3 MJ/K Indoor Air 8.17 1 1.006 Vehicle body Aluminum 237 2700 0.477 Vehicle body Steel 30–40 7850 0.896 awi = 36 kW/K Glass/mineral wool Vehicle body 0.031–0.041 20–110 0.840 Cv = 30 MJ/K (25–100 mm)

2.4. Energy Storage Technologies 2.4. Energy Storage Technologies To characterize energy storage (ESS) technologies, it is usual to determine the energy to weight To characterize energy storage (ESS) technologies, it is usual to determine the energy to weight ratio (or the energy to volume ratio), that is also called energy density, but in traction applications is ratio (or the energy to volume ratio), that is also called energy density, but in traction applications also significant the power density of the storage system (or the power to weight ratio) because is also significant the power density of the storage system (or the power to weight ratio) because locomotives demand high power peaks when the train accelerates. locomotives demand high power peaks when the train accelerates. Large energy density ratios mean that the ESS is capable of supplying power demand during Large energy density ratios mean that the ESS is capable of supplying power demand during long long periods, while large power density ratios translate in the ability to supply high levels of power periods, while large power density ratios translate in the ability to supply high levels of power pulses pulses in a short time. in a short time. Figure 6 presents a classification of different storage technologies depending on both Figure6 presents a classification of di fferent storage technologies depending on both parameters: parameters: Energy and power density. Energy and power density.

10 h 103 1 h Li-ion batteries 1 min 102 Ni-Cd batteries SMES

Lead-Acid 10 s Flywheels 101 batteries

Supercapacitors 100 1 s Energy density (Wh/kg) 10-1

Capacitors

10-2 101 102 103 104 Power density (W/kg) Figure 6.6. ClassificationClassification of of Energy Energy Storage Storage Systems Systems (ESS) (ESS) according according to data to availabledata available in references in references [61,62]. [61,62]. It is necessary to consider the trend rather than the absolute levels, as there could be discrepancies betweenIt is di ffnecessaryerent published to consider data, because the trend of the rather interdependency than the ofabsolute both parameters levels, as and there the publishingcould be date,discrepancies since these between systems different are currently published being data, developed, because of and the consequently, interdependency figures of both of performance parameters couldand the have publishing improved. date, since these systems are currently being developed, and consequently, figuresAlthough of performance there are could several have energy improved. storage technologies, the most common ESS employed in railwaysAlthough applications there are supercapacitors,several energy storage flywheels technologies, and batteries. the Batteries most common have a high ESS energy employed density, in butrailways a low applications power density are (a problemsupercapacitors, from the pointflywheels of view and of batteries. energy needs Batteries when have the train a high accelerates), energy whiledensity, supercapacitors but a low power have density a high (a power problem density, from but the a lowpoint energy of view density of energy (Figure needs6). Flywheels when the havetrain higheraccelerates), power while density supercapacitors than batteries have and a higher high power energy density, density but than a supercapacitors,low energy density but (Figure cost is an6). importantFlywheels drawback.have higher Other power important density than factors batterie to takes and into higher account energy are shown density in than Table supercapacitors,6. but cost is an important drawback. Other important factors to take into account are shown in Table 6. Table 6. Characteristics of ESS technologies most applied in transportation [63]. Table 6. Characteristics of ESS technologies most applied in transportation [63]. Specific Specific Energy Storage Price Self-Discharge Lifespan Energy Power Density System Specific Specific Energy (€/kWh) (%/day) (Cycles) (Wh/kg) (W/kg) (kWh/m3) Price Self-Discharge Lifespan Storage System Energy Power Density Li-ion (€/kWh) (%/day) 3 (Cycles)4 100–250 230–340 200–6203 200–1800 0.1 4 10 –4 10 Batteries (Wh/kg) (W/kg) (kWh/m ) × × Supercapacitors 0.1–15 0.1–10 10–25 300–4000 2 >5 105 3 4 Li-ion Batteries 100–250 230–340 200–620 200–1800 0.1 4× × 10 –4 × 10 Flywheels 5–130 400–1600 20–80 1000–3500 20 5 7 Supercapacitors 0.1–15 0.1–10 10–25 300–4000 2 10 –10 >5 × 105 Flywheels 5–130 400–1600 20–80 1000–3500 20 105–107 In order to choose what technology is the most appropriate for each railway application, it is necessaryIn order to consider to choose not what only technology their characteristics, is the most but appropriate also the place for where each therailway energy application, storage system it is necessary to consider not only their characteristics, but also the place where the energy storage Appl. Sci. 2019, 9, x FOR PEER REVIEW 16 of 41

Appl. Sci. 2019, 9, 3605 16 of 40 system is going to be installed. There are two possibilities for placing ESS, on board or wayside (normally in electrical substations) as stationary systems. is going to be installed. There are two possibilities for placing ESS, on board or wayside (normally in 2.5.electrical Energy substations) Storage Models as stationary systems. Modeling of ESS (batteries and supercapacitors in this work) is an important concern for sizing 2.5. Energy Storage Models ESS and developing control strategies of hybrid electric vehicles through simulation studies. DifferentModeling models of have ESS (batteries been reported and supercapacitors in the literature in thisand work)reviewed is an in important some specific concern reports for sizing for railways,ESS and developingfor instance control [62]. strategiesThe use of hybridbatteries electric [17] vehiclesand supercapacitors through simulation have been studies. previously Different reportedmodels haveand, in been some reported cases, for in thediesel literature traction and engines reviewed [64]. The in some simplest specific model reports for a forbattery railways, is a real for voltageinstance source [62]. (a The voltage use of source batteries with [17 a ]constant and supercapacitors or variable resistor have beenin series, previously according reported to SoC and, of the in battery).some cases, For for a dieselsingle tractionsupercapacitor, engines [ 64different]. The simplest models model are proposed for a battery in the is a realliterature: voltage From source an (a electrochemicalvoltage source withmodel a constantto electric or equivalent variable resistorcircuit model. in series, Electric according equivalent to SoC model of the is battery). very popular For a andsingle gives supercapacitor, good results. di Theyfferent use models from area first proposed order model in the literature:(the simplest From one, an with electrochemical a capacitor) model to a thirdto electric or fourth equivalent model circuit (with model.four capacitors Electric equivalentwith variable model capacity) is very [65]. popular When and modeling gives good with results. RC networksThey use fromis important a first order to take model the (thenumber simplest of RC one, elements with a capacitor)as low as topossible a third for or fourthpractical model reasons; (with includefour capacitors the non-linear with variable capacitance capacity) effect [65 ].(the When capacitance modeling depends with RC networkson voltage is important[64]) only toin takeone theC elementnumber and of RC finallyelements include as lowa parallel as possible leakage for resi practicalstor. The reasons; model include used for the simulation non-linear purposes capacitance in nexteffect sections, (the capacitance is a first order depends model on with voltage two [64 capacitors]) only in with one Cfitselement adequately and finallythe pattern include of response a parallel inleakage the order resistor. of seconds The model to some used minute for simulation [65]: A purposescapacitance in nextC, a sections,capacitance is a firstwhich order depends model with with voltagetwo capacitors respect with nominal fits adequately conditions the (i.e., pattern UN) ofK responsec (Ui−UN); in a the series order resistance of seconds (R toS) some and minutea parallel [65]: resistanceA capacitance (RP) C,(Figure a capacitance 7). Two which500 F dependscapacitors with from voltage Maxwell respect Technologies nominal conditions (model BMOD0500 (i.e., UN)Kc P016(U B02)U ); with a series 16 DC resistance working (R voltage) and a have parallel been resistance tested in (R the) (Figure laboratory7). Two in charge 500 F capacitors and discharge from i − N S P testsMaxwell to obtain Technologies the value (model of each BMOD0500 internal parameter P016 B02) withand 16test DC the working performance voltage of have the beenESS. testedResults in werethe laboratory that every in module charge andcould discharge be represented tests to obtainby two the resistors value of and each two internal capacitors: parameter RS = and2.1 m testΩ the; a parallelperformance resistance of the R ESS.P = 2.8 Results kΩ; A were capacitor that every C = module500 F and could Kc around be represented 18 F/V by[64]. two Some resistors of these and valuestwo capacitors: can be obtained RS = 2.1 in m Ωdatasheets; a parallel and resistance the litera RPture,= 2.8 but kΩ it; Ais capacitor interesting C =to500 check F and them Kc aroundin the laboratory.18 F/V[64]. The Some series of these and values parallel can beassociation obtained inneeded datasheets for each and theapplication literature, can but itbe is interestingbuilt and evaluatedto check them through in the a laboratory.state-space The representation series and parallel of the association aggregated needed model. for The each equivalent application of can the be associationbuilt and evaluated must take through into account a state-space Kc and the representation dependence of of the equivalent aggregated capacity model. versus The equivalentvoltage Ui). of the association must take into account Kc and the dependence of equivalent capacity versus voltage Ui).

Figure 7. Elemental model for a supercapacitor module (Maxwell MOD0500). This model is associated Figure 7. Elemental model for a supercapacitor module (Maxwell MOD0500). This model is in series and parallel to define the aggregated ESS model linked to traction or hotel loads. associated in series and parallel to define the aggregated ESS model linked to traction or hotel loads. 3. Results and Discussion 3. Results and Discussion 3.1. Acceleration and Power 3.1. Acceleration and Power In order to study the storage possibilities of the different locomotive vehicles, some simulations haveIn been order performed to study the in storage the railway possibilities route from of the Alcazar different de locomotive San Juan to vehicles, Cartagena. some In simulations case of the havefull-electric been performed locomotives in (S-253the railway and S-252), route althoughfrom Alcazar some de simulations San Juan to have Cartagena. been carried In case out of in the the full-electricwhole itinerary, locomotives nowadays (S-253 it is and only S-252), possible although to drive some along simulations the route have from been Alcazar carried de San outJuan in the to wholeChinchilla, itinerary, as the nowadays rest of the it railway is only trackpossible from to Chinchilla drive along to Cartagenathe route from is not Alcazar electrified. de San Juan to Chinchilla, as the rest of the railway track from Chinchilla to Cartagena is not electrified. Appl. Sci. 2019, 9, x FOR PEER REVIEW 17 of 41 Appl.Appl. Sci. Sci.2019 2019, 9, 9 3605, x FOR PEER REVIEW 17 of17 41 of 40 The simulator tool has been developed by authors in Matlab, based on the equations already describedThe simulator in Section tool 2 hasfor beenobtaining developed the resistan by authorsce forces in Matlab, and thebased tractive on the effort-velocity equations already and describedaccelerationThe simulator in curves. Section tool Once 2 has for the been obtaining resistance developed theforces resistan by (curve authorsce, railforces in vehicles Matlab, and andthe based grade,tractive on if the thiseffort-velocity equations force is against alreadyand describedaccelerationthe acceleration in Section curves. of2 the for Once train) obtaining the and resistance the the traction resistance forces effort (curve forces (motor, andrail and,vehicles the tractive in some and e grade,cases,ffort-velocity grade)if this forceis and computed, accelerationis against it curves.theis easy acceleration Once to obtain the resistance of the train)position, forces and (curve,speed,the traction railacceleration, vehicles effort (motor andpower grade, and, and in if some thisenergy force cases, that is grade) againstdetermine is thecomputed, accelerationthe train it ofisperformance the easy train) to andobtain in the each the traction tripposition, on eff ortthe speed, (motorroute. acceleration,For and, the in somesection power cases, been and grade) considered, energy is computed, that resistance determine it isforce, easy the due to train obtain to theperformancecurves position, is limited speed, in each (see acceleration, tripFigure on 1b).the power route.Moreover, and For energy theTalgo se thatctioncoaches determine been have considered, independent the train resistance performance wheels force, which in due each limit to trip oncurvesthese the route. effects is limited For respect the (see section toFigure conventional been 1b). considered, Moreover, (bogies) resistanceTalgo coaches. coaches force, A havepreliminary due independent to curves version is limited wheels of this (seewhich tool Figure limitwas1b). Moreover,thesepresented effects Talgo in Referencesrespect coaches to have [16,17].conventional independent (bogies) wheels coaches. which A limit preliminary these eff ectsversion respect of this to conventional tool was (bogies)presentedThe coaches. power in References Aprofile preliminary of [16,17]. the locomotives version of is this calculated tool was as presented the product in of References the propulsive [16,17 force]. and the velocityTheThe power ofpower the profile profiletrain. ofWhen of the the locomotives locomotivesthe power takes isis calculatedcalculated negative as as values, the the product product it means of of the the the propulsive propulsive engine isforce forcegenerating and and the the velocityvelocityenergy. of Acceleration theof the train. train. When could When the be the powerobtained power takes from takes negative Equati negativeon values, (2). values, Results it means it obtainedmeans the enginethe for engine the is acceleration generating is generating energy.and Accelerationenergy.power demand Acceleration could for be the obtainedcould different be obtained from locomotives Equation from simulatedEquati (2). Resultson (2).are Resultsshown obtained inobtained Figures for the for 8 acceleration and the 9.acceleration and powerand demandpower fordemand the di forfferent the different locomotives locomotives simulated simulated are shown are shown in Figures in Figures8 and9 .8 and 9.

(a) (b) (a) (b) Figure 8. Power and acceleration for locomotives (a) diesel (S-334); (b) hybrid (S-730). FigureFigure 8. 8.Power Power and and acceleration acceleration forfor locomotives ( (aa)) diesel diesel (S-334); (S-334); (b ()b hybrid) hybrid (S-730). (S-730).

(a) (b) (a) (b) FigureFigure 9. 9.Power Power and and accelerationacceleration forfor electric locomotives locomotives (a (a) )S-253; S-253; (b ()b S-252.) S-252. Figure 9. Power and acceleration for electric locomotives (a) S-253; (b) S-252. AsAs can can be be seen seen in in Figures Figures8 8and and9, 9, the the train train has has negative negative accelerationacceleration throughout throughout the the itinerary, itinerary, normallynormallyAs whencan when be it seen it has has in to to Figures stop stop in in a8 a station,and station, 9, the oror train itit reducesreduces has negative speed below belowacceleration the the limits limits throughout of of each each of the ofthe theitinerary, sections sections ofnormallyof the the route. route. when This This it negativehas negative to stop acceleration acceleration in a station, is isor achieved achievedit reduces through speed below braking. the limitsElectrical Electrical of each locomotives locomotives of the sections have have regenerativeofregenerative the route. braking brakingThis negative (the (the traction traction acceleration motor motor worksworksis achieved as a generator), through braking. but but sometimes sometimes Electrical the the locomotivesgenerated generated energy have energy regenerative braking (the traction motor works as a generator), but sometimes the generated energy cannotcannot be be returned returned through through the the catenary, catenary, so braking so braking is achieved is achieved through through the use the of use electrical of electrical resistors. cannot be returned through the catenary, so braking is achieved through the use of electrical Inresistors. this case, In the this simulation case, the softwaresimulation considers software theconsiders possibility the possibility to inject power to inject (if therepower are (if otherthere trainsare resistors. In this case, the simulation software considers the possibility to inject power (if there are onother the track trains section) on the track or the section) use of or pneumatic, the use of blendingpneumatic, or blending resistive or bank resistive braking bank options, braking i.e., options, there is otheri.e., there trains is onsome the feedbacktrack section) from or Electric the use and of pneu Kinematicalmatic, blending parts of or the resistive model. bank This braking is an important options, some feedback from Electric and Kinematical parts of the model. This is an important issue because i.e.,issue there because is some electromechanical feedback from limitations Electric and can Kinematical have a major parts impact of the on model. the kinematical This is an behavior important of electromechanicalissue because electromechanical limitations can havelimitations a major can impact have a on major the kinematicalimpact on the behavior kinematical of the behavior model (e.g.,of the variation in traction and braking curves, due to voltage values on overhead lines, which aﬀects accelerations and changes timetables). The interest and impacts of this feedback is analyzed in the Appl. Sci. 2019, 9, x FOR PEER REVIEW 18 of 41

Appl.the Sci. model2019, (e.g.,9, 3605 the variation in traction and braking curves, due to voltage values on overhead lines,18 of 40 which affects accelerations and changes timetables). The interest and impacts of this feedback is analyzed in the literature [66]. In the case of diesel units, they also have dynamic braking, in which literaturethe power [66 supplied]. In the caseby motors of diesel are units, used theyto feed also roof have resistors dynamic cooled braking, with inforced which ventilation. the power In supplied both bycases, motors energy areused is used to feedin a non-efficient roof resistors way. cooled with forced ventilation. In both cases, energy is used in a non-eIn orderfficient to way. improve the energy efficiency and make use of the energy generated in the regenerativeIn order to braking, improve the the idea energy is to e ffiinstallciency batteries and make and/or use ofsupercapacitors, the energy generated recovering in the and regenerative storing braking,the braking the idea energy. is to install The recovered batteries and energy/or supercapacitors, can be used for recovering feeding andthe storingsame train the brakingwhen it energy. is Theaccelerating, recovered energyreducing can the be power used for peak feeding consumption. the same trainIn addition, when it in is electrified accelerating, paths, reducing it could the feed power peakother consumption. locomotives circulating In addition, in inthe electrified same itinerary paths, or it give could it back feed to other the grid locomotives through the circulating catenary in(in the samethe case itinerary of reversible or give substations). it back to the grid through the catenary (in the case of reversible substations).

3.2.3.2. Cases Cases of of Study Study

3.2.1.3.2.1. Diesel-Electric Diesel-Electric Locomotive Locomotive (S-334)(S-334) S-334S-334 locomotive locomotive hashas beenbeen simulatedsimulated through the the railway railway itinerary itinerary between between Cartagena Cartagena and and Alcazar,Alcazar, with with a Talgoa Talgo IV IV coaches’ coaches’ configuration configuration (Table (Table2). 2). From From Alcazar Alcazar to to Cartagena, Cartagena, the the train train has has four intermediatefour intermediate stops (Villarrobledo,stops (Villarrobledo, Albacete, Albacete, Murcia, Murc Balsicas),ia, Balsicas), while while in the in opposite the opposite direction direction of travel, of it hastravel, five it intermediatehas five intermediate stops (Torrepacheco, stops (Torrepacheco, Balsicas, Balsicas, Murcia, Cieza,Murcia, Albacete). Cieza, Albacete). The diesel The engine diesel has anengine efficiency has an ratio efficiency of 37%, ratio and theof 37%, efficiency and the of ef theficiency main of generator the main is generator 90%. The is maximum 90%. The maximum velocity that thevelocity locomotive that the can locomotive reach is 200 can km reach/h. is 200 km/h. TheThe speed speed profile profile for for a a passenger passenger traintrain in both directions directions is is presented presented in in Figure Figure 10. 10 .

200 Train Speed Railway Speed Limit

150

100

0 00.511.522.533.5 Time (h) (a) (b)

FigureFigure 10. 10.Speed Speed proﬁle profile for dieselfor diesel locomotive locomotive S-334: S-334: (a) From (a) From Alcazar Alcazar to Cartagena; to Cartagena; (b) from (b Cartagena) from toCartagena Alcazar. to Alcazar.

SomeSome ﬁgures figures of of the the simulation simulation forfor passengerpassenger trains trains are are presented presented in in Table Table 7.7 .

TableTable 7.7. SimulationSimulation results for for diesel diesel locomotive locomotive S-334. S-334.

N° PrimaryPrimary Energy Energy ElectricityElectricity BrakingBraking Average Average DirectionDirection Stop N StopsDuration Duration ConsumptionConsumption 1 1 DemandDemand EnergyEnergy Power ◦ Power (kW) s (kWh)(kWh) (kWh)(kWh) (kWh)(kWh) (kW) Alcazar-CartagenaAlcazar-Cartagena 4 4 3 h 12 min 3 h 12 min 7607.8 7607.8 2417.2 2417.2 1257.3 1257.3 869.81 869.81 Cartagena-AlcazarCartagena-Alcazar 5 5 3 h 20 min 3 h 20 min 9648.3 9648.3 3229.7 3229.7 955.8 955.8 1144.6 1144.6 1 1 PrimaryPrimary energy energy from from fu fuelel in the locomotive.locomotive. The Cartagena-Alcazar itinerary has higher values of train energy consumption, as well as the averageThe Cartagena-Alcazar power consumption, itinerary due to the has ascending higher values slope and of train associated energy large consumption, grade resistances as well that as the averagethe train power has to consumption, overcome (see due Figure to the1). ascending slope and associated large grade resistances that the train has to overcome (see Figure1). Appl.Appl. Sci. Sci.2019 2019, 9,, 9 3605, x FOR PEER REVIEW 19 of19 41 of 40 Appl. Sci. 2019, 9, x FOR PEER REVIEW 19 of 41

3.2.2.3.2.2. Hybrid Hybrid Diesel-Electric Diesel-Electric andand ElectricElectric Unit (HDEMU S-730) S-730) 3.2.2. Hybrid Diesel-Electric and Electric Unit (HDEMU S-730) S-730S-730 locomotive locomotive has has been been simulated simulated through through the the same same itinerary, itinerary, studying studying possibilities possibilities for energyfor S-730 locomotive has been simulated through the same itinerary, studying possibilities for storageenergy associated storage associated with the usewith of regenerativethe use of regenerative braking and braking changes and in tractionchanges mode in traction throughout mode the energy storage associated with the use of regenerative braking and changes in traction mode throughout the route Alcazar-Chinchilla. The configuration of coaches is Talgo, see Table 2. This routethroughout Alcazar-Chinchilla. the route Alcazar-Chinchilla. The conﬁguration The of coaches configuration is Talgo, of see coaches Table 2is. ThisTalgo, hybrid see Table locomotive 2. This of hybrid locomotive of the HDEMU can develop a maximum speed of 180 km/h in diesel-electric thehybrid HDEMU locomotive can develop of the a maximumHDEMU can speed develop of 180 a km maximum/h in diesel-electric speed of 180 mode. km/h Speed in diesel-electric proﬁles for the mode. Speed profiles for the route are presented in Figure 11. routemode. are Speed presented profiles in Figurefor the 11route. are presented in Figure 11.

200 200 Train Speed RailwayTrain Speed Speed Limit Railway Speed Limit 150 150

100 100

50 50

0 000.511.522.53 00.511.522.53Time (h) (aTime) (h) (b) (a) (b) FigureFigure 11. 11.Speed Speed proﬁle profile for for hybridhybrid vehiclevehicle S-730: ( (aa)) From From Alcazar Alcazar to to Cartagena; Cartagena; (b () bfrom) from Cartagena Cartagena Figure 11. Speed profile for hybrid vehicle S-730: (a) From Alcazar to Cartagena; (b) from Cartagena toto Alcazar. Alcazar. to Alcazar. SomeSome results results of of the the simulation simulation forfor HDEMUHDEMU S-730 are are presented presented in in Table Table 8.8 . Some results of the simulation for HDEMU S-730 are presented in Table 8.

TableTable 8. 8. SimulationSimulation results for for hybrid hybrid locomotive locomotive S-730. S-730. Table 8. Simulation results for hybrid locomotive S-730. PrimaryPrimary Energy Electricity Total Average N Primary ElectricityTotal BrakingTotal Regenerative Average Direction N°◦ Duration ConsumptionEnergy 1 DemandElectricity Braking Regenerative AveragePower Direction StopsN° Duration Energy DemandEnergy Braking (kWh) EnergyEnergy (kWh) Power Direction Stops Duration Consumptio(kWh) (kWh)Demand Energy Energy (kW)Power Stops Consumptio (kWh) Energy (kWh) (kW) Alcazar–Cartagena 4 3 h 3 min n5143.8 1 (kWh) 2702.6 1638.6(kWh) 310.5 1020.0 1 (kWh) (kWh) (kW) 3 h 13 n (kWh) (kWh) Alcazar–CartagenaCartagena–Alcazar 5 4 3 h 3 min 8997.05143.8 3744.5 2702.6 1335.11638.6 442.4 310.5 1343.4 1020.0 Cartagena–AlcazarAlcazar–Cartagena 5 4 3 3h minh 13 3 min 8997.05143.8 3744.5 2702.6 1335.11638.6 442.4 310.5 1343.4 1020.0 Cartagena–Alcazar 5 3 h 113 min 8997.0 3744.5 1335.1 442.4 1343.4 1 PrimaryPrimary energy energy from from fuel inin the the locomotive. locomotive. 1 Primary energy from fuel in the locomotive. Figure 12 depicts the power demand and the regenerative energy profile on AJ-CH route. FigureFigure 12 12 depicts depicts the the power power demand demand andand thethe regenerativeregenerative energy energy profile proﬁle on on AJ-CH AJ-CH route. route.

300 300 200 200 100 (kW/10) r 100 (kW/10) r 0 0 -100 -100 -200 -200 Power gy (kWh),Powe gy r -300 EnergyPower gy (kWh),Powe gy r -300 Energy Ene -400 Ene -400 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4Time (h 0.6) 0.8 1 (aTime) (h) (b) (a) (b) Figure 12. Power for hybrid locomotive S-730 and energy profile for the foresee ESS device. Energy Figure 12. Power for hybrid locomotive S-730 and energy profile for the foresee ESS device. Energy Figuredraws 12. thePower possibilities for hybrid for storage locomotive behavior S-730 (dynamic and energy braking proﬁle fills ESS, for theandforesee then these ESS systems device. cover Energy draws the possibilities for storage behavior (dynamic braking fills ESS, and then these systems cover drawspart theof the possibilities demand to for accelerate storage behaviorthe unit): (dynamic(a) From brakingAlcazar ﬁllsto Cartagena; ESS, and then(b) from these Cartagena systems coverto part of the demand to accelerate the unit): (a) From Alcazar to Cartagena; (b) from Cartagena to partAlcazar. of the demand to accelerate the unit): (a) From Alcazar to Cartagena; (b) from Cartagena to Alcazar. Alcazar. Appl. Sci. 2019, 9, 3605 20 of 40

Appl.The Sci. maximum 2019, 9, x FOR energy PEER REVIEW recovered after a braking period is 203 kWh. It is also calculated the20 energyof 41 that the train needs to accelerate the first minute after a stop (maximum traction effort), obtaining a value ofThe 32 kWhmaximum (i.e., firstenergy energy recovered up and after down a braking cycle in peri Figureod is12 ).203 kWh. It is also calculated the energyTo store that thethe regenerativetrain needs to energy accelerate and, the in thisfirst way,minute reduce after thea stop train (maximum energy consumption, traction effort), it is studiedobtaining the a possibility value of 32 of kWh installing (i.e., first an energy ESS based up and on down Li-ion cycle batteries, in Figure a HESS 12). mixing batteries and supercapacitors,To store the and regenerative only supercapacitors. energy and, Parametersin this way, ofreduce Li-ion the batteries train energy have consumption, been obtained it fromis Referencesstudied the [67 possibility,68] and parameters of installing of an supercapacitors ESS based on fromLi-ion Reference batteries, [a69 HESS]. mixing batteries and supercapacitors,The characteristics and only of the supercapacitors. system are shown Paramete in Tablers of9. Li-ion batteries have been obtained from References [67,68] and parameters of supercapacitors from Reference [69]. The characteristics of the systemTable are shown 9. ESS in characteristics. Table 9.

Storage Energy Table 9. ESS characteristics. Lifespan Weight (kg) Volume (m3) Price (€) System (kWh) (Cycles) Energy Weight Volume Lifespan StorageBatteries System 400 2700 2 83,600Price (€) 10,000 (kWh) (kg) (m3) (Cycles) Batteries + 10,000 Batteries 320 + 15400 2140 + 65202700 1.6 + 0.592 66,880 +83,600120,000 10,000 supercapacitors (1,000,000 1) Batteries + 2140 + 66,880 + 10,000 Supercapacitors 200320 + 15 86,940 7.91.6 + 0.59 1600,000 1,000,000 supercapacitors 6520 120,000 (1,000,000 1) 1 Supercapacitors 200 Lifecycles86,940 of supercapacitors. 7.9 1600,000 1,000,000 1 Lifecycles of supercapacitors.

TheThe state state of of charge charge ofof thethe battery energy energy storag storagee system system (BESS) (BESS) and and the thehybrid hybrid ESS ESS(HESS) (HESS) is is represented in in Figure Figure 13. 13 In. Inorder order to increase to increase the thebatteries batteries lifetime, lifetime, the SOC the remains SOC remains between between 30% 30%and and 80%, 80%, and and the the majority majority of ofthe the time, time, it itis ismaintained maintained around around 50%. 50%. Red Red curves curves represent represent the the supercapacitors values, and blue lines show the behavior of the batteries. supercapacitors values, and blue lines show the behavior of the batteries.

(a) (b)

FigureFigure 13. 13.Energy Energy stored stored in in thethe ESSESS duringduring the itinerary CH-AJ-CH: CH-AJ-CH: (a ()a )BESS; BESS; (b ()b HESS.) HESS.

TheThe main main problem problem thatthat arises through through the the use use of ofHESS-as HESS-as on board on board energy energy storage storage system-is system-is the thehigher higher rates rates of ofweight weight and and volume volume per perkWh kWh of th ofe supercapacitors the supercapacitors compared compared with batteries with batteries (435 (435kg/kWh kg/kWh vs. vs. 6.75 6.75 kg/kWh kg/kWh and and 0.04 0.04 m3/kWh m3/kWh vs. vs.0.005 0.005 m3/kWh). m3/kWh). On the On one the hand, one hand, excessive excessive weight weight of of the ESS could substantially substantially modify modify the the dynamic dynamic behavi behavioror of of the the train. train. On On the the other other hand, hand, if there if there is is notnot enough enough space space in in the the locomotive, locomotive, itit couldcould bebe necessary to to add add a a service/power service/power car car to tothe the train train to to placeplace the the ESS ESS (notice (notice that that this this is is not not aa problemproblem for Talgo units, units, Table Table 2,2, because because they they usually usually integrate integrate power coaches for diesel and electric units. In the case of Talgo IV, the reason is to cover “hotel power coaches for diesel and electric units. In the case of Talgo IV, the reason is to cover “hotel loads” loads” in the case the supply from locomotive is not available). in the case the supply from locomotive is not available).

3.2.3.3.2.3. Electric Electric Locomotives Locomotives (S-252 (S-252 and and S-253)S-253) S-252 and S-253 are full electric locomotives (Figure 2). Therefore, in this case, the itinerary in S-252 and S-253 are full electric locomotives (Figure2). Therefore, in this case, the itinerary in which can circulate is only the section between Alcazar and Chinchilla (with 1 or 2 intermediate which can circulate is only the section between Alcazar and Chinchilla (with 1 or 2 intermediate stops). stops). These locomotives have been simulated not only in this section, but through the whole route These locomotives have been simulated not only in this section, but through the whole route (for (for comparison purposes). As can be seen in Figure 5a, both locomotives have similar traction Appl. Sci. 2019, 9, 3605 21 of 40 Appl. Sci. 2019, 9, x FOR PEER REVIEW 21 of 41 Appl. Sci. 2019, 9, x FOR PEER REVIEW 21 of 41 comparisoncurves, but purposes). the main difference As can be is seen that in S-252 Figure reaches5a, both 220 locomotives km/h, while have S-253 similar has a speed traction limit curves, of 140 but thekm/h,curves, main because dibutﬀerence the S-253 main is thatwas difference S-252designed reachesis thatas a S-252freight 220 km reaches locomotive./h, while 220 S-253km/h, haswhile a speed S-253 limithas a ofspeed 140 kmlimit/h, of because 140 S-253km/h, wasFigures because designed 14 S-253 and as 15 was a depict freight designed the locomotive. speed as a freightprofile locomotive.for a passenger train in both directions and the whole itinerary.FiguresFigures 14 14 and and 15 depictdepict the the speed speed profile proﬁle for a for passenger a passenger train in train both in directions both directions and the whole and the itinerary. whole itinerary. 200 Train Speed 200 RailwayTrain Speed Speed Limit Railway Speed Limit 150 150

100 100

50 50

0 00.511.522.53 0 00.511.522.53Time (h) (a) (bTime) (h) (a) (b) Figure 14. Speed profile for electric locomotive S-252: (a) From Alcazar to Cartagena; (b) from FigureCartagenaFigure 14. 14.Speed toSpeed Alcazar. proﬁle profile for electricfor electric locomotive locomotive S-252: S-252: (a) From (a) From Alcazar Alcazar to Cartagena; to Cartagena; (b) from (b Cartagena) from toCartagena Alcazar. to Alcazar.

(a) (b) (a) (b) FigureFigure 15. 15.Speed Speed profile profile for electricfor electric locomotive locomotive S-253: S-253: (a) From (a) From Alcazar Alcazar to Cartagena; to Cartagena; (b) from (b Cartagena) from Figure 15. Speed profile for electric locomotive S-253: (a) From Alcazar to Cartagena; (b) from toCartagena Alcazar. to Alcazar. Cartagena to Alcazar. SomeSome results results of of the the simulation simulation forfor passengerpassenger trains are are presented presented in in Table Table 10. 10 . Some results of the simulation for passenger trains are presented in Table 10. The duration of the travel increases in about 10 min by using the locomotive S-253, due to their speed limit of 140 kmTable/h (Table 10. Simulation 10, and Table results1, i.e., for electr S-252ic is locomotives a locomotive S-253 designed and S-252. for high-speed duties). Table 10. Simulation results for electric locomotives S-253 and S-252. However, the limitation of the maximum speed also reduces the energyTotal consumption, e.g., from Energy Total Averag 4673.2 kWh to 3837.0N° kWh (Cartagena–Alcazar direction),Energy thatEnergy means Brakin about 18%Regenerati of the totalAverag energy N° Locomoti Duratio Energy Deman Brakin Regenerati e consumption.Direction TheseStop resultsLocomoti show thatDuratio acceptingConsumpti slightly longerDeman travel times;g significantve Energy improvements e Direction Stop ve n Consumpti d g ve Energy Power s ve n on (kWh) d Energy (kWh) Power can be made in termss of energy efficiency. It is interestingon (kWh) to consider(kWh) thatEnergy some reports(kWh) [ 2] present(kW) fast (kWh) (kWh) (kW) trains (high-speed trains) as a more efficient3 h 03 way for transportation, but(kWh) this is mainly due to passenger occupation and supply systemS-252 (25 kV AC,3 h 03 that allows2900.7 power flow2637.0 to public1455.1 power 1357.6 system) and1038.2 not to Alcazar-Cartage S-252 min 2900.7 2637.0 1455.1 1357.6 1038.2 Alcazar-Cartage 4 min technologicalna reasons.4 For this reason,3 the h 12 improvement of braking potential is the main concern of na S-253 3 h 12 2197.4 1997.7 1051.6 982.5 648.5 this work to engage authoritiesS-253 and operatorsmin in the2197.4 improvement 1997.7 and1051.6 maintenance 982.5 of these 648.5 lines. 3min h 09 S-252 3 h 09 4673.2 4248.4 1746.2 1639.4 1548.4 Cartagena-Alcaz S-252 min 4673.2 4248.4 1746.2 1639.4 1548.4 Cartagena-Alcaz 5 min ar 5 3 h 21 ar S-253 3 h 21 3837.0 3488.2 1179.1 1152.4 1137.6 S-253 min 3837.0 3488.2 1179.1 1152.4 1137.6 Alcazar-Chinchi 2 S-252 1min h 02 1707.3 1552.1 449.3 444.9 1735.0 Alcazar-Chinchi 2 S-252 1 h 02 1707.3 1552.1 449.3 444.9 1735.0 Appl. Sci. 2019, 9, x FOR PEER REVIEW 22 of 41

1 h 13 S-253 1149.3 1044.8 240.7 99.73 912.3 min 1 h 02 S-252 1697.2 1542.9 990.8 941.2 1616.8 Chinchilla-Alcaz min Appl. Sci. 2019, 9, 3605 1 22 of 40 ar 1 h 10 S-253 824.8 749.2 251.8 226.7 651.02 min Table 10. Simulation results for electric locomotives S-253 and S-252. The duration of the travel increases in about 10 min by using the locomotive S-253, due to their Energy Energy Regenerative Average speed limit of 140N km/h (Table 10, and Table 1, i.e., S-252 is a locomotiveTotal Braking designed for high-speed Direction ◦ Locomotive Duration Consumption Demand Energy Power Stops Energy (kWh) duties). However, the limitation of the maximum(kWh) speed also(kWh) reduces the energy consumption,(kWh) (kW) e.g., from 4673.2 kWh to 3837.0S-252 kWh (Cartagena–Alcazar 3 h 03 min 2900.7 direction), 2637.0 that means 1455.1 about 1357.618% of the 1038.2 total Alcazar-Cartagena 4 energy consumption. TheseS-253 results 3 h 12show min that 2197.4accepting 1997.7slightly longer 1051.6 travel times; 982.5 significant 648.5 improvements can be madeS-252 in terms 3 h 09 of min energy 4673.2 efficiency. 4248.4 It is interesting 1746.2 to consider 1639.4 that 1548.4some Cartagena-Alcazar 5 reports [2] present fast trainsS-253 (high-speed 3 h 21 min trains) 3837.0 as a more 3488.2efficient way 1179.1 for transportation, 1152.4 but 1137.6 this is mainly due to passengerS-252 occupation 1 h 02 min and supply 1707.3 system 1552.1 (25 kV AC, 449.3 that allows 444.9 power flow 1735.0 to Alcazar-Chinchilla 2 public power system) andS-253 not to technological 1 h 13 min reasons. 1149.3 For 1044.8this reason, 240.7the improvement 99.73 of braking 912.3 potential is the main concernS-252 of this 1 h work 02 min to engage 1697.2 authorities 1542.9 and operators 990.8 in the 941.2 improvement 1616.8 Chinchilla-Alcazar 1 and maintenance of theseS-253 lines. 1 h 10 min 824.8 749.2 251.8 226.7 651.02

3.2.4.3.2.4. Freight Freight Transport Transport with with S-334 S-334 and and S-252 S-252 The locomotives S-334 (diesel-electric) and S-252 (electric) have also been used for freight The locomotives S-334 (diesel-electric) and S-252 (electric) have also been used for freight transport transport (it is usual for railway operators that older passenger locomotives be cascaded down to (it is usual for railway operators that older passenger locomotives be cascaded down to freight and freight and regional services). In this case, the maximum speed permitted for the circulation of the regional services). In this case, the maximum speed permitted for the circulation of the wagons is wagons is 100 km/h. The train is formed by 10 flat bogie wagons (container-wagon, see Table 2). 100 km/h. The train is formed by 10 ﬂat bogie wagons (container-wagon, see Table2). Both locomotives Both locomotives have been simulated throughout the whole itinerary (Figures 16 and 17 present havesome been results simulated of these throughout simulations). the whole itinerary (Figures 16 and 17 present some results of these simulations).

(a) (b)

Appl.Figure Sci.Figure 2019 16., 916.Speed, x FORSpeed proﬁlePEER profile REVIEW for dieselfor diesel locomotive locomotive S-334 S-334 for freight for freight transport: transport: (a) From (a Alcazar) From toAlcazar Cartagena; 23to of 41 (b)Cartagena; from Cartagena (b) from to Alcazar.Cartagena to Alcazar.

(a) (b)

FigureFigure 17. 17.Speed Speed proﬁleprofile forfor electricelectric locomotivelocomotive S-252 S-252 for for freight freight transport: transport: (a) ( aFrom) From Alcazar Alcazar to to Cartagena;Cartagena; (b ()b from) from Cartagena Cartagena to to Alcazar.Alcazar.

SomeSome results results of of the the simulation simulation forfor freightfreight trains are are presented presented in in Table Table 11. 11 It. Itis isinteresting interesting to to emphasizeemphasize the the high high potential potential for for energyenergy recoveryrecovery in these these trains. trains. This This potential potential could could justify justify the the need need for a change in transportation freight shares and the stimulation of intermodal services, both at national or international levels, for instance in Poland [3], or the OBOR initiative China-Europe [4].

Table 11. Simulation results for freight trains (S-334 and S-252).

Total Energy Energy Regenerative Average N° Braking Direction Locomotive Duration Consumpt Deman Energy Power Stops Energy ion (kWh) d (kWh) (kWh) (kW) (kWh) Alcazar-Cartage S-252 5 h 04 min 2355.9 2141.7 2634.2 2389.2 508.1 4 na S-334 5 h 29 min 6083.3 2080.5 2508.4 (2400.1) 470.5 Cartagena-Alca S-252 5 h 16 min 4484.0 4076.3 1487.6 1362.5 1003.8 5 zar S-334 6 h 04 min 11834.1 4047.3 1429.2 (1233.9) 974.9

3.2.5. Key Performance Indicators Some Key Performance Indicators (KPIs) have been calculated in order to evaluate the performance of the different types of locomotives and their ability to generate energy with regenerative braking. The KPIs have been defined as follows (according to ideas proposed by professional organizations [2]): KPI 1: Final net energy consumption per maximum track effort (kWh/kN-km). KPI 2: Final net energy consumption per train (kWh/train-km) KPI 3: Final net energy consumption per seat offered (passenger trains, kWh/seat-km) or per total trains mass (freight trains, kWh/“gross” tonnes-km) KPI 4: Final net energy consumption per number of passengers (passenger trains, kWh/pkm) or per mass transported (freight trains, kWh/”net” tonnes-km) KPI 5: Energy recuperation rate (Energy generate in regenerative braking per final gross energy consumption, kWh gen/kWh cons)

Passenger Trains Results obtained for KPIs in passenger trains are presented in Table 12. It is considered that regenerative braking can be used only between Alcazar and Chinchilla and that the diesel locomotive (S-334) cannot use the regenerative braking. Appl. Sci. 2019, 9, 3605 23 of 40 for a change in transportation freight shares and the stimulation of intermodal services, both at national or international levels, for instance in Poland [3], or the OBOR initiative China-Europe [4].

Table 11. Simulation results for freight trains (S-334 and S-252).

Energy Energy Regenerative Average N Total Braking Direction ◦ Locomotive Duration Consumption Demand Energy Power Stops Energy (kWh) (kWh) (kWh) (kWh) (kW) S-252 5 h 04 min 2355.9 2141.7 2634.2 2389.2 508.1 Alcazar-Cartagena 4 S-334 5 h 29 min 6083.3 2080.5 2508.4 (2400.1) 470.5 S-252 5 h 16 min 4484.0 4076.3 1487.6 1362.5 1003.8 Cartagena-Alcazar 5 S-334 6 h 04 min 11834.1 4047.3 1429.2 (1233.9) 974.9

3.2.5. Key Performance Indicators Some Key Performance Indicators (KPIs) have been calculated in order to evaluate the performance of the different types of locomotives and their ability to generate energy with regenerative braking. The KPIs have been defined as follows (according to ideas proposed by professional organizations [2]): KPI 1: Final net energy consumption per maximum track effort (kWh/kN-km). KPI 2: Final net energy consumption per train (kWh/train-km) KPI 3: Final net energy consumption per seat offered (passenger trains, kWh/seat-km) or per total trains mass (freight trains, kWh/“gross” tonnes-km) KPI 4: Final net energy consumption per number of passengers (passenger trains, kWh/pkm) or per mass transported (freight trains, kWh/”net” tonnes-km) KPI 5: Energy recuperation rate (Energy generate in regenerative braking per final gross energy consumption, kWh gen/kWh cons)

Table 12. Key Performance Indicators (KPIs) for passenger trains with regenerative braking only between Alcazar and Chinchilla.

KPI 1 KPI 2 KPI 3 KPI 4 KPI 5 kWh Direction Locomotive kWh/kN-km kWh/train-km kWh/seat-km kWh/pkm gen/kWh cons S-334 0.1064 18.78 0.0716 0.1225 0 S-730 0.0586 12.84 0.0490 0.0838 0.0604 Alcazar-Cartagena S-252 0.0207 6.20 0.0237 0.0404 0.1153 S-253 0.0152 4.56 0.0174 0.0297 0.0732 S-334 0.1453 25.63 0.0978 0.1672 0 S-730 0.1038 22.72 0.0867 0.1482 0.0492 Cartagena-Alcazar S-252 0.0323 9.68 0.0369 0.0632 0.1422 S-253 0.0272 8.16 0.0311 0.0532 0.1000

Results obtained for KPIs in passenger trains considering that regenerative braking can be used during the whole itinerary (all locomotives) are presented in Table 13. Appl. Sci. 2019, 9, 3605 24 of 40

Table 13. KPIs for passenger trains with regenerative braking available in the whole itinerary.

KPI 1 KPI 2 KPI 3 KPI 4 KPI 5 kWh Direction Locomotive kWh/kN-km kWh/train-km kWh/seat-km kWh/pkm gen/kWh cons S-334 0.0883 15.58 0.0594 0.1016 0.1704 S-730 0.0432 9.47 0.0361 0.0618 0.3070 Alcazar-Cartagena S-252 0.0113 3.40 0.0130 0.0222 0.5148 S-253 0.0090 2.70 0.0103 0.0176 0.4918 S-334 0.1314 23.19 0.0885 0.1513 0.0952 S-730 0.0931 20.40 0.0778 0.1331 0.1465 Cartagena-Alcazar S-252 0.0231 6.93 0.0264 0.0452 0.3859 S-253 0.0207 6.21 0.0237 0.0405 0.3304

In a 2015 report issued by the Spanish Government about the situation of railways in Spain [70], some data about energy consumption have been published, distinguishing among different routes and types of trains. From the studied itinerary (Madrid-Cartagena) and passenger trains, it has been reported a KPI 2 value of 20.68 kWh/train-km, a KPI 3 of 0.079 kWh/seat-km and a KPI 4 of 0.135 kWh/pkm. These values can be compared with the values obtained for the diesel locomotive S-334 (without considering regenerative braking), as it is nearly the only locomotive that operates passenger duties in this route in 2015. As can be seen in Table 12, the values obtained in the present study are similar to those reported (e.g., from Alcazar to Cartagena: KPI 2—18.78 kWh/train-km; KPI 3—0.0716 kWh/seat-km; and KPI 4—0.1225 kWh/pkm). Reference [20] studies the performance of several Sweden electrical locomotives in passenger services and presents some values for the Energy Recuperation Rate (KPI 5) that varies between 10–40%. Reference [20] evaluates KPI 3 with values which damp from 0.02 to 0.05 kWh/seat-km. These values are in agreement with values obtained in the current study for the electric locomotives (S-252 and S-253). Results validate the overall performance of the proposed model. Another study [71] about a Japanese electric train with regenerative braking shows an Energy Recuperation Rate that varies from 45% to 65%. Reference [72] develops a study about the regenerative potential in a Metro Line from Istanbul, with 32% of energy recuperated through regenerative braking. Using peak clipping strategies and OESS, savings reach 75% [73]. As noticeable for these results, the Energy Regenerative Rate is highly influenced by the conditions of the railway route, as well as the characteristic of the locomotive, but it is demonstrated that making use of braking energy has a high potential in reducing the energy consumption and improving the energy efficiency of railways operations in the future.

Freight Trains Results obtained for KPIs in freight trains (with the same considerations as in Table 12) are presented in Table 14.

Table 14. KPIs for freight trains with regenerative braking only between Alcazar and Chinchilla.

KPI 1 KPI 2 KPI 3 KPI 4 kWh/net KPI 5 kWh Direction Locomotive kWh/kN-km kWh/train-km kWh/gross tkm tkm gen/kWh cons S-334 0.0916 16.16 0.0182 0.0323 0 Alcazar-Cartagena S-252 0.0180 5.40 0.0061 0.0108 0.0521 S-334 0.1782 31.43 0.0353 0.0629 0 Cartagena-Alcazar S-252 0.0304 9.11 0.0103 0.0182 0.1586

Results obtained for KPIs in freight trains considering that regenerative braking can be used during the whole itinerary (and by both locomotives) are presented in Table 15. Appl. Sci. 2019, 9, 3605 25 of 40

Table 15. KPIs for freight trains with regenerative braking in the whole itinerary.

KPI 1 KPI 2 KPI 3 KPI 4 kWh/net KPI 5 kWh Direction Locomotive kWh/kN-km kWh/train-km kWh/gross tkm tkm gen/kWh cons S-334 0.0555 9.79 0.0110 0.0196 0.3945 Alcazar-Cartagena S-252 0.0022 0.66 0.0007 0.0013 1.1156 − − − − S-334 0.1596 28.16 0.0316 0.0563 0.1043 Cartagena-Alcazar S-252 0.0240 7.21 0.0081 0.0144 0.3343

In the case of freight locomotives, reports from the Spanish Government [74] calculate general data of the consumption rates, but it is not segregated in the different routes of the Spanish Railway System. There are reported values of 27.9 kWh/train-km for KPI 2, 0.03 kWh/gross tkm for KPI 3 and 0.07 kWh/net tkm for KPI 4. These values are slightly higher than those calculated in the present study for the diesel locomotive S-334 in the Alcazar-Cartagena direction, although there is not a big difference in the opposite direction (see Table 14: KPI 2—31.43 kWh/train-km; KPI 3—0.035 kWh/gross tkm; and KPI 4—0.0629 kWh/net tkm). Differences between both results could be explained in the peculiarities of this specific railway route and locomotive compared with the average characteristics of the Spanish railway freight transport (notice that mountain areas in Spain cover 20 to 40% of territory, and railway profiles are quite difficult in some areas, like in Italy, Norway or Switzerland railways).

3.2.6. Downsizing Diesel-Electric Power Generation The study of the possibility of a reduction (resizing) of the power engine for the hybrid HDEMU S-730 and the sizing of OESS alternatives (for its use in other sections) has been accomplished in this section. Trains with this type of locomotive are only used in passenger services and comprises of Appl. Sci. 2019, 9, x FOR PEER REVIEW 26 of 41 two 1.2 MW engines (in power/service coaches). The idea is to reduce in 0.4 MW each one of the powerof an engines,ESS that andincludes supply supercapacitors the default of powerand batteries. during peakThis demandESS stores with the the regenerative help of an ESS braking that includesenergy available supercapacitors in other andperiods. batteries. This ESS stores the regenerative braking energy available in otherTo periods. evaluate this possibility is necessary to perform simulations in the round trip between CartagenaTo evaluate and thisAlcazar. possibility Figure is necessary 18 presents to perform the electrical simulations power in the profile round for trip the between whole Cartagena itinerary and(starting Alcazar. and Figure ending 18 presentsin Alcazar) the electricalfor both power power proﬁle plants. for The the wholered line itinerary marks (starting the desired and ending power inreduction Alcazar) (i.e., for both from power 2.4 MW plants. to 1.6 The MW red in linethe marks3 kV DC the bus). desired power reduction (i.e., from 2.4 MW to 1.6 MW in the 3 kV DC bus).

FigureFigure 18. 18.Power Power proﬁle profile for for the the S-730 S-730 unit: unit: RedRed lineline marks marks the the aim aim of of power power reduction. reduction.

PowerPower peaks peaks that that ESS ESS must must cover cover (blue (blue line), line), the the regenerative regenerative braking braking profile profile (red (red line), line), and and the the storedstored energy energy necessary necessary to meetto meet the power the power demands demands (magenta) (magenta) are presented are presented in Figure 19in (remember Figure 19 that(remember braking ethatffort braking of thisS730 effort unit of isthis around S730 4unit MW, is Figurearound4, and4 MW, for simplicityFigure 4, and cost-efor simplicityffectiveness and remainscost-effectiveness unchanged remains in this scenario).unchanged in this scenario).

Figure 19. Power peaks reduction (blue line), braking profile (red line) and energy requirements (purple line) for the S-730 unit.

The characteristics of the three systems proposed are presented in Table 16.

Table 16. ESS characteristics.

Energy Weight Volume Storage System Price (€) Lifespan (Cycles) (kWh) (kg) (m3) Batteries 350 2300 1.75 73,150 10,000 Batteries + 51,205 + 245 + 15 1633 + 6520 1.23 + 0.59 10,000 (1,000,000 1) Supercapacitors 120,000 Supercapacitors 150 65,200 5.9 1,200,000 1,000,000 1 Lifecycles of supercapacitors. Appl. Sci. 2019, 9, x FOR PEER REVIEW 26 of 41

of an ESS that includes supercapacitors and batteries. This ESS stores the regenerative braking energy available in other periods. To evaluate this possibility is necessary to perform simulations in the round trip between Cartagena and Alcazar. Figure 18 presents the electrical power profile for the whole itinerary (starting and ending in Alcazar) for both power plants. The red line marks the desired power reduction (i.e., from 2.4 MW to 1.6 MW in the 3 kV DC bus).

Figure 18. Power profile for the S-730 unit: Red line marks the aim of power reduction.

Power peaks that ESS must cover (blue line), the regenerative braking profile (red line), and the stored energy necessary to meet the power demands (magenta) are presented in Figure 19 (remember that braking effort of this S730 unit is around 4 MW, Figure 4, and for simplicity and Appl.cost-effectiveness Sci. 2019, 9, 3605 remains unchanged in this scenario). 26 of 40

FigureFigure 19. 19.Power Power peaks peaks reduction reduction (blue (blue line), line), braking braking proﬁle profile (red line) (red and line) energy and requirementsenergy requirements (purple line)(purple for the line) S-730 for the unit. S-730 unit.

TheThe characteristics characteristics of of the the three three systems systems proposed proposed are are presented presented in in Table Table 16 16..

TableTable 16. 16.ESS ESS characteristics. characteristics.

Storage Energy Weight Volume Lifespan Storage SystemEnergy (kWh) Weight (kg) Volume (m3) PricePrice (€) (€) Lifespan (Cycles) System (kWh) (kg) (m3) (Cycles) BatteriesBatteries 350350 23002300 1.751.75 73,15073,150 10,00010,000 BatteriesBatteries+ + 51,205 + 10,000 245 + 15245 + 15 1633 + 16336520 + 6520 1.23 + 1.230.59 + 0.59 51,205 + 120,000 10,000 (1,000,000 1) SupercapacitorsSupercapacitors 120,000 (1,000,000 1) SupercapacitorsSupercapacitors 150150 65,20065,200 5.95.9 1,200,0001,200,000 1,000,0001,000,000 1 1 Lifecycles of of supercapacitors. supercapacitors. Appl. Sci. 2019, 9, x FOR PEER REVIEW 27 of 41

FigureFigure 20 20 depicts depicts the the energy energy stored stored through through ESS ESS systems systems in in both both cases. cases. The The red red line line represents represents the the supercapacitors, and blue lines show the behavior of the batteries. It can be clearly observed the supercapacitors, and blue lines show the behavior of the batteries. It can be clearly observed the effect effect of the supercapacitors in the charge and discharge of the battery system (HESS), slightly of the supercapacitors in the charge and discharge of the battery system (HESS), slightly reducing reducing the fluctuations that the batteries suffer in BESS. In both cases, the SOC of the batteries is the fluctuations that the batteries suffer in BESS. In both cases, the SOC of the batteries is maintained maintained between 30% and 80% to increase their lifetime. between 30% and 80% to increase their lifetime.

350

300

250

200

150 Energy (kWh)Energy 100

0 0123456 Time (h) (a) (b)

FigureFigure 20. 20.Energy Energy stored stored inin thethe ESSESS duringduring the whole whole itinerary: itinerary: (a ()a BESS;) BESS; (b ()b HESS.) HESS.

3.3.3.3. Last Last Mile Mile Applications Applications AnotherAnother significant significant application application of OESS of OESS is the is so-called the so-called “last-mile”. “last-mile”. Hybrid orHybrid electric or locomotives electric withlocomotives this feature with can crossthis non-electrifiedfeature can cross track non-electrified sections without track making sections use ofwithout overhead making line, improvinguse of theoverhead flexibility line, and improving energy effi theciency flexibility of the locomotive.and energy ef Thisficiency ESS canof the also locomotive. be used in This the hybrid ESS can locomotive also be used in the hybrid locomotive when switching between the diesel and the electric engine, producing smoother transitions (or in terminals with different catenary voltage, e.g., 3 kV DC vs. 25 kV AC for SNCF, RENFE operators). The ESS allows the locomotive to circulate in sidings, terminals, or factories, when normally another supporting diesel locomotive is used to help the train reach the last non-electrified part of the itinerary (called last-mile). In addition, it can be applied when there is a change in the nominal voltage of the catenary, e.g., in trains crossing borders (3 kV DC to 15 kV or 25 kV AC systems). Otherwise, it can also be used when the train is arriving at terminal stations, reducing in this way emissions and noise nearby the train station. Finally, another important application is its use as a backup and DER system (to be developed in Section 3.5), increasing the flexibility of the train and allowing the operation of trains when unforeseen catenary instabilities or failures are produced. The energy that is required for circulating during different distances (5, 10 and 15 km) at 50 km/h, with different gradients (between −15‰ and 15‰) is presented in Figure 21.

Figure 21. Energy required vs. slope for trains running at 50 km/h during 5, 10, and 15 km. Appl. Sci. 2019, 9, x FOR PEER REVIEW 27 of 41

Figure 20 depicts the energy stored through ESS systems in both cases. The red line represents the supercapacitors, and blue lines show the behavior of the batteries. It can be clearly observed the effect of the supercapacitors in the charge and discharge of the battery system (HESS), slightly reducing the fluctuations that the batteries suffer in BESS. In both cases, the SOC of the batteries is maintained between 30% and 80% to increase their lifetime.

350

300

250

200

150 Energy (kWh)Energy 100

0 0123456 Time (h) (a) (b)

Figure 20. Energy stored in the ESS during the whole itinerary: (a) BESS; (b) HESS.

3.3. Last Mile Applications Another significant application of OESS is the so-called “last-mile”. Hybrid or electric locomotives with this feature can cross non-electrified track sections without making use of Appl.overhead Sci. 2019 line,, 9, 3605 improving the flexibility and energy efficiency of the locomotive. This ESS can27 also of 40 be used in the hybrid locomotive when switching between the diesel and the electric engine, producing smoother transitions (or in terminals with different catenary voltage, e.g., 3 kV DC vs. 25 kV AC for whenSNCF, switching RENFE operators). between the diesel and the electric engine, producing smoother transitions (or in terminalsThe withESS diallowsfferent the catenary locomotive voltage, to circulate e.g., 3 kV in DC sidings, vs. 25 kVterminals, AC for SNCF,or factories, RENFE when operators). normally anotherThe ESSsupporting allows thediesel locomotive locomotive to circulateis used to in help sidings, the train terminals, reach the or factories,last non-electrified when normally part of anotherthe itinerary supporting (called diesel last-mile). locomotive In addition, is used toit can help be the applied train reach when the there last is non-electrified a change in the part nominal of the itineraryvoltage (calledof the last-mile).catenary, e.g., In addition, in trains it crossing can be applied borders when (3 kV there DC is to a change15 kV inor the25 nominalkV AC systems). voltage ofOtherwise, the catenary, it can e.g., also in trains be used crossing when borders the train (3 kVis arriving DC to 15 at kV terminal or 25 kV stations, AC systems). reducing Otherwise, in this way it canemissions also be usedand noise when nearby the train the is train arriving station. at terminal Finally, stations, another reducingimportant in application this way emissions is its use and as a noisebackup nearby and theDER train system station. (to be Finally, developed another in importantSection 3.5), application increasing is the its useflexibility as a backup of the and train DER and systemallowing (to bethe developed operation inof Sectiontrains when 3.5), increasing unforeseen the catenary flexibility instabilities of the train or and failures allowing are produced. the operation of trainsThe when energy unforeseen that is required catenary for instabilities circulating or failuresduring different are produced. distances (5, 10 and 15 km) at 50 km/h,The with energy different that is gradients required for(between circulating −15‰ during and 15‰) different is presented distances in (5, Figure 10 and 21. 15 km) at 50 km/h, with different gradients (between 15% and 15%) is presented in Figure 21. −

FigureFigure 21. 21.Energy Energy required required vs. vs. slope slope for for trains trains running running at at 50 50 km km/h/h during during 5, 5, 10, 10, and and 15 15 km. km.

In last-mile applications, normally the slope is going to be near to 0, so the energy that is needed for circulating 15 km is about 30 kWh. In order to maintain the SOC of the batteries between 80% and 40% and increase their lifespan, it should be necessary to install a battery system, sized at 75 kWh. There is also the possibility to install a HESS equipped with a supercapacitor of capacity of 7kWh (that allow the train accelerates and reaches 50 km/h) and a battery of capacity of 50 kWh that could maintain the speed for 15 km (Section 3.5). It is also possible to install a 30 kWh supercapacitor system, that increases the lifespan of the system, but the space required, and its weight increase substantially. The characteristics of the systems proposed are presented in Table 17.

Table 17. ESS characteristics.

Storage Lifespan Energy (kWh) Weight (kg) Volume (m3) Price (€) System (Cycles) Batteries 75 500 0.38 15,675 10,000 Batteries + 10,000 50 + 7 340 + 3000 0.25 + 0.28 10,450 + 56,000 Supercapacitors (1,000,000 1) Supercapacitors 30 13,000 1.2 240,000 1,000,000 1 Lifecycles of supercapacitors.

3.4. Evaluation and Characteristics of Aggregated Demand Once traction power needs have been evaluated, it is interesting to take some attention to the load profile in the electrified route (3 kV-DC, 150 km length, eight rectifier substations, Table3). For this purpose, the timetable presented in Figure3 has been used for a workday, and 52 trains have been simulated (freight and passenger with electric, diesel-electric and hybrid traction) for the electrified Appl. Sci. 2019, 9, x FOR PEER REVIEW 28 of 41

Table 17. ESS characteristics.

Energy Weight Volume Lifespan Storage System Price (€) (kWh) (kg) (m3) (Cycles) Batteries 75 500 0.38 15,675 10,000 Batteries + 10,450 + 10,000 50 + 7 340 + 3000 0.25 + 0.28 Supercapacitors 56,000 (1,000,000 1) Supercapacitors 30 13,000 1.2 240,000 1,000,000 1 Lifecycles of supercapacitors.

3.4. Evaluation and Characteristics of Aggregated Demand Once traction power needs have been evaluated, it is interesting to take some attention to the load profile in the electrified route (3 kV-DC, 150 km length, eight rectifier substations, Table 3). For Appl. Sci. 2019, 9, 3605 28 of 40 this purpose, the timetable presented in Figure 3 has been used for a workday, and 52 trains have been simulated (freight and passenger with electric, diesel-electric and hybrid traction) for the sectionelectrified of thesection route of (fromthe route AJ to(from CH). AJ Notice to CH). that Notice railway that railway timetables timetables are developed are developed to make to trainmake operationtrain operation robust robust and resilient and resilient to small to delays small anddelays these and can these have can a negativehave a negative or positive or positive effect. It effect. should It beshould taken be into taken account into thataccount delays that may delays occur may throughout occur throughout the route (stochasticthe route (stochastic theory helps theory in the helps study in ofthe these study possibilities of these possibilities and is considered and is inconsidered railway tra inffi railwayc management traffic management algorithms [14 algorithms], and that, [14], in some and cases,that, in dynamic some cases, braking dynamic cannot braking be employed. cannotThe be employed. “stochastic” The nature “stochastic” of the problem nature isofnot the newproblem in DR is andnot DERnew in policies DR and and DER models, policies some and of models, them proposed some of them by authors proposed [53 ]by and authors applied [53] for and this applied problem for withthis statisticproblem methodologies with statistic explainedmethodologies in Reference explaine [56d ].in Figure Reference 22a shows [56]. Figure this last 22a scenario: shows Brakingthis last isscenario: performed Braking through is performed resistive braking through (obviously, resistive br Altariaaking diesel(obviously, train servicesAltaria diesel with S-334train areservices not considered).with S-334 are Figure not 22 considered).a represents Figure the demand 22a repres for theents aggregated the demand load (onlyfor the traction aggregated requirements load (only are consideredtraction requirements for simplicity) are for considered the eight substationsfor simplicit fromy) for Alcazar the eight SJ to substations Chinchilla, whereasfrom Alcazar Figure SJ22 bto includesChinchilla, the whereas demand forFigure Altaria 22b (diesel-electric)includes the dema andnd Alvia for S-730Altaria (hybrid) (diesel-electric) trains. Figure and 22Alviac depicts S-730 the(hybrid) low synchronism trains. Figure between 22c depicts the brakingthe low potentialsynchronism of the between train in the the braking section potential and the percentage of the train of in brakingthe section being and eff ectivelythe percentage recovered of braking by other being trains. effectively Figure 22 drecovered shows the by possibilities other trains. that Figure offer the22d changeshows ofthe mode possibilities of traction that in HDEMUoffer the S-730 change (demand of mode for HDEMUof traction in electricin HDEMU mode S-730 is presented (demand in thefor greenHDEMU line, in Figure electric 22 b).mode This is change presented in the in operationthe green modelline, Figure (from 22b). electric This to diesel-electric)change in the operation clips the peakmodel demand (from electric target in to Figure diesel-electric) 22a. clips the peak demand target in Figure 22a.

12 Demand peaks above 10MW 10

Demand (MW) 4

Appl. Sci.0 2019, 9, x FOR PEER REVIEW 29 of 41 0 6 12 18 24 Time (h) ((aa)) ((bb))

FigureFigure 22.22. AggregatedAggregated demand demand profiles: proﬁles: (a ()a For) For the the eight eight substations; substations; (b) (substationsb) substations and and Altaria Altaria and andAlvia Alvia services; services; (c) (brakingc) braking potential potential and and estimation estimation of ofuse; use; (d (d) )peak peak clipping clipping using using S-730 asas DERDER resourceresource inin comparisoncomparison withwith FigureFigure 22 22aa(periods (periods around around 7 7 h h and and 20 20 h). h).

SeveralSeveral indicesindices havehave beenbeen deﬁneddefined forfor helpinghelping thethe readerreader inin thethe evaluationevaluation ofof resultsresults (notice(notice thatthat thesethese indicesindices are are for for an an aggregation aggregation of trainsof trains whereas whereas KPI KPI deﬁned defined in Section in Section 3.2.5 are3.2.5 valid are forvalid a single for a vehicle).single vehicle). For example, For example, the load the factor load (LF) factor (or its (LF) reciprocal, (or its reciprocal, the capacity the factor, capacity CF), thatfactor, is usually CF), that used is inusually Public used Power in SystemsPublic Power to evaluate Systems the to use evaluate of available the use capacity of available (i.e., the capacity rationality (i.e., of the investments) rationality isof usedinvestments) in this work is used for Railway in this Powerwork for System Railway (RPS). Power Traditional System policies (RPS). state Traditional that Supply-Side policies resourcesstate that Supply-Side resources are planned to follow Load Demand fluctuations, but this theory is changing, due to the deployment and use of DR. In conventional Power Systems, CF is around 1.2–1.7 (i.e., the load factor is around 0.6 to 0.8), whereas in manufacturing sector CF rages 1.1–1.3. Notice that Railway Power Systems (Table 18) presents very low LFs (or very high CFs) even at medium-high aggregation level (in our case at 66 kV sub-transport level of public power system, considering in this table the possibility that DC traction substation can exchange energy from a substation to other substations of the route; at present, a remote and complex possibility from technical and economic points of view). In this way, RPSs need some improvement in their use and management. Mean() tractive− demand Peak_() resource Capacity LF(%) = 100;CF = . (8) Max() tractive− demand Avg_(_) resource Mean Use

The next index, EStB (Energy Savings through Braking), evaluates the amount of energy generated during braking of a railway unit that can be used for other units in the same section of the overhead line of the route (i.e., at the same catenary/electrical section or substations) during a day:

RPS  gen_ usable = 24h EStB RPS 100 . (9)  gen_ braking 24h

Finally, the potential (available theoretically) generation of energy through braking (kWh) with respect to traction demand (kWh) in a section of RPS (or modified KPI5), MKPI5 is also considered:

RPS  gen_ braking = 24h MKPI5 RPS . (10)  net_ demand 24h

Several scenarios have been considered for simulation purposes in this subsection: Appl. Sci. 2019, 9, 3605 29 of 40 are planned to follow Load Demand ﬂuctuations, but this theory is changing, due to the deployment and use of DR. In conventional Power Systems, CF is around 1.2–1.7 (i.e., the load factor is around 0.6 to 0.8), whereas in manufacturing sector CF rages 1.1–1.3. Notice that Railway Power Systems (Table 18) presents very low LFs (or very high CFs) even at medium-high aggregation level (in our case at 66 kV sub-transport level of public power system, considering in this table the possibility that DC traction substation can exchange energy from a substation to other substations of the route; at present, a remote and complex possibility from technical and economic points of view). In this way, RPSs need some improvement in their use and management.

Mean(tractive demand) Peak_resource(Capacity) LF(%) = − 100; CF = . (8) Max(tractive demand) Avg_resource(Mean_Use) −

Table 18. Aggregation of Substations from Alcazar to Chinchilla (AJ-CH).

Peak and Peak Case/Scenario Load Factor (%) EStB (%) MKPI5 (%) Clipping (MW, %) 1 11 NA 1 NA 1 14.35, NA 1 2 16.58 2 NA 1 NA 1 9.56, NA 1 3 9.72 19.35 27.19 14.35, 0 4 13.66 2 39.4 2 27.19 9.56, 0 3 + 5 18.72 2 39.4 2 27.09 8.47, 11.4 1 NA: not applicable, 2 Maximum value.

RPSP gen_usable 24h EStB = 100. (9) RPSP gen_braking 24h

Finally, the potential (available theoretically) generation of energy through braking (kWh) with respect to traction demand (kWh) in a section of RPS (or modiﬁed KPI5), MKPI5 is also considered:

RPSP gen_braking 24h MKPI5 = . (10) RPSP net_demand 24h

Several scenarios have been considered for simulation purposes in this subsection:

1. Railway traffic in the route fulfills its timetable: This is theoretically possible, but difficult to accomplish in 100% of trains. In this case, trains usually use resistive braking to avoid incertitude and the possibility of an increase of voltage in catenary. 2. Railway traffic present delays in some passenger and freight services (a delay from 1 to 5 min is considered with a uniform distribution) and average values of demand have been considered with the aggregation methodology described in Reference [56]. Trains use resistive braking as the main braking system. This case represents an average scenario from the point of view of the demand for planning and operation purposes. Appl. Sci. 2019, 9, 3605 30 of 40

3. Railway traffic in the route fulfills its timetable, and electric trains use regenerative braking if the voltage remains around normal values. 4. Railway traffic present delays in some passenger and freight services (a delay from 1 to 5 min is considered again, and average values have been considered) and vehicles deploy regenerative braking: Average values of demand and generation have been considered. In this case, some delay can improve energy recovery indices. 5. Hybrid units (S-730) are able to change from conventional catenary supply to diesel-electric generator, in limited sections of the electrified route, to acts as a responsive load/generator (DER resource) for achieving an improved flexibility of demand and supply. 6. Wayside storage: Substations have a partial storage system to limit power in peak periods and reduce LFs. The load factor of the aggregated demand in Figure 22a has a very low value in all cases being considered (9.7 to 18.7). Notice that time windows (usually some seconds to one minute, due to dynamic train behavior) for the evaluation of energy and power needs from acceleration and braking are shorter than the time windows considered in Public Power Systems to compute some indices and record demand data (i.e., data aggregation acts as a high-pass filter). It should be noted that some peaks appearing on the load curve are in phase with S-730 demand. Merely, the change of mode of S-730 (from electrical mode to diesel) or the use of proposed storage of S-730 unit, during small periods, can raise the load factor up to 0.19 (i.e., +38%, or produce more than 2500 kW of flexibility during peak load periods). In the case of regenerative braking is considered, from 60% to 81% of generation, due to this possibility, is unable to be used by other trains because times do not match even in the case that eight traction substations are considered interconnected and reversible. This fact significantly limits the possibility to deploy regenerative braking based only on changes in the timetable. It is also interesting to consider a more real possibility: The demand in a specific DC substation without any possibility to inject power into other feeders of the RPS. To resume this scenario, and for simplicity, only two substations with the worst pattern have been considered: Rio Záncara (km 148 to 171) and Chinchilla (km 279 to 295). Figure 23 shows that only a small percentage of available energy from regenerative braking can be potentially used by other trains, or in other works: An interesting potentialAppl. Sci. 2019 for, 9 storage, x FOR PEER arises REVIEW from these figures and explains the need for some wayside storage31 of in41 traction substations.

8 11 Sub CH SUB RZ S-730 10 S-730 Altaria 6 8

6 4

4 Demand (MW) Demand (MW) 2 2

0 0 0 6 12 18 24 0 6 12 18 24 Time (h) Time (h) (a) (b)

FigureFigure 23.23. DailyDaily demanddemand andand AltariaAltaria andand AlviaAlvia demandsdemands inin aa singlesingle rectiﬁerrectifiersubstation: substation: ((aa)) CHCH substation;substation; ( b(b)) RZ RZ substation. substation.

TablesTables 1919 andand 2020 showshow thatthat thethe improvementimprovement ofof LFsLFs andand thethe potentialpotential generationgeneration throughthrough regenerativeregenerative braking braking are are lost, lost, including including the possibilitythe possibility of the of use the of use hybrid of hybrid S-730 units S-730 as DERunits resource as DER (i.e.,resource the change (i.e., the from change electric from to dieselelectric operation). to diesel operation). For these cases, For these in where cases, OESS in where does not OESS improve does thenot eimproveﬃciency the of the efficiency system of (only the asystem small 1.83%),(only a thesma solutionll 1.83%), is the a small solution wayside is a storagesmall wayside system storage (in the system (in the range 20–30 kWh) that significantly improves efficiency and load factor of both substations considered in the simulation (Tables 19 and 20).

Table 19. Substation at Rio Záncara (RZ).

Load Factor EStB MKPI5 Peak Clipping and Peak Case/Scenario (%) (%) (%) (%, MW) 1-Individual 2.65 NA NA - 2-Aggregated (considering 3.85 NA NA - delays) 3-Regenerative individual 2.59 1.83 32.71 0, 10.2 4-Regenerative Aggregated 3.48 19.71 32.71 0, 7.06 4 + 5-Hybrid S-730 as DER 3.48 1.83 32.71 0, 10.2 (individual) 6-Off-board 20 kWh storage 9.05 12.3 32.71 69.6, 3.1 (individual)

Table 20. Substation at Chinchilla (CH).

Load Factor EStB MKPI5 Peak Clipping and Peak Case/Scenario (%) (%) (%) (%, MW) 1-Individual 5.37 NA 1 NA 1 NA 1, 7.73 2-Aggregated (considering 7.47 NA 1 NA 1 NA 1, 5.56 delays) 3- Regenerative individual 5.09 4.83 30.03 0, 7.73 4- Regenerative Aggregated 6.91 10.53 30.03 0, 5.56 4 + 5 Hybrid S-730 as DER 4.98 4.83 30.03 0, 7.73 (individual) 6-Off-board 25 kWh storage 13.85 15.9 30.03 59.9, 3.1 (individual) 1 NA, baseline case. Appl. Sci. 2019, 9, 3605 31 of 40 range 20–30 kWh) that signiﬁcantly improves eﬃciency and load factor of both substations considered in the simulation (Tables 19 and 20).

Table 19. Substation at Rio Záncara (RZ).

Peak Clipping and Case/Scenario Load Factor (%) EStB (%) MKPI5 (%) Peak (%, MW) 1-Individual 2.65 NA NA - 2-Aggregated 3.85 NA NA - (considering delays) 3-Regenerative 2.59 1.83 32.71 0, 10.2 individual 4-Regenerative 3.48 19.71 32.71 0, 7.06 Aggregated 4 + 5-Hybrid S-730 as 3.48 1.83 32.71 0, 10.2 DER (individual) 6-Oﬀ-board 20 kWh 9.05 12.3 32.71 69.6, 3.1 storage (individual)

Table 20. Substation at Chinchilla (CH).

Peak Clipping and Case/Scenario Load Factor (%) EStB (%) MKPI5 (%) Peak (%, MW) 1-Individual 5.37 NA 1 NA 1 NA 1, 7.73 2-Aggregated 7.47 NA 1 NA 1 NA 1, 5.56 (considering delays) 3- Regenerative 5.09 4.83 30.03 0, 7.73 individual 4- Regenerative 6.91 10.53 30.03 0, 5.56 Aggregated 4 + 5 Hybrid S-730 as 4.98 4.83 30.03 0, 7.73 DER (individual) 6-Oﬀ-board 25 kWh 13.85 15.9 30.03 59.9, 3.1 storage (individual) 1 NA, baseline case.

3.5. Synchronized Timetables and DR Many railway administrations in Europe (Germany, France, Switzerland, etc.) use the so-called synchronized timetables (reports dealing with medium-term scenarios foresee a higher level of synchronization in several countries, e.g., Germany with 30 min interval for IC devices by 2030–2040 [6]). These timetables make easier the use of transport because the customer easily remembers the start of its trains (i.e., trains leave for their destination 5, 7, 13, 25 or 33 min past every hour). A well-known example is the Swiss Railways (SBB, Table 21). SBB-CFF-FFS operates 9000 passenger trains per day on its network, and has a well-known prestige to ensure safe and timely railway operation (i.e., high punctuality ratios). Speciﬁcally, SBB-CFF is proud because there are trains every half hour that connect the major population areas (e.g., at 7 h 49 or 8 h 49, and 8 h 25 or 9 h 25, from Zurich to Luzern, Table 21). The trend is ﬁrm because railway development plans in the future (PRODES 2035) foresee train departures every 15 min from Zurich to major populations in the Zurich area. Unfortunately, this policy also has several drawbacks: Its load curve peaks daily a value around 500 MW that can change up to 300 MW during short term intervals (1–5 min, short-term changes in demand has been discussed in the previous paragraph). This power is needed to accelerate trains (the reader can revisit Appl. Sci. 2019, 9, 3605 32 of 40

Section 3.1). Nevertheless, changes in Swiss Power System in the Zurich area, due to residential and commercial loads do not reach 35 MW in 15 min interval. To overcome this problem SBB is enrolled in a Demand Response research project in its loads, speciﬁcally to reduce train hotel load and heating of points (heating of trains from seconds to some minutes). The objective is to reduce peak load by 70 MW in 2023. The concern of this section is to demonstrate the potential available from the use of HVAC loads of trains as DR resource (demand ﬂexibility, DSF). Moreover, the use of on board storage in hybrid trains jointly with DR could help to support changes in demand and opens the possibility for the management of railway load curves during periods of high rates of increase of demand.

Table 21. Two examples of synchronized timetables of SBB-CFF-FFS system in Zurich HB station: Trains leaving and arriving to/from Luzern; and trains leaving from Zurich to St. Gallen. Data available in Reference [44] (Summer 2019).

Departures to Luzerne Departures to St Gallen Arrivals from Luzerne Train Type and Train Type and Train Type and Time Time Time Number Number Number 7 h 04 IR 70 7 h 09 IR 13 6 h 56 IR 70 7 h 35 IR 75 7 h 33 IC 1 7 h 25 IR 75 8 h 04 IR 70 7 h 39 IC 5 7h 56 IR 70 8 h 35 IR 75 8 h 02 IR 37 8 h 25 IR 75 9 h 04 IR 70 8 h 09 IR 13 8 h 56 IR 70 9 h 35 IR 75 8 h 33 IC 1 9 h 25 IR 75

The use of pantographs in passenger or in power coaches is a well-known and proven concept in European Railways (including Germany, Switzerland, Netherlands or Spain, Figure 24). The main concept proposed is that these coaches can include partial storage to be used for hotel/power loads, or also to be used as a buﬀer for static (Figure 24a) or dynamic (Figure 24b) storage to support high rates of demand growth, while other trains leave the terminal station. A scenario for the use of these coaches with the management of HVAC load has been simulated in the terminal station of the route Alcazar-Chinchilla: Madrid-Chamartin. Madrid-Chamartin has passenger traﬃc estimated in 6,144,000 passenger/year; 53 high-speed trains/day, and 39 Intercity and Regional Trains/day in 2018 (i.e., some of the trains being considered in the route Alcazar-Chinchilla in previous sections), excludingAppl. Sci. 2019 suburban, 9, x FOR services.PEER REVIEW 33 of 41

(a) (b)

FigureFigure 24. 24.Two Two examples examples of the useof ofthe pantographs use of pantographs in rolling stock: in (rollinga) Former stock: Talgo (a power-converter) Former Talgo coachpower-converter TG1z for Talgo coach III TG1z series for with Talg a “static”o III series pantograph with a “static” for conversion pantograph of for 3 kVDC conversion from of catenary 3 kVDC (static)from catenary or locomotive (static) (dynamic) or locomotive to 380 (dynamic)/400 V incase to 380/400 of change V in of case traction of change or train of supply traction without or train locomotive;supply without (b) restaurant locomotive; coaches (b) ARmz restaurant of DB (Germancoaches Operator)ARmz of andDB SBB-CFF-FFS (German Operator) (Swiss main and operator)SBB-CFF-FFS with a(Swiss dynamic main pantograph operator) with for use a dynamic in the route. pantograph for use in the route.

It is usual, in terminal stations in Spain, that trains are placed on the platforms 20–30 min before they leave the station to perform check-in of passengers. In this case, trains require power for “hotel loads” and are able to store or generate power to or from storage devices. Table 22 shows a timetable for Madrid-Chamartin is the time interval from 18 h to 19 h.

Table 22. Trains leaving Madrid-Chamartin from 18 h to 19 h, according to winter 2019 timetable (excluding suburban services).

Departures (Intercity and Regional) Arrivals (Intercity and Regional) Train Type and Train Train Type and Train Time Destination Time From Number Number 18 h 10 Valladolid AVANT 08389 17 h 58 Lugo ALVIA 00552 18 h 18 Albacete 1 MD Regional 18042 18 h 07 Barcelona MD Regional 17501 18 h 26 Gijon ALVIA 11761 18 h 07 Santander ALVIA 04142 18 h 33 Cartagena1 ALTARIA 00226 18 h 11 Alicante ALVIA 11781 18 h 40 Valladolid AVANT 08169 18 h 34 Murcia1 ALTARIA 11929 18 h 50 Santiago ALVIA 00351 18 h 50 Valladolid AVANT 08178 19 h 00 Soria MD Regional 17306 19 h 19 Jaen MD Regional 18035 1 Trains that use the route Albacete-Murcia-Cartagena (Altaria, Alvia, MD Regional). For simulation purposes, “Altaria” and “Alvia” trains to Cartagena and Gijon are supposed to be ready in platforms at 18 h00 (train 11761) and 18 h05 (train 00226). Regional MD service from Madrid to Albacete leaves at 18 h18. It is supposed that some of the trains (ALVIA, similar to HDEMU S-730, but without diesel generation) has a hybrid “last-mile” dynamic storage (results and sizing in Section 3.3) with 8.3 kWh in supercapacitors and 40 kWh in Li-ion batteries (400 V a 100 Ah). Altaria only has a “static” storage to support train hotel loads (battery in the power coach rechargeable through a static pantograph or the dynamic braking of some locomotive or EMU). Hotel load of Altaria is simulated for each of its nine coaches, and the overall train demand (hotel load) with the models proposed in Section 2.4 and parameters previously presented in Table 5, is depicted in Figure 25. An outdoor winter temperature ranging from 2 to 9 °C is used (input Xext in Figure 5a); 2.5 for COP (coefficient of performance of HVAC devices used in the coaches, appliance model in Figure 5a), 50% of occupancy (at Madrid terminal station, considering average rates for occupancy in Reference [70],) and a rate of ventilation of 20 m3/passenger-hour. Figure 25 represents the results. Appl. Sci. 2019, 9, 3605 33 of 40

Table 22. Trains leaving Madrid-Chamartin from 18 h to 19 h, according to winter 2019 timetable (excluding suburban services).

Departures (Intercity and Regional) Arrivals (Intercity and Regional) Train Type and Train Type and Time Destination Time From Train Number Train Number 18 h 10 Valladolid AVANT 08389 17 h 58 Lugo ALVIA 00552 MD Regional 18 h 18 Albacete 1 18 h 07 Barcelona MD Regional 17501 18042 18 h 26 Gijon ALVIA 11761 18 h 07 Santander ALVIA 04142 ALTARIA 18 h 33 Cartagena 1 18 h 11 Alicante ALVIA 11781 00226 18 h 40 Valladolid AVANT 08169 18 h 34 Murcia1 ALTARIA 11929 18 h 50 Santiago ALVIA 00351 18 h 50 Valladolid AVANT 08178 MD Regional 19 h 00 Soria 19 h 19 Jaen MD Regional 18035 17306 1 Trains that use the route Albacete-Murcia-Cartagena (Altaria, Alvia, MD Regional).

For simulation purposes, “Altaria” and “Alvia” trains to Cartagena and Gijon are supposed to be ready in platforms at 18 h00 (train 11761) and 18 h05 (train 00226). Regional MD service from Madrid to Albacete leaves at 18 h18. It is supposed that some of the trains (ALVIA, similar to HDEMU S-730, but without diesel generation) has a hybrid “last-mile” dynamic storage (results and sizing in Section 3.3) with 8.3 kWh in supercapacitors and 40 kWh in Li-ion batteries (400 V a 100 Ah). Altaria only has a “static” storage to support train hotel loads (battery in the power coach rechargeable through a static pantograph or the dynamic braking of some locomotive or EMU). Hotel load of Altaria is simulated for each of its nine coaches, and the overall train demand (hotel load) with the models proposed in Section 2.4 and parameters previously presented in Table5, is depicted in Figure 25. An outdoor winter temperature ranging from 2 to 9 ◦C is used (input Xext in Figure5a); 2.5 for COP (coe ﬃcient of performance of HVAC devices used in the coaches, appliance model in Figure5a), 50% of occupancy (at Madrid terminal station, considering average rates for occupancy in Reference [70],) and a rate of Appl. Sci. 2019, 9, x FOR PEER REVIEW 34 of 41 ventilation of 20 m3/passenger-hour. Figure 25 represents the results.

50 25

40 20

30 15

20 10 Temperature (ºC)

HVAC Demand (kW) 10 5 Indoor Vehicle 0 0 15 16 17 18 19 20 15 16 17 18 19 20 Time (h) Time (h) (a) (b)

FigureFigure 25. 25.Simulation Simulation of of HVAC HVAC loads loads for for Altaria Altaria coaches coaches ( a()a) Aggregated Aggregated electricity electricity demand; demand; ( b(b)) state state variablesvariables (indoor (indoor and and vehicle vehicle temperature, temperature, i.e., i.e., temperatures temperatures Xi Xi and and Xv Xv in in Figure Figure5). 5).

To cover all the “hotel loads”, the proposed battery ESS of Altaria (into the service/power coach) is sized at 100Ah@400 V (40 kWh) to cover HVAC demand (average load around 30 kWh, Figure 26a) and other internal loads for 30–40 min without the need to use its internal generator or the external supply from 3 kV DC catenary or through the locomotive. The HVAC “hotel load” of Altaria is a resource to reduce the load from substations of the terminal station. To evaluate the potential of this load, it has been taking into account that Altaria has used the braking energy of its locomotive or catenary in the shed to preheat its coaches to 23 °C. When the unit arrives at the terminal, HVAC loads can be switched-off (Figure 26a), and this DR policy reduces demand by 15–30 kW without significantly reduce the comfort of passengers (i.e., indoor temperature Xi, Figure 26b).

60 24 DR with HVAC Indoor HVAC uncontrolled 50 22 Vehicle

20 40 18 30 16 20

Temperature (ºC) 14 HVAC Demand (kW) 10 12

0 10 15 16 17 18 19 20 15 16 17 18 19 20 Time (h) Time (h) (a) (b)

Figure 26. Simulation of HVAC loads under control for Altaria coaches with preheating from 17 h tp 18 h: (a) Aggregated electricity demand with and without control; (b) state variables (indoor and vehicle temperature, i.e., temperatures Xi and Xv in Figure 5).

The simulation of the start of MD-Regional is presented in Figure 27a. The route outside the terminal is done at a reduced speed (45–50 km/h) and requires a 1.5 MW of peak power during 40 s and then a flat demand around 65–70 kW. This power is basically obtained from two sources: The Altaria OESS Supercapacitor (to achieve the initial acceleration, with energy requirements 8.3 kWh) and when the steady state is reached (time from 40 to 150 s) with the two batteries of Altaria and Alvia, and the help of DR “generation”, due to HVAC control policies. Figure 27b&c show the results. The model for the equivalent supercapacitor was discussed in Section 2.5. Appl. Sci. 2019, 9, x FOR PEER REVIEW 34 of 41

50 25

40 20

30 15

20 10 Temperature (ºC)

HVAC Demand (kW) 10 5 Indoor Vehicle 0 0 15 16 17 18 19 20 15 16 17 18 19 20 Time (h) Time (h) (a) (b)

Figure 25. Simulation of HVAC loads for Altaria coaches (a) Aggregated electricity demand; (b) state variables (indoor and vehicle temperature, i.e., temperatures Xi and Xv in Figure 5). Appl. Sci. 2019, 9, 3605 34 of 40 To cover all the “hotel loads”, the proposed battery ESS of Altaria (into the service/power coach)To coveris sized all theat 100Ah@400 “hotel loads”, V the(40 proposedkWh) to cover battery HVAC ESS of demand Altaria (into (average the service load /aroundpower coach) 30 kWh, is sizedFigure at 100Ah@40026a) and other V (40 internal kWh) to loads cover for HVAC 30–40 demand min wi (averagethout the load need around to use 30 its kWh, internal Figure generator 26a) and or otherthe external internal loadssupply for from 30–40 3 kV min DC without catenary the needor through to use itsthe internal locomotive. generator The orHVAC the external “hotel load” supply of fromAltaria 3 kV is DCa resource catenary to or reduce through the the load locomotive. from substations The HVAC of the “hotel terminal load” station. of Altaria To is evaluate a resource the topotential reduce theof this load load, from it substations has been taking of the into terminal account station. that Altaria To evaluate has used the potentialthe braking of thisenergy load, of itits haslocomotive been taking or catenary into account in the that shed Altaria to preheat has used its the coaches braking to energy23 °C. ofWhen its locomotive the unit arrives or catenary at the terminal, HVAC loads can be switched-off (Figure 26a), and this DR policy reduces demand by in the shed to preheat its coaches to 23 ◦C. When the unit arrives at the terminal, HVAC loads can be15–30 switched-o kW withoutﬀ (Figure significantly 26a), and reduce this DR the policy comfort reduces of passengers demand by (i.e., 15–30 indoor kW temperature without signiﬁcantly Xi, Figure reduce26b). the comfort of passengers (i.e., indoor temperature Xi, Figure 26b).

60 24 DR with HVAC Indoor HVAC uncontrolled 50 22 Vehicle

20 40 18 30 16 20

Temperature (ºC) 14 HVAC Demand (kW) 10 12

0 10 15 16 17 18 19 20 15 16 17 18 19 20 Time (h) Time (h) (a) (b)

FigureFigure 26. 26.Simulation Simulation ofof HVACHVAC loadsloads underunder control for Altaria coaches coaches with with preheating preheating from from 17 17 h htp tp18 18 h: h: (a (a) )Aggregated Aggregated electricity electricity demand demand with with and withoutwithout control;control; ((bb)) statestate variables variables (indoor (indoor and and vehiclevehicle temperature, temperature, i.e., i.e., temperatures temperatures Xi Xi and and Xv Xv in in Figure Figure5). 5).

TheThe simulation simulation of of the the start start of of MD-Regional MD-Regional is is presented presented in in Figure Figure 27 27a.a. The The route route outside outside the the terminalterminal is is done done at at a a reduced reduced speed speed (45–50 (45–50 km km/h)/h) and and requires requires a a 1.5 1.5 MW MW of of peak peak power power during during 40 40 s s andand then then a a flat flat demand demand around around 65–70 65–70 kW. kW. This This power power is is basically basically obtained obtained from from two two sources: sources: The The AltariaAltaria OESS OESS Supercapacitor Supercapacitor (to (to achieve achieve the the initial initial acceleration, acceleration, with with energy energy requirements requirements 8.3 8.3 kWh) kWh) andand when when the the steady steady state state is reachedis reached (time (time from from 40 to 40 150 to s) 150 with s) thewith two the batteries two batteries of Altaria of Altaria and Alvia, and andAlvia, the helpand the of DR help “generation”, of DR “generation”, due to HVAC due controlto HVAC policies. control Figure policies. 27b,c Figure show the27b&c results. show The the modelresults. for The the model equivalent for the supercapacitor equivalent supercapacitor was discussed was in Section discussed 2.5. in Section 2.5. Moreover, these policies that combine EES and DR benefits the possibilities to recover the energy deployed during regenerative braking. According to Table 22, if both technologies were deployed, there are some trains with similar characteristics that arrive at terminal station and that would use partially the regenerative braking in the substation area (Alvia 11781, Altaria 11929) and that could support the acceleration of MD regional service, contribute to refill of storage systems or control demand for auxiliary loads. Finally, it should be taken into account that terminal stations have conventional and flexible loads (e.g., heating and cooling that can be used to reduce the impact of synchronization in the Public Power Systems), and also commuter services that are not considered in Table 22, but which deploy their activity in the same area, increasing the potential resources for contributing to the reduction of demand peaks. Appl.Appl. Sci. Sci.2019 2019, 9, ,9 3605, x FOR PEER REVIEW 3535 ofof 40 41

1 1.5 Demand Accel 0.9

Super C 0.8 1 Batteries

0.7 SoC (%) 0.5 0.6

0.5

Demand (MW), Acceleration (m/s2) 0 0.4 01234 0 50 100 150 Distance from station (km) Time (s) (a) (b)

1400

1200

1000

800

600 SC and Li-ion 400 batteries batteries Power generation (kW) 200

0 0 50 100 150 Time (s) (c)

FigureFigure 27. 27.Simulation Simulation of of the the acceleration acceleration of of MDRegional MDRegional supported supported by by Alvia Alvia and and Altaria Altaria Storage Storage at at 1818 h h 10 10 (a ()a Demand) Demand for for unit unit until until next next substation substation (4 km (4 km away); away); (b) state(b) state of charge of charge of supercapacitor of supercapacitor and batteriesand batteries of units of supportingunits supporting the start the ofstart train; of train; (c) energy (c) energy supplied supplied by ESS by to ESS MD-Regional. to MD-Regional.

4. ConclusionsMoreover, these policies that combine EES and DR benefits the possibilities to recover the energyThis deployed paper presents during alternative regenerative solutions braking. for According increasing to the Table energy 22, e ffiif ciencyboth technologies of diesel-electric were anddeployed, hybrid trainsthere are without some impairing trains with on similar its dynamic characte characteristics.ristics that arrive These at solutions terminal (on station board and and that off boardwould storage) use partially enhance the the regenerative lifecycle of braking these units in the and substation the performance area (Alvia of energy 11781, infrastructuresAltaria 11929) and on lowthat and could medium support tra ffithec routes.acceleration To reach of MD these regional goals, several service, di ffcontributeerent alternatives to refill haveof storage been evaluated:systems or Storecontrol the demand energy of for the auxiliary diesel dynamic loads. Finally, braking it systems; should reducebe taken the into diesel account motor that size, terminal install wayside stations EEShave or conventional control of trains and as flexible DSF and loads DER (e.g., resources heating (specifically, and cooling the that change can be in tractionused to modereduce from the impact diesel toof electric synchronization or vice-versa, in the or Public the change Power in Systems), the service and or also climate commuter comfort services parameters that are of not trains considered which ainffects Table around 22, but 20–30% which of deploy overall their demand) activity in the in samethe same way area, that Publicincreasing Power the Systems potential does. resources For each for solution,contributing an energy to the storage reduction system of demand must be peaks. added with the appropriate capacity, or a portfolio of DER policies has been identified and simulated. Train functional models (traction and hotel loads) and DER models4. Conclusions have been linked and aggregated to improve the usefulness of simulations. Super-capacitors and diThisfferent paper technologies presents ofalternative batteries have solutions been chosenfor incr foreasing this purpose.the energy Dynamic efficiency braking of diesel-electric energy can, therefore,and hybrid be recuperated,trains without and impairing energy e ffionciency its dynamic improved characteristics. both in vehicles These and solutions in the Railway (on board Power and System.off board Energy storage) savings enhance by 10–20%the lifecycle are reportedof these units and nearby and the 70% performance improvement of energy in Load infrastructures Factor and peakon low shavings and medium (an important traffic concernroutes. forTo Publicreach these Power goals, Systems several and itsdifferent future challenges).alternatives Moreover,have been theevaluated: stochastic Store nature the of energy some events of the in diesel railways, dynami for example,c braking time systems; delays reduce with respect the diesel to the motor timetable, size, hasinstall been wayside evaluated. EES These or control delays of can trains be a as positive DSF and or negative DER resources effect on (specifically, energy efficiency the change through in regenerativetraction mode braking from diesel (and also, to electric their impact or vice-versa, on timetable or the performance). change in the Presentedservice or simulationsclimate comfort are validparameters for the itineraryof trains chosenwhich inaffects the example, around but20–30% the same of overall method demand) can be applied in the same through way the that software Public toPower any other Systems railway does. line For powered each bysolution, other vehicles. an energy storage system must be added with the appropriate capacity, or a portfolio of DER policies has been identified and simulated. Train functional models (traction and hotel loads) and DER models have been linked and aggregated to Appl. Sci. 2019, 9, 3605 36 of 40

In future developments of this work, detailed physical and thermal models of batteries, supercapacitors behavior, and improved models for “hotel loads” will be developed and validated in order to improve the integration of the ESS in the railway system, especially in on board solutions, in which space, weight and thermal requirements are vital to avoid affecting either the performance of trains or the customer comfort. Finally, the potential contribution and support of the reported flexibility of railway systems, which have been modelled in the paper, can help in the effective balance of RES unpredictability (volatility) in the eco-energy scenario that arises in the 2030–2050 horizon. This possibility will be of interest and consideration.

Author Contributions: A.G.-G. and A.G. conceived and designed the experiments and structure of the paper. A.G.-G. programmed the algorithms, developed and wrote the part concerning train simulation, generation and economic evaluation of ESS alternatives. A.G.-G. and A.G. developed and wrote the part concerning PBLM models. A.G. conceived DR policies and their integration. All authors have approved the final manuscript. Funding: This work was supported by the Ministerio de Ciencia, Innovación y Universidades, Spanish Government) under research project ENE-2016-78509-C3-2-P; Ministerio de Educación through grant FPU17/02753 and EU FEDER funds. Authors have also received funds from these grants for covering the costs to publish in open access. Acknowledgments: Authors are very grateful to the information, data and technical discussions provided by Patentes Talgo S.A. (Spain). This work was supported by the Ministerio de Ciencia, Innovación y Universidades (Spanish Government) under research project ENE-2016-78509-C3-2-P; Ministerio de Educación (Spanish Government) under grant FPU17/02753 and especially EU FEDER funds. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations and Nomenclature

AREA American Railway Engineering Association BESS Battery Energy Storage System CER Community of European Railway and Infrastructure Companies CF Capacity Factor DB Deutsche Bahn (German Railway Operator) DER Distributed Energy Resources DMU Diesel Multiple Unit DG Distributed Generation DR Demand Response DSF Demand Side Flexibility DSO Distribution System Operator EMD Electro-Motive Division (USA) EMU Electric Multiple Unit ESS Energy Storage Systems ICE Internal Combustion Engine HDEMU Hybrid Diesel and Electric Multiple Unit HESS Hybrid Energy Storage System HVAC Heat, Ventilation and Air Conditioning LF Load Factor OBOR One Belt One Road Initiative (China) OESS On-board Energy Storage System PBLM Physically Based Load Modeling RENFE Spanish Railway Operator RPS Railway Power System SBB Swiss Federal Railways (Schweizerische Bundesbahnen, SBB-CFF-FFS) SESS Stationary Energy Storage System SNCF French Railway Operator (Société Nationale des Chemins de fer Français) Appl. Sci. 2019, 9, 3605 37 of 40

SoC State of charge (water heater, battery of any ESS) TCL Thermostatically Controlled Loads TSO Transmission System Operator UIC International Union of Railways/Union International des Chemins de Fer VSI Voltage Source Inverter

Symbols in PBLM Model

A Area of coach: Windows, body, ceiling, etc. (outdoor) Ci Thermal capacity of indoor masses (energy storage) Cv Thermal capacity of body/frame masses (energy storage) D Differential operator ha Thermal losses, external exchange by convection (variable value f(speed)) aw0 Thermal losses coefficient (conduction) between the body/frame and indoor (cabin) awi Thermal losses coefficient (conduction) between the body/frame and outdoor m(t) Thermostat state (discrete, 0/1, or continuous) Kv Heat transfer coefficient (vehicle) K Heat transfer coefficient, global (according EN 13129-1) Hch Input. Heat gains/extraction, due to electric energy conversion to thermal energy (HVAC) Hv Input. Heat gains, due to ventilation (air quality) and infiltrations Hsw Input. Heat gains, due to solar radiation (coach windows) Hsw Input. Heat gains, due to solar radiation (coach body) Xi State variable. Indoor temperature (cabin). Xv State variable. Vehicle temperature (body/frame) Xext Input. Temperature of water in the input pipeline Xs Thermostat setting. MAX Xs Thermostat setting (maximum)

Symbols in Energy Analysis

EStB Energy Supplied through braking KPIi Key Performance Indicators MKPI5 Key Performance Indicator (RPS)

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