Lower energy consumption in railway networks thanks to smart IT behind the scene

Transcription

1 Lower energy consumption in railway networks thanks to smart IT behind the scene M. Montigel systransis Ltd., Switzerland. 1 Introduction Figure 1: Locomotive enters newly built Lötschberg railway base tunnel ( systransis 2006) Modern societies are hungry for fast and convenient mobility and heavily depend on efficient transportation systems. For instance, the capacity of the Lötschberg railway tunnel through the Swiss alps (see Figure 1), which was completed in 2007, has been used up to its limits almost immediately. This mobility comes at a price. A substantial amount of the total energy consumption of a modern society flows into transportation of people and goods. In Switzerland, in the year 2010, the distribution of overall energy consumption looks as shown in Figure 2.! "

2 Figure 2: Distribution of overall energy consumption in Switzerland 2009 [7],[8] 35% of the overall energy consumption is spent for transportation. Consequently strategies for energy conservation in transportation are very substantial for the overall energy balance of a developed country. In this contribution, the energy consumption patterns in transportation will be analysed in depth, which will lead to concrete values for the specific energy input needed for road and rail transportation. Further, it will be shown how modern Railway IT helps to conserve energy. Lastly, potentials for further energy conservation in the future and actions to harvest these potentials are proposed. 2 Energy consumption patterns in transportation 2.1 Production In order to be able to identify potentials for energy savings in transportation and elaborate on possible action plans, it is necessary to first analyse the energy consumption patterns in the transportation area. While there are various related studies, such as [9], the preferred methodology for the following considerations is to base on the overall transportation production and energy consumption figures of an entire country. By using these neutral and unchallenged official figures, the potential biases introduced in studies that favour particular sectors of transportation are completely avoided. All following figures are taken from the Swiss statistics for the year of First the production side is analysed. production consists of passenger and freight transportation. The total annual transportation production broken down to passenger and freight is shown in Figure 3. In passenger transportation, private car traffic is very dominant with 79% of the overall production. On the other hand, the rail traffic s share in fright transportation amounts to 38% Figure 3: Annual Production of Transport (left: Passenger [1]; right: Fright [2])! #

3 Since the overall production is required for the following considerations, passenger kilometres (P km) and freight ton kilometres (t km) must be combined in one value. Commonly, this is achieved by the definition of transportation units (TU), whereas 1 P km and 1 t km each conform to 1 TU. Summing up the values given in Figure 3 leads to the overall transportation production in Switzerland in 2007 shown in Figure 4, which indicates a market share of rail transportation of 21%. Figure 4: Annual Production of Transport, measured in Transport Units. 2.2 Energy Input for As mentioned in the introduction, Section 1, the transportation s share of the overall consumption amounts to 35% (see Figure 2). However, to be precise, the following considerations do not base on the consumption (of the end-user) but on the energy input into the production system and hence includes the differences in the efficiency of the distribution of energy for various means of energy supply. This is necessary because the distribution efficiency for electricity typically is considerably lower than the efficiency of distributing oil-based energy supply. The detailed methodology how to derive the relevant energy input for transportation, based on data provided in [3] [8], is given in Appendix A The relevant energy input can be broken down into input for oil-based energy and input for electricity, as shown in Figure 5, which clearly shows the massive dominance of oil-based energy supply (94%). Figure 5: Annual Energy Input for, [3] [8].! $

4 2.3 Comparison of Specific Energy Inputs Without undue oversimplification it can be assumed that in Switzerland almost all electric energy used for transportation serves to power rail traffic, while almost all oil-based energy drives road traffic. Comparing Figure 4 and Figure 5 leads to the obvious conclusion that rail transportation delivers a larger share of overall transportation production while requiring a considerably smaller share of energy. Hence, from this respect, rail transportation is much more energy-efficient than road transportation. This claim can be further supported by computing the values of specific energy inputs for both means of transportation. The specific energy input is defined as follows: E in P out! E denotes the total energy input for a transportation system, while in P denotes the total transportation out production output (in traffic units) of the system. The values for the specific energy input are given in Figure 6, which shows that road traffic! requires almost 4 times the energy input than rail traffic for the same transportation production, therefore confirming the initial informal conclusion.! Figure 6: Specific Energy Input for. The in-depth analysis of energy input clearly shows that rail traffic is already much more efficient than mobility on the road, which consequently seem to provide huge potentials for improvements. Nevertheless, in the sequel the focus will lie in methods to make rail traffic even more efficient, thereby trying to assist in a successful replacement of atomic energy in the future. 3 How modern Railway IT helps to conserve energy 3.1 Key Concepts Technologies for increasing energy efficiency in the context of railway operation are receiving increased attention. A review of these technologies can be found in [13]. In the sequel the focus lies on optimising the trains trajectories during real-time optimisation. Track occupation conflicts are frequent in standard railway operations today. Train drivers, who are not aware of such conflicts in advance, are forced to stop, which results in additional delays, timetable instability, and a waste of energy. This could be avoided if they were informed about the conflict and had a chance to adapt their driving behaviour accordingly. Theoretical concepts of increasing energy efficiency through energy-optimal train trajectories have been studied extensively. The possibilities for computing and using such trajectories are described in detail in! %

5 Albrecht [15], Howlett and Pudney [16], and Franke [17]. Lüthi [13] discusses the energy saved as a result of integrated real-time rescheduling. Mitchell [18] discusses the impact of advisory systems on energy savings and uses a simulation-based model to quantitatively estimate these savings. Figure 7: Modern Traffic Optimisation System for Lötschberg Base Tunnel ( systransis 2006). In 2007, for the first time anywhere in the world, some of these theoretical concepts have been put into practice in an innovative computer-based train control system for the aforementioned Lötschberg base tunnel (see Figure 7). It can reduce the negative effects of a dense operation by sending advisory speeds to the drivers of trains affected by conflicts. This system makes use of the fact that in a conflict-prone train operation not every trajectory leads to the same energy consumption: optimised trajectories consume less energy and even provide additional benefits such as reduction of delays a train will experience and the increased stability of overall operations. These concepts are shown in Figure 8. The red curves depict a trajectory without any optimisation. The train driver does not know that a conflict with another train awaits him ahead, indicated by the right dotted vertical line. He/she is driving at full speed, only to be halted by the train safety system. What follows is a tedious acceleration phase, starting from zero km/h, as soon as the track ahead is free.! &

6 Figure 8: Time gained by sending advisory speed. The green solid trajectory is optimised and avoids a halt. Figure 9: Distribution of the percentage energy saved due to sending advisory speeds. [12] The green curve shows the same case but solved with an optimised trajectory. The optimisation system has detected the conflict long ago and informed the driver about the impediment. Hence he/she is starting to brake much earlier to a nonzero speed, until it is possible to accelerate again. The latter point in time typically coincides with the moment in which the non-optimised train starts its braking process. This phenomenon stems from the fact that the acceleration rates of trains are much lower (factor 3-4) than the deceleration rates. More details on the computational aspects can be found in Montigel et al [10] and [11]. A comprehensive illustration is the movie [19]. 3.2 Resulting Energy Savings In a study undertaken to quantitatively estimate the impact of this optimisation on energy consumption using real operational data it was demonstrated that in an overwhelming majority of cases the energy savings due to sending advisory speeds amount to 10-20% (see Mehta et al. [12]). Figure 9 shows the! '

7 corresponding distribution. The percentage of total energy saved in the conflict area was calculated to be 12.4%. Although the absolute energy savings in kwh varies strongly for each train run, the percentage of the energy saved (with respect to the energy used to cover the area of interest in the non-optimised case) does not vary as significantly, as can be seen in Figure 9. Further, sending advisory speeds is estimated to save about 60 kwh of traction energy per conflict. What is worth mentioning is that energy savings are not directly taken into account in the calculation of advisory speeds. They are purely a side effect of reducing the delays resulting from a conflict. This case clearly demonstrates that considerable impacts are possible by taking relatively simple measures. In the sequel future potentials for energy savings are identified and potential concepts given how to harvest them. 4 Potentials for further energy conservation Figure 10: Model example for energy optimised railway line ( systransis 2006). Extrapolating the figures gained in Section 3.2 to the whole railway network in Switzerland, the countrywide potential can be estimated. The following assumptions on the daily train traffic from [20] and [21] are used: 1. About 1500 passenger journeys (only long-distance trains) occur each day, each experiencing on average 2 conflicts leading to an unplanned stop 2. About 2000 freight journeys occur each day, each experiencing on average 3 conflicts leading to an unplanned stop. Hence the considerable potential savings for Switzerland in railway traffic for one year amount to: 60 kwh * (1500 * * 3) * 365 " kwh = 200 GWh " 720 TJ This is around 7% of the electricity consumption of railways and also around 7% of the annual production of the nuclear power plant in Mühleberg.!! (

8 As a second estimate, the following is proposed: Assuming 12.4% savings per conflict case when considering the conflict prone areas, and assuming further that roughly! of the rail network are conflict prone, would result in 6.2% savings of the annual electricity consumption, which is sufficiently close to gain confidence in the estimates. Assuming a market price of CHF 0.20 per kwh this translates into a monetary saving of about CHF 40 million in energy costs alone, not to mention the added value and monetary gain due to fewer delays and better timetable stability, and reduced maintenance costs due to less wear and tear. Extrapolating this for Europe (for lack of better figures using the population figures) this leads to a potential for savings of more than EUR million, while the investment cost for such systems for whole Europe are estimated to amount to several hundred million per 5 to 10 years. Assuming other means of transportation find similar ways of saving 7 % of the total consumption of energy, this would amount to EUR million annually, which should be a huge motivation for research investments. This example clearly shows, which dramatic impact one single measure can have when adapted to an entire transportation network at once. 5 Conclusion: actions 1. Since the specific energy input is by far higher for road based transportation, reduction efforts should be directed to that area a. Make vehicles more efficient b. Renew flotilla c. Increase average utilisation by intelligent disposition systems for people and freight d. Break trend towards heavier cars e. Increase intelligent traffic management to avoid slow and standstill traffic f. Break daily demand peaks by road pricing 2. Since it is relatively easy to direct rail traffic by centralised optimisation systems these should be utilised in whole networks in the future! )

9 6 Appendix A: Methodology to derive the relevant energy inputs Switzerland Share of total energy consumption for Year % Share of total energy input for Year % Year 2007 Categorie Quantity Modifier Unit Value % Road Private 1'000'000 P km / y 87'492 79% Annual Passenger Production [01] Road Public 1'000'000 P km / y 5'138 5% Rail 1'000'000 P km / y 17'835 16% Total 1'000'000 P km / y 110' % Annual Freight Production [02] Annual Production Road 1'000'000 t km / y 16'993 62% Rail 1'000'000 t km / y 10'631 38% Total 1'000'000 t km / y 27' % Road 1'000'000 VE / y 109'623 79% Rail 1'000'000 VE / y 28'466 21% Total 1'000'000 VE / y 138' % Annual Energy Consumption for [03, 04, 06] Specific Energy Consumption for Petrol/gasoline 1 TJ / y 148'350 61% Diesel 1 TJ / y 85'484 35% Total Oil-based 1 TJ / y 233'834 96% Electricity 1 TJ / y 9'668 4% Total Land 1 TJ / y 243' % Average 1 kw h / VE % Road 1 kw h / VE % Rail 1 kw h / VE % Annual Energy Input for [07, 08] Specific Energy Input for Petrol/gasoline 1 TJ / y 194'422 60% Diesel 1 TJ / y 112'032 34% Total Oil-based 1 TJ / y 306'455 94% Electricity 1 TJ / y 19'554 6% Total Land 1 TJ / y 326' % Average 1 kw h / VE % Road 1 kw h / VE % Rail 1 kw h / VE % Energy Savings due to conflict resolution Computed via (# conflicts * savings per conflict) 1 TJ / y 710 7% Computed via estimated percentage saved * total 1 TJ / y 580 consumption Mühleberg Savings CH 1'000'000 CHF / y 39 Savings EUR 1'000'000 EUR / y 3'285 Complete Potential Savings EUR 1'000'000 EUR / y 51'484 Figure 11: Methodology of Computations of Specific! *

10 References [1] Swiss Federal Administration: Verkehrsleistung im Personenverkehr, [2] Swiss Federal Administration: Transportleistung im Güterverkehr, [3] Swiss Federal Administration: Endenergieverbrauch Verkehr, [4] Swiss Federal Administration: Treibstoffverbrauch Verkehr, [5] Swiss Federal Administration: Energieinhalte und CO 2 -Emissionsfaktoren von fossilen Energieträgern, [6] Swiss Federal Office for Energy: Überblick über den Energieverbrauch der Schweiz im Jahr 2010, [7] Swiss Federal Office for Energy: Gesamter Energieeinsatz und Endverbrauch, 2009, [8] Swiss Federal Office for Energy: Gesamtenergiestatistik 2009, [9] Treber M., Der spezifische Energieverbrauch des öffentlichen Verkehrs, DER NAHVERKEHR, 13. Jahrgang, Heft 12/95, S.25-28, 1995 [10] Montigel M., Kleiner C. & Achermann E., Experience with the Speed and Traffic Optimisation employed in the novel Train Traffic Control Center of the Lötschberg Base Tunnel in Switzerland, Proceedings of Railway Capacity The Engineering Challenge, [11] Montigel M., Operations control system in the Lötschberg Base Tunnel, RTR - European Rail Technology Review 02/2009, [12] Mehta F., Rößiger C. & Montigel M., Latent energy savings due to the innovative use of advisory speeds to avoid occupation conflicts, Proceedings of Comprail 2010, 2010 [13] Website for Energy Efficiency Technologies for Railways. [14] Lüthi M., Evaluation of energy saving strategies in heavily used rail networks by implementing an integrated real-time rescheduling system, Comprail 2008 Proceedings, [15] Albrecht, T. Energy-Efficient Train Operation, Chapter in Railway Timetable and Traffic, pp , Eurail Press, [16] Howlett, P.G., Pudney, P.J. Energy-efficient train control, Springer, Berlin, [17] Franke, R., Meyer, M., Terwiesch, P. Optimal Control of the Driving of Trains, Automatisierungstechnik 50(12), pp , [18] Mitchell, I, The Sustainable Railway Use of Advisory Systems for Energy Savings, IRSE Technical Paper, [19] Swiss Television, Einstein, Sparsam durch den Lötschberg, movie, [20] Information from Media centre SBB, [21] queried 24th November 2009 for timetable period 2009/10.! "+