Open Access Open Access  Restricted Access Subscription or Fee Access

Optimizing the Energy Efficiency of Electric Transportation Systems Operation Using a Genetic Algorithm

Giorgio Boschetti(1), Andrea Mariscotti(2*)

(1) DITEN, University di Genova, Italy
(2) DITEN, University of Genova, Italy
(*) Corresponding author


DOI: https://doi.org/10.15866/iree.v9i4.1532

Abstract


Energy consumption of a rail transit system depends on many parameters. One of the most effective methods of reducing energy consumption in a rail transit system is optimizing the speed profile of the trains along the route. A genetic algorithm (GA) based approach is proposed to search for the optimal train speed trajectory, given a journey time constraint, and its effectiveness is shown by simulation results. The proposed approach includes realistic system modeling using an integrated electromechanical simulation model to calculate train energy consumption and travelling time under different operating conditions, inter-station distances, track profiles.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Electromechanical Simulation; Guideway Transportation Systems; Genetic Algorithm; Optimization; Speed Profile

Full Text:

PDF


References


M. Ogasa, Energy saving and environmental measures in railway technologies: example with hybrid electric railway vehicles, IEEJ Transactions on Electrical and Electronic Engineering, vol. 3, Jan. 2008, pp. 15–20.
http://dx.doi.org/10.1002/tee.20227

J. W. Sheu and W. S. Lin, Automatic train regulation with energy saving using dual heuristic programming, IET Electric Systems Transportation, vol. 1, 2011, pp. 80–80.
http://dx.doi.org/10.1049/iet-est.2010.0074

W.-S. Lin and J.-W. Sheu, Optimization of train regulation and energy usage of metro lines using an adaptive-optimal-control algorithm, IEEE Transactions on Automation Science Engineering, vol. 8, Oct. 2011, pp. 855–855.
http://dx.doi.org/10.1109/TASE.2011.2160537

Y. Bavafa-Toosi, C. Blendinger, V. Mehrmann, A. Steinbrecher, and R. Unger, A new methodology for modeling, analysis, synthesis, and simulation of time-optimal train traffic in large networks, IEEE Transactions on Automation Science Engineering, vol. 5, Jan. 2008, pp. 43–52.
http://dx.doi.org/10.1109/TASE.2007.897613

E. Khmelnitsky, On an optimal control problem of train operation, IEEE Transactions on Automation and Control, vol. 45, Jul. 2000, pp. 1257–1257.
http://dx.doi.org/10.1109/9.867018

H.-S. Hwang, Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 28, Nov. 1998, pp. 791–802.
http://dx.doi.org/10.1109/3468.725350

K. K. Wong and T. K. Ho, Coast control for mass rapid transit railways with searching methods, IEE Proceedings - Electric Power Applications, vol. 151, 2004, pp. 365–376.
http://dx.doi.org/10.1049/ip-epa:20040346

C. Chang and S. Sim, Optimising train movements through coast control using genetic algorithms, IEE Proceedings - Electric Power Applications, vol. 144, 1997, pp. 65–73.
http://dx.doi.org/10.1049/ip-epa:19970797

Y. V. Bocharnikov, A. M. Tobias, C. Roberts, S. Hillmansen, and C. J. Goodman, Optimal driving strategy for traction energy saving on DC suburban railways, IET Electric Power Applications, vol. 1, 2007, pp. 675–682.
http://dx.doi.org/10.1049/iet-epa:20070005

H.-J. Chuang, C.-S. Chen, C.-H. Lin, C.-H. Hsieh, and C.-Y. Ho, Design of optimal coasting speed for saving social cost in mass rapid transit systems, Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, pp. 2833–2839, 2008.
http://dx.doi.org/10.1109/DRPT.2008.4523892

R. Liu and L. M. Golovitcher, Energy-efficient operation of rail vehicles, Transportation Research Part A: Policy and Practice, vol. 37, 2003, pp. 917–932.
http://dx.doi.org/10.1016/j.tra.2003.07.001

T. Albrecht, Railway timetable & traffic: analysis, modelling and simulation, Energy-Efficient Train Operation, pp. 83–105 (Eurailpress, DVV Rail Media, 2008).

P. Howlett, The optimal control of a train, Annals of Operation Research, vol. 98, 2000, pp. 65–87.
http://dx.doi.org/10.1023/A:1019235819716

Mandic, M., Uglesic, I., Milardic, V., Method for optimization of energy consumption of electrical trains, (2011) International Review of Electrical Engineering (IREE), 6 (1), pp. 292-299.

E. Khmelnitsky, On an optimal control problem of train operation, IEEE Transactions on Automation and Control, vol. 45, 2000, pp. 1257–1266.
http://dx.doi.org/10.1109/9.867018

H. Ko, T. Koseki, and M. Miyatake, Application of dynamic programming to the optimization of the running profile of a train, in Computers in Railway IX (J. Allan, C. Brebbia, R. Hill, G. Sciutto, and S. Sone, eds.), vol. 15, pp. 103–112, The Wessex Institute, 2004.

S. Effati and H. Roohparvar, The minimization of the fuel costs in the train transportation, Applied Mathematics and Computation, Vol. 175, Apr. 2006, pp. 1415–1431.
http://dx.doi.org/10.1016/j.amc.2005.08.037

W. Liu, Q. Li, and B. Tang, Energy saving train control for urban railway train with multi-population genetic algorithm, International forum on information technology and applications, IFITA '09, vol. 2, pp. 58–63, 15-17 May, 2009, Chengdu, China.

Aryani, N.K., Syai'in, M., Soeprijanto, A., Made Yulistya Negara, I., Optimal placement and sizing of distributed generation for minimize losses in unbalance radial distribution systems using quantum genetic algorithm, (2014) International Review of Electrical Engineering (IREE), 9 (1), pp. 157-164.

M. Domínguez, A. Fernández-Cardador, A. P. Cucala, R. R. Pecharromán, Energy Savings in Metropolitan Railway Substations Through Regenerative Energy Recovery and Optimal Design of ATO Speed Profiles, IEEE Transactions on Automation, Science and Engineering, Vol. 9, July 2012, pp. 496-504.

Computers in Railways V: Volume 1, Railway Systems and Management. South Hampton, Great Britain: Comput. Mechanic Pub., 1996, pp. 337–346.

J.H. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, MI, 1975).

D.T. Pham and D. Karaboga, Intelligent optimisation techniques: genetic algorithms, tabu search, simulated annealing and neural networks (Springer, 2000).
http://dx.doi.org/10.1007/978-1-4471-0721-7

D. Whitely and T. Hanson, Optimising neural network using faster, more accurate genetic search, Proc. 3rd Int. Conf. on Genetic Algorithms and their Applications, George Mason University, pp 370-374, 1989.

P.G. Howlett and P.J. Pudney, Energy-efficient train control, Springer, 1995.
http://dx.doi.org/10.1007/978-1-4471-3084-0

S.M. Howard, L.C. Gill and P.J. Wong, Review and Assessment of Train Performance Simulation Models, Transportation Research Record, n. 917, pp. 1-6, 1983.

B. P. Rochard and F. Schmid, A review of methods to measure and calculate train resistances, Proc. of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 214, 2000, pp. 185–199.
http://dx.doi.org/10.1243/0954409001531306

G. Boschetti and A. Mariscotti, The parameters of motion mechanical equations as a source of uncertainty for traction systems simulation, Proc. of XX Imeko World Congress, Busan South Korea, 2012.

G. Boschetti and A. Mariscotti, Integrated Electromechanical Simulation of Traction Systems: Relevant Factors for the Analysis and Estimation of Energy Efficiency, Proc. of the 2nd Intern. Conf. on Electrical Systems for Aircraft, Railway and Ship Propulsion, Bologna, Italy, Oct. 12-14, 2012.

V. Profillidis, Railway Engineering, Avebury Technical: Ashgate Publishing Limited, Aldershot, 1995. D. S. Armstrong and P. H. Swift, Lower energy technology. Part A, identification of energy use in multiple units. Report MR VS 077, British Rail Research, Derby, 20 July 1990.

A. Mariscotti, Distribution of the traction return current in AC and DC electric railway systems, IEEE Transactions on Power Delivery, vol. 18, Oct. 2003, pp. 1422-1432.
http://dx.doi.org/10.1109/TPWRD.2003.817786

R. Cella, G. Giangaspero, A. Mariscotti, A. Montepagano, P. Pozzobon, M. Ruscelli and M. Vanti, Measurement of AT Electric Railway System currents and validation of a Multiconductor Transmission Line model, IEEE Transactions on Power Delivery, vol. 21, July 2006, pp. 1721-1726.
http://dx.doi.org/10.1109/TPWRD.2006.874109

A. Mariscotti and P. Pozzobon, Determination of the Electrical Parameters of Railway Traction Lines: Calculation, Measurement and Reference Data, IEEE Transactions on Power Delivery, vol. 19, Oct. 2004 pp. 1538-1546.
http://dx.doi.org/10.1109/TPWRD.2004.835285

A. Mariscotti, Voltage coupled to wayside interconnecting cables, Proc. of the 2nd Intern. Conf. on Electrical Systems for Aircraft, Railway and Ship Propulsion, Bologna, IT, Oct. 21-23, 2010.

A. Mariscotti, M. Ruscelli and M. Vanti, Modeling of Audiofrequency Track Circuits for validation, tuning and conducted interference prediction, IEEE Transactions on Intelligent Transportation Systems, vol. 11, March 2010, pp. 52-60.
http://dx.doi.org/10.1109/TITS.2009.2029393

A. Mariscotti, P. Pozzobon and M. Vanti, Simplified modelling of 2x25 kV AT Railway System for the solution of low frequency and large scale problems, IEEE Transactions on Power Delivery, vol. 22, Jan. 2007, pp. 296-301.
http://dx.doi.org/10.1109/TPWRD.2006.883020

Ciccarelli, F., Clemente, G., Iannuzzi, D., Lauria, D., An analytical solution for optimal design of stationary Lithium-ion capacitor storage device in light electrical transportation networks, (2013) International Review of Electrical Engineering (IREE), 8 (3), pp. 989-999.

TraXim, Traffic and traction simulator, [online] www.astm-e.ch


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize