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

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.

[1]  Eugene Khmelnitsky,et al.  On an optimal control problem of train operation , 2000, IEEE Trans. Autom. Control..

[2]  A. Mariscotti.  Voltage coupled to wayside interconnecting cables , 2010, Electrical Systems for Aircraft, Railway and Ship Propulsion.

[3]  Masafumi Miyatake,et al.  Application of dynamic programming to the optimization of the running profile of a train , 2004 .

[4]  Tang Bing,et al.  Energy Saving Train Control for Urban Railway Train with Multi-population Genetic Algorithm , 2009, 2009 International Forum on Information Technology and Applications.

[5]  Rongfang Rachel Liu,et al.  Energy-efficient operation of rail vehicles , 2003 .

[6]  Adi Soeprijanto,et al.  Optimal Placement and Sizing of Distributed Generation for Minimize Losses in Unbalance Radial Distribution Systems Using Quantum Genetic Algorithm , 2014 .

[7]  Chung-Fu Chang,et al.  Optimising train movements through coast control using genetic algorithms , 1997 .

[8]  Phil Howlett,et al.  The Optimal Control of a Train , 2000, Ann. Oper. Res..

[9]  P. Pozzobon,et al.  Simplified Modeling of 2$\,\times\,$ 25-kV AT Railway System for the Solution of Low Frequency and Large-Scale Problems , 2007, IEEE Transactions on Power Delivery.

[10]  Clive Roberts,et al.  Optimal driving strategy for traction energy saving on DC suburban railways , 2007 .

[11]  A. Mariscotti,et al.  Integrated electromechanical simulation of traction systems: Relevant factors for the analysis and estimation of energy efficiency , 2012, 2012 Electrical Systems for Aircraft, Railway and Ship Propulsion.

[12]  V. A. Profillidis.  Railway Engineering , 1995 .

[13]  R. R. Pecharromán,et al.  Energy Savings in Metropolitan Railway Substations Through Regenerative Energy Recovery and Optimal Design of ATO Speed Profiles , 2012, IEEE Transactions on Automation Science and Engineering.

[14]  Duc Truong Pham,et al.  Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks , 2011 .

[15]  P. Pozzobon,et al.  Measurement of AT electric railway system currents at power-supply frequency and validation of a multiconductor transmission-line model , 2006, IEEE Transactions on Power Delivery.

[16]  Phil Howlett,et al.  Energy-efficient train control , 1994 .

[17]  Chao-Shun Chen,et al.  Design of optimal coasting speed for saving social cost in Mass Rapid Transit systems , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[18]  Tin Kin Ho,et al.  Coast control for mass rapid transit railways with searching methods , 2004 .

[19]  Milivoj Mandić,et al.  Method for Optimization of Energy Consumption of Electrical Trains , 2011 .

[20]  Jih-Wen Sheu,et al.  Automatic train regulation with energy saving using dual heuristic programming , 2011 .

[21]  Wei-Song Lin,et al.  Optimization of Train Regulation and Energy Usage of Metro Lines Using an Adaptive-Optimal-Control Algorithm , 2011, IEEE Transactions on Automation Science and Engineering.

[22]  An Analytical Solution for Optimal Design of Stationary Lithium-Ion Capacitor Storage Device in Light Electrical Transportation Networks , 2013 .

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[24]  Masamichi Ogasa.  Energy Saving and Environmental Measures in Railway Technologies: Example with Hybrid Electric Railway Vehicles , 2008 .

[25]  Andrea Mariscotti,et al.  Modeling of Audiofrequency Track Circuits for Validation, Tuning, and Conducted Interference Prediction , 2010, IEEE Transactions on Intelligent Transportation Systems.

[26]  Roman Unger,et al.  A New Methodology for Modeling, Analysis, Synthesis, and Simulation of Time-Optimal Train Traffic in Large Networks , 2008, IEEE Transactions on Automation Science and Engineering.

[27]  Sohrab Effati,et al.  The minimization of the fuel costs in the train transportation , 2006, Appl. Math. Comput..

[28]  A. Mariscotti,et al.  Distribution of the Traction Return Current in AC and DC Electric Railway Systems , 2002, IEEE Power Engineering Review.

[29]  P J Wong,et al.  REVIEW AND ASSESSMENT OF TRAIN PERFORMANCE SIMULATION MODELS , 1983 .

[30]  Felix Schmid,et al.  A review of methods to measure and calculate train resistances , 2000 .

[31]  P. Pozzobon,et al.  Determination of the electrical parameters of railway traction lines: calculation, measurement, and reference data , 2004, IEEE Transactions on Power Delivery.

[32]  Hee-Soo Hwang,et al.  Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway , 1998, IEEE Trans. Syst. Man Cybern. Part A.