A Multi-Objective Simulator for Optimal Power Dimensioning on Electric Railways using Cloud Computing

Jesus Carretero, Silvina Caino, Felix Garcia-Carballeira, Alberto Garcia

2015

Abstract

Power dimensioning and energy saving have been traditionally two main issues regarding the deployment of electric grids. Electric railways are also concerned about these issues, and simulators have been traditionally used to test such infrastructure deployments. The main goal of this paper is to present the Railway electric Power Consumption Simulator, a simulation model and tool for the railway energy provisioning problem. This simulator aims to propose electric railway infrastructure deployments, optimizing the quality of the electric flow supplied to train, as well as saving as much energy as possible. The paper describes the simulator structure, as well as the ontology used to translate railway infrastructure elements into an electric circuit. Because these two objectives are conflicting, a multi-objective optimization problem is formulated and solved. Finally, a standard railway scenario is used to illustrate the capabilities of the tool, trying to find the best electric substation placements in order to optimize such objectives. The evaluation shows how the tool can handle hundreds of simulated scenarios using Cloud Computing techniques.

References

  1. Abrahamsson, L. and Soder, L. (2009). Railway power supply investment decisions considering the voltage drops-assuming the future traffic to be known. In Intelligent System Applications to Power Systems, 2009. ISAP'09. 15th International Conference on, pages 1- 6. IEEE.
  2. AENOR (2004). UNE-EN 50163: Railway Applications - Supply voltages of traction systems. AENOR.
  3. Angeli, D. and Masala, E. (2012). A cost-effective cloud computing framework for accelerating multimedia communication simulations. Journal of Parallel and Distributed Computing, 72(10):1373 - 1385.
  4. Augugliaro, A., Dusonchet, L., Favuzza, S., and Sanseverino, E. (2004). Voltage regulation and power losses minimization in automated distribution networks by an evolutionary multiobjective approach. Power Systems, IEEE Transactions on, 19(3):1516-1527.
  5. Bobi, J. d. D. S., Marcos, F. J., and Nú n˜ ez, J. G. (2007). A simulation tool for sizing electrical railway lines. In ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference, pages 269-277. American Society of Mechanical Engineers.
  6. Carrano, E., Soares, L., Takahashi, R., Saldanha, R., and Neto, O. (2006). Electric distribution network multiobjective design using a problem-specific genetic algorithm. Power Delivery, IEEE Transactions on, 21(2):995-1005.
  7. Carrano, E., Takahashi, R., Cardoso, E., Saldanha, R., and Neto, O. (2005). Optimal substation location and energy distribution network design using a hybrid gabfgs algorithm. Generation, Transmission and Distribution, IEE Proceedings-, 152(6):919-926.
  8. CENELEC (2012). UNE-EN 50388: Railway Applications - Power supply and rolling stock - Technical criteria for the coordination between power supply (substation) and rolling stock to achieve interoperability. AENOR.
  9. CENELEC (2015). prEN 50641 (draft): Railway Applications - - Fixed installations - Requirements for the validation of simulation tools used for the design of traction power supply systems. CENELEC: Europeean Committee for Electrotechnical Standardization.
  10. Chang, C., Wang, W., Liew, A., and Wen, F. (1998). Bicriterion optimisation for traction substations in mass rapid transit systems using genetic algorithm. Electric Power Applications, IEE Proceedings -, 145(1):49- 56.
  11. Chang, C., Wang, W., Liew, A., Wen, F., and Srinivasan, D. (1995). Genetic algorithm based bicriterion optimisation for traction substations in dc railway system. In Evolutionary Computation, 1995., IEEE International Conference on, volume 1, pages 11-.
  12. Dean, J. and Ghemawat, S. (2008). Mapreduce: Simplified data processing on large clusters. Communications of the ACM, 51(1):107-113.
  13. Decraene, J., Zeng, F., Low, M. Y. H., Cai, W., Cheng, Y. Y., and Choo, C. S. (2011). Evolutionary design of experiments using the mapreduce framework. In Proceedings of the 2011 Summer Computer Simulation Conference, SCSC 7811, pages 76-83, Vista, CA. Society for Modeling & Simulation International.
  14. Deelman, E., Singh, G., Livny, M., Berriman, B., and Good, J. (2008). The cost of doing science on the cloud: The montage example. In High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for, pages 1-12.
  15. del Valle, Y., Venayagamoorthy, G., Mohagheghi, S., Hernandez, J.-C., and Harley, R. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. Evolutionary Computation, IEEE Transactions on, 12(2):171-195.
  16. García, A., Gó mez, C., García-Carballeira, F., and Carretero, J. (2014). Enhancing the structure of railway infrastructure simulators. In Proceedings of the 1st International Conference on Engineering and Applied Sciences Optimization (OPT-i), pages 352-363.
  17. Gomez, J., Khodr, H., De Oliveira, P., Ocque, L., Yusta, J., Villasana, R., and Urdaneta, A. (2004). Ant colony system algorithm for the planning of primary distribution circuits. Power Systems, IEEE Transactions on, 19(2):996-1004.
  18. S., Margraf, M., Habchi, Jacob, R. Qucs technical http://qucs.sourceforge.net/tech/node14.html.
  19. accessed April 2015. and
  20. Kim, B. S., Lee, S. J., Kim, T. G., and Song, H. S. (2014). Mapreduce based experimental frame for parallel and distributed simulation using hadoop platform. In 28th European Conference on Modelling and Simulation, ECMS 2014, Brescia, Italy, May 27-30, 2014, pages 664-669.
  21. Liu, Z., Liu, F., Zhang, B., Ma, F., and Gao, S. (2010). Research on cloud computing and its application in railway. Beijing Jiaotong Daxue Xuebao(Journal of Beijing Jiaotong University), 34(5):14-19.
  22. Mendoza, F., Bernal-Agustin, J., and Dominguez-Navarro, J. (2006). Nsga and spea applied to multiobjective design of power distribution systems. Power Systems, IEEE Transactions on, 21(4):1938-1945.
  23. Nguyen, P. H., Kling, W. L., and Ribeiro, P. F. (2011). Smart power router: a flexible agent-based converter interface in active distribution networks. Smart Grid, IEEE Transactions on, 2(3):487-495.
  24. Parada, V., Ferland, J., Arias, M., and Daniels, K. (2004). Optimization of electrical distribution feeders using simulated annealing. Power Delivery, IEEE Transactions on, 19(3):1135-1141.
  25. Pilo, E., Mazumder, S., and Gonzalez-Franco, I. (2015). Smart electrical infrastructure for ac-fed railways with neutral zones. Intelligent Transportation Systems, IEEE Transactions on, 16(2):642-652.
  26. Pilo, E., Rouco, R., Fernandez, A., and Hernandez-Velilla, A. (2000). A simulation tool for the design of the electrical supply system of high-speed railway lines. In Power Engineering Society Summer Meeting, 2000. IEEE, volume 2, pages 1053-1058 vol. 2.
  27. Radenski, A. (2013). Using mapreduce streaming for distributed life simulation on the cloud. In Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems. ECAL 2013., pages 284-291.
  28. Ramirez-Rosado, I. and Bernal-Agustin, J. (1998). Genetic algorithms applied to the design of large power distribution systems. Power Systems, IEEE Transactions on, 13(2):696-703.
  29. Ramirez-Rosado, I. and Dominguez-Navarro, J. (2004). Possibilistic model based on fuzzy sets for the multiobjective optimal planning of electric power distribution networks. Power Systems, IEEE Transactions on, 19(4):1801-1810.
  30. Soler, M., Lopez, J., Mera Sanchez de Pedro, J., and Maroto, J. (2015). Methodology for multiobjective optimization of the ac railway power supply system. Intelligent Transportation Systems, IEEE Transactions on, PP(99):1-12.
  31. Strbac, G. and Djapic, P. (1995). A genetic based fuzzy approach to optimisation of electrical distribution networks. In Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995. GALESIA. First International Conference on (Conf. Publ. No. 414), pages 194-199.
Download


Paper Citation


in Harvard Style

Carretero J., Caino S., Garcia-Carballeira F. and Garcia A. (2015). A Multi-Objective Simulator for Optimal Power Dimensioning on Electric Railways using Cloud Computing . In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-120-5, pages 428-438. DOI: 10.5220/0005573404280438


in Bibtex Style

@conference{simultech15,
author={Jesus Carretero and Silvina Caino and Felix Garcia-Carballeira and Alberto Garcia},
title={A Multi-Objective Simulator for Optimal Power Dimensioning on Electric Railways using Cloud Computing},
booktitle={Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2015},
pages={428-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005573404280438},
isbn={978-989-758-120-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - A Multi-Objective Simulator for Optimal Power Dimensioning on Electric Railways using Cloud Computing
SN - 978-989-758-120-5
AU - Carretero J.
AU - Caino S.
AU - Garcia-Carballeira F.
AU - Garcia A.
PY - 2015
SP - 428
EP - 438
DO - 10.5220/0005573404280438