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Improving the Traffic Model to Be Used in the Optimisation of Mass Transit System Electrical Infrastructure

Author

Listed:
  • Álvaro J. López-López

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Ramón R. Pecharromán

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Antonio Fernández-Cardador

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Asunción P. Cucala

    (Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

Among the different approaches for minimising the energy consumption of mass transit systems (MTSs), a common concern for MTS operators is the improvement of the electrical infrastructure. The traffic on the lines under analysis is one of the most important inputs to the studies devoted to improving MTS infrastructure, since it represents where and how frequently it is possible to save energy. However, on the one hand, MTS electrical studies usually simplify the traffic model, which may lead to a misrepresentation of the energy interactions between trains. On the other hand, if the stochastic traffic is rigorously modelled, the size of the simulation problem could grow excessively, which in turn could make the time to obtain results unmanageable. To cope with this issue, this paper presents a method to obtain a reduced-size set of representative scenarios. Firstly, a traffic model including the most representative stochastic traffic variables is developed. Secondly, a function highly correlated with energy savings is proposed to make it possible to properly characterise the traffic scenarios. Finally, this function is used to select the most representative scenarios. The representative scenario set obtained by the application of this method is shown to be sufficiently accurate with a limited number of scenarios. The traffic approach in this paper improves the accuracy with respect to the usual traffic approach used in the literature.

Suggested Citation

  • Álvaro J. López-López & Ramón R. Pecharromán & Antonio Fernández-Cardador & Asunción P. Cucala, 2017. "Improving the Traffic Model to Be Used in the Optimisation of Mass Transit System Electrical Infrastructure," Energies, MDPI, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1134-:d:106772
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    References listed on IDEAS

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    1. Bin Wang & Zhongping Yang & Fei Lin & Wei Zhao, 2014. "An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing," Energies, MDPI, vol. 7(10), pages 1-25, October.
    2. Huan Xia & Huaixin Chen & Zhongping Yang & Fei Lin & Bin Wang, 2015. "Optimal Energy Management, Location and Size for Stationary Energy Storage System in a Metro Line Based on Genetic Algorithm," Energies, MDPI, vol. 8(10), pages 1-23, October.
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    Cited by:

    1. Sahil Bhagat & Jacopo Bongiorno & Andrea Mariscotti, 2023. "Influence of Infrastructure and Operating Conditions on Energy Performance of DC Transit Systems," Energies, MDPI, vol. 16(10), pages 1-26, May.

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