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Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Author

Listed:
  • Regina Lamedica

    (Department of Astronautics, Electrical and Energetic Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Alessandro Ruvio

    (Department of Astronautics, Electrical and Energetic Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Laura Palagi

    (Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy)

  • Nicola Mortelliti

    (Department of Astronautics, Electrical and Energetic Engineering, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

The paper proposes an optimal siting and sizing methodology to design an energy storage system (ESS) for railway lines. The scope is to maximize the economic benefits. The problem of the optimal siting and sizing of an ESS is addressed and solved by a software developed by the authors using the particle swarm algorithm, whose objective function is based on the net present value (NPV). The railway line, using a standard working day timetable, has been simulated in order to estimate the power flow between the trains finding the siting and sizing of electrical substations and storage systems suitable for the railway network. Numerical simulations have been performed to test the methodology by assuming a new-generation of high-performance trains on a 3 kV direct current (d.c.) railway line. The solution found represents the best choice from an economic point of view and which allows less energy to be taken from the primary network.

Suggested Citation

  • Regina Lamedica & Alessandro Ruvio & Laura Palagi & Nicola Mortelliti, 2020. "Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line," Energies, MDPI, vol. 13(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6271-:d:452586
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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. Mihaela Popescu & Alexandru Bitoleanu, 2019. "A Review of the Energy Efficiency Improvement in DC Railway Systems," Energies, MDPI, vol. 12(6), pages 1-25, March.
    3. Serdar Dindar & Sakdirat Kaewunruen & Min An, 2018. "Identification of appropriate risk analysis techniques for railway turnout systems," Journal of Risk Research, Taylor & Francis Journals, vol. 21(8), pages 974-995, August.
    4. 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. Meishner, Fabian & Ünlübayir, Cem & Sauer, Dirk Uwe, 2023. "Model-based investigation of an uncontrolled LTO wayside energy storage system in a 750 V tram grid," Applied Energy, Elsevier, vol. 331(C).
    2. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
    4. Regina Lamedica & Marco Maccioni & Alessandro Ruvio & Federico Carere & Nicola Mortelliti & Fabio Massimo Gatta & Alberto Geri, 2022. "Optimization of e-Mobility Service for Disabled People Using a Multistep Integrated Methodology," Energies, MDPI, vol. 15(8), pages 1-22, April.
    5. Szymon Haładyn, 2021. "The Problem of Train Scheduling in the Context of the Load on the Power Supply Infrastructure. A Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.

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