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Supercapacitor State Based Control and Optimization for Multiple Energy Storage Devices Considering Current Balance in Urban Rail Transit

Author

Listed:
  • Zhongping Yang

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Zhihong Yang

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Huan Xia

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Fei Lin

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

  • Feiqin Zhu

    (School of Electrical Engineering, Beijing Jiaotong University, No. 3 Shangyuancun, Beijing 100044, China)

Abstract

The use of supercapacitors (SCs) to store regenerative braking energy from urban rail trains is able to achieve a good energy saving effect. This paper analyzes the current balance method of stationary energy storage devices (ESDs). At the beginning of the paper, the mathematical model of the DC traction power system, which includes trains, ESDs and traction substations, is established. Next, based on this, the SC state-based control strategy (SCSCS) is proposed, which can adjust the charging voltage of the ESD according to the SC voltage and current, then the charging current of the ESD can be reasonably distributed under the voltage difference of ESDs, and the SC voltage and current stress can be reduced. In order to determine the optimal controlling parameters, the optimization model is proposed and solved by the genetic algorithm. The analysis of the case study also shows the effectiveness of the proposed control strategy and optimization algorithm. Finally, the rationality of the proposed strategy is verified by experiments.

Suggested Citation

  • Zhongping Yang & Zhihong Yang & Huan Xia & Fei Lin & Feiqin Zhu, 2017. "Supercapacitor State Based Control and Optimization for Multiple Energy Storage Devices Considering Current Balance in Urban Rail Transit," Energies, MDPI, vol. 10(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:520-:d:95603
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    References listed on IDEAS

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    1. Chang Ye & Shihong Miao & Qi Lei & Yaowang Li, 2016. "Dynamic Energy Management of Hybrid Energy Storage Systems with a Hierarchical Structure," Energies, MDPI, vol. 9(6), pages 1-15, May.
    2. Diego Iannuzzi & Enrico Pagano & Pietro Tricoli, 2013. "The Use of Energy Storage Systems for Supporting the Voltage Needs of Urban and Suburban Railway Contact Lines," Energies, MDPI, vol. 6(4), pages 1-19, March.
    3. Fei Lin & Xuyang Li & Yajie Zhao & Zhongping Yang, 2016. "Control Strategies with Dynamic Threshold Adjustment for Supercapacitor Energy Storage System Considering the Train and Substation Characteristics in Urban Rail Transit," Energies, MDPI, vol. 9(4), pages 1-18, March.
    4. Sergio Ignacio Serna-Garcés & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2016. "Sliding-Mode Control of a Charger/Discharger DC/DC Converter for DC-Bus Regulation in Renewable Power Systems," Energies, MDPI, vol. 9(4), pages 1-27, March.
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    Cited by:

    1. Stefano Menicanti & Marco di Benedetto & Davide Marinelli & Fabio Crescimbini, 2022. "Recovery of Trains’ Braking Energy in a Railway Micro-Grid Devoted to Train plus Electric Vehicle Integrated Mobility," Energies, MDPI, vol. 15(4), pages 1-25, February.
    2. 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).
    3. Flavio Ciccarelli & Luigi Pio Di Noia & Renato Rizzo, 2018. "Integration of Photovoltaic Plants and Supercapacitors in Tramway Power Systems," Energies, MDPI, vol. 11(2), pages 1-14, February.
    4. Pan, Deng & Zhao, Liting & Luo, Qing & Zhang, Chuansheng & Chen, Zejun, 2018. "Study on the performance improvement of urban rail transit system," Energy, Elsevier, vol. 161(C), pages 1154-1171.
    5. Ivan Radaš & Ivan Župan & Viktor Šunde & Željko Ban, 2021. "Route Profile Dependent Tram Regenerative Braking Algorithm with Reduced Impact on the Supply Network," Energies, MDPI, vol. 14(9), pages 1-22, April.

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