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Optimal Energy Management, Location and Size for Stationary Energy Storage System in a Metro Line Based on Genetic Algorithm

Author

Listed:
  • Huan Xia

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

  • Huaixin Chen

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

  • Zhongping Yang

    (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)

  • Bin Wang

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

Abstract

The installation of stationary super-capacitor energy storage system (ESS) in metro systems can recycle the vehicle braking energy and improve the pantograph voltage profile. This paper aims to optimize the energy management, location, and size of stationary super-capacitor ESSes simultaneously and obtain the best economic efficiency and voltage profile of metro systems. Firstly, the simulation platform of an urban rail power supply system, which includes trains and super-capacitor energy storage systems, is established. Then, two evaluation functions from the perspectives of economic efficiency and voltage drop compensation are put forward. Ultimately, a novel optimization method that combines genetic algorithms and a simulation platform of urban rail power supply system is proposed, which can obtain the best energy management strategy, location, and size for ESSes simultaneously. With actual parameters of a Chinese metro line applied in the simulation comparison, certain optimal scheme of ESSes’ energy management strategy, location, and size obtained by a novel optimization method can achieve much better performance of metro systems from the perspectives of two evaluation functions. The simulation result shows that with the increase of weight coefficient, the optimal energy management strategy, locations and size of ESSes appear certain regularities, and the best compromise between economic efficiency and voltage drop compensation can be obtained by a novel optimization method, which can provide a valuable reference to subway company.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:11618-11640:d:57236
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

    1. Olave-Rojas, David & Álvarez-Miranda, Eduardo, 2021. "Towards a complex investment evaluation framework for renewable energy systems: A 2-level heuristic approach," Energy, Elsevier, vol. 228(C).
    2. 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.
    3. Timur Yunusov & Maximilian J. Zangs & William Holderbaum, 2017. "Control of Energy Storage," Energies, MDPI, vol. 10(7), pages 1-5, July.
    4. David Roch-Dupré & Carlos Camacho-Gómez & Asunción P. Cucala & Silvia Jiménez-Fernández & Álvaro López-López & Antonio Portilla-Figueras & Ramón R. Pecharromán & Antonio Fernández-Cardador & Sancho Sa, 2021. "Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers," Energies, MDPI, vol. 14(16), pages 1-19, August.
    5. Zhang, Huan & Zhu, Chunguang & Zheng, Wandong & You, Shijun & Ye, Tianzhen & Xue, Peng, 2016. "Experimental and numerical investigation of braking energy on thermal environment of underground subway station in China's northern severe cold regions," Energy, Elsevier, vol. 116(P1), pages 880-893.
    6. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
    7. Petru Valentin Radu & Adam Szelag & Marcin Steczek, 2019. "On-Board Energy Storage Devices with Supercapacitors for Metro Trains—Case Study Analysis of Application Effectiveness," Energies, MDPI, vol. 12(7), pages 1-22, April.
    8. Guifu Du & Dongliang Zhang & Guoxin Li & Chonglin Wang & Jianhua Liu, 2016. "Evaluation of Rail Potential Based on Power Distribution in DC Traction Power Systems," Energies, MDPI, vol. 9(9), pages 1-20, September.
    9. Shuai Su & Tao Tang & Yihui Wang, 2016. "Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model," Energies, MDPI, vol. 9(2), pages 1-19, February.
    10. 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.
    11. Wei, Shaoyuan & Murgovski, Nikolce & Jiang, Jiuchun & Hu, Xiaosong & Zhang, Weige & Zhang, Caiping, 2020. "Stochastic optimization of a stationary energy storage system for a catenary-free tramline," Applied Energy, Elsevier, vol. 280(C).
    12. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    13. 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.
    14. Hammad Alnuman & Daniel Gladwin & Martin Foster, 2018. "Electrical Modelling of a DC Railway System with Multiple Trains," Energies, MDPI, vol. 11(11), pages 1-20, November.

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