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An Improved Genetic Algorithm for Optimal Stationary Energy Storage System Locating and Sizing

Author

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  • Bin Wang

    (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)

  • Wei Zhao

    (Beijing Metro R&D Center, Beijing 100044, China)

Abstract

The application of a stationary ultra-capacitor energy storage system (ESS) in urban rail transit allows for the recuperation of vehicle braking energy for increasing energy savings as well as for a better vehicle voltage profile. This paper aims to obtain the best energy savings and voltage profile by optimizing the location and size of ultra-capacitors. This paper firstly raises the optimization objective functions from the perspectives of energy savings, regenerative braking cancellation and installation cost, respectively. Then, proper mathematical models of the DC (direct current) traction power supply system are established to simulate the electrical load-flow of the traction supply network, and the optimization objections are evaluated in the example of a Chinese metro line. Ultimately, a methodology for optimal ultra-capacitor energy storage system locating and sizing is put forward based on the improved genetic algorithm. The optimized result shows that certain preferable and compromised schemes of ESSs’ location and size can be obtained, acting as a compromise between satisfying better energy savings, voltage profile and lower installation cost.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:10:p:6434-6458:d:40983
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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. Hadjipaschalis, Ioannis & Poullikkas, Andreas & Efthimiou, Venizelos, 2009. "Overview of current and future energy storage technologies for electric power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1513-1522, August.
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    Cited by:

    1. 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.
    2. José de Jesús Jaramillo Serna & Jesús M. López-Lezama, 2019. "Calculation of Distance Protection Settings in Mutually Coupled Transmission Lines: A Comparative Analysis," Energies, MDPI, vol. 12(7), pages 1-32, April.
    3. 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.
    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.
    5. Tusongjiang Kari & Wensheng Gao & Ayiguzhali Tuluhong & Yilihamu Yaermaimaiti & Ziwei Zhang, 2018. "Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers," Energies, MDPI, vol. 11(9), pages 1-19, September.
    6. 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.
    7. 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.
    8. 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.
    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. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.

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