IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4565-d603305.html
   My bibliography  Save this article

Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System

Author

Listed:
  • Marcin Szott

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, St Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland)

  • Marcin Jarnut

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, St Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland)

  • Jacek Kaniewski

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, St Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland)

  • Łukasz Pilimon

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, St Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland)

  • Szymon Wermiński

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, St Prof. Z. Szafrana 2, 65-516 Zielona Góra, Poland)

Abstract

This paper introduces the concept of fault-tolerant control (FTC) of a multi-string battery energy storage system (BESS) in the dynamic reduction system of a traction substation load (DROPT). The major task of such a system is to reduce the maximum demand for contracted peak power, averaged for 15 min. The proposed concept, based on a multi-task control algorithm, takes into account: a three-threshold power limitation of the traction substation, two-level reduction of available power of a BESS and a multi-string structure of a BESS. It ensures the continuity of the maximum peak power demand at the contracted level even in the case of damage or disconnection of at least one chain of cells of the battery energy storage (BES) or at least one converter of the power conversion system (PCS). The proposed control strategy has been tested in a model of the system for dynamic reduction of traction substation load with a rated power of 5.5 MW. Two different BESS implementations have been proposed and several possible cases of failure of operations have been investigated. The simulation results have shown that the implementation of a multi-string BESS and an appropriate control algorithm (FTC) may allow for maintenance of the major assumption of DROPT, which is demanded power reduction (from 3.1 MW to 0.75 MW), even with a reduction of the BESS available power by at least 25% and more in the even in fault cases.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4565-:d:603305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4565/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qing Gu & Tao Tang & Fang Cao & Hamid Reza Karimi & Yongduan Song, 2013. "Peak Power Demand and Energy Consumption Reduction Strategies for Trains under Moving Block Signalling System," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, November.
    2. Li Chen & Yuqi Tong & Zuomin Dong, 2020. "Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems," Energies, MDPI, vol. 13(7), pages 1-19, April.
    3. Hyeongig Kim & Jae-Haeng Heo & Jong-Young Park & Yong Tae Yoon, 2017. "Impact of Battery Energy Storage System Operation Strategy on Power System: An Urban Railway Load Case under a Time-of-Use Tariff," Energies, MDPI, vol. 10(1), pages 1-15, January.
    4. 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.
    5. Kyoung-min Kwon & Jaeho Choi, 2019. "Single-Phase 13-Level Power Conditioning System for Peak Power Reduction of a High-Speed Railway Substation," Energies, MDPI, vol. 12(23), pages 1-26, November.
    6. Yu-Shan Cheng & Yi-Hua Liu & Holger C. Hesse & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2018. "A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community," Energies, MDPI, vol. 11(2), pages 1-18, February.
    7. 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.
    8. Petru Valentin Radu & Miroslaw Lewandowski & Adam Szelag, 2020. "On-Board and Wayside Energy Storage Devices Applications in Urban Transport Systems—Case Study Analysis for Power Applications," Energies, MDPI, vol. 13(8), pages 1-29, April.
    9. Mao-Chia Huang & Cheng-Hsien Yang & Chien-Chih Chiang & Sheng-Cheng Chiu & Yun-Feng Chen & Cong-You Lin & Lu-Yu Wang & Yen-Liang Li & Chang-Chung Yang & Wen-Sheng Chang, 2018. "Influence of High Loading on the Performance of Natural Graphite-Based Al Secondary Batteries," Energies, MDPI, vol. 11(10), pages 1-12, October.
    10. 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.
    11. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    12. Marcin Szott & Szymon Wermiński & Marcin Jarnut & Jacek Kaniewski & Grzegorz Benysek, 2021. "Battery Energy Storage System for Emergency Supply and Improved Reliability of Power Networks," Energies, MDPI, vol. 14(3), pages 1-21, January.
    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. 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.
    15. Yu Miao & Patrick Hynan & Annette von Jouanne & Alexandre Yokochi, 2019. "Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements," Energies, MDPI, vol. 12(6), pages 1-20, March.
    16. Peter Haidl & Armin Buchroithner & Bernhard Schweighofer & Michael Bader & Hannes Wegleiter, 2019. "Lifetime Analysis of Energy Storage Systems for Sustainable Transportation," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nurul Hiron & Nundang Busaeri & Sutisna Sutisna & Nurmela Nurmela & Aceng Sambas, 2021. "Design of Hybrid (PV-Diesel) System for Tourist Island in Karimunjawa Indonesia," Energies, MDPI, vol. 14(24), pages 1-24, December.
    2. Nikita Dmitrievich Senchilo & Denis Anatolievich Ustinov, 2021. "Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption," Energies, MDPI, vol. 14(21), pages 1-25, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Timur Yunusov & Maximilian J. Zangs & William Holderbaum, 2017. "Control of Energy Storage," Energies, MDPI, vol. 10(7), pages 1-5, July.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. Mohammadmahdi Ghiji & Vasily Novozhilov & Khalid Moinuddin & Paul Joseph & Ian Burch & Brigitta Suendermann & Grant Gamble, 2020. "A Review of Lithium-Ion Battery Fire Suppression," Energies, MDPI, vol. 13(19), pages 1-30, October.
    8. Desreveaux, A. & Bouscayrol, A. & Trigui, R. & Hittinger, E. & Castex, E. & Sirbu, G.M., 2023. "Accurate energy consumption for comparison of climate change impact of thermal and electric vehicles," Energy, Elsevier, vol. 268(C).
    9. Shujuan Meng & Binyu Xiong & Tuti Mariana Lim, 2019. "Model-Based Condition Monitoring of a Vanadium Redox Flow Battery," Energies, MDPI, vol. 12(15), pages 1-16, August.
    10. 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.
    11. Anisa Surya Wijareni & Hendri Widiyandari & Agus Purwanto & Aditya Farhan Arif & Mohammad Zaki Mubarok, 2022. "Morphology and Particle Size of a Synthesized NMC 811 Cathode Precursor with Mixed Hydroxide Precipitate and Nickel Sulfate as Nickel Sources and Comparison of Their Electrochemical Performances in an," Energies, MDPI, vol. 15(16), pages 1-15, August.
    12. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    13. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    14. Jiang Liu & Tian-tian Li & Bai-gen Cai & Jiao Zhang, 2020. "Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line," Energies, MDPI, vol. 13(8), pages 1-25, April.
    15. Piotr Krawczyk & Anna Śliwińska, 2020. "Eco-Efficiency Assessment of the Application of Large-Scale Rechargeable Batteries in a Coal-Fired Power Plant," Energies, MDPI, vol. 13(6), pages 1-16, March.
    16. Jack E. N. Swallow & Michael W. Fraser & Nis-Julian H. Kneusels & Jodie F. Charlton & Christopher G. Sole & Conor M. E. Phelan & Erik Björklund & Peter Bencok & Carlos Escudero & Virginia Pérez-Dieste, 2022. "Revealing solid electrolyte interphase formation through interface-sensitive Operando X-ray absorption spectroscopy," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    17. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    18. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    19. González, L.G. & Cordero-Moreno, Daniel & Espinoza, J.L., 2021. "Public transportation with electric traction: Experiences and challenges in an Andean city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    20. Harika Dasari & Eric Eisenbraun, 2021. "Predicting Capacity Fade in Silicon Anode-Based Li-Ion Batteries," Energies, MDPI, vol. 14(5), pages 1-16, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4565-:d:603305. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.