IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2217-d1388616.html
   My bibliography  Save this article

Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction

Author

Listed:
  • Krzysztof Sozański

    (Institute of Automation, Electronics and Electrical Engineering, University of Zielona Góra, ul. Szafrana 9, 65-516 Zielona Góra, Poland)

  • Szymon Wermiński

    (Institute of Automation, Electronics and Electrical Engineering, University of Zielona Góra, ul. Szafrana 9, 65-516 Zielona Góra, Poland)

  • Jacek Kaniewski

    (Institute of Automation, Electronics and Electrical Engineering, University of Zielona Góra, ul. Szafrana 9, 65-516 Zielona Góra, Poland)

Abstract

This study describes a laboratory model of a battery energy storage system (BESS) designed for testing algorithms aimed at reducing peak power consumption in railway traction substations. The system comprises a DC/DC converter and battery energy storage. This article details a laboratory model of a bidirectional buck-boost DC/DC converter, which is used to transfer energy between the battery energy storage and a DC line. It presents an analysis of DC/DC converter systems along with simulation studies. Furthermore, the results of laboratory tests on the DC/DC converter model are also provided. The control algorithm of the system in the traction substation is focused on reducing peak power, offering benefits such as lower charges for the railway operator due to the possibility of reducing contracted power requirements. From the perspective of the power grid, the reduction in power fluctuations and, consequently, voltage sags, is advantageous. This paper includes a description of a hardware simulator for verifying the system’s control algorithms. The verification of the control algorithms was performed through experimental tests conducted on a laboratory model (a hardware simulator) of the system for dynamic load reduction in traction substations, on a power scale of 1:1000 (5.5 kW). The experimental tests on the laboratory model (hardware simulator) demonstrated the effectiveness of the algorithm in reducing the peak power drawn from the power source.

Suggested Citation

  • Krzysztof Sozański & Szymon Wermiński & Jacek Kaniewski, 2024. "Small-Scale Battery Energy Storage System for Testing Algorithms Aimed at Peak Power Reduction," Energies, MDPI, vol. 17(9), pages 1-23, May.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2217-:d:1388616
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2217/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/9/2217/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Hameed, Zeenat & Hashemi, Seyedmostafa & Ipsen, Hans Henrik & Træholt, Chresten, 2021. "A business-oriented approach for battery energy storage placement in power systems," Applied Energy, Elsevier, vol. 298(C).
    3. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
    Full references (including those not matched with items on IDEAS)

    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. Ahmed M. Hussien & Jonghoon Kim & Abdulaziz Alkuhayli & Mohammed Alharbi & Hany M. Hasanien & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado, 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    2. Konečná, Eva & Teng, Sin Yong & Máša, Vítězslav, 2020. "New insights into the potential of the gas microturbine in microgrids and industrial applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    4. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    5. Zhang, Tao & Li, Hong-Zhou & Xie, Bai-Chen, 2022. "Have renewables and market-oriented reforms constrained the technical efficiency improvement of China's electric grid utilities?," Energy Economics, Elsevier, vol. 114(C).
    6. Md Masud Rana & Akhlaqur Rahman & Moslem Uddin & Md Rasel Sarkar & SK. A. Shezan & C M F S Reza & Md. Fatin Ishraque & Mohammad Belayet Hossain, 2022. "Efficient Energy Distribution for Smart Household Applications," Energies, MDPI, vol. 15(6), pages 1-19, March.
    7. Panyawoot Boonluk & Sirote Khunkitti & Pradit Fuangfoo & Apirat Siritaratiwat, 2021. "Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand," Energies, MDPI, vol. 14(5), pages 1-20, March.
    8. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    9. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    10. Harsh, Pratik & Das, Debapriya, 2022. "Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    11. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    12. Yong-Rae Lee & Hyung-Joon Kim & Mun-Kyeom Kim, 2021. "Optimal Operation Scheduling Considering Cycle Aging of Battery Energy Storage Systems on Stochastic Unit Commitments in Microgrids," Energies, MDPI, vol. 14(2), pages 1-21, January.
    13. Francisco Durán & Wilson Pavón & Luis Ismael Minchala, 2024. "Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid," Energies, MDPI, vol. 17(2), pages 1-21, January.
    14. Fang, Xichen & Guo, Hongye & Zhang, Xian & Wang, Xuanyuan & Chen, Qixin, 2022. "An efficient and incentive-compatible market design for energy storage participation," Applied Energy, Elsevier, vol. 311(C).
    15. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    16. Álvarez-Arroyo, César & Vergine, Salvatore & de la Nieta, Agustín Sánchez & Alvarado-Barrios, Lázaro & D’Amico, Guglielmo, 2024. "Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources," Renewable Energy, Elsevier, vol. 229(C).
    17. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    18. Umer, Khalid & Huang, Qi & Khorasany, Mohsen & Afzal, Muhammad & Amin, Waqas, 2021. "A novel communication efficient peer-to-peer energy trading scheme for enhanced privacy in microgrids," Applied Energy, Elsevier, vol. 296(C).
    19. Ali M. Jasim & Basil H. Jasim & Bogdan-Constantin Neagu & Simo Attila, 2023. "Electric Vehicle Battery-Connected Parallel Distribution Generators for Intelligent Demand Management in Smart Microgrids," Energies, MDPI, vol. 16(6), pages 1-29, March.
    20. Ouyang, Tiancheng & Zhang, Mingliang & Qin, Peijia & Tan, Xianlin, 2024. "Flow battery energy storage system for microgrid peak shaving based on predictive control algorithm," Applied Energy, Elsevier, vol. 356(C).

    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:17:y:2024:i:9:p:2217-:d:1388616. 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.