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An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

Author

Listed:
  • Wan Chen

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Baolian Liu

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Muhammad Shahzad Nazir

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Ahmed N. Abdalla

    (Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an 223003, China
    Jiangsu Foreign Expert Workshop, Jiangsu Provincial Department of Sciences and Technology, Huai’an 223003, China)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt)

  • Zujun Ding

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Muhammad Shoaib Bhutta

    (School of Automotive and Transportation Engineering, Guilin University of Aerospace Technology, Guilin 541004, China)

  • Mehr Gul

    (Department of Electrical Engineering, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta 1800, Pakistan)

Abstract

To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. A brief description of the virtual synchronous generator control strategy is given. The capacity allocation is based on different optimization goals and the optimal energy storage capacity configuration of the coordinated frequency modulation (FM) control strategy. The detail of the dual-loop control strategy is carried out by establishing the grid-connected transfer function model of the synchronverter energy storage and a theoretical model of life cycle cost is established. The optimal control strategy of coordinated FM for wind storage is implemented using MATLAB software. The simulation showed that the proposed strategy provided the energy storage capacity at high wind speed, which is configured to be 5.9% of the installed capacity of the wind turbine, marking a reduction of 26% compared with the 8% capacity required for independent support. In addition, the proposed method has improved the energy storage capacity configuration of the coordinated FM control strategy.

Suggested Citation

  • Wan Chen & Baolian Liu & Muhammad Shahzad Nazir & Ahmed N. Abdalla & Mohamed A. Mohamed & Zujun Ding & Muhammad Shoaib Bhutta & Mehr Gul, 2022. "An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8510-:d:860580
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    References listed on IDEAS

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    1. El-Bidairi, Kutaiba S. & Nguyen, Hung Duc & Mahmoud, Thair S. & Jayasinghe, S.D.G. & Guerrero, Josep M., 2020. "Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia," Energy, Elsevier, vol. 195(C).
    2. Jian Chen & Tao Jin & Mohamed A. Mohamed & Andres Annuk & Udaya Dampage, 2022. "Investigating the Impact of Wind Power Integration on Damping Characteristics of Low Frequency Oscillations in Power Systems," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    3. Muhammad Shahzad Nazir & Ahmed N. Abdalla & Ahmed Sayed M. Metwally & Muhammad Imran & Patrizia Bocchetta & Muhammad Sufyan Javed, 2022. "Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
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    Cited by:

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    2. Ahmed Ismail M. Ali & Zuhair Muhammed Alaas & Mahmoud A. Sayed & Abdulaziz Almalaq & Anouar Farah & Mohamed A. Mohamed, 2022. "An Efficient MPPT Technique-Based Single-Stage Incremental Conductance for Integrated PV Systems Considering Flyback Central-Type PV Inverter," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    3. Michell J. Quintero-Durán & John E. Candelo-Becerra & Mario Eduardo González-Niño & Saúl Andrés Hernández-Moreno & Rodolpho Fernando Váz, 2025. "Synchronverter Control Strategy: A Review of Different Improvements and Applications," Energies, MDPI, vol. 18(13), pages 1-39, July.
    4. Fahad M. Almasoudi, 2023. "Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
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    6. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    7. Felipe Ramos & Aline Pinheiro & Rafaela Nascimento & Washington de Araujo Silva Junior & Mohamed A. Mohamed & Andres Annuk & Manoel H. N. Marinho, 2022. "Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
    8. Riyadh Kamil Chillab & Aqeel S. Jaber & Mouna Ben Smida & Anis Sakly, 2023. "Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    9. Xianyang Cui & Yulong Liu & Ding Yuan & Tao Jin & Mohamed A. Mohamed, 2023. "A Hierarchical Coordinated Control Strategy for Power Quality Improvement in Energy Router Integrated Active Distribution Networks," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
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