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Grid-Forming Operation of Energy-Router Based on Model Predictive Control with Improved Dynamic Performance

Author

Listed:
  • Mahdieh Najafzadeh

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Natalia Strzelecka

    (Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland)

  • Oleksandr Husev

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Indrek Roasto

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Kawsar Nassereddine

    (Faculty of Engineering, Lebanese University, Beirut 6573, Lebanon)

  • Dmitri Vinnikov

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Ryszard Strzelecki

    (Faculty of Electrical and Control Engineering, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

Abstract

The focus of this study is on the grid-forming operation of the Energy Router (ER) based on Model Predictive Control (MPC). ER is regarded as a key component of microgrids. It is a converter that interfaces the microgrid (s) with the utility grid. The ER has a multiport structure and bidirectional energy flow control. The ER concept can be implemented in Nearly Zero-Energy Buildings (NZEB) to provide flexible energy control. A concept is proposed where the ER works as a single grid-forming converter. The challenge is to keep the predefined reference voltage and frequency inside the NZEB in all possible modes, including the idle operation mode, current sources, and nonlinear load control. To gain stability and output voltage quality, the MPC is proposed. The design of the modified MPC algorithm with improved dynamics performance is explained. PLECS software is utilized to verify the proposed algorithm. The results demonstrate the suitable performance of the proposed control method in terms of total harmonic distortion of the output voltage. The influence of weighting coefficiencies is evaluated, showing the higher impact of the capacitor filter voltage on lowering the total harmonics distortion of the output voltage. Finally, the capability of the control system toward step change in the reference value is evaluated.

Suggested Citation

  • Mahdieh Najafzadeh & Natalia Strzelecka & Oleksandr Husev & Indrek Roasto & Kawsar Nassereddine & Dmitri Vinnikov & Ryszard Strzelecki, 2022. "Grid-Forming Operation of Energy-Router Based on Model Predictive Control with Improved Dynamic Performance," Energies, MDPI, vol. 15(11), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4010-:d:827523
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    References listed on IDEAS

    as
    1. Yingpei Liu & Yan Li & Haiping Liang & Jia He & Hanyang Cui, 2019. "Energy Routing Control Strategy for Integrated Microgrids Including Photovoltaic, Battery-Energy Storage and Electric Vehicles," Energies, MDPI, vol. 12(2), pages 1-16, January.
    2. Md Alamgir Hossain & Hemanshu Roy Pota & Walid Issa & Md Jahangir Hossain, 2017. "Overview of AC Microgrid Controls with Inverter-Interfaced Generations," Energies, MDPI, vol. 10(9), pages 1-27, August.
    3. Indrek Roasto & Oleksandr Husev & Mahdiyyeh Najafzadeh & Tanel Jalakas & Jose Rodriguez, 2019. "Voltage Source Operation of the Energy-Router Based on Model Predictive Control," Energies, MDPI, vol. 12(10), pages 1-15, May.
    4. Yingshu Liu & Yue Fang & Jun Li, 2017. "Interconnecting Microgrids via the Energy Router with Smart Energy Management," Energies, MDPI, vol. 10(9), pages 1-19, August.
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    Cited by:

    1. Eros D. Escobar & Tatiana Manrique & Idi A. Isaac, 2022. "Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control," Energies, MDPI, vol. 15(21), pages 1-19, October.
    2. Escobar, Eros D. & Betancur, Daniel & Manrique, Tatiana & Isaac, Idi A., 2023. "Model predictive real-time architecture for secondary voltage control of microgrids," Applied Energy, Elsevier, vol. 345(C).

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