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A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control

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  • Rao, Yingqing
  • Yang, Jun
  • Xiao, Jinxing
  • Xu, Bingyan
  • Liu, Wenjing
  • Li, Yonghui

Abstract

The microgrid contains many distributed power sources which have great randomness and volatility, so it is difficult to maintain the stable operation. As a mobile energy storage component with the capability of Vehicle-to-Grid (V2G), electric vehicles (EVs) provide a solution for the safe and stable operation of multimicrogrids. First, a microgrid system including micro gas turbines, distributed power and EVs which consider the random behavior of EV users as dynamic limitations is established. A coupled multimicrogrid system is then established. An improved robust model predictive frequency control strategy of multimicrogrids with EVs is proposed, and the whole control process is transformed into linear matrix inequalities (LMIs). A linear quadratic regulator (LQR) is added to further optimize control quantities to ensure the stability of the output. Finally, different scenarios are set up in the microgrids, and the simulation results are compared with other control methods. The simulation results show that the proposed control method can better suppress the frequency fluctuation with a faster response speed than other methods. The regulation of LQR is verified, and the proposed algorithm is shown to be effective in solving the uncertainty of system parameters. The establishment of multimicrogrids increases the anti-interference ability of the system.

Suggested Citation

  • Rao, Yingqing & Yang, Jun & Xiao, Jinxing & Xu, Bingyan & Liu, Wenjing & Li, Yonghui, 2021. "A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002127
    DOI: 10.1016/j.energy.2021.119963
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    References listed on IDEAS

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    Cited by:

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    2. Adlan Pradana & Mejbaul Haque & Mithulanathan Nadarajah, 2023. "Control Strategies of Electric Vehicles Participating in Ancillary Services: A Comprehensive Review," Energies, MDPI, vol. 16(4), pages 1-36, February.
    3. Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    4. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    5. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    6. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.

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