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Multiagent Based Distributed Control with Time-Oriented SoC Balancing Method for DC Microgrid

Author

Listed:
  • Tao Wu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yanghong Xia

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Liang Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Wei Wei

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Based on the droop control, voltage regulation at the secondary control is required to eliminate the deviation of the average voltage across the microgrid. Meanwhile, to prevent any of energy storage (ESs) from over-charging or over-discharging, State-of-Charge (SoC) balancing should be added in the secondary control. This paper proposes a distributed secondary control in the DC microgrid based on the multiagent system (MAS). This controller consists of voltage regulation and time-oriented SoC balancing. In voltage regulation, a PI controller adjusts the droop parameters according to the discrepancy between the average voltage and the reference voltage. In SoC balancing, controller operates in charging mode or discharging mode according to changes of the global average SoC. Being different from the conventional method, the time-oriented SoC balancing method is designed to balance charge/discharge time rather than to balance SoC directly. Thus, SoCs reach a consensus only at the last moment when all ES nodes charge or discharge completely. Furthermore, characteristics, global dynamic model, and steady-state analysis of the proposed control method are studied. Finally, MATLAB/Simulink simulations are performed to verify the effectiveness of the proposed control.

Suggested Citation

  • Tao Wu & Yanghong Xia & Liang Wang & Wei Wei, 2020. "Multiagent Based Distributed Control with Time-Oriented SoC Balancing Method for DC Microgrid," Energies, MDPI, vol. 13(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2793-:d:365985
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    Citations

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    Cited by:

    1. Woon-Gyu Lee & Thai-Thanh Nguyen & Hak-Man Kim, 2022. "Multiagent-Based Distributed Coordination of Inverter-Based Resources for Optimal Operation of Microgrids Considering Communication Failures," Energies, MDPI, vol. 15(10), pages 1-19, May.
    2. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    3. Hussein M. Abdeltawab & Yasser A. I. Mohamed, 2020. "Distributed Battery Energy Storage Co-Operation for Renewable Energy Sources Integration," Energies, MDPI, vol. 13(20), pages 1-24, October.
    4. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2021. "Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review," Energies, MDPI, vol. 14(21), pages 1-45, November.
    5. Xiang Li & Zhenya Ji & Fengkun Yang & Zhenlan Dou & Chunyan Zhang & Liangliang Chen, 2022. "A Distributed Two-Level Control Strategy for DC Microgrid Considering Safety of Charging Equipment," Energies, MDPI, vol. 15(22), pages 1-20, November.

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