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Distributed Variable Droop Curve Control Strategies in Smart Microgrid

Author

Listed:
  • Changhong Deng

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Yahong Chen

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Jin Tan

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Pei Xia

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Ning Liang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Weiwei Yao

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

  • Yuan-ao Zhang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, Hubei, China)

Abstract

In micro grid (MG), active/reactive power sharing for all dis-patchable units is an important issue. To meet fluctuating loads’ active and reactive power demands, the units generally adopt primary P-f and Q-U droop control methods. However, at different state of charge (SOC) values, the capability of Lead Acid Battery Bank (LABB) based units to take loads varies in a large range; active power should not be shared according to the units P capacities in a constant ratio. Besides, influenced by the output and line impedance between units, reactive power is not able to be shared in proportion to the units Q capacities. Another problem, after MG power balance requirement is satisfied, frequency and voltage are deviating from their rated values thus power quality is reduced. This paper presents a new smart MG which is based on the multi agent system. To solve the problems mentioned above, P-f and Q-U droop curves are adjusted dynamically and autonomously in local agents. To improve the power quality, secondary restoration function is realized in a decentralized way, the computation tasks are assigned to local, the computation capability and communication reliability requirements for central PC are low, and operation reliability is high. Simulation results back the proposed methods.

Suggested Citation

  • Changhong Deng & Yahong Chen & Jin Tan & Pei Xia & Ning Liang & Weiwei Yao & Yuan-ao Zhang, 2017. "Distributed Variable Droop Curve Control Strategies in Smart Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:24-:d:124053
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    References listed on IDEAS

    as
    1. Demin Li & Bo Zhao & Zaijun Wu & Xuesong Zhang & Leiqi Zhang, 2017. "An Improved Droop Control Strategy for Low-Voltage Microgrids Based on Distributed Secondary Power Optimization Control," Energies, MDPI, vol. 10(9), pages 1-18, September.
    2. Hyeong-Jun Yoo & Thai-Thanh Nguyen & Hak-Man Kim, 2017. "Multi-Frequency Control in a Stand-Alone Multi-Microgrid System Using a Back-To-Back Converter," Energies, MDPI, vol. 10(6), pages 1-18, June.
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    Cited by:

    1. Kewei Cai & Belema Prince Alalibo & Wenping Cao & Zheng Liu & Zhiqiang Wang & Guofeng Li, 2018. "Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network," Energies, MDPI, vol. 11(11), pages 1-18, November.

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