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Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm

Author

Listed:
  • Sang-Ji Lee

    (Central Area Branch Office, KPX, 32, Yongam 6-gil, Buk-myeon, Dongnam-gu, Cheonan-si, Chungcheongnam-do 31242, Korea)

  • Jin-Young Choi

    (LG CNS, 28F, FKI Tower, 24, Yeoui-daero, Yeongdeungpo-gu, Seoul 07320, Korea)

  • Hyung-Joo Lee

    (Department of Electrical Engineering, Inha University, 100, Inha-ro, Nam-gu, Incheon 22212, Korea)

  • Dong-Jun Won

    (Department of Electrical Engineering, Inha University, 100, Inha-ro, Nam-gu, Incheon 22212, Korea)

Abstract

Microgrids (MGs) in which power generation and consumption occur locally have gained prominence, and MG demonstration tests have been widely carried out. In accordance with the increase in the number of MG installations, studies regarding the cooperative control of multiple MGs are proceeding in various forms. In this paper, the distributed control strategy of a multi-microgrid (MMG) is proposed. Distributed control is the method in which agents of the electric power facility autonomously control their facility through communication with the neighboring agents only. In this process, a consensus algorithm is utilized to obtain the global information required to control the overall system. In this distributed control strategy, a single MG is operated at an optimal economic point using the equality incremental cost constraints while maintaining the balance between the generation and demand. The control strategy of a MMG is that the flow of the point of common coupling (PCC) is maintained at a particular value needed by the utility and the internal change in power is distributed to the MGs according to their reserves. The proposed algorithm is verified in the MG level and the MMG level through a simulation model using PSCAD/EMTDC software in the C language.

Suggested Citation

  • Sang-Ji Lee & Jin-Young Choi & Hyung-Joo Lee & Dong-Jun Won, 2017. "Distributed Coordination Control Strategy for a Multi-Microgrid Based on a Consensus Algorithm," Energies, MDPI, vol. 10(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1017-:d:105096
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    Citations

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    Cited by:

    1. Yusuf Izmirlioglu & Loc Pham & Tran Cao Son & Enrico Pontelli, 2024. "A Survey of Multi-Agent Systems for Smartgrids," Energies, MDPI, vol. 17(15), pages 1-62, July.
    2. Rosero, D.G. & Díaz, N.L. & Trujillo, C.L., 2021. "Cloud and machine learning experiments applied to the energy management in a microgrid cluster," Applied Energy, Elsevier, vol. 304(C).
    3. Su-Been Hong & Thai-Thanh Nguyen & Jinhong Jeon & Hak-Man Kim, 2020. "Distributed Operation of Microgrids Considering Secondary Frequency Restoration Based on the Diffusion Algorithm," Energies, MDPI, vol. 13(12), pages 1-14, June.
    4. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Dat Thanh Tran & Al Faris Habibullah & Kyeong-Hwa Kim, 2022. "Seamless Power Management for a Distributed DC Microgrid with Minimum Communication Links under Transmission Time Delays," Sustainability, MDPI, vol. 14(22), pages 1-29, November.
    6. Thanh Van Nguyen & Kyeong-Hwa Kim, 2019. "Power Flow Control Strategy and Reliable DC-Link Voltage Restoration for DC Microgrid under Grid Fault Conditions," Sustainability, MDPI, vol. 11(14), pages 1-27, July.
    7. Giuseppe Barone & Giovanni Brusco & Alessandro Burgio & Daniele Menniti & Anna Pinnarelli & Michele Motta & Nicola Sorrentino & Pasquale Vizza, 2018. "A Real-Life Application of a Smart User Network," Energies, MDPI, vol. 11(12), pages 1-23, December.
    8. Claudio Giovanni Mattera & Hamid Reza Shaker & Muhyiddine Jradi, 2019. "Consensus-Based Method for Anomaly Detection in VAV Units," Energies, MDPI, vol. 12(3), pages 1-17, February.

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