IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p1017-d105096.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/1017/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/1017/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1017-:d:105096. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.