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Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control

Author

Listed:
  • Chendan Li

    (Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

  • Mehdi Savaghebi

    (Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

  • Ernane A. A. Coelho

    (Núcleo de Pesquisa em Eletrônica de Potência (NUPEP), Faculdade de Engenharia Elétrica (FEELT), Universidade Federal de Uberlandia (UFU), Uberlandia, Minas Gerais 38400-902, Brazil)

  • Juan C. Vasquez

    (Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark)

Abstract

Recently, microgrids are attracting increasing research interest as promising technologies to integrate renewable energy resources into the distribution system. Although many works have been done on droop control applied to microgrids, they mainly focus on achieving proportional power sharing based on the power rating of the power converters. With various primary source for the distributed generator (DG), factors that are closely related to the operation cost, such as fuel cost of the generators and losses should be taken into account in order to improve the efficiency of the whole system. In this paper, a multiagent-based distributed method is proposed to minimize the operation cost in AC microgrids. In the microgrid, each DG is acting as an agent which regulates the power individually using a novel power regulation method based on frequency scheduling. An optimal power command is obtained through carefully designed consensus algorithm by using sparse communication links only among neighbouring agents. Experimental results for different cases verified that the proposed control strategy can effectively reduce the operation cost.

Suggested Citation

  • Chendan Li & Mehdi Savaghebi & Josep M. Guerrero & Ernane A. A. Coelho & Juan C. Vasquez, 2016. "Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control," Energies, MDPI, vol. 9(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:717-:d:77470
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    Citations

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    Cited by:

    1. Kaveh Dehghanpour & Christopher Colson & Hashem Nehrir, 2017. "A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids," Energies, MDPI, vol. 10(5), pages 1-25, May.
    2. Silvia Marzal & Raul González-Medina & Robert Salas-Puente & Emilio Figueres & Gabriel Garcerá, 2017. "A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids," Energies, MDPI, vol. 10(9), pages 1-25, August.
    3. Khan, Muhammad Waseem & Wang, Jie, 2017. "The research on multi-agent system for microgrid control and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1399-1411.
    4. Stephanus Antonius Ananda & Jyh-Cherng Gu & Ming-Ta Yang & Jing-Min Wang & Jun-Da Chen & Yung-Ruei Chang & Yih-Der Lee & Chen-Min Chan & Chia-Hao Hsu, 2016. "Multi-Agent System Fault Protection with Topology Identification in Microgrids," Energies, MDPI, vol. 10(1), pages 1-21, December.
    5. Haifeng Liang & Yue Dong & Yuxi Huang & Can Zheng & Peng Li, 2018. "Modeling of Multiple Master–Slave Control under Island Microgrid and Stability Analysis Based on Control Parameter Configuration," Energies, MDPI, vol. 11(9), pages 1-18, August.
    6. Woltmann, Stefan & Kittel, Julia, 2022. "Development and implementation of multi-agent systems for demand response aggregators in an industrial context," Applied Energy, Elsevier, vol. 314(C).

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