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Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

Author

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  • Hyun-Koo Kang

    (Korea Electric Power Research Institute (KEPRI), Korea Electric Power Company (KEPCO), 105 Munji-Ro, Yuseong-Gu, Daejeon 34056, Korea)

  • Il-Yop Chung

    (School of Electrical Engineering, Kookmin University, Seoul 136-702, Korea)

  • Seung-Il Moon

    (School of Electrical Engineering and Computer Scirnce, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 151-744, Korea)

Abstract

This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented.

Suggested Citation

  • Hyun-Koo Kang & Il-Yop Chung & Seung-Il Moon, 2015. "Voltage Control Method Using Distributed Generators Based on a Multi-Agent System," Energies, MDPI, vol. 8(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12411-14025:d:60435
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    References listed on IDEAS

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    1. Sang-Jin Oh & Cheol-Hee Yoo & Il-Yop Chung & Dong-Jun Won, 2013. "Hardware-in-the-Loop Simulation of Distributed Intelligent Energy Management System for Microgrids," Energies, MDPI, vol. 6(7), pages 1-21, July.
    2. Pepermans, G. & Driesen, J. & Haeseldonckx, D. & Belmans, R. & D'haeseleer, W., 2005. "Distributed generation: definition, benefits and issues," Energy Policy, Elsevier, vol. 33(6), pages 787-798, April.
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    Cited by:

    1. 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.
    2. Fabio Bignucolo & Alberto Cerretti & Massimiliano Coppo & Andrea Savio & Roberto Turri, 2017. "Impact of Distributed Generation Grid Code Requirements on Islanding Detection in LV Networks," Energies, MDPI, vol. 10(2), pages 1-16, January.
    3. Andrés Felipe Pérez Posada & Juan G. Villegas & Jesús M. López-Lezama, 2017. "A Scatter Search Heuristic for the Optimal Location, Sizing and Contract Pricing of Distributed Generation in Electric Distribution Systems," Energies, MDPI, vol. 10(10), pages 1-16, September.
    4. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.
    5. Qing Yang & Bo Zhang & Jiaquan Ran & Song Chen & Yanxiao He & Jian Sun, 2017. "Measurement of Line-to-Ground Capacitance in Distribution Network Considering Magnetizing Impedance’s Frequency Characteristic," Energies, MDPI, vol. 10(4), pages 1-14, April.
    6. Trinh Phi Hai & Hector Cho & Il-Yop Chung & Hyun-Koo Kang & Jintae Cho & Juyong Kim, 2017. "A Novel Voltage Control Scheme for Low-Voltage DC Distribution Systems Using Multi-Agent Systems," Energies, MDPI, vol. 10(1), pages 1-20, January.

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