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Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

Author

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  • Cheol-Hee Yoo

    (School of Electrical Engineering, Kookmin University, 861-1, Jeongneung-dong, Seongbuk-gu, Seoul 136-702, Korea)

  • Il-Yop Chung

    (School of Electrical Engineering, Kookmin University, 861-1, Jeongneung-dong, Seongbuk-gu, Seoul 136-702, Korea)

  • Hak-Ju Lee

    (Korea Electric Power Research Institute, 105 Munji-Ro, Yuseong-Gu, Daejeon 305-760, Korea)

  • Sung-Soo Hong

    (School of Electrical Engineering, Kookmin University, 861-1, Jeongneung-dong, Seongbuk-gu, Seoul 136-702, Korea)

Abstract

Microgrids can be considered as controllable units from the utility point of view because the entities of microgrids such as distributed energy resources and controllable loads can effectively control the amount of power consumption or generation. Therefore, microgrids can make various contracts with utility companies such as demand response program or ancillary services. Another advantage of microgrids is to integrate renewable energy resources to low-voltage distribution networks. Battery energy storage systems (BESSs) can effectively compensate the intermittent output of renewable energy resources. This paper presents intelligent control schemes for BESSs and autonomous energy management schemes of microgrids based on the concept of multi-agent systems. The proposed control scheme consists of two layers of decision-making procedures. In the bottom layer, intelligent agents decide the optimal operation strategies of individual microgrid entities such as BESSs, backup generators and loads. In the upper layer, the central microgrid coordinator (MGCC) coordinates multiple agents so that the overall microgrid can match the load reduction requested by the grid operator. The proposed control scheme is applied to Korea Power Exchange’s Intelligent Demand Response Program.

Suggested Citation

  • Cheol-Hee Yoo & Il-Yop Chung & Hak-Ju Lee & Sung-Soo Hong, 2013. "Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management," Energies, MDPI, vol. 6(10), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:10:p:4956-4979:d:29076
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    References listed on IDEAS

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    1. Lagorse, Jeremy & Paire, Damien & Miraoui, Abdellatif, 2010. "A multi-agent system for energy management of distributed power sources," Renewable Energy, Elsevier, vol. 35(1), pages 174-182.
    2. 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.
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    Cited by:

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    2. Liuming Jing & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2016. "A Novel Protection Method for Single Line-to-Ground Faults in Ungrounded Low-Inertia Microgrids," Energies, MDPI, vol. 9(6), pages 1-16, June.
    3. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    4. Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
    5. Changsun Ahn & Huei Peng, 2013. "Decentralized and Real-Time Power Dispatch Control for an Islanded Microgrid Supported by Distributed Power Sources," Energies, MDPI, vol. 6(12), pages 1-16, December.
    6. Vu, Ba Hau & Chung, Il-Yop, 2022. "Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids," Renewable Energy, Elsevier, vol. 195(C), pages 1137-1154.
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    8. Javier Rodríguez-García & David Ribó-Pérez & Carlos Álvarez-Bel & Elisa Peñalvo-López, 2019. "Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy," Energies, MDPI, vol. 12(13), pages 1-23, July.
    9. Coelho, Vitor N. & Weiss Cohen, Miri & Coelho, Igor M. & Liu, Nian & Guimarães, Frederico Gadelha, 2017. "Multi-agent systems applied for energy systems integration: State-of-the-art applications and trends in microgrids," Applied Energy, Elsevier, vol. 187(C), pages 820-832.
    10. Tine L. Vandoorn & Jan Van de Vyver & Louis Gevaert & Lieven Degroote & Lieven Vandevelde, 2015. "Congestion Control Algorithm in Distribution Feeders: Integration in a Distribution Management System," Energies, MDPI, vol. 8(6), pages 1-20, June.
    11. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim, 2016. "Optimal Electric and Heat Energy Management of Multi-Microgrids with Sequentially-Coordinated Operations," Energies, MDPI, vol. 9(6), pages 1-18, June.
    12. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim & Yong Hoon Im & Jae Yong Lee, 2015. "Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations," Energies, MDPI, vol. 8(8), pages 1-20, August.
    13. Wang, Linyuan & Zhao, Lin & Mao, Guozhu & Zuo, Jian & Du, Huibin, 2017. "Way to accomplish low carbon development transformation: A bibliometric analysis during 1995–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 57-69.
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