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Multi-Agent System-Based Microgrid Operation Strategy for Demand Response

Author

Listed:
  • Hee-Jun Cha

    (Department of Electrical Engineering, Inha University, 100, Inha-ro, Nam-gu, Incheon 402-751, Korea)

  • Dong-Jun Won

    (Department of Electrical Engineering, Inha University, 100, Inha-ro, Nam-gu, Incheon 402-751, Korea)

  • Sang-Hyuk Kim

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

  • Il-Yop Chung

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

  • Byung-Moon Han

    (Department of Electrical Engineering, Myongji University, 116, Myongji-ro, Cheoin-gu, Yongin-si, Gyeonggi-do 449-728, Korea)

Abstract

The microgrid and demand response (DR) are important technologies for future power grids. Among the variety of microgrid operations, the multi-agent system (MAS) has attracted considerable attention. In a microgrid with MAS, the agents installed on the microgrid components operate optimally by communicating with each other. This paper proposes an operation algorithm for the individual agents of a test microgrid that consists of a battery energy storage system (BESS) and an intelligent load. A microgrid central controller to manage the microgrid can exchange information with each agent. The BESS agent performs scheduling for maximum benefit in response to the electricity price and BESS state of charge (SOC) through a fuzzy system. The intelligent load agent assumes that the industrial load performs scheduling for maximum benefit by calculating the hourly production cost. The agent operation algorithm includes a scheduling algorithm using day-ahead pricing in the DR program and a real-time operation algorithm for emergency situations using emergency demand response (EDR). The proposed algorithm and operation strategy were implemented both by a hardware-in-the-loop simulation test using OPAL-RT and an actual hardware test by connecting a new distribution simulator.

Suggested Citation

  • Hee-Jun Cha & Dong-Jun Won & Sang-Hyuk Kim & Il-Yop Chung & Byung-Moon Han, 2015. "Multi-Agent System-Based Microgrid Operation Strategy for Demand Response," Energies, MDPI, vol. 8(12), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12430-14286:d:60827
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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.
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    Cited by:

    1. Mayank Singh & Rakesh Chandra Jha, 2019. "Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization," Energies, MDPI, vol. 12(3), pages 1-25, January.
    2. 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.
    3. Yuan Hong & Shengbin Wang & Ziyue Huang, 2017. "Efficient Energy Consumption Scheduling: Towards Effective Load Leveling," Energies, MDPI, vol. 10(1), pages 1-27, January.
    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. Yongming Zhang & Zhe Yan & Feng Yuan & Jiawei Yao & Bao Ding, 2018. "A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings," Energies, MDPI, vol. 12(1), pages 1-17, December.
    6. Anh-Duc Nguyen & Van-Hai Bui & Akhtar Hussain & Duc-Huy Nguyen & Hak-Man Kim, 2018. "Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System," Energies, MDPI, vol. 11(6), pages 1-18, June.
    7. Hyung-Joon Kim & Mun-Kyeom Kim, 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response," Energies, MDPI, vol. 12(21), pages 1-28, October.
    8. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Diffusion Strategy-Based Distributed Operation of Microgrids Using Multiagent System," Energies, MDPI, vol. 10(7), pages 1-21, July.
    9. Hossein Abedini & Tommaso Caldognetto & Paolo Mattavelli & Paolo Tenti, 2020. "Real-Time Validation of Power Flow Control Method for Enhanced Operation of Microgrids," Energies, MDPI, vol. 13(22), pages 1-19, November.
    10. V, Kavitha & V, Malathi & Guerrero, Josep M. & Bazmohammadi, Najmeh, 2022. "Energy management system using Mimosa Pudica optimization technique for microgrid applications," Energy, Elsevier, vol. 244(PA).
    11. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    12. 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).
    13. Fan, Dongming & Ren, Yi & Feng, Qiang & Liu, Yiliu & Wang, Zili & Lin, Jing, 2021. "Restoration of smart grids: Current status, challenges, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    14. Haesum Ali & Akhtar Hussain & Van-Hai Bui & Jinhong Jeon & Hak-Man Kim, 2019. "Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy," Energies, MDPI, vol. 12(19), pages 1-18, September.
    15. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.
    16. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.

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