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A multi-agent solution to energy management in hybrid renewable energy generation system

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  • Jun, Zeng
  • Junfeng, Liu
  • Jie, Wu
  • Ngan, H.W.

Abstract

This paper presents a multi-agent (MAS) solution to energy management in a distributed hybrid renewable energy generation system. An introduction and analysis of the system, including its constituents, characteristics and excitation mechanism are presented first. Then, validation of the MAS solution demonstrates its feasibility in meeting all the requirements of the system. Five kinds of agents are proposed to present the Energy Management System. Each agent is built as a three layered architecture. A macro MAS is also presented in detail with a framework containing its overall optimisation function based on JADE (Java Agent Development). Discussion about the agents’ behaviours and a scenario case study on the MAS and its testing process are included in the paper. It indicates that the MAS is a suitable solution for the energy management of the distributed hybrid renewable energy generation system.

Suggested Citation

  • Jun, Zeng & Junfeng, Liu & Jie, Wu & Ngan, H.W., 2011. "A multi-agent solution to energy management in hybrid renewable energy generation system," Renewable Energy, Elsevier, vol. 36(5), pages 1352-1363.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:5:p:1352-1363
    DOI: 10.1016/j.renene.2010.11.032
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    14. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
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    17. Zhao, Bo & Xue, Meidong & Zhang, Xuesong & Wang, Caisheng & Zhao, Junhui, 2015. "An MAS based energy management system for a stand-alone microgrid at high altitude," Applied Energy, Elsevier, vol. 143(C), pages 251-261.
    18. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    19. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    20. Pipattanasomporn, Manisa & Feroze, Hassan & Rahman, Saifur, 2012. "Securing critical loads in a PV-based microgrid with a multi-agent system," Renewable Energy, Elsevier, vol. 39(1), pages 166-174.
    21. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
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    23. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
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    25. 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.

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