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Multi-agent based multi objective renewable energy management for diversified community power consumers

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  • Xiong, Linyun
  • Li, Penghan
  • Wang, Ziqiang
  • Wang, Jie

Abstract

This paper proposes a multi-objective renewable energy management scheme to satisfy the needs of diversified community power consumers, wherein the multi-agent system is employed for coordinating and controlling the power generation and consumption units. Firstly, a multi-agent based microgrid and home energy management system is constructed. Subsequently, system models for renewable energy sources, electrical vehicles, residential loads and the cost function are constructed to form the microgrid operation constraints. With these constraints, three optimization models are proposed to minimize the electricity bills, the power purchased from the main grid, and optimize the power quality. The three objectives are proposed to reflect the power consumer’s needs for cost saving, green consumption, and power reliability, respectively. Meanwhile, a coordination strategy which balances the three objectives is proposed. After that, a novel multi agent system is proposed to realize the proposed objectives. Four case studies and a sensitivity analysis are conducted to verify the effectiveness of the proposed method. The case study results show that: in electricity minimization, 2–6.5% of total electricity bills can be saved; in main grid power consumption minimization, the peaks of the power consumption profiles are shaved while the valleys of the profiles are filled; in power quality enhancement, the steady state frequency drop is reduced for 0.35 Hz to 0.38 Hz. The sensitivity analysis shows that communities equipped with energy storage systems accounting for 30% of the total load can achieve best optimization outcomes; the increase in renewable energy usage will lead to higher electricity bills and poorer power quality if the renewable energy sources are limited, while the availability of more renewable energy sources will compensate for the negative effects of using renewable energy sources.

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  • Xiong, Linyun & Li, Penghan & Wang, Ziqiang & Wang, Jie, 2020. "Multi-agent based multi objective renewable energy management for diversified community power consumers," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318276
    DOI: 10.1016/j.apenergy.2019.114140
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    14. Madler, Jochen & Harding, Sebastian & Weibelzahl, Martin, 2023. "A multi-agent model of urban microgrids: Assessing the effects of energy-market shocks using real-world data," Applied Energy, Elsevier, vol. 343(C).
    15. Yu, Biying & Sun, Feihu & Chen, Chen & Fu, Guanpeng & Hu, Lin, 2022. "Power demand response in the context of smart home application," Energy, Elsevier, vol. 240(C).
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    17. Meryem Hamidi & Abdelhadi Raihani & Omar Bouattane, 2023. "Sustainable Intelligent Energy Management System for Microgrid Using Multi-Agent Systems: A Case Study," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    18. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    19. Wang, Xiaobo & Huang, Wentao & Li, Ran & Tai, Nengling & Zong, Ming, 2023. "Frequency-based demand side response considering the discontinuity of the ToU tariff," Applied Energy, Elsevier, vol. 348(C).
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    21. Kawakib Arar Tahir & Javier Ordóñez & Juanjo Nieto, 2024. "Exploring Evolution and Trends: A Bibliometric Analysis and Scientific Mapping of Multiobjective Optimization Applied to Hybrid Microgrid Systems," Sustainability, MDPI, vol. 16(12), pages 1-29, June.

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