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A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid

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  • Xi, Lei
  • Yu, Tao
  • Yang, Bo
  • Zhang, Xiaoshun
  • Qiu, Xuanyu

Abstract

This paper proposes a novel electric power autonomy to satisfy the requirement of power generation optimization of smart grid and decentralized energy management system. A decentralized virtual tribes control (VTC) is developed which can effectively coordinate the regional dispatch centre and the distributed energy. Then a wolf pack hunting (WPH) strategy based VTC (WPH-VTC) is designed through combining the multi-agent system stochastic game and multi-agent system collaborative consensus, which is called the multi-agent system stochastic consensus game, to achieve the coordination and optimization of the decentralized VTC, such that different types of renewable energy can be effectively integrated into the electric power autonomy. The proposed scheme is implemented on a flexible and dynamic multi-agent stochastic game-based VTC simulation platform, which control performance is evaluated on a typical two-area load–frequency control power system and a practical Guangdong power grid model in southern China. Simulation results verify that it can improve the closed-loop system performances, increase the utilization rate of the renewable energy, reduce the carbon emissions, and achieve a fast convergence rate with significant robustness compared with those of existing schemes.

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  • Xi, Lei & Yu, Tao & Yang, Bo & Zhang, Xiaoshun & Qiu, Xuanyu, 2016. "A wolf pack hunting strategy based virtual tribes control for automatic generation control of smart grid," Applied Energy, Elsevier, vol. 178(C), pages 198-211.
    • Handle: RePEc:eee:appene:v:178:y:2016:i:c:p:198-211
    DOI: 10.1016/j.apenergy.2016.06.041
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    Cited by:

    1. Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    2. Ren, Yi & Fan, Dongming & Feng, Qiang & Wang, Zili & Sun, Bo & Yang, Dezhen, 2019. "Agent-based restoration approach for reliability with load balancing on smart grids," Applied Energy, Elsevier, vol. 249(C), pages 46-57.
    3. Yin, Linfei & Yu, Tao & Zhang, Xiaoshun & Yang, Bo, 2018. "Relaxed deep learning for real-time economic generation dispatch and control with unified time scale," Energy, Elsevier, vol. 149(C), pages 11-23.
    4. Pappachen, Abhijith & Peer Fathima, A., 2017. "Critical research areas on load frequency control issues in a deregulated power system: A state-of-the-art-of-review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 163-177.
    5. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    6. Huaizhi Wang & Xian Zhang & Qing Li & Guibin Wang & Hui Jiang & Jianchun Peng, 2018. "Recursive Method for Distribution System Reliability Evaluation," Energies, MDPI, vol. 11(10), pages 1-15, October.
    7. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).

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