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Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid

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  • Zhang, Xiaoshun
  • Yu, Tao
  • Yang, Bo
  • Li, Li

Abstract

This paper proposes a decentralized collaborative control framework of autonomous VGT (virtual generation tribe) for smart grid. A VGT-CCA (VGT based collaborative consensus algorithm) is firstly developed to solve the dynamic GCD (generation command dispatch) optimization of the AGC (automatic generation control) under an ideal communication network. Then a novel CCA VGT-RCCA (VGT based robust CCA) is designed by introducing the consensus gain functions and virtual consensus variables, which provides significant robustness to a practical communication network consisted with switching topology, transmission delay and noise. The performance of VGT-CCA and VGT-RCCA has been evaluated on a typical two-area load frequency control model and the China southern power grid model, respectively. Simulation results verify the effectiveness of the proposed algorithms.

Suggested Citation

  • Zhang, Xiaoshun & Yu, Tao & Yang, Bo & Li, Li, 2016. "Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid," Energy, Elsevier, vol. 101(C), pages 34-51.
    • Handle: RePEc:eee:energy:v:101:y:2016:i:c:p:34-51
    DOI: 10.1016/j.energy.2016.02.009
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    References listed on IDEAS

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    1. Lund, Henrik & Andersen, Anders N. & Østergaard, Poul Alberg & Mathiesen, Brian Vad & Connolly, David, 2012. "From electricity smart grids to smart energy systems – A market operation based approach and understanding," Energy, Elsevier, vol. 42(1), pages 96-102.
    2. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    3. Coronado Mondragon, Adrian E. & Coronado, Etienne S. & Coronado Mondragon, Christian E., 2015. "Defining a convergence network platform framework for smart grid and intelligent transport systems," Energy, Elsevier, vol. 89(C), pages 402-409.
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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. Qu, Kaiping & Yu, Tao & Huang, Linni & Yang, Bo & Zhang, Xiaoshun, 2018. "Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market," Energy, Elsevier, vol. 149(C), pages 779-791.
    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. Wang, Haiyang & Zhang, Chenghui & Li, Ke & Ma, Xin, 2021. "Game theory-based multi-agent capacity optimization for integrated energy systems with compressed air energy storage," Energy, Elsevier, vol. 221(C).
    5. Xianyong Zhang & Yaohong Huang & Li Li & Wei-Chang Yeh, 2018. "Power and Capacity Consensus Tracking of Distributed Battery Storage Systems in Modular Microgrids," Energies, MDPI, vol. 11(6), pages 1-25, June.
    6. Pengcheng Ni & Zhiyuan Ye & Can Cao & Zhimin Guo & Jian Zhao & Xing He, 2023. "Cooperative Game-Based Collaborative Optimal Regulation-Assisted Digital Twins for Wide-Area Distributed Energy," Energies, MDPI, vol. 16(6), pages 1-17, March.

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