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Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy

Author

Listed:
  • Ju, Liwei
  • Zhang, Qi
  • Tan, Zhongfu
  • Wang, Wei
  • Xin, He
  • Zhang, Zehao

Abstract

The paper introduces multi-agent system (MAS) for intelligent scheduling model of multi-MG autonomy-cooperative operation. Firstly, wind power plants (WPPs), photovoltaic generators (PVs), conventional gas turbines, energy storage systems (ESSs) and controllable loads (CLs) are integrated into MGs with the price-based demand response (PBDR). Then, a 3-layer coordinate control system framework is designed for MGs, namely, a distribution management system (DMS), an MG central controller and MG controllable elements. Further, the influence of ESS modes on MG operation is discussed, namely, the longest life cycle (LLC) mode and the optimal economic efficient (OEE) mode. Finally, MORE MICROGRIDS project in Europe is taken as simulation platform. The results suggest: (1) the proposed strategy can be applied to the optimal scheduling of MG operation both in grid-connected and island modes. (2) Different ESS modes directly affect MGs scheduling, with the OEE mode optimizing MGs scheduling and with the LLC mode ensuring the ESS life cycle. (3) PBDR can smooth the load demand curve for promoting the output of WPPs and PVs. Note that the PBDR can stronger improve the MG operation in the ESS’s LLC mode, because the output of the ESS in the LLC mode is lower compared with the OEE mode.

Suggested Citation

  • Ju, Liwei & Zhang, Qi & Tan, Zhongfu & Wang, Wei & Xin, He & Zhang, Zehao, 2018. "Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy," Energy, Elsevier, vol. 157(C), pages 1035-1052.
    • Handle: RePEc:eee:energy:v:157:y:2018:i:c:p:1035-1052
    DOI: 10.1016/j.energy.2018.06.097
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    References listed on IDEAS

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    Cited by:

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    2. Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    3. Jihed Hmad & Azeddine Houari & Allal El Moubarek Bouzid & Abdelhakim Saim & Hafedh Trabelsi, 2023. "A Review on Mode Transition Strategies between Grid-Connected and Standalone Operation of Voltage Source Inverters-Based Microgrids," Energies, MDPI, vol. 16(13), pages 1-41, June.
    4. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    5. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    6. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    7. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    9. Yanbin Li & Yanting Sun & Junjie Zhang & Feng Zhang, 2022. "Optimal Microgrid System Operating Strategy Considering Variable Wind Power Outputs and the Cooperative Game among Subsystem Operators," Energies, MDPI, vol. 15(18), pages 1-20, September.

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