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Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty

Author

Listed:
  • Wang, Luhao
  • Zhang, Bingying
  • Li, Qiqiang
  • Song, Wen
  • Li, Guanguan

Abstract

This paper addresses the energy dispatch problem for multi-stakeholder multiple microgrids (MMGs) under uncertainty while considering independent market operators (IMOs) based energy trading forms. Firstly, a collaborative hierarchical dispatch framework is proposed to adapt to decentralized multiple stakeholders and coordinate energy trading between IMOs and microgrids (MGs). And then this framework is further decomposed into different independent optimization problems for stakeholders based on an analytical target cascading (ATC) algorithm, in which Lagrangian penalty terms are introduced to ensure consistency in energy trading. In these optimization problems, energy trading and production of an individual MG is formulated as a two-stage adaptive robust optimization model to hedge against uncertainties from random renewable energy sources and loads. Moreover, in order to realize parallel computing for all independent optimization problems, a diagonal quadratic approximation method is applied to linearize quadratic terms. We integrate the ATC algorithm with a column-and-constraint generation algorithm to derive robust energy dispatch schemes in parallel. Finally, simulations on different cases are conducted to testify the rationality and validity of the proposed robust distributed energy dispatch approach. The results show that the hierarchical energy dispatch framework with IMOs has advantages over that without IMOs. Moreover, the proposed approach can reduce the impacts of uncertainties on distributed decision making of multiple stakeholder and enhance the computational efficiency.

Suggested Citation

  • Wang, Luhao & Zhang, Bingying & Li, Qiqiang & Song, Wen & Li, Guanguan, 2019. "Robust distributed optimization for energy dispatch of multi-stakeholder multiple microgrids under uncertainty," Applied Energy, Elsevier, vol. 255(C).
  • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315326
    DOI: 10.1016/j.apenergy.2019.113845
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    Cited by:

    1. Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(C).
    2. Mohseni, Shayan & Pishvaee, Mir Saman, 2023. "Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization," Applied Energy, Elsevier, vol. 350(C).
    3. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    4. Singh, Kuljeet & Hachem-Vermette, Caroline, 2021. "Economical energy resource planning to promote sustainable urban design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    5. Yuwei Wang & Yuanjuan Yang & Liu Tang & Wei Sun & Huiru Zhao, 2019. "A Stochastic-CVaR Optimization Model for CCHP Micro-Grid Operation with Consideration of Electricity Market, Wind Power Accommodation and Multiple Demand Response Programs," Energies, MDPI, vol. 12(20), pages 1-33, October.
    6. Zhu, Ziqing & Wing Chan, Ka & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2021. "Real-Time interaction of active distribution network and virtual microgrids: Market paradigm and data-driven stakeholder behavior analysis," Applied Energy, Elsevier, vol. 297(C).
    7. Kong, Xiangyu & Liu, Dehong & Wang, Chengshan & Sun, Fangyuan & Li, Shupeng, 2020. "Optimal operation strategy for interconnected microgrids in market environment considering uncertainty," Applied Energy, Elsevier, vol. 275(C).
    8. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    9. Chang, Weiguang & Yang, Qiang, 2023. "Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading," Applied Energy, Elsevier, vol. 351(C).
    10. Qiu, Haifeng & You, Fengqi, 2020. "Decentralized-distributed robust electric power scheduling for multi-microgrid systems," Applied Energy, Elsevier, vol. 269(C).
    11. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    12. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    13. Gabriela Almeida Marcon Nora & Anete Alberton & Diego Hernando Florez Ayala, 2023. "Stakeholder theory and actor‐network theory: The stakeholder engagement in energy transitions," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 673-685, January.
    14. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    15. Hachem-Vermette, Caroline & Singh, Kuljeet, 2022. "Optimization of energy resources in various building cluster archetypes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    16. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    17. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    18. Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    19. Shama Naz Islam, 2024. "A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies," Energies, MDPI, vol. 17(7), pages 1-18, April.
    20. Chang, Weiguang & Dong, Wei & Yang, Qiang, 2023. "Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market," Applied Energy, Elsevier, vol. 336(C).
    21. He, Shuaijia & Gao, Hongjun & Liu, Junyong & Zhang, Xi & Chen, Zhe, 2022. "Distribution system planning considering peak shaving of energy station," Applied Energy, Elsevier, vol. 312(C).
    22. Qiu, Haifeng & Vinod, Ashwin & Lu, Shuai & Gooi, Hoay Beng & Pan, Guangsheng & Zhang, Suhan & Veerasamy, Veerapandiyan, 2023. "Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling," Applied Energy, Elsevier, vol. 350(C).
    23. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    24. Diptish Saha & Najmeh Bazmohammadi & Juan C. Vasquez & Josep M. Guerrero, 2023. "Multiple Microgrids: A Review of Architectures and Operation and Control Strategies," Energies, MDPI, vol. 16(2), pages 1-32, January.

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