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Distributed real-time economic dispatch for islanded microgrids with dynamic power demand

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  • Huang, Lei
  • Sun, Wei
  • Li, Qiyue
  • Li, Weitao

Abstract

The plugging and unplugging of high-power loads result in dynamically changing power demands for islanded microgrids. However, the existing distributed real-time dispatch assumes the power demand to be static. If the power demand changes during the distributed iterative solution process, existing dispatching algorithms would yield a mismatched solution. To address this problem, a distributed real-time dispatching algorithm is proposed to adapt to the dynamic power demand. First, the role of real-time dispatch in the hierarchical control of microgrids is analyzed. Then, the real-time dispatch is modeled as an optimization problem with a dynamic equality constraint. Combining the alternating direction method of multipliers (ADMM) and the distributed resource allocation (DRA) algorithms, the distributed iterative algorithms for solving the established optimization problem with static and dynamic constraints are deduced. Lastly, the effectiveness of the proposed algorithm is verified through case studies. Compared with the existing real-time dispatching algorithm, the proposed algorithm improves the economic efficiency of islanded microgrids with time-varying power demand.

Suggested Citation

  • Huang, Lei & Sun, Wei & Li, Qiyue & Li, Weitao, 2023. "Distributed real-time economic dispatch for islanded microgrids with dynamic power demand," Applied Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923005202
    DOI: 10.1016/j.apenergy.2023.121156
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    References listed on IDEAS

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    1. Jiayi, Huang & Chuanwen, Jiang & Rong, Xu, 2008. "A review on distributed energy resources and MicroGrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(9), pages 2472-2483, December.
    2. Lin, Zhiyi & Song, Chunyue & Zhao, Jun & Yin, Huan, 2022. "Improved approximate dynamic programming for real-time economic dispatch of integrated microgrids," Energy, Elsevier, vol. 255(C).
    3. Tayab, Usman Bashir & Roslan, Mohd Azrik Bin & Hwai, Leong Jenn & Kashif, Muhammad, 2017. "A review of droop control techniques for microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 717-727.
    4. L. Xiao & S. Boyd, 2006. "Optimal Scaling of a Gradient Method for Distributed Resource Allocation," Journal of Optimization Theory and Applications, Springer, vol. 129(3), pages 469-488, June.
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    Cited by:

    1. Huang, Lei & Sun, Wei & Li, Qiyue & Mu, Daoming & Li, Weitao, 2024. "A two-layer energy management for islanded microgrid based on inverse reinforcement learning and distributed ADMM," Energy, Elsevier, vol. 301(C).

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