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Reactive coordinated optimal operation of distributed wind generation

Author

Listed:
  • Xiang, Yue
  • Zhou, Lili
  • Huang, Yuan
  • Zhang, Xin
  • Liu, Youbo
  • Liu, Junyong

Abstract

Large-scale distributed wind generation (DWG) integration brings new challenges to the optimal operation of the distribution network. The reactive supports from wind turbines (WTs) and reactive power resources can improve both the operation economy and renewable energy consumption. In this paper, a multi-period reactive coordinated optimal operation model for DWG in the distribution network is established. The active-reactive power coordination characteristics of two typical types of WTs are considered and the operating strategy of reactive power resources is integrated in the model. The second-order cone programming (SOCP) is developed to transform the original nonlinear power flow model into a linear and convex model, which would significantly improve the power flow calculation efficiency for DWG penetrated distribution network. The simulation results show that the integration of reactive power resources can further promote the consumption of DWG and improve the operating profits of the distribution network.

Suggested Citation

  • Xiang, Yue & Zhou, Lili & Huang, Yuan & Zhang, Xin & Liu, Youbo & Liu, Junyong, 2021. "Reactive coordinated optimal operation of distributed wind generation," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s036054422032524x
    DOI: 10.1016/j.energy.2020.119417
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    References listed on IDEAS

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    1. Xiang, Yue & Zhou, Lili & Su, Yunche & Liu, Jichun & Huang, Yuan & Liu, Junyong & Lei, Xia & Sun, Zhang & Xu, Weiting & Zhang, Wentao, 2018. "Coordinated DG-Tie planning in distribution networks based on temporal scenarios," Energy, Elsevier, vol. 159(C), pages 774-785.
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    Citations

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    Cited by:

    1. Yi Zhang & Pengtao Liu, 2023. "Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm," Energies, MDPI, vol. 16(20), pages 1-20, October.
    2. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    3. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).

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