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A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability

Author

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  • Xiping Ma

    (Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China)

  • Wenxi Zhen

    (Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China)

  • Rui Xu

    (Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China)

  • Xiaoyang Dong

    (Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China)

  • Yaxin Li

    (Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China)

Abstract

With the integration of large-scale wind power clusters into the power system, wind farms play a crucial role in grid reactive power regulation. However, the range of its reactive power remains uncertain, posing challenges in formulating a viable program for regulating reactive power to ensure the safe and cost-effective operation of the power system. Based on this, this paper develops a bi-level reactive power optimization for wind clusters integrating the power grid while considering the reactive capability. Firstly, this paper carries out a refined analysis of the wind power clusters, taking into account the characteristics of different areas to estimate the exact value of the reactive power capability in wind power clusters. Secondly, a bi-level reactive power optimization model is established. The upper-layer optimization aims to minimize active losses and voltage deviation in power system operation, while the lower-layer optimization focuses on maximizing reactive power margin utilization in wind farms. To solve this bi-level optimization model, an improved artificial fish swarm algorithm (AFSA) is employed, which decouples real variables and integer variables to enhance the optimization ability of the algorithm. Finally, the effectiveness of our proposed optimization strategy and algorithm is validated through the simulation results.

Suggested Citation

  • Xiping Ma & Wenxi Zhen & Rui Xu & Xiaoyang Dong & Yaxin Li, 2024. "A Bi-Level Reactive Power Optimization for Wind Clusters Integrating the Power Grid While Considering the Reactive Capability," Energies, MDPI, vol. 17(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3910-:d:1452063
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    References listed on IDEAS

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    1. Ghasemi, Mojtaba & Aghaei, Jamshid & Akbari, Ebrahim & Ghavidel, Sahand & Li, Li, 2016. "A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems," Energy, Elsevier, vol. 107(C), pages 182-195.
    2. Jin, Haowei & Guo, Jue & Tang, Lei & Du, Pei, 2024. "Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix," Energy, Elsevier, vol. 286(C).
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