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Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization

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  • Zhou, Yu
  • Li, Zhengshuo
  • Wang, Guangrui

Abstract

This paper suggests leveraging the reactive power range embedded in wind farms to improve safety and optimality during the power system reactive power optimization process. First, three typical reactive power range approaches are analysed, and a two-stage robust linear optimization-based reactive power range evaluation method is proposed. This method yields a reactive power range that can be leveraged by an upstream system operator while ensuring wind farm operational security against wind farm uncertainty. Simplified DistFlow equations are employed to balance computational accuracy and cost. Next, an uncertain reactive power optimization problem that involves the wind farm reactive power range is introduced, through which system operators ensure system-wide security and optimality in the base case and against any possible deviation caused by system-wide load uncertainty and uncertain renewable generation. Steady-state models of automatic generation control and local voltage control against deviations are captured. This uncertain reactive power optimization problem is recast as a deterministic optimization problem that is readily solvable. Case studies confirm that even with notable uncertainty, wind farms are competent reactive power resources that provide a considerable reactive power range.

Suggested Citation

  • Zhou, Yu & Li, Zhengshuo & Wang, Guangrui, 2021. "Study on leveraging wind farms' robust reactive power range for uncertain power system reactive power optimization," Applied Energy, Elsevier, vol. 298(C).
  • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921005717
    DOI: 10.1016/j.apenergy.2021.117130
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    Cited by:

    1. Utama, Christian & Meske, Christian & Schneider, Johannes & Ulbrich, Carolin, 2022. "Reactive power control in photovoltaic systems through (explainable) artificial intelligence," Applied Energy, Elsevier, vol. 328(C).
    2. Zhang, Zhaoyi & Shang, Lei & Liu, Chengxi & Lai, Qiupin & Jiang, Youjin, 2023. "Consensus-based distributed optimal power flow using gradient tracking technique for short-term power fluctuations," Energy, Elsevier, vol. 264(C).

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