A stochastic power flow-based static security assessment under uncertain scenarios

Yuyue Zhang, Xin Tian, Lina Zhang
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Abstract

With the gradual increase in the grid-connected capacity of renewable energy sources, the uncertainty in the operation of power systems has increased, posing challenges to static security assessment considering N-1 contingency scanning. To address this, this article first establishes a static security calculation model based on stochastic power flow. Then, it proposes stochastic component-level safety indexes and system-level safety indexes. Finally, using the analytic hierarchy process to analyze the obtained weighting coefficients, the article establishes a system of static security assessment indexes for power systems. A data-driven simulation method based on extreme gradient boosting (XGBoost) is proposed to tackle the high time consumption of multi-scenario static security assessment, which brings difficulties in model debugging and application. Case studies based on the IEEE 39-bus system demonstrate the effectiveness of the proposed model and the rapidity of the data-driven approach.
不确定情景下基于随机电力流的静态安全评估
随着可再生能源并网容量的逐步增加,电力系统运行的不确定性也随之增加,这给考虑 N-1 应急扫描的静态安全评估带来了挑战。为此,本文首先建立了基于随机功率流的静态安全计算模型。然后,提出了随机元件级安全指标和系统级安全指标。最后,文章利用层次分析法对得到的权重系数进行分析,建立了电力系统静态安全评估指标体系。针对多场景静态安全评估耗时长,给模型调试和应用带来困难的问题,提出了一种基于极梯度提升(XGBoost)的数据驱动仿真方法。基于 IEEE 39 总线系统的案例研究证明了所提模型的有效性和数据驱动方法的快速性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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