An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events
An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events
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DOI：
10.24251/hicss.2020.381
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Yijun Xu;M. Korkali;L. Mili;Xiao Chen
中科院分区：
文献类型：
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作者：
Yijun Xu;M. Korkali;L. Mili;Xiao Chen
低频、高影响罕见事件引起的电力系统故障风险评估对电力系统规划者和运行者至关重要。本文提出了一种基于多可信度模型的多代理风险评估方法，用于概率潮流分析中罕见事件下的风险评估。具体而言，多个多项式混沌扩展为基础的代理模型构建再现电力系统响应的随机变化的负荷和随机发生的组件停电。然后，这些代理以可忽略的计算成本传播大量样本，从而有效地筛选出与高风险罕见事件相关的样本。然而，由替代产生的结果，可能会偏向于位于低概率尾部区域的样本，这是关键的电力系统风险评估。为了解决这个问题，原始的高保真电力系统模型，采用微调的估计结果的低保真度的替代品，重新评估只有一小部分的样本。这种多保真度模型方法在不牺牲计算精度的情况下，大大提高了传统蒙特卡罗方法在计算罕见事件下的风险事件概率时的计算效率。
Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.