电力系统可靠性评估中基于相关变量的解集模型
首发时间:2020-03-25
摘要:节点负荷,风电出力等相关随机变量的引入对电力系统的运行安全构成了很大的挑战。而如何精确的构建这些相关性模型对于电力系统的可靠性评估至关重要。其中最常用的方法是估计多元联合概率密度函数(PDF),但这样的方法忽略了加和约束,为了考虑加和约束,本文提出了可以将集总变量(例如系统负荷)随机分解为非集总变量(例如节点负荷)的非参数解集(NPD)模型(即非参数条件PDF)。此外,基于非参数解集模型,本文还提出了一种改进的三阶段条件采样方法,可以通过NPD模型灵活的,准确的对相关性变量进行建模,并通过改进的RBTS和IEEE-RTS79进行可靠性评估来验证其有效性。
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Power system reliability assessment considering multiple dependent variables based on non-parametric disaggregation model
Abstract:The correlated random variation of the bus loads, wind powers, etc., has posed a challenge to the power system operation security. How to construct the accurate dependence model becomes vital for power system reliability evaluation. Most currently used methods in estimating the multivariate joint probability density function (PDF) ignore the aggregation constraint ,To incorporate the aggregation constraint, a non-parametric disaggregation (NPD) model (i.e. multivariate conditional PDF by non-parametric estimation) which can randomly disaggregating an aggregate variable (e.g. system load) into the non-aggregate variables (e.g. bus loads) is introduced. Moreover, an effective three-stage conditional sampling method is also presented for the proposed non-parametric disaggregation model (NPD). The dependence can be modeled flexibly and accurately by the NPD model, and its validity is verified by the reliability evaluation of the modified RBTS and IEEE-RTS79.?????
Keywords: reliability assessment non-parametric disaggregation ;kernel density estimation dependence
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