基于模糊C-均值聚类的时序概率潮流快速计算方法

为衡量光伏出力与负荷的时序变化特性对电力系统运行状态的影响,基于模糊C-均值聚类算法提出一种时序概率潮流快速计算方法.将一天分为24个时段,采用自适应扩散核密度估计法分别建立光伏出力与负荷的概率密度分布模型,提高概率模型局部适应性,并通过Copula理论描述二者之间的相关关系;利用模糊C-均值聚类法划分光伏出力与负荷场...

Fast calculation method of time sequence probabilistic power flow based on fuzzy C-means clustering

In order to measure the influence of time sequence changing characteristics of photovoltaic output and load on the operation state of power system, a fast calculation method of time sequence probabilistic power flow is proposed based on fuzzy C-means clustering algorithm. A day is divided into 24 periods, and the adaptive diffusion kernel density estimation method is adopted to build the probability density distribution models of photovoltaic output and load respectively, which improves the local adaptability of the probability model, and the correlation between them is described by Copula theory. The fuzzy C-means clustering method is used to divide the photovoltaic output and load scenarios, and the scenario clustering center and scenario occurrence probability are used to substitute Monte Carlo simulation process for probabilistic power flow calculation, which greatly reduces the calculation times. The simulation and analysis are carried out based on the measured data of a certain place in Northwest China and IEEE 30-bus system, and the results show that the proposed method can improve the calculation speed of time sequence probabilistic power flow on the premise of ensuring accuracy.