基于智能电能表的智慧城市峰值负荷概率估计

Peak load probability estimation of smart city based on smart meter

充足的变电站和馈线的容量规划主要取决于对未来高峰电力需求的准确估计。然而,传统的未来峰值需求估计是基于经验值进行度量,在多样性最大需求之后进行的,表示个体峰值消费水平和多居民需求多样性。针对智能城市智能电能表的特点,提出了一种基于细粒度智能电能表数据和用户社会人口统计数据的数据驱动的概率峰值用电量估算框架。特别是,在提出的方法中集成了四个主要阶段:负荷建模和抽样;通过所提出的可变截断R-vine连接方法;基于相关性的客户分组;归一化最大多样化需求估计和新客户的概率峰值需求估计。利用平均绝对百分误差和弹球损失函数定量地证明了该方法在点估计值和概率结果上的优越性。

Adequate capacity planning of substations and feeders mainly depends on accurate estimation of future peak power demand.However,the traditional future peak demand estimation is based on the empirical measurement,which is conducted after the maximum diversity demand,and represents the individual peak consumption level and the diversity of multi-resident demand.According to the characteristics of the smart meter in smart city,a data-driven peak probability power consumption estimation framework based on fine-grained smart meter data and user social demographic data is proposed in this paper.In particular,four major phases are integrated into the proposed approach,including load modeling and sampling,the proposed variable truncation R-vine linkage approach,correlation-based customer grouping,normalized maximum diversification demand estimation,and probabilistic peak demand estimation for new customers.The superiority of the proposed method in point estimation and probability results is proved quantitatively by means of mean absolute percentage error and marble loss function.