一种面向需求侧管理的用户负荷形态组合分析方法

摘要

Traditional electrical load analysis methods have disadvantages on gaining the representative load profile of a single customer as well as evaluating similarity between the different load patterns. This paper proposes a new combinational load analysis method by using the density based clustering to gain the representative load profile of a single customer and clustering different customers into different clusters according to the cosine similarity function. After applying this method to a real power system’s data set, the result is compared with the one using traditional analysis method and the applications in demand side management (DSM) are discussed. The results show that this new method could effectively find the representative load profiles and recognize the different load profiles, and thus confirm the value of this method in DSM applications.%　　提出了一种面向需求侧管理的用户负荷形态组合分析方法，改善了传统方法在单一用户典型负荷形态提取以及不同负荷形态相似性度量上的不足。该方法以密度聚类算法剔除单一用户异常日用电形态的影响，提取其典型负荷形态，再以余弦相似度度量不同用户典型负荷形态的相似波动特性，对用户进行分类。将该方法应用于实际电力负荷数据，将获得的用户分类结果与传统方法进行了比较，并以此为基础对负荷聚类在需求侧管理中的应用做了探讨。结果表明该方法能够有效地提取单一用户的典型负荷形态，并能够较好地度量不同负荷形态之间的相似波动特性，在需求侧管理中具有实用价值。