基于改进型密度峰值算法的电力负荷聚类分析

在海量异质灵活资源参与含高比例新能源电网的运行调节背景下，针对用户用电特性分析的准确性、鲁棒性、计算效率的高要求问题，文中提出了一种基于特征指标完善和改进型密度峰值算法的电力负荷聚类分析方法。首先，通过提取9个完备的特征指标进行指标降维和完善以代替日负荷曲线组成的功率向量作为聚类输入；其次，采用熵权法对各项特征指标赋予权重保证负荷曲线的形态特征；最后，采用一种改进型密度峰值聚类算法对日负荷进行聚类分析。基于某地区实际负荷数据进行算例分析，结果表明文中所提方法在鲁棒性、聚类质量等方面相比于传统电力负荷聚类算法均具有优越性，聚类结果能真实有效地反映用户的实际用电特性，为制定精准的电力用户画像、需求侧响应策略提供了态势感知基础。

Clustering analysis of power load based on improved characteristic index and improved density peak algorithm

Under the background of massive heterogeneous flexible resources participating in the operation regulation of power grid with high proportion renewable energy, an analysis method for power load clustering based on complete feature index and improved density peak algorithm to the high requirements of accuracy, robustness and computational efficiency in the analysis of user power consumption characteristics is proposed in this paper. Firstly, nine complete characteristic indexes are extracted for index reduction and improvement to replace the power vector composed of daily load curves as clustering input. Secondly, the entropy weight method is used to assign weight to each characteristic index to ensure the morphological characteristics of load curves. Finally, an improved density peak clustering algorithm is applied to clustering analysis of daily load. Case studies are carried out based on a certain area actual load data, and the results show that the proposed method is superior to the traditional power load clustering algorithm in terms of robustness and clustering quality. The clustering results can reflect the actual power consumption characteristics of users truly and effectively, which will provide a situational awareness basis for formulating accurate user portrait and demand response strategy.