基于改进K-means聚类的轨道交通基础设施分布式光伏发电典型场景生成及出力特性分析

Typical Scenario Generation and Output Characteristics Analysis of Distributed Photovoltaic Power Generation for Railway Transportation Infrastructure Based on Improved K-means Clustering

受限于自然条件,光伏出力具有很强的随机性。为准确评估轨道交通基础设施分布式光伏发电的光伏出力特性,提出一种基于改进K-means聚类算法的轨道交通基础设施分布式光伏发电典型场景生成方法,并基于此进行光伏出力特性分析。首先,基于分布式光伏发电设施以及气象数据,利用PVsyst软件模拟光伏发电出力数据。然后,针对基本K-means聚类算法聚类参数和初始聚类中心盲目性高的问题,结合聚类有效性指标(Density based index,DBI)和层次聚类对其进行改进并利用改进K-means聚类算法生成光伏典型日出力场景。最后,基于华中地区某地轨道交通基础设施分布式光伏系统对所提方法的有效性和优越性进行验证,并通过定性和定量分析各典型场景的出力特性揭示轨道交通基础设施分布式光伏出力的规律和特点。

Restricted by the natural conditions,the PV output is highly stochastic.In order to accurately assess the PV output characteristics of distributed PV power generation for rail transit infrastructure,a typical scenario generation method for distributed PV power generation for rail transit infrastructure based on the improved K-means clustering algorithm is proposed,and based on which the PV output characteristics are analysed.Firstly,based on the distributed PV power generation facilities as well as meteorological data,PV output data are simulated using PVsyst software.Then,for the problem of high blindness of the clustering parameters and initial clustering centre of the basic K-means clustering algorithm,it is improved by combining the density based index(DBI)index and hierarchical clustering,and a typical daily PV output scenario is generated by the improved K-means clustering algorithm.Finally,the effectiveness and superiority of the proposed method is verified based on the distributed PV system of rail transit infrastructure in central China,and the law and characteristics of the distributed PV output of rail transit infrastructure are revealed through qualitative and quantitative analysis of the output characteristics of each typical scenario.