基于改进K-means聚类的风光发电场景划分
摘要：针对可再生能源发电，尤其是风力、光伏发电的出力不确定性问题，结合改进后的K-means聚类方法对发电的状态进行场景划分。首先建立风力、光伏发电的不确定性模型，选用合适的概率密度函数进行拟合；之后结合密度聚类和提出的混合评价函数，对基本的K-means聚类算法进行改进，解决了算法的初始聚类中心和聚类个数难以选取的问题；然后运用改进后的K-means聚类对某地风力、光伏发电场景进行聚类划分，从而将不确定性问题转化成确定性问题。最后通过对场景划分的算例进行分析，验证了所提方法的工程实用性。
Abstract:In view of the uncertainty of power generation in renewable energy, especially wind power and photovoltaic power generation, the improved K-means clustering method was used to segment the state of power generation. Firstly, the uncertainty model of wind power and photovoltaic power generation was established, and the appropriate probability density function was used to fit. Then the basic K-means clustering algorithm was improved by combining density clustering and proposed hybrid evaluation function, to solve the problem that the initial clustering center and the number of clusters were difficult to select. The improved K-means clustering was used to cluster the wind and photovoltaic scenes in a certain place, thus transforming the uncertainty problem into a deterministic problem. Finally, the practicability of the proposed method was verified by analyzing an example of scenario division.
标题：基于改进K-means聚类的风光发电场景划分
title:Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering
作者：宋学伟, 刘玉瑶
authors:Xuewei SONG, Yuyao LIU
关键词：风力发电,光伏发电,密度聚类,K-means聚类,场景划分,
keywords:wind power generation,photovoltaic power generation,density clustering,K-means clustering,scenario division,
发表日期：2020-12-31
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