EN基于改进K-means聚类的风光发电场景划分
Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering
- 发电技术 2020年41卷第6期 页码：625-630
- 作者机构：
1.上海电机学院电气学院, 上海市 浦东新区 201306
2.国网东营市垦利区供电公司, 山东省 东营市 257000
- 作者简介：
[ "宋学伟(1994), 男, 硕士, 从事负荷预测、主动配电网优化调度、孤岛运行研究, soongxuewei@163.com" ]
[ "刘玉瑶(1994), 女, 工程师, 从事可再生能源、电网计量工作" ]
- 基金信息：国家自然科学基金项目(51477099);上海市自然科学基金项目(15ZR1417300);上海市自然科学基金项目(14ZR1417200)
DOI：10.12096/j.2096-4528.pgt.19090
中图分类号： TK81;TM615收稿：2020-05-25，
纸质出版：2020-12-31
- 稿件说明：
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宋学伟, 刘玉瑶. 基于改进K-means聚类的风光发电场景划分[J]. 发电技术, 2020,41(6):625-630.
Xuewei SONG, Yuyao LIU. Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering[J]. Power Generation Technology, 2020, 41(6): 625-630.
宋学伟, 刘玉瑶. 基于改进K-means聚类的风光发电场景划分[J]. 发电技术, 2020,41(6):625-630. DOI： 10.12096/j.2096-4528.pgt.19090.
Xuewei SONG, Yuyao LIU. Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering[J]. Power Generation Technology, 2020, 41(6): 625-630. DOI： 10.12096/j.2096-4528.pgt.19090.
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.
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references
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