文章摘要
焦田利,章坚民,李熊,等.基于空间相关性的大规模分布式用户光伏空间分群方法[J].电力系统自动化,2019,43(21):97-102. DOI: 10.7500/AEPS20180422003.
JIAO Tianli,ZHANG Jianmin,LI Xiong, et al.Spatial Clustering Method for Large-scale Distributed User Photovoltaics Based on Spatial Correlation[J].Automation of Electric Power Systems,2019,43(21):97-102. DOI: 10.7500/AEPS20180422003.
基于空间相关性的大规模分布式用户光伏空间分群方法
Spatial Clustering Method for Large-scale Distributed User Photovoltaics Based on Spatial Correlation
DOI:10.7500/AEPS20180422003
关键词: 分布式用户光伏  光伏空间分群  功率预测  空间相关性  K-means聚类
KeyWords: distributed user photovoltaic(PV)  spatial clustering of photovoltaics  power prediction  spatial relativity  K-means clustering
上网日期:2019-07-09
基金项目:国家自然科学基金资助项目(51677047)
作者单位E-mail
焦田利 杭州电子科技大学自动化学院, 浙江省杭州市 310018  
章坚民 杭州电子科技大学自动化学院, 浙江省杭州市 310018 zhangjmhzcn@hdu.edu.cn 
李熊 国网浙江省电力有限公司电力科学研究院, 浙江省杭州市 310014  
朱军 国网浙江省电力有限公司杭州市供电公司, 浙江省杭州市 310020  
叶方彬 国网浙江省电力有限公司电力科学研究院, 浙江省杭州市 310014  
麻吕斌 浙江华云信息科技有限公司, 浙江省杭州市 310012  
摘要:
      提出一种面向大规模分布式用户光伏出力预测的光伏空间分群方法,目的在于为气象站点优化部署或多光伏用户基于“空间—时间关联”的功率预测提供依据。将气象对光伏出力的影响分为大气候和小气候2类。大气候主要是日照或5类天气类型影响,通过光伏实际出力占额定出力的比例来划分,从而将历史数据时段划分为5类天气类型样本群;小气候认为是光伏安装高程、温度、湿度以及周围地理环境等广义小气候影响。对5类天气类型历史样本群,进行空间相关的聚类分析,得到用户光伏地域分块划分。综合分块中不合群的用户光伏点数量和分块气象一致性来决定最优地域分块方案为用户光伏空间分群策略。以具有丰富气候带和地貌的某市遍布全境的2 887个分布式用户光伏群为例,分群方法得到了较好的验证。
Abstract:
      A spatial clustering method for large-scale distributed user photovoltaic output prediction is proposed. The aim is to provide a basis for the deployment of meteorological stations and power prediction based on “space-time correlation” for photovoltaic users. The impact of meteorology on photovoltaic output is divided into major climate factor and microclimate factor. The major climate factor is mainly attributed by sunlight in five types of weather, which is modeled by the ratio of the actual photovoltaic output to the rated output so that the historical periodical data can be divided into five weather type sample groups. The microclimate factor is considered as the general micro-climate influence of photovoltaic installation elevation, temperature, humidity and surrounding geographic environment. Spatial correlation clustering analysis is carried out for five types of weather historical sample groups, and user photovoltaic region partition is obtained. Considering the number of photovoltaic sites that are not clustered in sub-blocks and the meteorological consistency of sub-blocks, the optimal regional sub-blocking plan is determined as a photovoltaic spatial clustering strategy. Taking 2 887 distributed user photovoltaic clusters across the whole city with rich climatic zones and landforms as examples, the proposed clustering method has been well verified.
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