文章摘要
焦田利,章坚民.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. DOI: 10.7500/AEPS20180422003.
JIAO Tianli,ZHANG Jianmin.Minimization Meteorological Stations Approach for Large Scale Distributed PVs Generation Output Forecasting by Comprehensive Spatial Relativity Clustering[J].Automation of Electric Power Systems. 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
Minimization Meteorological Stations Approach for Large Scale Distributed PVs Generation Output Forecasting by Comprehensive Spatial Relativity Clustering
DOI:10.7500/AEPS20180422003
关键词: 大规模分布式用户光伏  空间光伏分群  功率预测  空间相关性  K-means聚类  
KeyWords: large scale distributed photovoltaic(PV)  spatial clustering of PVs  power prediction  spatial relativity  K-means clustering analysis.
上网日期:2019-07-09
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位E-mail
焦田利 杭州电子科技大学 zhangjmhzcn@hdu.edu.cn 
章坚民 杭州电子科技大学 zhangjmhzcn@hdu.edu.cn 
摘要:
      本文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最少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;首先将气象对光伏出力的影响分为大气候和小气候两类:大气候主要是日照或五类天气类型影响,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五类天气类型样本群;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,对历史五类天气类型样本群,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丰富地貌的某县级市的遍布全境2887个分布式用户光伏群为例,得到了较好的验证。
Abstract:
      This paper proposes a spatial clustering method for large-scale geographic distributed photovoltaic stations (DPVs) in purpose of the power output prediction; first, the impacts of meteorology on photovoltaic output is divided into two categories, i.e., 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 to be the generalized by the microclimate influence as photovoltaic installation elevation, temperature, humidity, surrounding geographical environment, etc.; for the five historical periodical weather sample groups, spatially related clustering analysis is performed to obtain the photovoltaic station regional division; considering the number of photovoltaic sites that are not clustered in sub-blocks and the meteorological consistency of sub-regions, the optimal regional sub-blocking plan is determined as a photovoltaic sub-group strategy, that is the minimum plan for meteorological sites,or a spatial clustering for distributed PV to implement generation forecasting based on spatial-temporal correlation. Taking 2887 distributed photovoltaic power plant clusters across the whole territory of a county-level city with rich climatic zones and rich landforms as examples, it has been well verified.
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