摘要
随着光伏发电在电网中的应用越来越广泛,如何建立有效的光伏发电概率模型成为亟待研究的问题。传统的光伏发电模型一般基于参数估计,需要对辐照度的概率分布形式做出预先假设,且无法计及日总辐射与小时辐射间的内在"加和"约束。为了克服传统光伏发电模型存在的不足,利用解集及条件核密度估计技术提出一种新的光伏发电时序概率模型。该模型属于非参数模型,无需对辐照度的概率分布形式做出任何限制,不仅能够计及日/小时辐照度之间的时序相关性,而且能够计及日总辐射与小时辐射间的内在"加和"约束,从而可以更加精确地反映光伏发电的随机变动规律。算例分析表明该模型能够以更高精度反映辐照度的变动规律,因而具有明显的优越性和实用性。
With the grow ing use of photovoltaic（ PV） generation in pow er system,establishing effective probabilistic model for PV generation becomes an urgent problem to be settled. Conventional chronological probability models of PV generation are based on parametric estimation,w hich require to assume the probability distribution type of irradiance,and cannot consider the ＇additive constraint＇ betw een day＇s and hour＇s irradiance sequence. In order to overcome the draw backs of conventional models,this paper proposes a new photovoltaic sequence probabilistic model based on disaggregation theory and conditional kernel density estimation. Without limiting the probability distribution type of irradiance,the proposed nonparametric model is the non-parametric model,and can capture not only the chronological correlation,but also the＇additive constraint＇ betw een day＇s and hour＇s irradiance sequence,w hich can more accurately reflect the random fluctuation law of photovoltaic generation. The example analysis show s that the model can reflect the change rule of irradiance w ith higher precision,w hich has obvious superiority and practicality.
出处
《电力建设》
北大核心
2016年第7期27-32,共6页
Electric Power Construction
基金
国家自然科学基金项目(50977094)~~
关键词
光伏发电
概率模型
加和约束
时序相关性
条件核密度估计
photovoltaic generation
probabilistic model
additive constraint
chronological correlation
conditional kernel density estimation
