Photovoltaic output probability modeling method and system for describing photovoltaic multi-dimensional correlation based on R-Vine Copula

Abstract

The invention discloses a photovoltaic output probability modeling method and system for describing photovoltaic multi-dimensional correlation based on R-Vine Copula, relates to the field of uncertainty analysis of a power system, and aims to adapt to different actual use conditions and precision requirements by flexibly selecting a kernel density estimation bandwidth, so that a nonparametric model based on kernel density estimation can better provide an ideal marginal distribution model. Various Copula functions are hierarchically combined to more accurately represent the nonlinear correlation of photovoltaic output, and the description precision of the photovoltaic dependent structure is improved. According to the method, the defects that an existing traditional photovoltaic probability modeling method is single in marginal distribution and correlation form, large in fitting error, difficult to adapt to a nonlinear photovoltaic power generation scene and large in modeling and actual operation condition deviation are overcome, the randomness and correlation of photovoltaic output can be represented more accurately, and the method is suitable for large-scale popularization and application. Therefore, reliable and stable operation of the power system under the background of large-scale photovoltaic access is ensured.

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