Correlation analysis of combined wind and solar power output

As large-scale, high-proportion new energy sources are integrated into traditional power systems, their output, characterized by randomness and volatility, along with the susceptibility of power system components to extreme weather conditions, introduces new challenges to the stable operation of power systems. Therefore, in the operation of power systems, accurately and reasonably describing the correlation characteristics between wind and solar power outputs and accurately depicting changes in the operational state of power systems under extreme weather conditions have become important directions for conducting risk assessment research on power systems containing large-scale new energy. Regarding the temporal correlation characteristics between wind and solar power outputs, a multivariate normal distribution probability density function is used to describe the temporal correlation of wind and solar power outputs under two-dimensional variables. To explore the correlation under high-dimensional variables, this paper introduces the R-vine structure Copula function, which is formed based on actual wind and solar power output data. Finally, the correlation analysis of high-dimensional data is achieved according to this vine structure function.