Power System Risk Assessment Considering Wind-Solar Output Correlation

The large-scale incorporation of wind and solar energy presents notable difficulties for maintaining the stable operation of power grids. Due to similarities in regional climates, the output from wind and solar generation often shows a significant degree of correlation, which is essential for the scheduling and risk analysis of power systems. This study utilizes a non-parametric kernel density estimation approach to model wind and solar generation output, effectively capturing their variability. The Copula function is then employed to describe the correlation between wind and solar power outputs, and a Copula-based technique for modeling wind-solar output correlations is introduced. Using Monte Carlo simulations and K-means clustering algorithms, typical wind-solar generation scenarios are created. Through scenario analysis, this research builds a risk assessment framework that incorporates wind-solar correlation, developing an indicator system that focuses on two main factors: voltage violations at nodes and power flow violations in lines. Finally, an IEEE 33-node system is utilized for validation of the case study. The results indicate that the proposed risk assessment method, which accounts for wind-solar output correlations, provides a more accurate reflection of the real operational risks in the power grid, offering a valuable tool for power system scheduling decisions.