Following rigorous accuracy and research on constructing scenario sets for combined wind and solar power output based on kernel density estimation and copula functions
Abstract
The inherent uncertainties of renewable energy sources—specifically their intermittent and volatile nature—create significant obstacles for power system planning. With the growing integration of renewables into the grid, mapping the precise interdependencies among wind generation, solar photovoltaic (PV) yield, and power demand has become crucial for designing and managing resilient power networks. To address the challenge of simulating representative scenarios for correlated wind and solar outputs, this study initially employs non-parametric kernel density estimation (KDE) to model extensive empirical datasets. Following rigorous accuracy and goodness-of-fit validation, specific KDE formulas for both wind and solar resources are derived. Subsequently, the research constructs various Copula-based joint probability models to represent the combined power generation of wind and solar farms. The performance of these models is comparatively assessed using Maximum Likelihood Estimation (MLE) alongside Akaike and Bayesian Information Criteria (AIC/BIC). reduce nce, the most suitable Copula function is identified to capture the joint probabilistic behavior of wind and PV systems. Ultimately, this optimal Copula model drives the generation of annual power output profiles for wind and solar energy. Computational experiments and validation procedures confirm that the simulated yearly scenarios accurately preserve the underlying correlation structures of the original data.