Methodfor Modeling Photovoltaic Uncertainties Based on Improved Conditional Generative Adversarial Networks

With the extensive adoption of renewable energy, the uncertainty of photovoltaic (PV) output poses significant challenges to power grid scheduling and planning. To address the issue of PV output uncertainty, this paper proposes an improved Conditional Generative Adversarial Network (CGAN)-based modeling method for PV uncertainty. The method in-corporates the characteristic data of PV output as conditional information for the CGAN and employs the Wasserstein distance as the discriminator's loss function. Subsequently, through adver-sarial training of the CGAN, the generator learns the mapping relationship between random noise and the real historical dataset, enabling the efficient generation of scenarios closely resem-bling the real distribution. Comparative analysis and validation through case studies demonstrate that the proposed method accurately captures the uncertainty of renewable energy output.