A DVAE-MFA Framework for Wind–Photovoltaic Scenario Generation Considering Fluctuation Characteristics and Spatial–Temporal Correlations

The large-scale integration of wind and solar photovoltaic (PV) power is a cornerstone of low-carbon, sustainable energy systems. However, the uncertainty of the output brings great challenges to the operation and dispatching of power systems. To clearly describe the fluctuation characteristics of wind–PV power output and the spatial–temporal coupling relationship, a two-stage wind–PV scenario-generation method is proposed. This method is based on Difference-Constrained Variational Autoencoder and Mixture of Factor Analyzers (DVAE-MFA). In the first stage, a differential constraint term is added to the reconstruction loss of the Variational Autoencoder (VAE) to build the Difference-Constrained Variational Autoencoder (DVAE) model. This helps the model better learn the fluctuation characteristics of output sequences. In the second stage, to solve the problem of the posterior distribution of the DVAE latent variables deviating from the standard normal prior, the Mixture of Factor Analyzers (MFA) model is introduced for secondary probability modeling of the latent space. The simulation experiment results show that the proposed DVAE-MFA model outperforms comparison models in terms of the scenario temporal fluctuation characteristics, spatial–temporal correlations, and statistical distribution similarity. The generated output scenarios can reproduce the features of historical data, providing high-quality data support for the stochastic optimization scheduling of sustainable power systems.