Photovoltaic Scene Generation via Self-Attention Augmented Wasserstein Generative Adversarial Networks

With the gradual spread of renewable energy sources such as photovoltaics, accurate modeling of PV output with uncertainty becomes critical. As a key method to solve this challenge, scenario generation has received a lot of attention. This paper studies the application of generation model based on generative adversarial network (GAN) and its improved version WGAN-GP in generating renewable energy power scenarios. In order to deal with the complexity of dynamic weather and nonlinear power conversion, self-attention mechanism is introduced to improve the ability of the model to capture long-distance dependencies, and enhance the modeling ability of the generator and discriminator. Through experiments, the model shows a good generation effect, can efficiently capture the complex characteristics of renewable energy output, and provides reliable data support for probabilistic prediction and risk assessment of power system. The experimental results show that the model has high diversity and authenticity in the generation of photovoltaic power generation scenes, demonstrating its potential and effectiveness in practical applications.