# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 1 Introduction
[PERSON] [15] was the first to apply Generative Adversarial Networks (GANs) to the scenario generation of wind-solar power output, while [PERSON] [16] introduced a GAN loss function based on Wasserstein distance and incorporated a gradient penalty term (Wasserstein Generative Adversarial Networks, WGAN-GP), thereby enhancing the stability of GAN model training and the quality of generated outputs. These GAN-based methods effectively capture the stochastic characteristics of renewable energy; however, due to the use of random noise as input, the types of generated scenarios exhibit randomness and unpredictability, making them difficult to control. To address this issue, Conditional Generative Adversarial Networks (CGANs) incorporate additional label information into the inputs of both the generator and the discriminator, thus guiding the scenario generation process. References [17; 18] utilized renewable energy scenario prediction data as label information to develop a CGAN-based short-term scenario generation model for renewable energy. Although the conditional information within the CGAN model can guide scenario generation to some extent, the reliance on a single type of condition limits the interpretability of the adjustment process, while discretized condition information cannot achieve continuous and smooth control over scenario generation. Most of the current literature about scenario generation focuses on a single typical scenario of wind and solar loads [19]. Few studies on extreme scenario generation consider the wind-solar-load correlation. On the one hand, in the same region, the wind-solar-load has a certain correlation [20], and the consideration of the wind-solar-load correlation in the process of scenario generation can help improve its accuracy. On the other hand, in the process of power system operation, extreme scenarios often threaten the power supply and consumption of the power system. Studying methods for extreme scenario generation that consider the wind-solar-load correlation to maintain the power supply and consumption of the power system is very important [21]. In summary, this paper mainly focuses on the wind-solar-load extreme scene generation problem and proposes information-maximizing generative adversarial networks (InfoGANs) for extreme scene generation extraction that considers the source-load correlation. First, on the basis of the hierarchical clustering technique, historical wind-solar-load data are evaluated to analyze the joint probability distribution of the historical wind-solar-load output data and the distribution characteristics under different extreme weather conditions and to verify the correlation of the wind-solar-load data. Subsequently, the basis for determining extreme scenarios, such as long-term wind-solar-load output, low output, and abnormal fluctuations considering extreme weather, is proposed. In the InfoGAN algorithm, the extreme scenario set is extracted from the scenario set. The extreme scenarios are used as boundary conditions to simulate the operation of a power system, analyze the operation of the power system under extreme scenarios, and propose risk warning indicators for the power system to protect the power supply and consumption. Finally, the effectiveness of the method proposed in this paper is verified with practical examples.