# Method for Wind-Solar-Load Extreme Scenario Generation Based on an Improved InfoGAN
## 1 Introduction  
To achieve the dual-carbon goal, China is actively developing a new type of power system with a high proportion of renewable energy access [1]. However, with the large-scale use of renewable energy sources, mainly wind power and solar power, the intermittency and volatility of the power output affect the reliability and stability of power system operation, resulting in great challenges in the optimal scheduling of power systems [2]. On the demand side, with the rapid growth of flexible loads, such as electric vehicles, the uncertainty of the load profile of a power system is also increasing [3]. To address the source-demand uncertainty in a power system, the scenario generation method, which is a type of uncertainty modeling method used to generate wind-solar-demand power profiles by using historical wind-solar-load data and referring to various related factors, such as weather and data, has gained substantial attention [4]. In recent years, with the increasing severity of the El Nino phenomenon, extreme weather has frequently occurred. Under extreme weather conditions, wind-solar are often characterized by long-term low output or unpredictable fluctuations in power output [5]. On the one hand, long-term low renewable energy output makes it difficult to guarantee the reliability with which a power system will supply power to uncertain loads. On theother hand, power systems face serious consumption problems when the renewable energy output remains high for a long period [6]. Therefore, in power system operations, both typical and extreme scenarios need to be considered. Therefore, a method for extreme scenario generation is needed to explore in depth the characteristics of different extreme scenarios, such as wind-solar-load, and to provide extreme scenario boundary conditions for power system operations [7]. Currently, domestic and international research on scenario generation for renewable energy can be divided into two main categories: scenario generation methods based on probabilistic statistical features and data-driven scenario generation methods [8; 9]. Statistical feature-based scenario generation methods require prior assumptions about the probability distribution of renewable energy output and relevant model parameters from historical data [10]. Reference [11] assumes that the wind speed follows a Weber distribution and that the solar intensity follows a Beta distribution, and it builds the wind-scenery output on the basis of these distributions. A copula function is used to capture the spatial correlation between wind farms, and Monte Carlo sampling is used to generate wind power scenarios [12]. These methods, although simple and easy to handle, suffer from major shortcomings in terms of accuracy. The method assumes that the statistical distribution is based on historical data; however, because of geographical location and time differences, wind power and solar power may not align with the assumed distribution. If assumptions from the distribution of sampling are used to generate scenarios, the calculated results will seriously deviate from the actual value, affecting the power system to make the right decision. Methods for data-driven scenario generation are based on data [13]. With the development of artificial intelligence technology, this type of method has begun to receive widespread attention. This kind of method mainly generates scenarios by feature extraction from historical data via neural network training; this approach does not need the distribution type to be formulated in advance but adopts an unsupervised learning approach to learn the relevant features of the data itself [14]. Among them, generative adversarial networks are increasingly used in scenario generation.