Renewable Scenario Generation Based on Improved Conditional Generative Adversarial Networks

Driven by the “dual-carbon” goals, the high proportion of renewable energy integration has intensified the uncertainty in power system operation and planning. Traditional scenario generation methods rely on statistical models and prior assumptions, making it difficult to effectively characterize the complex spatiotemporal correlations and weather-coupled mechanisms in renewable energy output. This paper proposes a renewable energy scenario generation method based on an improved conditional generative adversarial network (CGAN), aiming to overcome the limitations of traditional generation techniques. By introducing Wasserstein distance with gradient penalty (WGAN-GP) to optimize model training stability, designing a multilayer perceptron (MLP) module to compress high-dimensional weather condition information and reduce redundancy and noise sensitivity, and embedding a differentiable conditional control layer to dynamically adjust output distribution, the proposed method achieves significant improvements. Experiments demonstrate that the generated scenario sets outperform traditional methods in terms of temporal correlation, coverage rate, and power interval width, effectively balancing physical laws and data-driven characteristics. This provides an efficient and reliable solution for modeling renewable energy uncertainty.