Short-Term Wind Power Scenario Generation Based on Conditional Latent Diffusion Models

Quantifying short-term uncertainty in wind power plays a crucial role in power system decision-making. In recent years, the scenario generation community has conducted numerous studies employing generative models. Among these generative models, diffusion models have shown remarkable capabilities with excellent posterior representation. However, diffusion models are seldom used to quantify renewable energy uncertainty. To fill this research gap, this manuscript proposes a novel conditional latent diffusion model (CLDM) adapted for short-term scenario generation. CLDM decomposes the wind power scenario generation task into deterministic forecasting and forecast error scenario generation. The embedding network is used to regress deterministic forecasts, which reduces the denoising complexity of diffusion models. The denoising network generates forecast error scenarios in a latent space. Subsequently, the wind power scenarios are reconstructed by combining deterministic forecasts and forecast error scenarios. The case study compares with existing state-of-the-art methods, CLDM demonstrates superior evaluation metrics and enhances the denoising efficiency.