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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.

Power systems with high penetration of renewable energy contain various uncertainties. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to the Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight the high accuracy and computational efficiency of the proposed model over state-of-the-art models, and it makes an ideal candidate for large-scale scenario reduction problems.