Diffusion Model with Multi-Source Data for Day-Ahead Renewable Energy Scenario Generation
TL;DR
Results on the open dataset released by the Chinese State Grid Renewable Energy Generation Forecasting Competition show that the proposed VAE-CLDM achieves the lowest root mean square error (RMSE), mean absolute error (MAE), and maximum mean discrepancy (MMD) among the tested models on the training-statistics-standardized renewable-power benchmark.
Abstract
High-quality renewable energy (RE) scenario generation is essential for secure, reliable, and economic power system operation under high RE penetration. To address the limitations of existing methods in preserving scenario fidelity, diversity, spatiotemporal dependence, and engineering consistency, this paper proposes a three-stage framework that combines a variational autoencoder (VAE) with a conditional latent diffusion model (CLDM), hereafter referred to as VAE-CLDM, for day-ahead renewable energy scenario generation. First, multi-source features are constructed by integrating renewable power outputs, meteorological variables, temporal lag information, and spatial correlation characteristics among wind farms and photovoltaic stations. Then, VAE compresses the high-dimensional features into a compact latent space while retaining key statistical and spatiotemporal information. Based on this latent representation, a CLDM generates realistic scenarios by progressively denoising random noise under meteorological conditions. A spatiotemporal feature modeling strategy is incorporated to better represent temporal fluctuations and inter-site correlations, while a diversity regulation factor is selected on the validation set to balance scenario fidelity and tail-event coverage. Finally, the generated scenarios are reconstructed into the physical space and checked using output-bound and ramp-consistency correction to improve their practical usability. Results on the open dataset released by the Chinese State Grid Renewable Energy Generation Forecasting Competition show that the proposed framework achieves the lowest root mean square error (RMSE), mean absolute error (MAE), and maximum mean discrepancy (MMD) among the tested models on the training-statistics-standardized renewable-power benchmark, with RMSE of 0.3013±0.0022, MAE of 0.3636±0.0010, and MMD of 0.04269±0.00040. After post-correction, lower- and upper-bound violations are reduced to 0.00%, and the ramp-violation rate is reduced to 0.08%, indicating that the proposed VAE-CLDM can provide useful scenario inputs for day-ahead dispatch and risk assessment in renewable-dominated power systems.