电网技术2025，Vol.49Issue(2)：511-521,中插34-中插36,14.DOI:10.13335/j.1000-3673.pst.2024.1528
基于改进去噪扩散概率模型和模型迁移的新能源场站超短期出力场景生成
Ultra-short-term Output Scenario Generation for Renewable Energy Plants Based on Improved Denoising Diffusion Probabilistic Models and Model-based Transfer Learning
摘要
Abstract
The output of renewable energy plants exhibits strong uncertainty,posing significant challenges to the dispatching and controlling of new-type power systems.To achieve accurate modeling of renewable energy output scenarios,first,addressing the uncertainty of ultra-short-term output for renewable energy plants,this paper proposes an improved Denoising Diffusion Probabilistic Model(DDPM).This model uses an enhanced self-attention mechanism to design a neural network architecture tailored for ultra-short-term output scenario generation of renewable energy plants better to capture the correlation of renewable energy output time series and fit its probability distribution.Subsequently,addressing the issue of insufficient historical data for newly built renewable energy plants,a framework for output scenario generation of newly built renewable energy plants based on model-based transfer learning is proposed to complete the construction of the scenario generation model under the condition of small sample.Finally,case studies are conducted on the wind and solar output dataset open-sourced by the National Renewable Energy Laboratory(NREL)in the United States.Combined with the proposed evaluation metrics,the results of case studies indicate that the model proposed in this paper significantly outperforms generative adversarial networks,variational autoencoders and models without model-based transfer learning.关键词
新能源/场景生成/去噪扩散概率模型/自注意力机制/模型迁移Key words
renewable energy/scenario generation/denoising diffusion probabilistic model/self attention/model-based transfer learning分类
信息技术与安全科学引用本文复制引用
戴宇欣,张俊,乔骥,沈阳武,余及舟,许沛东,张科,高天露,白昱阳..基于改进去噪扩散概率模型和模型迁移的新能源场站超短期出力场景生成[J].电网技术,2025,49(2):511-521,中插34-中插36,14.基金项目
新一代人工智能国家科技重大专项(No.2021ZD0112700).Project Supported by the National Key Research and Development Program of China(No.2021ZD0112700). （No.2021ZD0112700）
