南方电网技术2026，Vol.20Issue(2)：105-114,10.DOI:10.13648/j.cnki.issn1674-0629.2026.02.011
基于条件流模型的可再生能源短期出力场景生成
Short-Term Output Scenario Generation of Renewable Energy Based on Conditional Flow Model
摘要
Abstract
Effectively and accurately portraying the short-term output uncertainty of renewable energy is an important foundation for the stable operation of power systems.In recent years,researchers conduct a lot of studies on generative models,among which flow-based generative models show great potential.However,flow models are rarely used to portray the uncertainty of renewable energy.Therefore,a short-term output scenario generation method of renewable energy based on conditional flow model is proposed.Firstly,a series of invertible functions are trained using real output data to map the probability distribution of the data to a standard Gaussian distribution.After the training is completed,the new output curve can be generated by inputting random noise obeying Gaussian distribution into the trained flow model.The method can be trained directly by maximum likelihood estimation and employs a series of specially designed invertible transformations,which can efficiently compute the training target.In addition,a generated scene evaluation index system is constructed to assess the quality of generated scenes,achieving interpretability of the results generated by the artificial intelligence black box model.The examples verify the effectiveness and sophistication of the proposed method.关键词
可再生能源/不确定性/场景生成/生成模型/流模型Key words
renewable energy/uncertainty/scenario generation/generative model/flow model分类
信息技术与安全科学引用本文复制引用
施永浩,罗萍萍,林济铿..基于条件流模型的可再生能源短期出力场景生成[J].南方电网技术,2026,20(2):105-114,10.基金项目
国家自然科学基金资助项目(51177107). Supported by the National Natural Science Foundation of China(51177107). （51177107）
