基于改进条件生成扩散模型的新能源日前场景生成方法  

The Day-ahead Scenario Generation Method for New Energy Based on An Improved Conditional Generative Diffusion Model

作　　者：王长刚 刘伟 曹宇[1,2] 梁栋[3] 李扬 莫静山[1,2] WANG Changgang;LIU Wei;CAO Yu;LIANG Dong;LI Yang;MO Jingshan(Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology(Northeast Electric Power University),Ministry of Education,Jilin 132012,Jilin Province,China;School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,Jilin Province,China;Substation Secondary Maintenance Center,Jilin Power Supply Company,Jilin 132012,Jilin Province,China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林省吉林市132012 [2]东北电力大学电气工程学院,吉林省吉林市132012 [3]吉林供电公司变电二次检修中心,吉林省吉林市132012

出　　处：《电网技术》2025年第4期1358-1368,I0022-I0025,共15页Power System Technology

基　　金：国家自然科学基金项目(52307084)。

摘　　要：在新能源发电占比不断提高的背景下,如何准确生成新能源出力场景对电力系统日前调度至关重要。基于深度学习的场景生成方法能够完成上述任务,但其黑盒性质使之存在可解释性差等问题。对此,文章提出一种基于改进条件生成扩散模型的新能源日前场景生成方法。该方法的构建基于马尔可夫链和变分推理的理论框架,先通过扩散过程将历史数据转化为纯噪声,再由条件信息引导去噪过程从而生成满足条件分布的场景。并改进噪声表为余弦形式,提高生成场景质量。最后,将方法应用于实际风光出力数据中,结果表明,该方法能够有效生成新能源出力场景,且适应性较好。In the context of the rising share of new energy generation,accurately generating new energy output scenarios is crucial for day-ahead power system scheduling.Deep learning-based scenario generation methods can address this need,but their black-box nature raises concerns about interpretability.To tackle this issue,this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model.This method is built on the theoretical framework of Markov chains and variational inference.It first transforms historical data into pure noise through a diffusion process,then uses conditional information to guide the denoising process,ultimately generating scenarios that satisfy the conditional distribution.Additionally,the noise table is improved to a cosine form,

关 键 词：新能源 不确定性 场景生成 改进条件生成扩散模型 余弦噪声表 

分 类 号：TM715[电气工程—电力系统及自动化]