电力系统保护与控制2026，Vol.54Issue(17)：26-37,12.DOI:10.19783/j.cnki.pspc.260095
基于改进CGAN和ARIMA组合模型的风光典型场景生成
Typical wind-PV scenario generation based on improved CGAN-ARIMA hybrid model
摘要
Abstract
With the high penetration of renewable energy,the complexity of uncertainties faced by power systems has intensified,making operational planning increasingly reliant on refined scenario generation.To address the issues of insufficient interpretability and difficulty in ensuring physical feasibility in a single deep generative model,a typical wind and PV output scenario generation method based on an improved conditional generative adversarial network(CGAN)and autoregressive integrated moving average(ARIMA)hybrid model is proposed,which integrates the implicit learning capability of deep generative models with the explicit modeling capability of parametric statistical models.First,the ARIMA model is used to capture the low-frequency trend components of the output data,while extracting the high-frequency residual sequences.Then,an improved CGAN model integrated with long short-term memory network is constructed.A statistical regularization term is introduced into the generator loss function to refine the distribution characteristics of residuals,thereby achieving high-frequency residual sequence modeling.Moreover,kernel principal component analysis and a Gaussian mixture model are applied to perform nonlinear dimensionality reduction,adaptive clustering,and probability weighting on the generated high-dimensional scenario matrices,thereby extracting typical scenarios with probability weights.Finally,case studies indicate that the proposed approach reduces the Wasserstein distance index by more than 50%and the conditional value-at-risk error by over 40%,confirming its effectiveness.关键词
场景生成/条件生成对抗网络/核主成分分析/高斯混合模型/新能源出力Key words
scenario generation/conditional generative adversarial network/kernel principal component analysis/Gaussian mixture model/renewable energy outputs引用本文复制引用
李晓阳,曹永吉,李常刚,李娜,衣彦霖,李宝亮..基于改进CGAN和ARIMA组合模型的风光典型场景生成[J].电力系统保护与控制,2026,54(17):26-37,12.基金项目
This work is jointly supported by Smart Grid-National Major Science and Technology Project of China(No.2024ZD0801100). 智能电网国家科技重大专项资助(2024ZD0801100) （No.2024ZD0801100）
山东省重点研发计划项目资助(2025TSGCCZZB0081) （2025TSGCCZZB0081）
