工业技术与职业教育2025，Vol.23Issue(2)：8-12,18,6.
基于生成对抗网络的风光出力场景生成
Wind-Photovoltaic Power Generation Scenario Generation Based on GAN
摘要
Abstract
The operation and planning of the new power system involve a large number of modeling and analysis of new energy scenarios,and the rationality of the scenarios has a significant impact on the computational efficiency and reliability of system operation and planning.To this end,this study proposes a scenario generation method for wind and solar generation based on the VAE-DCGAN model,which uses a variational autoencoder as the generator part of the generative adversarial network,learns the power generation characteristics of data,and generates a large number of wind and solar power generation scenarios through a game training with the discriminator.The superiority of the generated scenarios is evaluated using metrics such the difference distance of the cumulative probability distribution and the maximum mean.The results show that the MMD distance between the generated data and the real data of the VAEDCGAN model is 0.032 9,and the model can better learn the power generation characteristics of historical wind and solar data.关键词
场景生成/变分自动编码器/生成对抗网络/新能源出力/最大均值差异距离Key words
scenario generation/variational autoencoder/generative adversarial networks/renewable energy power output/maximum mean discrepancy lespedeza bicolor分类
信息技术与安全科学引用本文复制引用
郭子铭..基于生成对抗网络的风光出力场景生成[J].工业技术与职业教育,2025,23(2):8-12,18,6.
