南方电网技术2025，Vol.19Issue(3)：141-152,12.DOI:10.13648/j.cnki.issn1674-0629.2025.03.013
基于GAN-RL最优新能源消纳的运行方式样本智能生成方法
Intelligent Generation Method for Operation Mode Samples of Optimal New Energy Consumption Based on GAN-RL
摘要
Abstract
Aiming at the insufficient new energy consumption capacity in actual power grid operation,an intelligent generation method is proposed for operating mode samples of optimal new energy consumption based on generative adversarial networks(GAN)and reinforcement learning(RL).The method is used to establish generator and discriminator models through deep neural networks with a small sample set of actual power grid operation modes.And based on the objective function of GAN,the generator and discriminator can be trained for maximum and minimum adversarial training to generate false operating mode samples,distribution characteristics of which approach the real samples.During the training process of the generator,a reward and punishment feedback function is defined for new energy consumption to provide a quantitative good or bad response to the"generation action".On this basis,the reinforcement learning method is introduced.Following the optimization principle of accumulating the most rewards,the generator parameters are continuously optimized in real-time by the reinforcement learning method with policy gradients.Operating mode samples are intelligently generated with optimal new energy consumption characteristics.The proposed method is tested in the actual power grid of a certain region.The results show that it can effectively generate operation mode samples that meet the optimal new energy consumption,and provide data support for improving the current operation scheduling decision of insufficient new energy consumption.关键词
新能源消纳/生成对抗网络/强化学习/智能运行方式生成Key words
new energy consumption/generative adversarial network/reinforcement learning/intelligent operation mode generation分类
动力与电气工程引用本文复制引用
李帅虎,李汉典,邹谈,王炜宇,曹一家..基于GAN-RL最优新能源消纳的运行方式样本智能生成方法[J].南方电网技术,2025,19(3):141-152,12.基金项目
国家自然科学基金联合基金资助项目(U23B200694) （U23B200694）
湖南省自然科学基金资助项目(2023JJ30024).Supported by the Joint Funds of the National Natural Science Foundation of China(U23B200694) （2023JJ30024）
the Natural Science Foundation of Hunan Province of China(2023JJ30024). （2023JJ30024）
