高压电器2025，Vol.61Issue(5)：218-227,10.DOI:10.13296/j.1001-1609.hva.2025.05.023
基于CNN-LSTM风光荷预测的主动配电网双层扩展规划方法
Double Layer Expansion Planning Method for Active Distribution Network Based on CNN-LSTM Wind,Solar and Load Prediction
摘要
Abstract
With access of a large amount of renewable energy to the distribution network,expansion planning of the distribution network is required due to the uncertainty of its output.To this end,a method based on convolutional neu-ral network and long short-term memory network is firstly proposed to predict the wind and solar load output,and then a dual-layer expansion planning model of the active distribution network is constructed.The upper-level plan-ning model takes the lowest annual comprehensive cost as the optimization goal and,at the same time,the transfor-mation and upgrading of the line and various costs is considered.While,the lower-level operation model takes the lowest annual comprehensive operation cost and the smallest node voltage offset as the optimization goal and the oper-ating conditions,distributed power supply and energy storage planning are also considered.After the upper and lower layer correlation modeling,the two-layer model is transformed into a multi-objective optimization problem,and then the normalized normal constraint method is used for solution so to obtain a uniformly distributed pareto front.Final-ly,the effectiveness of the method is verified through example.关键词
主动配电网/卷积神经网络/长短期记忆网络/双层规划模型/归一化法向约束法Key words
active distribution network/convolutional neural network/long short-term memory network/two-level planning model/NNC method引用本文复制引用
朱夏,陈颂,袁明瀚,刘扬洋..基于CNN-LSTM风光荷预测的主动配电网双层扩展规划方法[J].高压电器,2025,61(5):218-227,10.基金项目
国网上海电力公司技术项目(52090022004J).Project Supported by State Grid Shanghai Municipal Electric Power Company Technology Project(52090022004J). （52090022004J）
