电力系统自动化2026，Vol.50Issue(18)：57-67,11.DOI:10.7500/AEPS20251101004
基于深度学习的配电网需求响应能力与潜力分析
Analysis of Demand Response Capability and Potential in Distribution Networks Based on Deep Learning
摘要
Abstract
In the background of the continuous advancement of electricity market-oriented reform,in order to improve the utilization efficiency of demand response in distribution networks,a method for analyzing demand response capability and potential in distribution networks based on deep learning is proposed.Firstly,a convolutional neural network(CNN)-bi-directional long short-term memory(BiLSTM)-Transformer two-layer prediction model is constructed to achieve accurate and dynamic prediction of demand response capability.Secondly,load models for various industries and user types are established.The demand response potential is analyzed from three perspectives of user side,distribution network side,and development trends.A long-and short-term demand response potential quantification method based on spatial envelope domain is proposed.Meanwhile,with the objective of minimizing the comprehensive cost of the distribution network,an optimization model based on short-term demand response potential is constructed,and a targeted incentive pricing strategy is proposed.Finally,simulation case studies verify the effectiveness and superiority of the proposed method in prediction accuracy,potential assessment,and market application.关键词
配电网/需求响应/电力市场/深度学习/动态预测/激励价格/TransformerKey words
distribution network/demand response/electricity market/deep learning/dynamic forecasting/incentive price/Transformer引用本文复制引用
陈骏鹏,苗世洪,何海飞,王杰,周华锋,彭超逸..基于深度学习的配电网需求响应能力与潜力分析[J].电力系统自动化,2026,50(18):57-67,11.基金项目
智能电网国家科技重大专项资助项目(2025ZD0804400). This work is supported by Smart Grid-National Science and Technology Major Project(No.2025ZD0804400). （2025ZD0804400）
