基于深度学习的配电网需求响应能力与潜力分析OA

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.