In the reliability analysis of electrical integrated energy systems,traditional numerical algorithms struggle with the high computational demands of natural gas dynamics,making timely dynamic analysis difficult.This paper presents a novel approach that replaces these algorithms with a neural network-based method using multi-scale dilated convolution and attention mechanisms.The model utilizes convolutional neural networks(CNN)for feature extraction and long short-term memory(LSTM)networks to capture time series characteristics.Multi-scale dilated convolutions expand the receptive field,while attention mechanisms enhance sensitivity to critical changes.This sequence-to-sequence learning process accurately models complex relationships between time steps,resulting in a dynamic surrogate model for the gas network.The gas network model is integrated with the power system flow model,allowing for a comprehensive reliability analysis using Monte Carlo methods and multi-state models.Tests on a distribution-level electric-gas integrated energy system show that the CNN-LSTM model not only accurately simulates gas dynamics but also significantly improves computational efficiency,meeting the reliability assessment needs of large-scale integrated energy systems.
electrical integrated energy systemoperational reliabilitynatural gas dynamicssurrogate modeldata driven
维普
万方数据
