Reliability analysis of integrated electrical energy systems considering the dynamics of gas network
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
万方数据
维普
