摘要：To ensure gas supply reliability and reduce the risk of gas shortage for customers, a deep learning-based method for the optimization of peak shaving of natural gas pipeline was proposed. Firstly, a forecasting model of node pressure based on deep learning was developed, aiming to capture the impact of boundary transient changes on the node pressure. Then, a multi-objective optimization model with minimum operation cost and maximum pipeline gas storage capacity was established, which could dynamically optimize the gas storage and gas compressor operating scenarios. Finally, the proposed method was validated on a natural gas pipeline network. The results show that, compared with the operation scenario without optimization, the average gas storage capacity of the pipeline increased to 33. 628 million cubic meters after optimization, and the operating cost was saved by RMB 106 000. The results can provide a new insight into the peak shaving of natural gas pipeline systems. ? 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

ISSN号：1671-1815

卷、期、页：卷25期23：9793-9799

发表日期：2025-01-01

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：Science Technology and Engineering

参与作者：玉德俊,杨超,宁喜风

通讯作者：范霖

第一作者：苏怀,张劲军

论文类型：期刊论文

论文概要：范霖,玉德俊,苏怀,杨超,宁喜风,张劲军，Method for the Multi-objective Optimization of Peak Shaving of Natural Gas Pipelines Based on Pressure Prediction，Science Technology and Engineering，2025，卷25期23：9793-9799

论文题目：Method for the Multi-objective Optimization of Peak Shaving of Natural Gas Pipelines Based on Pressure Prediction