管道(软件)
天然气
遗传算法
维数(图论)
管道运输
生产(经济)
石油工程
数学优化
网络模型
工程类
工艺工程
模拟
环境科学
计算机科学
环境工程
数学
废物管理
机械工程
数据挖掘
宏观经济学
经济
纯数学
作者
Jun Zhou,Zhao Yunxiang,Tiantian Fu,Xuan Zhou,Guangchuan Liang
出处
期刊:Energy
[Elsevier]
日期:2022-07-01
卷期号:256: 124651-124651
被引量:1
标识
DOI:10.1016/j.energy.2022.124651
摘要
With the increasing proportion of natural gas consumption in the energy market, in order to meet the demand for seasonal peak regulation and emergency gas supply, it is urgent to research and develop the underground natural gas storage (UNGS). Different from the conventional oil and gas fields, the UNGS pipeline network needs to consider the boundary constraints under both injection and production conditions. Therefore, considering the characteristics of injection and production technology, this paper aims to constructs a Multiple Condition Hybrid model (MCH model) for optimizing the design parameters of UNGS pipeline network. This paper proposes a Hybrid Genetic Algorithm (HGA) for solving the MCH model of pipeline network design. In the solution of Case 1, HGA has a 10%–13% lower investment cost than GA while shortening the GA iterations by 50%–70%. Case 2 is revealed that the MCH model can be optimized to obtain lower pipeline network costs under the boundary of injection and production conditions. Finally, HGA is used to optimize the design parameters of the MCH model for the field example Case 3, and the pipeline network parameters are obtained that are about 17% lower than the field costs. • Established a MCH model for dimension optimization of UNGS pipeline network. • MCH model coupling injection and production conditions. • Proposed a Hybrid Genetic Algorithm based on MFDM and GA. • Verified the MCH model and HGA by three cases.
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