scholarly journals A Robustness Evaluation Method of Natural Gas Pipeline Network Based on Topological Structure Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Xueyi Li ◽  
Huai Su ◽  
Jinjun Zhang ◽  
Nan Yang
Keyword(s):  
Natural Gas ◽  
Natural Disasters ◽  
Topological Structure ◽  
Structural Changes ◽  
Evaluation Method ◽  
Gas Pipeline ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Random Disturbances ◽  
Component Failures

As the total mileage of natural gas pipeline network continues to increase, the topological structure of natural gas pipeline network will become more and more complex. The complicated topological structure of natural gas pipeline network is likely to cause inherent structural defects, which have serious impacts on the safe operation of natural gas pipeline network. At present, related researches mainly focused on the safe and reliable operation of natural gas pipeline network, which has become a research hotspot, but few of them considered the complexity of natural gas pipeline network and its potential impacts. In order to understand the complexity of natural gas pipeline network and its behaviors when facing structural changes, this paper studied the robustness of natural gas pipeline network based on complex network theory. This paper drew on the methods and experience of robustness researches in other related fields, and proposed a robustness evaluation method for natural gas pipeline network which is combined with its operation characteristics. The robustness evaluation method of natural gas pipeline network is helpful to identify the key components of the pipeline network and understand the response of the pipeline network to structural changes. Furthermore, it can provide a theoretical reference for the safe and stable operation of natural gas pipeline network. The evaluation results show that natural gas pipeline network shows strong robustness when faced with random disturbances represented by pipeline accidents or component failures caused by natural disasters, and when faced with targeted disturbances represented by terrorist disturbances, the robustness of natural gas pipeline network is very weak. Natural gas pipeline network behaves differently in the face of different types of random disturbances. Natural gas pipeline network is more robust when faced with component failures than pipeline accidents caused by natural disasters.

Using Machine Learning Tools for Forecasting Natural Gas Market Demand

2020 ◽  
Author(s):  
Kai Yang ◽  
Lei Hou
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Evaluation Method ◽  
Gas Pipeline ◽  
Prediction Methods ◽  
Error Evaluation ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Gas Consumption ◽  
Gas Supply

Abstract Providing reliable and accurate forecasts of natural gas consumption can keep supply and demand of natural gas pipelines in balance, which can increase profits and reduce supply risks. In order to accurately predict the short-term load demand of different gas nodes in the natural gas pipeline network, a hybrid optimization strategy of integrated genetic optimization algorithm and support vector machine are proposed. Factors such as holidays, date types and weather were taken into account to build a natural gas daily load prediction model based on GA-SVM was established. A natural gas pipeline network in China includes three gas supply nodes of different user type gas is forecasted, and a variety of error evaluation method, the GA-SVM evaluation index compared with other prediction methods, and through different data set partition is discussed in the periods of peak gas and gas resources in the GA — the applicability of the SVM prediction model, the ends of a natural gas pipeline network in China includes four gas supply nodes of different user type gas is forecasted, and a variety of error evaluation method, the GA-SVM evaluation index compared with other prediction methods, The applicability of the method is also discussed by dividing different data sets. By predicting the gas load forecast of the three nodes, the results show that GA-SVM hybrid prediction model has high prediction accuracy compared with other single models, and the three gas nodes MAPE of GA-SVM is respectively 3.66%, 5.17% and 3.43%. Through further analysis, even with the data samples reduced, the winter gas peak of gas prediction can still maintain good prediction effects. The research shows that the GA-SVM model has high accuracy and strong applicability in predicting gas consumption at different nodes of the natural gas pipeline network. This study can provide a research basis for analysis of gas supply uncertainty and further gas supply reliability evaluation of pipeline network.

scholarly journals GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

2020 ◽  
Vol 75 ◽  
pp. 86
Author(s):  
Yue Xiang ◽  
Peng Wang ◽  
Bo Yu ◽  
Dongliang Sun
Keyword(s):  
Natural Gas ◽  
Large Scale ◽  
Parallel Simulation ◽  
Simulation Software ◽  
Gas Pipeline ◽  
Processing Unit ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Pipeline Networks ◽  
Speedup Ratio

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

scholarly journals Analysis and Design of Interruptible Gas Contract in China under Energy Market Reform

2020 ◽  
Vol 12 (2) ◽  
pp. 506
Author(s):  
Jian Chai ◽  
Liqiao Wang
Keyword(s):  
Natural Gas ◽  
Option Price ◽  
Gas Pipeline ◽  
Shaanxi Province ◽  
Stable Operation ◽  
Short Term ◽  
Peak Shaving ◽  
Pipeline Network ◽  
Analysis And Design ◽  
Natural Gas Pipeline

Under the background of economic development, energy security and environmental demands, the development of clean and low-carbon energy has promoted natural gas and non-fossil energy to become the main direction of world energy development. China’s natural gas consumer market has wide seasonal peaks and valleys. Because China’s natural gas peak shaving practices have some problems, we concluded that interruptible gas management has become a viable short-term emergency peak shaving method for natural gas systems in the transition period. In this paper, we take Shaanxi Province as an example. From the perspective of option pricing, this paper explains the method of using interruptible gas management to deal with the short-term supply and demand imbalance of natural gas. Therefore, we propose an interruptible gas contract trading mode, discuss the content of the interruptible gas contract and the relevant market organization form, and try to use the Black–Scholes model to calculate the option price of the interruptible gas contract. Finally, based on the price of interruptible gas and the option price of the interruptible gas contract to meet the maximum capacity shortage constraint, a provincial natural gas pipeline network company’s optimal purchase model for the interruptible gas was established, and the model was solved using the dynamic queuing method. The results show that the interruptible gas contract can not only reduce the market risk of the provincial natural gas pipeline network company and maintain the stable operation of the gas pipeline, but also reduce the cost of the interruptible users and make up for gas shortage losses.

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