Response of natural gas distribution pipeline networks to ambient temperature variation (unsteady simulation

Abstract Natural gas is transported through pipeline networks such as transmission and distribution to the final consumers. Local natural gas companies should make the continuous delivery of natural gas under any ambient temperature and demand. Natural gas demand varies due to ambient temperature variation (ATV) in different seasons or even during a day. The continuous supply of natural gas could be achieved only if the response of natural gas pipeline network to ATV is fully studied and understood. This study investigated the response of a typical natural gas distribution pipeline network to ATV. Firstly, a multilayer perceptron neural network model has been developed to forecast natural gas demand at any ambient temperature. Secondly, a new approach has been presented to simulate the natural gas distribution pipeline network in unsteady conditions. This approach is developed to predict the response of the distribution pipeline to ATV. The city of Semnan, Iran, was selected as the case study. Natural gas consumption in 4 coldest days of a year was extracted from the metering points. According to the results, the forecast data for natural gas demand has about 1% division compared with the actual values. Also, the node pressures significantly dropped on the coldest day of the year due to the increase in natural gas demand. In addition, the effect of natural gas composition on node pressure investigated. Results show that the natural gas with higher molecular weight has a lower pressure in all network nodes.

[1]  T. A. Adeosun,et al.  Development of unsteady-state Weymouth equations for gas volumetric flow rate in horizontal and inclined pipes , 2009 .

[2]  E. Shashi Menon,et al.  Gas pipeline hydraulics , 2005 .

[3]  M. Farzaneh-Gord,et al.  Numerical procedures for natural gas accurate thermodynamic properties calculation , 2012 .

[4]  G. P. Greyvenstein.  An implicit method for the analysis of transient flows in pipe networks , 2002 .

[5]  Jolanta Szoplik,et al.  Forecasting of natural gas consumption with artificial neural networks , 2015 .

[6]  Tomislav Šarić,et al.  Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia , 2014 .

[7]  Morteza Behbahani-Nejad,et al.  The accuracy and efficiency of a reduced-order model for transient flow analysis in gas pipelines , 2010 .

[8]  Tatsuhiko Kiuchi,et al.  An implicit method for transient gas flows in pipe networks , 1994 .

[9]  João C.C. Henriques,et al.  Dynamic behaviour of high-pressure natural-gas flow in pipelines , 2005 .

[10]  Hesam Ahmadian Behrooz,et al.  Modeling and state estimation for gas transmission networks , 2015 .

[11]  Morteza Behbahani-Nejad,et al.  The accuracy and efficiency of a MATLAB-Simulink library for transient flow simulation of gas pipelines and networks , 2010 .

[12]  Bo Yu,et al.  Comparison study on the accuracy and efficiency of the four forms of hydraulic equation of a natural gas pipeline based on linearized solution , 2015 .

[13]  Selim Zaim,et al.  Forecasting natural gas consumption in İstanbul using neural networks and multivariate time series methods , 2012, Turkish Journal of Electrical Engineering and Computer Sciences.

[14]  Hamid Reza Rahbari,et al.  Unsteady natural gas flow within pipeline network, an analytical approach , 2016 .

[15]  A. O. Olatunde,et al.  Direct Calculation of Unsteady-State Weymouth Equations for Gas Volumetric Flow Rate with Different Friction Factors in Horizontal and Inclined Pipes , 2012 .

[16]  Edris Ebrahimzadeh,et al.  Simulation of transient gas flow using the orthogonal collocation method , 2012 .

[17]  Michael A. Adewumi,et al.  Simulation of non-isothermal transients in gas/condensate pipelines using TVD scheme , 2000 .

[18]  A. Osiadacz,et al.  OPTIMAL NUMERICAL METHOD FOR SIMULATING DYNAMIC FLOW OF GAS IN PIPELINES , 1983 .

[19]  M. Chaczykowski,et al.  Transient flow in natural gas pipeline – The effect of pipeline thermal model , 2010 .

[20]  Andrzej J. Osiadacz,et al.  Comparison of isothermal and non-isothermal pipeline gas flow models , 2001 .

[21]  M. Chaczykowski,et al.  Sensitivity of pipeline gas flow model to the selection of the equation of state , 2009 .

[22]  Harun Kemal Ozturk,et al.  Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks , 2009 .

[23]  Jan Fredrik Helgaker,et al.  Validation of 1D flow model for high pressure offshore natural gas pipelines , 2014 .

[24]  E. Benjamin Wylie,et al.  Network: System Transient Calculations by Implicit Method , 1971 .

[25]  M. A. Adewumi,et al.  Higher-Resolution Numerical Solution for 2-D Transient Natural Gas Pipeline Flows , 1996 .

[26]  Rezvan Alamian,et al.  A state space model for transient flow simulation in natural gas pipelines , 2012 .

[27]  Feng Yu,et al.  A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network , 2014 .

[28]  Andrzej J. Osiadacz Osiadacz,et al.  Simulation and Analysis of Gas Networks , 1987 .

[29]  C. Luongo,et al.  An Efficient Program For Transient Flow Simulation In Natural Gas Pipelines , 1986 .

[30]  Shankar Narasimhan,et al.  Simulation and State Estimation of Transient Flow in Gas Pipeline Networks Using a Transfer Function Model , 2006 .

[31]  S. Haaland.  Simple and Explicit Formulas for the Friction Factor in Turbulent Pipe Flow , 1983 .

[32]  E. Benjamin Wylie,et al.  Fluid Transients in Systems , 1993 .

[33]  D. G. Papanikas,et al.  Transient gas flow simulation using an Adaptive Method of Lines , 2003 .

[34]  F. Gorucu.  Artificial Neural Network Modeling for Forecasting Gas Consumption , 2004 .

[35]  Junyang Zhou,et al.  Simulation of Transient Flow In Natural Gas Pipelines , 1995 .

[36]  José L. Risco-Martín,et al.  Modeling and simulation of a gas distribution pipeline network , 2009 .

[37]  W. Yow,et al.  Analysis and control of transient flow in natural gas piping systems , 1971 .

[38]  Mahmood Farzaneh-Gord,et al.  Developing novel correlations for calculating natural gas thermodynamic properties , 2011 .

[39]  V. Ismet Ugursal,et al.  Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector , 2008 .

[40]  Alireza Khotanzad,et al.  Combination of artificial neural-network forecasters for prediction of natural gas consumption , 2000, IEEE Trans. Neural Networks Learn. Syst..

[41]  H. C. Ti,et al.  Transient analysis of gas pipeline network , 1998 .

[42]  H. C. Ti,et al.  Transient analysis of isothermal gas flow in pipeline network , 2000 .

[43]  Miha Kovačič,et al.  Genetic programming prediction of the natural gas consumption in a steel plant , 2014 .