Improving the natural gas transporting based on the steady state simulation results

The work presents an example of practical application of gas flow modeling results in the network, that was obtained for the existing gas network and for real data about network load depending on the time of day and air temperature. The gas network load in network connections was estimated based on real data concerning gas consumption by customers and weather data in 2010, based on two-parametric model based on the number of degree-days of heating. The aim of this study was to elaborate a relationship between pressure and gas stream introduced into the gas network. It was demonstrated that practical application of elaborated relationship in gas reduction station allows for the automatic adjustment of gas pressure in the network to the volume of network load and maintenance of gas pressure in the whole network at possibly the lowest level. It was concluded based on the results obtained that such an approach allows to reduce the amount of gas supplied to the network by 0.4% of the annual network load.

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

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

[3]  Janusz Skorek,et al.  Real gas flow simulation in damaged distribution pipelines , 2012 .

[4]  Christine W. Chan,et al.  Applications of artificial intelligence for optimization of compressor scheduling , 2006, Eng. Appl. Artif. Intell..

[5]  Abdurrahman Satman,et al.  Heating and cooling degree-hours for Turkey , 1999 .

[6]  F. Monforti,et al.  A MonteCarlo approach for assessing the adequacy of the European gas transmission system under supply crisis conditions , 2010 .

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

[8]  D. Brkić.  An improvement of Hardy Cross method applied on looped spatial natural gas distribution networks , 2009 .

[9]  William Y. Svrcek,et al.  The effect of major parameters on simulation results of gas pipelines , 2007 .

[10]  E. Andrew Boyd,et al.  Efficient operation of natural gas transmission systems: A network-based heuristic for cyclic structures , 2006, Comput. Oper. Res..

[11]  Mohd Amin Abd Majid,et al.  Simulation model for natural gas transmission pipeline network system , 2011, Simul. Model. Pract. Theory.

[12]  Maziar Yazdani Damavandi,et al.  New approach to gas network modeling in unit commitment , 2011 .

[13]  Richard A. Dawe,et al.  Review of ways to transport natural gas energy from countries which do not need the gas for domestic use , 2003 .

[14]  Hulya Sarak,et al.  The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study , 2003 .

[15]  Jolanta Szoplik,et al.  The Gas Transportation in a Pipeline Network , 2012 .

[16]  Omar Fayez Mohamed El-Mahdy,et al.  Computer aided optimization of natural gas pipe networks using genetic algorithm , 2010, Appl. Soft Comput..

[17]  Hesam Najibi,et al.  Effect of different parameters on optimum design for high pressure natural gas trunk-lines , 2011 .

[18]  Suming Wu,et al.  Model relaxations for the fuel cost minimization of steady-state gas pipeline networks , 2000 .

[19]  Stefan Lochner,et al.  Identification of congestion and valuation of transport infrastructures in the European natural gas , 2011 .

[20]  M. Steinbach.  On PDE solution in transient optimization of gas networks , 2007 .

[21]  Benjamin F. Hobbs,et al.  Future evolution of the liberalised European gas market: Simulation results with a dynamic model , 2008 .

[22]  Murat Kenisarin,et al.  Energy saving potential in the residential sector of Uzbekistan , 2007 .

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

[24]  Weijun Gao,et al.  A procedure to design the mainline system in natural gas networks , 2009 .

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

[26]  Z. Şen,et al.  An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul , 2000 .

[27]  N. I. Voropai,et al.  Detection of “bottlenecks” and ways to overcome emergency situations in gas transportation networks on the example of the European gas pipeline network , 2012 .

[28]  Sepehr Sanaye,et al.  Modeling and optimizing a CHP system for natural gas pressure reduction plant , 2012 .

[29]  Maryam Fasihizadeh,et al.  Improving gas transmission networks operation using simulation algorithms: Case study of the National Iranian Gas Network , 2014 .

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

[31]  J. Szoplik,et al.  The Steady-state Simulations for the Gas Flow in a Pipeline Network. , 2010 .

[32]  M. H. Fazel Zarandi,et al.  Forecasting semi-dynamic response of natural gas networks to nodal gas consumptions using genetic fuzzy systems , 2015 .

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

[34]  D. Matko,et al.  Pipeline simulation techniques , 2000 .

[35]  Victor M. Zavala,et al.  Stochastic optimal control model for natural gas networks , 2014, Comput. Chem. Eng..

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

[37]  Liang Sun,et al.  Mathematical modeling of the flow in a pipeline with a leak , 2012, Math. Comput. Simul..