Transient simulation of gas pipeline networks using intelligent methods

Abstract Simulation of gas pipeline network has an important role in control and design of the natural gas transmission system. Transient simulation provides several advantages in energy consumption optimization where compressor stations variables are manipulated regarding to contract pressures. In this paper, a novel approach based on intelligent algorithms and three basic functions is proposed for dynamic simulation of gas pipeline networks. An optimization tool is used to find the inlet flow rates of the network. If the inlet flow rates are calculated correctly, all network variables can be computed using three basic functions. In each sample of time, the optimization tool called particle swarm optimization gravitational search algorithm (PSOGSA) offers some candidate solutions for inlet flow rates of the network. For each of these candidate solutions, the network is analyzed using three basic functions and then, outlet pressures are calculated. The differences between calculated outlet pressures and the reference values are considered as an error or fitness function of optimization tool. Finally, the optimization tool finds the optimum inlet flow rates at that sample of time which lead to minimum error. The proposed method is straight forward and easy to implement while its error percentage is near zero and converges faster than some well-known optimization algorithms. Numerical results confirm the accuracy and efficiency of the suggested algorithm.

[1]  Jan Fredrik Helgaker,et al.  Modelling of Natural Gas Pipe Flow with Rapid Transients-case Study of Effect of Ambient Model☆ , 2015 .

[2]  Javad Mahmoudimehr,et al.  Minimization of fuel consumption in cyclic and non-cyclic natural gas transmission networks: Assessment of genetic algorithm optimization method as an alternative to non-sequential dynamic programing , 2012 .

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

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

[5]  Aliasghar Montazar,et al.  Optimize of all Effective Infiltration Parameters in Furrow Irrigation Using Visual Basic and Genetic Algorithm Programming , 2012 .

[6]  Aliasghar Montazar,et al.  Sensitive analysis of optimized infiltration parameters in SWDC model , 2012 .

[7]  A. Osiadacz,et al.  Simulation of transient gas flows in networks , 1984 .

[8]  K. S. Chapman,et al.  Nonisothermal Transient Flow in Natural Gas Pipeline , 2008 .

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

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

[11]  Berna Dengiz,et al.  An integrated simulation model for analysing electricity and gas systems , 2014 .

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

[13]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[14]  M. Morari,et al.  Model Predictive Control of Gas Pipeline Networks , 1986, 1986 American Control Conference.

[15]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[16]  Morteza Behbahani-Nejad,et al.  A MATLAB Simulink Library for Transient Flow Simulation of Gas Networks , 2008 .

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

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

[19]  Lorenz T. Biegler,et al.  Economic Nonlinear Model Predictive Control for periodic optimal operation of gas pipeline networks , 2013, Comput. Chem. Eng..

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

[21]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[22]  Chia-Nan Ko,et al.  An orthogonal-array-based particle swarm optimizer with nonlinear time-varying evolution , 2007, Appl. Math. Comput..

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

[24]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[25]  Michael Herty,et al.  Gas Pipeline Models Revisited: Model Hierarchies, Nonisothermal Models, and Simulations of Networks , 2011, Multiscale Model. Simul..

[26]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[27]  Armin Fügenschuh,et al.  Validation of nominations in gas network optimization: models, methods, and solutions , 2015, Optim. Methods Softw..

[28]  Roger Z. Ríos-Mercado,et al.  Optimization problems in natural gas transportation systems. A state-of-the-art review , 2015 .

[29]  Mohammad Ebrahim Banihabib,et al.  Monthly Inflow Forecasting using Autoregressive Artificial Neural Network , 2012 .

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