Dynamic optimization of natural gas pipeline networks with demand and composition uncertainty

Abstract Pipelines are one of the most efficient methods to transport large quantities of natural gas from gas reserves to main markets. However, because of the unsteady nature of gas flow, the operation of pipelines is always dynamic. Furthermore, the uncertainties in demand and the fluctuation of gas composition also make the efficient operation of these dynamic processes challenging. This study addresses the problem of determining the optimal operation to minimize compression costs, while considering demand and gas composition uncertainties. A dynamic pipeline network model is developed with rigorous thermodynamic equations, allowing accurate calculation of gas compressibility factor at any temporal and spatial point. The supply gas composition and demand nodes flow rates are assumed to be uncertain. To deal with these uncertainties, a robust optimization algorithm is applied using back-off constraints calculated from Monte Carlo simulation. Through successive iterations, the algorithm terminates at a solution that is robust to a specified level of process variability with minimal cost. We show from the case studies that the formulated model and the algorithm can successfully address the problem with acceptable computational cost.

[1]  W. A. Poe,et al.  Handbook of Natural Gas Transmission and Processing , 2006 .

[2]  Marc C. Steinbach,et al.  Computational optimization of gas compressor stations: MINLP models versus continuous reformulations , 2016, Mathematical Methods of Operations Research.

[3]  Ignacio E. Grossmann,et al.  Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty , 2015, Comput. Chem. Eng..

[4]  Weihang Zhu,et al.  A multi-objective optimization model for gas pipeline operations , 2017, Comput. Chem. Eng..

[5]  Hesam Ahmadian Behrooz,et al.  Dynamic optimization of natural gas networks under customer demand uncertainties , 2017 .

[6]  David L. Woodruff,et al.  Pyomo: modeling and solving mathematical programs in Python , 2011, Math. Program. Comput..

[7]  D. Peng,et al.  A New Two-Constant Equation of State , 1976 .

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

[9]  Jianjun Liu,et al.  Single and multiple objective optimization of a natural gas liquefaction process , 2017 .

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

[11]  Hari S. Viswanathan,et al.  The shale gas revolution: Barriers, sustainability, and emerging opportunities , 2017 .

[12]  Mikhail V. Lurie,et al.  Modeling of Oil Product and Gas Pipeline Transportation , 2008 .

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

[14]  Lorenz T. Biegler,et al.  Optimization of grade transitions in polyethylene solution polymerization process under uncertainty , 2016, Comput. Chem. Eng..

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

[16]  Moritz Diehl,et al.  An approximation technique for robust nonlinear optimization , 2006, Math. Program..

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

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

[19]  Lorenz T. Biegler,et al.  MPEC strategies for cost optimization of pipeline operations , 2010, Comput. Chem. Eng..

[20]  Hesam Ahmadian Behrooz.  Managing demand uncertainty in natural gas transmission networks , 2016 .

[21]  Stefano Campanari,et al.  Dynamic modeling of natural gas quality within transport pipelines in presence of hydrogen injections , 2017 .

[22]  Björn Geißler,et al.  Solving Highly Detailed Gas Transport MINLPs: Block Separability and Penalty Alternating Direction Methods , 2018, INFORMS J. Comput..

[23]  Felipe da Silva Alves,et al.  Multi-objective design optimization of natural gas transmission networks , 2016, Comput. Chem. Eng..

[24]  Luis A. Ricardez-Sandoval,et al.  Stochastic back‐off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty , 2018 .

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

[26]  Dominique Bonvin,et al.  Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty , 2003, Comput. Chem. Eng..

[27]  Martin Schmidt,et al.  High detail stationary optimization models for gas networks , 2015 .

[28]  Alexander Martin,et al.  MIP-based instantaneous control of mixed-integer PDE-constrained gas transport problems , 2018, Comput. Optim. Appl..

[29]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[30]  Nikolaos V. Sahinidis,et al.  Global optimization of an industrial gas network operation , 2016 .

[31]  Thorsten Koch,et al.  Evaluating Gas Network Capacities , 2015, MOS-SIAM Series on Optimization.

[32]  Victor M. Zavala,et al.  pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations , 2018, Math. Program. Comput..

[33]  Jan Van Impe,et al.  Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method , 2016 .

[34]  O. Redlich,et al.  On the thermodynamics of solutions; an equation of state; fugacities of gaseous solutions. , 1949, Chemical reviews.

[35]  Ignacio E. Grossmann,et al.  An equation-oriented approach for handling thermodynamics based on cubic equation of state in process optimization , 2010, Comput. Chem. Eng..

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

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

[38]  Victor M. Zavala,et al.  Large-scale optimal control of interconnected natural gas and electrical transmission systems , 2016 .

[39]  Hannsjörg Freund,et al.  Efficient calculation of constraint back-offs for optimization under uncertainty: A case study on maleic anhydride synthesis , 2018, Chemical Engineering Science.

[40]  Halit Üster,et al.  Optimization for Design and Operation of Natural Gas Transmission Networks , 2014 .

[41]  Frank Pettersson,et al.  Linearization of an MINLP model and its application to gas distribution optimization , 2017 .

[42]  Argimiro Resende Secchi,et al.  Novel method for looped pipeline network resolution , 2017, Comput. Chem. Eng..