Compressor stations in natural gas networks should perform such that time-varying demands of customers are fulfilled while all of the system constraints are satisfied. Power consumption of compressor stations impose the most operational cost to a gas network so their optimal performance will lead to significant money saving. In this paper, the gas network transient optimization problem is addressed. The objective function is the sum of the compressor's power consumption that should be minimized where compressor speeds and the value status are decision variables. This objective function is nonlinear which is subjected to nonlinear and combinatorial constraints including both discrete and continuous variables. To handle this challenging optimization problem, a novel approach based on using two different structure intelligent algorithms, namely the particle swarm optimization (PSO) and cultural algorithm (CA), is utilized to find the optimum of the decision variables. This approach removes the necessity of finding an explicit expression for the power consumption of compressors as a function of decision variables as well as the calculation of objective function derivatives. The objective function and constraints are evaluated in the transient condition by a fully implicit finite difference numerical method. The proposed approach is applied on a real gas network where simulation results confirm its accuracy and efficiency.
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March 2019
Research-Article
Transient Optimization of Natural Gas Networks Using Intelligent Algorithms
Esmaeel Khanmirza,
Esmaeel Khanmirza
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: khanmirza@iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: khanmirza@iust.ac.ir
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Reza Madoliat,
Reza Madoliat
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: r_madoliat@iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: r_madoliat@iust.ac.ir
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Ali Pourfard
Ali Pourfard
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: pourfard@mecheng.iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: pourfard@mecheng.iust.ac.ir
Search for other works by this author on:
Esmaeel Khanmirza
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: khanmirza@iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: khanmirza@iust.ac.ir
Reza Madoliat
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: r_madoliat@iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: r_madoliat@iust.ac.ir
Ali Pourfard
Mechanical Engineering Department,
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: pourfard@mecheng.iust.ac.ir
Iran University of Science and Technology,
Tehran 1684613114, Iran
e-mail: pourfard@mecheng.iust.ac.ir
1Corresponding author.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 28, 2017; final manuscript received April 16, 2018; published online September 14, 2018. Editor: Hameed Metghalchi.
J. Energy Resour. Technol. Mar 2019, 141(3): 032901 (11 pages)
Published Online: September 14, 2018
Article history
Received:
April 28, 2017
Revised:
April 16, 2018
Citation
Khanmirza, E., Madoliat, R., and Pourfard, A. (September 14, 2018). "Transient Optimization of Natural Gas Networks Using Intelligent Algorithms." ASME. J. Energy Resour. Technol. March 2019; 141(3): 032901. https://doi.org/10.1115/1.4040073
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