Research Papers: Petroleum Engineering

Transient Optimization of Natural Gas Networks Using Intelligent Algorithms

[+] Author and Article Information
Esmaeel Khanmirza

Mechanical Engineering Department,
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

Ali Pourfard

Mechanical Engineering Department,
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 141(3), 032901 (Sep 14, 2018) (11 pages) Paper No: JERT-17-1184; doi: 10.1115/1.4040073 History: Received April 28, 2017; Revised April 16, 2018

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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Fig. 1

Feasible domain of the compressor

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Fig. 5

Desired outlet flow rates over the 72 h period

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Fig. 2

The CA algorithm pseudo-code

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Fig. 3

The schematic structure of the gas network

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Fig. 4

Flowchart of the proposed approach

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Fig. 9

The calculated inlet flow rates

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Fig. 6

The optimum speeds of the two compressor stations

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Fig. 7

The optimum status of the network valve

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Fig. 8

The optimum power of the two compressor stations

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Fig. 10

The passing flow rate over the network valve

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Fig. 11

Outlet pressures of the network

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Fig. 12

Computational time comparison of the PSO or CA algorithm at different time levels

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Fig. 13

Performance comparison of CA and PSO at different time levels



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