doi: 10.4304/jsw.6.3.452-459
Adaptive Genetic Algorithm for Steady-State Operation Optimization in Natural Gas Networks
- School of Petroleum Engineering, Southwest Petroleum University, Chengdu, China
Abstract
Natural gas is normally transported through a vast network of pipelines. A pipeline network is generally established to connect gas wells with gas processing fields (gathering network) or to transmit gas at high pressure from gas sources to regional demand points (trunk network) or to distribute gas to consumers at low pressure from regional demand points (distribution network). The problems involved in optimizing the operation conditions of networks to promote benefit belong to a class of non-liner optimization problems. The operation benefit of gas network is combined with the purchase and sale prices of gas, the quantity bought and sold of gas and the management costs. Aimed at the maximum operation benefit, the paper proposes an operation optimization model of gas network with consideration of quantity input (output) constraints of each node, operation pressure constraints of pipelines, compressor constraints, valve constraints and hydraulic constraints of the pipeline system. The model adapts to all kinds of pipeline structures, followed with our presentation of a global approach, which is based on the method of adaptive genetic algorithm, to the optimization model. Afterwards, computer software is developed to optimize the operation conditions of gas trunk networks, gas gathering and distribution networks. Finally, an application example is demonstrated.
Keywords
- gas pipeline network
- operation optimiza
How to Cite
Changjun Li, Wenlong Jia, Yi Yang, and Xia Wu, "Adaptive Genetic Algorithm for Steady-State Operation Optimization in Natural Gas Networks," Journal of Software, vol. 6, no. 3, pp. 452-459, 2011. https://doi.org/ 10.4304/jsw.6.3.452-459
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
