Name
Title | ||
|---|---|---|
Decision of optimal scheduling scheme for gas field pipeline network based on hybrid genetic algorithm |
Abstract | ||
|---|---|---|
A mathematical model of optimal scheduling scheme for natural gas pipeline network is established, which takes minimal annual operating cost of compressor stations as objective function after comprehensively considering the resources of gas field, operating parameters of compressor stations and work conditions of pipeline system. In the light of the characteristics of the objective function such as nonliner, more optimal variables and complicated constraint conditions, based on the thought of modern heuristic evolutionary-algorithm, this paper presented a new hybrid genetic algorithm, which is featured by global search, fast convergence and strong robustness. It combined the reproduction strategy of differential evolution algorithm with the crossover and mutation of genetic algorithm. With the dynamic calibration of fitness and the elitism strategy of the optimal individual, this algorithm can improve the ability of searching and avoid the premature convergence effectively. The case study of a certain pipeline network system with 11 nodes, 11 pipelines,2 compressor stations demonstrates the effectiveness and application of the established model and algorithm. The optimal scheduling scheme could be adapted to daily operation and future retrofit of gas pipeline network. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1543834.1543883 | GEC Summit |
Keywords | Field | DocType |
natural gas pipeline network,gas pipeline network,genetic algorithm,gas field pipeline network,optimal scheduling scheme,new hybrid genetic algorithm,objective function,certain pipeline network system,optimal individual,compressor station,differential evolution algorithm,mathematical model,optimization,natural gas,differential evolution,evolutionary algorithm,premature convergence | Compressor station,Heuristic,Mathematical optimization,Pipeline transport,Crossover,Premature convergence,Computer science,Scheduling (computing),Robustness (computer science),Artificial intelligence,Machine learning,Genetic algorithm | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||