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首页> 外文会议>Biennial International Pipeline Conference(IPC 2004) vol.3; 20041004-08; Calgary(CA) >Multi-objective Optimization of Large Pipeline Networks Using Genetic Algorithm
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Multi-objective Optimization of Large Pipeline Networks Using Genetic Algorithm

机译：基于遗传算法的大型管道网络多目标优化

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摘要

This paper presents application of Genetic Algorithm (GA) methodologies to multi-objective optimization of two complex gas pipeline networks to achieve specific operational objectives. The first network contains 10 compressor stations resulting in 20 decision variables and an optimization space of 6.3x10~(29) cases. The second system contains 25 compressor stations resulting in 54 decision variables and an optimization space of 1.85x10~(78) cases. Compressor stations generally included multiple unit sites, where the compressor characteristics of each unit is taken into account constraining the solution by the surge and stonewall limits, maximum and minimum speeds and maximum power available. A key challenge to the optimization of such large systems is the number of constraints and associated penalty functions, selection of the GA operators such as crossover, mutation, selection criteria and elitism, as well as the population size and number of generations. The paper discusses the approach taken to arrive at optimal values for these parameters for large gas pipeline networks. Examples for two-objective optimizations, referred to as Pareto fronts, include maximum throughput and minimum fuel, as well as, minimum linepack and maximum throughput in typical linepack/throughput/fuel envelopes.
机译：本文介绍了遗传算法（GA）方法在两个复杂的天然气管道网络的多目标优化中实现特定操作目标的应用。第一个网络包含10个压缩站，产生20个决策变量和6.3x10〜（29）个案例的优化空间。第二个系统包含25个压缩机站，产生54个决策变量，优化空间为1.85x10〜（78）个案例。压缩机站通常包括多个单元站点，其中考虑了每个单元的压缩机特性，它们受喘振和石壁限制，最大和最小速度以及可用的最大功率限制了解决方案。优化此类大型系统的关键挑战是约束条件和相关惩罚函数的数量，GA算子的选择（例如交叉，变异，选择标准和精英），以及种群规模和世代数。本文讨论了为大型天然气管道网络获得这些参数的最佳值所采用的方法。称为Pareto前沿的两个目标优化的示例包括最大吞吐量和最小燃料量，以及典型的线组/吞吐量/燃料包络线中的最小线组和最大吞吐量。

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