微粒群进阶算法在天然气管道优化运行中的应用

Application of Advanced Particle Swarm Optimization Algorithm in Optimal Operation of Natural Gas Pipeline

将动态惩罚函数引入标准微粒群算法,结合输气管道工艺计算,基于C语言编制了一种可应用于求解天然气管道优化问题的微粒群进阶算法。以中亚天然气管道A/B线为例,对比验证了此算法在水力热力求解上的准确性,并将其应用于管道定流量稳态能耗优化运行研究中,实现了管道沿线压缩机组的能耗优化。借助微粒群进阶算法,将机组进、出口压力及末站交接压力作为边界控制条件,合理制定了该管道在不同输量台阶下的管存优化控制原则。并将目标、允许及安全管存控制区与管道优化运行研究相结合,通过微粒群进阶算法实现了管道在较优管存控制下的优化运行,为长输天然气管道的能耗及管存优化运行分析提供了一种新方法。

The self-adaptive dynamic penalty function is introduced into the basic particle swarm optimization(PSO)algorithm.Com⁃bined with the processing calculation of natural gas pipeline,an advanced particle swarm optimization algorithm for natural gas pipe⁃line optimization is developed based on C language.Taking A/B lines of China--Central Asia gas pipeline as an example,the accuracy of the algorithm in hydraulic thermodynamics solution is comparatively demonstrated.The algorithm is further applied to the steadystate energy consumption optimization operation with constant flow-rate of the A/B lines,and the energy consumption of compressor units along the pipelines is optimized.Taking the inlet/outlet pressure of the compressor units and the handover pressure at the termi⁃nal station of the pipeline as boundary control conditions,the optimization control principle of the pipeline gas inventory under differ⁃ent throughput steps is reasonably formulated.Combining goal,permission and safety stock control areas with the optimal operation re⁃search of pipeline,the optimal operation of pipeline under optimal stock control is realized by the advanced particle swarm algorithm.The research in this paper provides a new method for the optimal operation analysis of energy consumption and stock control of longdistance natural gas pipeline.