Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network, they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany’s largest transmission system operators to develop a decision support system for their dispatching center, we investigated how to automatize the control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed-integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real-world data, we demonstrate the performance of our approach.

压缩机站是每个高压气体输送网络的心脏。它们位于网络的交叉区域，被包含在巨大的复杂工厂中，在这里它们与阀门和调节器结合在一起，负责路由和推动天然气通过网络。由于其复杂性和数据压缩站的缺乏，通常在科学文献中以高度简化和理想化的方式来处理。作为正在进行的项目的一部分，该项目与德国最大的传输系统运营商之一一起为其调度中心开发决策支持系统，我们研究了如何自动控制压缩机站。每个工作站必须采用特定的配置，导致与附近其他元素的组合导致相交区域中多达2000种可能的可行操作模式的离散集合。由于工作站的期望性能会随时间变化，因此必须适应工作站的配置。我们的目标是在满足预设的性能范围或需求方案的同时，将随时间变化的整体操作模式和相关元素的必要变化减至最小。本文介绍了选择的模型以及已实现的基于混合整数编程的算法来解决这一难题。通过在现实世界的数据上显示大量的计算结果，我们证明了该方法的性能。我们的目标是在满足预设的性能范围或需求方案的同时，将随时间变化的整体操作模式和相关元素的必要变化减至最小。本文介绍了选择的模型以及已实现的基于混合整数编程的算法来解决这一难题。通过在现实世界的数据上显示大量的计算结果，我们证明了该方法的性能。我们的目标是在满足预设的性能范围或需求方案的同时，将随时间变化的整体操作模式和相关元素的必要变化减至最小。本文介绍了选择的模型以及已实现的基于混合整数编程的算法来解决这一难题。通过在现实世界的数据上显示大量的计算结果，我们证明了该方法的性能。