To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment of contracts under these new circumstances, a vast number of possible scenarios, incorporating uncertain supply and demand, has to be simulated ahead of time. This many-query task can be accelerated by model order reduction, yet, large-scale, nonlinear, parametric, hyperbolic partial differential (-algebraic) equation systems, modeling gas transport, are a challenging application for model reduction algorithms. For this industrial application, we bring together the scientific computing topics of: mathematical modeling of gas transport networks, numerical simulation of hyperbolic partial differential equation, and model order reduction for nonlinear parametric systems. This research resulted in the "morgen" (Model Order Reduction for Gas and Energy Networks) software platform, which enables modular testing of various combinations of models, solvers, and model reduction methods. In this work we present the theoretical background on systemic modeling and structured, data-driven, system-theoretic model reduction for gas networks, as well as the implementation of "morgen" and associated numerical experiments testing model reduction adapted to gas network models.

中文翻译：

---

天然气和能源网络的模型订单减少

为了应对可再生能源的波动性，天然气网络起着至关重要的作用。但是，为了确保在这些新情况下履行合同，必须提前模拟大量不确定的情况，其中包括不确定的供求关系。可以通过模型降阶来加速此多查询任务，但是，大规模，非线性，参数化，双曲偏微分（-代数）方程组系统，模型化气体传输对模型约简算法而言是具有挑战性的应用。对于该工业应用，我们将以下科学计算主题汇集在一起​​：气体传输网络的数学建模，双曲型偏微分方程的数值模拟以及非线性参数系统的模型阶数约简。这项研究导致了“摩根” （天然气和能源网络的模型降阶）软件平台，可以对模型，求解器和模型降阶方法的各种组合进行模块化测试。在这项工作中，我们介绍了用于天然气网络的系统建模和结构化，数据驱动，系统理论模型简化的理论背景，以及“摩根”的实现以及适用于天然气网络模型的相关数值实验测试模型的简化。