  To counter the volatile nature of renewable energy sources, gas networks take
a vital role. But, to ensure fulfillment of contracts under these
circumstances, a vast number of possible scenarios, incorporating uncertain
supply and demand, has to be simulated ahead of time. This many-query gas
network simulation task can be accelerated by model reduction, yet,
large-scale, nonlinear, parametric, hyperbolic partial differential(-algebraic)
equation systems, modeling natural gas transport, are a challenging application
for model order 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 parametric model
reduction for nonlinear 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.
