  Optimizing the transient control of gas networks is a highly challenging
task. The corresponding model incorporates the combinatorial complexity of
determining the settings for the many active elements as well as the non-linear
and non-convex nature of the physical and technical principles of gas
transport. In this paper, we present the latest improvements of our ongoing
work to solve this problem for real-world, large-scale problem instances: By
adjusting our mixed-integer non-linear programming model regarding the gas
compression capabilities in the network, we reflect the technical limits of the
underlying units more accurately while maintaining a similar overall model
size. In addition, we introduce a new algorithmic approach that is based on
splitting the complexity of the problem by first finding assignments for
discrete variables and then determining the continuous variables as locally
optimal solution of the corresponding non-linear program. For the first task,
we design multiple different heuristics based on concepts for general
time-expanded optimization problems that find solutions by solving a sequence
of sub-problems defined on reduced time horizons. To demonstrate the
competitiveness of our approach, we test our algorithm on particularly
challenging historic demand scenarios. The results show that high-quality
solutions are obtained reliably within short solving times, making the
algorithm well-suited to be applied at the core of time-critical industrial
applications.
