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Optimizing the transient control of gas networks is a highly challengingtask. The corresponding model incorporates the combinatorial complexity ofdetermining the settings for the many active elements as well as the non-linearand non-convex nature of the physical and technical principles of gastransport. In this paper, we present the latest improvements of our ongoingwork to solve this problem for real-world, large-scale problem instances: Byadjusting our mixed-integer non-linear programming model regarding the gascompression capabilities in the network, we reflect the technical limits of theunderlying units more accurately while maintaining a similar overall modelsize. In addition, we introduce a new algorithmic approach that is based onsplitting the complexity of the problem by first finding assignments fordiscrete variables and then determining the continuous variables as locallyoptimal solution of the corresponding non-linear program. For the first task,we design multiple different heuristics based on concepts for generaltime-expanded optimization problems that find solutions by solving a sequenceof sub-problems defined on reduced time horizons. To demonstrate thecompetitiveness of our approach, we test our algorithm on particularlychallenging historic demand scenarios. The results show that high-qualitysolutions are obtained reliably within short solving times, making thealgorithm well-suited to be applied at the core of time-critical industrialapplications.

Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics