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

TL;DR

This paper presents a novel MIP-based heuristic approach for optimizing transient control in large-scale gas networks, effectively handling non-linearities and combinatorial complexity for real-world instances.

Contribution

The authors introduce an improved mixed-integer non-linear programming model and a new splitting algorithm with heuristics for discrete and continuous variables, enhancing solution quality and speed.

Findings

High-quality solutions achieved reliably within short times

Algorithm outperforms existing methods on challenging scenarios

Approach suitable for time-critical industrial applications

Abstract

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.