3 research outputs found
Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics
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
Controlling transient gas flow in real-world pipeline intersection areas
Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network, they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany’s largest transmission system operators to develop a decision support system for their dispatching center, we investigated how to automatize the control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed-integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real-world data, we demonstrate the performance of our approach.TU Berlin, Open-Access-Mittel – 2020BMBF, 05M14ZAM, Forschungscampus Modal - Mathematical Optimization and Data Analysis Laboratories. Antrag auf die erste Hauptphase (Implementierung) des Forschungscampus Moda