Model Order Reduction for Gas and Energy Networks

TL;DR

This paper develops a specialized model order reduction framework and software platform for efficient simulation of large-scale, nonlinear gas transport networks, aiding renewable energy integration.

Contribution

It introduces the 'morgen' platform combining system-theoretic model reduction with gas network modeling and provides theoretical and numerical validation.

Findings

Successful reduction of complex gas network models

Enhanced simulation speed for multiple scenarios

Validation of reduction methods on real-world models

Abstract

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.