Model order reduction for gas and energy networks
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 g...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-07-01
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| Series: | Journal of Mathematics in Industry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13362-021-00109-4 |
| _version_ | 1819137183660900352 |
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| author | Christian Himpe Sara Grundel Peter Benner |
| author_facet | Christian Himpe Sara Grundel Peter Benner |
| author_sort | Christian Himpe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T10:46:50Z |
| format | Article |
| id | doaj.art-218fe3b852014f9f93962e0e84abd612 |
| institution | Directory Open Access Journal |
| issn | 2190-5983 |
| language | English |
| last_indexed | 2024-12-22T10:46:50Z |
| publishDate | 2021-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Mathematics in Industry |
| spelling | doaj.art-218fe3b852014f9f93962e0e84abd6122022-12-21T18:28:54ZengSpringerOpenJournal of Mathematics in Industry2190-59832021-07-0111114610.1186/s13362-021-00109-4Model order reduction for gas and energy networksChristian Himpe0Sara Grundel1Peter Benner2Computational Methods in Systems and Control Theory Group at the Max Planck Institute for Dynamics of Complex Technical SystemsComputational Methods in Systems and Control Theory Group at the Max Planck Institute for Dynamics of Complex Technical SystemsComputational Methods in Systems and Control Theory Group at the Max Planck Institute for Dynamics of Complex Technical SystemsAbstract 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.https://doi.org/10.1186/s13362-021-00109-4Digital twinGas networkModel reductionEmpirical GramiansHyperbolic systems |
| spellingShingle | Christian Himpe Sara Grundel Peter Benner Model order reduction for gas and energy networks Journal of Mathematics in Industry Digital twin Gas network Model reduction Empirical Gramians Hyperbolic systems |
| title | Model order reduction for gas and energy networks |
| title_full | Model order reduction for gas and energy networks |
| title_fullStr | Model order reduction for gas and energy networks |
| title_full_unstemmed | Model order reduction for gas and energy networks |
| title_short | Model order reduction for gas and energy networks |
| title_sort | model order reduction for gas and energy networks |
| topic | Digital twin Gas network Model reduction Empirical Gramians Hyperbolic systems |
| url | https://doi.org/10.1186/s13362-021-00109-4 |
| work_keys_str_mv | AT christianhimpe modelorderreductionforgasandenergynetworks AT saragrundel modelorderreductionforgasandenergynetworks AT peterbenner modelorderreductionforgasandenergynetworks |