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 tr...
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| Format: | Article |
| Language: | English |
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Université de Montpellier
2024-05-01
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| Series: | Open Journal of Mathematical Optimization |
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| Online Access: | https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.29/ |
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| author | Hennings, Felix Hoppmann-Baum, Kai Zittel, Janina |
| author_facet | Hennings, Felix Hoppmann-Baum, Kai Zittel, Janina |
| author_sort | Hennings, Felix |
| collection | DOAJ |
| description | 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 tackle 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 historical demand scenarios. The results show that high-quality solutions are obtained reliably within short run times, making the algorithm well-suited to be applied at the core of time-critical industrial applications. |
| format | Article |
| id | doaj-art-1d0acf0abd61463a835544558e9ecfe5 |
| institution | Kabale University |
| issn | 2777-5860 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Université de Montpellier |
| record_format | Article |
| series | Open Journal of Mathematical Optimization |
| spelling | doaj-art-1d0acf0abd61463a835544558e9ecfe52025-02-07T14:01:17ZengUniversité de MontpellierOpen Journal of Mathematical Optimization2777-58602024-05-01513410.5802/ojmo.2910.5802/ojmo.29Optimizing transient gas network control for challenging real-world instances using MIP-based heuristicsHennings, Felix0https://orcid.org/0000-0001-6742-1983Hoppmann-Baum, Kai1https://orcid.org/0000-0001-9184-8215Zittel, Janina2https://orcid.org/0000-0002-0731-0314Technische Universität Berlin Chair of Software and Algorithms for Discrete Optimization Straße des 17. Juni 135, 10623 Berlin, GermanyZuse Institute Berlin Applied Algorithmic Intelligence Methods Department Takustraße 7, 14195 Berlin, GermanyZuse Institute Berlin Applied Algorithmic Intelligence Methods Department Takustraße 7, 14195 Berlin, GermanyOptimizing 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 tackle 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 historical demand scenarios. The results show that high-quality solutions are obtained reliably within short run times, making the algorithm well-suited to be applied at the core of time-critical industrial applications.https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.29/Transient Gas Network OptimizationSequential Mixed-Integer ProgrammingRolling Horizon HeuristicAggregated Horizon HeuristicReal-World Historical InstancesIndustry-Ready |
| spellingShingle | Hennings, Felix Hoppmann-Baum, Kai Zittel, Janina Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics Open Journal of Mathematical Optimization Transient Gas Network Optimization Sequential Mixed-Integer Programming Rolling Horizon Heuristic Aggregated Horizon Heuristic Real-World Historical Instances Industry-Ready |
| title | Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics |
| title_full | Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics |
| title_fullStr | Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics |
| title_full_unstemmed | Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics |
| title_short | Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics |
| title_sort | optimizing transient gas network control for challenging real world instances using mip based heuristics |
| topic | Transient Gas Network Optimization Sequential Mixed-Integer Programming Rolling Horizon Heuristic Aggregated Horizon Heuristic Real-World Historical Instances Industry-Ready |
| url | https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.29/ |
| work_keys_str_mv | AT henningsfelix optimizingtransientgasnetworkcontrolforchallengingrealworldinstancesusingmipbasedheuristics AT hoppmannbaumkai optimizingtransientgasnetworkcontrolforchallengingrealworldinstancesusingmipbasedheuristics AT zitteljanina optimizingtransientgasnetworkcontrolforchallengingrealworldinstancesusingmipbasedheuristics |