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Für die Energiesystemforschung sind Software-Modelle ein Kernelement zur Analyse von Szenarien. Das Forschungsprojekt UNSEEN hatte das Ziel eine bisher unerreichte Anzahl an modellbasierten Energieszenarien zu berechnen, um Unsicherheiten – vor allem unter Nutzung linear optimierender Energiesystem-Modelle - besser bewerten zu können. Hierfür wurden umfangreiche Parametervariationen auf Energieszenarien angewendet und das wesentliche methodische Hindernis in diesem Zusammenhang adressiert: die rechnerische Beherrschbarkeit der zu lösenden mathematischen Optimierungsprobleme. Im Vorläuferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open-Source-Lösers PIPS-IPM++ die Grundlage für den Einsatz von High-Performance-Computing (HPC) zur Lösung dieser Modelle gelegt. In UNSEEN war dieser Löser die zentrale Komponente eines Workflows, welcher zur Generierung, Lösung und multi-kriteriellen Bewertung von Energieszenarien auf dem Hochleistungscomputer JUWELS am Forschungszentrum Jülich implementiert wurde. Zur effizienten Generierung und Kommunikation von Modellinstanzen für Methoden der mathematischen Optimierung auf HPC wurde eine weitere Workflow-Komponente von der GAMS Software GmbH entwickelt: der Szenariogenerator. Bei der Weiterentwicklung von Lösungsalgorithmen für linear optimierende Energie-Systemmodelle standen gemischt-ganzzahlige Optimierungsprobleme im Fokus, welche für die Modellierung konkreter Infrastrukturen und Maßnahmen zur Umsetzung der Energiewende gelöst werden müssen. Die in diesem Zusammenhang stehenden Arbeiten zur Entwicklung von Algorithmen wurden von der Technischen Universität Berlin verantwortet. Bei Design und Implementierung dieser Methoden wurde sie vom Zuse Instituts Berlin unterstützt.
Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions
to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.
BEAM-ME: Accelerating Linear Energy Systems Models by a Massively Parallel Interior Point Method
(2020)
Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.
Energy system optimization models are increasing in scope and resolution, integrating detailed technology representations, sector coupling, and multiple scenarios reflecting uncertainty in future energy demand. These advances yield large and challenging linear programs whose efficient solution remains a bottleneck in practical energy system analyses. For a long time, the standard way to address such problems has relied on shared-memory interior-point methods (IPM), which combine robustness and accuracy but face scalability limits as model instance size grows.
Specialized solver architectures are beginning to change this picture. Two promising directions have emerged: (i) GPU-accelerated first-order methods (FOM) such as the primal-dual linear programming approach; and (ii) distributed-memory IPM, exemplified by the open-source solver PIPS-IPM++, which can exploit block structure that arises in many energy system models and achieves high parallelism on highperformance computing systems. These developments open new opportunities for large-scale optimization in energy system analysis.
This paper presents a computational study comparing these solver classes on a diverse test set of largescale linear programs arising from energy system analysis, including scenario-based formulations derived from stochastic programming. We investigate how parallelization strategies affect solution time and numerical accuracy. The results illustrate that distributed-memory IPM can leverage problem structure to deliver substantial speed-ups on specific problems with block-angular structures. GPU-accelerated FOMs demonstrate strong scalability but may yield solutions with higher relative infeasibilities, which, depending on the use case and model uncertainty, can still be acceptable.
Overall, our findings indicate that recent algorithmic and hardware advances substantially broaden the computational toolbox available to the energy system optimization community. Each solver class exhibits distinct advantages: shared-memory IPMs remain a powerful tool for reliably obtaining high-accuracy solutions; distributed-memory IPMs can extend scalability to hundreds of cores for certain structured models, enabling faster time-to-solution; and GPU-based FOM can deliver fast solutions when such lower accuracy levels are appropriate. Together, they help make high-resolution, multi-scenario energy system optimization models tractable across a broader range of problem sizes and computing environments.