001     911684
005     20250317091733.0
024 7 _ |a 2128/32730
|2 Handle
037 _ _ |a FZJ-2022-04939
100 1 _ |a Breuer, Thomas
|0 P:(DE-Juel1)138707
|b 0
|e Corresponding author
|u fzj
111 2 _ |a ISC High Performance 2022
|g ISC 2022
|c Hamburg
|d 2022-05-29 - 2022-06-02
|w Germany
245 _ _ |a Evaluation of uncertainties in linear energy system optimization models using HPC and neural networks
260 _ _ |c 2022
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
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336 7 _ |a Output Types/Conference Poster
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336 7 _ |a Poster
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520 _ _ |a Within the interdisciplinary BMWK-funded project UNSEEN, experts from High Performance Computing, mathematical optimization and energy systems analysis combine strengths to evaluate uncertainties in modeling and planning future energy systems with the aid of High Performance Computing (HPC) and neural networks. Energy System Models (ESM) are central instruments for realizing the energy transition. These models try to optimize complex energy systems in order to ensure security of supply while minimizing costs for power production and transmission. In order to derive reliable and robust policy advice for decision makers, hundreds or even thousands of ESM problems need to be solved in order to address uncertainties in a given model and dataset.Mixed-integer linear programs (MIPs), a direct extension of Linear programs (LPs), can be used to formulate and compute more concrete and realistic energy systems. Since the availability of fast LP solvers is a major prerequisite for optimizing MIPs, the development of an open-source scalable distributed-memory LP solver, called PIPS-IPM++, was started in a preceding project and can already outperform state-of-the-art solvers. A second prerequisite for efficient MIP solving is the availability of MIP heuristics. For this purpose, we develop a generic MIP framework including reinforcement learning methods. Moreover, we aim to implement an efficient automated HPC workflow for generating, solving, and postprocessing numerous ESM problems with a special structure in order to develop new tools for better predictions about the future of our energy system. This novel approach couples multiple existing and new software packages to achieve the project goals.
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
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536 _ _ |a Verbundvorhaben: UNSEEN ' Bewertung der Unsicherheiten in linear optimierenden Energiesystem-Modellen unter Zuhilfenahme Neuronaler Netze, Teilvorhaben: Entwicklung einer integrierten HPC-Workflow Umgebung zur Kopplung von Optimierungsmethoden mit Methode (03EI1004F)
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536 _ _ |0 G:(DE-Juel-1)ATMLAO
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588 _ _ |a Dataset connected to DataCite
700 1 _ |a Cao, Karl-Kiên
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Fiand, Fred
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Fuchs, Benjamin
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Koch, Thorsten
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Vanaret, Charlie
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Wetzel, Manuel
|0 P:(DE-HGF)0
|b 6
856 4 _ |u https://juser.fz-juelich.de/record/911684/files/UNSEEN_ISC_2022_Poster.pdf
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909 C O |o oai:juser.fz-juelich.de:911684
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Deutsches Zentrum für Luft- und Raumfahrt
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|6 P:(DE-HGF)0
910 1 _ |a GAMS Software GmbH
|0 I:(DE-HGF)0
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|6 P:(DE-HGF)0
910 1 _ |a Deutsches Zentrum für Luft- und Raumfahrt
|0 I:(DE-HGF)0
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|6 P:(DE-HGF)0
910 1 _ |a Zuse Institute Berlin
|0 I:(DE-HGF)0
|b 4
|6 P:(DE-HGF)0
910 1 _ |a Technische Universität Berlin
|0 I:(DE-HGF)0
|b 5
|6 P:(DE-HGF)0
910 1 _ |a Deutsches Zentrum für Luft- und Raumfahrt
|0 I:(DE-HGF)0
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913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
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|v Enabling Computational- & Data-Intensive Science and Engineering
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914 1 _ |y 2022
915 _ _ |a OpenAccess
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920 1 _ |0 I:(DE-Juel1)JSC-20090406
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980 1 _ |a FullTexts
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406


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