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000911684 1001_ $$0P:(DE-Juel1)138707$$aBreuer, Thomas$$b0$$eCorresponding author$$ufzj
000911684 1112_ $$aISC High Performance 2022$$cHamburg$$d2022-05-29 - 2022-06-02$$gISC 2022$$wGermany
000911684 245__ $$aEvaluation of uncertainties in linear energy system optimization models using HPC and neural networks
000911684 260__ $$c2022
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000911684 520__ $$aWithin the interdisciplinary BMWK-funded project UNSEEN, experts from High Performance Computing, mathematical optimization and energy systems analysis combine strengths to evaluate uncertainties in modeling and planning future energy systems with the aid of High Performance Computing (HPC) and neural networks. Energy System Models (ESM) are central instruments for realizing the energy transition. These models try to optimize complex energy systems in order to ensure security of supply while minimizing costs for power production and transmission. In order to derive reliable and robust policy advice for decision makers, hundreds or even thousands of ESM problems need to be solved in order to address uncertainties in a given model and dataset.Mixed-integer linear programs (MIPs), a direct extension of Linear programs (LPs), can be used to formulate and compute more concrete and realistic energy systems. Since the availability of fast LP solvers is a major prerequisite for optimizing MIPs, the development of an open-source scalable distributed-memory LP solver, called PIPS-IPM++, was started in a preceding project and can already outperform state-of-the-art solvers. A second prerequisite for efficient MIP solving is the availability of MIP heuristics. For this purpose, we develop a generic MIP framework including reinforcement learning methods. Moreover, we aim to implement an efficient automated HPC workflow for generating, solving, and postprocessing numerous ESM problems with a special structure in order to develop new tools for better predictions about the future of our energy system. This novel approach couples multiple existing and new software packages to achieve the project goals.
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000911684 536__ $$0G:(BMWi)03EI1004F$$aVerbundvorhaben: UNSEEN ' Bewertung der Unsicherheiten in linear optimierenden Energiesystem-Modellen unter Zuhilfenahme Neuronaler Netze, Teilvorhaben: Entwicklung einer integrierten HPC-Workflow Umgebung zur Kopplung von Optimierungsmethoden mit Methode (03EI1004F)$$c03EI1004F$$x1
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000911684 7001_ $$0P:(DE-HGF)0$$aCao, Karl-Kiên$$b1
000911684 7001_ $$0P:(DE-HGF)0$$aFiand, Fred$$b2
000911684 7001_ $$0P:(DE-HGF)0$$aFuchs, Benjamin$$b3
000911684 7001_ $$0P:(DE-HGF)0$$aKoch, Thorsten$$b4
000911684 7001_ $$0P:(DE-HGF)0$$aVanaret, Charlie$$b5
000911684 7001_ $$0P:(DE-HGF)0$$aWetzel, Manuel$$b6
000911684 8564_ $$uhttps://juser.fz-juelich.de/record/911684/files/UNSEEN_ISC_2022_Poster.pdf$$yOpenAccess
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000911684 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Deutsches Zentrum für Luft- und Raumfahrt $$b3
000911684 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Zuse Institute Berlin$$b4
000911684 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Technische Universität Berlin$$b5
000911684 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Deutsches Zentrum für Luft- und Raumfahrt $$b6
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000911684 9141_ $$y2022
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