Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/176750
Authors: 
Schwarz, Hannes
Kotthoff, Lars
Hoos, Holger
Fichtner, Wolf
Bertsch, Valentin
Year of Publication: 
2017
Series/Report no.: 
Working Paper Series in Production and Energy 24
Abstract: 
The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their large-scale, deterministic equivalent formulations. Unfortunately, using this approach, even when leveraging a high degree of parallelism on large high-performance computing (HPC) systems, finding close-to-optimal solutions still requires long computation. In this work, we present a procedure to reduce this computational effort substantially, using a stateof-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storages, modeled as a two-stage stochastic mixed-integer linear program (MILP). We demonstrate substantially reduced computing time and costs of up to 50% achieved by our procedure. Our methodology can be applied to other, similarly-modeled energy systems.
Subjects: 
OR in energy
large-scale optimization
stochastic programming
uncertainty modeling
automated algorithm configuration
sequential model-based algorithm configuration
Persistent Identifier of the first edition: 
Document Type: 
Working Paper
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