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Schwarz, Hannes
Kotthoff, Lars
Hoos, Holger
Fichtner, Wolf
Bertsch, Valentin
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Working Paper Series in Production and Energy 24
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.
OR in energy
large-scale optimization
stochastic programming
uncertainty modeling
automated algorithm configuration
sequential model-based algorithm configuration
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