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Using automated algorithm configuration to improve the optimization of decentralized energy systems modeled as large-scale, two-stage stochastic programs

Hannes Schwarz, Lars Kotthoff, Holger Hoos, Wolf Fichtner and Valentin Bertsch

No 24, Working Paper Series in Production and Energy from Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP)

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.

Keywords: OR in energy; large-scale optimization; stochastic programming; uncertainty modeling; automated algorithm configuration; sequential model-based algorithm configuration (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-ore
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