Using automated algorithm configuration to improve the optimization of decentralized energy systems modeled as large-scale, two-stage stochastic programs
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.
Year of publication: |
2017
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Authors: | Schwarz, Hannes ; Kotthoff, Lars ; Hoos, Holger ; Fichtner, Wolf ; Bertsch, Valentin |
Publisher: |
Karlsruhe : Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP) |
Subject: | OR in energy | large-scale optimization | stochastic programming | uncertainty modeling | automated algorithm configuration | sequential model-based algorithm configuration |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 10.5445/IR/1000072492 [DOI] 895053713 [GVK] hdl:10419/176750 [Handle] RePEc:zbw:kitiip:24 [RePEc] |
Source: |
Persistent link: https://ebvufind01.dmz1.zbw.eu/10011812611