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

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

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.


Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Forschungsbericht
Jahr 2017
Sprache Englisch
Identifikator DOI(KIT): 10.5445/IR/1000072492
ISSN: 2196-7296
URN: urn:nbn:de:swb:90-724925
KITopen ID: 1000072492
Verlag Karlsruhe
Umfang 17 S.
Serie Working Paper Series in Production and Energy ; 24
Schlagworte OR in energy, large-scale optimization, stochastic programming, uncertainty modeling, automated algorithm configuration, sequential model-based algorithm configuration
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