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Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems

Kotthoff, Lars and Schwarz, Hannes and Hoos, Holger and Fichtner, Wolf and Bertsch, Valentin (2019) Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems. Annals of Operations Research. Springer. DOI: 10.1007/s10479-018-3122-6 ISSN 0254-5330

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Official URL: http://link.springer.com/article/10.1007/s10479-018-3122-6

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 systems, finding close-to-optimal solutions still requires substantial computational effort. In this work, we present a procedure to reduce this computational effort substantially, using a state-of-the-art automated algorithm configuration method. We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storage units, modeled as a two-stage stochastic mixed-integer linear program. We demonstrate that the computing time and costs can be substantially reduced by up to 50% by use of our procedure. Our methodology can be applied to other, similarly-modeled energy systems

Item URL in elib:https://elib.dlr.de/126364/
Document Type:Article
Title:Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kotthoff, LarsUNSPECIFIEDUNSPECIFIED
Schwarz, HannesUNSPECIFIEDUNSPECIFIED
Hoos, HolgerUNSPECIFIEDUNSPECIFIED
Fichtner, WolfUNSPECIFIEDUNSPECIFIED
Bertsch, ValentinUNSPECIFIEDUNSPECIFIED
Date:28 January 2019
Journal or Publication Title:Annals of Operations Research
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1007/s10479-018-3122-6
Publisher:Springer
ISSN:0254-5330
Status:Published
Keywords:OR in energy Large-scale optimization Stochastic programming Uncertainty modeling Automated algorithm configuration Sequential model-based algorithm configuration
HGF - Research field:Energy
HGF - Program:Technology, Innovation and Society
HGF - Program Themes:Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales
DLR - Research area:Energy
DLR - Program:E SY - Energy Systems Analysis
DLR - Research theme (Project):E - Systems Analysis and Technology Assessment
Location: Stuttgart
Institutes and Institutions:Institute of Engineering Thermodynamics > Systems Analysis and Technology Assessment
Deposited By: Borovleva, Tatiana
Deposited On:22 Mar 2019 16:43
Last Modified:22 Mar 2019 16:43

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