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

  • Hannes SchwarzEmail author
  • Lars Kotthoff
  • Holger Hoos
  • Wolf Fichtner
  • Valentin Bertsch
S.I.: Stochastic Modeling and Optimization, in memory of András Prékopa
  • 25 Downloads

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.

Keywords

OR in energy Large-scale optimization Stochastic programming Uncertainty modeling Automated algorithm configuration Sequential model-based algorithm configuration 

Notes

Acknowledgements

The authors would like to gratefully acknowledge the funding provided by the Helmholtz Association of German Research Centers via the Research Programme “Storage and Cross-Linked Infrastructures”. In addition, the authors also acknowledge support by the state of Baden-Württemberg through bwHPC and the German Research Foundation (DFG) through Grant No. INST 35/1134-1 FUGG, as well as through an NSERC Discovery Grant. We also acknowledge the use of Compute Canada/Calcul Canada computing resources. Valentin Bertsch acknowledges funding from the ESRI’s Energy Policy Research Centre. All omissions and errors are our own.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chair of Energy Economics, Institute for Industrial Production (IIP)Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.Department of Computer ScienceUniversity of WyomingLaramieUSA
  3. 3.Leiden Institute of Advanced Computer Science (LIACS)Universiteit LeidenLeidenThe Netherlands
  4. 4.Department of Computer ScienceUniversity of British Columbia (UBC)VancouverCanada
  5. 5.Economic and Social Research Institute (ESRI)DublinIreland
  6. 6.Department of Economics, Trinity College DublinDublinIreland
  7. 7.Department of Energy Systems Analysis, German Aerospace Center (DLR)StuttgartGermany
  8. 8.University of StuttgartStuttgartGermany

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