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

Author

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  • 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.

Suggested Citation

  • Schwarz, Hannes & Kotthoff, Lars & Hoos, Holger & Fichtner, Wolf & Bertsch, Valentin, 2017. "Using automated algorithm configuration to improve the optimization of decentralized energy systems modeled as large-scale, two-stage stochastic programs," Working Paper Series in Production and Energy 24, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    • Handle: RePEc:zbw:kitiip:24
    DOI: 10.5445/IR/1000072492
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    References listed on IDEAS

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    1. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    2. Alper Atamtürk & Martin Savelsbergh, 2005. "Integer-Programming Software Systems," Annals of Operations Research, Springer, vol. 140(1), pages 67-124, November.
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    Keywords

    OR in energy; large-scale optimization; stochastic programming; uncertainty modeling; automated algorithm configuration; sequential model-based algorithm configuration;
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