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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper Series in Production and Energy ; No. 24

Classification and Topics

• Classification: Wirtschaft
• Subject: OR in energy ; large-scale optimization ; stochastic programming ; uncertainty modeling ; automated algorithm configuration ; sequential model-based algorithm configuration

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Schwarz, Hannes ; Kotthoff, Lars ; Hoos, Holger ; Fichtner, Wolf ; Bertsch, Valentin
• Event: Veröffentlichung
• (who): Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP)
• (where): Karlsruhe
• (when): 2017

Further information

• DOI: doi:10.5445/IR/1000072492
• Handle: http://hdl.handle.net/10419/176750
• URN: urn:nbn:de:swb:90-724925
• Last update: 10.03.2025, 11:42 AM CET