Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems
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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.
KeywordsOR in energy Large-scale optimization Stochastic programming Uncertainty modeling Automated algorithm configuration Sequential model-based algorithm configuration
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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