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

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