Improving Computation Time for Optimization Runs of Modelica-Based Energy Systems

Sven Klute (), Markus Hadam, Mathias van Beek and Marcus Budt
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Sven Klute: Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, 46047 Oberhausen, Germany
Markus Hadam: Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, 46047 Oberhausen, Germany
Mathias van Beek: Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, 46047 Oberhausen, Germany
Marcus Budt: Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT, 46047 Oberhausen, Germany

Energies, 2024, vol. 17, issue 1, 1-13

Abstract: Mathematical optimization is a widespread method in order to improve, for instance, the efficiency of energy systems. A simulation approach based on partial differential equations can typically not be formulated as an optimization problem, thus requiring interfacing to an external optimization environment. This is, among others, also true for the programming language Modelica. Because of high computation time, such coupled approaches are often limited to small-scale optimization problems. Since simulation models tend to get more complex, simulation time and, in turn, associated optimization time rise significantly. To enable proper sampling of the search space, individual optimization runs need to be solved in acceptable times. This paper addresses the search for a proper optimization approach and tool to couple with Modelica/Dymola. The optimization is carried out on an exemplary power plant model from the ClaRa-Library using an evolutionary algorithm (SPEA2-based) with Ansys optiSlang. To verify and evaluate the results, a comparison with the standard Dymola optimization library is performed. Both parallelization and indirect optimization with surrogate models achieved a significant runtime reduction by a factor of up to 5.4. The use of meta models is particularly advantageous for repetitive optimization runs of the same optimization problem but may lead to deviations due to the calculated approximations.

Keywords: mathematical optimization; runtime reduction; parallelization methods; energy systems; meta model; modelica; dymola; Ansys optiSLang (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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