Authors: Sergio A. Dorado-Rojas (sergio.dorado.rojas@gmail.com), Manuel Navarro Catalan (manosnavarro@gmail.com), Marcelo de Castro Fernandes (decasm3@rpi.edu), Luigi Vanfretti (luigi.vanfretti@gmail.com)
This work has been submitted to the American Modelica Conference 2020.
For pulling, contact Sergio A. Dorado-Rojas (sergio.dorado.rojas@gmail.com) or Manuel Navarro Catalan (manosnavarro@gmail.com)
In this paper, a Python-based approach to automate Modelica time-domain simulations of a power system model is presented. This routine is employed to benchmark the performance of a commercial (Dymola) against an open-source (OpenModelica) simulation tool with different solver settings. Python scripts are developed to execute a fairly large dynamic simulation of a model of about 800 states in three different scenarios. This degree of automation makes it easier to change solver settings straightforwardly. The performance of each of the tools is assessed through metrics such as execution time and CPU utilization. The quantitative comparison results provide a clear reference to the performance of the tools and solvers for the execution of time-domain simulations with a significant degree of complexity.