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This paper offers systematic guidelines for modeling power systems components in the phasor time-domain using the Modelica language and their verification. It aims to share the authors’ experience in power system modeling with Modelica and the approaches used to meet the high expectations of the power industry w.r.t. to the models’ simulation results. While the modeling guidelines are generic, the verification procedure includes the validation against a domain-specific commercial software tool called PSS®E that is the de facto tool used for power system transmission planning and analysis. To formalize the proposed approaches, a schematic description of the processes of model implementation and validation is elicited through flowcharts. Challenging use cases are presented to point out some of the major difficulties that can be faced in the modeling steps because of unclear or missing documentation of the models’ dynamics in the reference tool. Finally, unique features of the Modelica language that allow for power system modeling and verification unavailable in traditional tools are illustrated.

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In this paper, a benchmark between solvers and Modelica tools for time-domain simulations of a power system model is presented. A Python-based approach is employed to automate Modelica simulations and compute performance metrics. This routine is employed to compare the performance of a commercial (Dymola) against an open-source (OpenModelica) simulation tool with different solver settings. Python scripts are developed to execute a dynamic simulation of a common model for power system studies with 49 states and 420 variables in three different scenarios. This degree of automation makes it easier to change solver settings and tools during execution. 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. The commercial tool offers better performance for variable-step solver, but the performance of the open-source software shows significantly faster results for fixed-step solvers.