This paper discusses FMI usage in an in-house simulation tool landscape where it helps to open doors between different tools.However, limitations due to missing physical connectors or missing model structure are faced and are described with the help of use cases.Information hiding in FMI can turn out obstructive in in-house applications.Experiences from implementing FMI support in in-house simulation tools are shared.

A combined cycle power plant are modeled and considered for calibration. The dynamic model, aimed for start-up optimization, contains 64 candidate parameters for calibration. The number of parameter sets that can be created are huge and an algorithm called subset selection algorithm is used to reduce the number of parameter sets. The algorithm investigates the numerical properties of a calibration from a parameter Jacobean estimated from a simulation of the model with reasonably chosen parameter values. The calibrations were performed with a Levenberg-Marquardt algorithm considering the least squares of eight output signals. The parameter value with the best objective function value resulted in simulations in good compliance to the process dynamics. The subset selection algorithm effectively shows which parameters that are important and which parameters that can be left out.

This paper shows how different kinds of optimization related task such as offline optimization or optimal control are solved using a combination of Modelica, Optimica, JModelica.organd Python.The application examples presented in this paper are all real industrial applications in the field of Combined Cycle Power Plants.Therefore different workflows have to be combined to solve the underlying task.This paper shows that these workflows can be conveniently connected using Python.

In the electricity market of today, with increasing demand on electricity production on short notice, the combined cycle power plant stands high regarding fast start-ups and efficiency. In this thesis it has been shown how the start-up procedure of a combined cycle power plant can be optimized using JModelica.org, proposing a way to minimize the start-up time while maximizing the power production during start-up. The physical models have been developed in Modelica, adapted to suit optimization purposes and extended to optimization problems with the Optimica extension. Constraints keeping the lifetime consumption of critically stressed components under control have been limiting factors and the plant models have been successfully optimized to full load.

The industrial usage of the open-source Modelica tool OpenModelica was limited so far for power plant applications, due to the performance of large fluid systems. This paper presents some efforts to improve the simulation time on benchmark fluid models proposed by Siemens Energy. The main aspects presented here to achieve a faster simulation are an efficient evaluation of the jacobian matrix by a coloring technique, that exploits the sparsity pattern of a modelica model. Therefore the techniques are scratched and applied to