The flexible operation of combined cycle power plants (CCPPs) has recently become more and more important. Therefore an efficient, fast and highly predictable power plant start up is necessary. Model predictive control for power plant start up can be used to determine optimized setpoints for all relevant control variables (e.g. gas turbine load and steam turbine control valves) resulting in a fast plant startup while keeping all constraints. This paper presents the set up of a model predictive controller, its modeling particularities and solutions to the real time capability problem. The developed controller prototype was successfully tested online on a real plant. The measurement results, which are also presented in the paper, prove the large benefit of this technology within the scope of power plant start up. Modeling details are not within the scope of this paper, which rather focuses on the overall controller setup and the online applicability.

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.

The control test application shown in this paper demonstrates the usability of Modelica to run comprehensive tests for plant controllers involving large parts of the physical plant and the control system. However it highlights the need for further development; in areas such as: the test system; the discrete part of the models; the Modelica language; Modelica Association standards such as FMI and ModelicaXML and the tool support for these.

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

Fired steam generators are the dominating technology of coal combustion in power plants. This paper presents a model for pulverized coal fired steam generators in Modelica. The model components are designed as an extension of SiemensPower, a Modelica library for transient simulation of power plants. The focus is on coal combustion, gas-particle-flow and radiation heat transfer in the furnace. The dispersed flow of flue gas has to be modeled because radiation heat transfer gets considerably intensified by the contained coal and ash particles. Customized connectors for the dispersed flow of flue gas had to be developed on the base of Modelica.Fluid. The component oriented approach supports the adaption of the model to different simulation tasks, such as stability analysis of the evaporator or influence of the coal mill on plant dynamics. Component structure, parametrization and spatial discretization were important aspects for the development of maintainable and re-usable components of the model library.

This paper deals with the Thermal Separation Library, which is intended to be used for absorption and rectification processes. Two example calculations show how the simulation speed can be increased by choosing the right way to set up the equations. One example refers to the ordering of the substances in the substance vector and one refers to the modelling of equilibrium processes. An example of use presented is the CO2 absorption in a post-combustion carbon capture plant. The transient simulation results are compared to measurement data obtained in a Siemens pilot plant.

This article describes dynamic models of the carbon dioxide (CO2) -removal units which are coupled with conventional models to form a complete model of an IGCC power plant with CO2 capture. Therefore some components of the Modelica_Fluid 1.0 library and packages of the Modelica.Media library from Modelica 3.0 were used. Not yet available components were developed.

To optimize the performance of this kind of power plants and their components it is necessary to simulate and analyze their dynamic behavior. Therefore; a general model approach for steam turbines within Modelica has been developed. This model approach is based on a general model; which can be adjusted to the necessary model depth as described in this paper.

Modeling of large fluid systems requires in-house (specialized) tools, since applicability of Modelica and existing environments is limited. Nevertheless Modelica is a very powerful and descriptive modeling language, which is best suited for physical modeling in a heterogeneous environment. Its object oriented approach, the built-in documentation and the availability of commercial and free libraries justifies the decision for Modelica as the preferred modeling language within Siemens Energy. For an appropriate analysis of transient power plant processes, there often are large fluid systems to be modeled, i.e. there can be several thousand states. For such plant models, we use our in-house tool Dynaplant (DP), which is specialized for large fluid systems. A comparison between DP and Dymola[1] reveals some deficiencies of the Modelica world concerning performance and plant model construction: Especially, successive initialization and sparse matrix solvers are important features in need.