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 benchmark models provided by Siemens Energy.
In the electricity market of today, with increasing demand for electricity production on short notice, the combined cycle power plant stands high regarding fast start-ups and efficiency. In this paper, it has been shown how the dynamic start-up procedure of a combined cycle power plant can be optimized using direct collocation methods, proposing a way to minimize the start-up time while maximizing the power production during start-up. Physical models derived from first principles have been developed in Modelica specifically for optimization purposes, in that the models contain no discontinuities. Also, the models used for optimization are simpler than typical highfidelity simulation models. Two different models used for optimization in four different start-up scenarios are presented in the paper. A critically limiting factor during start-up is the stress of important components, e.g., the evaporator. In order to take this aspect into account, constraints on the stress levels of such components have been introduced in the optimization formulation. In particular, it is shown how a pressure dependent stress constraint, similar to what is used in actual operation, can be applied in optimization. Also, different assumptions about which control variables to optimize are explored. Results are encouraging and show that energy production during start-up can be significantly increased by increasing the number of control inputs available to the optimizer, while maintaining desirable lifetime of critical components by introducing constrains on acceptable stress levels.
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.org and 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.
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.
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