Predictive control can substantially improve the energy performance of buildings during operation, but it requires a model of the building to be implemented. Gray-box model identification starts from a physics-based model (white-box element) and complements it with measurements from the operation of the building (black-box element). The level of detail of the original model is limited by the optimization problem that needs to be solved when estimating its parameters. Consequently, it is common to heavily simplify building models hindering the intelligibility of their parameters and limiting their application potential. This paper investigates the accuracy and scalability of different transcription methods for parameter estimation of building models. The methodology starts from a Modelica model as an initial guess which is transferred to CasADi using the Functional Mockup Interface to solve the parameter estimation problem. The study demonstrates the high effectiveness of multiple shooting. Single shooting and direct collocation could be more suitable for setups with faster integration times or with increased granularity in the training data, respectively.

Green production of hydrogen and its derivatives is becoming a cornerstone of industry decarbonation. Apart from the technological development point of view, optimizing the overall production chains dynamically is essential for the competitiveness of these systems.

In this paper, we describe how we built, validated and used a Modelica-based library dedicated to the simulation and optimization of energy process for the production of green molecules. Especially, models of complex media, salt cavity hydrogen storage and electrolysis module are presented.

An example application shows that the models of the library are particularly handy for the modeling of a 5MW electrolysis module, which is used for the calibration of an optimization model.

This paper presents the recently added support for import and export of functional mockup units (FMUs) in CasADi, an open-source software framework for numerical optimization. Of particular interest is the efficient calculation of derivatives, especially in the context of sensitivity analysis and dynamic optimization. We show how the import interface allows for both first and second derivatives can be efficiently and accurately calculated and - importantly - validated for correctness. We also outline the FMU export interface, which leverages CasADi mature and efficient support for forward and adjoint derivative calculation and C code generation. Finally, potential future developments of the support are discussed.

This paper presents the implementation of successive linearization based model predictive control (SLMPC) efforts through the interfacing of OpenModelica and Matlab using the OMMatlab tool. The dynamic system (here a chemical process) and the model predictive control (MPC) algorithm are implemented in OpenModelica and Matlab, respectively. The model linearization procedure is carried out through OMMatlab, which is highly optimized in terms of run-time by using a single executable file and adapting it at each sample time. Also, necessary theories for a continuous model discretization are discussed for both nonlinear Modelica and linearized continuous models. A procedure for constructing an Extended Kalman Filter (EKF) from a continuous Modelica model is also presented. The usability of the OpenModelica-Matlab interface for SLMPC is demonstrated by control of liquid

levels in a tanks-in-series problem.