When simulating a Modelica model, non-linear algebraic loops may be present, which involves solving multiple equations simultaneously. The classical Newton-Raphson method is commonly employed for solving a non-linear equation system (NLS). However, the computational burden of using this method during simulation can be significant. To tackle this issue, utilizing artificial neural networks (ANNs) to approximate the solution of algebraic loops is a promising approach. While ANN surrogates offer fast performance, ensuring the correctness of the computed solution or quantifying reliability can be challenging.

This publication presents a prototype, based on the OpenModelica compiler (OMC), that automates the extraction of time-consuming algebraic loops. It generates training data, trains ANNs using machine learning (ML) methods, and replaces the algebraic loops with ANN surrogates in the simulation code. A hybrid approach, combining the trained surrogate with the nonlinear Newton solver, is then used to compute the solution with a desired level of accuracy.

New robust and yet powerful Modelica libraries as the DLR ThermoFluid Stream library or the introduction of dialectic mechanics use a special modeling approach that uses linear implicit equilibrium dynamics. In this paper, we study the basic motivation of this approach, its benefits and drawbacks before finally showing how to get from the models to applicable simulation code.

The need for modeling different aspects of microgrid design and operation has seen the development of various tools over time for different analysis purposes. In this study, Modelica has been adopted as the language of choice to construct a University Campus Microgrid model, utilizing the Modelica Standard Library and the OpenIPSL library. This paper explores the advantages of utilizing Modelica for campus microgrid modeling, emphasizing its benefits and unique features. Modelica features, such as the use of record structures and replaceable templates prove to be particularly advantageous for the modeling task, enabling flexibility and efficiency in the modeling process. Furthermore, comprehensive validation tests are conducted to ensure the accuracy and reliability of sub-systems (e.g. specific power generator systems), before assembling the microgrid network model as a whole. The results demonstrate the efficacy of Modelica in accurately modeling and simulating microgrids, highlighting its potential for advancing microgrid research and development.

A key feature of the Modelica language is its object-oriented nature: components are instances of classes and they can aggregate other components, so that extremely large models can be efficiently designed as ``trees of components''. However, the structural analysis of Modelica models, a necessary step for generating simulation code, often relies on the flattening of this hierarchical structure, which undermines the scalability of the language and results in widely-used Modelica tools not being able to compile and simulate such large models.

In this paper, we propose a novel method for the modular structural analysis of Modelica models. An adaptation of Pryce's Sigma-method for non-square DAE systems, along with a carefully crafted notion of component interface, make it possible to fully exploit the object tree structure of a model. The structural analysis of a component class can be performed once and for all, only requiring the information provided by the interface of its child components. The resulting method alleviates the exponential computation costs that can be yielded by model flattening; hence, its scalability makes it ideally suited for the modeling and simulation of large cyber-physical systems.