98 research outputs found
The OpenModelica integrated environment for modeling, simulation, and model-based development
OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica- UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.Fil: Fritzson, Peter. Linköping University; SueciaFil: Pop, Adrian. Linköping University; SueciaFil: Abdelhak, Karim. Fachhochschule Bielefeld; AlemaniaFil: Asghar, Adeel. Linköping University; SueciaFil: Bachmann, Bernhard. Fachhochschule Bielefeld; AlemaniaFil: Braun, Willi. Fachhochschule Bielefeld; AlemaniaFil: Bouskela, Daniel. Electricité de France; FranciaFil: Braun, Robert. Linköping University; SueciaFil: Buffoni, Lena. Linköping University; SueciaFil: Casella, Francesco. Politecnico di Milano; ItaliaFil: Castro, Rodrigo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Franke, Rüdiger. Abb Group; AlemaniaFil: Fritzson, Dag. Linköping University; SueciaFil: Gebremedhin, Mahder. Linköping University; SueciaFil: Heuermann, Andreas. Linköping University; SueciaFil: Lie, Bernt. University of South-Eastern Norway; NoruegaFil: Mengist, Alachew. Linköping University; SueciaFil: Mikelsons, Lars. Linköping University; SueciaFil: Moudgalya, Kannan. Indian Institute Of Technology Bombay; IndiaFil: Ochel, Lennart. Linköping University; SueciaFil: Palanisamy, Arunkumar. Linköping University; SueciaFil: Ruge, Vitalij. Fachhochschule Bielefeld; AlemaniaFil: Schamai, Wladimir. Danfoss Power Solutions GmbH & Co; AlemaniaFil: Sjolund, Martin. Linköping University; SueciaFil: Thiele, Bernhard. Linköping University; SueciaFil: Tinnerholm, John. Linköping University; SueciaFil: Ostlund, Per. Linköping University; Sueci
Future Perspectives of Co-Simulation in the Smart Grid Domain
The recent attention towards research and development in cyber-physical
energy systems has introduced the necessity of emerging multi-domain
co-simulation tools. Different educational, research and industrial efforts
have been set to tackle the co-simulation topic from several perspectives. The
majority of previous works has addressed the standardization of models and
interfaces for data exchange, automation of simulation, as well as improving
performance and accuracy of co-simulation setups. Furthermore, the domains of
interest so far have involved communication, control, markets and the
environment in addition to physical energy systems. However, the current
characteristics and state of co-simulation testbeds need to be re-evaluated for
future research demands. These demands vary from new domains of interest, such
as human and social behavior models, to new applications of co-simulation, such
as holistic prognosis and system planning. This paper aims to formulate these
research demands that can then be used as a road map and guideline for future
development of co-simulation in cyber-physical energy systems
Simulation-Based Evaluation and Optimization of Control Strategies in Buildings
Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.Research leading to these results has been partially supported by the Modelling Optimization of Energy
Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from
the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517.
Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission
H2020-EeB5-2015 project “Optimised Energy Efficient Design Platform for Refurbishment at District Level” under
Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial
support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning
Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen
gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded
by MOEEBIUS project. This paper reflects only the authors’ views and the Commission is not responsible for any
use that may be made of the information contained therein
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