Olivier Chilard
EDF Research and development, Clamart, France
Jérémy Boes
Institut de Recherche en Informatique de Toulouse (IRIT), SMAC, Toulouse University, Universitå Paul Sabatier, France
Alexandre Perles
Institut de Recherche en Informatique de Toulouse (IRIT), SMAC, Toulouse University, Universitå Paul Sabatier, France
Guy Camilleri
Institut de Recherche en Informatique de Toulouse (IRIT), SMAC, Toulouse University, Universitå Paul Sabatier, France
Marie-Pierre Gleizes
Institut de Recherche en Informatique de Toulouse (IRIT), SMAC, Toulouse University, Universitå Paul Sabatier, France
Jean-Philippe Tavella
EDF Research and development, Clamart, France
Dominique Croteau
EDF Research and development, Clamart, France
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http://dx.doi.org/10.3384/ecp15118189Published in: Proceedings of the 11th International Modelica Conference, Versailles, France, September 21-23, 2015
Linköping Electronic Conference Proceedings 118:20, p. 189-196
Published: 2015-09-18
ISBN: 978-91-7685-955-1
ISSN: 1650-3686 (print), 1650-3740 (online)
The smart power grids will extensively rely on network control to increase efficiency, reliability, and safety; to enable plug-and-play asset integration, such as in the case of distributed generation and alternative energy sources; to support market dynamics as well as reduce peak prices and stabilize costs when supply is limited. In turn, network control requires an advanced communication infrastructure with support for safety and real-time communication.
Simulating such complex systems is a key objective for the development of Smart Grids. Several simulation tools are available on the market but these tools have two major drawbacks:
• They are generally not designed to import models developed for other tools.
• They are not adapted to large scale complex system of systems or cyber-physical systems as smart grids which require time-consuming calculation.
One solution to bypass these drawbacks is to use a co-simulation platform which can connect together several simulators and FMUs (Functional Mock-up unit).
ER and D is funding the development of its own co-simulation platform dedicated to the Smart Grids in partnership with LORIA-INRIA. A first release of this tool named MECSYCO is available under the Affero GPL license v3 (http://mecsyco.loria.fr/). The next published version (at the end of 2015) will upgrade MECSYCO with the coupling of different types of discrete-time or continuous-time simulators (including the FMUs) divided in three domains:
• The physics domain (process) : FMUs exported according to the FMI 2.0 standard from Dymola with models built from the EDF Modelica library GridSysPro or historical tools widely used at EDF (e.g. EMTP-RV) now compatible with the FMI standard;
• The telecommunication domain: NS-3, OMNeT++ or OPNeT ;
• The Information System domain with models designed with UML/SysML oriented tools.
MECSYCO is based on the Multi-Agent concept (one agent per simulator to describe a heterogeneous multi-model) and on the DEVS formalism (to conceive a decentralized execution algorithm respecting the causality constraints).
This paper provides first an overview of the ER and D Modelica library GridSysPro (GSP) composed of electrical components mapped on the zone related to the process of a Smart Grid. Besides that, to comply with the modeling of large scale electrical networks, a solution to co-initialize several interconnected FMUs exported from Dymola is described.
Julien Vaubourg, Yannick Presse, Benjamin Camus, Christine Bourjot, Laurent Ciarletta, Vincent Chevrier, Jean-Philippe Tavella, Hugo Morais, Boris Deneuville, & Olivier Chilard (PAAMS 2015). SmartGrid Simulation with MECSYCO.
Brax, N., Andonoff, E., Gleizes, M.-P., & Glize, P. (2013). Self-adaptive Aided Decision-making - Application to Maritime Surveillance. International Conference on Agents and Artificial Intelligence (ICAART), (pp. 419-422). Barcelona: INSTICC - Institute for Systems and Technologies of Information, Control and Communication.
Capera, D., Georgé, J.-P., Gleizes, M.-P., & Glize, P. (2003). The AMAS Theory for Complex Problem Solving Based on Self-organizing Cooperative Agents. International Workshop on Theory And Practice of Open Computational Systems, pp. 389-394.
Capera, D., Gleizes, M.-P., & Glize, P. (2004). Mechanism Type Synthesis based on Self-Assembling Agents. Journal of Applied Artificial Intelligence, 921-936.
Ferber, J. (1999). Multi-agent systems: an introduction to distributed artificial intelligence. Addison-Wesley Reading.
Georgé, J.-P., Gleizes, M.-P., & Camps, V. (2011). Cooperation. (G. Di Marzo Serugendo, M.-P. Gleizes, & A. Karageorgos, Eds.) Selforganising Software, 193–226.
Jorquera, T., Georgé, J.-P., Gleizes, M.-P., & Régis, C. (2013). A Natural Formalism and a MultiAgent Algorithm for Integrative Multidisciplinary Design Optimization. IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), Atlanta, USA, 17/11/2013-20/11/2013 (pp. 146 - 154).
IEEE Computer Society.
Whitehead, D. (2008). The el farol bar problem revisited : Reinforcement learning in a potential game. ESE Discussion Papers, 186.
Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. Knowledge engineering review, 10(2), 115-152.
