Dynamic Data-Driven Experiments in the Smart Grid Domain with a Multi-agent Platform

  • Zülküf GençEmail author
  • Michel Oey
  • Hendrik van Antwerpen
  • Frances Brazier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9568)


Pervasive information and communication technologies and large-scale complex systems, are strongly influencing today’s networked society. Understanding the behaviour and impact of such distributed, often emergent systems on society is of vital importance. This paper proposes a new approach to better understand the complexity of large-scale participatory systems in the context of smart grids. Multi-agent based distributed simulations of realistic multi-actor scenarios incorporating real-time dynamic data and active participation of actors is the means to this purpose. The Symphony experiment platform, developed to study complex emergent behaviours and to facilitate the analysis of the system dynamics and actor interactions, is the enabler.


Smart Grid Autonomous Actor High Level Architecture Virtual Power Plant Smart Grid Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Symphony has been partly developed within the EIT ICT Labs projects Open SES Experience Labs (11831) and European Virtual Smart Grid Lab (11814). Partners in these projects include TU-Delft, CWI The Netherlands, Imperial College London, Fortiss, Fraunhofer FOKUS, DFKI, TU-Berlin, and DAI-Labor, to whom the authors are very grateful.


  1. 1.
    AgentScape - Distributed Agent Middleware.
  2. 2.
    Nist framework and roadmap for smart grid interoperability standards, release 3.0. Technical report, National Institute of Standards and Technology (2014)Google Scholar
  3. 3.
    Ahrenholz, J., Danilov, C., Henderson, T., Kim, J.: CORE: a real-time network emulator. In: Military Communications Conference, MILCOM 2008, pp. 1–7. IEEE, November 2008Google Scholar
  4. 4.
    Amin, S.M., Wollenberg, B.F.: Toward a smart grid: power delivery for the 21st century. Power and Energy Magazine 3(5), 34–41 (2005)CrossRefGoogle Scholar
  5. 5.
    Bar-Yam, Y.: Dynamics of complex systems, vol. 213. Addison-Wesley, Reading (1997)zbMATHGoogle Scholar
  6. 6.
    Chassin, D., Schneider, K., Gerkensmeyer, C.: GridLAB-D: an open-source power systems modeling and simulation environment. In: Transmission and Distribution Conference and Exposition, 2008, T&D, pp. 1–5. IEEE/PES, April 2008Google Scholar
  7. 7.
    Chassin, D.P., Fuller, J.C., Djilali, N.: GridLAB-D: an agent-based simulation framework for smart grids. J. Appl. Math. 2014(12) (2014)Google Scholar
  8. 8.
    Dahmann, J.S., Fujimoto, R.M., Weatherly, R.M.: The department of defense high level architecture. In: Proceedings of the 29th Conference on Winter Simulation, WSC 1997, pp. 142–149. IEEE Computer Society, Washington, D.C. (1997)Google Scholar
  9. 9.
    Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Gellings. C.: Estimating the costs, benefits of the smart grid: a preliminary estimate of the investment requirements and the resultant benefits of a fully functioning smart grid. Electric Power Research Institute (EPRI), Technical report (1022519) (2011)Google Scholar
  11. 11.
    IEC-TC57-WG10/11/12. Communications Networks and Systems in Substations. Standard IEC 61850, International Electrotechnical Commission, Geneva, CHGoogle Scholar
  12. 12.
    Ketter, W., Collins, J., Reddy, P.: Power tac: a competitive economic simulation of the smart grid. Energy Economics 39, 262–270 (2013)CrossRefGoogle Scholar
  13. 13.
    McArthur, S.D., Davidson, E.M., Catterson, V.M., Dimeas, A.L., Hatziargyriou, N.D., Ponci, F., Funabashi, T.: Multi-agent systems for power engineering applications–part i: concepts, approaches, and technical challenges. IEEE Transactions on Power Systems 22(4), 1743–1752 (2007)CrossRefGoogle Scholar
  14. 14.
    Mosshammer, R., Kupzog, F., Faschang, M., Stifter, M.: Loose coupling architecture for co-simulation of heterogeneous components. In: 39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013, pp. 7570–7575, November 2013Google Scholar
  15. 15.
    North, M., Collier, N., Ozik, J., Tatara, E., Macal, C., Bragen, M., Sydelko, P.: Complex adaptive systems modeling with repast simphony. Complex Adaptive Systems Modeling 1(1), 1–26 (2013)CrossRefGoogle Scholar
  16. 16.
    Oey, M.A., Genc, Z., Ogston, E., Brazier, F.M.T.: Symphony – agent-based platform for distributed smart grid experiments. In: Corchado, J.M., Bajo, J., Kozlak, J., Pawlewski, P., Molina, J.M., Gaudou, B., Julian, V., Unland, R., Lopes, F., Hallenborg, K., García Teodoro, P. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 238–249. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Repast Development Team. The Repast Suite.
  18. 18.
    Schutte, S., Scherfke, S., Troschel, M.: Mosaik: a framework for modular simulation of active components in smart grids. In: 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS), pp. 55–60, October 2011Google Scholar
  19. 19.
    Tan, S., Song, W.-Z., Dong, Q., Tong, L.: SCORE: Smart-grid common open research emulator. In: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 282–287, November 2012Google Scholar
  20. 20.
    Team SimPY. SimPy Simulation Package.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zülküf Genç
    • 1
    Email author
  • Michel Oey
    • 1
  • Hendrik van Antwerpen
    • 1
  • Frances Brazier
    • 1
  1. 1.The Faculty of Technology, Policy and ManagementDelft University of TechnologyDelftThe Netherlands

Personalised recommendations