Validating Intelligent Power and Energy Systems – A Discussion of Educational Needs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10444)


Traditional power systems education and training is flanked by the demand for coping with the rising complexity of energy systems, like the integration of renewable and distributed generation, communication, control and information technology. A broad understanding of these topics by the current/future researchers and engineers is becoming more and more necessary. This paper identifies educational and training needs addressing the higher complexity of intelligent energy systems. Education needs and requirements are discussed, such as the development of systems-oriented skills and cross-disciplinary learning. Education and training possibilities and necessary tools are described focusing on classroom but also on laboratory-based learning methods. In this context, experiences of using notebooks, co-simulation approaches, hardware-in-the-loop methods and remote labs experiments are discussed.


Cyber-Physical Energy Systems Education Learning Smart grids Training Validation 



This work is supported by the European Communitys Horizon 2020 Program (H2020/2014–2020) under project “ERIGrid” (Grant Agreement No. 654113).


  1. 1.
    Crawley, E.F., Malmqvist, J., Lucas, W.A., Brodeur, D.R.: The CDIO syllabus v2.0. An updated statement of goals for engineering education. In: 7th International CDIO Conference, Copenhagen, Denmark (2011)Google Scholar
  2. 2.
    Deese, A.: Development of smart electric power system (SEPS) laboratory for advanced research and undergraduate education. In: 2015 IEEE Power Energy Society General Meeting, p. 1 (2015)Google Scholar
  3. 3.
    Farhangi, H.: The path of the smart grid. IEEE Power Energ. Mag. 8(1), 18–28 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gottschalk, M., Uslar, M., Delfs, C.: The Use Case and Smart Grid Architecture Model Approach: The IEC 62559-2 Use Case Template and the SGAM Applied in Various Domains. Springer, Heidelberg (2017). doi: 10.1007/978-3-319-49229-2 CrossRefGoogle Scholar
  5. 5.
    Gungor, V., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G.: Smart grid technologies: communication technologies and standards. IEEE Trans. Ind. Inf. 7(4), 529–539 (2011)CrossRefGoogle Scholar
  6. 6.
    Hu, Q., Li, F., Chen, C.F.: A smart home test bed for undergraduate education to bridge the curriculum gap from traditional power systems to modernized smart grids. IEEE Trans. Educ. 58(1), 32–38 (2015)CrossRefGoogle Scholar
  7. 7.
    Karady, G.G., Heydt, G.T., Olejniczak, K.J., Mantooth, H.A., Iwamoto, S., Crow, M.L.: Role of laboratory education in power engineering: is the virtual laboratory feasible? Part I. In: 2000 IEEE Power Engineering Society Summer Meeting, vol. 3, pp. 1471–1477 (2000)Google Scholar
  8. 8.
    Kotsampopoulos, P., Kleftakis, V., Hatziargyriou, N.: Laboratory education of modern power systems using PHIL simulation. IEEE Trans. Power Syst. PP(99), 1 (2016)Google Scholar
  9. 9.
    Liserre, M., Sauter, T., Hung, J.: Future energy systems: integrating renewable energy sources into the smart power grid through industrial electronics. IEEE Ind. Electron. Mag. 4(1), 18–37 (2010)CrossRefGoogle Scholar
  10. 10.
    Martinez, J., Dinavahi, V., Nehrir, M., Guillaud, X.: Tools for analysis and design of distributed resources - part IV: future trends. IEEE Trans. Power Deliv. 26(3), 1671–1680 (2011)CrossRefGoogle Scholar
  11. 11.
    Mets, K., Ojea, J.A., Develder, C.: Combining power and communication network simulation for cost-effective smart grid analysis. IEEE Commun. Surv. Tutor. 16(3), 1771–1796 (2014)CrossRefGoogle Scholar
  12. 12.
    Neureiter, C., Engel, D., Trefke, J., Santodomingo, R., Rohjans, S., Uslar, M.: Towards consistent smart grid architecture tool support: from use cases to visualization. In: 2014 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) (2014)Google Scholar
  13. 13.
    Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. IEEE Comput. Sci. Eng. 9(3), 21–29 (2007)CrossRefGoogle Scholar
  14. 14.
    Pochacker, M., Sobe, A., Elmenreich, W.: Simulating the smart grid. In: IEEE PowerTech Grenoble (2013)Google Scholar
  15. 15.
    Podmore, R., Robinson, M.: The role of simulators for smart grid development. IEEE Trans. Smart Grid 1(2), 205–212 (2010)CrossRefGoogle Scholar
  16. 16.
    Rohjans, S., Lehnhoff, S., Schütte, S., Scherfke, S., Hussain, S.: Mosaik - a modular platform for the evaluation of agent-based smart grid control. In: IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE) (2013)Google Scholar
  17. 17.
    Schütte, S., Scherfke, S., Tröschel, M.: Mosaik: a framework for modular simulation of active components in smart grids. In: IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS), pp. 55–60 (2011)Google Scholar
  18. 18.
    Steinbrink, C.: A nonintrusive uncertainty quantification system for modular smart grid co-simulation. Ph.D. thesis, University of Oldenburg (2016)Google Scholar
  19. 19.
    Strasser, T., Stifter, M., Andrén, F., Palensky, P.: Co-simulation training platform for smart grids. IEEE Trans. Power Syst. 29(4), 1989–1997 (2014)CrossRefGoogle Scholar
  20. 20.
    Strasser, T., Andren, F., Kathan, J., Cecati, C., Buccella, C., Siano, P., Leitao, P., Zhabelova, G., Vyatkin, V., Vrba, P., Marik, V.: A review of architectures and concepts for intelligence in future electric energy systems. IEEE Trans. Ind. Electron. 62(4), 2424–2438 (2015)CrossRefGoogle Scholar
  21. 21.
    Strasser, T., Pröstl Andrén, F., Lauss, G., et al.: Towards holistic power distribution system validation and testing—an overview and discussion of different possibilities. e & i Elektrotech. Informationstechnik 134(1), 71–77 (2017)CrossRefGoogle Scholar
  22. 22.
    Vournas, C.D., Potamianakis, E.G., Moors, C., Cutsem, T.V.: An educational simulation tool for power system control and stability. IEEE Trans. Power Syst. 19, 48–55 (2004)CrossRefGoogle Scholar
  23. 23.
    Vrba, P., Marik, V., Siano, P., Leitao, P., Zhabelova, G., Vyatkin, V., Strasser, T.: A review of agent and service-oriented concepts applied to intelligent energy systems. IEEE Trans. Ind. Inf. 10(3), 1890–1903 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National Technical University of AthensAthensGreece
  2. 2.AIT Austrian Institute of TechnologyViennaAustria
  3. 3.HAW Hamburg University of Applied SciencesHamburgGermany
  4. 4.OFFIS e.VOldenburgGermany
  5. 5.Delft University of TechnologyDelftThe Netherlands
  6. 6.Technical University of DenmarkLyngbyDenmark
  7. 7.European Distributed Energy Resources Laboratories (DERlab) e.VKasselGermany
  8. 8.TECNALIA Research and InnovationBilbaoSpain
  9. 9.University of StrathclydeGlasgowUK

Personalised recommendations