Power Network Reliability Computations Using Multi-agent Simulation

  • Aleš Horák
  • Miroslav Prýmek
  • Tadeusz Sikora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7430)


The reliability of a power network as a whole is limited by the reliability of the weakest element in the network. Ensuring the reliability just with nothing more than a readiness to repair or replace broken parts is not feasible economically, and planned element replacements in the middle of their working life is often a waste of working resources.

This article presents a multi-agent simulation system designed for modelling all aspects of power network elements and power network processes. The modelling of this system is able to predict the weak points of a simulated power network over an extended time span (decades). This mode of prediction takes into account the network topology, which means that each element is modelled in a context that is close to the real one.

To show the system capabilities, we present two models of computations based on real power networks of 10 kV. Both models make use of the main feature of the system, which is the ability to use local interactions even for global analytic computations, such as the computation of a power network steady state or locating the least reliable point in the network.


power network simulation power network reliability economic modelling of power network multi-agent simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arrillaga, J., Watson, N.: Computer Modelling of Electrical Power Systems. Wiley (2001)Google Scholar
  2. 2.
    Brown, R.E.: Electric power distribution reliability. Power engineering. CRC Press, Boca Raton (2008)CrossRefGoogle Scholar
  3. 3.
    DAISY, s.r.o. PAS DAISY Bizon Projektant (2010)
  4. 4.
    Prýmek, M., Horák, A.: Multi-agent Approach to Power Distribution Network Modelling. Integrated Computer-Aided Engineering 17(4), 291–304 (2010)Google Scholar
  5. 5.
    Qudaih, Y., Hiyama, T.: Reconfiguration of Power Distribution System Using Multi Agent and Hierarchical Based Load Following Operation with Energy Capacitor System. In: Power Engineering Conference, IPEC 2007, pp. 223–227 (2007)Google Scholar
  6. 6.
    Tolbert, L., Qi, H., Peng, F.: Scalable Multi-Agent System for Real-time Electric Power Management. In: Power Engineering Society Summer Meeting, vol. 3, pp. 1676–1679 (2001)Google Scholar
  7. 7.
    Hcbson, E.: Network constrained reactive power control using linear programming. IEEE Transactions on Power Apparatus and Systems PAS-99(3), 868–877 (1980)CrossRefGoogle Scholar
  8. 8.
    Schweppe, F.: Power system static-state estimation, Part III: Implementation. IEEE Transactions on Power Apparatus and Systems, 130–135 (1970)Google Scholar
  9. 9.
    Krishnamoorthy, S., Chowdhury, M.: Investigation and a practical compact network model of thermal stress in integrated circuits. Integrated Computer-Aided Engineering 16(2), 131–140 (2009)Google Scholar
  10. 10.
    Střída, F., Stacho, B., Rusek, S.: Network steady-state modelling in the Bizon projektant program. In: 10th International Scientific Conference Electric Power Engineering (EPE 2009), Ostrava, Czech Republic, VŠB TU Ostrava, pp. 186–189 (2009)Google Scholar
  11. 11.
    Walling, R., Saint, R., Dugan, R., Burke, J., Kojovic, L.: Summary of distributed resources impact on power delivery systems. IEEE Transactions on Power Delivery 23(3), 1636–1644 (2008)CrossRefGoogle Scholar
  12. 12.
    Gan, C., Silva, N., Pudjianto, D., Strbac, G., Ferris, R., Foster, I., Aten, M.: Evaluation of alternative distribution network design strategies. In: 20th International Conference and Exhibition on Electricity Distribution-Part 1, CIRED 2009, pp. 1–4. IET (2009)Google Scholar
  13. 13.
    Kundur, P., Balu, N., Lauby, M.: Power system stability and control. McGraw-Hill Professional (1994)Google Scholar
  14. 14.
    Billinton, R., Li, W.: Reliability assessment of electric power systems using Monte Carlo methods. Plenum Publishing Corporation (1994)Google Scholar
  15. 15.
    Anderson, C., Cardell, J.: Analysis of wind penetration and network reliability through Monte Carlo simulation. In: IEEE Winter Simulation Conference (WSC 2009), pp. 1503–1510 (2009)Google Scholar
  16. 16.
    Stefano, A., Santoro, C.: Supporting agent development in erlang through the exat platform. In: Walliser, M., et al. (eds.) Software Agent-Based Applications, Platforms and Development Kits. Whitestein Series in Software Agent Technologies and Autonomic Computing, pp. 47–71. Birkhuser, Basel (2005)Google Scholar
  17. 17.
    Antonella, S., Santoro, C.: eXAT: an Experimental Tool for Programming Multi-Agent Systems in Erlang. In: 4th AI*IA/TABOO Joint Workshop ”From Objects to Agents”: Intelligent Systems and Pervasive Computing, Villasimius, CA, Italy, Pitagora Editrice Bologna (2003)Google Scholar
  18. 18.
    Medvec, Z., Prokop, L.: Cost calculation based on public sources for industrial company during interruption of electrical power supply. In: Kolcun, M., Kurimsky, J. (eds.) Proceedings of 4th International Scientific Symposium on Electric Power Engineering - Elektroenergetika, pp. 563–565 (2007)Google Scholar
  19. 19.
    Prokop, L., Kral, V., Hradilek, Z.: Software OCENS - a tool for outage costs and energy not supply calculation. Przeglad Elektrotechniczny 85(3), 227–230 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aleš Horák
    • 1
  • Miroslav Prýmek
    • 1
  • Tadeusz Sikora
    • 2
  1. 1.Faculty of InformaticsMasaryk University BrnoBrnoCzech Republic
  2. 2.Department of Electrical Power Engineering, FEECSVB – Technical University of OstravaOstravaCzech Republic

Personalised recommendations