Advertisement

Developing a Model of Topological Structure Formation for Power Transmission Grids Based on the Analysis of the UNEG

  • Sergey MakrushinEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 247)

Abstract

Methods of the current research are based on the nodes degrees distribution analysis. Information about the United National Electricity Grid (UNEG)—Russia’s power transmission grid is used in the paper as a source of empirical data about power grids topology. As a result of the analysis, we get a conclusion that universal models of complex network theory are not applicable to the UNEG modelling. Nevertheless, the creation of a compound model, which generates networks with nodes degrees distributions similar to the negative binomial distribution, is promising. The analysis of the UNEG nodes degrees distribution helped us to identify the key principles for a compound model. According to these principles, we chose from the ad hoc models of power network formation the Random Growth Model (RGM) as a good base for creation of the compound model. However, the RGM has a significant flaw in the mechanism of formation of transit nodes in a power grid. We found a way to fix it by adding to the RGM an intermediate phase of network growth, which occurs between the phase of global optimal growth and the phase of self-organized growth. A new’project’ stage of network formation could correctly depict the formation of transit nodes, which are created in large-scale projects of long power line routes creation.

Keywords

Power grid Degree distribution Network topology Network growth 

References

  1. 1.
    Barabsi, A., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barthelemy, M.: Spatial Networks. Condensed Matter, Statistical Mechanics, Physics Reports 499, 1–101 (2011). arXiv:1010.0302MathSciNetGoogle Scholar
  3. 3.
    Erdös, P., Rényi, A.: On random graphs I. Publ. Math. Debrecen 6, 290–297 (1959)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Makrushin, S.: Analysis of Russian power transmission grid structure: small world phenomena detection. In: Kalyagin V., Nikolaev A., Pardalos P., Prokopyev O. (eds) Models, Algorithms, and Technologies for Network Analysis. NET 2016, Springer Proceedings in Mathematics & Statistics, vol. 197. Springer, Cham (2017)Google Scholar
  5. 5.
    Matthew, O.: Jackson Social and Economic Networks. Princeton University Press (2010)Google Scholar
  6. 6.
    (H9) Moslonka-Lefebvre, M., Bonhoeffer, S., Alizon, S.: Weighting for sex acts to understand the spread of STI on networks. J. Theor. Biol. 311(Oct 21), 46–53 (2012)Google Scholar
  7. 7.
    Neporozhnii, P.S.: 50th anniversary of the Lenin Golro Plan and Hydropower Development. Power Technol. Eng. 4(12), 1089–1093 (1970)Google Scholar
  8. 8.
    NetworkX (2017). https://networkx.github.io. Accessed 30 Sept 2017
  9. 9.
    OpenStreetMap (2017). http://www.openstreetmap.org. Accessed 30 Sept 2017
  10. 10.
    Order of the Ministry of Energy of Russia: Shema i programma razvitiya ENES na 2013–2019 godi (Scheme and development program of the UNES on 2013–2019 years). Order of the Ministry of Energy of Russia from 19.06.2013 309 (2013)Google Scholar
  11. 11.
    Pagani, G. A., Aiello, M.: The Power Grid as a Complex Network: a Survey. Physica A: Statistical Mechanics and its Applications, 392 (11) (2011)Google Scholar
  12. 12.
    Penrose, M.: Random Geometric Graphs. Oxford University Press, Oxford (2003)CrossRefGoogle Scholar
  13. 13.
    Schultz, P., Heitzig J., Kurths J.: A Random Growth Model for Power Grids and Other Spatially Embedded Infrastructure Networks. Eur. Phys. J. 223(2593) (2014)Google Scholar
  14. 14.
    Services for technological connection: power distribution centers (2017). http://portaltp.fsk-ees.ru/sections/Map/map.jsp. Accessed 30 Sept 2017
  15. 15.
    Soltan, S., Zussman, G.: Generation of Synthetic Spatially Embedded Power Grid Networks. In: Proceedings of the IEEE PES-GM’16, July 2016Google Scholar
  16. 16.
    Wang, Z., Scaglione A., Thomas, R.J.: Generating statistically correct random topologies for testing smart grid communication and control networks. IEEE Trans. Smart Grid, 1(1), 5463043, 28–39 (2010)Google Scholar
  17. 17.
    Watts, D.J., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Financial University under the Government of the Russian FederationMoscowRussia

Personalised recommendations