Spatio-temporal database design for backbone power grid of Russia

View or download the article (Rus)

About the Authors

Andrey M. Karpachevskiy

Lomonosov Moscow State University, Faculty of Geography,
Leninskie gory 1, 119991, Moscow, Russia;

German S. Titov

Lomonosov Moscow State University, Faculty of Geography,
Leninskie gory 1, 119991, Moscow, Russia;


Backbone power lines in Russia have a complex spatial structure. There are no systematized and topologically consistent spatio-temporal data about them. however, the study of their evolution requires not only data mining, but also a comprehensive design of the structure of the spatio-temporal database. The structure should provide effective data storage, be convenient for filling the database and editing data, provide the ability to reconstruct the network for a given period and apply spatial analysis methods.

Open sources like power grid operator reports, schemes and programs of power grid development, public cadastral map, information from Situational and Analytical Center of the Ministry of Energy and very high spatial resolution remote sensing data are the main data sources.

Users do not have direct access to the database but refer to it using queries. Interaction with the database is carried out through Application programming Interface (API). This allows downloading data from the database as well as embedding them into external systems, for example, connecting analysis tools to them, creating cartographic web applications with this data.

Data preprocessing is performed in python using the Arcpy module, the database is created with PostgreSQl, the API works on PostgREST.

Consistent multi-temporal spatial database serves as the basis for analyzing the structural features of electrical networks, makes it possible to visualize the history of the development of the power grid of the territory in an interactive web-based mapping application, allows to apply geoprocessing tools and special network analysis tools. The detailed study of the evolution of backbone power grids is crucial in long-term strategies for the development of the power grid.

Abroad, studies of the evolution of electrical networks usually operate with a schematic graph of a network without reference to real spatial geometry, therefore, there is no problem of designing the structure of spatio-temporal database. yet, ignoring topomorphological relationships in the network leads to the loss of information about electrical networks, which leads to a loss in the quality of spatial analysis.


geographical networks, GIS-analysis, network analysis, network evolution, power grid.


  1. Buzna L. The evolution of the topology of high-voltage electricity. International Journal of Critical Infrastructures, 2009. V. 5. No. 1/2. P. 72–85.
  2. Crucitti P., Latora V., Marchiori M. Locating critical lines in high-voltage electrical power grids. Fluctuation and Noise Letters, 2005. No. 2 (5). P. 201–208.
  3. Faddeev A. Assessment of vulnerability of power systems of Russia, CIS countries and Europe to cascade accidents. Bulletin of Moscow University. Series 5: Geography, 2016. No. 1. P. 46–53 (in Russian).
  4. Kargashin P.E., Novakovsky B.A., Prasolova A.I., Karpachevskiy A.M. Study of the spatial configuration of power grids from satellite images. geodesy and cartography, 2016. No. 3. P. 50–55 (in Russian).
  5. George B., Shekhar S. Time-Aggregated Graphs for Modeling Spatio-temporal Networks. Journal on Data Semantics XI. Lecture Notes in Computer Science, 2008. V. 5383. P. 191–213. DOI: 10.1007/978-3-540-92148-6_7.
  6. Medjroubi W., Vogt T. Open source data and models for a sustainable power grid modelling and analysis. 1st Int. Conf. on Large-Scale Grid Integration of Renewable Energy, New Delhi, India, 2017.
  7. Rosas-Casals M. Power grids as Complex Networks: Topology and Fragility. Complexity in Engineering, 2010. P. 21–26. DOI: 10.1109/COMPENG.2010.23.
  8. Rosas-Casals M., Valverde S., Solé R.V. Topological vulnerability of the European power grid under errors and attacks. International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, 2007. V. 17 (7). P. 2465–2475.
  9. Wang E., Cook D., Hyndman R.J. A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational and Graphical Statistics, 2020. V. 29. No. 3. P. 466–478.
  10. Wickham H. Tidy data. Journal of statistical software. 2014. V. 59. No. 10. P. 1–23.

For citation: Karpachevskiy A.M., Titov G.S. Spatio-temporal database design for backbone power grid of Russia InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2021. V. 27. Part 2. P. 306–314. DOI: 10.35595/2414-9179-2021-2-27-306-314 (In Russian)