Experience of network modeling and mapping based on spatio-temporal database on the backbone electric networks

DOI: 10.35595/2414-9179-2022-1-28-556-567

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;
E-mail: karpach-am@yandex.ru

German S. Titov

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory 1, 119991, Moscow, Russia;
E-mail: titovgs@my.msu.ru

Nadezhda I. Tulskaya

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory 1, 119991, Moscow, Russia;
E-mail: tnadya@mail.ru

Anna I. Prasolova

Lomonosov Moscow State University, Faculty of Geography,
Leninskie Gory 1, 119991, Moscow, Russia;
E-mail: prasolova.geo@yandex.ru

Abstract

A unique spatio-temporal database of the backbone electric networks of the Moscow power system was previously based on various information sources and published as a cartographic web service. In this study, we consider some mapping possibilities based on calculated parameters, including network analysis methods. To represent the data correctly for each studied year from 1936 to 2020, we have developed algorithms for verifying data integrity, as well as for automated creation of a topologically correct network model. Bringing the network to a topologically correct form implies the snapping of the end vertices of the lines to the point objects of the power system, the elimination of hanging dangles, as well as the elimination of self-intersections. The integrity check is carried out in three stages: 1) coordination of the time frame for the existence of network segments; 2) checking the connectivity of each power line for each time slice; 3) checking the connectivity of the entire network as a whole for each year. The age of the network, betweenness centrality, electric grid centrality, closeness centrality in this paper are taken as an example of local parameters, i. e. indicators confined to specific elements of the network (edges or vertices). In addition, we have considered a global indicator characterizing the network as a whole—the average shortest path in the network, which can be calculated in three ways: without taking into account the weight, taking into account the length of the lines or taking into account its capacitance characteristics, depending on voltage.

Keywords

web service, geographic networks, network analysis, topological model, data integrity

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For citation: Karpachevskiy A.M., Titov G.S., Tulskaya N.I., Prasolova A.I. Experience of network modeling and mapping based on spatio-temporal database on the backbone electric networks. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2022. V. 28. Part 1. P. 556–567. DOI: 10.35595/2414-9179-2022-1-28-556-567 (in Russian)