Open AccessGIS-Analysis Of The Ural Power Grid Vulnerability To The Impact Of Sleet And Wind
Author(s) -
Andrey Karpachevskiy,
Oksana Filippova,
Pavel Kargashin
Publication year - 2022
Publication title -
geography, environment, sustainability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.316
H-Index - 8
eISSN - 2542-1565
pISSN - 2071-9388
DOI - 10.24057/2071-9388-2021-082
Subject(s) - betweenness centrality , centrality , grid , computer science , wind power , power (physics) , index (typography) , electric power system , transport engineering , geography , statistics , engineering , mathematics , electrical engineering , physics , geodesy , quantum mechanics , world wide web
In this paper, we describe an experiment of complex power grid structure and wind and sleet mapping of territory using two different network indices: standard edge betweenness centrality and new author’s index – electrical grid centrality. Such analysis of the network allows to identify power lines with high load which could be vulnerable elements of the power grid. It is very important for strategic planning of power grids to reduce the risk of accidents by distributing loads across several lines so that they will be able to reserve each other. As a case territory for this research, we took the Ural united power system in Russia which is greatly exposed to different sleet and wind according to the statistics of the power grid operator. The degree of natural hazard consequences could be compensated by the network structure through alternative paths of energy supply or vice versa – increased if they are absent. At the same time, in this paper we consider that power grids have their own features from the graph theory point of view, for example multiple (parallel) edges, branches, different types of vertices. The existing index of edge betweenness centrality does not perfectly cope with them. We compare two indices characterizing power line importance within the system – betweenness centrality and electrical grid centrality and analyze the network structure features together with the spatial distribution of sleet and wind. As a result, we could identify bottlenecks in the study network. According to this study the most vulnerable power lines were detected, for example 500 kV Iriklinskaya CHP – Gazovaya and 500 kV Yuzhnouralskaya CHP-2 – Shagol power lines, that supply big cities such as Chelyabinsk and Orenburg and a bunch of industries around them.