A Variable Neighborhood Descent approach for electrical grids as an overload reduction method

Nowadays, there is a growing need to analyse systems using complex networks and graphs, especially in critical infrastructures. That includes transmission and distribution systems, where a single fault may cause power interruption for several consumers. A special approach to this problem uses Optima...

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Veröffentlicht in:Revista IEEE América Latina 2021-10, Vol.19 (10), p.1674-1683
Hauptverfasser: De Oliveira Caetano, Henrique, Maciel, Carlos, Bessani, Michel, Neto, Luiz
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Sprache:eng
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Zusammenfassung:Nowadays, there is a growing need to analyse systems using complex networks and graphs, especially in critical infrastructures. That includes transmission and distribution systems, where a single fault may cause power interruption for several consumers. A special approach to this problem uses Optimal Transmission Switching (OTS), where edges are comutated to change the network topology, and improves fault response. Because of its computational complexity, heuristics are proposed to the problem. This paper aims to introduce Variable Neighborhood Descent (VND) to the OTS problem, because of its local search feature, as well as the ability to deal with local minimuns. For that, the neighborhood structures and objetive function were adapted to address the peculiarities of the electrical grids, and a power redistribution algorithm was implemented. Failures and attacks were simulated, and the overload reduction was compared between the original topology and the one found by the VND (by line-switching). For power overload failures, results were better in intermediate overload levels, for both topologies. For node removal, best results were found in scale-free graphs, especially in intentional attacks, which shows that the local search phase, presented in VND, works well in a subset of edges limited to the proximity of the failure, especially with networks that have hubs. The computacional time shows the potential of the heuristic to be used in real time analysis.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2021.9477270