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

Authors

Keywords:

Optimization, Line-Switching, Metaheuristics, Electrical grids, Complex Networks

Abstract

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.

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Author Biographies

Henrique de Oliveira Caetano, University of São Paulo (USP)

Currently studying Electrical Engineering at University of São Paulo (USP) Brazil, and doing research at the Signal Processing Laboratory (LPS - USP). I'm interested in the following topics: Resilience and robustness of electrical power systems; heuristics methods and signal processing.

Carlos Maciel, São Carlos School of Engineering, University of São Paulo (EESC - USP)

Graduated in Electronics at IME Brazil (1989) and PhD in Biomedical Engineering from COPPE/UFRJ Brazil (2000). I am currently an Associate Professor at the USP (University of São Paulo) Brazil and have experience in the area of Signal Processing, Instrumentation and Large Scale System analysis. I work mainly on the following topics: signal processing, probabilistic models (Dynamic Bayes Networks), computationally intense algorithms, resilience and system planning. I teach Signal and Systems and Digital Signal Processing for undergraduate students and Pattern Recognition and Statistical Signal Processing for the graduate program.

Michel Bessani, Electrical Engineering Department, Federal University of Minas Gerais (DEE - UFMG)

Michel is Graduated in Electrical Engineering, has Master (2015) and PhD (2018) with the São Carlos School of Engineering at the University of São Paulo (EESC - USP). Currently works at the Electrical Engineering Department, Federal University of Minas Gerais, and does research in System Reliability and Resilience and Computational Intelligence.

Luiz Neto, São Carlos School of Engineering, University of São Paulo (EESC - USP)

Luiz has a degree in Electrical Engineering - Emphasis in Electronics from the University of São Paulo (2016). He is interested in the topics of systems resilience and reliability, routing, system reconfiguration, intelligent systems, bioinspired algorithms and optimization.

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Published

2021-04-12

How to Cite

de Oliveira Caetano, H., Maciel, C., Bessani, M., & Neto, L. (2021). A Variable Neighborhood Descent approach for electrical grids as an overload reduction method. IEEE Latin America Transactions, 19(10), 1674–1683. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/4791