A Short-Term Preventive Maintenance Scheduling Method for Distribution Networks with Distributed Generators and Batteries

Reference

J. Fu, A. Núñez, and B. De Schutter, "A Short-Term Preventive Maintenance Scheduling Method for Distribution Networks with Distributed Generators and Batteries," IEEE Transactions on Power Systems, vol. 36, pp. 2516-2531, May 2021.

Abstract

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses.

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Bibtex entry

@article{FuNun:21-004,
author={J. Fu and A. N{\'{u}}{\~{n}}ez and B. {D}e Schutter},
title={A Short-Term Preventive Maintenance Scheduling Method for Distribution Networks with Distributed Generators and Batteries},
journal={IEEE Transactions on Power Systems},
volume={36},
pages={2516--2531},
month=may,
year={2021},
doi={10.1109/TPWRS.2020.3037558}
}

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