Dynamic Evaluation of Power Distribution Lines for Determining the Number of Repair Teams and Prioritizing the Resolution of Faults Caused by High-Impact Low-Probability Events

TL;DR

This paper presents a model for prioritizing power distribution line repairs during high-impact low-probability events, aiming to optimize repair team deployment and restore critical loads efficiently.

Contribution

It introduces a novel prioritization method considering economic value, failure probability, and network topology to determine repair needs during critical events.

Findings

Topology factor significantly influences line prioritization.

Model effectively determines the number of repair teams needed.

Tested on IEEE 33-bus network with promising results.

Abstract

In order to increase the resilience of distribution systems against high-impact low-probability (HILP) events, it is important to prioritize the damaged assets so that the lost loads, especially critical and important loads, can be restored faster. In addition, correctly predicting the number of repair teams during critical times contributes to restoring the network to the initial resilience level. For this reason, this paper discusses the prioritization of electricity supply lines for evaluating the number of required repair teams. To this end, the economic value of distribution system lines has been considered as a criterion representing the sensitivity of the network to hurricanes. The modeling is based on value, in which the load value, failure probability of the poles, fragility curve, duration of line repair by the maintenance team, and the topology factor have been considered. This is so that the significance of the demand side, the failure extent and accessibility of the lines, the importance of time, and the network configuration are considered. The results provide a list of line priority for fault resolution, in which the topology factor has a larger effect. The number of repair teams required to restore critical and important loads is determined from this model. This modeling has been tested on an IEEE 33-bus network. Keywords: Resilience, distribution systems, asset evaluation, restoration prioritization, repair team