Distribution systems resilience enhancement via pre- and post-event actions

Babak Taheri; Amir Safdarian; Moein Moeini-Aghtaie; Matti Lehtonen

Distribution systems resilience enhancement via pre- and post-event actions

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Author(s): Babak Taheri¹ ; Amir Safdarian¹ ; Moein Moeini-Aghtaie² ; Matti Lehtonen³
- Affiliations: 1: Department of Electrical Engineering , Sharif University of Technology , Tehran 11365–11155 , Iran ;
  2: Department of Energy Engineering , Sharif University of Technology , Tehran 11365–11155 , Iran ;
  3: Department of Electrical Engineering , Aalto University , Espoo 02150 , Finland
Source: Volume 2, Issue 4, December 2019, p. 549 – 556
DOI: 10.1049/iet-stg.2019.0035 , Online ISSN 2515-2947

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 27/01/2019, Accepted 02/07/2019, Revised 28/06/2019, Published 03/07/2019

Recently, resilience studies have become an indispensable tool for sustainable operation of energy infrastructure. In line with the need, this study presents a mathematical model to enhance resilience level of power distribution systems against natural disasters. The model is designed as a three-stage algorithm according to system operators’ actions. The first stage schedules pre-event actions. At this stage, forecasts about the approaching disaster as well as fragility curves of system components are used to identify failure probability of system components. The failure probabilities are used to trip out the lines as much as possible to defensively operate the distribution network, and advantages of alternatives such as distributed energy resources and normally-open switches are taken to serve critical loads. The second stage is to monitor system operating conditions during the event and identify the status of system components. The third stage mainly focuses on scheduling post-event actions. At this stage, based on real data about different elements of the network, available alternatives are taken to restore as much critical load as possible. To evaluate performance of the model, it is applied to a distribution test system and the results are discussed in detail.

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