Distribution systems resilience enhancement via pre‐ and post‐event actions. Issue 4 (24th July 2019)

• Record Type: Journal Article
• Authors: Taheri, Babak; Safdarian, Amir; Moeini‐Aghtaie, Moein; Lehtonen, Matti
• Is Part Of: IET smart grid. Volume 2:Issue 4(2019)
• Page Start: 549
• Page End: 556
• Publication Date: 2019-07-24
• Subjects: distributed power generation -- Internet -- scheduling -- disasters -- distributed processing -- distribution networks -- switchgear -- probability -- failure analysis; distribution systems resilience enhancement -- resilience studies -- indispensable tool -- sustainable operation -- energy infrastructure -- mathematical model -- resilience level -- power distribution systems -- natural disasters -- three‐stage algorithm -- system operators -- stage schedules pre‐event actions -- approaching disaster -- system components -- failure probability -- distribution network -- distributed energy resources -- critical load -- system operating conditions -- scheduling post‐event actions -- distribution test system; B8120J Distribution networks -- B8370 Switchgear -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0035
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.