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

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.
- Sharif University of Technology Iran (Islamic Republic of)
- Aalto University Finland
- Sharif University of Technology Iran (Islamic Republic of)
system components, Failure analyses, probability, sustainable operation, Distributed processing, Switchgear, disasters, resilience studies, distributed energy resources, Distributed power generation, Disasters, distributed power generation, energy infrastructure, scheduling, scheduling post-event actions, switchgear, distribution network, Probability, distribution systems resilience enhancement, Internet, system operating conditions, critical load, Scheduling, three-stage algorithm, stage schedules pre-event actions, failure analysis, indispensable tool, approaching disaster, TK1-9971, failure probability, distribution networks, natural disasters, Distribution networks, power distribution systems, system operators, distributed processing, internet, distribution test system, Electrical engineering. Electronics. Nuclear engineering, mathematical model, resilience level
system components, Failure analyses, probability, sustainable operation, Distributed processing, Switchgear, disasters, resilience studies, distributed energy resources, Distributed power generation, Disasters, distributed power generation, energy infrastructure, scheduling, scheduling post-event actions, switchgear, distribution network, Probability, distribution systems resilience enhancement, Internet, system operating conditions, critical load, Scheduling, three-stage algorithm, stage schedules pre-event actions, failure analysis, indispensable tool, approaching disaster, TK1-9971, failure probability, distribution networks, natural disasters, Distribution networks, power distribution systems, system operators, distributed processing, internet, distribution test system, Electrical engineering. Electronics. Nuclear engineering, mathematical model, resilience level
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).26 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
