Large scale data analytics for resilience of energy networks

Massive power failures are induced frequently by natural disasters. Two fundamental challenges arise in face of such failures: First, how recovery can be resilient to the increasing severity of disruptions and their impact on service users in a changing climate. Second, how can we measure the impact of failures and recovery and its heterogeneity on customers with different characteristics. We conduct a large-scale study on recovery from 169 failure events at two operational distribution grids in the states of New York and Massachusetts. Guided by unsupervised learning from non-stationary data, our analysis finds that under the widely adopted prioritization policy favoring large failures, recovery exhibits a scaling property where a majority (90%) of customers recovers in a small fraction (10%) of total downtime. However, recovery degrades with the severity of disruptions: large failures that cannot recover rapidly increase by 30% from the moderate to extreme events. Prolonged small failures dominate entire recovery processes. Further, our analysis demonstrates the promise of mitigating the degradation by enhancing recovery of a small fraction of large failures through distributed generation and storage. Next, a dynamic resilience metric is developed using spatiotemporal failure and recovery processes incorporating the cost imposed on individual customers. The resilience metric is then combined with randomization inference to design a framework on how to study the dynamic cost and its heterogeneity on customers with different characteristics. Our framework is validated on publicly available data from multiple states in the US.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Afsharinejad, Amir Hossein

Advisor dc:contributor.advisor

• Ji, Chuanyi

Committee members dc:contributor.committeemember

• Ganz, Scott
• Davenport, Mark
• Divan, Deepakraj
• Thomas, Valerie

• Resilience of recovery services
• Large-scale power failures
• Data analytics at scale
• Infrastructure enhancement

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/72985

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/72985