{"653197":{"#nid":"653197","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Amir Afsharinejad","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003ELarge Scale Data Analytics for Resilience of Energy Networks\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Chuanyi Ji, ECE, Chair, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Scott Ganz, School of Business, George Town University\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mark Davenport, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Deepak Divan, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Valerie Thomas, ISyE\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003EMassive power failures are induced frequently by natural disasters in a changing climate. 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 customers. The resilience metric is then combined with 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 large scale data from multiple states in the US.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Large Scale Data Analytics for Resilience of Energy Networks"}],"uid":"28475","created_gmt":"2021-11-29 20:37:13","changed_gmt":"2021-11-29 20:37:43","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-12-03T13:00:00-05:00","event_time_end":"2021-12-03T15:00:00-05:00","event_time_end_last":"2021-12-03T15:00:00-05:00","gmt_time_start":"2021-12-03 18:00:00","gmt_time_end":"2021-12-03 20:00:00","gmt_time_end_last":"2021-12-03 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}