Large-scale data analytics, modeling and resilience of energy infrastructure and service
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Large scale power failures induced by severe weather have become frequent and damaging in recent years, causing millions of people to be without electricity service for days. Although the power industry has been battling weather-induced failures for years, it is unknown how resilient the energy infrastructure and services really are to severe weather disruptions. This thesis addresses the research issues up to date and challenges on resilience. The focus is on studying fundamental challenges and advanced approaches for quantifying resilience. In particular, a first aspect of this problem is how to model large-scale failures, recoveries and impacts, involving the infrastructure, service providers, customers, and weather. The thesis has developed spatiotemporal models failures and recoveries based on non-stationary random processes, where the models are derived from by including dynamic network topology. A second aspect is to identify and characterize generic vulnerability (i.e., non-resilience) in the infrastructure and services through large-scale data analytics. Using data obtained from power distribution grids across multiple service regions, the thesis has found that local power failures have a disproportionally large non-local impact on customers, where top 20% failures affected 80% customers during severe weather events and daily operations. In contrast, an aggregation of small disruptions and commonplace devices result in major cost in customer downtime, where bottom 89% failures that affected 34 customers amount to 56% of the total cost. Extreme weather does not cause but exacerbates the vulnerabilities in daily operations.