Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics

Abstract

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 weatherinduced failures for years, it is largely unknown how resilient the energy infrastructure and services really are to severe weather disruptions. What fundamental issues govern the resilience? Can advanced approaches such as modeling and data analytics help industry to go beyond empirical methods? This paper discusses the research to date and open issues related to these questions. The focus is on identifying 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. A second aspect is how to identify generic vulnerability (i.e., non-resilience) in the infrastructure and services through large-scale data analytics. And, a third is to understand what resilience metrics are needed and how to develop them.

Proactive Resilience of Power Systems Against Natural Disasters: A Literature Review

“…Nowadays, the abundant monitoring data collected from various power network edges and deployed monitoring equipment (e.g., SCADA, PMU) enables better awareness of the potential proactive actions [123], with or without human expert knowledge, prepared hour-ahead or day-ahead before the actual occurrence of extreme events. By fully leveraging these system status data and critical infrastructure resources (e.g., recloser, energy storage system, and mobile compensator) availability information, machine learning-based or aided approaches can significantly increase the efficiency and fast response of the decision-making process at the proactive stage for power system resiliency enhancement [124]. Additionally, machine learning-based methods are able to overcome some hurdles that are difficult for other methods (e.g., optimization-based, rule-based) of implementing proactive strategies.…”

A novel planning‐attack‐reconfiguration method for enhancing resilience of distribution systems considering the whole process of resiliency

“…22 The strategies of both DG planning and network reconfiguration are used to enhance the structural resilience for both strengthening infrastructures and improving the restoration rate of loads for services to customers. 22 The strategies of both DG planning and network reconfiguration are used to enhance the structural resilience for both strengthening infrastructures and improving the restoration rate of loads for services to customers.…”

“…The measures for enhancing the resilience of distribution systems to respond to extreme natural disasters generally involve strengthening infrastructures and improving the restoration rate of services to customers. 22 The strategies of both DG planning and network reconfiguration are used to enhance the structural resilience for both strengthening infrastructures and improving the restoration rate of loads for services to customers. Therefore, DG is an important guarantee for both aspects.…”

Resilience engineering: theory and practice in interdependent infrastructure systems

“…For example, the Adaptation and Resilience in Energy Systems (ARIES) programme in the UK provided a risk framework to assess the resilience of energy systems, to ensure a balance between changing patterns of demand and supply, helping to identify how energy providers can best anticipate the physical and economic impacts of climate change on current and new energy generation technologies, providing a range of adaptation options (ARCC 2018). Ji et al (2017) consider a range of modelling approaches and metrics which could provide insight into how disruptions or damage to the energy distribution network caused by adverse weather events might be limited spatially (i.e. absorb), and how services could be quickly restored (i.e.…”