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Abstract Climate-change-imposed challenges in the form of heightened frequency and intensity of weather events exert additional pressure on securing the imperative continuous and reliable power supply, leading to increased power outages. This research proposes a comprehensive framework for enhancing the resilience of electric power networks (EPNs) through reliability-based risk assessment, promoting predictions and proactive decisions. The presented research discusses weather phenomena, their association with climate change, and their projected impacts. The numerical weather prediction model, WRF 3.4.1, with a 4 km resolution cell grid, gives a more accurate projection of high winds’ frequency and intensity. The simulation period from 2086 to 2099 is based on a reference control period spanning from 2000 to 2013, with adjustments made to background conditions using climate model output consistent with projections for the late century, a pseudo-global warming (PGW) technique. The presented research focuses on the wooden power distribution poles. The reliability assessment approach employs fragility development and analysis against wind scenarios through advanced modeling techniques and statistical analysis used to mimic historical and projected wind scenarios and to allow numerous factors on both the demand and capacity sides and their inherent uncertainties to be considered. The annual probability of failure is obtained by performing a mathematical convolution of the fragility and the hazard curves, showing the reflection of the effects of climate change on the annual probability of failure. Scaling these results to a system-level resilience assessment will facilitate the flexible energy design strategies integration and allow smoother net-zero standards incorporation and adaptation to the changing environmental conditions. This understanding will allow the decision-makers to evaluate the critical locations within a distribution line and plan to address the vulnerabilities by hardening the assets or implementing modern microgrid techniques or distributed energy resource integration. 

Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts. 

The electric distribution system is a cornerstone of modern life, playing a critical role in the daily activities and well-being of individuals. As the world transitions toward a decarbonized future, where even mobility relies on electricity, ensuring the resilience of the grid becomes paramount. This paper introduces novel resilience metrics designed to equip utilities and stakeholders with actionable tools to assess performance during storm events. The metrics focus on emergency storm response and the resources required to improve customer service. The practical calculation of the metrics from historical utility data is demonstrated for multiple storm events. Additionally, the metrics’ improvement with added crews is estimated by “rerunning history” with faster restoration. By applying this resilience framework, utilities can enhance their restoration strategies and unlock potential cost savings, benefiting both providers and customers in an era of heightened energy dependency. 

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large‐cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data‐driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.