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Extended power transmission outages caused by weather events can significantly impact the economy, infrastructure, and residents’ quality of life in affected regions. One of the challenges is providing early, accurate warnings for these disruptions. To address this challenge, we introduce HMN-RTS, a hierarchical multiplex network designed to predict the duration of a forced transmission outage by leveraging a multi-modal approach. We investigate outage duration prediction over two years at the county level, focusing on the states of the Pacific Northwest region, including Idaho, California, Montana, Washington, and Oregon. The multiplex network layers collect diverse data sources, including information about power outages, weather data, weather forecasts, lightning, land cover, transmission lines, and social media. Our findings demonstrate that this approach enhances the accuracy of predicting power outage duration. The HMN-RTS model improves 3 hours ahead outage predictions, achieving a macro F1 score of 0.79 compared to the best alternative of 0.73 for a five-class classification. The HMN-RTS model provides valuable predictions of outage duration across multiple time horizons and seasons, enabling grid operators to implement timely outage mitigation strategies. Overall, the results underscore the HMN-RTS model’s capability to deliver early and practical risk assessments.

Geomagnetic storms represent a critical yet sometimes overlooked factor affecting the reliability of modern power systems. This study examines the relationship between geomagnetic storm activity—characterized by the Dst index and categorized into weak, moderate, strong, severe, and extreme intensities—and reported power outages of unknown or unusual origin in the United States from 2006 to 2023. Outage data come from the DOE OE-417 Annual Summaries, while heliospheric and solar wind parameters (including proton density, plasma speed, and the interplanetary magnetic field) were obtained from NASA’s OMNIWeb database. Results indicate that years with a higher total count of geomagnetic storms, especially those featuring multiple strong or severe events, exhibit elevated incidences of unexplained power interruptions. Correlation analyses further reveal that increasingly negative Dst values, enhanced solar wind velocity, and higher alpha/proton ratios align with greater numbers of outages attributed to unknown causes, underscoring the pivotal role of solar wind–magnetosphere coupling. A simple regression model confirms that storm intensity and average magnetic field strength are statistically significant predictors of unexplained outages, more so than broad indicators such as sunspot number alone. These findings highlight the importance of monitoring high-intensity geomagnetic storms and associated heliospheric variables to mitigate potential risks. Greater attention to space weather impacts and improved reporting of outage causes could bolster grid resilience, helping operators anticipate and manage disruptions linked to geomagnetic disturbances.

Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to hurricanes. Existing outage models use inputs including power system information, environmental parameters, and demographic parameters. This paper reviews the existing power outage models, highlighting their strengths and limitations. Existing models were developed and validated with data from a few utility companies and regions, limiting the extent of their applicability across geographies and hurricane events. Instead, we train and validate these existing outage models using power outages from multiple regions and hurricanes, including hurricanes Harvey (2017), Michael (2018), and Isaias (2020), in 1910 US cities. The dataset includes outages from 39 utility companies in Texas, 5 in Florida, 5 in New Jersey, and 11 in New York. We discuss the limited ability of state-of-the-art machine learning models to (1) make bounded outage predictions, (2) extrapolate predictions to high winds, and (3) account for physics-informed outage uncertainties at low and high winds. For example, we observe that existing models can predict outages higher than the number of customers (in 19.8 % of cities with an average overprediction ratio of 5.2) and cannot capture well the outage variance for high winds, especially above 70 m s−1. Our findings suggest that further developments are needed for power outage models for proper representation of hurricane-induced outages.

Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards. We examined the associations between localized outages and social vulnerability factors (demographic characteristics), controlling for environmental factors (weather), in Washington State between 2018–2021. We additionally analyzed the validity of PowerOutage.us data compared to federal datasets. The population included 27 counties served by 14 electric utilities. We developed a continuous measure of daily outage burden using PowerOutage.us data and operationalized social vulnerability using four factors: poverty level, unemployment, disability, and limited English proficiency. We applied zero-altered lognormal generalized additive mixed-effects models to characterize the relationship between social vulnerability and daily power outage burden, controlling for daily minimum temperature, maximum wind speed, and precipitation, from 2018 to 2021 in Washington State. We found that social vulnerability factors have non-linear relationships with outages. Wind and precipitation are consistent drivers of outage occurrence and duration. There are seasonal effects that vary by county-utility area. Both PowerOutage.us and federal datasets have missing and inaccurate outage data. This is the first study evaluating differential exposure to localized outages as related to social vulnerability that has accounted for weather and temporal correlation. There is a lack of transparency into power outage distribution for those most vulnerable to climate impacts, despite known contributions by electric utilities to climate change. For effective public health surveillance of power outages and transparency, outage data should be made available at finer spatial resolution and temporal scales and/or utilities should be required to report differential exposure to power outages for socially vulnerable populations.

Strong winds induced by extratropical storms cause a large number of power outages, especially in highly forested countries such as Finland. Thus, predicting the impact of the storms is one of the key challenges for power grid operators. This article introduces a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. We start by identifying storm objects from wind gust and pressure fields by using contour lines of 15 m s−1 and 1000 hPa, respectively. The storm objects are then tracked and characterized with features derived from surface weather parameters and forest vegetation information. Finally, objects are classified with a supervised machine-learning method based on how much damage to the power grid they are expected to cause. Random forest classifiers, support vector classifiers, naïve Bayes processes, Gaussian processes, and multilayer perceptrons were evaluated for the classification task, with support vector classifiers providing the best results.

On 7–9 January 2005 Storm Erwin (known as Storm Gudrun in the Nordic countries) passed across northern Europe causing damage and interrupting power and transportation networks from Ireland to the eastern Baltic region. In northern England the storm was associated with severe river flooding around Carlisle that cut transportation links into the city and necessitated evacuations. In Scandinavia strong winds were reported, resulting in large scale forest damage and power outages. In Denmark, wind energy was impacted as wind speeds crossed the 25 m s−1 cut-off threshold for turbine operations, leading to a mass shut down of wind turbines and requiring electricity to be imported to make up the shortfall. In Sweden, there were widespread power outages as transmissions lines were blown down in the winds, and coastal nuclear power plants were shut down when sea spray caused short-circuiting problems in electricity transmission. The storm was associated with a notable coastal surge and flooding, particularly in Denmark and the eastern Baltic. The present contribution gives an overview of how the storm affected infrastructure as well as other societal impacts. During Storm Britta in 2006 there was important wave strike damage to offshore energy infrastructure across the North Sea, motivating a review of other North Sea severe storms for comparable impacts. A detailed analysis of tide gauge and wave data is conducted to understand the surge and possible meteotsunamis and rogue waves during Storm Erwin. While the storm caused severe wind-related damage onshore, shipping and offshore energy production were less affected compared with other storms of the past 40 years.

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services, financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time (GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives; (2) the relationship between the duration of power outage and power system variables; and (3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

Background Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death worldwide with continuous rise. Limited studies indicate that COPD was associated with major storms and related power outages (PO). However, significant gaps remain in understanding what PO’s role is on the pathway of major storms-COPD. This study aimed to examine how PO mediates the major storms-COPD associations. Methods In this time-series study, we extracted all hospital admissions with COPD as the principal diagnosis in New York, 2001–2013. Using distributed lag nonlinear models, the hospitalization rate during major storms and PO was compared to non-major storms and non-PO periods to determine the risk ratios (RRs) for COPD at each of 0–6 lag days respectively after controlling for time-varying confounders and concentration of fine particulate matter (PM 2.5 ). We then used Granger mediation analysis for time series to assess the mediation effect of PO on the major storms-COPD associations. Results The RRs of COPD hospitalization following major storms, which mainly included flooding, thunder, hurricane, snow, ice, and wind, were 1.23 to 1.49 across lag 0–6 days. The risk was strongest at lag3 and lasted significantly for 4 days. Compared with non-outage periods, the PO period was associated with 1.23 to 1.61 higher risk of COPD admissions across lag 0–6 days. The risk lasted significantly for 2 days and was strongest at lag2. Snow, hurricane and wind were the top three contributors of PO among the major storms. PO mediated as much as 49.6 to 65.0% of the major storms-COPD associations. Conclusions Both major storms and PO were associated with increased hospital admission of COPD. PO mediated almost half of the major storms-COPD hospitalization associations. Preparation of surrogate electric system before major storms is essential to reduce major storms-COPD hospitalization.

Even though the storm system killed five people in Arkansas and injured hundreds more, it could have been so much worse. Skot Covert, a THV 11 News meteorologist, told the Rotary Club of Little Rock last week that the National Weather Service nailed this, and that helped everyone pre-pare for the unthinkable. Selflessness: Individual Arkansans, churches, nonprofits and businesses were quick to step up in the storms wake, volunteering for cleanup, setting up shelters, donating clothing and other goods, and pro-viding free meals.

In mountainous regions, forested areas, and other zones prone to natural disasters, power equipment faces heightened risks of shutdown. Such disruptions significantly elevate the risk of secondary cascading failures within the power grid. Consequently, devising cascading failure mitigation strategies from an operational perspective is of paramount importance for containing the spread of cascading failures in the power system during disasters and minimizing the losses incurred from disaster incidents. Firstly, based on the severity of natural disaster accident risks, this paper establishes a risk index for power equipment for the first time, providing a new perspective for the refined analysis of the development model of cascading failures in power systems. Subsequently, a new collaborative mitigation strategy for system cascading failures is proposed at the operational control level. This strategy, in conjunction with proactive prevention and control measures, aims to promptly sever potential cascading failure paths upon the occurrence of a disaster, thereby ensuring that the area of power outage is minimized to the greatest extent possible. The effectiveness of the proposed strategy is verified through simulation cases. The results show that in the scenario set in this article, the risk of cascading failures under natural disasters is nearly five times higher than that without natural disasters. At the same time, the cascading failure control method proposed in this study can reduce the risk of cascading failure by about 80%.