Explore the vast range of titles available.

Many developing countries in Africa face a “double tragedy” when it comes to electrification. Electricity access rates are low, while those who have access to electricity face frequent outages. There are ongoing efforts aimed at increasing access to electricity on the continent. However, the need to improve the reliability of electricity supply receives limited attention. Unreliable electricity impacts users by limiting electricity utilization and the benefits that should accrue from having an electricity connection. Using data from 496 household survey questionnaires, this study examines the impacts of electricity outages in urban households in Accra, Ghana. The study applies correlation and regression analyses to identify which household characteristics are associated with or predict households reporting outage impacts. Outages were found to impact household safety/security, access to food, and access to social services and were found to cause appliance damage as well. Factors that are significantly correlated with reporting certain outage impacts include respondent’s annual income and employment status, frequency of electricity outages, and household size. Significant predictors of reporting outage impacts are socioeconomic disadvantage, high exposure to outages, and living in a large family setting. The study’s findings underscore the need for interventions to eliminate, or at least minimize, electricity supply interruptions in developing countries if sustainable social and economic development is to be achieved.

As demand for electricity increases on an already strained electrical supply due to urbanization, population growth, and climate change, the likelihood of power outages will also increase. While researchers understand that the number of electrical grid disturbances is increasing, we do not adequately understand how increased power outages will affect a society that has become increasingly dependent on a reliable electric supply. This systematic review aims to understand how power outages have affected society, primarily through health impacts, and identify populations most vulnerable to power outages based on the conclusions from prior studies. Based on search parameters, 762 articles were initially identified, of which only 50 discussed the social impacts of power outages in North America. According to this literature, racial and ethnic minorities, especially Blacks or African Americans, those of lower socioeconomic status, children, older adults, and those living in rural areas experienced more significant impacts from previous power outages. Additionally, criminal activity increased during prolonged power outages with both pro-social and anti-social behaviors observed. Providing financial assistance or resources to replace spoiled goods can reduce crime. Future research on this topic must consider the financial effects of power outages, how power outage impacts seasonally vary, and the different durations of power outage impacts.

BackgroundPrecipitated by an unusual winter storm, the 2021 Texas Power Crisis lasted February 10 to 27 leaving millions of customers without power. Such large-scale outages can have severe health consequences, especially among vulnerable subpopulations such as those reliant on electricity to power medical equipment, but limited studies have evaluated sociodemographic disparities associated with outages.ObjectiveTo characterize the 2021 Texas Power Crisis in relation to distribution, duration, preparedness, and issues of environmental justice.MethodsWe used hourly Texas-wide county-level power outage data to estimate geographic clustering and association between outage exposure (distribution and duration) and six measures of racial, social, political, and/or medical vulnerability: Black and Hispanic populations, the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), Medicare electricity-dependent durable medical equipment (DME) usage, nursing homes, and hospitals. To examine individual-level experience and preparedness, we used a preexisting and non-representative internet survey.ResultsAt the peak of the Texas Power Crisis, nearly 1/3 of customers statewide (N = 4,011,776 households/businesses) lost power. We identified multiple counties that faced a dual burden of racial/social/medical vulnerability and power outage exposure, after accounting for multiple comparisons. County-level spatial analyses indicated that counties where more Hispanic residents resided tended to endure more severe outages (OR = 1.16, 95% CI: 1.02, 1.40). We did not observe socioeconomic or medical disparities. With individual-level survey data among 1038 respondents, we found that Black respondents were more likely to report outages lasting 24+ hours and that younger individuals and those with lower educational attainment were less likely to be prepared for outages.SignificancePower outages can be deadly, and medically vulnerable, socioeconomically vulnerable, and marginalized groups may be disproportionately impacted or less prepared. Climate and energy policy must equitably address power outages, future grid improvements, and disaster preparedness and management.

Weather is a key driving factor of power outages. In this article, a methodology to forecast weather‐related power distribution outages one day‐ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modelling challenge for small and rural electric utilities. The weights for outage and non‐outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather‐related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.

Automating the process of restoring service to customers after a large‐scale outage event have significant impacts on the agility and speed of recovery in distribution systems. This study develops a set of probabilistic metrics to assess the impact of automation in enhancing the resilience of power distribution systems. The proposed metrics capture the features and detailed process of automatically locating and isolating faults and restoring the service to customers in distribution systems. In addition, this study develops a model to evaluate the spatio–temporal impacts of hurricane on power distribution systems, which is used to generate hurricane‐induced outage scenarios to calculate the resilience metrics given different automation schemes. The proposed model is utilised to evaluate the resilience of bus number four of Roy Billinton Test System in the face of a passing hurricane. The metrics are calculated to evaluate the impact of different levels of automation throughout the network, the intensity of the hurricane, and line hardening on the resilience of the system.

Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.

Power outages threaten public health. While outages will likely increase with climate change, an aging electrical grid, and increased energy demand, little is known about their frequency and distribution within states. Here, we characterize 2018–2020 outages, finding an average of 520 million customer-hours total without power annually across 2447 US counties (73.7% of the US population). 17,484 8+ hour outages (a medically-relevant duration with potential health consequences) and 231,174 1+ hour outages took place, with greatest prevalence in Northeastern, Southern, and Appalachian counties. Arkansas, Louisiana, and Michigan counties experience a dual burden of frequent 8+ hour outages and high social vulnerability and prevalence of electricity-dependent durable medical equipment use. 62.1% of 8+ hour outages co-occur with extreme weather/climate events, particularly heavy precipitation, anomalous heat, and tropical cyclones. Results could support future large-scale epidemiology studies, inform equitable disaster preparedness and response, and prioritize geographic areas for resource allocation and interventions. The authors find within-day, seasonal, and regional differences in county-level power outages from 2018–2020. Outages commonly co-occur with climate events. Counties in the south and Michigan faced high social and medical vulnerabilities and outages.

Sometimes a power failure can be fairly local, but other times, a seemingly identical initial failure can cascade to cause a massive and costly breakdown in the system. Yang et al. built a model for the North American power grid network based on samples of data covering the years 2008 to 2013 (see the Perspective by D'Souza). Although the observed cascades were widespread, a small fraction of all network components, particularly the ones that were most cohesive within the network, were vulnerable to cascading failures. Larger cascades were associated with concurrent triggering events that were geographically closer to each other and closer to the set of vulnerable components. Science , this issue p. eaan3184 ; see also p. 860 Cascading failures in the U.S. power grid are most likely to occur within cohesive parts of the grid. The understanding of cascading failures in complex systems has been hindered by the lack of realistic large-scale modeling and analysis that can account for variable system conditions. Using the North American power grid, we identified, quantified, and analyzed the set of network components that are vulnerable to cascading failures under any out of multiple conditions. We show that the vulnerable set consists of a small but topologically central portion of the network and that large cascades are disproportionately more likely to be triggered by initial failures close to this set. These results elucidate aspects of the origins and causes of cascading failures relevant for grid design and operation and demonstrate vulnerability analysis methods that are applicable to a wider class of cascade-prone networks.

Power systems are the most complex systems and have great importance in modern life. They have direct impacts on the modernization, economic, political and social aspects. To operate such systems in a stable mode, several control and protection techniques are required. However, modern systems are equipped with several protection schemes with the aim of avoiding the unpredicted events and power outages, power systems are still encountering emergency and mal-operation situations. The most severe emergencies put the whole or at least a part of the system in danger. If the emergency is not well managed, the power system is likely to have cascading failures that might lead to a blackout. Due to the consequences, many countries around the world have research and expert teams who work to avoid blackouts on their systems. In this paper, a comprehensive review on the major blackouts and cascading events that have occurred in the last decade are introduced. A particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world and it is also due to the ready availability of data for the past events. The paper also highlights the root causes of different blackouts around the globe. Furthermore, blackout and cascading analysis methods and the consequences of blackouts are surveyed. Moreover, the challenges in the existing protective schemes and research gaps in the topic of power system blackout and cascading events are marked out. Research directions and issues to be considered in future power system blackout studies are also proposed.