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Natural disasters and extreme weather events have caused long-duration power outages in Brazil in recent years, partly due to the lack of resilience of the power grid. These outages can have disastrous impacts on the lives and livelihoods of the people who lose access to electricity. These impacts are most severe for socially vulnerable populations who struggle to prepare for or recover from a power outage. However, no index of social vulnerability specific to power outages in Brazil currently exists. To fill this gap, this paper develops an index of social vulnerability to long-duration power outages tailored to Brazil. Results are demonstrated on a case study of Rio de Janeiro using publicly available data to create indices of vulnerability in three dimensions of health, preparedness, and evacuation, as well as an index of overall vulnerability. The vulnerability maps are reported at the municipality, weighting area, and/or census tract levels. The results indicate that the most socially vulnerable regions are also highly susceptible to extreme weather events and natural disasters. The vulnerability maps can be used for targeted decision making in terms of infrastructural hardening, grid reinforcement, and preemptive event preparation as well as to inform risk-based resilient operation strategies. This study also discusses policy barriers and opportunities for vulnerability-informed resilience approaches in Brazil. Overall, this index is a valuable tool for policymakers and electric utilities to understand who is vulnerable during a power outage and to build a more equitable and resilient power grid. [ABSTRACT FROM AUTHOR]

Power outages have become pervasive in many Global South countries causing serious disruption to economies, psycho-social well-being, and industries including libraries. Occasionally, these power outages and their implications are felt in Global North countries too, mainly as a result of natural disasters whose occurrence has become too common due to global warming. This study sought to assess the impact of power outages on library services. The method employed was desk research which relied on a literature review and the lived experiences of the researcher who is based in South Africa, one of the countries that experienced almost daily periodic power outages as a result of the imposition of load shedding. The results show that power outages disrupt daily services of libraries resulting in difficulties in accessing electronic resources and services causing library hours to be adjusted and even closures. Power outages often lead to great frustration and anger from clients and librarians alike. Based on the available literature, some remedies are suggested to cushion the blow to clients and libraries. However, these remedies can only be temporary solutions. Ultimately, it is recommended that libraries should tackle the issue of load shedding collectively by either negotiating favourable deals with alternative energy providers and/or in negotiations with governments to, as far as possible, exempt and prioritise libraries in cases of power outages. This will aid in stemming the negative effects on education, access to information, and other efforts to actualise sustainable development.

Abstract: Climate change induced extreme weather events will increase in intensity and frequency, leading to longer and widespread electricity outages. As an example, Winter Storm Uri in Texas left over 4.5 million customers without power between 14 and 18 February 2021. The social justice consequences of these events remain an outstanding question, as outage data are, typically, only available at the county level, obscuring detailed experiences. We produce a first-of-its-kind unique spatially resolved dataset of interruptions using satellite data on nighttime lights to track blackouts at the census block group (CBG) level. Correlating this dataset with demographic data reveals that minority CBGs were 1.5–3 times more likely to suffer from interruptions compared to predominantly white CBGs, whereas income status was positively correlated with the likelihood of interruption. The presence of critical facilities—including police and fire stations, hospitals, and water treatment facilities—in a CBG reduced the chances of interruptions by around 16 % , a small difference that does not otherwise explain the disparity among communities. We suggest explanations, test a subset of them, and propose further work needed to explain what drives these disparities.

This paper provides a literature review of methods and modeling techniques to estimate the cost of power system outages, along with the value of outage mitigation or system resilience. Regulators, policymakers, and infrastructure owners have a growing need to understand the methods for estimating the benefits of resilience improvements of electric infrastructure against natural and man-made disasters. There is a broad literature that estimates the cost of short-duration outages and a small but developing literature on estimating the cost of long-duration outages. This article reviews the models used to estimate the cost of outages and discusses their relative strengths. Additionally, this paper identifies key questions from stakeholders regarding resilience investment and maps them to the relevant models that would help answer them. We include recommendations for future work to include recent advances in regional economic modeling that can estimate region and demographic-specific costs and the distributional consequences of potential resilience projects.

Operational challenges arising from public utility obstacles are widespread globally, leading to production disruptions, decreased sales, and escalated logistics costs, ultimately contributing to a decline in firm labor productivity growth. Despite the criticality of this issue, there is a scarcity of studies in the field of operations management that investigate factors capable of mitigating these adverse impacts. Taking power outages and transportation obstacles as pivotal examples, our study aims to examine whether national culture can moderate the impact of public utility obstacles on firm labor productivity growth. To achieve this objective, we conduct an empirical analysis using a multi‐country dataset from the World Bank Enterprises Survey, which covers detailed firm‐level information across 28 industries in 41 countries, with a total of 17,227 firm‐year observations. Our findings indicate that the detrimental effects of power outages and transportation obstacles on firm labor productivity growth are contingent on various dimensions of national culture. Specifically, cultural power distance and uncertainty avoidance would amplify the negative impact of power outages, whereas long‐term orientation would alleviate this impact. Meanwhile, individualism and masculinity can help mitigate the adverse effects of transportation obstacles. Our results provide valuable insights for managers. Highlights: National culture is a key contingency that influences the relationship between public utility obstacles and firm labor productivity growth.Power distance and uncertainty avoidance amplify the negative impact of power outages on labor productivity growth, whereas long‐term orientation mitigates this impact, and individualism and masculinity attenuate the negative impact of transportation obstacles on labor productivity growth.Firms should consider the impact of various national cultures on global operations and understand which cultures contribute to mitigating the negative effects of public utilities, such as power outages and transportation obstacles, when making plant location decisions to achieve sustainable operations. [ABSTRACT FROM AUTHOR]

Copyright of Risk, Hazards & Crisis in Public Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure.

Climate change will increase the frequency of extreme weather events. This means climate-driven events like wildfires and power outages will likely co-occur more often, potentially magnifying their health risks. We characterized three types of climate-driven events—anomalously warm temperatures, wildfire burn zone disasters, and long power outages—in 58 California counties during 2018–2019. We defined county-day anomalously warm temperatures when daily average temperatures exceeded 24 °C and the 85th percentile of the long-term county average. We defined county-day wildfire burn zone disasters when an active wildfire burn zone intersected a county, burned 1+ structures, killed a civilian, or received a Federal Emergency Management Agency Fire Management Declaration, and overlapped with a community. For a subset of the 38 counties (66%), long power outage county days were identified using PowerOutage.us data when an outage affected >0.5% of county customers for 8+ h. Co-occurring events were when 2+ of these events occurred on the same county day. Using the CDC/ATSDR Social Vulnerability Index (SVI), we determined whether co-occurring events disproportionately affected vulnerable populations. Nearly every county (97%) experienced at least one day of anomalously warm temperatures, 57% had at least one wildfire burn zone disaster day, and 63% (24/38 counties with available data) had at least one long power outage day. The most common co-occurring events (anomalously warm temperatures and wildfire burn zone disasters) impacted 24 (41%) counties for 144 total county-days. We did not find a clear connection between co-occurring events and social vulnerability. We observed an inverse correlation between co-occurring wildfire burn zone disasters and long power outage days with SVI, and a positive correlation between co-occurring anomalously warm and long power outage days with SVI. This analysis can inform regional resource allocation and other state-wide planning and policy objectives to reduce the adverse effects of climate-driven events.

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM's performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid. [ABSTRACT FROM AUTHOR]

Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure. [ABSTRACT FROM AUTHOR]

Recent increases in extreme weather events such as severe thunderstorms, floods, and hurricanes are leading to destruction in power system equipment (transmission and distribution poles and lines, substations, power plants, etc.) and are causing widespread prolonged power outages. These outages often cause inconveniences in critical services (health care, transportation, national security, etc.) and significant losses in the economy, leading to human suffering. Therefore, understanding the spatiotemporal correlation of these events with power systems is crucial to planning and for maintaining reliable operation and control under such events. However, developing such correlation requires several datasets, including weather events and power outage datasets, along with coordination from multiple entities (e.g., electric utilities, government agencies, and research organizations). Also, high-resolution data collection is a time-consuming and tedious task because different interest groups are involved in the process. To this end, we propose an automated data framework that maps severe weather events with power outages to quantify power system resilience. This framework uses the publicly available National Weather Service dataset and Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) power outage dataset to quantify the power system resilience. The proposed work can quantify power system resilience against extreme weather events at the county/state level for different weather event types (e.g., hurricanes, severe thunderstorms, and floods). The outcome of the proposed work will be useful for identifying vulnerability hot spots, developing weather event-based planning strategies (planning strategies might change with events types), developing asset management strategies, and developing predictive analysis tools.

This article studies the Transmission Restoration Problem with Mobile Substation Resources, a novel mixed-integer linear programming model that prescribes the most effective usage of mobile-substation resources to enhance the resilience of a power transmission system against a particular, widespread flood event. The model is a two-stage stochastic program in which each scenario captures a different potential progression of flood heights at substations over the event horizon. The first stage concerns the pre-event selection and positioning of mobile-substation resources. The second stage concerns the coordination of mobile-substation resource deployment and permanent-substation restoration to maintain and recover service within the horizon. Experiments in the IEEE 24-Bus System and a synthetic Houston grid confirm the efficacy of the model. Even when isolated from effects related to restoration of permanent substations, the effect of four mobile transformers and eight mobile breakers for a realistic set of flood scenarios in the synthetic Houston grid was found to be an average total-cost reduction of approximately ${\$}$ 35MM (i.e., approximately 8% of a default optimal objective value). Additionally, a novel, parallel heuristic is designed that can efficiently solve the problem as well as, with minor modifications, similar stochastic problems on pre-selection of mobile resources or placement of static ones. For a 40-scenario model instance in the IEEE 24-Bus System, the extensive form was not able to find an integer-feasible solution in six hours, yet the heuristic achieved an optimality gap no worse than 4.5% in two hours.

The complex interactions between the weather, the environment, and electrical infrastructure that result in power outages are not fully understood, but because of the threat of climate change, the need for models that describe how these factors produce power grid failures is acute. Without them, it remains difficult to understand the amount of weather-related damage we may expect in the future, as well as how changes or upgrades to the infrastructure may mitigate it. To address this problem, a modeling framework is proposed in this article that integrates data derived from structural vulnerability analysis into a machine-learning based weather-related power outage prediction model to create a model that is sensitive both to the weather and the technical configuration of the infrastructure. This Physics Informed Machine Learning (PIML) approach is demonstrated using data from a major power utility operating in the US State of Connecticut, and is compared against a fragility curve modeling approach using some of the same data. The validation of the PIML model shows superior predictive ability, as well as variable sensitivities that follow expected patterns. These results suggest that the model would be able to evaluate the influence that different configurations of the infrastructure would have on the occurrence of power outages caused by severe storms, allowing for the anticipated effects of investments in infrastructural upgrades to be quantified and optimized.

In the United States, weather-related power outages cost the economy tens of billions annually, and there has been an upward trend in billion-dollar disasters over the last two decades. Thus, it is of growing importance to be able to predict outages and understand local resilience. However, many outage prediction models rely on utility infrastructure and outage data, which can be difficult to obtain when a study domain covers many utility territories. This study demonstrates two gradient-boosting machine-learning models driven by utility-agnostic non-proprietary data, eliminating the need for utility-specific data, and allowing individuals or communities to build and use such models for emergency planning or vulnerability analysis. Further, the framework is novel for its ability to incorporate data from various ecoregions, utilize infrastructure proxy data, and provide outage predictions for a breadth of storm types over a large and scalable domain. In this study, vegetation, land cover, energy infrastructure proxy, and weather data are used as model inputs to evaluate 15,872 events across 17 states in the Eastern U.S., where an event is defined as a unique combination of geographic county and storm episode ID. The model predicting all storm types except thunderstorms was validated using 10-fold cross-validation where folds were split chronologically, and demonstrates an r-squared value between predicted and actual outages of 0.61. Similarly, the thunderstorm-only model demonstrates an r-squared of 0.31. For future work, the addition of flooding data may be considered as the r-squared for the various-storm-type model increases to 0.77 when data from New York and New Jersey for Hurricane Sandy are removed. Additionally, the framework demonstrated here can be used to create a real-time outage prediction forecasting tool for storm events, and can be used to analyze resilience at a county resolution under future climate scenarios.

A novel approach combining thermal energy storage (TES) and a thermosiphon was investigated for cold storage. The use of TES units for cooling systems has been studied for many years, as they are well suited for short-term energy storage. A cold latent heat accumulator was designed to replace the function of any vapour compression cycle in the event of electrical failure without using any electrical device but rather the thermosiphon principle. A laboratory prototype of a thermosiphon combined with the cold accumulator was developed using a paraffin mixture as a phase change material (PCM). The accumulator was connected to the vapour compression system of a closed display cabinet. An experimental study was carried out by simulating 1.5 h compressor shutdowns with and without the accumulator. The thermosiphon loop effectively transferred 97 % of the stored energy to the display cabinet. The air and product temperatures in the cabinet, the behaviour of the compressor during restart, and the charging and discharging processes of the accumulator were analysed. The results showed that shutting down the compressor with the cold accumulator significantly reduces the increase of air and product temperatures compared to shutting down without the accumulator. The air temperature in the rear duct was maintained within the acceptable temperature range for 72 min with the accumulator, compared to 3 min without. A default in the design of the accumulator was observed during the charging phase, as some areas of the accumulator never reached 80 % of charge. This new approach extends demand-side management and renewable energies to all end users of vapour compression machines. [ABSTRACT FROM AUTHOR]

Stated preference studies are often based on the assumptions that proposed outcomes would realize with certainty and respondents believe their survey responses are consequential. This paper uses split sample treatments to test whether survey consequentiality and outcome uncertainty lead to differences in welfare measures, focusing on a discrete choice experiment on improving quality of electricity supply among business enterprises in Tanzania. Our results show that incorporating uncertainty not only affects the preferences for the attribute with uncertainty (duration of power outage) but also for a choice attribute with a precautionary feature (advanced outage notification). While outcome uncertainty and an additional survey script (a formal letter from a state-owned electric utility) to strengthen consequentiality have some influence on preferences and willingness to pay (WTP) estimates for certain attributes, we do not find significant implications on overall welfare estimates. [ABSTRACT FROM AUTHOR]

The reliability of the electricity supply is important since any interruption to the supply has direct and indirect consequences for its users. A reliable electricity supply requires a reliable electrical grid system to transmit and distribute the power from the generating plants to the consumers. This study reviewed the literature to find out how the reliability concept has been understood with a special focus on grid electricity reliability, what factors influence grid electricity reliability, what measures have been used to measure grid electricity reliability, which theories and methodologies have been applied to study grid electricity reliability and what are the likely research gaps that require future address. This review found that the literature documents four categories of factors that influence grid electricity reliability, and these are environmental, security, organizational and technical. The biggest influencers of grid electricity reliability were the technical-related factors followed by the environmental-related factors. In addition, we found that sixty studies focused on one subsystem, eleven on two subsystems while seven studies considered three subsystems. Most studies were found to address the distribution of subsystem reliability. As per the methodology adopted, this review found that eleven studies used a qualitative approach, forty-five studies used a quantitative approach, while eleven studies used a case study approach to study the concept of grid electricity reliability. In addition, we found that thirty-seven studies used the duration and frequency of power outages to measure grid electricity reliability. [ABSTRACT FROM AUTHOR]

For power plant networks in developing countries like Iraq, balancing electricity demand and generation continues to be a major challenge. Energy management (EM) in either demand-side (DS) or generation-side (GS) strategies, which is frequently utilized in Iraq due to a lack of adequate power generation, has a small impact on the power balancing mechanism. Most previous studies in similar countries discussed only the application of DS strategies. The purpose of this paper is to contrast and review various energy management methodologies being used in developing nations facing power outages, to be able to recommend suitable ones according to the country's situation. To assess potential EM-based solutions to improve the total energy efficiency of the Iraqi electrical community, a thorough and methodical analysis was carried out. The main objective of this review paper is to discuss the causes of power outages and the energy management strategies addressed here as methods to mitigate or avoid power outages. Unlike existing reviews that concentrated on demand-side energy management, this study specifically focuses on power outage causes in developing countries like Iraq rather than all management strategies. It also introduces the consequences of power outages including analysis of distribution power losses, financial loss from power blackouts, and power blackouts in firms in a typical month. Therefore, it presents readers with state-of-the-art strategies and recommends a generation-based EM strategy to mitigate such issues. [ABSTRACT FROM AUTHOR]

Millions of Americans experienced impacts from the 2020 wildfire season, including unhealthy air quality from smoke. We examine how exposure to poor air quality during wildfires relates to public opinion toward Public Safety Power Shutoffs (PSPSs). PSPSs have been increasingly deployed in the Western U.S. during extreme wildfire conditions to reduce ignition risk from power equipment by de-energizing sections of the electrical grid, potentially leaving impacted areas without electricity for multiple days. We surveyed Oregon residents (n = 1,308), a state where few PSPSs have been deployed, and found that a majority of respondents supported PSPSs, and that poorer air quality during wildfires, recorded from monitoring stations proximal to respondents, was related to increased support for PSPSs. We also found that females and those with liberal political orientations were more supportive of PSPSs. This research has implications for utilities, emergency managers, and policymakers as they consider deployment of PSPSs to mitigate wildfire. [ABSTRACT FROM AUTHOR]

Purpose: This research investigates the relationship between power shortages and the sales performance of businesses in South Asia (SAR). The study seeks to reveal the impact of electricity supply quality on commercial operations in the region by utilizing a mix of qualitative and quantitative measures, based on data from World Bank enterprise surveys. Design/Methodology/Approach: The study employs Ordinary Least Squares (OLS) estimation techniques with industry and country fixed effects to account for national and industry-level variations. It utilizes data from the World Bank Enterprise Surveys to assess the influence of electricity supply on business sales, considering both perception-based and quantitative metrics. Findings: The research consistently demonstrates an inverse relationship between the reliability of electricity supply and business sales performance in SAR. Whether measured by perception-based indicators or quantitative measures, power interruptions and deficiencies are detrimentally linked to sales. While the correlation is statistically significant, it is not exceptionally strong, necessitating caution in formulating broad policy recommendations solely based on these findings. Implications/Originality/Value: From a policy perspective, the study emphasizes the critical role of perceived electricity infrastructure quality. A reliable electricity supply is found to be paramount for enhancing enterprise sales, regardless of the severity of hindrances identified by businesses. This underscores the importance of prioritizing dependable electricity supply to bolster business operations in the region, offering valuable insights for policymakers and business leaders in South Asia. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.

Many studies have estimated the effects of rising air temperatures due to climate change on electricity infrastructure systems, but none have quantified impacts in terms of potential outages down to the neighborhood scale. Using high-resolution climate projections, infrastructure maps, and forecasts of peak electricity demand for Los Angeles County (LAC), we estimated vulnerabilities in the electricity infrastructure to 2060. We considered rising air temperatures under IPCC RCP 4.5 and RCP 8.5 at 2 km2 grid cell resolution, two local government population growth scenarios, different efficiency implementations of new residential and commercial buildings, air conditioners (AC), and higher AC penetration. Results were that generators, substations, and transmission lines could lose up to 20% of safe operating capacities (MW). Moreover, based on recent historical load factors for substations in the Southern California Edison service territory, 848–6724 MW (4–32%) of additional capacity, distributed energy resources, and/or peak load shifting could be needed by 2060 to avoid hardware overloading and outages. If peak load is not mitigated, and/or additional infrastructure capacity not added, then all scenarios result in > 100% substation overloading in Santa Clarita, which would trigger automatic outages, and > 20% substation overloading in at least Lancaster, Palmdale, and Pomona in which protection gear could trip outages within 30 min. Several climate change adaptation options are discussed for electricity infrastructure and building stock with consideration for trade-offs in system stability and other energy and environmental goals.

Hydrogen is a bright energy vector that could be crucial to decarbonise and combat climate change. This energy evolution involves several sectors, including power backup systems, to supply priority facility loads during power outages. As buildings now integrate complex automation, domotics, and security systems, energy backup systems cause interest. A hydrogen-based backup system could supply loads in a multi-day blackout; however, the backup system should be sized appropriately to ensure the survival of essential loads and low cost. In this sense, this work proposes a sizing of fuel cell (FC) backup systems for low voltage (LV) buildings using the history of power outages. Historical data allows fitting a probability function to determine the appropriate survival of loads. The proposed sizing is applied to a university building with a photovoltaic generation system as a case study. Results show that the sizing of an FC–battery backup system for the installation is 7.6% cheaper than a battery-only system under a usual 330-minutes outage scenario. And 59.3% cheaper in the case of an unusual 48-hours outage scenario. It ensures a 99% probability of supplying essential load during power outages. It evidences the pertinence of an FC backup system to attend to outages of long-duration and the integration of batteries to support the abrupt load variations. This research is highlighted by using historical data from actual outages to define the survival of essential loads with total service probability. It also makes it possible to determine adequate survival for non-priority loads. The proposed sizing is generalisable and scalable for other buildings and allows quantifying the reliability of the backup system tending to the resilience of electrical systems.

Key Takeaways: Modeling tools can simulate possible disasters to water distribution systems, including pipe breaks, power outages, and changes to water source availability. Resilience analysis can help water utilities compare emergency response strategies for disasters that could affect their water distribution systems. Socioeconomic and environmental factors are useful to identify priority areas within a water distribution system to improve equitable resilience. [ABSTRACT FROM AUTHOR]

Power outages are a common outcome of hurricanes in the USA with potentially serious implications for community wellbeing. Understanding how power outage recovery is influenced by factors such as the magnitude of the outage, storm characteristics, and community demographics is key to building community resilience. Outage data are a valuable tool that can help to better understand how hurricanes affect built infrastructure and influence the management of short-term infrastructure recovery process. We conduct a spatial regression analysis on customers experiencing outages and the total power recovery time to investigate the factors influencing power outage recovery in Louisiana after Hurricane Isaac. Our interest was in whether infrastructure damage and recovery times resulting from a hurricane disproportionately affect socioeconomically vulnerable populations and racial minorities. We find that median income is a significant predictor of the time it takes to restore 50%, 80%, and 95% of the total outages within a ZIP Code Tabulation Area, even after controlling for hurricane characteristics and total outages. Higher income geographies and higher income adjacent geographies experience faster recovery times. Our findings point to possible inequities associated with income in power outage recovery prioritization, which cannot be explained by exposure to outages, storm characteristics, or the presence of critical services such as hospitals and emergency response stations. These results should inform more equitable responses to power outages in the future helping to improve overall community resilience. [ABSTRACT FROM AUTHOR]

This study investigates Iraq's challenging electricity landscape, exacerbated by the cumulative impacts of four wars, leading to daily power outages. The reliance on neighborhood diesel generators (NDG) as a temporary fix is critically assessed, with a strong expert consensus via the Delphi method advocating for a transition to solar photovoltaic (SPV) panels. The Delphi survey involved 20 experts, with 85% agreeing on the necessity of this transition, and high consensus (90% or higher) achieved on key questions regarding the inadequacy of NDG and the suitability of SPV as a replacement. The scarcity of local load data prompted the adaptation of Spain's load profiles to Iraq using the innovative Rosetta transform, identifying the optimal number of SPV panels needed for low, base, and high consumption scenarios as 7, 9, and 11 panels, respectively. In a first approach to the viability of such an SPV installation, it is deduced that the minimum prices per kWh should be between $0.106 and $0.078, depending on the scenario, for it to be viable, well above the current prices in Iraq. A deeper analysis was then performed to evaluate Hybrid Microgrid Systems (HMGS) integrating SPVs, batteries and gasoline generators, both off-grid and grid-connected, and taking into account NDG-related savings. This analysis evidenced the viability of a grid-connected HMGS, leveraging SPV and battery storage, as the most economically viable solution, achieving payback periods up to 3.6 years in the best case. This research underscores the need for a policy shift towards sustainable energy solutions in Iraq and similar contexts, highlighting the technical and economic advantages of adopting clean, renewable energy systems over traditional NDG, and paving the way for a sustainable energy future. • Solar energy and hybrid microgrids in Iraq can greatly reduce fossil fuel reliance. • Iraq's daily power outages show the urgent need for reliable, sustainable energy. • Delphi survey shows neighborhood diesel generators are an inefficient, costly fix. • Our Rosetta method adapts Spanish load profiles to reflect Iraqi energy patterns. • Economic analysis confirms that grid-tied SPV-battery HMGS offers good payback. [ABSTRACT FROM AUTHOR]

Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

This perspective investigates the impacts of disparate energy disruptions during Winter Storm Uri, which in 2021 caused severe power outages across the state of Texas leading to numerous deaths and billions of dollars of damage to property and infrastructure. The paper focuses on the potential injustice along ethnic lines in the distribution of power outages across the state. By analyzing data from the hourly rate of power outages aggregated by counties in Texas we find that among rural populations, Hispanic households faced substantially higher outage rates throughout the storm compared to other racial and ethnic groups. This reveals an inequitable distribution of energy disruptions. We propose that further nuances may be uncovered if more granular data were made available, delving into intra-county disparities. Only through comprehensive examination and proactive measures can we mitigate the adverse effects of future energy disruptions and safeguard the well-being and lives of all citizens, irrespective of their race, ethnicity, or socioeconomic status.

The electricity crisis, or power outages, has been a global phenomenon for decades and South Africa is part of this crisis. The SA economy has been impacted immensely by its effects, resulting in industry shutdowns, a steep decline in productivity, unemployment, negative healthcare impacts and an education crisis, to name but a few. In 2022 there was more loadshedding than in all the previous years together. The intention of this paper is to shed light on the devastating impact of loadshedding on the South African economy. A literature review was used, coupled with a documentary research design. It was found that the current loadshedding crisis in South Africa is the culmination of poor leadership, poor management, an aging energy infrastructure and a complex regulatory environment that has hampered economic growth and the livelihoods of its citizens. It has a devastating effect on every sphere of the socio-economic life of the country, with vast industries shutting down, lack of international investments, unemployment, collapsing health and education systems, the continuous darkness that citizens experience daily, the alternative resources needed to survive the loadshedding, etc. The following key pillars are needed to avoid worsening the situation: to upgrade or build new energy infrastructure, find alternative energy sources such wind, solar and green energy, make energy prices fair and market related, and prioritise energy efficiency technologies to reduce demand and outsourcing to independent power producers. [ABSTRACT FROM AUTHOR]

The work is devoted to the development of a model and method for calculating a backup photovoltaic (BPV) system designed to maintain a consumer load under the power grid. The proposed method and algorithm are based on the energy balance equations in the system during the day for given daily profiles of load and outage scenario. The method allows calculate the sizing parameters of BPV: the power of PV array Pinst and the capacity of batteries Qinst. As an example, various outage scenarios were simulated for BPV located in the Kyiv region, which provides support for a daily load of 10 kW. As a result of simulation the dependences of sizing parameters on the outages characteristics were obtained. The proposed method for sizing problem solving can be used in the design of local backup power supply systems for residential buildings or small businesses in a military situation. The reliability of the backup power supply is estimated when the energy balance is disturbed due to unplanned consumers power outages. [ABSTRACT FROM AUTHOR]