The impact of power outages on Ecuadorian science

As global average temperatures rise, so does the frequency and intensity of El Niño-induced droughts, which in turn threaten the reliability of hydropower. 1.4 billion people live in countries where hydropower constitutes more than a quarter of the electricity production and which have experienced El Niño droughts, meaning many more power outages can be expected around the world. Little research has been conducted on the impact of power outages on mental health. This study takes Zambia as its case study to examine the impact that El Niño droughts have had on the lives of householders connected to a highly hydropower-dependant electricity grid, and includes the impact it has had on their physical and self-reported mental health. Using 54 online responses to a survey, we found that the greatest impacts of outages spoiled food, compromised entertainment, compromised ability to work and limitation in cooking options. More than a fifth of respondents reported experiencing self-reported depression to a major degree or all of the time due to power outages, with individuals writing their own responses that they felt debilitated, experienced reduced communication and reduced activities, and stress. Using Bayesian inference, we found that changes in sleeping patterns arising from power outages was a statistically significant predictor of self-reported depression. 63% of surveyed households were willing to pay approximately USD 0.10/kWh as of the end of 2019, about double the tariff that they did, to ensure reliable electricity supply. Household income was a statistically significant predictor of willingness to pay more.

Purpose: Electricity, being the most important resource for any organization has substantial impact on working capital. So, it is substantial to analyze the impact of acute power shortage on firm’s liquidity and profitability. As acute power shortages in the country adversely affects the performance of firms by reducing profits and enhancing working capital investment cycle, it is noteworthy to analyze the effect of electricity crisis on firm’s need for liquidity. The aim of the research is to find the impact of power outage on working capital and profitability.
 Design/Methodology/Approach: Using annual financial data of 102 textiles firms from 2008-2020, the study employed panel data analysis to measure the impact of acute power outage on firm’s working capital management.
 Findings: The results revealed that acute power shortage significantly impact working capital management in textile firms of Pakistan. So, firms must go for alternative energy sources for long-run resulting into huge savings from losses for these firms.
 Implications/Originality/Value: Motivated by the scarcity of empirical evidences from emerging economies and the importance of working capital efficiency, current study is the need of the day. The impact of power outages need to be explored in detail to gauge its impact on industry’s financial performance.

Power outages are common. They can result in exposure to extreme temperatures by shutting off temperature-controlling devices, and thereby also cause stress. Consequently, outages may precipitate cardiovascular disease (CVD)-related hospitalizations. We assessed this relationship among older adults. We leveraged 2017-2018 data from 245,452 New York State Medicare Fee-for-Service beneficiaries (65+ years) with 390,530 CVD hospitalizations. Using NY Department of Public Services data, we calculated total hours without power 1 day, 1-2 days, and 1-3 days before case and control periods, with an outage ZIP Code Tabulation Area (ZCTA)-hour defined based on ≥10% of customers in a ZCTA-hour without power in primary analyses. We used a case-crossover study design and ran conditional logistic regression to assess associations separately within each urbanicity level: New York City (NYC), non-NYC urban, and rural areas. We additionally stratified models by warm versus cool season, individual-level age and sex, and ZCTA-level socioeconomic factors. Secondarily, we considered emergency (n = 298,910) and nonemergency hospitalizations separately. We generally observed null associations between power outages and all CVD hospitalizations across New York State and within subgroups. For example, in NYC, we observed a rate ratio of 1.05 (95% confidence interval: 0.85, 1.30) for each additional power outage hour 1 day prior. The case-crossover design we used eliminated time-fixed confounding, but there were a limited number of exposed cases, limiting statistical power. Future studies should investigate co-occurring severe weather, span additional years, and evaluate other and broader geographic areas.

In this study, we propose a method that uses the Transformer model to enhance power outage alerts by integrating data from multiple sources. By integrating data from various sources, including operational data from the local distribution network and meteorological data, we have constructed a comprehensive multi-source data framework for power outage warnings. The Transformer model, known for its ability to capture complex dependencies and patterns, has been employed to extract features and make accurate predictions. Results on actual power system data have shown that our approach significantly boosts the accuracy and stability of predictions. The fusion of multi-source data has enabled timely maintenance and protection measures, reducing the duration and impact of power outages. The findings from this study have provided valuable insights for power outage warnings and future research on multi-source data fusion.

This chapter reveals that SMEs in South Africa are highly vulnerable to electricity crises, with many experiencing significant financial losses and business closures. The impact of power outages and load shedding varies across sectors, with manufacturing and retail businesses particularly affected. SMEs also face challenges in accessing alternative energy sources, such as solar power, due to high costs and limited availability. Factors contributing to SMEs' vulnerability include their reliance on the national electricity grid, limited access to finance for alternative energy solutions, and inadequate government support. Many SMEs are also struggling to compete with larger firms that have the resources to invest in backup power solutions and other resilience measures. Further, the chapter identifies several policy interventions that could support SMEs in mitigating the impact of electricity crises, including incentives for renewable energy, increased access to finance for energy-saving solutions, and targeted business support programs.

In developing countries, access to electricity has received much attention. However, the reliability of its supply has been given less focus, though power outages happen frequently and are expected to limit gains from electricity connection. In this paper, I go beyond electricity connection and provide an average estimate of monthly defensive expenditures at different monthly hours of power outages for urban households in Ethiopia, using the generalized propensity score method. I also elicit households' willingness to pay for improved electricity supply, using a stated preference method, to account for non-monetary costs of outages. Based on the average estimated results, a back-of-the-envelope calculation for urban households of Ethiopia with electricity connection provides a monthly defensive expenditure of US$14.8 million and a monthly willingness to pay of US$6.2 million for improved electricity supply, on top of the regular electricity bill. The study underscores that connection to electricity is not enough; the reliability of its supply is also important.

The Impact of Power Outages on Small Businesses in the City of Johannesburg

Traditional cloud stacks are designed to tolerate server or rack-level failures, that are unpredictable and uncorrelated. � Such stacks successfully deliver highly-available cloud services at global scale. The increasing criticality of cloud services to the overall world economy is causing concern about the impact of power outages, cyber-attacks, configuration errors, or other causes of datacenter or larger-scale failures on cloud availability. Recent experience shows that these events can trigger cascading failures and global-scale service outages. We study the impact of correlated, datacenter resource failures, exploring distributed protocols (widely-used in Cassandra) across varied configurations and resource availability. Our study reveals that using such protocols to achieve high availability on resources with large-scale, correlated outages are costly in storage and update traffic, requiring replication factors of 10 or more. Further analysis reveals that this limitation arises from from inflexible replication and quorum.

Securing electric power sources for modern disaster risk reduction in Japan

The article examines the peculiarities of implementing digital technologies in the educational process and consulting activities using the example of the Institute of Energy, Automation, and Energy Saving in the context of modern challenges, particularly wartime conditions and a shortage of specialists in the energy sector. The methods of adapting the educational process to the needs of students who combine studying with work at critical infrastructure enterprises are described.The study explores the issue of ensuring academic integrity in the conditions of distance learning and conducting assessment activities. The risks associated with the unauthorized use of information resources, the application of artificial intelligence for generating responses, and the challenges of student identification during online exams are identified. Global experience in maintaining academic integrity and effective assessment methods, such as step-by-step monitoring, individualized assignments, and video recording of responses, are analyzed.The article also considers the impact of power outages on the educational process and ways to overcome these challenges, including the use of alternative communication methods, video conferencing services, video hosting platforms (YouTube), and corporate e-learning platforms (elearn.nubip.edu.ua). Integrated approaches to organizing the learning process are proposed, ensuring close interaction between the university and enterprises through dual education. This not only improves students’ knowledge levels but also facilitates their adaptation to real working conditions.It is concluded that the combination of digital technologies and flexible learning strategies ensures the quality of education and academic integrity even in crisis situations. The use of e-learning platforms, individualized assignments, video control, and industry integration enhances the effectiveness of the educational process and strengthens the interaction between universities and employers.

The Impact of Power Outages on Households in Zambia

A resilient power distribution network can reduce length and impact of power outages, maintain continuous services, and improve reliability. One effective way to enhance the system's resilience is to form microgrids during outages. In this paper, a novel dynamic microgrid formation-based service restoration method using deep reinforcement learning is proposed, and it is treated as a Markov decision process (MDP) while taking operational and structural limitations of microgrids into account. The deep Q-network is employed to obtain optimal control strategies for microgrid formation. We have introduced a new way for the agent to choose actions when building a microgrid using the deep Q-learning method, which ensures that the microgrid has a feasible radial structure. The proposed service restoration method enables real-time computing to facilitate online formation of dynamic microgrids and adapts to changing conditions. The influence of optimal switch placement on service restoration using proposed method is also investigated. The effectiveness of proposed service restoration method is validated by case studies using the modified IEEE 33-node test system and a real 404-node distribution system operated by Saskatoon Light and Power in Saskatoon, Canada.

A resilient power distribution network can reduce length and impact of power outages, maintain consistent services, and improve reliability. One effective way to enhance the system’s resilience is to form microgrids during outages. In this paper, a novel dynamic microgrid formation-based service restoration method using deep reinforcement learning is proposed, and it is treated as a Markov decision process (MDP) while taking operational and structural limitations of microgrids into account. The deep Q-network is employed to obtain optimal control strategies for microgrid formation. We have introduced a new way for the agent to choose actions when building a microgrid using the deep Q-learning method, which ensures that the microgrid has a feasible radial structure. The proposed restoration method enables real-time computing to facilitate online formation of dynamic microgrids and can adapt to changing conditions. The proposed method is validated through case studies using the IEEE 33-node test system.

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

Post-earthquake functional state assessment of communication base station using Bayesian network