Electric Power Outages Research Articles

Is it the people or the place? The remarkable 30-year period of integrative physiology research in the Carlson Gymnasium at the University of Colorado Boulder.

Historically, programs of physical education and sport were housed in gymnasium buildings on academic campuses. As physical education evolved to the more scientifically focused successor departments of exercise science and kinesiology, faculty specialization developed in the physiology of exercise. With time, some faculty broadened their research to study the integrative physiology of other biological states and stressors. Through this series of events, a small group of integrative physiologists was formed in the Carlson Gymnasium at the University of Colorado Boulder during the 1990s with the goal of conducting novel biomedical research. The challenges were daunting: no contemporary core laboratory facilities, lack of temperature control, piercing external noise, pests, regular flooding, electrical power outages, and lack of funds for renovation. Despite these obstacles, the group established an innovative program of translational physiological research ranging from high-throughput molecular analyses to cell models to rodent studies to clinical trials in humans. These investigators supported their work with grant awards from the National Institutes of Health (NIH), Department of Defense, National Aeronautics and Space Administration (NASA), American Heart Association, and private research foundations totaling ∼$80 M in direct costs from the late 1980s to 2020. Collectively, the faculty and their laboratory personnel published ∼950 articles in peer-reviewed scientific journals. Over that period, 379 undergraduate students, 340 graduate students, 84 postdoctoral fellows, and dozens of junior research faculty received scientific training in Carlson, supported by >$21 M in extramural funding. What was accomplished by this handful of integrative physiologists speaks to the importance of the qualities of the investigators rather than their research facilities in determining scientific success.

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

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.

Household Adaptations to and Impacts from Electric Power and Water Outages in the Texas 2021 Winter Storm

Household Adaptations to and Impacts from Electric Power and Water Outages in the Texas 2021 Winter Storm

Local power outages, heat, and community characteristics in New York City

Electrical power outages are of increasing interest to US urban scholars, government officials and stakeholders, as they have increased in number and duration with significant health and economic, among other, impacts. This analysis examines reports of power outages in New York City in relation to socioeconomic and health characteristics of neighborhoods. Using the city's 311-call database we examine complaint calls for power outages from 2014 to 2022. While 311-calls for power outages occur all year long, volume trended higher during the warmer months (June, July and August), and as minimum daily temperatures exceeded 20 °C (68°F), the number of calls increased dramatically. Spatial clusters of high call areas were in Census tracts with high energy burdens, lower-income households, and high percentages of people of color. Furthermore, we found the higher call areas were associated with higher vulnerability to heat-exacerbated deaths. As climate change is expected to raise temperatures and increase the frequency and intensity of heat waves around the world, and as power outages are becoming more common, these findings will help to provide guidance for adaptation and energy reliability policies in New York City and have implications for other cities globally.

Are utilities overspending on electric power resilience? How can that be?

Few people doubt that the U.S. will encounter future electric power outages of long durations, disrupting a large number of people and businesses. Most industry observers believe that any improvement in the resilience of the U.S. electric-power network would be cost-beneficial. From an economic perspective, the desirability of improved resilience depends on the marginal benefits and marginal costs. Marginal benefits are especially hard to measure. There is also the question of whether utilities are applying the most cost-effective actions to improve resilience, which is difficult to judge but often ignored by state utility regulators in evaluating utility “resilience” plans. State utility regulators and electric system operators face the burden of answering to the heated public after an extended power outage. They will, not surprisingly, tend to err on the side of excessive resilience, which translates into higher electricity prices. Two probable explanations for this behavior are probability neglect and the precautionary principle. This essay concludes by posing less standard alternatives for policymakers to consider in addressing electric power resilience and the “outage” problem. Two alternatives are exploiting the price mechanism and compensating utility customers for extended power outages.

Analytical fragility curves for trees subject to ice loading in a changing climate

ABSTRACT Recent severe ice storms across the United States severely damaged trees resulting in extensive electrical power outages. Furthermore, trees and branches can fall on nearby roads, blocking traffic flow and reducing the safety of drivers. In this study, trees subjected to ice loads were analyzed using the finite element method and Monte Carlo simulation to develop analytical fragility curves. Two-dimensional, fractal trees were constructed with randomly generated geometric and mechanical parameters for four deciduous tree species: Acer saccharum, Tilia americana, Fagus grandifolia, and Quercus alba. Two load case scenarios were considered – with and without the effects of leaves – which were then subjected to varying ice accumulation thicknesses. The resulting fragility curves suggest that leaves have a substantial impact on tree branch damage under ice loads, which is significant because of the increase in unseasonably early ice storms due to climate change.

Household Adaptations to Infrastructure System Service Interruptions

When critical infrastructure system services are disrupted, households typically respond by reducing, delaying, or relocating their demand (e.g., delaying laundry), or augmenting supply (e.g., using a generator). While this phenomenon is well known, there has been little systematic empirical investigation of it. Focusing on electric power and water service interruptions and using revealed and stated preference survey data from Los Angeles County, California, we develop 24 mixed logit models, one each to predict the probability an individual undertakes a specified adaptation as a function of outage duration and characteristics of the individual. The analysis aims to determine: (1) how common different household adaptations are; (2) how adaptation implementation varies with infrastructure type, outage duration, and uses of the service; (3) what household characteristics are associated with implementation of different adaptations; and (4) how adaptations tend to occur together. The percentage of individuals who report doing an adaptation varies greatly across adaptations and outage durations, from 2% to 88%. In general, adaptations that require moving out of the home are the least common of those investigated. For electric power outages, adaptations that could be done at home are less likely as the outage duration increases, while those that require going somewhere are more likely as the duration increases. For water outages, all adaptations (except delaying consumption) are more likely as an outage lasts longer. Using electric power or water for medical devices and/or work and business has a large effect on the likelihood of implementing many adaptations. Preevent conservation habits are also associated with an increased likelihood of implementing adaptations. The influence of household characteristics varies greatly across adaptations. There is evidence that some adaptations tend to occur together (e.g., using water from lakes and the government) and others tend not to (e.g., delaying electricity use and going to a hotel).

Optimal scheduling method of regenerative electric heating for emergency residential building heating: An affine arithmetic‐based model predictive control approach

AbstractResidential heating faces the challenge of heating interruption when an electric power outage occurs. As a promising heating electrification form, regenerative electric heating (REH) equipped with thermal energy storage (TES) has the flexibility of maintaining the building indoor temperature within the desired range during power outages and reducing the operation cost during normal operation states. However, the allocation and scheduling of the limited thermal energy in TES for the above two purposes is impacted by many uncertainties, for example, outdoor temperature, irradiation, and duration of power outages. Overestimation of the thermal energy required for power outages in the TES can improve the heating supply reliability, but it will also increase the REH operation cost to some extent, and vice versa. To address this problem, an affine arithmetic‐based model predictive control approach (AA‐MPC) for an optimal REH scheduling method is proposed to balance the heating supply reliability during power outages and operation economy of REH at the same time. An REH‐based residential building energy system model is developed to describe the building thermal load associated with the outdoor temperature and irradiation. Then, the required thermal energy for emergency building heating provided by the hot water tank (HWT) is determined using the minimum thermal demand of residents during a power outage, which is constrained by the minimum comfort temperature threshold. Based on this, an AA‐MPC approach that takes the thermal energy for emergency building heating as a time‐varying constraint of the HWT is developed to determine the optimal REH scheduling that considers emergency residential building heating under the above uncertainties. Numerical studies show that the proposed method can maintain minimum thermal demand for at least 2 h when a power outage occurs under uncertainties. At the same time, it can reduce the impact of uncertainties on the operation cost and reduce economic problems caused by emergency heating to a certain extent. Compared to the interval arithmetic‐based model predictive control approach, the operation cost intervals of the proposed method are reduced by 57.3%, 0.3%, and 32.5% under low, middle, and high prediction error levels respectively.

Zinc cathodes produced with highly-variable DC current simulated from solar irradiance data

This work is the first of series of two papers with focus on the applicability of highly-variable direct (DC) current for producing high-quality cathodes by electrowinning. Different profiles of electrical current were simulated from solar irradiance data and weather parameters and then applied to the electrowinning cell using a precision, programmable power supply. This approach was motivated considering the use of photovoltaic power plant to directly supply DC current to industrial tankhouses, especially in scenarios of blackout of electric grid, power outages and most expensive electricity hours. The simulated DC current was applied to zinc electrowinning, which was selected as case study due to its intensive energy consumption. The electrowinning tests were carried out under operating conditions similar to those practiced in industry. The zinc cathodes were characterized by morphology, microstructure, crystal structure and mechanical resistance (i.e., bending behavior). Average current densities in the range 261–809 A m−2 were applied to the electrowinning cell, which reflect the different current profiles simulated for each type of solar irradiance variability. Even under wide range of current densities and high amplitude variations (peak values as high as 1816 A m−2 were momently applied to the cell), the current efficiencies and specific energy consumptions were considered satisfactory (>90% and < 3000 kWh t−1, respectively). Analysis by scanning electron microscopy showed that the zinc deposits produced with highly-variable DC current are noticeably rougher than those produced at typical constant currents. The microstructure analysis confirmed that high current densities promote grain refinement. Bending measurements of different materials revealed increasing resistances by increasing the average current density, which, however, did not lead to their fracture or breakage during the stripping.

Comparing the Structure, Function, Value, and Risk of Managed and Unmanaged Trees along Rights-of-Way and Streets in Massachusetts

Trees provide numerous benefits in urban areas, including improving stormwater retention and filtration, removing gaseous and particulate pollutants from the air, sequestering atmospheric carbon, and reducing ambient temperature. However, trees also pose risks in urban areas. Trees growing near overhead electrical utility lines cause a large proportion of electrical power outages. To mitigate this risk, arborists frequently and sometimes severely prune trees near overhead utilities for clearance and to reduce the likelihood of failure. Ostensibly, urban trees distant from utility lines are not pruned as frequently or severely. This study aimed to (i) assess factors related to both individual trees and the sample populations of trees growing near and away from overhead utility lines, and (ii) determine whether those factors differed between the two groups. In total, 200 utility easement plots and 200 non-utility control plots were distributed in Eversource Energy’s distribution territories, encompassing 2361 trees in total. Diameter at breast height (DBH), crown height and spread, percent crown missing, percent twig dieback, and likelihood of failure were gathered for each tree in the study. These variables were compared individually among study groups, and used as inputs to calculate estimated ecosystem service delivery using USFS iTree Eco v6. Overall, trees in control plots were larger and delivered more ecosystem services, per tree, than trees in utility plots. However, on a population level, trees in utility plots were more populous and delivered more aggregate ecosystem services than those in control plots. Although the aesthetics of utility tree pruning is often debated, there were no differences in likelihood of failure ratings between trees in control and utility plots. These findings may help to frame trees near overhead utility lines, commonly seen as risks or eyesores, as valuable green infrastructure and community assets.

Distributed Generation Power Systems in Wastewater Management

The article concerns the energy security of a wastewater treatment process caused by unforeseen situations related to the risk of electrical power outages. In this case, renewable energy sources based on distributed generation power systems can solve this problem in each wastewater treatment plant. The article highlights e related challenges and proposes the direction of solutions in this regard based on Czech conditions. The first part of the paper deals with the consequences of long-term outage of wastewater treatment plants on the population and the environment. There are several solutions presented for blackout conditions, and model calculations are made based on data from a Czech wastewater treatment plant. Diesel engine-generators, biogas as a cogeneration source of heat and electricity, solar panels with storage systems and combined biogas and solar systems were considered as approaches to provide energy autonomy during a blackout in a wastewater treatment plant. Special attention was paid to a combination of CHP units with solar panels and batteries. The results were evaluated for three different locations for this combination. It was concluded that biogas combustion in the CHP unit was the most profitable option, allowing the production of electricity independently of the grid for its own consumption and possibly for other operations. The last part of the paper deals with the transition to island operation, which must occur during a blackout. This transition is more difficult for both solar panels and cogeneration units if they were to supply electricity to the grid before a blackout. The transition to energy island operation could be ensured by frequency relay and processor devices to control the circuit breaker. Then, to maintain island operation, it would be necessary to have an automatic load shedding/application system.

Techno-economic analysis of a PV system with a battery energy storage system for small households: A case study in Rwanda

Due to the inadequacy of distribution networks in developing countries, especially in small residential areas, there are frequent interruptions in the electrical energy provided by the grid. This problem negatively affects the life quality and productivity of the people living in these regions. This problem can be overcome by integrating BESS-supported renewable energy sources into the distribution system. These distributed energy resources contribute significantly to providing energy directly to consumers. On a small scale, such a system is supported by the grid, when possible, to ensure energy supply continuity. This study presents a techno-economic analysis, using PV*SOL simulation software, of a grid-connected solar PV system with BESS that is used to supply a small residential community in Rwanda, Muhanga district, Shyogwe sector. The consumers were a group of one hundred households around a wetland valley. The energy generated from the solar PV system was used to supply home appliances and a water pumping system for agricultural activities. The simulation results showed that the annual energy requirement is 82.34 MWh with a peak load of 30.4 kW. The simulation results also revealed that a PV system, with an installed capacity of 57.33 kWp integrated with a BESS of 89.2 kWh storage capacity, can supply the load with own power consumption of 68.65%, a level of self-sufficiency of 64.38%, and a performance ratio of 86.05% when the desired ratio is set to 110% with a year as the reference period. The financial analysis demonstrated a return on assets of 9.14% and an amortization period of 9.65 years. These results indicate that the proposed method is technically and economically feasible for use in addressing the issue of electrical power outages in developing countries.

Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.

UPIoT based power monitoring of transformer

Due to the rise in electric vehicles, air conditioning, and home appliances in India, the demand for electricity is rising quickly. The uninterrupted supply of electricity to vital loads like hospitals and consumers is crucial and is a major issue for the electricity provider. Many consumers in India are impacted by electric power outages. One of the causes is faulty distribution transformers. Local distribution transformers are more susceptible to faults since they don't receive routine maintenance. The overloading of the transformer during peak hours is another factor. In this study, a transformer power monitoring system is proposed. It measures the transformer's current, voltage, and temperature, and uploads the recorded data to the cloud. The data helps to see any abnormal condition in the transformer and alerts the authority to take action. The proposed system can be used to prevent the overloading of the transformer by using controllable loads like electric vehicles. These loads can be disconnected from the transformer during peak loading to reduce the chance of failure causing an electrical power outage for all.

Evaluation of the effects of electrical power outages on small and medium scale enterprises (SMEs)

This study evaluated the effects of electrical power outages on Small and Medium Scale Enterprises (SMEs). The study was guided by two specific objectives of determining the cost of alternative sources of power supply and its impact on the profitability of businesses of selected SMEs in Elelenwo community in Obio/Akpor Local Government of Rivers State and to suggest possible ways of reducing the power outages in Elelenwo Community. The costs of power supply using private power generation and that of public power generation from Port Harcourt Electricity Distribution (PHED) were collected from selected SMEs in the study area. Findings established the fact that electrical power outages have significant negative effects on the businesses of small and medium scale enterprises (SMEs) which in turns affect their profitability and sustainability. This was established by finding the correlations of the costs of running businesses with private power generation and that of using public power generation for selected SMEs and the results yielded negative correlation coefficients. The study also suggested possible ways of reducing the power outages.

Technical and financial analysis of large-scale solar-PV in eThekwini Municipality: Residential, business and bulk customers

Regulatory changes, economic challenges, environmental concerns, and changing public perception have contributed to the profound changes observed globally in the electricity industry. Since 2008, South Africa has been experiencing electric power deficits and outages. This has been due in part to generation capacity constraints, belated investment in new electricity infrastructure, deferred maintenance of existing power assets, load growth in areas which were not adequately planned for, high population and economic growth over the last two decades. This has resulted in peak electricity demand outstripping available power generation capacity, leading to electricity shortages and load shedding, which is now impeding economic growth. In South Africa, forced under frequency load shedding, rising electricity tariffs, energy efficiency, declining cost of solar PV systems, the introduction of Carbon taxes, high cost of unserved energy has led consumers to explore embedded generation options to assist with reducing their energy bills, hence investments in solar PV has become an option to municipal customers. The simple payback period of solar PV systems is an important indicator for customers to ascertain whether to invest in these systems or not. Revenue loss remains a significant concern for municipalities who have historically designed single and two-rate bundled tariffs, which rely on municipalities selling electricity to ensure its business’s sustainability. Municipalities have now proposed new tariff structures designed to minimize the adverse impact of reducing electricity sales from solar PV by creating net billing tariffs with a built in network access charge component based on the customer’s inverter size. Case studies were carried out to better understand the impact on the feasibility of solar PV systems with and without the implementation of these new tariffs and its impact on the customer’s payback periods. A calculation of the levelised cost of electricity and customers rate of return for the different customer classes were also calculated to provide a better picture on the financial feasibility of rooftop solar PV. Results obtained from these case studies indicate lucrative payback periods for customers installing solar PV systems with improved revenue recovery for the municipality.

Compensating Customers for Power Outages: A Bad Regulatory Policy?

Few people doubt that the United States will continue experiencing long‐lasting, highly damaging electric power outages—like outages from Superstorm Sandy, Hurricane Maria in Puerto Rico, the Texas Blackout, record‐breaking heat waves causing wildfires and infrastructure damage, and more tornadic activity and severe hurricanes in Florida and other coastal states. Many weather experts believe that hurricane strength, precipitation intensity, thunderstorm frequency, and winter storm activity—all partially attributed to climate change—will continue to escalate in the future. These occurrences will likely lead to more frequent and longer‐lasting power outages with the potential to inflict significant harm on businesses, individuals and local economies.

Individual and Collective Strategies to Limit the Impacts of Large Power Outages of Long Duration

As modern society becomes ever more dependent on the availability of electric power, the costs that could arise from individual and social vulnerability to large outages of long duration (LLD-outages) increases. During such an outage, even a small amount of power would be very valuable. This article compares individual and collective strategies for providing limited amounts of electric power to residential customers in a hypothetical New England community during a large electric power outage of long duration. We develop estimates of the emergency load required for survival and assess the cost of strategies to address outages that last 5, 10, and 20 days in either winter or summer. We find that the cost of collective solutions could be as much as 10 to 40 times less than individual solutions (less than $2 per month per home). However, collective solutions would require community-wide coordination, and if local distribution system lines are destroyed, only individual back-up systems could provide contingency power until those lines are repaired. Costs might be reduced if more robust distributed generation were employed that could be operated continuously with the ability to sell power back to the grid. Our cost-effectiveness analysis only assesses what could be done, developing estimates of preparedness cost. A decision about what should be done would require additional input from a range of stakeholders as well as some form of analytical deliberative process.