Major Power Outages Research Articles

Assessing the Impact of Catastrophic Electricity Loss on the Food Supply Chain

The food supply chain’s heavy reliance on electricity poses significant vulnerabilities in the event of prolonged and widespread power disruptions. This study introduces a system-dynamics model that integrates five critical infrastructures—electric grid, liquid fossil fuels, Internet, transportation, and human workforce—to evaluate the resilience of food supply chains to major power outages. We validated the model using the 2019 Venezuelan blackouts as a case study, demonstrating its predictive validity. We then explored how more extreme electricity losses would disrupt the supply chain. More specifically, we modeled the impact of a large-scale cyberattack on the US electric grid and a high-altitude electromagnetic pulse (HEMP) event. A cyberattack severely damaging the US electric grid and allowing for recovery within a few weeks or months would lead to substantial drops in food consumption. However, it would likely still be possible to provide adequate calories to everyone, assuming that food is equitably distributed. In contrast, a year-long recovery from a HEMP event affecting most of the continental United States could precipitate a state of famine. Our analysis represents a first attempt at quantifying how food availability progressively worsens as power outages extend over time. Our open-source model is made publicly available, and we encourage its application to other catastrophic scenarios beyond those specifically considered in this work (for example, extreme solar storms, high-lethality pandemics).

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

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.

Microgrid-based operational framework for grid resiliency enhancement: A case study at KUET campus in Bangladesh

This study investigates the role of microgrids in enhancing the resilience of power systems in response to the power outages caused by severe conditions. A three-step analysis is carried out to achieve this objective. Firstly, the general backgrounds of resilience in power systems are presented, comprising resilience modeling, resilience analysis methods, and resilience enhancement efforts by different sections. Secondly, a time-dependent simulation model is designed for KUET, which incorporates standard indices for measuring, evaluating, and monitoring grid resilience. In this simulation, the total demand for KUET was assumed to be close to 1 MW, and case studies are conducted to determine the performance of the microgrid system under different operating conditions. Load prioritization management strategies have been performed. Finally, a unified resilience evaluation and operational enhancement approach is proposed, which includes a procedure for assessing the impact of severe conditions on power systems and a risk-based defensive islanding procedure. The proposed adaptive islanding method aims to mitigate the grid functionality drop. The results of the case studies illustrate the influence of microgrid structural and operational characteristics on grid resilience. The microgrid implementation improves the Grid Resilience Index (RC) from 0.9 to 1.35, with faster recovery and increased grid hardiness. However, there may be a potential trade-off with a decline in Operational Resilience (Roperational) from 0.133 to 0.069, which needs further investigation. This study contributes to the development of a framework for the measurement of resilience in power systems and provides insights into the use of microgrids as a potential solution for dealing with major power outages.

Evaluating the impact of coordinated multiple mobile emergency resources on distribution system resilience improvement

Extreme weather events in recent years have resulted in numerous incidents of major power outages and significant economic losses. Therefore, it is crucial to find potential solutions that can improve the resilience of the distribution system (DS). In this regard, this paper proposes a two-stage resilient restoration model for distribution systems utilizing Electric Vehicles (EVs) and Mobile Energy Resources (MERs) to address the issue. The impact of a transportation network on the two-stage model is represented by the consideration of interdependence between vehicle routes and distribution lines. Initially, the charging and repair stations are strategically placed on the transportation network before an extreme event by considering the distribution lines that have a high probability of failure during the event. An integer nonlinear programming model is used to determine the optimal number and staging locations of the charging and repair stations during the development of the first stage. The second stage involves the design of a mixed integer linear programming model to restore loads and recover the DS via optimizing the dispatch of EVs with MERs on the transportation network. The proposed method is tested on modified IEEE 123-Bus system. The utilization of EVs alongwith MERs under post-disaster conditions helps to reduce the ENS by 42.1038 %. The ENS found to be 7.9150 MWh utilising the proposed method is much less compared to the ENS obtained by existing method and hence this demonstrates the effectiveness of the proposed method.

A decision-making model for efficient fair electricity rationing under major power outrage emergencies

Major power outages emergencies (MPOEs) are increasingly occurring with greater frequency and wreaking havoc, necessitating the effective decision-making for electricity rationing to mitigate economic losses incurred and maintain social stability. To address this issue, this paper proposes an efficient fair electricity rationing decision-making model under MPOEs, including two methods to quantify the efficiency and fairness of the electricity rationing. Firstly, the inoperability inputoutput model is employed to quantify the efficiency by assessing the business interruption costs caused by MPOEs. Secondly, the fairness is quantified by the fairness perception of the affected regions, which consider their bounded rational comparisons based on the Prospect theory. Then, the NSGA-II is utilized to solve the model. Finally, Sichuan MPOE in 2022 is designed as a numerical experiment to validate the proposed model, accompanied by corresponding discussions to demonstrate the impact of “supercities” on electricity rationing and the significance of the balance between efficiency and fairness.

Review of Low Inertia in Power Systems Caused by High Proportion of Renewable Energy Grid Integration

With the power industry moving toward a green and low-carbon direction, renewable energy is occupying an increasingly larger share in the power system. However, compared with traditional thermal power generation, the instability of new energy generation is very prominent, which also leads to a decrease in the inertia of the power system after the grid integration of a high proportion of renewable energy. If no measures are taken, this may lead to frequency collapse accidents. Therefore, this paper first introduces two international major power outage accidents that have occurred in recent years, analyzing the causes, and then summarizes the inspiration obtained from the accidents. Subsequently, some research results on low inertia-related issues in the power system caused by the high proportion of new energy grid integration in recent years were summarized and analyzed from three aspects: inertia evaluation methods, optimal operation measures for the power system, and under frequency load-shedding (the abbreviation “ULFS” in the following text stands for it) schemes. Finally, suggestions were made for future research directions.

Optimal defense strategy selection method for CPS considering integrated cyber–physical losses

Cyber-attacks against the cyber–physical power system (CPPS) may lead to major power outages and even system collapse in severe cases. In order to quantify the attack losses and select optimal strategies to defend against cyber-attacks, this paper proposes a computational model of integrated cyber–physical losses based on the theory of interdependent network. Based on the multiple coupling relationship between cyber system and physical network, the monitoring system is regarded as the cyber–physical interaction layer, and the information network operation constraint, physical system load reduction constraint, and monitoring equipment operation constraint are established respectively to analyze the impact on another network after a failed node appears in one layer of network, and proposed the cyber–physical integrated loss objective function. The offensive and defensive processes are abstracted into a two-player zero-sum game model, which evolves into a multi-stage game according to the changes in the system state during the game, and the integrated cyber–physical loss is used as the game gain of both the attacker and defender, and the Minimax-Q algorithm is introduced to solve for the optimal gain and select the optimal defense strategy. Finally, the IEEE 30 node cyber–physical system is used as a simulation example to verify whether the proposed loss calculation model can effectively quantify the losses caused by the attack on the information network and the power grid, and has practical guidance for the selection of the optimal defense strategy.

Meteorological Factors Responsible for Major Power Outages during a Severe Freezing Rain Storm over Eastern Canada

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Optimal operation method for load restoration of distribution networks under disturbance scenario

Extreme events can cause damages to transmission system equipment, resulting in a major power outage. In disturbance scenario, local distributed energy resources (DERs) and switches can be fully utilized to restore power loads, which is critical for enhancing the resiliency. A service restoration model for distribution network with soft open point (SOP) is proposed, in which the ZIP loads are considered. Traditional tie switch (TS) operations and the power output capabilities of local DERs are coordinated to restore loads in outage area. On this basis, SOP flexibility is explored to further improve the restoration performance. The restoration model includes nonlinear constraints and integer variables, which is usually a mixed-integer nonlinear programming (MINLP) problem, and is difficult to obtain a feasible solution within an acceptable time. In particular, the ZIP load model is not directly amenable with conic solvers. To improve solution efficiency, convex relaxation, linear approximation, and big-M methods are introduced to convert MINLP problem into a mixed-integer second-order cone programming (MISOCP) problem. Simulation tests are studied to verify the effectiveness of the proposed model.

Development of a 230MHz Interference Source Identification Device for Power Wireless Private Networks

With the development of the times, the demand for power wireless private networks in the power industry is increasing day by day. Power wireless private network planning is the basis for building a ubiquitous reliable terminal communication access network, is a close link for residential users, inaccurate planning and optimization in time will evolve into a major power outage accident of the power grid, affecting people's production and life. This paper lays a theoretical foundation for the development and design of future power wireless interference source monitoring devices by studying the perception identification and positioning technology of wireless communication interference sources for 230MHz private networks, provides a theoretical and technical foundation for the promotion of future power wireless private networks and corresponding base station detection and inspection, and provides further guarantees for the further promotion of power wireless private networks.

Power outage mediates the associations between major storms and hospital admission of chronic obstructive pulmonary disease

BackgroundChronic obstructive pulmonary disease (COPD) is the third-leading cause of death worldwide with continuous rise. Limited studies indicate that COPD was associated with major storms and related power outages (PO). However, significant gaps remain in understanding what PO’s role is on the pathway of major storms-COPD. This study aimed to examine how PO mediates the major storms-COPD associations.MethodsIn this time-series study, we extracted all hospital admissions with COPD as the principal diagnosis in New York, 2001–2013. Using distributed lag nonlinear models, the hospitalization rate during major storms and PO was compared to non-major storms and non-PO periods to determine the risk ratios (RRs) for COPD at each of 0–6 lag days respectively after controlling for time-varying confounders and concentration of fine particulate matter (PM2.5). We then used Granger mediation analysis for time series to assess the mediation effect of PO on the major storms-COPD associations.ResultsThe RRs of COPD hospitalization following major storms, which mainly included flooding, thunder, hurricane, snow, ice, and wind, were 1.23 to 1.49 across lag 0–6 days. The risk was strongest at lag3 and lasted significantly for 4 days. Compared with non-outage periods, the PO period was associated with 1.23 to 1.61 higher risk of COPD admissions across lag 0–6 days. The risk lasted significantly for 2 days and was strongest at lag2. Snow, hurricane and wind were the top three contributors of PO among the major storms. PO mediated as much as 49.6 to 65.0% of the major storms-COPD associations.ConclusionsBoth major storms and PO were associated with increased hospital admission of COPD. PO mediated almost half of the major storms-COPD hospitalization associations. Preparation of surrogate electric system before major storms is essential to reduce major storms-COPD hospitalization.

Mobile and Portable De-Icing Devices for Enhancing the Distribution System Resilience Against Ice Storms: Preventive strategies for damage control

Climate change has reportedly increased the frequency and intensity of extreme weather events such as heatwaves, heavy rains, hurricanes, tornados, flood, fire, and ice storms. Ice storms are considered one of the most severe natural disasters that can disrupt people's daily lives and incur infrastructural damages. As shown in Figure 1, the accumulated weight of ice on surfaces can easily snap trees and power lines and damage distribution and transmission structures. Just six to seven millimeters of ice accumulation can add hundreds of pounds of weight per line span. Ice storms are the leading cause of large-scale power outages in the United States and elsewhere during the winter season. The damage caused by ice storms can easily result in major power outages, blackouts, and at times shut down entire metropolitan areas.

Research on the method of observing the safety level of the grid cascading trip based on node injection power

Cascading trips generally occur in the early stages of major power outages. They are usually caused by power flow shifts after the initial failure. Although their form is relatively simple and if they are not adequately evaluated in advance, which will cause disasters in the power grid. Aiming at the grid cascading trip, this paper discusses the relationship between the occurrence of cascading trip and the node injection power, and presents a method for evaluating the current operating status of the grid to the safety level of the cascading trip. For this method, this paper firstly gives the expression of the current operating status of the power grid to the safety level of the cascading trip, and then uses this expression to further give a mathematical model describing the safety level of the grid to the cascading trip state. This model is an optimization model. Based on the DC power flow calculation method and the particle swarm optimization algorithm, this paper gives the specific algorithm flow of the optimization model. Finally, the calculation examples are demonstrated on IEEE-39 node system and IEEE14-bus system, and the results of the cases show the effectiveness of the model and method.

Planning resilient networks against natural hazards: Understanding the importance of correlated failures and the value of flexible transmission assets

Natural hazards cause major power outages as a result of spatially-correlated failures of network components. However, these correlations between failures of individual elements are often ignored in probabilistic planning models for optimal network design. We use different types of planning models to demonstrate the impact of ignoring correlations between component failures and the value of flexible transmission assets when power systems are exposed to natural hazards. We consider a network that is hypothetically located in northern Chile, a region that is prone to earthquakes. Using a simulation model, we compute the probabilities of spatially-correlated outages of transmission and substations based on information about historical earthquakes in the area. We determine optimal network designs using a deterministic reliability criterion and probabilistic models that either consider or disregard correlations among component failures. Our results show that the probability of a simultaneous failure of two transmission elements exposed to an earthquake can be up to 15 times higher than the probability simultaneous failure of the same two elements when we only consider independent component failures. Disregarding correlations of component failures changes the optimal network design significantly and increases the expected levels of curtailed demand in scenarios with spatially-correlated failures. We also find that, in some cases, it becomes optimal to invest in HVDC instead of AC transmission lines because the former gives the system operator the flexibility to control power flows in meshed transmission networks. This feature is particularly valuable to systems exposed to natural hazards, where network topologies in post-contingency operating conditions might differ significantly from pre-contingency ones.

On-Line Health Condition Monitoring of Power Connectors Focused on Predictive Maintenance

Electrical power connectors are critical points of electrical networks. Failure in high-voltage connectors may result in major power outages, safety risks and important economic consequences. Therefore, there is an imperious need to tackle such issue by developing suitable on-line condition monitoring strategies to minimize the aforementioned problems and to ease the application of predictive maintenance tasks. This work develops an on-line condition monitoring method to predict early failures in power connectors from data acquired on-line (electric current and voltage drop across the connector, and temperature) to determine the instantaneous value of the connector resistance, since it is used as a signature or indicator of its health condition. The proposed approach combines a parametric degradation model of the resistance of the connector, whose parameters are identified by means of the Markov chain Monte Carlo stochastic method, which also provides the confidence intervals of the electrical resistance. This fast approach allows an on-line diagnosis of the health condition of the connector, anticipating its failure and thus, easing the application of predictive maintenance plans. Laboratory results emulating the ageing conditions of the connectors prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus.

Power Outages and Community Health: a Narrative Review.

Power outages, a common and underappreciated consequence of natural disasters, are increasing in number and severity due to climate change and aging electricity grids. This narrative review synthesizes the literature on power outages and health in communities. We searched Google Scholar and PubMed for English language studies with titles or abstracts containing "power outage" or "blackout." We limited papers to those that explicitly mentioned power outages or blackouts as the exposure of interest for health outcomes among individuals living in the community. We also used the reference list of these studies to identify additional studies. The final sample included 50 articles published between 2004 and 2020, with 17 (34%) appearing between 2016 and 2020. Exposure assessment remains basic and inconsistent, with 43 (86%) of studies evaluating single, large-scale power outages. Few studies used spatial and temporal control groups to assess changes in health outcomes attributable to power outages. Recent research linked data from electricity providers on power outages in space and time and included factors such as number of customers affected and duration to estimate exposure. The existing literature suggests that power outages have important health consequences ranging from carbon monoxide poisoning, temperature-related illness, gastrointestinal illness, and mortality to all-cause, cardiovascular, respiratory, and renal disease hospitalizations, especially for individuals relying on electricity-dependent medical equipment. Nonetheless the studies are limited, and more work is needed to better define and capture the relevant exposures and outcomes. Studies should consider modifying factors such as socioeconomic and other vulnerabilities as well as how community resiliency can minimize the adverse impacts of widespread major power outages.

Data‐driven prediction for the number of distribution network users experiencing typhoon power outages

Typhoons have substantial impacts on power systems and may result in major power outages for distribution network users. Developing prediction models for the number of users going through typhoon power outages is a high priority to support restoration planning. This study proposes a data-driven model to predict the number of distribution network users that may experience power outages when a typhoon passes by. To improve the accuracy of the prediction model, twenty six explanatory variables from meteorological factors, geographical factors and power grid factors are considered. In addition, the authors compared the application effect of five different machine learning regression algorithms, including linear regression, support vector regression, classification and regression tree, gradient boosting decision tree and random forest (RF). It turns out that the RF algorithm shows the best performance. The simulation indicates that the accuracy of the optimal model error within ±30% can reach up to 86%. The proposed method can improve the prediction accuracy through continuous learning on the existing basis. The prediction results can provide efficient guidance for emergency preparedness during typhoon disaster, and can be used as a basis to notify the distribution network users who are likely to lose power.

A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study’s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs.

Availability Assessment Based Case-Sensitive Power System Restoration Strategy

Increasingly frequent severe weather events in recent years threaten the security of power systems and result in major power outages throughout the world. The development of reasonable power system restoration (PSR) solutions is therefore urgently needed to speed up the recovery of the power supply while at the same time steering clear of vulnerable and risky equipment. This paper aims to develop a case-sensitive PSR model for power transmission systems that can adjust restoration solutions according to the evaluated availability of outage equipment in specific blackout scenarios and weather conditions. A novel PSR model that integrates the startup of generating units, formulation of the restoration network, renewable energy sources, and availability assessment of devices is proposed. A reformulated model is also proposed to relieve the computational burden of complex PSR problems. The availability of outage equipment is comprehensively assessed based on historical operating records, fault diagnosis results, and weather conditions. The assessed availability results are sensitive to the characteristics of real blackout cases and will support system operators generate case-sensitive PSR solutions while mitigating the vulnerable equipment. The feasibility and effectiveness of the proposed PSR model and its reformulations are verified through case studies.

Revamping doctoral training for a decarbonised future

Revamping doctoral training for a decarbonised future