Power Outage Duration Research Articles

Optimal scheduling of smart home appliances with a stochastic power outage: A two-stage stochastic programming approach

This study primarily concentrates on enhancing the scheduling of electric appliances within a smart home equipped with a photovoltaic solar array and a storage battery capable of redistributing excess electricity to the grid. Furthermore, the investigation takes into consideration the unpredictability of a power outage, analyzing the scheduling of these appliances to minimize consumer electricity expenses. To tackle this issue, a two-stage stochastic programming methodology is developed to effectively model the uncertainty surrounding both the onset time and duration of power outages. Additionally, a sample average approximation algorithm (SAA) is devised to efficiently address the problem. Through extensive computational experiments, the outcomes demonstrate that the SAA yields shorter CPU processing times, albeit without guaranteeing optimal solutions. The total average percent deviation of the SAA's upper bound from its lower bound and the optimal solution stands at 4.16 % and 4.5 %, respectively. Moreover, it is demonstrated that utilizing the stochastic approach, as opposed to the deterministic one, can enhance solution quality by 6.2 %. Furthermore, a comprehensive sensitivity analysis is provided, focusing on probability distribution functions of outage start time and duration, alongside an analysis of the solar panel and battery storage capacities. It is revealed that when adopting a Normal distribution instead of a Uniform distribution, the performance of the SAA experiences a slight decline.

Reliability enhancement through optimal placement of photovoltaic power plant and battery energy storage in distribution system

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS).

Multi-source data fusion for power outage warning based on transformer model

Abstract 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.

Corporate Governance Implications for Sustainable Performance: Focus on Leading Energy Producers in Denmark, Estonia, Latvia, Lithuania, and Sweden

This paper aims to evaluate corporate governance in relation to enterprise performance indicators in order to enhance it. The intention is not only to align with the interests of shareholders, but also to foster competitive, sustainable, and inclusive growth. For this purpose, the leading energy producer in each of the five countries—Denmark, Estonia, Latvia, Lithuania, and Sweden—was investigated to evaluate their corporate governance performance. An analysis was conducted, employing regression analysis, Pearson correlation, and descriptive statistics. The influence of corporate governance on the performance of chosen enterprises was examined, utilising specifically developed models. The findings reveal that the corporate governance variables are diverse, and financial metrics exhibit significant variability, reflecting the complexity of the energy industry. The research results confirm that larger and more varied boards positively impact the performance of state-owned power suppliers and increase their net income. The presence of independent members was also found to contribute to the net income growth of state-owned power suppliers. However, the study indicated that the frequency of audit meetings does not necessarily increase earnings. Still, larger audit committees can contribute to CG decision-making processes concerning debt management. The results also implied the need to consider the qualifications of the board members and its composition for proper power interruption management to minimise the frequency and duration of power outages. Therefore, it must be of pivotal focus for respective corporate governance duties. In this respect, the need for more specific and regular assessments was also found to be justified regarding industry-specific challenges related to power system disruptions. Customer-centric strategies should deserve relevant attention as well. The enforcement of the management audit function could be a solution. Consequently, assessing the governance structures and decision-making processes must be systematic for energy producers due to the business dynamics leading to the revaluation of the evolving challenges and possible solutions aimed at the competitive and sustainable development of the energy sector.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Untangling the relationship between power outage and population activity recovery in disasters

Despite recognition of the relationship between infrastructure resilience and community recovery, very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure services contribute to the speed of community recovery. To address this gap, this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts, focusing on the recovery dynamics of population activity and power infrastructure restoration. Empirical observational data were utilized to analyze the extent of impact, recovery duration, and recovery types of both systems in the aftermath of Hurricane Ida. The study reveals three key findings. First, power outage duration positively correlates with outage extent until a certain impact threshold is reached. Beyond this threshold, restoration time remains relatively stable regardless of outage magnitude. This finding underscores the need to strengthen power infrastructure, particularly in extreme weather conditions, to minimize outage restoration time. Second, power was fully restored in 70% of affected areas before population activity levels normalized. This finding suggests the role infrastructure functionality plays in post-disaster community recovery. Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation, housing damage, and business interruptions. Finally, if power outages last beyond two weeks, community activity resumes before complete power restoration, indicating adaptability in prolonged outage scenarios. This implies the capacity of communities to adapt to ongoing power outages and continue daily life activities. These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems, such as power outages, during extreme weather events. They also enhance our empirical understanding of how infrastructure resilience influences community recovery. By identifying the critical thresholds for power outage functionality and duration that affect population activity recovery, this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions. Hence, the findings can inform infrastructure operators, emergency managers, and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.

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.

Public values failure associated with Hurricane Ian power outages

Power outages from extreme weather events can diminish community resilience, making it difficult for the areas impacted to bounce back after such events. For socially vulnerable populations, the frequency and duration of power outages can be even more severe. Governments have an obligation to protect public values, or those values that are most fundamental to society, which includes equitable resilience. Using Jørgensen and Bozeman's inventory of public values, this manuscript explores how power outages from extreme weather events create public values failures. More specifically, the manuscript evaluates intraorganizational aspects of public administration during power outages in Florida during Hurricane Ian in 2022. Framing power outages as a public values failure may motivate greater time and effort toward improving equitable access to more resilient power systems.

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.

Effects of technical and security factors on grid electricity reliability: evidence from Uganda national electricity grid network

PurposeAn unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.Design/methodology/approachThis study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.FindingsThe findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.Originality/valueThis paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.

Review of Concepts and Determinants of Grid Electricity Reliability

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.

Design, control, reliability, economic and energy management of microgrid: A review

Currently, fossil fuels still dominate as the primary energy source, accounting for around 80% of global output. However, the COVID-19 pandemic has severely affected various industries, including the electric power sector. Consequently, the energy industry has recognized the importance of renewable energy sources in addressing environmental concerns and establishing a sustainable future. Recently, microgrid systems based on renewable energy have emerged as crucial players in reducing pollution and promoting environmental sustainability. This paper offers a comprehensive assessment of microgrid systems, starting with the optimal design of the microgrid and extending to the evaluation of its control system. Through a detailed analysis of existing literature and case studies, the review identifies several key findings. Firstly, effective design and control strategies are crucial for optimizing the operation of microgrid's and maximizing their economic and energy management potential. Secondly, the integration of renewable energy sources and energy storage systems can significantly enhance the reliability and resilience of microgrid's. Thirdly, advanced control techniques and optimization algorithms play a vital role in achieving optimal energy management, cost reduction, and efficient load scheduling within microgrid systems. Furthermore, the paper explores energy management, reliability assessment, and economic analysis within the microgrid context. Lastly, the paper highlights the application of cutting-edge trends like artificial intelligence, data analysis, and blockchain in the field of microgrid systems. The numerical results showcase the enhanced reliability and resilience of microgrid's with the integration of energy storage systems, reducing the frequency and duration of power outages by 30% compared to traditional grids.

Power system fault detection and notification with Wi-Fi

In this study, a prototype model was designed and implemented for power system failure. A mobile application was developed for notification of faults that may occur in power systems. The line current data was transmitted with Wi-Fi via module on mobile application and so negative impacts of the production sector was eliminated and the ordinary flow of life by minimizing the duration of power outages due to technical failures were prevented. By measuring the current in the prototype, it was possible to determine a short circuit situation, and to warn the operator with the mobile application on the mobile phone, and then to quickly dispatch them to the defective area. Protection in the power system is carried out more healthily with instant data flow with the mobile application

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.

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

A COMPREHENSIVE APPROACH TO THE PREDICTIVE MAINTENANCE OF SECONDARY DISTRIBUTION SUBSTATIONS: DEVICES AND METHODS

The present work addresses the predictive maintenance of Secondary Distribution Substations (SDS) by resorting to IoT models and technologies. Consisting of a fully modular platform, this solution encompasses a number of different wireless smart sensors whose data are locally integrated in an edge unit (information handler and gateway to the cloud). A comprehensive diagnosis, involving both data trending and the detection of single events, is remotely fed to people in charge. Thus, it contributes not only to improve the quality of service, by reducing the number and duration of power outages, but also to reduce costs to both operational and long-term investments.

Power Outage Sensing Device based on IOT for Service Quality Evaluation in the PLN Distribution System

In overcoming electrical disturbances, PLN implements an equipment protection system that aims to reduce the areas experiencing power outage. However, this protection system has not been integrated with the SCADA system. As a parameter to maintain customer service continuity, PLN uses customer-based indexes, i.e., the Customer Average Interruption Duration Index (CAIDI) and Customer Average Interruption Frequency Index (CAIFI). In the calculation of those index values, the record of power outage occurrence at PLN is still not accurate because the duration and frequency of power outage based on customer complaints. In this study, a Power Outage Sensing device is installed on the secondary distribution system transformer 380/220 which can ensure the location, record the frequency and duration of power outage and serve customers in real time based on IOT using WEMOS D1 and optocoupler sensors which can then be accessed on a web. In its implementation, all power outage is recorded in the database and can be accessed on the web interface. This device can speed up the recovery of disturbances in the system after an outage and perform accurate CAIDI and CAIFI index calculations as customer service evaluations.

Decision support tool for enabling resiliency in an underground power distribution system

Natural disasters cause substantial damage to power distribution infrastructure with long duration of power outages. A resilient Power Distribution System (PDS) should be able to quickly absorb, adapt and recover from the catastrophic events. Enabling resiliency in the PDS is a new imperative for utilities to reduce financial losses, enhance customer satisfaction and supply critical loads in the network. Modern underground PDS comprise of underground feeders and overhead components. Though, these PDS are considered to be highly reliable; however, they still remain vulnerable to catastrophic events by virtue of their locations, age, maintenance and other factors. These factors have a direct impact on resiliency assessment of the system. Quick and accurate decision making by the network operator during and immediately after a natural disaster can make significant impact on the restoration of the system, particularly critical loads. This paper proposes a set of metrics for evaluation of resiliency for underground power distribution networks and a decision support tool for the network operator to enable resiliency in a PDS during network planning and operational contingency scenarios. It facilitates the decision making process to supply all critical loads in the network under all possible scenarios. The proposed decision support tool to enable resiliency is tested through simulation study on a real 110 Bus, 11 kV, underground PDS of CESC Limited, in Patuli area of Kolkata, India.

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.