Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S

Taimoor, Naveed; Khosa, Ikramullah; Jawad, Muhammad; Akhtar, Jahanzeb; Ghous, Imran; Qureshi, Muhammad Bilal; Ansari, Ali; Nawaz, Raheel

doi:10.1109/access.2020.3043630

Cited by 14 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is important to choose an appropriate machine learning algorithm which may perform well on such data. In the literature, several machine learning algorithms have been employed for different applications, such as support vector machines, random forest, decision trees, the artificial neural network, and linear models [12,35,37]. However, random forest (RF) has proved to be the best candidate, particularly in the case of non-linear data with high diversity and outliers, considering multiple research domains [12,17,35,[38][39][40][41][42].…”

Section: Methodsmentioning

confidence: 99%

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

et al. 2022

Self Cite

View full text Add to dashboard Cite

Severe weather conditions not only damage electric power infrastructure, and energy systems, but also affect millions of users, including residential, commercial or industrial consumers. Moreover, power outages due to weather-related natural disasters have been causing financial losses worth billions of US dollars. In this paper, we analyze the impact of power outages on the revenue of electric power suppliers, particularly due to the top five weather-related natural disasters. For this purpose, reliable and publicly available power outage events data are considered. The data provide the time of the outage event, the geographic region, electricity consumption and tariffs, social and economic indicators, climatological annotation, consumer category distribution, population and land area, and so forth. An exploratory analysis is carried out to reveal the impact of weather-related disasters and the associated electric power revenue risk. The top five catastrophic weather-related natural disaster categories are investigated individually to predict the related revenue loss. The most influencing parameters contributing to efficient prediction are identified and their partial dependence on revenue loss is illustrated. It was found that the electric power revenue associated with weather-related natural disasters is a function of several parameters, including outage duration, number of customers, tariffs and economic indicators. The findings of this research will help electric power suppliers estimate revenue risk, as well as authorities to make risk-informed decisions regarding the energy infrastructure and systems planning.

show abstract

Section: Methodsmentioning

confidence: 99%

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Power is restored to the majority of customers within 48 hours after an extreme weather event [21]. Thus, we considered any outages that occurred within the time frame of 3 hours before (to accommodate the uncertainty with NWS data recording) to 48 hours after the expiration time of the weather event group to be correlated with the weather event group.…”

Section: Mapping Eagle-i Datasets and The Nws Datasetsmentioning

confidence: 99%

Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping

Lee,

Chinthavali,

Bhusal

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan).

show abstract

“…Forecasting probability of the detriment of the transmission line by using random forest (RF) [44] using the weather forecasting to obtain the probability of outage of the component [49] using MC and the restoration cost to forecasting the resiliency factors [68] prediction the voltage and over load by using MPC [74] the scenarios of outages and damages are forecasted by considering the inception and location of the fault [46] infinite Markov switching autoregressive model to improve the prediction accuracy [55] using existing data by employing the multi-variable autoregressive-moving-average time series to load forecasting [13] short term of the wind power based on Bayesian [55] the probability of the component outage based on machine learning via logistic regression [89] using the RF method to predict the outage time [87] Assessment security-constrained based unit commitment for a preventive operation [49] using radial basis function, partial dependency plot and the neural network [87] intrusion response system to monitor the dynamics of the system [52] using reliability indices [60] using RAW index [60] Cyber security analyzing eigenvalues of matrix A [14] uncertain energy management using block chain and directed acyclic graph [58] affects the performance of WF and power system is the compensation of the lost WT(s) output power. In this case, after a HILP such as wind storm, the number of WTs is reduced (Some of WTs may have been damaged and some of them have been turned off to prevent damage).…”

Section: Uncertainty Of Wind and Coordinated Controlmentioning

confidence: 99%

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

Modaberi

Tohidi

Zadeh

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

Recently, due to the increase of the frequency of high impact low probability (HILP) events (manmade and natural events), assessment and enhancement of resilience has become very important in the operation and planning procedures of future power systems. In this regard, several researches have been conducted on operation and planning strategies of the power system to improve resilience. Here, assessment and enhancement approaches of power system resilience by focusing on the role of wind farms and wind turbines are reviewed. To do so, a comprehensive review of the related metrics, hazard types, modelling approaches, assessment methods, and enhancement solutions is presented. Then, the assessment and enhancement methods of power system resilience are categorized in three phases before event (preventive proactive actions), during event (corrective active actions) and after event (restorative reactive actions) from the point of view of operation and planning actions. Finally, the current challenges, research gaps and future trends are discussed.

show abstract

Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S

Cited by 14 publications

References 37 publications

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

Financial Hazard Prediction Due to Power Outages Associated with Severe Weather-Related Natural Disaster Categories

Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

Contact Info

Product

Resources

About