Outage prediction models for snow and ice storms

Cerrai, Diego; Koukoula, Marika; Watson, Peter L.; Anagnostou, Emmanouil N.

doi:10.1016/j.segan.2019.100294

Cited by 40 publications

(19 citation statements)

References 63 publications

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“…They were able to forecast the post-storm restoration times. In [19], Cerrai et al presented outage predictions for ice and snow related storms using Bayesian regression tree and random forest models. The techniques were able to predict both lower impact events and extreme events.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

et al. 2022

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The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

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Section: Introductionmentioning

confidence: 99%

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

et al. 2022

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“…21 In addition to economic losses, natural disasters can lead an environmental and health issues as using diesel generators or charcoal grills, which in turn may cause suffocation to users. 22 Moreover, healthcare facilities and emergency response systems generally cannot temporarily suspend operations, especially in difficult circumstances that rely on electrical energy to supply many operations and devices such as electronic health records and respirators. For instance, in 2003, the power delivery to eight states stopped, and as a result of this blackout, many hospitals faced numerous internal problems, including loss of heating, ventilation, air conditioning, and water pressure.…”

Section: Background and Assumptionsmentioning

confidence: 99%

“…In addition to economic losses, natural disasters can lead an environmental and health issues as using diesel generators or charcoal grills, which in turn may cause suffocation to users 22 . Moreover, healthcare facilities and emergency response systems generally cannot temporarily suspend operations, especially in difficult circumstances that rely on electrical energy to supply many operations and devices such as electronic health records and respirators.…”

Section: Background and Assumptionsmentioning

confidence: 99%

A novel approach for sizing battery storage system for enhancing resilience ability of a microgrid

Zahraoui

Alhamrouni

Mekhilef

et al. 2021

Int Trans Electr Energ Syst

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The deployment of battery energy storage systems (BESS) can provide numerous benefits including increased renewable energy penetration, improvements in power quality and reliability, reduction of demand peaks, and reduced greenhouse gas (GHG) emissions. During the implementation of BESS, one of the most crucial factors is to determine the optimal size of the BESS to permit the balance between the technical characteristics of the BESS and the additional overall cost including the economic operation of the microgrid (MG). Therefore, the optimal sizing of the BESS allows the storage of sufficient power during the change of dispatch from the distributed energy resources (DERs) or in critical events such as natural disasters. The proposed framework determines the optimal size of the BESS based on forecasting the natural disasters or the outage cases, to enhance the reliability and economic operation of the MG. In this study, two stages are proposed to determine the optimal size of BESS in the MG. The first stage is the forecasting stage, which predicts the location of the outage or the faults that may occur. In the second stage, obtain the optimal size of the BESS to minimize the total operation cost for an economic operation of the MG. The teaching-learning-based optimization (TLBO) combined with quadratic programming (QP) is applied to solve the proposed objective formulation

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“…Severe storms pose a serious threat to electric distribution networks, generating great repair costs and disrupting quality of life for millions of Americans every year [1]. Several increasingly sophisticated outage prediction models (OPMs) were developed and validated in recent years for hurricanes [2][3][4][5][6][7][8], thunderstorms [9][10][11][12], rain and wind [9][10][11], snow [13] and ice storms [13,14]. Except for an isolated case [7] that studied the dynamics of Hurricane Irene, all other studies focused on the prediction of storm total outages.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Power Outages Caused by Thunderstorms

et al. 2020

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Thunderstorms are complex weather phenomena that cause substantial power outages in a short period. This makes thunderstorm outage prediction challenging using eventwise outage prediction models (OPMs), which summarize the storm dynamics over the entire course of the storm into a limited number of parameters. We developed a new, temporally sensitive outage prediction framework designed for models to learn the hourly dynamics of thunderstorm-caused outages directly from weather forecasts. Validation of several models built on this hour-by-hour prediction framework and comparison with a baseline model show abilities to accurately report temporal and storm-wide outage characteristics, which are vital for planning utility responses to storm-caused power grid damage.

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Outage prediction models for snow and ice storms

Cited by 40 publications

References 63 publications

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting

A novel approach for sizing battery storage system for enhancing resilience ability of a microgrid

Dynamic Modeling of Power Outages Caused by Thunderstorms

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