Dynamic Modeling of Power Outages Caused by Thunderstorms

Alpay, Berk A.; Wanik, David W.; Watson, Peter L.; Cerrai, Diego; Liang, Guannan; Anagnostou, Emmanouil N.

doi:10.3390/forecast2020008

Cited by 27 publications

(15 citation statements)

References 32 publications

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“…Computer models and simulations provide key insights as storms are forecasted. Power outage estimation models exist for tropical cyclones and hurricanes [11][12][13][14], thunderstorms [15], and various weather and vegetation inputs [16][17][18]. Han et al [12] recognized that accurate storm estimates are necessary to have the proper crews in place to make restoration as efficient as possible.…”

Section: Resiliencymentioning

confidence: 99%

“…The ABM presented in [25] can be utilized, as shown in this study, to aid emergency managers in Resiliency Zones 1-3. As the storm is predicted in Zone 1, the weather forecasts can be utilized to predict the expected outages [11,15,17,18,26], which can then allow emergency managers to begin understanding the necessary resources. As the storm is better understood in Zones 2 and 3, the ABM can be run with the updated outage predictions to see how the predicted restoration time would change with available resources.…”

Section: Resiliencymentioning

confidence: 99%

“…Additionally, three machine-learning algorithms were used to predict the outages: Random forest, gradient boosted trees, and Bayesian additive regression trees. Although this is only one of many outage prediction models [11][12][13][14][15][16][17][18], the results from Wanik et al [11] will be used as the inputs for the study presented here.…”

Section: Case Study On Power Outage Impacts From Future Hurricane Sanmentioning

confidence: 99%

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Estimated Time to Restoration of Hurricane Sandy in a Future Climate

et al. 2020

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Power outage restoration following extreme storms is a complicated process that couples engineering processes and human decisions. Emergency managers typically rely on past experiences and have limited access to computer simulations to aid in decision-making. Climate scientists predict that although hurricane frequency may decrease, the intensity of storms may increase. Increased damage from hurricanes will result in new restoration challenges that emergency managers may not have experience solving. Our study uses agent-based modeling (ABM) to determine how restoration might have been impacted for 30 different scenarios of Hurricane Sandy for a climate in 2112 (Sandy2112). These Sandy2112 scenarios were obtained from a previous study that modeled how outages from Hurricane Sandy in 2012 might have been affected in the future as climate change intensified both wind and precipitation hazards. As the number of outages increases, so does the expected estimated time to restoration for each storm. The impact of increasing crews is also studied to determine the relationship between the number of crews and outage durations (or restoration curves). Both the number of outages and the number of crews impact the variability in time to restoration. Our results can help emergency managers and policy makers plan for future hurricanes that are likely to become stronger and more impactful to critical infrastructure.

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Section: Resiliencymentioning

confidence: 99%

Section: Resiliencymentioning

confidence: 99%

Section: Case Study On Power Outage Impacts From Future Hurricane Sanmentioning

confidence: 99%

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Estimated Time to Restoration of Hurricane Sandy in a Future Climate

et al. 2020

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“…Similar approaches and findings can be seen in the few studies in the literature that specifically focus on predicting thunderstorm-related power outages. In Alpay et al, the authors take a rapid-refresh nowcasting approach to modeling thunderstorm-related outages, using an LSTM neural network trained on data from a rapidly updating radaringesting weather model from NOAA [33]. The works of Shield and Kabir et al both describe a thunderstorm outage prediction system trained on weather data from the National Digital Forecast Database for an area in Alabama [34,35].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System

2021

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Thunderstorms are one of the most damaging weather phenomena in the United States, but they are also one of the least predictable. This unpredictable nature can make it especially challenging for emergency responders, infrastructure managers, and power utilities to be able to prepare and react to these types of events when they occur. Predictive analytical methods could be used to help power utilities adapt to these types of storms, but there are uncertainties inherent in the predictability of convective storms that pose a challenge to the accurate prediction of storm-related outages. Describing the strength and localized effects of thunderstorms remains a major technical challenge for meteorologists and weather modelers, and any predictive system for storm impacts will be limited by the quality of the data used to create it. We investigate how the quality of thunderstorm simulations affects power outage models by conducting a comparative analysis, using two different numerical weather prediction systems with different levels of data assimilation. We find that limitations in the weather simulations propagate into the outage model in specific and quantifiable ways, which has implications on how convective storms should be represented to these types of data-driven impact models in the future.

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“…There are several studies where the research motivation is to assess the impact of geomagnetic storms on the electric power system [32][33][34]. In context with natural disasters, there are several studies in the literature which present the impact assessment of other kind of severe-weather related events such as thunderstorm, rainstorms and heavy winds [35][36][37][38][39]. Most of the existing studies present the analysis and prediction of outages in context to specific kind of event which trigger the power outage as discussed earlier.…”

Section: Introductionmentioning

confidence: 99%