Power outage prediction for natural hazards using synthetic power distribution systems

Zhai, Chengwei; Chen, Thomas Ying‐Jeh; White, Anna; Guikema, Seth D.

doi:10.1016/j.ress.2020.107348

Cited by 37 publications

(24 citation statements)

References 32 publications

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“…The power system model we use here, though physics-based, cannot resolve all details during a power outage and recovery process. Given that distribution networks and protective devices data are generally not available, star-like network 35 and minimal spanning tree (MST) 73 models (and models combining these two 73 , 74 ) are usually used to generate synthetic distribution networks, and features that may have minor effects on predicting the daily-scale power outage and recovery under hurricanes including protective devices are usually neglected 73 – 75 . Based on these assumptions, the adopted simulation framework is arguably the best model that we can use to capture the power outage and recovery process at a mesoscale level, e.g., for each zip code or census tract, rather than at the individual household level, for risk and resilience analysis.…”

Section: Methodsmentioning

confidence: 99%

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

2022

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Tropical cyclones (TCs) have caused extensive power outages. The impacts of TC-caused blackouts may worsen in the future as TCs and heatwaves intensify. Here we couple TC and heatwave projections and power outage and recovery process analysis to investigate how TC-blackout-heatwave compound hazard risk may vary in a changing climate, with Harris County, Texas as an example. We find that, under the high-emissions scenario RCP8.5, long-duration heatwaves following strong TCs may increase sharply. The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%) over the 21st century. We also reveal that a moderate enhancement of the power distribution network can significantly mitigate the compound hazard risk. Thus, climate adaptation actions, such as strategically undergrounding distribution network and developing distributed energy sources, are urgently needed to improve coastal power system resilience.

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Section: Methodsmentioning

confidence: 99%

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

2022

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“…Large EWO are less frequent and therefore short-term comparison (as in Table 1) gives only partial information about the long term effect of EWO relative to NWO. The majority of outage forecasting models in literature have focused on EWO [14,16,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] while neglecting the impact of NWO whose long-term cumulative effects are substantial. There have been a few NWO studies that have focused on subsets of these outages, such as animal and vegetation related outages.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

Owolabi

Sunter

2022

Energies

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Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.

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“…There are some studies that have focused on power outage prediction during normal weather condition [16][17][18], which are not applicable under windstorms. To address this concern, a variety of methods have been proposed in the past-published studies to predict the storm-caused outages, which can be broadly classified into two main categories: 1fragility-based approaches 2-statistical models [19]. In the first category, reference [19] estimates power outages during disasters based on fragility curves.…”

Section: Introductionmentioning

confidence: 99%

“…To address this concern, a variety of methods have been proposed in the past-published studies to predict the storm-caused outages, which can be broadly classified into two main categories: 1fragility-based approaches 2-statistical models [19]. In the first category, reference [19] estimates power outages during disasters based on fragility curves. In the second category, [20] develops an outage-forecasting model based on the random forest method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pre-Storm Island Formation Framework to Improve Distribution System Resilience Considering Tree-Caused Failures

et al. 2022

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This paper presents a new framework for island formation prior to windstorms, which considers tree-caused failures of distribution networks. In the proposed framework, both direct and indirect effects of windstorms on distribution lines are quantified. Thus, a novel discrete Markov chain model is proposed for representing the failure modes of trees in each time interval of windstorm duration. This model categorizes ''healthy'', ''uprooted'', ''stem breakage'', and ''branch breakage'' states of a tree. In addition, a new linetree interaction model is presented for calculating tree-caused failure probability of overhead lines. The results of the proposed Markov model are taken as inputs by the developed line-tree interaction model. In these models, the different characteristics of windstorms are taken into account. Tree vulnerability to

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Power outage prediction for natural hazards using synthetic power distribution systems

Cited by 37 publications

References 32 publications

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

Tropical cyclone-blackout-heatwave compound hazard resilience in a changing climate

Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

A Novel Pre-Storm Island Formation Framework to Improve Distribution System Resilience Considering Tree-Caused Failures

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