Estimated Time of Restoration (ETR) Guidance for Electric Distribution Networks

Electric distribution utilities have an obligation to inform the public and government regulators about when they expect to complete service restoration after a major storm. In this study, we explore methods for calculating the estimated time of restoration (ETR) from weather impacts, defined as the time it will take for 99.5% of customers to be restored. Actual data from Storm Irene (2011), the October Nor’easter (2011) and Hurricane Sandy (2012) within the Eversource Energy-Connecticut service territory were used to calibrate and test the methods; data used included predicted outages, the peak number of customers affected, a ratio of how many outages a restoration crew can repair per day, and the count of crews working per day. Data known before a storm strikes (such as predicted outages and available crews) can be used to calculate ETR and support pre-storm allocation of crews and resources, while data available immediately after the storm passes (such as customers affected) can be used as motivation for securing or releasing crews to complete the restoration in a timely manner. Used together, the methods presented in this paper will help utilities provide a reasonable, data-driven ETR without relying solely on qualitative past experiences or instinct

Assessment of Natural Hazards Impacts on Critical Infrastructure Systems

Reliable energy is a staple of modern society; without it, industry grinds to a halt, communication systems go silent, and the public’s welfare is at risk. In this dissertation, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. First, we show a performance evaluation of outage prediction models for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to \u3e15,000 outages). Second, we present an analysis that shows how incorporating high-resolution infrastructure, vegetation management and LiDAR-derived hazardous tree pixels (HazPix) data can improve the spatial accuracy of outage predictions during hurricanes. Third, we demonstrate how crew-related variables (i.e. the number of crews working), the peak number of customers affected, and estimates from the previously calibrated outage prediction model can be used to forecast the storm outage restoration duration (the time it takes to repair 99.5% of outages during a storm event). Lastly, we combine the three previous objectives into an evaluation of i) how a future Hurricane Sandy (strengthened from large-scale thermodynamic climate change) might impact outages in Connecticut; ii) how different vegetation management strategies can decrease outages; and iii) the number of restoration crews that would be needed to repair the future outages in a timely manner. Each of these sub-objectives can be used to motivate proactive storm resilience initiatives (such as increased vegetation management or infrastructure hardening). This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes

Weather-Based Damage Prediction Models for Electric Distribution Networks

From thunderstorms to hurricanes, electric distribution networks are subject to a wide range of warm weather storm events. Tropical Storm Irene (2011) and Hurricane Sandy (2012) are two events in recent memory that disrupted over half of The Connecticut Light and Power Company’s (CL&P) service territory, which left some customers without power for up to eleven days. This research study investigates a damage prediction framework for both thunderstorms and hurricanes that combines two generalized linear models to probabilistically determine the occurrence and extent of damages, known as trouble spots, to the overhead power distribution network. The models are inputted with high-resolution weather simulations from the Weather and Research Forecasting (WRF) Model along with distributed information on CL&P’s infrastructure, tree canopy density, and land cover data. The models were subjected to cross validation based on 30 major storm cases including the two tropical storms (Storm Irene and Hurricane Sandy), and exhibited a median percent error less than 30% for predicting the counts of trouble spots per event. Additionally, we explore an operational example of these models by using forecasts from 48 and 24 hours ahead of landfall by Hurricane Sandy to demonstrate how a real-time damage prediction system might operate

Agent Based Model to Estimate Time to Restoration of Storm-Induced Power Outages

Extreme weather can cause severe damage and widespread power outages across utility service areas. The restoration process can be long and costly and emergency managers may have limited computational resources to optimize the restoration process. This study takes an agent based modeling (ABM) approach to optimize the utility storm recovery process in Connecticut. The ABM is able to replicate past storm recoveries and can test future case scenarios. We found that parameters such as the number of outages, repair time range and the number of utility crews working can substantially impact the estimated time to restoration (ETR). Other parameters such as crew starting locations and travel speeds had comparatively minor impacts on the ETR. The ABM can be used to train new emergency managers as well as test strategies for storm restoration optimization

Community power outage prediction modeling for the Eastern United States

In the United States, weather-related power outages cost the economy tens of billions annually, and there has been an upward trend in billion-dollar disasters over the last two decades. Thus, it is of growing importance to be able to predict outages and understand local resilience. However, many outage prediction models rely on utility infrastructure and outage data, which can be difficult to obtain when a study domain covers many utility territories. This study demonstrates two gradient-boosting machine-learning models driven by utility-agnostic non-proprietary data, eliminating the need for utility-specific data, and allowing individuals or communities to build and use such models for emergency planning or vulnerability analysis. Further, the framework is novel for its ability to incorporate data from various ecoregions, utilize infrastructure proxy data, and provide outage predictions for a breadth of storm types over a large and scalable domain. In this study, vegetation, land cover, energy infrastructure proxy, and weather data are used as model inputs to evaluate 15,872 events across 17 states in the Eastern U.S., where an event is defined as a unique combination of geographic county and storm episode ID. The model predicting all storm types except thunderstorms was validated using 10-fold cross-validation where folds were split chronologically, and demonstrates an r-squared value between predicted and actual outages of 0.61. Similarly, the thunderstorm-only model demonstrates an r-squared of 0.31. For future work, the addition of flooding data may be considered as the r-squared for the various-storm-type model increases to 0.77 when data from New York and New Jersey for Hurricane Sandy are removed. Additionally, the framework demonstrated here can be used to create a real-time outage prediction forecasting tool for storm events, and can be used to analyze resilience at a county resolution under future climate scenarios

Dynamic Modeling of Power Outages Caused by Thunderstorms

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

Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas

Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data

Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)

Using molecular simulation for adsorbent screening is computationally expensive and thus prohibitive to materials discovery. Machine learning (ML) algorithms trained on fundamental material properties can potentially provide quick and accurate methods for screening purposes. Prior efforts have focused on structural descriptors for use with ML. In this work, the use of chemical descriptors, in addition to structural descriptors, was introduced for adsorption analysis. Evaluation of structural and chemical descriptors coupled with various ML algorithms, including decision tree, Poisson regression, support vector machine and random forest, were carried out to predict methane uptake on hypothetical metal organic frameworks. To highlight their predictive capabilities, ML models were trained on 8% of a data set consisting of 130,398 MOFs and then tested on the remaining 92% to predict methane adsorption capacities. When structural and chemical descriptors were jointly used as ML input, the random forest model with 10-fold cross validation proved to be superior to the other ML approaches, with an <i>R</i>² of 0.98 and a mean absolute percent error of about 7%. The training and prediction using the random forest algorithm for adsorption capacity estimation of all 130,398 MOFs took approximately 2 h on a single personal computer, several orders of magnitude faster than actual molecular simulations on high-performance computing clusters