Search NASA⌕ Search

Engineering topics

Chinthavali, Supriya

Publications and source records attributed to Chinthavali, Supriya.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

H3 Geospatial Mapping Resolution Recommendations

This report provides a brief overview of the Hexagonal Hierarchical Geospatial Indexing System (H3) and its benefits and use cases; a comparison of population estimates of various H3 resolutions with administrative boundaries (county, zip code, etc.); and H3 resolution recommendations for electric outage data reporting for US states considering optimal balance between accuracy, computational efficiency, and privacy preservation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Correlating Power Outage Spread with Infrastructure Interdependencies During Hurricanes

Power outages caused by extreme weather events, such as hurricanes, can significantly disrupt essential services and delay recovery efforts, underscoring the importance of enhancing our infrastructure's resilience. This study investigates the spread of power outages during hurricanes by analyzing the correlation between the network of critical infrastructure and outage propagation. We leveraged datasets from Hurricanemapping.com, the North American Energy Resilience Model Interdependency Analysis (NAERM-IA), and historical power outage data from the Oak Ridge National Laboratory (ORNL)'s EAGLE-I system. Our analysis reveals a consistent positive correlation between the extent of critical infrastructure components accessible within a certain number of steps (k-hop distance) from initial impact areas and the occurrence of power outages in broader regions. This insight suggests that understanding the interconnectedness among critical infrastructure elements is key to identifying areas indirectly affected by extreme weather events.

Bose, Avishek↗
DOE OSTI2024

Historical Power Outages of the United States and the Social Vulnerability Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). Because the SVI is calculated based on the summed rank of multiple vulnerability factors for environmental hazards, it can include factors irrelevant to power outages caused by extreme events. This work performs a detailed correlation analysis for social vulnerability and power outages by considering different SVI themes (e.g., socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation) and power outages with and without a threshold for extreme weather events. Although there is some relation between specific themes and aspects of power outages and the SVI in the results, there is no strong distinction between power outage durations and low vs. high SVI values. These results point to the need for further research that grounds the specific factors and methods used to develop SVI and related indices to energy services and power systems disruptions.

Bhusal, Narayan↗
DOE OSTI2024

Analysis of Historical Power Outages of the United States and the National Risk Index

Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). However, the relationship between National Risk Index (NRI) and power outages is yet to be explored. This work analyzes the NRI indices such as Risk, Expected Annual Loss, Social Vulnerability, and Community Resilience with several resilience metrics such as event duration, impact duration, recovery duration, impact level, impact rate, recovery rate, recovery to impact ratio, and area under the outage curves to see the correlation of NRI indices with the resilience metrics. The results show that NRI indices such as Risk and Expected Annual Loss increase with the increase of event duration, impact duration, and recovery duration. All Other metrics are indifferent to the change in the Risk and EAL ratings. The results also show that there is no strong relationship between all the metrics and community resilience and social vulnerability. This work also performed the sensitivity analysis of the extreme event selection process. This sensitivity analysis reveals that the way of identifying extreme events has a significant impact on the evaluation of the events.

Bhusal, Narayan↗
DOE OSTI2024

Impact Study of Thunderstorms on the US Power Grid Using Publicly Available Datasets

This work analyzes the impact of thunderstorms on the US power grid based on publicly available data. Since thunderstorms can bring lightning, heavy precipitation, and wind storms, analyzing their impact on the power system provides a combined correlation of lightning strikes, floods, and wind storms on power outages. This paper leverages publicly available thunderstorm datasets from the National Weather Service (NWS) and power outage datasets from Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) to study the correlation between thunderstorms and power outages. This work is analyzing the patterns of thunderstorms from 2013-2022, which shows that the thunderstorms are not slowing down and will seem to continue their impact on human life in the future. This work also analyzes the monthly and yearly pattern of the impact of thunderstorms on power systems at the national, state, and county level.

Bhusal, Narayan↗
DOE OSTI2024

The Challenges of Modeling Distributed Energy Resources (DERs) as Blackstart Resources and for Volt-VAR Optimality

Modeling, testing and instituting Distributed Energy Resources (DERs) as Blackstart Resoucrces presents several challenges due to the fundamental differences between traditional black start resources (e.g., large generators) versus DERs like solar PV systems with battery storage. This paper addresses some of these key challenges. These include intermittency of coordinating DERs to provide continuous power during a black start event, especially during extended periods of cloud cover or when battery energy storage is depleted. This paper additionally addresses the collapsing voltage and stability control challenges specific to maintaining bulk power system stability during black start synchronization These physical and engineering limitations require careful engineering design, modeling and engineering to ensure that DERs can support critical loads and substations during black start events. A variety of additional challenges also exist. Additionally, there are challenges associated with feeder location and low voltage secondary system impacts on DER functions and settings. We compare typical functions and settings for DERs for power factor control and correction. We also demonstrate how voltag control via Volta-VAR power factor correction can be done

Mukherjee, Srijib↗
DOE OSTI2024

A dataset of recorded electricity outages by United States county 2014–2022

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-I TM ), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Electric Utility Industry Standards Landscape

The electric utility industry relies on robust communication protocols to manage complex electrical grid data. The inherent networked nature of electrical grids, coupled with the radial structure of the “last mile” portion delivering power to end-use customers, presents difficulties in describing electrical models using simple data constructs. The paper provides an overview of communication protocols that address electric utility data, including grid data, and classifies these protocols through identification key characteristics that make them suitable for different electric grid data domains. Because of their variety, grid edge devices and their associated dedicated protocols are assessed by groups: those primarily designed for energy production and storage, those related to flexible loads, and those related to electric vehicles. The intent of this report is to provide guidance for stakeholders to navigate the challenges posed by the numerous overlapping protocols available to address electric grid data. Although further industry review, refinement, and validation of the categorization of these protocols is recommended, the authors propose this categorization as a start to improve electric grid awareness and understanding. In addition, this report includes three recommended industry actions regarding protocols to improve communications on the electric grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Winter Storm Elliott: Energy Emergency Alert Lessons Learned

Power blackouts in the United States in 2022 highlighted the need for better communication and planning among power grid stakeholders. This need was further demonstrated at a 2021 workshop held by the Grid Reliability Initiative Utilizing Data Standardization (GRIDS) Project. The GRIDS Project was established to understand and address the gaps in communication between the transmission and distribution operators. With the ongoing energy transition including electrification, distributed energy resource penetration, and so on, this project aims for improved energy emergency communications at the transmission-distribution interface. In 2022, Winter Storm Elliott affected 60% of the United States population (Weather Underground 2022), resulting in rolling blackouts in many areas of the eastern United States (Knoxville Utilities Board [KUB] 2023). Information presented here is based on the GRIDS workshop findings and subsequent research and interviews with stakeholders. After a brief summary of the effects of Winter Storm Elliott, this report covers the standard energy emergency communications processes currently in use, how the storm affected power management operations for the Tennessee Valley Authority (TVA) and its local utility companies, and related experiences in other states. The report concludes with lessons learned and recommendations for improvements and a path forward.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Feedback on the Electric Emergency Incident and Disturbance Report, OE-417

The US Department of Energy (DOE) Office of Electricity (OE) mandates requirements for reporting electric emergencies and disruptions in the United States. Specific electric power industry organizations (such as balancing authorities, reliability coordinators, some generating entities, and electric utilities) report this information through the OE-417 Electric Emergency Incident and Disturbance Report via Form OE-417. Entities are required to submit Form OE-417 when at least one of the qualifying 26 criteria is met pursuant to Section 13(b) of the Federal Energy Administration Act of 1974 (Public Law 93-275). Entities are required to report, within 6 hours of the incident, loss of electric service to 50,000 customers or more for 1 hour or longer. The deadline for submitting Form OE-417 depends on the nature of the incident. An updated Form OE-417 Schedule 1 and all of Schedule 2 are both due within 72 hours of the incident to provide complete disruption information. Additionally, a Final Report must be filed within 72 hours of the incident, unless an interim update has been provided. If the incident meets specific criteria, the form must be filed within one hour, six hours, or by the later of 24 hours after the recognition of the incident or by the end of the next business day.1 The ability of DOE to respond quickly to energy emergencies that could affect the nation’s infrastructure and help alleviate or prevent further disruptions depends on the industry’s prompt and complete submission of information. This report summarizes current issues with Form OE-417 and the reporting process and presents recommendations for improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets

Extreme weather events, such as hurricanes, severe thunderstorms, and floods can significantly disrupt power grid systems, leading to electrical outages that result in inconvenience, economic losses, and life-threatening situations. There is a growing need for a robust and precise predictive model to forecast power outages, which will help prioritize emergency response before, during, and after extreme weather events. In this paper, we introduce machine-learning models that predict power outage risk at the state level during and after extreme weather events. We jointly utilized two publicly available datasets: the U.S. historical power outage data collected by the Environment for Analysis of Geo-Located Energy Information (EAGLE-I™) system, and the National Weather Service historical weather alert data sets. We highlight our initial result and discuss future work aimed at enhancing the model's robustness and accuracy for real-world applications.

Lee, Sangkeun (Matt)↗
DOE OSTI2023

Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts

As the frequency and intensity of severe weather has increased, its effect on the electric grid has manifested in the form of significantly more and larger outages in the United States. This has become especially true for regions that were previously isolated from weather extremes. In this paper, we analyze the weather impacts on the electric power grid across a variety of weather conditions, draw correlations, and provide practical insights into the operational state of these systems. High resolution computational modeling of specific meteorological variables, computational approaches to solving power system models under these conditions, and the types of resiliency needs are highlighted as goal-oriented computing approaches are being built to address grid resiliency needs. An example analysis correlating outages to 1km day-ahead weather from two historical winter storms, calculated on a large cluster using a combination of interpolated and extrapolated inputs from multiple instrumented sites to workflows that produce primary meteorological outputs, is shown as initial proof of concept.

analysis↗
DOE OSTI2023

EAGLE-I Power Outage Data 2014 - 2022

The provided EAGLE-I historic dataset includes eight years of power outage information at the county level from 2014 to 2022 at 15-minute intervals collected by the EAGLE-I program at ORNL. The data has been collected from utility's public outage maps using an ETL process. The dataset details FIPS code, county name, state name, total number of customers without power, and a date/timestamp. Also included is the EAGLE-I coverage of each state for each year. For detailed metadata, refer to the metadata DOI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

EAGLE-I Power Outage Data Information

This DOI contains information on the EAGLE-I power outage dataset and serves as a blanket DOI for all EAGLE-I historic data. Historic power outage data from specific years have been linked to this DOI.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Analysis of Correlation between Cold Weather Meteorological Variables and Electricity Outages

The significance of the impact of weather on the electric grid has grown as climate change continues to increase the frequency and intensity of extreme weather events. In recent years (2021-2022) in particular, extreme winter weather has affected the grid in locations in the US rarely exposed to extreme low temperatures, snow and icing conditions. Here we analyze the correlation between cold weather meteorological variables and electricity outages during two large winter storm events, Uri (February 2021) and Landon (February 2022) using Random Forest machine learning and Pearson’s correlation coefficient. Our geographical focus across the two storms is the state of Texas. Extrapolation of the method to winter weather impacts over other years and additional locations is proposed.

Dumas, Melissa↗
DOE OSTI2022

An Alternative Timing and Synchronization Approach for Situational Awareness and Predictive Analytics

Accurate and synchronized timing information is required by power system operators for controlling the grid infrastructure (relays, Phasor Measurement Units (PMUs), etc.) and determining asset positions. Satellite-based global positioning system (GPS) is the primary source of timing information. However, GPS disruptions today (both intentional and unintentional) can significantly compromise the reliability and security of our electric grids. A robust alternate source for accurate timing is critical to serve both as a deterrent against malicious attacks and as a redundant system in enhancing the resilience against extreme events that could disrupt the GPS network. To achieve this, we rely on the highly accurate, terrestrial atomic clock-based network for alternative timing and synchronization. In this paper, we discuss an experimental setup for an alternative timing approach. The data obtained from this experimental setup is continuously monitored and analyzed using various time deviation metrics. We also use these metrics to compute deviations of our clock with respect to the National Institute of Standards and Technologys (NIST) GPS data. The results obtained from these metric computations are elaborately discussed. Finally, we discuss the integration of the procedures involved, like real-time data ingestion, metric computation, and result visualization, in a novel microservices-based architecture for situational awareness.

Chinthavali, Supriya↗
DOE OSTI2022

COVID-19 pandemic ramifications on residential Smart homes energy use load profiles

The COVID-19 pandemic has significantly affected people’s behavioral patterns and schedules because of stay-at-home orders and a reduction of social interactions. Therefore, the shape of electrical loads associated with residential buildings has also changed. In this paper, we quantify the changes and perform a detailed analysis on how the load shapes have changed, and we make potential recommendations for utilities to handle peak load and demand response. Our analysis incorporates data from before and after the onset of the COVID-19 pandemic, from an Alabama Power Smart Neighborhood with energy-efficient/smart devices, using around 40 advanced metering infrastructure data points. This paper highlights the energy usage pattern changes between weekdays and weekends pre– and post–COVID-19 pandemic times. The weekend usage patterns look similar pre– and post–COVID-19 pandemic, but weekday patterns show significant changes. We also compare energy use of the Smart Neighborhood with a traditional neighborhood to better understand how energy-efficient/smart devices can provide energy savings, especially because of increased work-from-home situations. HVAC and water heating remain the largest consumers of electricity in residential homes, and our findings indicate an even further increase in energy use by these systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗