Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

Patrick D. Royer; Wei Du; Kevin Schneider

doi:10.1007/s13753-022-00413-6

Volume 13 Issue 3

Jul. 2022

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Article Navigation > International Journal of Disaster Risk Science > 2022 > 13(3): 415-434

Patrick D. Royer, Wei Du, Kevin Schneider. Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology[J]. International Journal of Disaster Risk Science, 2022, 13(3): 415-434. doi: 10.1007/s13753-022-00413-6

Citation:

Patrick D. Royer, Wei Du, Kevin Schneider. Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology[J]. International Journal of Disaster Risk Science, 2022, 13(3): 415-434. doi: 10.1007/s13753-022-00413-6

Citation:

Patrick D. Royer, Wei Du, Kevin Schneider. Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology[J]. International Journal of Disaster Risk Science, 2022, 13(3): 415-434. doi: 10.1007/s13753-022-00413-6

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Rapid Evaluation and Response to Impacts on Critical End-Use Loads Following Natural Hazard-Driven Power Outages: A Modular and Responsive Geospatial Technology

doi: 10.1007/s13753-022-00413-6

Energy and Environment, Pacific Northwest National Laboratory, Richland, WA, 99352, USA

Funds:

This work was supported by the United States Department of Energy, Office of Energy Efficiency and Renewable Energy, Solar Energy Technology Program. The Pacific Northwest National Laboratory is operated for the U.S. Department of Energy by the Battelle Memorial Institute under Contract DE-AC05-76RL01830.

Available Online: 2022-07-06

Abstract

Abstract

The disparate nature of data for electric power utilities complicates the emergency recovery and response process. The reduced efficiency of response to natural hazards and disasters can extend the time that electrical service is not available for critical end-use loads, and in extreme events, leave the public without power for extended periods. This article presents a methodology for the development of a semantic data model for power systems and the integration of electrical grid topology, population, and electric distribution line reliability indices into a unified, cloud-based, serverless framework that supports power system operations in response to extreme events. An iterative and pragmatic approach to working with large and disparate datasets of different formats and types resulted in improved application runtime and efficiency, which is important to consider in real time decision-making processes during hurricanes and similar catastrophic events. This technology was developed initially for Puerto Rico, following extreme hurricane and earthquake events in 2017 and 2020, but is applicable to utilities around the world. Given the highly abstract and modular design approach, this technology is equally applicable to any geographic region and similar natural hazard events. In addition to a review of the requirements, development, and deployment of this framework, technical aspects related to application performance and response time are highlighted.
- Electrical grid,
- Hurricane response,
- Puerto Rico,
- Resilience and geospatial technology,
- Software applications

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References(35)

References

Aigner, W., S. Miksch, W. Müller, H. Schumann, and C. Tominski. 2008. Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics 14(1):47-60.

Akker, J.D. 2014. Fault location, isolation, and service restoration technologies reduce outage impact and duration. Addis Ababa, Ethiopia:Ministry of Health, Federal Democratic Republic of Ethiopia.

Alam, M.M., Z. Zhu, E.B. Tokgoz, J. Zhang, and S. Hwang. 2020. Automatic assessment and prediction of the resilience of utility poles using unmanned aerial vehicles and computer vision techniques. International Journal of Disaster Risk Science 11(1):119-132.

Codd, E.F. 1979. Extending the database relational model to capture more meaning. ACM Transactions on Database Systems 4(4):397-434.

Dobson, J., E. Bright, P. Coleman, R. Durfee, and B. Worley. 2000. LandScan:A global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing 66(7):849-857.

Granell, C., and F.O. Ostermann. 2016. Beyond data collection:Objectives and methods of research using VGI and geo-social media for disaster management. Computers, Environment and Urban Systems 59:231-243.

Hammer, M., and D. McLeod. 1978. The semantic data model:A modeling mechanism for data base applications. In Proceedings of the 1978 ACM SIGMOD International Conference on Management of Data, 31 May 1978-2 June 1978, Austin, Texas, USA, 26-36.

Han, S., D. Guikema, and S.M. Quiring. 2009. Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models. Risk Analysis 29(10):1443-1453.

Heydt, G., and T.J. Graf. 2010. Distribution system reliability evaluation using enhanced samples in a Monte Carlo approach. IEEE Transactions on Power Systems 25(4):2006-2008.

Jeffers, R.F., M.J. Baca, A.M. Wachtel, S. Derosa, A. Staid, W. Fogleman, A. Outkin, and F. Currie. 2018. Analysis of microgrid locations benefitting community resilience for Puerto Rico. Technical report. Albuquerque, New Mexico:Sandia National Laboratory.

Keim, D.A. 2002. Visual exploration of large data sets. Communications of the ACM 8(1):1-8.

Lam, N.S.-N., Y. Qiang, H. Arenas, P. Brito, and K.B. Liu. 2015. Mapping and assessing coastal resilience in the Caribbean region. Cartography and Geographic Information Science 42(4):315-322.

Mensah, A.F., and L. Dueñas-Osorio. 2016. Efficient resilience assessment framework for electric power systems affected by hurricane events. Journal of Structural Engineering.. Mensah, A.F., and L. Dueñas-Osorio. 2016. Efficient resilience assessment framework for electric power systems affected by hurricane events. Journal of Structural Engineering. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001423.

Mukherjee, S., R. Nateghi, and M. Hastak. 2018. A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S. Reliability Engineering and System Safety 175(C):283-305.

Nateghi, R., S. Guikema, and S.M. Quiring. 2014. Power outage estimation for tropical cyclones:Improved accuracy with simpler models. Risk Analysis 34(6):1069-1078.

NOAA (National Oceanic and Atmospheric Administration). 2020. U.S. billion-dollar weather and climate disasters, 1980-present. Asheville, NC:NOAA National Centers for Environmental Information.

Ofli, F., P. Meier, M. Imran, C. Castillo, D. Tuia, N. Rey, J. Briant, and P. Millet et al. 2016. Combining human computing and machine learning to make sense of Big (Aerial) Data for disaster response. Big Data 4(1):47-59.

Ouyang, M., and L. Dueñas-Osorio. 2014. Multi-dimensional hurricane resilience assessment of electric power systems. Structural Safety 48:15-24.

Riley, S.J., S.D. DeGloria, and R. Elliot. 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences 5(1-4):23-27.

Rudin, C., S. Ertekin, R. Passonneau, A. Radeva, A. Tomar, B. Xie, S. Lewis, and M. Riddle et al. 2014. Analytics for power grid distribution reliability in New York City. Informs Journal on Applied Analysis 44(4):364-383.

Staid, A., S.D. Guikema, R. Nateghi, S.M. Quiring, and M.Z. Gao. 2014. Simulation of tropical cyclone impacts to the U. S. power system under climate change scenarios. Climatic Change 127(3):535-546.

Tokgoz, B., M. Safa, and S. Hwang. 2017. Resilience assessment for power distribution systems. International Journal of Civil and Environmental Engineering 11(7):806-811.

U.S. Environmental Protection Agency. 2016. Climate change indicators:Weather and climate. https://www.epa.gov/climate-indicators/weather-climate. Accessed 2 Feb 2020.

Van, S.B., B.N. Judson, S.V.T. Nguyen, and W.D. Ross. 2012. Microgrid study:Energy security for DoD installations. Technical report 1164. Cambridge, MA:Lincoln Laboratory, Massachusetts Institute of Technology.

Voigt, V., T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl. 2007. Satellite image analysis for disaster and crisis-management support. IEEE Transactions on Geoscience and Remote Sensing 45(6):1520-1528.

Vugrin, E., D. Warren, and M.A. Ehlen. 2011. A resilience assessment framework for infrastructure and economic systems:Quantitative and qualitative resilience analysis of petrochemical supply chains to a hurricane. Process Safety Progress 30(3):280-290.

Vyron, A., and C. Potsiou. 2020. A deep learning method to accelerate the disaster response process. Remote Sensing 12(3):Article 544.

Weems, C.F., S.E. Watts, M.A. Marsee, L.K. Taylor, N.M. Costa, M.F. Cannon, V.G. Carrion, and A. Pina. 2007. The psychosocial impact of Hurricane Katrina:Contextual differences in psychological symptoms, social support, and discrimination. Behaviour Research and Therapy 45(10):2295-2306.

Wood, A.J., and B.F. Wollenberg. 1996. Power generation, operation, and control, 2nd edn. New York:John Wiley & Sons.

Wong, P.C., K. Schneider, P. MacKey, H. Foote, G. Chin, R. Guttromson, and J. Thomas. 2009. A novel visualization technique for electric power grid analytics. IEEE Transactions on Visualization and Computer Graphics 15(3):410-423.

Yang, F., D.W. Wanik, D. Cerrai, M. Bhuiyan, and E.N. Anagnostou. 2020. Quantifying uncertainty in machine learning-based power outage prediction model training:A tool for sustainable storm restoration. Sustainability 12(4):Article 1525.

Yi, J., Y. Kang, J.T. Stasko, and J.A. Jacko. 2007. Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics 13(6):1224-1231.

Zhai, C., T. Chen, A. White, and S. Guikema. 2021. Power outage prediction for natural hazards using synthetic power distribution systems. Reliability Engineering & System Safety 208:Article 107348.

Zhang, L., and J. Yi. 2010. Management methods of spatial data based on PostGIS. In Proceedings of 2010 2nd Pacific-Asia Conference on Circuits, Communications and System, 1-2 August 2010, Beijing, China, 410-413.

Zimmerman, R., and C.E. Restrepo. 2009. Analyzing cascading effects within infrastructure sectors for consequence reduction. Proceedings of the 2009 IEEE Conference on Technologies for Homeland Security, 11-12 May 2009, Waltham, Massachusetts, United States, 157-162.

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