Simulation of Power Grid Outages from Historical Landfalling Hurricanes in the Northeast United States

Hurricanes are considered as one of the most catastrophic weather events that impact the U.S. East Coast due to the synergy of high winds and intense precipitation, and the consequent floods. Under such extreme weather conditions, ensuring the resilience of electric power systems is crucial, given its impact on socio-economic activities as well as public health and safety. In the Northeastern coastal states, direct hurricane landfalls were not frequent, but their frequency of occurrences will increase throughout the 21st century (Marsooli et al. 2019). In this regard, a vulnerability assessment of the current power system under the influence of historical major hurricanes can provide valuable information to guide long-term resilience improvement investments for operating, maintaining, and upgrading the respective electrical assets. The University of Connecticut Outage Prediction Model (UConn OPM), which has been developed and improved for over a decade, is a suitable tool for quantitatively assessing power outages caused by weather events (Cerrai et al. 2019; Watson et al. 2022; Yang et al. 2023). This study will adapt the UConn OPM to apply on historical hurricanes. Specifically, we will (i) select the most intense historical landfalling hurricanes of the Northeast United States in the past century; (ii) generate event-based high-resolution weather data by downscaling ECMWF's atmospheric reanalysis of the 20th century (ERA-20C); and (iii) create a machine learning framework by properly adapting the existing UConn OPM to predict power outages using the downscaled hurricane dataset generated in (i) and the current power infrastructure information. The results will show the number and location of power outages that can be potentially triggered by the selected historical hurricanes given the current power system condition. Finally, the impact of the selected historical hurricanes will be compared with the more recent severe events that affected the Northeast United States.

References
Cerrai, D., Wanik, D. W., Bhuiyan, M. A. E., Zhang, X., Yang, J., Frediani, M. E., & Anagnostou, E. N. (2019). Predicting storm outages through new representations of weather and vegetation. IEEE Access, 7, 29639-29654.
Marsooli, R., Lin, N., Emanuel, K., & Feng, K. (2019). Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns. Nature communications, 10(1), 1-9.
Watson, P. L., Spaulding, A., Koukoula, M., & Anagnostou, E. (2022). Improved quantitative prediction of power outages caused by extreme weather events. Weather and Climate Extremes, 37, 100487.
Yang, F., Koukoula, M., Emmanouil, S., Cerrai, D., & Anagnostou, E. N. (2023). Assessing the power grid vulnerability to extreme weather events based on long-term atmospheric reanalysis. Stochastic Environmental Research and Risk Assessment, 1-16.