A performance-based probabilistic framework to model risk to power systems from hurricanes - Ceferino, Luis; ICASP14; Arora, Prateek (My University, 2023)
Utility companies are often prepared for small-scale blackouts under normal operating conditions. However, they face crucial challenges with extreme weather events, such as hurricanes. Utilities must make risk-informed decisions to prioritize their limited resources (e.g., for grid hardening) in cities expected to experience larger and longer hurricane-induced outages. Probabilistic outage models can capture the uncertainty in power outages and characterize the grid's vulnerabilities to local environmental conditions, such as winds responsible for falling trees and poles that can affect the outages. We employ a probabilistic outage model developed for the 3.6 million historical outages caused by Hurricane Harvey (2017), Hurricane Michael (2018), and Hurricane Isaias (2020) in the United States states to investigate the expected performance of power systems to future hurricanes. This paper presents a generalized probabilistic framework coupling the expected frequency of future hurricane hazard levels and probabilistic outage predictions to understand the frequency of power system performance indices, namely the System Average Interruption Frequency Index and System Average Interruption Duration Index, from hurricanes. Results show that factors other winds, such as land use patterns, influence the risk of power outages across cities in New Jersey from future hurricanes.
14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
PUBLISHED