Framework for probabilistic simulation of power transmission network performance under hurricanes

Structures in a power transmission network, such as towers and conductors, are vulnerable to hurricanes. Failures of these structures trip transmission lines and usually result in large scale power outages in the region. This paper presents a technique for the probabilistic simulation of power transmission systems under hurricane events and provides fundamental insights on the modeling and quantification of power system performance and resilience. The study models the power transmission system as a network of connected individual components, which are subjected to wind-induced mechanical failure and power flow constraints. A realistic power transmission network is developed for the study region. The geographical data are obtained for all components in the network based on a data collection and image processing campaign to reflect the realistic properties of the network serving the Lehigh Valley, PA. A hurricane simulator is utilized to generate a hurricane scenario providing time-varying wind intensities and wind directions for the component failure analysis. The spatio-temporal impact of the hurricane is investigated: a pool of component fragilities is generated to effectively incorporate the uncertainties in structural capacities into the analysis; the spatial correlation among structures is modeled efficiently by a random field based technique. At the system level, Monte Carlo simulation is adopted to determine the failure probability of transmission lines. The unmet demand of the system is computed probabilistically, based on the alternate current optimal power flow analysis, capacity constraints and load shedding process of the system. The simulation results can be used to quantify and visualize the power network performance, and help decision makers to identify critical components in the network to optimize the short-term pre-event preparation for an approaching hurricane and long-term retrofit strategy to enhance system resilience.