Importance of tree type and precipitation estimates for modeling hurricane-induced power outages

Hurricanes can severely damage an electrical power system; thus, advance prediction of their impacts on the power grid could significantly benefit pre-storm planning efforts for power companies and the general public. Statistical, regression-based modeling can be used to provide an estimate of the number and locations of outages prior to hurricane landfall. This information would allow crews to place themselves in the areas of greatest impact ahead of the storm. Prior hurricane power outage models have integrated hurricane variables (such as wind speed or central pressure), environmental variables (such as soil or elevation characteristics), and power system variables (such as the number of poles or transformers per unit area) with positive results. This newest version of the hurricane power outage model builds upon a model created by Dr. Seth Guikema (Johns Hopkins University) and Dr. Steven Quiring (Texas A&M University) and will include two new predictor variables: tree type and storm total precipitation. The model will be run for hurricanes Dennis, Ivan, and Katrina over the region of a major Gulf Coast utility company that includes much of Alabama and Mississippi. The results will then be compared with previous model versions run over the same study region and using the same storms. It is anticipated that integration of these two new predictors will further improve model accuracy. Future research will include further refinement of the hurricane power outage model, with a potential release of the model to power companies, and eventually the release of a simplified, online version of the model to the general public.