Using data gathered from Hurricane Katrina and four other destructive storms, researchers with Johns Hopkins and Texas A&M universities say they have discovered a way to accurately predict the number of power outages that will occur before a hurricane comes ashore. The study published in the current issue of the journal Risk Analysis.
The data garnered from the computer models could potentially result is significant financial savings for utility companies that could then conceivably be passed on to customers, according to the researchers say. In addition, the information could help companies determine appropriate utility crew levels and placements to facilitate rapid restoration of power following a severe storm.
The research focused on two common challenges facing utility companies as a hurricane is approaches -- deciding how many repair crews to request from other utilities, a decision that may cost the provider millions of dollars, and where to locate these crews within its service areas to enable fast and efficient restoration of service after the hurricane passes. The ability to accurately estimate the number of outages and where they will occur will allow utilities to better plan for the number and location of these crews, the researchers say.
What makes the research team's computational approach unique and increases its accuracy is the combination of more detailed information about the storm, the area it is impacting and the power system of the area, together with more appropriate statistical models.
"If the power company overestimates, it has spent a lot of unnecessary money," says Steven Quiring, an assistant professor of geography at Texas A&M. "If it underestimates, the time needed to restore power can take several extra days or longer, which is unacceptable to them and the people they serve. So these companies need the best estimates possible, and we think this study can help them make the best possible informed decision."
In addition, more accurate models "provide a much better basis for preparing for restoring power after the storm," Seth Guikema, an assistant professor of geography and environmental engineering at Johns Hopkins and formerly of Texas A&M says, adding that "the goal is to restore power faster and save customers money."
In developing their computer model, the researchers looked at damage data from five hurricanes: Dennis (1995), Danny (1997), Georges (1998), Ivan (2004) and Katrina (2005). In the areas studied, Ivan created 13,500 power outages; Katrina, more than 10,000; Dennis, about 4,800; Georges, 1,075; and Danny, 620.
For the worst of these storms, some customers were without power for up to 11 days. The research team collected information about the locations of outages in these past hurricanes, with an outage defined as permanent loss of power to a set of customers due to activation of a protective device in the power system.
The information also includes data about the power system in each area (poles, transformers, etc.), hurricane wind speeds, wetness of the soil, long-term average precipitation, the land use, local topography and other related factors. This was then used to train and validate a statistical regression model called a Generalized Additive Model, a particular form of model that can account for nonlinear relationships between the variables.
Source: Steven Quiring, Texas A&M
Writer: Walaika Haskins
Related links:
The team's "Risk Analysis" study:
http://www3.interscience.wiley.com/cgi-bin/fulltext/122542675/HTMLSTART
Seth Guikema's Web Page:
https://jshare.johnshopkins.edu/sguikem1/public_html/
Johns Hopkins Department of Geography and Environmental Engineering:
http://engineering.jhu.edu/~dogee/
Steven Quiring's Web Page:
http://geog.tamu.edu/~squiring/
Texas A&M video with Steven Quiring:
http://www.youtube.com/watch?v=gcnltgtiemQ&