2B.2 Predicting the Impact of Tropical Storms on Electrical Power Systems

Monday, 26 June 2017: 10:45 AM
Mt. Roan (Crowne Plaza Tennis and Golf Resort)
Steven M. Quiring, Ohio State Univ., Columbus, OH; and J. V. Pino, S. D. Guikema, and D. B. McRoberts

Tropical storms (e.g., typhoons or hurricanes) can cause significant damage to the electrical power system, leading to prolonged power interruptions to a large number of customers. The estimated annual cost to the U.S. economy from storm-related power outages is >$20 billion. One approach to deal with this problem is to develop predictive techniques for assessing how weather events will impact the power grid, which will help utilities, customer and first responders, and city/state planners to better prepare for the outages. For example, utilities must determine how many repair crews to request from other utilities, the amount of material and equipment they will need to make repairs, and where in their geographically expansive service area to station crews and materials. Accurate forecasts of the impact of an approaching hurricane within their service area are critical for utilities in balancing the costs and benefits of different levels of resources. The Hurricane Outage Prediction Model (HOPM) are a family of statistical models that utilize predictions of tropical cyclone windspeed and duration of strong winds, along with power system and environmental variables (e.g., soil moisture, long-term precipitation), to forecast the number and location of power outages. This presentation summarizes the data-driven power outage models that we have developed for the U.S. Department of Energy and a number of investor-owned electrical utilities in the United States. These models are used to support decision making for near-term events (e.g., pre-storm preparation) and longer-term planning. The development and validation of our models will be presented and our approach for quantifying uncertainty will also be discussed. The talk will highlight the challenges and opportunities for high-resolution modeling of the impact of tropical storms on power systems.
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