Jacob Hawkes, Schneider Electric, Calgary, Canada
Tom Layton, Eversource, Connecticut, USA 1. Abstract
Tree damage caused by severe weather is the leading cause of electric power outages, and can cause widespread damage to electrical distribution systems. Increased weather variability is resulting in more frequent extreme weather events, which cause catastrophic damage to these systems.
Utilities are increasingly turning to decision support systems to aid in the management of the grid, and many of these tools rely on accurate and relevant weather observations and forecast data.
Existing official weather stations are often dispersed over wide areas, leaving large sections of a utility’s service area without local weather observations. Forecasts based on these stations cannot take into account the local effects of geography and population that will affect a utility’s infrastructure, and restoration decisions.
Analysing the utility’s service area, their infrastructure and the locations of existing official stations can reveal strategic locations for the placement of private weather stations. These stations will provide hyper-local observations, which will aid in the situational awareness for extreme weather events. Further, these hyper-local weather stations provide critical input into outage forecast modelling, enabling a utility to efficiently mobilize resources to reduce restoration time.
Eversource launched a joint effort with the University of Connecticut to develop an Outage Prediction Model, which will aid the utility in predicting how the forecasted weather will affect their infrastructure, and guide in their outage management planning. To ensure the model has the best observational and forecast data, they embarked on a local weather station initiative and installed 10 utility-grade weather stations. This new source of weather data will be incorporated into the Outage Prediction Model to produce more accurate outage forecasts.
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