6.3 The Economic Case for Predicting Storm Damages at Electric Distribution Utilities

Thursday, 26 January 2017: 11:00 AM
606 (Washington State Convention Center )
Shylesh Muralidharan, Schneider Electric, Burnsville, MN; and D. W. Wanik

The Economic case for predicting Storm damages at Electric Distribution Utilities

To achieve goals of reliability electric service, utilities strive to keep service outages at a minimum, especially if they occur during severe weather. Utilities accomplish this by optimizing storm response and restoration efforts during and after large-scale storm events that damage overhead infrastructure, which can leave customers without power for days. To plan for an effective storm response and restoration effort, it is important that utilities start with a good prediction model of asset damage from an incoming storm impacting their service territory. Schneider Electric in collaboration with University of Connecticut is building such a software-based solution to predict asset damage based on data from previous storms, utilities asset data and relevant environmental data to make decisions on (1) crew staging, (2) estimating a time of restoration that can be used for planning restoration efforts, and (3) designing a communication strategy to inform their customers about expected time of restoration, thereby ensuring superior customer satisfaction and operational reliability indices. The biggest challenge that Utilities have today to embracing such a system as part of their restoration effort is to justify the investment. Examples of these costs include emergency planning staffing, mutual assistance, and carrying right amount of inventory to ensure that customer impact is kept to a minimum.

In this talk, we will present model that details the economic feasibility of any utility investing in the aforementioned solution. The cost of building and running this solution is taken into account and compared with all the costs driving the quality of a utility’s response to an event based on the predictive solution in terms of workforce, inventory and other cost drivers that scale-up during any large-scale storm event. For costs, the model will consider both direct costs involved in setting up and operating this system as well the indirect costs impacting storm response and restoration decisions because the predictive solution is available for decision-making. This has to be weighed against the economic benefits of having an estimate early in the planning phase and all corresponding opportunity costs of being better prepared due to the availability of damage estimates. Such a model will be very useful for Utilities to evaluate the business case and estimate a Return on Investment (ROI) so that emergency preparedness managers can present a justification for investing in such a predictive solution. This will also help utilities develop risk intelligence to help respond to natural hazards in an effective, science-based manner.

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