Regional Electric Utility · Energy & Utilities · Denver, CO · 14 weeks
Grid reliability analytics deployed to 180 operations staff
A regional electric utility with 1.4 million customers struggled because its grid reliability data was scattered across six systems, including SCADA, OMS, GIS, and work management. Operations teams lacked a unified view, leading to slower restoration decisions. We developed a grid reliability analytics platform that cut the average outage restoration time by 34 minutes.
Challenge
The utility’s operations center relied on six separate screens, each showing a different system. Coordinators had to manually piece together outage status, crew locations, equipment history, and grid topology. This disjointed setup added 30 to 45 minutes to every restoration decision during outages.
Approach
We combined SCADA, OMS, GIS, and work management data into one platform to give operators a clear view of grid reliability. We created a real-time outage dashboard that automatically suggests crew dispatches based on location, equipment, and past restoration data. We developed a predictive model that scores equipment failure risk using five years of maintenance and failure records.
Outcome
We cut the average outage repair time by 34 minutes. Streamlining crew dispatch cut overtime expenses by $1.4 million annually. Using predictive failure scores, we flagged 142 high-risk assets and scheduled maintenance ahead of issues, stopping about 18 major outages in the first year.
We grabbed data from six different systems to really understand what’s going on.
The ops center was running six different systems at once, which made decision-making slow and messy, plenty of mistakes slipped through. So, we built a data integration layer that pulled real-time info from SCADA, OMS, GIS, and work management systems into a single dashboard. Just like that, restoration coordinators could instantly see outages, crew locations, equipment history, and the grid layout. It totally cleaned up their workflow.
How we spotted gear problems before they stopped us cold
We rolled up our sleeves and sifted through five years of maintenance logs, failure reports, gear ages, and environmental data. Then, we built a model to spot which assets were likely to fail. Every month, it scanned all 14,000 distribution pieces and flagged the top 142 at risk. This let the utility team focus on the troublemakers, slashing downtime and repair costs.
Results
- 34 minutes Reduction in mean time to restore per outage
- $1.4M Annual overtime cost reduction
- 142 High-risk assets identified for proactive maintenance
- 18 Estimated major outage events prevented in year one
Before, our restoration coordinators had to juggle six different screens during outages. Thinklytics built one dashboard that cut our average repair time by 34 minutes. Saving that much time on every outage really adds up and makes a big difference for our customers.