Turning Data into a More Resilient Grid
Clay Tutaj is Manager Data Science at Exelon’s Baltimore Gas and Electric. Neha Dave is Senior Data Scientist at Exelon’s Baltimore Gas and Electric.
Vegetation has long been one of the most stubborn challenges in maintaining electric reliability in the U.S. This has been compounded by an uptick in severe weather, with storms and high winds impacting on the many miles of overhead nearby vegetation on our system.
For many utilities the standard response is routine or cycle-based tree trimming and hazard tree removal, usually based on visual inspections and field expertise. It’s an approach that has served us for decades, but it isn’t always precise.
Crews often clear areas on a set cycle and with limited information, not necessarily where the risk is highest. That can lead to missed opportunities, uneven performance, and inefficient use of limited budgets.
Targeted, Risk-Based Approach
At BGE, a more targeted approach has taken shape through a partnership of Vegetation Management and the Data Science team; one that relies on data to drive decisions, not just following routine.
By combining detailed reliability modeling, vegetation data, historical outage records, and cost information, we identify which trees pose the greatest risk reduction per dollar spent. It’s a shift away from cycle-based trimming and removal toward an information-based strategy guided by reliability, risk, and value.