Charlie Sanchez leads Infrastructure Advisory at Black & Veatch, drawing on 25+ years of delivering infrastructure solutions for clients.
Utilities know where many of their problems reside; the hard part is deciding which one to solve first.
At first glance, this might seem counterintuitive. Most systems can call up years of reports and operating data. Operators usually know which transformers are aging or which substations deserve attention. But what they don’t have are unlimited crews, unlimited capital or unlimited outage windows.
Today, the biggest challenge is no longer visibility but decision-making across the system. Utilities don’t have the luxury of doing things in a disconnected way. Rather, the onus is on them to plan with a mindset that factors in all those variables to trade them off as the need arises.
How The Engineering Problem Has Changed
Previously, utilities conducted inspections and replaced equipment when it wore out. While that work hasn’t gone away, the facts surrounding a decision have changed and, in many cases, increased. Electricity demand continues to grow, requiring the grid to deliver power at levels it was not originally designed to handle. Distributed energy resources are changing how power actually moves.
Meanwhile, utilities are asking more of their existing infrastructure that, in many cases, was engineered decades ago. The engineering problem has changed because the system has changed. Engineers can no longer evaluate one part of the network without considering how it affects the rest.
That means a utility can’t look at a transformer, pumping station or pipeline segment in isolation. Replace it, and the utility has solved one problem, but only if that asset was actually the constraint. If a more serious issue was sitting somewhere else in the system, the utility has now spent money, spent crew time and created an outage window, all without making the system meaningfully better.
Age alone, for example, doesn’t necessarily tell a utility which asset poses the greatest risk. It’s not unheard of for a utility to replace a 31-year-old asset simply because its nameplate life was 30 years, even though it’s performing well. Meanwhile, an asset with an expected 50-year life could be performing poorly after only 25 years. A risk- or value-based approach uses performance data and operating history to determine which investment matters more rather than automatically replacing the oldest equipment first.
Utilities can’t ignore how one move affects another. A planned outage will limit what happens elsewhere on the network. A project or task that looks urgent because of equipment age or condition may deliver only modest improvement if another constraint continues to hold the system back.
From Data To System-Level Decisions
Today’s infrastructure realities require a different approach. Existing data can inform better decisions when properly identified, connected and analyzed as part of a larger system. Inspection reports, maintenance records, sensor data and operating histories all describe different parts of the same problem. An alert or open work order by itself doesn’t tell you whether to pull equipment offline today or keep it running until the next planned outage.
However, this information becomes extremely valuable when viewed together, alongside the inspection history, operating conditions and an understanding of how that equipment influences the rest of the network. Utilities have no shortage of operational information at hand; the challenge is to connect it with predictive analysis, so operators can move beyond reacting to failures and toward anticipating them.
As I have supported electrical grid modernization efforts, I’ve seen why this system-level view matters. Much of today’s conversation focuses on building new generation to meet rising demand. While new generation is necessary, power still has to move across transmission lines and through substations onto distribution networks before it reaches customers. If one part of that chain can’t keep up, additional generation doesn’t solve the problem. It simply shifts the bottleneck somewhere else.
That’s why grid modernization extends well beyond replacing aging hardware. Transmission, distribution, instrumentation, controls and operational software all influence how the system performs. Planning those investments independently no longer reflects the way the grid actually operates. Now, every decision changes conditions somewhere else on the network.
Digital modeling is one strategy that has changed how engineers approach those decisions. Instead of committing to a project and discovering the consequences much later after work has already started, they can evaluate alternatives before construction starts. Expand one substation, but what happens to power flowing elsewhere? Reinforce one transmission corridor—does it relieve congestion or simply move it? Could a relatively modest investment eliminate the need for a much larger capital project?
Those are engineering questions the technical experts can now answer with much greater confidence than they could before because they’re able to evaluate the system before making any changes.
Artificial intelligence can also play a role by helping engineers assemble information from maintenance records, operating data, inspection reports and other systems quickly enough to evaluate more alternatives before committing crews and capital. Together, approaches like digital and AI capabilities make it possible to take a more systemic and increasingly real-time view of infrastructure performance.
For example, utilities can start with one upcoming investment decision, using digital and AI capabilities to bring together maintenance and inspection reports as well as operating data so teams can identify constraints across the network. Bring planning, engineering, operations and maintenance staff together to check those findings against firsthand experience, and use digital models to compare how different options would affect reliability, risk, crew availability, outage schedules or any other system-specific priorities before committing resources.
