As North America's electrical grid infrastructure continues to age, utility executives are under mounting pressure to make increasingly nuanced asset management decisions. The question of when to repair and when to replace is no longer just a matter of scheduling, it’s a strategic consideration with direct implications on capital planning, regulatory compliance, and long-term grid resilience.
Asset-intensive organizations like electric utilities are tasked with maximizing performance from legacy equipment while investing in modern infrastructure to support reliability and the energy transition. Making the wrong call, whether replacing too early or repairing too late, can carry significant financial, operational, and reputational costs.
Fortunately, the growing availability of real-time asset condition data is helping to shift this conversation. Utilities now have the opportunity to replace generalized life expectancy models with data-driven lifecycle strategies that extend asset lifespan, prioritize high-risk components, and align maintenance with measurable asset health, not just age.
The Flaws of Time-Based Lifecycle Management
Traditionally, utilities have relied on time-based maintenance and end-of-life assumptions to guide asset decisions. Transformers might be scheduled for replacement at 40 years, breakers at 25, regardless of their condition or usage. This approach offers simplicity, but increasingly, it lacks relevance.
Grid assets do not degrade in uniform patterns. Environmental exposure, loading variability, manufacturing differences, and maintenance history all influence how an asset performs over time. Two transformers installed in the same year can have drastically different failure risks after two decades in service. Yet under a purely time-based model, both are treated the same.
This not only leads to premature capital spending on assets that may have years of useful life remaining, but also leaves high-risk components in service far longer than is prudent, especially if their degradation is invisible until failure occurs.
Shifting to a Data-Driven Lifecycle Strategy
A modern asset lifecycle strategy is no longer just about timelines, it’s about real-time insights. With the deployment of thermal and visual sensors, remote monitoring platforms, and integration with SCADA and asset management systems, utilities can now track how each asset behaves in the field, 24/7.
This data allows planners and executives to make more informed decisions based on actual asset health, not averages. Rather than replacing an entire fleet of transformers at 40 years, utilities can selectively retire the 15% that show signs of thermal stress, corrosion, or phase imbalance, while keeping the rest in service, monitored and maintained, for several additional years.
Critically, a data-driven approach enables a shift from time-based to condition-based. Maintenance can be prioritized for assets trending toward failure, while non-critical or healthy units can be deferred. The result is better allocation of limited capital, fewer emergency outages, and improved asset utilization.
Challenges and Considerations for Executive Teams
Moving from calendar-driven to condition-driven asset management is a meaningful transition, and not without its complexities.
For one, it requires robust sensor infrastructure and reliable data streams. Utilities must also train planners and engineers to interpret condition data within the context of risk, criticality, and operating environment. Moreover, governance models must evolve. Asset decisions, once made on spreadsheet cycles, now require a collaborative process involving operations, engineering, risk, and finance.
Another major consideration is the alignment between capital expenditure (CapEx) and operational expenditure (OpEx). Repairing assets tends to fall under OpEx, while replacements are capitalized. This can create internal tension, especially when performance-based regulation or rate-case scrutiny incentivizes capital projects. Executives need to manage this balance carefully to ensure short-term budget decisions don’t undermine long-term resilience.
A Practical Framework for Decision-Making
To help guide these decisions, utilities can incorporate structured models that weigh condition, cost, and consequence. While these models are not a replacement for expertise, they can sharpen decision-making and bring transparency to lifecycle planning.
Condition-Based ROI:
Evaluates the trade-off between repairing now versus replacing later based on actual health, risk of failure, and potential cost impact of waiting.
Remaining Useful Life (RUL) Forecasting + Cost Indexing:
Combines sensor data with historical trends and procurement cost projections to forecast when an asset is likely to fail, and what waiting will cost in escalating prices, downtime, or service penalties.
Asset Prioritization Matrix:
Ranks assets by condition, criticality, and cost exposure to help executives focus spending where it delivers the greatest reliability and risk mitigation.
These models support a measured, intelligent approach to asset decision-making, helping utilities plan replacements when necessary, extend asset life where possible, and justify those actions to regulators and boards with confidence.
Extending Asset Life Without Compromising Reliability
The average age of transformers in many North American grids now exceeds 40 years. Replacing them all is not financially or logistically realistic. What is realistic and necessary is a smarter strategy that uses condition-based insights to stretch asset value while maintaining reliability.
By investing in real-time monitoring and adopting structured asset health frameworks, utilities can reduce unnecessary replacements, better plan for capital needs, and limit the likelihood of catastrophic failures. More importantly, they gain the ability to adapt, to build a grid strategy that evolves with real conditions rather than rigid timelines.
For executives tasked with planning the next decade of grid modernization, this isn’t just a cost management exercise. It’s a shift toward asset stewardship that supports resilience, transparency, and long-term value.