We are at the doorstep of an energy crisis trilemma- security, affordability, and sustainability of power generation and distribution sources. Furthermore, exogeneous factors like weather fluctuation can also put the national energy system under pressure. Limited non-renewable stock, insufficient renewable storage, local market disturbances and relatively low investment in the hydrocarbon sector causes additional imbalance. Such fiscal stresses limit the timely accomplishment of investment programs for smooth energy transitions. However, as simple as it may sound, energy transition is neither easy and cheap, nor stable at the moment.
At the time of this article, rising energy prices in Italy illustrated the tension between affordability, security of supply and decarbonization. Expanding low-carbon generation also requires adequate networks, storage and flexibility. Asset management is one part of that system, and it should be judged against the service it can realistically support.
Generation costs depend on fuel, plant utilization, capital and operating costs, among other factors. Feed-in tariffs are a support mechanism, not a complete measure of those costs. Technologists can contribute by improving asset availability, investigating developing faults and helping plan interventions. That is where the relationship between maintenance and reliability becomes practical.
Firstly, maintenance and reliability are NOT the same thing. Just because something is well-maintained does not mean it is reliable. If something has been reliable up until now does not mean you can slack on the maintenance part of it. Interestingly, the root cause that makes people to often interchange these terms on the basis of “life-extension” guarantee. As ironic as it may sound right now, life-extension itself is a misnomer. We often talk about the bathtub curve of asset reliability. The more resource we spend at maintenance the more we expect the curve to elongate. But is that how it really works?
Companies develop risk functions to connect asset decisions with operational consequences. In a simple quantitative model, risk combines the probability of a defined event over a stated period with its consequences. A more severe consequence increases the risk estimate; an exponential relationship should not be assumed. The calculation needs traceable inputs, explicit assumptions and an account of uncertainty.
A risk function for the energy sector can identify asset improvement opportunities using overall asset health status. This can enable criticality analysis and definitely become a decision support tool. It can reduce negative consequences of unplanned downtime and provide better control in resource allocation for the service provider.
This is where asset indexing strategies, including transformer health indexing models, can help organize diagnostic evidence. DGA and moisture measurements inform assessment of the oil-paper insulation system, but they do not provide a fixed percentage of insight into every failure mode. Main-tank oil does not directly represent a sealed bushing or a separate tap-changer compartment. Those components require their own diagnostic evidence. Combining condition data with asset criticality and estimated outage consequences can support fleet prioritization and repair or replacement planning, provided the uncertainty remains visible.
Smart analytics and artificial intelligence can help make that evidence usable. Their value depends on the decisions they support, the failure modes they cover and the gaps they leave for inspection and testing.
