Before the energy transition, planning in the low-voltage network was comparatively straightforward. Demand profiles were stable and predictable, and transformers were installed with substantial capacity buffers. According to demand forecasts at the time, those capacity limits were unlikely ever to be approached. The situation today is markedly different. The ageing of assets coincides with rising demand from heat pumps, electric vehicle charging and other electrification trends. At the same time, consumers have increasingly become prosumers, feeding power into the network through residential PV systems. Together, these developments are driving low-voltage transformers closer to their capacity limits.
Operational challenges are intensified by workforce constraints and extended procurement lead times for new transformers, making asset replacement more complex and less flexible. As a result, grid operators require far more precise assessments of the remaining useful life (RUL) of their assets. Yet many low-voltage transformers – originally designed to operate for 50–80 years – still run without any meaningful condition data. With limited visibility of thermal behaviour or asset condition, planners must rely on conservative loading assumptions and age-based replacement limits, often leading to premature retirements or unexpected failures. Figure 1 outlines the typical mechanism behind conventional replacement decisions.
A recent pilot project undertaken jointly by a distribution system operator (DSO) in Germany and Danish company Oktogrid explored a new approach to estimating RUL. By combining continuous measurements with CNAIM probability of failure modelling and long-term load scenarios, Oktogrid was able to present a data driven approach to estimating RUL and facilitating replacement planning for the operator.
CNAIM (Common Network Asset Indices Methodology) was jointly developed by all six UK distribution network operators and recognised by UK energy regulator Ofgem. It provides a standardised method for evaluating asset condition and probability of failure, based on inspection and maintenance data from more than 10 000 distribution transformers.
Non-invasive monitoring, condition and performance data, and probability of failure
To better understand the condition and utilisation of its low-voltage transformer fleet, the German DSO equipped ten 10–20 kV/0.4 kV transformers with Oktogrid’s non-invasive monitoring system. Over an eight-month period, around 2.7 million measurements were collected for the entire fleet. The monitored parameters included:
- transformer load;
- dynamic thermal rating (according to IEC 60076-7);
- top-oil and hot-spot temperatures;
- total harmonic current distortion;
- vibration response;
- acoustic sound level;
- partial discharge activity (ultrasonic emissions); and
- ambient temperature and humidity.
Using those parameters it was possible to estimate the performance and condition of the transformers.
This condition information was then incorporated into the CNAIM model to calculate the future probability of failure (PoF) for each transformer. Condition data was combined with static factors such as age, installation environment and inspection history. CNAIM provided a structured framework to quantify health indices and establish RUL. The inclusion of condition data allowed a refined PoF calculation beyond conventional age-based assessments.
Taking account of future loading
Because condition alone does not determine replacement timing, Oktogrid also evaluated future load development using Scenarios A (light rise in energy demand) and C (substantial rise in energy demand) from the German grid development plan (Netzentwicklungsplan) 2037/2045. These scenarios were extrapolated to 2075 to reflect long-term trends in energy demand. Historically, many DSOs define end-of-life at approximately 100% rated load. However, thermal modelling based on real-time top-oil and hot-spot temperature indicated that actual dynamic thermal rating could be significantly higher. The ten monitored transformers showed:
- minimum additional capacity: +8%;
- maximum additional capacity: +26%.
This difference reflects the gap between nameplate-based planning assumptions and the true thermal state recorded in operation for the lowest observed additional capacity per transformer. Figure 2 displays the utilisation prognosis for a selected transformer with rated and dynamic thermal rating limit.
The analysis then combined:
- CNAIM-derived probability of failure rates; and
- future grid development plans.
For every transformer, the calculated replacement year corresponded to whichever criterion reached its limit first — either the projected probability of failure or the allowable thermal loading.
This combined assessment offered a more accurate view of RUL than either input alone or traditional age-based replacement method.
Figure 3 shows the different replacement scenarios for a selected transformer.
Result: 141 additional years of operation
Under Scenario C (high growth), the analysis showed that the ten monitored transformers could collectively provide 141 additional service years beyond conventional replacement planning.
This uplift represents the difference between a traditional age- or rated-load-based retirement decision and a data-driven assessment of transformer condition and dynamic thermal loading limits.
The pilot project highlighted several operational benefits for DSOs:
- Enhanced asset lifetime. Condition-based insights allow transformers to remain in service longer, deferring capital expenditure and reducing demand on supply chains.
- Increased network capacity. Accurate thermal assessment enables higher utilisation of existing assets, a critical factor in constrained low-voltage networks.
- Improved investment planning. Combining load forecasts and health indices provides a more stable foundation for long-term network development.
The method illustrates a broader trend in distribution networks: the shift from time-based maintenance towards data-driven asset management. By capturing the real operating environment of transformers, DSOs can refine PoF models, adjust loading strategies and align replacement timing with actual need rather than fixed age thresholds. This becomes increasingly important as Europe’s low-voltage transformer population approaches critical age and as electrification accelerates. Digital monitoring offers a means to optimise existing assets while supporting long-term reliability.
Unlocking hidden capacity and extending life
The German pilot project shows how continuous transformer monitoring, paired with established modelling frameworks and long-term planning scenarios, can provide DSOs with a clearer, more actionable understanding of asset health and utilisation. With 141 additional transformer-years identified across only ten units, the potential system-wide impact is substantial.
As grid operators seek to balance reliability, cost efficiency and sustainability, real-time monitoring offers a practical pathway to unlock hidden capacity and extend the life of ageing infrastructure. With growing operational complexity across distribution networks, the value of accurate, real-time data will only increase.
