Project Summary

Electrical Losses are the difference in the energy that enters the electrical network and that which reaches consumers premises. Without intervention losses are forecast to increase with increasing electrification. They not only directly impact on customer's bill and our carbon footprint, but losses due to theft often lead to serious safety incidents. Current methods of identifying and monitoring losses are outdated and inefficient. The I-LAD project will utilise modern data techniques to improve: automating and modernising losses data collection, identification and modelling, understanding of total losses landscape improving cross sector coordination of losses interventions automating ongoing losses monitoring and measurement.

Innovation Justification

I-LAD addresses Challenge 4 - Accelerating towards net zero (...), specifically: Develop solutions to reduce efficiency loss (...).It's currently impossible to fully understand true level, location and source of losses across the energy systems, directly impacting on Customers' bills.TL, while inevitable, are directly dependant on demand at the time. Current technologies only allow for estimations of losses, making it challenging for DNOs to target losses reduction technologies effectively. While NTL are somewhat preventable, current identification methods are piecemeal, rely on 3rd party intelligence, use of tools like google maps and manual information exchange between all parties. Additionally, no tools exist to monitor all losses in real/near-real time. Improvement in losses management requires a radical new approach rather than refinement of existing processes. I-LAD proposes to address this by:

1.Developing novel approaches to automating and modernising losses data
collection, identification and modelling: e.g., currently energy theft is difficult to
detect, and even when it is, by the very nature of bypassing the meter, DNOs
won't have a load profile dataset from a smart meter which can be used train a
model. To address this, we will use techniques like synthetic data (where very
small amount of "real" data available is used to generate "fake" examples of NTL
to train the model). Those techniques have been applied in other industries (e.g.
fraud detection) but are untested in this context.

2.Fully understanding total losses landscape using model developed in item 1
create a new digital service allowing DNOs to automatically identify, model and
record losses at a granular level through innovative use of Machine Vision, AI and
Machine Learning and traditional analysis of network data.

3.Novel approach to losses governance: develop new data sharing options to
address data privacy barriers (GDPR) and drive improved cross-sector
collaboration. Improved visibility of losses will give better clarity on their sources
and who is best placed to address them.

4.Automating losses monitoring and measurement: to evidence effectiveness
of losses mitigation. This will be unique and will help reflect the dynamically
changing network we expect during the energy transition.
Use of complex, sensitive data and novel data techniques is risky and innovative,
making this project well suited to SIF criteria.
The project builds on previous technology and innovation with a reasonably high
TRL level 6 and IRL level 4 with core innovation looking at processes and
algorithms that could bring it to commercial readiness.

R4SIF_Discovery_ILAD_Appendix_FINAL.pdf 

Impacts and Benefits

This project will deliver tools to identify, classify and monitor electricity losses more efficiently using novel data and modelling techniques, resulting actions and coordinated losses interventions, leading to measurable losses reduction at a higher level than is currently achieved. It's estimated that losses account for 5% - 8% of the total distributed units, costing
a typical household around £100pa and accounting for around 90% of a DNO's total Greenhouse Gas emissions. Whilst already high, losses are expected to significantly increase in coming years from electrification of heat and transport, increasing volumes of low carbon technology altering and increasing power flows and heightened cost of living pressures impacting the levels of NTL. Although one of the key outcomes of this project will be full quantification of the possible value of benefits of improved losses identification and coordinated intervention methods, we know that a reduction in losses will provide two main
streams of benefits: Financial: the greater the losses, the greater the costs to customers through their electricity bills. This is due to having to generate more electricity to cover losses.
Therefore, losses reduction will directly contribute to a relative reduction on customers' bills.
E.g., It is estimated that total annual losses due to theft across the networks are approximately 2.2TWh. The largest source of theft is believed to be cannabis farms, accounting for around 0.75TWh. A methodology which improved their detection allowing even 10% of them to be removed would save a staggering 75GWh. Using standard values in Ofgem's ED2 CBA this equates to a saving of over £4mpa, and nearly 20,000 tCO2 Environmental: The greater the losses, the greater the carbon emissions and environmental impact to society. This is due to losses representing fuel consumed and emissions produced in the process of electricity generation. Other: Reducing different sources of losses can have widely varying social benefits. E.g., theft can be associated with significant health and safety risks. Illegal modification to the network is often linked to other illegal activity, therefore
better identification of these may support identification. While reducing losses can lead to substantial benefits (including bill savings for consumers and emissions reductions), however, they are notoriously difficult to identify. For example, in its RIIO-ED2 final determination, Ofgem removed the Losses Discretionary Reward in part due to the challenges in accurately measuring activities to reduce losses. The techniques developed as part of I-LADwill help mitigate this issue.