I-LAD: Innovating Losses Analysis and Detection (SIF Round 4)

Project Overview

Description

Electrical losses are an unavoidable consequence of transferring energy across electricity networks, where they have financial and environmental impacts, and are forecast to increase significantly as we accelerate electrification of heat and transport.

Technical Losses, while inevitable, are directly dependent 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 Non-Technical Losses 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.

Expected 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 Non-Technical Losses.

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 £4m pa, 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.

Progress

This project is due to start in February 2025.

Funding

Total Cost – £175,005
SIF Funding – £149,167

Start/End Date

3rd February to 30th April 2025