Project Summary

The connection request queue is at least 723GW and growing, driven by lowcarbon technologies, renewable energy and new developments. Assessing the
overall network impact from this volume of applications is challenging, making it
difficult to assess available headroom and future investment needs. FastTrack, an
AI solution, aims to simulate the impact of both small and large-scale connection
requests using data on network capacity, load, and external factors, to present a
"rolled up" view of overall demand. This will provide DNOs with risk-weighted
insights to make faster, more informed decisions on future investments, helping
network planners prioritise interventions improving delivery times.

Innovation Justification

FastTrack builds new capability to address how DNOs assess the cumulative effect of how new connection requests "roll up" to impact the upstream system. To date, work has focused on streamlining specific aspects of the connections request process (e.g. quote generation: UKPN HV Auto Quote; process efficiencies from applicant's perspective: ENA Connect Direct, NGED Click2Connect), rather than to provide network planners/connections teams with tools to rapidly assess cumulative, risk-weighted impacts across the whole queue at GSP, which FastTrack would provide.

This 'system-view' of the impact of connections stack at GSP level would provide connections teams/network planners with new tools and capability to understand network risk - information that's currently missing with existing tools. Integrating diverse connection data into a unified model rolled up to GSP level, will facilitate better decision-making, potentially enabling additional connections, betteridentifying investment needs and improved information sharing with Transmission Operator.

Leveraging advanced AI techniques including probabilistic forecasting and simulation, FastTrack surpasses current state of the art by a) projecting aggregated impacts at substation-by-substation level and b) rolling these up to develop a probabilistic outcome at the GSP; an unmet need that isn't addressed by existing solutions; this is entirely new capability.

Currently, FastTrack is TRL2, anticipating TRL4 by the end of Discovery, demonstrating experimental proof of concept is feasible at Alpha; IRL and CRL will increase from Level 2 to Level 3 by the end of Discovery. Discovery allows us to efficiently identify user-needs, validate technical feasibility, and refine our solution design, to streamline solution build at Alpha. The solution would ultimately be designed to apply to the entirety of the connections queue.

SIF funding is crucial because this custom AI solution is at TRL2 and cannot be advanced under BAU. Its ambitious scope, including projecting the impact of the connections queue stack at the substation level and modelling the roll-up to GSP, makes it inherently risky and unsuitable for development under BAU.

Alternative approaches, e.g. expanding existing resource capability, were considered but found inadequate. A key benefit of AI solutions is they can be rapidly scaled across the network, in a way that existing approaches (e.g. bottomup substation-by-substation analyses) cannot. Existing tools lack aggregate, forward-looking impact assessments at GSP level; this cannot be addressed by repurposing existing tools or capability. An AI-driven approach is preferred for its inherent ability to incorporate diverse connection requests (and other data).

https://apply-for-innovation-funding.service.gov.uk/application/10142974/form/question/44109/forminput/123465/file/767370/download

Impacts and Benefits

Pre-innovation baseline

DNOs currently face significant challenges in assessing the impact of high-volume connection requests. Existing rules-based processes could lead to network capacity not being fully utilised, which could delay connection dates. A lack of tools that enable connections teams/ network planners to assess applications in the context of the cumulative impact across the network, make informed decisions on available capacity and investment needs more challenging, which can slow the release of capacity for new connections. Key baseline metrics include:

*Some customers offered connection dates in late 2030s;

*Connections queue for both Distribution and Transmission estimated to rise to
800GW by end-2024;

Forecasted benefits to energy consumers

Financial

*Future reductions in network operating costs: Improved connections forecasting enables more accurate, lower-cost forward investment. Probabilistic forecasting enables risk-weighted prioritisation of investment needs, making the network easier to monitor and maintain. Anticipated operational cost savings will be investigated at Discovery.

*Cost savings on consumer energy bills: Reducing deep reinforcement costs through accurate connections queue forecasting and investment targeting.

Environmental

*Direct CO2 savings: FastTrack may enable customers to adopt low carbon energy solutions earlier than the counterfactual world, enabling benefits from reduced carbon emissions to be realised earlier, improving chances of meeting GB's net zero targets. The accelerated and wider scale adoption of low carbon pathways by consumers will result in reduced direct CO2 emissions and direct CO2 savings per annum.

*Indirect CO2 savings: Potentially reduced requirement for network infrastructure investments and reinforcements may result in carbon emission savings from otherwise required construction works (embodied carbon emissions).

New to market

*Novel data analysis: Combining connection queue data with third-party data (e.g., macro-economic indicators like house prices) provides new insights into connection request behaviours, enabling DNO engineers to process applications more effectively.

Wider benefits

*UK Government has formally recognised the challenge of grid connections within Connections Action Plan with Ofgem. FastTrack directly supports delivery of this objective, as well as wider Net Zero ambitions.

*By improving connection forecasting to better assess impacts on not only the distribution system but the upstream transmission system should allow more lowcarbon technologies to connect sooner.

Example metrics for impact quantification

*Operational measures: Reduction in connection delivery times and increase in acceptance volumes (GWh), indicating FastTrack's effectiveness in driving efficiencies.

*Operating costs: Decrease in costs associated with connection driven investment relative to baseline.

*CO₂ reduction: Increase in LCT sites connected and their export capacity compared to prior years