Optimised assets and practices

SGN Innovation is exploring how to support its operational colleagues in implementing tools and practices to mitigate emissions coming from pipeline operations. This work is driven by SGN’s GD3 Environmental Action Plan which sets a target to reduce operational carbon emissions by 46% relative to the 2019 baseline with a long-term target of net zero by 2045.

Whilst the Environmental Action Plan and the Network Asset Management Strategy articulate what SGN will invest in there be an “operational pathway” in how these ambitions translate into day-to-day operational decision-making as well as ensuring that the competing requirements on operational staff (efficiency vs emissions reduction for example) are addressed and appropriate guidance and prioritisation given.

This project creates a pragmatic approach to bridge the identified operational gap by establishing a decision framework and playbook for commissioning and decommissioning pipelines that embeds gas drawdown as a default activity with the potential if successful to expand to other areas of implementing the Environmental Action Plan. The aim is to enable SGN’s and other Network workforce to deliver consistent efficient emissions reductions across GD3.

To support the UK achieving net zero by 2050 there is a need to decarbonise the current gas networks of transmission and distribution levels. The conversion of the NTS into a hydrogen transmission network has been widely discussed and extensive work is underway to prove the technical capability and commercial viability of a 100% hydrogen network. There is also additional work to support the governments clean power targets and a three-molecule approach has been adopted within National Gas to consider (bio)methane hydrogen (including hydrogen blends) and carbon dioxide.

The gas networks need to be prepared to operate and safely manage the transportation of all three molecules especially with the ambition to develop a 100% hydrogen network in the future upskilling and training the current workforce and the workforce of the future is a fundamental step to ensuring the facilitation of the energy transition.

Identifying the skills and competencies required both during the transition and after the transition to maintain the future systems was discovered in the Skills and Competencies NIA that closed in Q4 2023. A competency framework was developed that will provide a baseline for the training and resourcing strategy proposed for operational and technical skills and competency requirements for current and future workforces.

The project produced a comprehensive plan to identify the known gaps and to provide a roadmap for key developments of standards and policies which will drive the training and competency needs. Furthermore it identified potential training facilities to support the development of the plan and ultimately facilitate rollout. The project also enabled a large-scale training and competency programme to be developed alongside the relevant technical standards and policies in readiness for deployment to the relevant engineers.

National Gas would therefore like to understand how AI tools can be used to accurately and efficiently produce training materials and create a more effective personalised training experience.

We’re developing a low-cost easy-to-install solution to measure gas flow at regulator stations. The goal is to keep the equipment as simple and non-intrusive as possible.

To measure the flow we’ll use two methods:

• One method checks how open the regulator is and the pressure difference across it to estimate the flow.
• The other uses a small sensor that creates a slight temperature change at the outlet which also helps estimate the flow.

By combining these two methods with the regulator’s technical details we aim to measure the flow with an accuracy of about ±10%.

Gas networks supply embedded power stations that support the electricity network. These embedded generators can fire up without any warning to GDNs and is causing significant challenges to gas networks.

GDNs are required to submit hourly gas demand nominations to National Gas for each offtake point within specified time deadlines.

Embedded generators are small. They are not included in the UNC’s requirements to notify their GDN of intended offtake activity due to their size being below the threshold for NExAs (network exit agreements). Despite this GDNs must include the demand from these embedded generators in their nominations to ensure there is sufficient gas within their network. This causes numerous challenges for SGN and other GDNs.

GDNs’ current forecasting process does not specifically forecast embedded gas generation and current models do not take inputs from the electricity market. Embedded generators act in a variety of electricity markets yet GDNs don’t have visibility of this demand.

It is anticipated that additional embedded generators will connect in the coming months/years as the demand for electricity increases.The challenge of not having knowledge of embedded generator’s demand and its potential to contribute to a storage shortage has been acknowledged by both EGRIT (Electricity and Gas Resilience Task Group) and NESO (National Energy System Operator). The benefits of creating a notification platform supported by a ML engine are various. Namely to develop an ML-enabled forecasting tool to predict gas demand from embedded generators with increased accuracy as delivery time approaches. In addition to create a notification platform to improve real-time visibility of embedded generator activities within the electricity and gas networks.

This NIA project aims to progress the FEmGE forecasting tool from TRL 1 to TRL 7 delivering a fully functional MVP. NGN will be funding this project to the value of £92333 and SGN to £184666 of the total of £276999.