Future Energy Networks

This project involves a comprehensive set of tasks aimed at implementing and validating a domestic safety system for hydrogen use, including excess flow valves.

As the Gas Transmission network responds to a changing energy system, from drivers including the transition to net zero and to changes in supply and demand, we are required to decommission our large site based assets in certain locations. Decommissioning is a multifaceted endeavour that goes beyond the conclusion of an asset’s lifespan and encompasses a complex deconstruction process. This project will implement an innovative AI tool to help National Gas manage decommissioning to drive benefits such as increasing the accuracy of cost estimation, ways to reduce carbon emissions, identify re-use potential and lower the overall time taken to decommission.

UK’s Net Zero Emissions Target and the Role of Hydrogen: The UK has committed to a legally binding net zero emissions target by 2050. Achieving this target necessitates the integration of hydrogen, particularly in hard-to-decarbonize industrial applications and peaking power generation. The recent publication of the Climate Change Committee’s Seventh Carbon Budget highlights hydrogen’s significant role within the electricity supply sector. Hydrogen is identified as a crucial source of long-term storable energy that can be dispatched as needed and as a feedstock for synthetic fuels. For hydrogen to fully contribute to a future hydrogen system, its production, storage, and transportation must be considered collectively.

East Coast Hydrogen (ECH) Project: In recent years, Cadent, in partnership with National Gas and Northern Gas Networks (NGN), has developed the East Coast Hydrogen (ECH) Project. The ECH project aims to decarbonize primarily industry and power sectors. As part of this initiative, Cadent has developed the East Midlands Hydrogen Pipeline (EMHP), which aims to connect hydrogen production at Uniper’s Ratcliffe on Soar site to major industrial and power off-takers in the East Midlands. The project seeks to transport hydrogen to major population centres, including Nottingham, Leicester, Melton Mowbray, Derby, and Burton upon Trent. During the development of the EMHP, it became evident that hydrogen storage plays a critical role in establishing a resilient and efficient hydrogen system. Consequently, a consortium was formed to explore the feasibility of storage, leading to the East Midlands Storage Project (EMSTOR).

Discovery Phase of EMSTOR: During the Discovery Phase, EMSTOR evaluated various technologies for large-scale hydrogen storage in the East Midlands. The technologies considered included lined rock caverns, lined rock shafts, silos, and geological storage options such as aquifers and disused hydrocarbon fields. After comparing these technologies against several technical parameters, including Technology Readiness Level (TRL), cost, size, and location relative to pipelines, it was determined that hydrogen storage in geological fields, particularly disused hydrocarbon fields, is the most viable option. Therefore, disused hydrocarbon fields in geological formations were selected for further consideration in the Alpha Phase.

Alpha Phase Consortium: To execute the Alpha Phase, a consortium led by Cadent and including Star Energy Ltd, Centrica Energy Storage, National Grid, British Geological Society, University of Edinburgh, and Uniper was established. This consortium will focus on advancing the feasibility and implementation of hydrogen storage in disused hydrocarbon fields.

Develop a flexible and adaptable toolset for the rapid analysis of Local Area Energy Plans (LAEPs). This will convert qualitative statements to quantified metrics and identify key network specific planning parameters.

In metering applications, Equations of State (EoS) are mathematical models that are used to convert measured volumes to standard units. This enables transfer from volume to mass, allowing customers to be billed and for the networked to be balanced in energy. Metering and network balancing cannot be performed in volume, as it doesn’t account for relative, varying gas component concentrations – and therefore CV.

The EoS currently used (AGA8) is acceptable for up to 5% hydrogen, but after this point it’s uncertainty is unknown – meaning the network may be unable to maintain accurate billing or system balancing. This project will obtain experimental data for a range of net zero gases and compare the output of several EoS for accuracy against real, measured, NTS-representative conditions.

This project will help to inform UK Gas Distribution Network Operators (GDNOs) and wider industry on the long-term suitability of existing Excess Flow Valve (EFV) designs in a future where hydrogen is being distributed in network pipelines. A risk to normal EFV functionality exists in the event that an ignition occurs within the downstream gas installation pipework and this project will help to understand the effectiveness of existing EFV designs to manage this risk, identifying any necessary modifications to existing EFV designs where appropriate.

This innovation project proposal is centred on trialling the development of a predictive model to identify customers in vulnerable situations whose heat comes from Cadent delivered gas that are missing out on the protections that the Priority Service Register (PSR) brings because they are “hidden” behind a non-domestic supply contract. The aim of the predictive model would be to aid Cadent to find these customers so that it can be ensured that they receive the support that they need in the event of an interruption to supply.

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 £92,333 and SGN to £184,666 of the total of £276,999.

The project will develop an integrated hierarchical network modelling framework for simulating the operation of future GB energy system scenarios with highly interconnected gas and power networks. The realistic modelling of power-to-gas and storage operators’ behaviour will be emphasised. The integrated models will be demonstrated on a simulation platform as real-time digital twins for future system scenarios.

Considerable novelty will lie in the combination of modelling scale and granularity; representation of many autonomous decentralised agents making sub-optimal decisions; and the optimal resolution of dilemmas arising from the finite energy budgets constraining primarily weather-driven low to zero carbon scenarios.