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Reference Number ENA_10037416
Title Intelligent Gas Grid
Status Started
Energy Categories HYDROGEN and FUEL CELLS(Hydrogen) 5%;
FOSSIL FUELS: OIL, GAS and COAL(Oil and Gas, Refining, transport and storage of oil and gas) 90%;
RENEWABLE ENERGY SOURCES(Bio-Energy) 5%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 75%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
SGN - Southern England
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 August 2022
End Date 01 February 2023
Duration 6 months
Total Grant Value £601,426
Industrial Sectors Energy
Region South East
Programme
 
Investigators Principal Investigator Project Contact , SGN - Southern England (100.000%)
Web Site https://smarter.energynetworks.org/projects/ENA_10037416
Objectives
Abstract Although the project has relevance to the challenges on whole system integration and heat, its scope is most clearly associated with the challenge for data and digitalisation:Automated pressure management software, and the use of near real time data and machine-learning techniques, will contribute to better coordination, planning and network optimisationIncreased injection of biomethane and hydrogen into the network will enable progress towards net zero and enable strategic outcomes from other challenges e.g., decarbonisation of heatThe project directly addresses as its primary focus points 7 and 9 in the challenge scope definition:Point 7: this project will use novel sensor technology to improve visibility of the condition of network infrastructure and make data-driven decisions about that infrastructure.Point 9: this project will use data, combined with machine-learning and AI techniques, to improve the forecasting abilities of both demand on the network, and required maintenance and interventions.The principal innovation underscoring the project is use of data-driven techniques based on AI and machine-learning to address each Opportunity Area (OA). These would constitute novel methods which, combined with modular dashboards that integrate the solutions with data analytics, will help SGN continue its positive journey to delivering a digitalised network.During Discovery, the project evolved by researching network user needs, identifying underlying motivations and enabling deeper understanding of the opportunities. This allowed the refinement of problem statements, outline AI solutions, and impact and complexity assessments. During Alpha, progress will continue by refining benefits cases and undertaking bench-testing of solutions ahead of Beta field trials.The solutions address the challenges in several ways:ML/AI models optimise the pressure in the Low Pressure (LP) network to reduce leakage.In the Medium Pressure (MP) networks, the models optimise the injection of biomethane enabling progress towards net zero.Data from the Utonomy system is used for network anomaly detection leading to improved maintenance. This data is combined with other sources of data to predict the distribution of reported escapes.A dashboard is used to display relevant data and KPIs. This enables operators to make faster and more effective interventions.Utonomy will be the main project partner for Alpha. The Utonomy engineering team has capabilities in electronics design for hazardous areas, data science and machine learning, industrial IoT and digital communications technologies, cyber security, and cloud-hosted software applications. Utonomy has already collaborated successfully with SGN and Wales & West Utilities on developing, trialling and proving remote pressure control & management technology. Utonomy has carried out initial field trials with SGN of its Intelligent Gas Grid Control software concept, developed via an Innovate UK funded project completed in March 2022.Utonomy will use Faculty Science Limited as lead subcontractor; who are uniquely placed to deliver state-of-the-art AI solutions from teams formed from over 200 professionals comprising both technical and commercial experts. In delivering AI solutions, in-house developed AI Engines allow specialised techniques to be applied to customer problems and to optimise performanceThe solutions will be primarily used by two sets of users; the network maintenance team, responsible for managing pressuresand undertaking maintenance, and the network planning team, responsible for overall network planning, performance and analysis.The maintenance team will use the solution to adjust governor pressures remotely, and automatically, to minimise leakage and optimise biomethane feed-in. They will also use the data to diagnose and resolve network or asset faults more quickly. The planning team will use the solution to track KPIs such as leakage reduction or biomethane injection. They will also take decisions based on data and analysis provided by the solutions.
Publications (none)
Final Report (none)
Added to Database 14/10/22