go to top scroll for more

Distribution Network Information Modelling (DNIM)

Reference Number
ENA_10052119
Title
Distribution Network Information Modelling (DNIM)
Status
Completed
Energy Categories
Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas)
Research Types
Applied Research and Development
Science and Technology Fields
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)
UKERC Cross Cutting Characterisation
Not Cross-cutting
Principal Investigator
Project Contact
SGN
Award Type
Network Innovation Allowance
Funding Source
Ofgem
Start Date
01 April 2023
End Date
01 June 2023
Duration
2 months
Total Grant Value
£15,234
Industrial Sectors
Energy
Region
South East
Programme
Investigators
Principal Investigator
Web Site
Objectives
Abstract
"Meeting the SIF challenge: This project meets all 4 of the aims of improving energy system resilience and robustness category by:By accurately mapping our network, DNIM will enable future challenges and risks to be identified in a quick and cost effective manner. This will help create a more resilient network that can adapt to the energy transition.Technology developed under the DNIM platform includes Artificial Intelligence and Machine Learning as well as robotic automation hardware. These technologies offer significant opportunities to facilitate hydrogen and heat energy system configurations for example.Improving our operational activities as we transition to net-zero whilst reducing impact to customers. With DNIM surveying the network autonomously without excavations, the system offers the gas networks a chance to improve resilience and robustness for a future green gas scenario in a sustainable and relatively clean manner.Overall strengthens our operation activities by having a better understanding of our assets and their precise locations. With this knowledge and the data collected, condition of the energy system and how those changes with future energy system configurations will be able to be analysed and evaluated.SGN: SGN is one of the largest utility companies, distributing natural and green gas safely and reliably through our 74,000km of pipes to 5.9 million homes and businesses across Scotland and southern England. We are committed to exceeding the expectations of our stakeholders by delivering value for money and exceptional customer service as well as providing a safe, secure and sustainable future for our network.SGN are the lead participant in this project and will provide clear direction and insight to the project partners. SGN will also provide insight and expertise from a gas distribution network perspective for the project, ensuring alignment to the challenge area and realisation of benefits to be captured.ULC Technologies: ULC Technologies has over 20 years of experience developing robotic solutions and deploying them as services using their field teams. ULCs team includes engineers (mechanical, electrical, software, robotics), research scientists, and technicians. This enables ULC to tackle highly complex and multi-functional problems with innovative solutions. These solutions may then be driven deployed in-house using ULCs extensive field teams which has had success deploying robots in the UK and the US for over two decades."
Data

No related datasets

Projects

No related projects

Publications

No related publications

Added to Database
18/10/23