SP Energy Networks and Digital Catapult, in a consortium with University of Strathclyde and National Grid ESO, have received funding from Ofgem’s Strategic Innovation Fund (SIF) to explore how an innovative, open and interoperable digital twin of the UK’s electricity transmission and distribution networks can aid decision making when managing and balancing energy resources and assets.
The project will also help to improve understanding of the potential role of advanced digital technologies in achieving the UK’s net zero targets.
The discovery project will determine the “art of the possible” based on currently available technologies, as well as outlining the use case and minimum viable product for a digital twin of the electricity transmission and distribution networks.
Digital twins are systems that gather or present data from the physical, ‘real’ world which is then used to build a model in the digital world (‘the twin’) that carries out analyses and enables smarter decision making in the physical world.
For complex systems – like the electricity network – digital twins offer the potential for dynamic and automatic decision making in operation, planning and forecasting, helping to cut costs, waste and improve assets’ lifecycles.
As part of this project, development of a digital twin of the electricity transmission and distribution network will allow the partners to understand the complete system in real time, providing the ability to visualise – and simulate – how and when the electricity transmission and distribution network is being used, in order balance the system in the most optimal, safe, and cost-effective way.
The initial phase of the project (Discovery) is the first of three in the SIF process, identifying relevant use cases, functional requirements as well as current and future sources of asset data at different voltage levels to assess the feasibility of developing a simulated digital twin that can run ‘what-if’ scenarios and support national transmission system (NTS) balancing decisions by National Grid.
Subsequent phases will build on these learnings to develop a proof-of-concept (Alpha phase) to further understand and de-risk a larger scale trial that we will aim to develop in the final SIF (Beta) phase.
