Navigating the new energy landscape
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COMMON STANDARDS: DIGITAL TWINS AND DATA INSTITUTIONS
For instance, data from electric vehicles (EVs) could benefit transport planning. Industry buy-in to the idea of digital twins is high – but there’s a problem. Currently, businesses are all following their own paths. For digital twins to facilitate data sharing, Enzer explained: We all need to be speaking the same language. Semantic precision enables consistency of data that enables frictionless sharing. Therefore, the Task Group is working to develop common standards. This is likely to be achieved via a combination of market forces and regulation. Enzer anticipates that to create a fully optimised National Digital Twin network is a 30-year, multi-generational programme, but is simultaneously working towards a shorter- term 3-year roadmap too, to demonstrate the worth of the initiative. Other tools outlined at the event to help develop an appropriate infrastructure for sharing data included ideas such as data catalogues, libraries, co-operatives, trusts or other kinds of data institutions which would increase data access while reducing the costs of data stewarding. Again, this requires a concerted effort to make data shareable, before it can be shared.
The adoption of common standards for the use of data and the inter-operability of digitised models is vital if the energy industry is to reap the benefits of this technological revolution. Nowhere is this more obvious than in the development of “digital twins” – a new tool which is fast gaining traction as its benefits become clearer, said Mark Enzer, Chief Technical Officer at Mott MacDonald and chair of the Centre for Digital Built Britain’s Digital Framework Task Group. A digital twin is a digital representation of an asset, for instance from a single solar panel, to a whole solar energy farm or even an entire power network. The purpose: to optimise assets via a feedback loop. Raw data from the physical asset is fed into the digital “twin”, which can for instance use AI or machine learning to create sophisticated algorithms or simulation engines, to deliver insights that can help make better decisions around the physical asset, creating better outcomes. Ideally, these digital twins will ultimately feed into a national ecosystem of connected digital twins, to add even more value by opening up access to information which can be harnessed by the whole energy sector – or even other related sectors, creating better outcomes on a much bigger scale.
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