Britain’s energy system is transforming as it benefits from the advances of digital transformation.
Data sharing has typically enabled organisations and businesses to unlock greater value, improve existing services and devise new solutions to wider challenges.
Now it is time for the energy system to adapt and maximise the power of data.
Promoting data sharing in energy can drive greater efficiency, collaboration, and innovation across the sector.
Sharing data more widely across stakeholders will also accelerate data democratisation within the industry, offering greater transparency as we seek to co-create solutions alongside industry partners.
© Supplied by National Grid ESOPowered by the ESO, the Virtual Energy System will facilitate the creation of an ecosystem of connected digital twins across Great Britain’s entire energy system, working as interoperable digital assets in synchronisation with the physical system.
Interoperability is crucial, without it, data cannot be shared between digital twins, leading to sub-optimal decisions within data silos.
The Virtual Energy System will realise its full potential through seamless data sharing, ensuring the most comprehensive modelling possible.
As more and more data is shared within the system, it can deliver increasingly efficient, whole-system, data-driven decision-making, resulting in better overall outcomes for society, the economy, and the environment.
Delivering on the potential of the Virtual Energy System
The Virtual Energy System will serve as a vessel for every element of the British energy industry to publish its data onto a decentralised system.
With unprecedented access to information providing oversight of the entire industry, we can facilitate and inform decision-making, supercharging ideas to cut real-world carbon emissions, drive efficiency and lower costs.
The insights generated by the Virtual Energy System will also enable actors to develop new market opportunities and drive innovation, ensuring decision-making processes keep pace with industry changes.
Delivering the Virtual Energy System is a socio-technical challenge, one that will require a collaborative and principled approach.
To support this, 14 socio-technical factors have been identified to ensure we have a shared vision of success.
Among these, six priority factors will shape how we build a strong governance model that upholds the needs of the industry and how we encourage policymakers to develop regulations supporting a unified digital energy system.
The priority factors will also be instrumental in shaping how the industry builds data models that can communicate on a machine-to-machine level and how we ensure interoperability so all data can communicate as part of the Virtual Energy System in a transparent and secure way.
Through the six priority factors, actors will be empowered to embrace the power of a digital energy system, helping craft strong, actionable use cases that leverage data to improve how we manage Britain’s energy.
Putting shared data into practice
It has been reported that the volume of unstructured data within the energy industry alone is growing by up to 65% each year.
Without system interoperability, siloed data can result in incomplete datasets and reduced visibility, hindering cross-industry collaboration and progress.
As the Virtual Energy System drives greater interconnectivity, it will enable more detailed scenario-building and forecasting to better inform decision-making across the entire energy ecosystem.
With each new use case built, the additional data shared within the Virtual Energy System creates the opportunity to deliver more accurate insights, so the system is constantly improving.
Use cases such as the Advanced Dispatch Optimiser (ADO), which will process large quantities of data, can help improve dispatch decisions within our transforming energy system.
The ADO will facilitate the management of increasingly complex grid operations through adaptive input models and machine learning.
As part of the Virtual Energy System, the ADO will then be able to aid the storage of data in a performant, secure and scalable way for analytical and simulation purposes.
In doing so, it will help grid operators plan for a range of potential system scenarios, improving decision-making about grid flexibility and security.
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