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What is the minimum required (and desired) data standards and processes are required to enable AI/ML to effectively engage with Energy System (e.g. optimising millions of distributed assets with greater precision thus reducing the reliance on blunt capacity market instruments)?

Background

BEIS has committed to ending the UK’s contribution to global warming by achieving net zero greenhouse gas emissions by 2050. Our work towards becoming a leader in green technologies and clean energy will drive economic growth, all whilst accelerating global climate action through strong international leadership.

Next steps

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Source

This question was published as part of the set of ARIs in this document:

Beis areas research interest interim update 2020

Related UKRI funded projects

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    Why might this be relevant?

    The project develops an AI-powered digital twin of the UK's electricity grid, providing better visibility of energy demand and network constraints, which can help optimize distributed assets and reduce reliance on capacity market instruments.

  • Supergen Network Plus in Artificial Intelligence for Renewable Energy (SuperAIRE)

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  • Manchester Metropolitan University and Oak Tree Power Limited KTP 22_23 R3

    To develop novel Artificial Intelligence and Machined Learning capabilities for a Sustainable Energy Management System....

    Funded by: Innovate UK

    Lead research organisation: MANCHESTER METROPOLITAN UNIVERSITY

  • OptiGen - Optimising community energy generation for real time demand

    The project addresses the challenge of dealing with data for the management of energy supply and demand. There are a number of software tools available for the configuration and sizing of decentralised energy plants, whi...

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    Why might this be relevant?

    The project aims to integrate energy plant design methods and datasets, which could partially address the question of data standards and processes for enabling AI/ML to engage with the energy system, but does not fully answer the question.

  • DEPENDABLE SMART ENERGY SYSTEMS

    The development of innovative technologies and solutions, which support the EU, UK and its citizens, industries and businesses by responding to the unprecedented climatic and environmental-related challenges, is the def...

    Funded by: Horizon Europe Guarantee

    Lead research organisation: University of Manchester

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    With the fast development of network technology and computing power, a huge amount of data has been generated in almost every aspect of our lives. The International Data Corporation reported that 90 ZB of data will be cr...

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    Lead research organisation: University of Warwick

  • Centre for Energy Systems Integration

    Energy systems are vitally important to the future of UK industry and society. However, the energy trilemma presents many complex interconnected challenges. Current integrated energy systems modelling and simulation tech...

    Funded by: EPSRC

    Lead research organisation: Newcastle University

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    In this project, we will seamlessly combine two disciplines that have been historically received continuous government and industrial funding: physics-based modelling, which is generalisable and robust but may require tr...

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    Lead research organisation: Imperial College London

  • Demonstration of a digitized energy system integration across sectors enhancing flexibility and resilience towards an efficient, sustainable, cost-optimised, affordable, secure and stable energy supply (ELEXIA)

    ELEXIA will develop/upgrade validated tools for planning and managing integrated energy systems in different conditions and will integrate and combine energy systems across vectors and sectors towards a cost optimised as...

    Funded by: Horizon Europe Guarantee

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    To develop a machine learning architecture and embedded capability to enable Upside Energy to continuously evolve its ensemble of algorithms for grid prediction and demand response portfolio management....

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