The government is seeking stakeholder engagement on its strategy for artificial intelligence-enabled clean energy as it looks to drive decarbonisation, lower prices and improve resilience and security
The UK government has published a call for evidence on the opportunities and risks of an AI-enabled clean energy system, as it explores the technology’s potential to create efficiencies, lower emissions, reduce household bills, and provide more secure energy.
The call for evidence by the Department for Energy Security and Net Zero is aimed at industry, academia, system operators, regulators and other energy stakeholders and will inform policy development and the UK AI for Clean Energy Strategy.
The 21-page document, which was published on 8 September, also sets out the government’s vision for how artificial intelligence could transform the energy system.
“The government’s aim is to ensure that artificial intelligence supports a cleaner, more affordable, efficient and secure energy system,” it said. “The vision sets out emerging thinking on the opportunities AI could create, including through better forecasting, planning, optimisation and coordination, while recognising that its deployment will also present risks and wider system implications that need to be managed.”
Through the call for evidence, the government said it seeks to test its understanding of how AI could affect the energy system in the near and longer term, and to identify the barriers preventing beneficial AI applications from being adopted.
It also seeks to understand where government action may be needed, including to support “coordination, standards and regulatory clarity”, and to gather evidence to inform future policy development.
Questions to stakeholders include where and how AI can add the most value in the energy system; where the UK may have competitive advantage over other countries; specific constraints limiting deployment; what effective assurance looks like at the system-level and how accountability could be maintained; and what the main risks of more autonomous and integrated AI deployment are and how these could be managed.
In the foreword of the call for evidence, Martin McCluskey, minister for local energy and jobs, said that the only long-term solution to making energy affordable for everyone is to “reduce our reliance on volatile global fossil fuel markets and move to a clean homegrown power system that we control”.
He called this the “great industrial challenge of our time”, and said it would require government to “create the conditions to move from promising trials into tangible results”.
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Benefits, risks, and barriers to adoption
“Used well, AI could help make the energy system cleaner, more productive and more adaptable,” the government said, citing the benefits of better prediction and system optimisation, more effective operation and maintenance, demand management, and the opportunity for more targeted investment.
It added that AI could also accelerate innovation by helping researchers, engineers and firms test ideas, improve designs and shorten the path to deployment, and that if the UK moves quickly “it can be the place where new start-ups building AI applications for the energy system, scale up”.
The document also cites analysis that suggests that if existing AI applications were adopted across industry, transport and buildings sectors globally by 2035, energy-related emissions would fall by around 5%, while current use of AI in the energy sector could deliver up to US$170bn in annual cost savings globally by 2030.
As well as the potential benefits, it also acknowledges that “safe and effective deployment depends on high-quality, timely, and interoperable data, plus robust governance and assurance”, and identifies a number of barriers to near-term adoption including technical systems integration, trust, capability, and culture.
The document notes that AI is already being adopted across the energy system.
“This will continue with or without government action. But as with previous technologies, adoption may be uneven, slowed by structural challenges, or pose new risks to the energy system and the consumer. So, the question is not whether AI will be adopted in the energy sector, but whether government acts to shape adoption early and actively enough to capture its benefits and manage its risks.”
The government also set out risks associated with a future move to agentic AI.
“AI could fundamentally reshape how the energy system is planned, operated and governed. But the scale and nature of that impact is uncertain. To explore this, the document examines an illustrative future in which highly autonomous agentic AI is embedded across the energy system.
“This raises key questions, such as how decision making, control and accountability shift when optimisation becomes more automated; whether current market, regulatory and governance arrangements stay robust when AI systems coordinate activity at greater speed and scale; or what risks emerge at system-level from interactions between multiple AI-enabled components.”
‘The grid we need now’ review
The call for evidence closes on 6 November, and the responses gathered will “strengthen the evidence base for government action” alongside the independent review of AI deployment in the electricity markets, produced by the government’s AI champion for clean energy, Lucy Yu, and published earlier this month.
There are 34 recommendations in her review, each aimed either at the government, the Department for Energy Security and Net Zero, the National Energy System Operator (NESO), the regulator Ofgem, or UK Research and Innovation (UKRI).
Recommendations for the government, for example, include setting a strategic intent “to move to full probabilistic, risk-based grid operations by 2035” and set out a roadmap for implementation; work with the NESO and researchers to create a shared capability for system-level testing of agentic AI systems; and develop a long-term roadmap for updating accountability and liability mechanisms to account for AI-enabled autonomy in the grid.
The review also suggests that the government continue to prioritise and accelerate the digitalisation of the energy system; invest in high-resilience sovereign AI compute that can underpin future grid operations; and fund a national AI for Grid Innovation Delivery hub.
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