AI could help run Britain’s future energy system faster, smarter and more cheaply but only if the sector can move beyond trials and start deploying it at scale.
An interim report by Lucy Yu, AI Champion for the Clean Energy sector, says artificial intelligence is already being used across parts of the electricity system but warns adoption remains uneven, fragmented and far from fully embedded.
The report draws on more than 80 survey responses, four multi-stakeholder roundtables and more than 40 expert interviews carried out as part of an independent review into AI deployment in electricity networks.
That final review is due later this summer and is expected to set out recommendations for government, regulators and industry.
The core message is clear. Britain’s energy system is becoming too complex, too decentralised and too weather-dependent to be managed using old tools alone.
More wind and solar, more electric vehicles, more heat pumps, more batteries and more flexible assets mean the grid is no longer a one-way system built around large central power stations.
AI could help manage that complexity by improving forecasting, optimising assets and supporting faster decisions across the system.
The report says AI is already being applied to grid control, maintenance, planning and market design but its potential could go much further.
One of the biggest ideas is the creation of what the report describes as the “world’s most experienced control room operator”, trained on real grid operations and physics-based simulations.
That could include rare and extreme events, such as the Iberian Peninsula power outage, allowing operators to prepare for situations that may happen only once in a generation.
The report also points to AI agents managing supply chains in near real time, spotting problems early and helping create “always-aware, reactive and self-healing systems”.
At household level, the shift could be just as significant. As more homes install batteries, rooftop solar, plug-in solar, electric heating and EVs, AI could help manage whole neighbourhoods as virtual power plants.
That would mean predicting when people need heat and power, balancing local demand and supply and responding to volatile weather patterns with less effort and lower cost.
The report says the long-term prize is a system that moves away from rigid rules and towards “true probabilistic management”, where decisions are based on risk-aware optimisation.
It also says AI could support more decentralised decision-making and even allow some grid functions to operate automatically within strict safety limits.
But the report is blunt about the barriers.
The first is data. AI depends on high-quality, timely and usable data but much of the energy system still suffers from fragmented ownership, inconsistent standards and poor access.
In many cases, the data exists but is not available in the right form, at the right speed or at the right level of detail to support operational use.
That lack of observability means operators cannot always see what is happening across the system in real time.
The report says synthetic data could help, by creating realistic datasets for testing and training AI systems where real-world data is limited, sensitive or difficult to access.
However, it warns that this will need clear standards for quality, validation and governance.
The second barrier is scaling. The UK has strong support for innovation, pilots and early-stage deployment but the jump from trial to normal operations remains difficult.
In many cases, the benefits of AI are felt across the whole system while the costs and risks sit with individual companies.
That weakens the incentive to invest and leaves promising technologies stuck in demonstration mode.
The third challenge is trust.
AI in electricity networks raises serious questions about safety, accountability and assurance says the report. If an AI system is making or supporting decisions in a safety-critical environment, the sector needs to know how it is tested, validated, monitored and controlled over time.
Without clear rules, organisations are likely to default to caution, even where the technology is technically ready.
The report also warns that the energy system is not just a technical machine, it is made up of markets, institutions, governance structures and regulations designed for an older, more centralised world.
That means AI deployment is not only a software problem. It is also a question of skills, leadership, regulation and institutional readiness.
The report highlights existing activity including the National Energy System Operator’s Project Volta, innovation funding for AI in networks, open data platforms, regulatory sandboxes and AI-enabled products from companies such as Open Climate Fix and Kraken.
But it says these initiatives will not be enough on their own.
The bigger opportunity is to make the UK one of the best places in the world to develop, test and deploy AI and frontier technologies for the future grid. If that happens, the benefits could reach far beyond the energy sector.
A smarter grid could use existing assets better, reduce the need for new infrastructure, cut emissions and lower system costs for households and businesses. It could also create exportable technology and attract investment into UK clean energy companies.
The final review will look at how AI can support new operating models across electricity networks, including probabilistic operation, decentralised management and greater autonomy.
The report makes clear these elements cannot be treated separately.
Better forecasting means little unless the system can act on it. Decentralised control could create problems without enough visibility. Autonomy without safety and assurance frameworks could introduce new risks.
Government is expected to respond to the final review as part of a wider AI in Clean Energy strategy before the end of the year.
Copyright © 2026 Energy Live News LtdELN