Executive Summary

• Technologies like software-defined automation, edge computing, and industrial AI can lead to ‘measurable gains in productivity, efficiency, safety and sustainability’ for the energy transition, according to Schneider Electric
• Grid edge controllers can provide a more granular view of the grid and ‘expand control options beyond the control centre and into the field’, providing better monitoring for the likes of voltage control systems, breakers, and even EV chargers
• Landis+Gyr notes the benefits of waveform-sensing meters for emerging grid needs, which requires built-in edge computing power for signal processing and analysis

The backlog facing the UK grid right now is no secret, with a major number of renewable energy, battery storage and industrial projects waiting for connection. Record volumes of power are being connected, but there remains a pipeline of projects approved by network operators but not connected up.

8 December 2025 saw a coordinated release from both Ofgem and NESO (National Energy System Operator): the former unveiled sweeping proposals to speed up grid connections; the latter announced a new pipeline of deliverable, ready-to-go projects would be prioritised for connection to the grid, representing the ‘biggest reform of the grid connections process to date.’

The race is therefore on to ensure Britain hits its clean power targets by 2030. Yet looking at the grid directly, can there be a technological shift to ensure faster progress? Schneider Electric, in a piece outlining its 2026 UK trends, noted: “Technologies like software-defined automation, edge computing, and industrial AI can lead to measurable gains in productivity, efficiency, safety and sustainability – setting new benchmarks.

“In 2026, the real differentiator for UK industry will be the strategic selection of scalable and fit-for-context technologies, facilitated by trusted partners who can simplify integration and execution,” the company added. “Together, electrification and digitalisation will be the twin engines powering UK industry’s next phase of growth and competitiveness.”

Saket Singh of Tech Mahindra, writing for the World Economic Forum, outlines the rationale for edge AI in building smarter, more resilient energy systems. Edge AI ‘puts intelligence right where it’s needed most – at the edges of our power networks… work[ing] locally on or near the grid’s sensors and devices’, Singh noted. Using it for critical energy infrastructure is ‘the next logical step.’

Looking at the smart grid edge – according to one study, the market for edge AI in smart grids is set to increase by 25% year on year in 2026 – Rick Kephart of Emerson, writing in Power, notes the importance of grid edge controllers. In a piece titled ‘Edge Computing May Be the Future of Power Distribution’, Kephart noted edge grid controllers ‘expand control options beyond the control centre and into the field, where equipment like voltage control systems, breakers, DERs (distributed energy resources) on the grid, or even EV chargers, must be monitored and controlled.’

The benefit is a more granular view of the grid, effecting ‘much better visibility into what is happening on the distribution grid, improved ability to control the many connected devices, and much faster speed of reaction’, Kephart added.

But what can this look like in practice? In October, Landis+Gyr published an interesting whitepaper outlining the benefits of waveform-sensing meters for emerging grid needs.

Waveform-sensing meters analyse the actual waveform of power flow by continuously sampling current and voltage at a very high frequency – and alongside the high sample rate, they continuously monitor the grid. Built-in edge computing power is required for signal processing and analysis. Waveform-sensing meters can also accommodate apps, which can manage the analytics at the grid edge.

The approach, Landis+Gyr notes, contrasts with traditional smart meters, which may sample up to 4000 times per second, but only bring back data at fixed intervals, such as every 15 minutes – making the data already historical. With the ‘right apps and communication in place’, waveform meters could ‘detect EV chargers or air conditioners in use during hours with high electricity rates and alert customers or even curtail the load’, the whitepaper notes.

“Our evolving grid, the condition of our infrastructure, and the demands modern life places on the power sector call for more than smart metering,” the whitepaper concludes. “A connected grid edge utilising waveform-sensing meters is now a crucial operational tool.”