Smart ‘matrix’ will help the UK meet the energy needs of future electric vehicles


A £2 million (US$2.7 million) machine-learning tool is creating a Matrix-like simulation of the electricity network in Southeast England. It has the potential to unlock energy capacity equivalent to 1,371 new rapid electric car chargers by 2028.

UK Power Networks’ Envision project is simulating how power is flowing through its networks across London, the South and the Southeast of England. From this data-led approach providing better ‘visibility’ of everything that is happening across hundreds of thousands of miles of network, the company will be able to run the network more efficiently and safely allow more low carbon technologies like heat pumps and EV chargers to connect.

Experts forecast Envision could release almost 70MW of electricity capacity by 2028, creating more space for EV chargers or low carbon heat pumps. This means engineers will not need to physically upgrade the network to release capacity, leading to significant cost and time savings: up to £4 million (US$5.4 million) in total over the next five years.

Connecting more electric car chargers and electric heat pumps is critical to tackling the climate crisis and reaching the UK’s ‘Net Zero’ carbon emissions target by 2050. There are currently more than 150,000 electric cars and 20,000 heat pumps in the areas served by UK Power Networks, but its analysis forecasts over 2.6million electric vehicles and 712,000 heat pumps by 2030. These need ‘space’ on the electricity network, so quickly and efficiently unlocking more capacity is a vital part of the network operator’s strategy.


Envision is building new predictive models that combine UK Power Networks’ data with external and real-time data from monitoring devices connected to substations. The machine learning algorithm will create a simulation of the electrical ‘load’ in specific areas and expand it across the entire network. Engineers will compare the simulation to real life physical monitors; feeding the software more and better data over time so the algorithm gets more accurate

“Our customers rightly expect us to do everything we can to make the switch to electric cars and low carbon heating as affordable as possible,” says Ian Cameron, head of customer services and innovation at UK Power Networks. “Through Envision, we’re thinking outside the box and reimagining traditional ways of working, to make it happen.”

“The aim of the Envision model is to generate a ‘virtual sensing network’ that uses advanced data capabilities and machine learning to simulate the behaviour of the network at scale, accurately estimating changing network load profiles,” says Simone Torino, head of product and business development at CKDelta, which is collaborating on the project. “In a world where the uptake of new distributed energy resources and the increasing electrification of transport are impacting electrical demand and distribution network constraints like never before, having this type of modelling and predictive analytics capabilities is a game changer for the utilities sector and has potential to reshape how we approach demand and supply in other sectors such as transport.”

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Lauren is a regular contributor to Traffic Technology International (TTi) and a freelance technical journalist. Over the past 15 years, she has worked on a wide variety of B2B publications and websites, including a stint as deputy editor of Traffic Technology International from 2014-2016. She has a degree in English from the University of Exeter. Lauren is mum to two busy little girls. She is always in demand!