Proving the Business Case for the Internet of Things

Smart grid could save $16.6 billion for UK economy: EA Technology

Iain Morris
April 1, 2014

UK networks and consumers could save as much as £10 billion ($16.6 billion) between now and 2050 by deploying smart-grid technology, according to new research from EA Technology.

The forecast is based on the use of EA’s (Chester, UK) Transform Model, which the company claims has already helped to lower the cost of deploying new electrical network infrastructure between 2015 and 2023 by around £200 million.

“If the network operators had not used the Transform Model, the cost for this period would have been £1.2billion, rather than the £1bn planned expenditure given,” said Sprawson. “This is a saving of an extra £200m the networks would have needed from Ofgem and, by extension, consumers.”

Developed specifically for techno-economic modeling by electricity networks, the Transform Model is recognized by UK industry bodies including regulatory authority Ofgem and the Energy Networks Association.

It has been designed to determine how much electrical network investment is needed to support growth in the use of low-carbon technologies such as electric vehicles, heat pumps and solar panels.

“The figure of £10 billion was worked out by examining the costs of performing network upgrades in a ‘conventional way’ and comparing them using smart technologies that are within the Transform Model,” said Mark Sprawson, the head of advanced network solutions at EA Technology.

“After populating the model with published government scenarios for how the country will achieve the 2050 carbon reduction targets, we found that the average saving of the UK going smart was £10billion,” added Sprawson. “As an example, if every UK network operator used the smart technology recommended by the Transform Model, Britain would avoid laying 28,000km of cable by 2030 – the equivalent of laying a cable from the UK to Australia and back again.”

According to Sprawson, the Transform Model uses data from a number of sources, including distribution networks, local authorities and central government.

“The model then overlays onto this network the anticipated future demands that will come from various low carbon technologies, considering both ‘conventional solutions’ and ‘smart solutions’,” said Sprawson. “These are modelled side by side, and the best value options are selected.”
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