The UK grid, like many others, is aging and often struggles to match supply and demand. There is a lack of capacity within existing infrastructure, while a multi-year backlog of clean energy projects are waiting to connect to it.
More investment is clearly needed to upgrade the transmission infrastructure. However this will take time. Emerging technologies like AI and digital modelling have the potential to uncover significant amounts of latent capacity within the existing power lines. By utilising these technological advances, utilities could squeeze more performance out of what we already have.
Unlocking latent capacity
Latent capacity is hiding in plain sight within the existing infrastructure; the challenge is finding it and defining the circumstances where it is safe to use. Traditional line rating methods that evaluate capacity usage are still largely manual: with individual engineers being sent out into the field to painstakingly record, compile and analyse data from each stretch of the network. This is an ilabour-intensive process that is superseded by new technology.
Utilities know that they have extra capacity but tend to be cautious so they do not overload lines with potentially dangerous levels of current.
Digital modelling, AI and machine learning technologies are starting to change this by providing engineers with the tools to conduct line rating analyses digitally, instead of in the field, and to scale the analysis from individual lines to assess the entire network. Multiple sources - including LiDAR, geospatial and cloud point data - can be more efficiently and effectively combined to create clearer and more accurate network visibility quickly and accurately. This means that utilities can conduct line rating analyses in a fraction of the time, identifying even small pockets of available capacity that, in aggregate, can increase practicable loads in the network.
There are still limits to how much energy utilities can run at once, but instead of applying a crude, overall standard to the entire network, technology can now help utilities to implement controls at a line-by-line level which offers critical flexibility.
AI systems have shown in pilot projects in New South Wales, Australia, and Texas, that it is possible to increase loads by up to 50% or even in some cases double the capacity by using these systems and digital modelling to improve the load management of the existing grid infrastructure.