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AI is rapidly evolving the way the energy sector operates. However, writing for Utility Week, Dan Clarke, head of innovation at the Energy Networks Association, believes that it has the potential to transform the sector even further.
It is scarcely credible how quickly AI has shifted from a niche interest of specialists like computer scientists, futurists and cryptographers to something anyone in the business of innovation must have a credible plan to use.
Energy networks, like financial markets, could be a system ripe for AI driven innovation, given the size of the data sets we work with and their complexity. Already AI is predicting energy consumption patterns, creating more seamless administrative processes and optimising grid performance. However, in many areas there is potential ready to be tapped. 
In the interests of full disclosure, I did try to get an AI to assist with the writing of this article, only to end up with a dizzying pile of platitudes and silicon valley pseudomysticism so there’s clearly still limits.
ENA Connect Direct, launched this month, is one example of AI replacing repetitive manual data processing tasks with ones that are automated and work almost instantly. When domestic low-carbon technology installers need to submit a connection application, they can now visit a single portal that serves the whole of the UK. Once there, AI image recognition software is used to assess whether pictures, provided by installers, show electrical cut-outs that are suitable for the type of connection described in the associated application.
In exceptional cases, where software can’t determine this or it is ambiguous, the image is sent to a technical expert for checking. But for the large majority of cases, AI assessment has been shown to be just as accurate as human assessment while significantly reducing the processing time to mere seconds. This is great news for low-carbon technology installers, and especially small to medium sized businesses reliant on speedy decisions.
Another example can be found at Electricity North West, who have been using AI and drone-enabled photography to assess the steel towers in their transmission network. AI identifies potential defects for review and also identifies key features that need closer inspection. This support for the manual assessment of drone-generated images allows for improved performance in terms of the efficiency and quality of each inspection. Costs were also saved and safety risks reduced as previously a helicopter or climbing team would be used to carry out such assessments.
At SP Energy Networks, they launched the ENSIGN project, in collaboration with their academic partners, with ambitious goals to create a detailed digital representation of their network encompassing physical infrastructure, asset data and real-time data streams. This ‘digital twin’ aims to enable network operators to predict and prevent outages, optimise asset management, enhance network operations, and develop smart grid solutions.
At the strategic level, ESO’s Advanced Dispatch Optimiser aims to revolutionise the electricity control room by using AI and machine-learning, to optimise scheduling and dispatch decisions amid renewable generation uncertainties. It’s a solution that leverages cutting-edge technologies to achieve real world impact.
Similarly the Dynamic Reserve Setting (DRS) harnesses the power of probabilistic machine-learning and AI to produce more accurate predictions of reserve for the ESO’s control room engineers. In its first usage, DRS successfully informed this decision to cut an impressive 1GW of reserve.
For the near future, ENA’s member companies are starting to explore the use of AI to enhance health, safety and environmental management practices. AI can support more effective operations through training, horizon scanning for risks and fostering an engaged culture supported by improved communication. For example, AI-powered training tools can create bespoke modules for individual employee needs, simulating real-world scenarios that are tailored for skill development. AI tracks progress, provides feedback, and scans for emerging risks by analysing data from various sources. These tools can instantly provide the type of data rich insights that drive sustained and tangible improvements in safety.
Even beyond innovations with an explicit AI focus, energy networks are increasingly using tools with AI woven into their fabric, including databases, design software and managerial tools. C-Suite executives around the world are already factoring the projected benefits of AI into their medium and long-term objectives.
In our Innovation Atlas, as we look ahead to what innovation ‘way points’ we need to focus on as we move towards our 2050 decarbonisation goals, the projected benefits of AI underpin the realisation of many projects. The UK is well positioned to fully benefit from the super charged evolution AI can potentially provide, whilst maintaining the safeguards that prevent unintended consequences. For the moment, I would argue making best use of AI is invaluable for energy innovators in every field.
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