The pace at which Artificial Intelligence (AI) is developing is thrilling, but what’s even more exciting is that we are still only in the initial phase of developing and deploying this technology. More transformation is yet to come.
The energy sector is no different; AI is already having a significant impact on the UK’s energy grid. AI-enabled grid monitoring is revolutionising how we maintain and operate our energy distribution networks. Imagine being able to know the health status of invisible assets like cables without digging them up. With this AI-enabled visibility, we can accurately plan asset maintenance and replacement, just as we would with assets like transformers that we can physically inspect.
AI also gives us foresight into potential asset failures, allowing us to proactively arrange resources and pre-empt repairs instead of reacting after failures occur. The benefits include operational efficiency by enabling preventative work that can be planned in an organised manner rather than emergency repairs. It unlocks productivity gains by allowing teams to target localised areas alongside upgrade or maintenance programmes. Work gets done under optimal conditions, not in harsh weather, reducing risk. Predictive maintenance reduces lengthy power cuts for customers and expensive emergency repairs as well as downtime, all of which impact billpayers.
Every cable failure that gets pre-emptively fixed saves money for utilities and consumers, as well as improving reliability and customer service. To give an example of this, Lucy Electric’s Synaps AI and Machine Learning (ML) technology cuts the time and cost of fault detection by two-thirds. The technology also improves energy security and reliability, which will only grow more critical as low-carbon technologies like EVs and heat pumps become widespread and bring additional challenges for grid capacity and use.
This is another area where AI technology is leading, and policy and regulations need to catch up. From a regulatory perspective, while DNOs are incentivised by minimising power outages, so much more can be unlocked with the advent of modern deep-learning methods. For example, harnessing AI grid monitoring could demonstrate the direct value of improved asset health ratings and potentially unlock additional funding from Ofgem by understanding the indirect benefits on safety, service and cost.
Applying AI to grid operations unlocks significant operational, financial, and customer service benefits while enhancing energy reliability and security – a true win-win.
Will AI replace us?
When it comes to the role of AI in the energy workforce, it’s important to look beyond the common fear that it will just replace human jobs. The reality is that AI unlocks immense new opportunities and creates diverse roles that never existed before. A perfect example is in the area of maintenance planning for energy networks.
Predicting equipment failures and providing actionable intelligence about assets is a monumental computational challenge that the human brain simply cannot handle effectively on its own. It involves millions of calculations, interpretation of vast complex datasets, and repetitive intricate computations. This is a critical capability that was impossible to achieve before the advent of AI and advanced analytics. Maintenance planners now have greater information to inform the scheduling of workloads, enhanced safety and reducing the amount of emergency repairs undertaken in unpleasant conditions or late into the night.
As these technologies have emerged, distribution network operators are increasingly hiring data scientists and nurturing AI-focused roles to leverage these powerful new tools. Rather than replacing existing maintenance crews, AI actually augments and elevates their capabilities. Humans can now focus on higher-level analysis, decision-making, and oversight, while AI handles the intense data processing workloads that fuel optimised maintenance planning.”
Advances discovered for the networks are highly applicable to highways too.
AI grid monitoring in action: how AI is changing our highways
Highways England has already put this tech into practise enabling the automation of data gathering and analysis to reduce the cost, resource and disruption of lighting maintenance. Led by National Highways and working with Kier Highways and Lucy Electric, the team embarked on the joint ‘GridKey Cable Smart’ project to explore this continuous monitoring option.
The aim was to replace the need for manual testing by fitting measurement devices in feeder pillars and lighting columns, to transmit data 24/7 for analytics, generating test certificates or triggering alarms as necessary.
The pilot project focuses on Highways Area 9 lighting circuits, where the GridKey measurement units (MCU) were fitted in each of the pillars together with the newly developed AutoLoop units being installed in the last column of each circuit. These provide a range of real-time electrical measurement data for each of the circuits, including Zs measurements to test the integrity of the earthing and detecting current spikes indicative of cable or column fault.
Information is then analysed and displayed at the central Data Centre through a bespoke portal, and further insight is also available through auto-generated daily safety certificates.
Results: strong technical and commercial viability
Based on the first six months’ data, real-time information from automated monitoring has already proven valuable, allowing significantly faster reaction time to support early intervention and eliminating the need for traffic management for each test.
Rather than waiting for the next six years’ manual test, the technology effectively carries out a check every 24 hours, which has helped identify safety concerns on up to ten percent of circuits, enabling remedial work to address them before they become urgent. Real-time information further eases the maintenance work by identifying the exact nature of faults and allowing the monitoring of results following the remedial work.
Based on a national roll-out of the technology and extrapolated from analysis of the current test time on sections of the M6 and the M5 motorways, approximately 500,000 man-hours of time on the road network would be saved each year, adding up to a total of estimated annual savings of £4million and a payback for the system installation of less than a year.
Beyond immediate financial benefits, the potential of the Cable Smart system is expected to be far wider reaching, with possible long-term forecasts of circuit failures up to five years ahead; in addition to the opportunity to expand to further applications, such as Highways England’s technology circuits.
