Hina Sharma, Product Manager at Gridkey & Data Systems, Lucy Electric, examines how AI-driven monitoring, fault detection, and data analysis are reshaping electrical grids.
Over the past two decades, the landscape of artificial intelligence has undergone a remarkable transformation, evolving from niche applications to a cornerstone of innovation across various sectors. Nowadays, the integration of AI into everyday operations is becoming increasingly critical, particularly in sectors where the demand for efficiency and sustainability is paramount.
Moreover, AI’s ability to analyse vast amounts of data in real time allows us to identify trends and make informed predictions, driving operational efficiencies that were previously unattainable. This shift not only enhances productivity but also fosters innovation, as companies leverage AI to develop new products and services that meet the evolving needs of consumers.
Just like AI, the energy sector has undergone a remarkable transformation over the past two decades, characterised by widespread electrification across industries and infrastructures, with energy demand and supply continuously expanding. Take solar power for example, in 2006 generation capacity in Britain was about 12 MW, while in 2024 this capacity reached 17 GW. For wind, the numbers are similarly impressive, offshore wind alone has seen an increase from 951 MW in 2009 to 15 GW in 2024.
This drastic increase poses momentous challenges for grid operators, as infrastructure struggles to keep up with demand. Understanding loads on networks and electricity usage requires advanced technologies to enable ‘always on’ grid monitoring and accurate reporting back to the Distribution Network Operators.
As grid upgrades are expensive and take time, AI is playing a crucial role in revolutionising how power is managed and distributed, enabling smarter grids to adapt to fluctuating demand while incorporating renewable energy sources more effectively. But how does this work in practice?
AI applications for grid operators
Two key application areas dominate the way AI enables more efficient grids – monitoring and fault detection.
Monitoring
Monitoring plays a critical role in managing the modern electricity grid. It enables near real-time assessment of load profiles and electricity demand, providing operators with valuable insights into the behaviour of the network.
By collecting and analysing data from substations and feeders, monitoring systems offer a granular view of how electricity is distributed and consumed across the grid.
AI-driven monitoring transforms the grid from a static system into a dynamic, intelligent network that can adapt to the demands of modern electricity consumption. This capability not only supports operational efficiency but also lays the foundation for a smarter, more resilient energy infrastructure.
Fault detection
Fault detection is an equally critical application of AI, solving one of the biggest challenges for operators. Allowing DNOs to move from a reactive approach to managing faults on the network to a proactive, predictive strategy, identifying and locating faults before they cause widespread disruptions.
Traditionally, operators would depend on customers reporting outages to respond, however, when outages are reported by customers the fault has already progressed so far that it led to disruptions for the end user. But not anymore.
The latest AI technology
The integration of fault detection systems with AI fundamentally changes the way grids are managed. Instead of waiting for failures to occur, operators can now predict and prevent outages, ensuring a more resilient and reliable grid. This shift not only improves operational efficiency and customer service, but also reduces costs associated with emergency repairs, regulatory penalties, and customer compensation.
Together, these types of technologies play a pivotal role in reducing financial penalties due to reduced customer interruptions and minimising customer minutes lost. Understanding load profiles across low-voltage networks helps to address technical losses and theft while ensuring compliance with power quality standards.
DNO to DSO: Making the transition
Capabilities such as load profiling, advanced fault detection, and monitoring of power quality, help shape smarter, more flexible grids. As the role of networks evolves from a DNO to a Distribution System Operator (DSO) and requires more active engagement with smaller generation sources and consumers, these AI technologies will be instrumental in optimising grid operations.
The integration of AI-driven data analysis helps operators find the best solutions for expanding network capacity while managing expenditure. Ultimately, these solutions enable better management of the system as the demand for clean energy and low carbon technologies continues to grow.
