How AI could have stopped the 2025 Iberian blackout
Posted on: 3 December, 2025
By Linda Serck
On 28 April 2025, the power systems of Spain and Portugal suffered a full-scale blackout. Millions of homes, businesses and critical infrastructures plunged into darkness in seconds.
Why was there a blackout in Spain and Portugal?
Initial investigations by the European Network of Transmission System Operators for Electricity (ENTSO‑E) highlighted a “lack of voltage control” in the Iberian grid as a key systemic vulnerability.
Initially, renewable energy – or renewables – got the blame on social media and in press reports. This is because wind and solar output naturally rises and falls with weather conditions. Because of this variability, it’s easy for the general public to assume that any major grid incident must be connected to sudden changes in renewable generation.
However, as WindFarm states: “A sharp increase in voltage led to a cascading loss of generation which affected the whole Iberian system. Fossil, nuclear and renewable power plants alike isolated themselves automatically from the electricity system to protect their electrical equipment from overvoltage. This further increased the voltage peaks until the whole system shut down.”
The incident shows that modern-day grids – with high shares of renewables and state-of-the-art assets – are becoming increasingly difficult to manage without more sophisticated digital intelligence, including AI-assisted monitoring and control.
Enquire now about our new MSc in Renewable Energy and AI, launching in September 2026
Dr Mahmoud Dhimish, the programme leader of the University’s new MSc in Renewable Energy and Artificial Intelligence, said how this incident illustrates how the gap between the renewable energy sector and AI urgently needs to be bridged to avoid such a catastrophe from happening again.
He said: “A rapid surge or dip in voltage can set off a chain of protective shutdowns across the network. To prevent that, we need engineers who build not only wind turbines and solar systems, but also the AI algorithms that detect these fluctuations early and stabilise the grid in real time.”
Why renewable energy requires greater grid intelligence
Professionals skilled in both renewables and AI are necessary because the transition to renewable energy has completely transformed grid design:
- Traditional synchronous generators (coal, gas, nuclear) rely on large rotating components that naturally provide inertia and help stabilise grid frequency. Whereas most renewable technologies generate electricity in a form that can’t be sent directly into the grid but must first pass through an inverter, and most inverter-based renewables don’t provide a stabilising support system.
- As generation becomes more distributed, more variable and less synchronous, it becomes harder to manage essential functions such as voltage control and damping oscillations across the network.
- Disturbances in real time – whether a voltage surge, an oscillation, or a line fault – can now spread through the system more quickly, meaning they require faster, more automated and more intelligent responses to keep the grid stable.
Where AI enters the picture
AI offers a transformation of grid resilience in three ways:
- Forecasting and anomaly detection – AI models trained in historical grid behaviour and real-time sensor data can identify unusual patterns such as oscillations, voltage spikes and rapid frequency changes before they become failures.
- Adaptive control and response – Once an anomaly is detected, AI systems can suggest or even autonomously initiate corrective actions.
- Virtual-lab simulation and training – Before deployment, the grid must be tested under many “what-if” and “worst-case” scenarios. Virtual labs allow engineers and AI systems to train on real data, test strategies and visualise outcomes in a safe environment.
Bridging the professional skills gap
With the technical complexity of combining renewables and AI for grid resilience, there is a growing skills gap that the University’s MSc in Renewable Energy and AI [page link] addresses.
Professionals need to understand both worlds:
- The physics and engineering of renewable generation, storage and grid control.
- The algorithms, forecasting data and AI models that enable intelligent operation.
Industry relevance and future careers
The industry is already embracing partnerships that blend AI and grid resilience. In the UK, National Grid announced a live trial with Emerald AI to demonstrate how AI-powered datacentres can support grid stability by dynamically adjusting energy consumption.
Graduates of the University’s Renewable Energy and AI Master’s degree will be able to step into careers such as Renewable Energy Engineer, AI Engineer for renewables companies, Grid Analytics Specialist, Real-Time Control Systems Engineer and AI Consultant for Utilities.
Dr Dhimish said: “AI-based forecasting and rapid detection could have mitigated the impact of the Iberian blackout and secured grid stability.
“Going forward, by embedding AI into renewable-energy systems, we make the transition not just about greener power but about smarter power.”
Enquire now about our new MSc in Renewable Energy and AI, launching in September 2026
