Javier Cavada, President and CEO, EMEA, Mitsubishi Power looks tab the role of AI in electricity networks.
Electricity networks around the world were designed for an era when power flowed one way: from power stations to homes, businesses, and factories. Those traditional grids were built for predictability and reliability. They assumed steady demand and centralised supply.
But that world no longer exists. We are electrifying everything, everywhere, driving enormous changes in the grid. Energy grids are becoming more decentralised as generation now comes from distributed networks with thousands of touchpoints instead of generating power from a few fossil-fuel based plants.
Instead, the 21st century grid must balance fluctuations from renewable generation, the increase in electric vehicles, the rise of digital industries, and the growing expectations of billions of people who rely on uninterrupted power. Today’s electricity network is under pressure to do more, adapt faster, and operate smarter than ever before.
While AI is often portrayed as an insatiable, power-hungry monster, the reality is much more complex. Although it is true that it represents a source of significant new demand, it is also making our energy systems more efficient, reliable and resilient.
AI helps find the balance
Every hour, power networks must manage a torrent of variables resulting from shifting weather patterns, volatile market prices, unpredictable demand peaks, and the intermittent flow of solar and wind power. No human operator, no matter how skilled, can process such complexity in real time. Yet AI can. By analysing billions of data points every second, from weather satellites, smart meters, and sensors embedded across the grid, AI systems can predict demand, forecast generation, and balance and integrate renewable energy sources dynamically. This allows energy to flow where it is needed most, reduces waste, and prevents costly blackouts.
Take for example the way it is optimising the use of natural gas in the grid. While renewables are the backbone of decarbonised grid systems, gas provides the reliability needed to balance supply when wind or solar generation dips. AI is transforming operations by analysing weather data, electricity demand, and market prices to determine the optimal times to run gas turbines, ensuring they operate efficiently and in harmony with renewables rather than in competition with them. The result is reduced emissions, lower fuel consumption, and improved system flexibility.
AI-driven insight and predictive maintenance
One of the biggest challenges of moving to a renewables-powered energy system is intermittency. AI helps overcome this hurdle by improving the way we forecast and manage renewable generation. Advanced algorithms now predict solar and wind output hours, or even days, in advance with remarkable accuracy. These forecasts allow grid managers to plan ahead, scheduling backup generation in the form of natural gas or energy storage to ensure stable supply. In Spain and Portugal, for instance, such systems are already cutting renewable curtailment by better anticipating fluctuations in generation.
Another area where AI is delivering real impact is by helping utilities avoid or delay costly infrastructure upgrades. Expanding the grid or replacing aging equipment often requires years of planning and vast capital investment. AI can extend the useful life of assets by identifying stress points, predicting failures, and optimising load distribution in real time. This can postpone the need for new substations or transmission lines by several years, freeing up funds for clean energy development.
AI is already being used to monitor the health of power generation assets like gas turbines, detecting performance anomalies, and scheduling maintenance before failures occur. What used to take teams of engineers weeks to analyse, can now be done in seconds. This predictive capability not only saves costs but also prevents unplanned downtime, a critical factor in maintaining the reliability of national grids.
This is not only an engineering achievement but an economic one too. By turning data into insight, AI allows us to extract more value from every unit of existing capacity, a crucial advantage at a time when energy demand is increasing worldwide.
A smarter energy network
The same intelligence that manages turbines and grids is improving how data centres, factories, and transport systems interact with the energy network. AI enables “smart demand,” allowing large consumers to adjust their energy use based on grid conditions. In Ireland, for example, some data centres now reduce power consumption during peak hours, helping stabilise the system. These flexible loads turn what was once seen as a problem, AI’s own energy use, into part of the solution.
Of course, the electricity appetite of AI itself cannot be ignored. Data centres require significant power and cooling, and their growth must be managed responsibly. But rather than this being a zero-sum challenge, we should see it as an opportunity for innovation. If AI workloads are powered increasingly by clean energy and managed intelligently through flexible grid integration, they can become enablers of stability rather than sources of strain.