Powering the future: Advances and applications of AI in power systems

Posted: June 16, 2025

Plenty of attention has been paid to the high energy cost of AI. By 2030, the IEA forecasts that data centers, driven in large part by AI workloads, may consume as much electricity as the entire country of Japan and its 125 million residents^.[1] Less attention, though, has been paid to the ways AI is poised to enhance the efficiency of the power sector.

What is AI in power systems?

AI is already unlocking new levels of efficiency, reliability, and flexibility across the electricity value chain, and even more transformative applications are in development:

• AI-infused predictive analytics
• AI-assisted load and generation forecasting
• Virtual power plants and AI-coordinated DERs
• AI-automated grid operations

Let’s take a look at how AI can help utilities, producers, and grid operators navigate an increasingly complex, decentralized, and decarbonized energy landscape today and in the years ahead.

What are the applications of AI in power systems?

AI-infused predictive analytics

One of the most widespread use cases for AI in power generation and transmission today is predictive analytics and predictive maintenance. Machine learning algorithms analyze historical and real-time data, detect patterns, and forecast future equipment failures or performance issues weeks or even months in advance. By learning from data over time, these models give grid operators, power producers, utilities, and other asset-owning players in the power sector the time and the foresight they need to remediate issues before they become big problems, minimizing downtime, improving reliability, and dramatically reducing operating costs.

AI-assisted load and generation forecasting

In a traditional, fossil fuel-based system, load and generation forecasting is relatively straightforward: demand for electricity follows predictable patterns based on time of day, season, and historical usage, and then power plants—dispatchable sources of generation—are ramped up or down accordingly. Solar and wind energy pose a significant complication to this process. Unlike conventional power plants, these energy sources are intermittent, dependent on the weather and time of day, which means they are non-dispatchable. Suddenly, accurate forecasting requires much more sophisticated techniques and tools. That’s where experts expect AI can make a transformative impact.^[2]

And in fact, that impact is already underway. Chile’s grid operator, for instance, is using AI to help grid planners run simulations far faster than traditional methods allow, and to run many scenarios at once.^[3] This project, and others like it, are largely in early, pilot phases, but already they are demonstrating AI’s ability to enhance the accuracy of day-ahead wind and solar forecasting, enabling a more stable, efficient power grid.

Virtual power plants of AI-orchestrated DERs

By 2050, experts project that global electricity demand will more than double.^[4] New power plants, however, require massive capital and infrastructure investment and take years to come online. To improve grid stability and close that gap between supply and projected demand, we are increasingly recognizing the potential of distributed energy resources (DERs).

That’s where virtual power plants (VPPs) come in. VPPs use software to aggregate and coordinate energy assets across homes, businesses, and communities into an optimized, dispatchable network of DERs, which can help balance the grid much like a traditional peaker plant, only faster, more flexibly, and without centralized fuel-based generation—in theory at least, if not yet at scale.

Orchestrating thousands of decentralized devices in real time isn’t simple. These DERs vary in type, ownership, and connectivity, and they’re subject to ever-changing conditions like weather, pricing, and user behavior. AI is emerging as the perfect tool for solving this complexity. By forecasting with greater accuracy, scheduling more efficiently, and automating decision-making, AI can ensure that DERs reliably support the grid when and where they’re needed most^[6]

Autonomous grid operations

We’ve been automating steady-state operations for some time already—adjusting setpoints automatically to maintain a process, balancing equipment loads to optimize performance, etcetera. As more powerful AI systems emerge, we’re piloting new systems capable of automating transient operations—startups, shutdowns, major disruptions, and other dynamic states of flux in a system. Using rigorous first-principal simulation with deep reinforcement learning, operators in the power sector may soon be able to optimize more complex, transient operations, like reducing startup time, stabilizing processes, and more.

Now, even more transformative forms of AI are in development: industrial generative AI, and agentic AI, which create a totally new experience, broadening and deepening the possibilities of industrial automation and autonomous decision-making. Generative AI can synthesize plans, scenarios, and recommendations based on vast datasets—real and synthetic—while agentic AI systems can act on those outputs in real time, adapting strategies as conditions change.

These advances are poised to open the door to more resilient, self-optimizing grid operations that can anticipate and respond to disruptions, coordinate distributed energy resources, and balance supply and demand with minimal human intervention.

How will AI continue to shape the power industry?

Yes, AI workloads will place strain on energy systems, and yet paradoxically, AI is proving itself to be one of the best tools we have to alleviate that strain. From spotting problems before they happen to helping us manage renewables and distributed resources, it’s already making the grid more agile and efficient. And we’re only just getting started. As these technologies mature, AI won’t just help the power industry keep the lights on; it will help us reimagine how the system works.

^{[1] International Energy Agency. (2025, April 10). AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works. https://www.iea.org/news/ai-is-set-to-drive-surging-electricity-demand-from-data-centres-while-offering-the-potential-to-transform-how-the-energy-sector-works
[2] International Energy Agency. (2023, July 5). Why AI and energy are the new power couple. https://www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple
[3] S&P Global. (2024). AI’s role in propelling the energy sector. https://www.spglobal.com/en/research-insights/special-reports/look-forward/ai-role-in-propelling-the-energy-sector
[4] Chen, P., Grünewald, T., Noffsinger, J., & Samseth, E. (2024, January 16). Global Energy Perspective 2023: Power outlook. McKinsey & Company. https://www.mckinsey.com/industries/oil-and-gas/our-insights/global-energy-perspective-2023-power-outlook
[5] International Energy Agency. (2022). Unlocking the potential of distributed energy resources. IEA. https://www.iea.org/reports/unlocking-the-potential-of-distributed-energy-resources
[6] Zhou, Y., Li, X., Wang, J., & Chen, M. (2024). Artificial intelligence applications in virtual power plants: A comprehensive review. Energy, 295, 117123. https://doi.org/10.1016/j.energy.2024.117123}