As artificial intelligence technology races toward its potential, a significant limiting factor has become increasingly evident: the energy required to drive its massive processing capabilities. While a chatbot inquiry might only take a few seconds, it can consume the same amount of energy as powering a lightbulb for several minutes. More complex generative AI tasks, like creating an image or video, can draw as much energy as running a microwave for an hour.
All of these tasks, and more sophisticated ones, are processed in data centers — warehouse-sized buildings filled to the doors with servers that perform the trillions of calculations per second to provide the chatbot, search engine, or AI agent’s response.
By some calculations each data center has an annual energy demand equivalent to hundreds of thousands, or even millions of homes. And while there are already several thousand data centers across the country, nearly as many are planned for construction over the next five years.
Accommodating this volume of energy demand with a grid that still strains to keep up during increasingly frequent extreme weather events will be no small feat — with potential ripple effects for natural resources, the environment and consumers. To better understand the challenges this poses and the opportunities it creates for innovative solutions to upgrade the country’s aging power grid, The Drexel News Blog tapped Ali Hasan, PhD, an assistant teaching professor in the College of Engineering. Hasan studies how power systems manage fluctuations in demand. He suggests the tidal wave of energy demand accompanying the proliferation of AI data centers will certainly pose a challenge, but it will also create an opportunity for the grid to evolve and integrate new sources of energy production — growth that could be enabled by AI technology itself.
How much energy do AI data centers consume?
AI data centers are rapidly becoming one of the largest new sources of electricity demand. A single hyperscale AI facility can consume hundreds of megawatts of power comparable to the electricity use of a mid-sized city. Globally, the energy footprint of data centers is expected to grow significantly as artificial intelligence becomes integrated into nearly every sector.
Why do they require so much energy?
The primary reason for data centers’ high energy consumption is computational intensity. Training and running modern AI models require thousands of high-performance processors operating continuously. These processors generate substantial heat, so large amounts of energy are also required for cooling systems to maintain safe operating conditions. In many facilities, cooling alone can account for a significant fraction of total energy consumption. As AI models become more complex and widely used, both computing and cooling demands continue to rise.
What happens when a large energy consumer, like a data center, comes online?
When a major electricity consumer such as an AI data center connects to the grid, utilities must carefully plan to ensure reliable supply. This often involves upgrading substations, reinforcing transmission lines and sometimes adding new generation capacity.
From a power systems perspective, large continuous loads can affect voltage stability, frequency regulation and local congestion. Utilities therefore coordinate years in advance with developers to integrate these facilities without compromising reliability for existing customers.
What challenges could the rapid addition of data centers post to America’s energy grid?
The biggest challenge is the pace of demand growth relative to the speed at which infrastructure can be built. Power plants and transmission projects can take a decade or more to complete, while data centers can be deployed much faster. Key challenges include regional grid congestion, increased peak demand and ensuring that new electricity needs are met without slowing the transition to clean energy. At the same time, this surge in demand could stimulate major investments in grid modernization, renewable energy and energy storage which would ultimately strengthen the energy system.
Is the U.S. energy grid ready? What upgrades or changes might be necessary?
The U.S. grid is robust but was not originally designed for highly concentrated, energy-intensive digital infrastructure. Meeting future demand will require expanding transmission capacity, integrate large amounts of renewable generation and deploy advanced grid management technologies.
A smarter, more flexible grid supported by digital monitoring, automation and predictive analytics will be essential. These upgrades will allow operators to balance supply and demand dynamically while maintaining reliability.
What will the increased demand generated by data centers mean for household energy customers?
The impact on households will vary by region and regulatory decisions. In the near term, infrastructure investments could influence electricity prices. However, large industrial customers can also help fund grid improvements that benefit all users.
Over the longer term, if managed strategically, data center demand could accelerate clean energy deployment, improve grid reliability and stabilize energy costs. The key factor will be thoughtful planning and investment.
What are some strategies that could help to manage the increased energy demand?
In the short term, improving data center efficiency is critical, including advanced cooling technologies, energy-efficient hardware and demand-response programs that shift consumption away from peak periods.
Long-term strategies include expanding renewable generation, deploying grid-scale energy storage, building new transmission infrastructure and developing localized microgrids.
How might AI technology be deployed to improve the efficiency of managing the energy grid?
Artificial intelligence itself will play a transformative role. AI can enhance demand forecasting, optimize power flow, detect anomalies and coordinate distributed energy resources such as solar panels and batteries. My research focuses on applying machine learning to power systems to enable exactly this type of intelligent, adaptive grid operation. By leveraging AI, we can make the energy system more efficient, resilient and capable of supporting emerging technologies like large-scale AI computing.
Reporters interested in speaking with Hasan should contact Britt Faulstick, executive director of News & Media Relations, at bef29@drexel.edu.
