Integrating AI into the Power Grid: Challenges and Opportunities

 

     With power-hungry AI technology growing immensely, there has been much chatter about powering it. Nuclear, including deep underground nuclear and the possible re-opening of the Three Mile Island nuclear plant, natural gas, and renewables are set to power more and more AI as time goes on. Realistically, natural gas will likely be the main power source since it can be built and integrated faster than nuclear and will be more reliable than renewables.

     The simple fact is that while AI can be a great technology for human benefit, it is not so great for the climate due to its very high energy use. It is a far more useful technology than the other main power-hungry ‘processing power’ technology, cryptocurrencies, which have very few societal benefits and some societal detriments.

     Manav Mittal, a senior project manager at Consumers Energy, wrote an opinion piece for Utility Dive about optimizing AI integration into the grid. Power demand from AI data centers is already growing fast and that pace will continue for at least several years. He acknowledges that there will be environmental and infrastructure costs to integrating AI, but these can be minimized with foresight. Mittal offers four areas where AI integration can be optimized for sustainability: energy efficiency, renewable energy, modernizing the grid, and demand response.

     Improvements in AI data center energy efficiency are very possible. The first area where this is so is in the process of cooling data centers. Older inefficient HVAC-based air-cooling systems can be replaced by liquid cooling and immersion cooling. Another area where efficiency improvements are possible is hardware, especially in developing more energy-efficient processors and GPUs. Mittal thinks data centers should be incentivized to adopt these newer and more efficient technologies.

     The use of renewable energy to power data centers is a long-established practice of tech companies like Meta who want to power up to 100% of their data centers with renewable energy. Collaboration with renewable energy developers and utilities is required. The power purchase agreement (PPA) for long-term renewable energy supply is key to these deals. Onsite renewables and storage can help these facilities be more self-sufficient and less grid-dependent.

     Modernizing the grid is essential to integrating AI and is easier said than done. The use of real-time data and sensors can better manage energy distribution. The ability to adjust power flow based on demand on smaller scales can help integrate AI. Oddly perhaps, AI itself can help to integrate AI data centers by utilizing machine learning to optimize power flows.

“By integrating AI into grid management, utilities can anticipate and respond to shifts in energy demand caused by data centers, ensuring a more stable grid overall.”

He also advocates for more energy storage systems, including large-scale batteries.

     Demand response programs, where businesses and consumers are incentivized to reduce their power usage during power demand peaks, is another tool that can help integrate AI data centers.

“Data centers are prime candidates for demand response because they can adjust their operations — such as shifting workloads to off-peak hours — without negatively impacting performance.”

He also thinks that deeper and smarter collaboration between technology companies, utilities, policymakers and local communities can help. Government incentives can be helpful.

     Stresses on power grids are now coming from many different sources including EVs, electrification of processes, heat pumps, and AI-based tech like smart cities, autonomous vehicles, AI-capable PCs and laptops, and Internet of Things (IoT) devices. Kathryn Ackerman wrote a July 2024 article for Sourceability that addresses some of these concerns. She emphasizes the need for smart grids and upgraded transmission:

Over 70% of U.S. transmission lines are over 25 years old and approaching the end of their 50-80-year lifecycle. According to the U.S. Department of Energy, “This has major consequences on our communities: power outages, susceptibility to cyber-attacks, or community emergencies caused by faulty grid infrastructure.”

In late mid-October 2023, the Department of Energy (DOE) tackled this problem with investments in the Grid Resilience and Innovation Partnerships (GRIP) Program to strengthen grid resilience and reliability. However, the power industry is still stuck between a rock and a hard place: costly upgrades and an unclean energy grid due to the lack of renewable energy located within a close distance.

“Demand for electricity in 2030 will be 14% to 19% higher than 2021 levels,” according to an analysis from REPEAT (Rapid Energy Policy Evaluation and Analysis Toolkit), an energy policy project led by Princeton professor Jesse Jenkins, states.

“A 21st-century grid has to accommodate steadily rising electricity demand to power electric vehicles, heat pumps, industrial electrification, and hydrogen electrolysis, and it needs to extend to new parts of the country to harness the best wind and solar resources. Both factors mean we simply need a bigger grid with more long-distance transmission,” Jenkins told CNBC.

     Ackerman proposes five power components that can reduce the strain on a grid from AI power demand: high-efficiency power converters, uninterruptible power supplies (UPSs), energy storage systems, smart grid technologies, and advanced cooling solutions.

     Power converters are devices that convert AC into DC and vice versa. High-efficiency power converters can minimize energy loss leading to greater efficiency.

“New technologies such as silicon carbide (SiC) and gallium nitride (GaN) are becoming popular in power electronics due to their superior efficiency and thermal performance compared to traditional silicon-based components.”

Many original component manufacturers (OCMs) are utilizing SIC components.

     Uninterruptible power supplies (UPSs) are required for many AI applications, especially training models. Typical UPS systems can last 8-15 years.

     Energy storage systems, when discharged, can help power grids during demand peaks and can prevent power fluctuations.

     Smart grid technologies can integrate AI into grid management, thereby helping to optimize the integration of AI data centers. These AI-based technologies can also help with demand response management and improve grid reliability.

“Innovative grid technologies that leverage AI can enhance grid reliability by offering predictive maintenance, load forecasting, and real-time grid monitoring. These systems can dynamically adjust power distribution based on demand patterns, optimizing energy usage and reducing the risk of blackouts.”

     Advanced cooling solutions include liquid cooling systems and advanced air-cooling technologies. She mentions some other cooling solutions without explanation, including solar, geothermal-driven, and free cooling. 

     Integrating AI has the same challenges as integrating other forms of electrification, namely how to make the grid and its processes more efficient, cleaner, and more responsive to changes in demand.

 

 

 

References:

 

Opinion. From code to current: How to keep AI data centers in check for a sustainable grid. Manav Mittal. Utility Dive. January 3, 2025. From code to current: How to keep AI data centers in check for a sustainable grid | Utility Dive

The Need for a Strong Power Grid Infrastructure in the Age of AI. Kathryn Ackerman. July 16, 2024. Sourceability. The Need for a Strong Power Grid Infrastructure in the Age of AI