AI adoption is skyrocketing. While witnessing such a large-scale technological revolution can be exciting, the sudden uptick in data center demand comes with some concerns. As AI continues to grow, it becomes harder to ignore its impact on the power grid.
Many energy companies are actively investing in AI to improve operations. At the same time, this technology may place excessive strain on an already struggling grid.
How AI May Strain the Power Grid
AI relies on data centers, and these facilities use a considerable amount of electricity. In 2022 alone, they consumed 460 terawatt-hours (TWh) of energy, and that was before the generative AI boom. As AI adoption continues on its current trajectory, this consumption could surpass 1,000 TWh by 2026.
Rising power demands are nothing new for the industry, but AI is driving them at an unprecedented scale. Without any adjustment, data centers could push electrical demands faster than the grid’s ability to keep up.
Compounding the issue is the current state of the nation’s energy infrastructure. The majority of transmission lines in the U.S. are over 25 years old, with 70% nearing the end of their useful service lives. Power outages and related issues have become increasingly common due to this aging, so an additional demand spike could push the infrastructure past its breaking point.
How AI Could Improve Grid Operations
At the same time, AI has several positive implications for the grid. As operators upgrade power lines and substations to prevent blackouts, AI could reveal the optimal way forward. By analyzing past trends and predicting future consumption, machine learning can identify which infrastructure needs attention first to make the biggest impact.
AI-powered grid technologies can also prevent power supply issues by making the grid more flexible. Smart transformers analyze usage and generation in real time to distribute the optimal amount of electricity to each area at any moment. This adaptation helps them reduce energy waste by 15% and improve voltage profiles by 12 percent. Such optimizations may mitigate strain and stop outages.
That flexibility may also prove vital in transitioning to renewable energy. Wind and solar are intermittent, so the grid must be able to store surplus electricity at peak generation and distribute stored power at peak demand. The only way to do so is through real-time analytics and adaptation, which smart transformers provide.
How to Balance AI and Energy Infrastructure Needs
Given its potential to both strengthen and harm the power grid, AI usage in this industry requires strategic planning. Utility companies can balance AI’s growth with grid reliability by following these steps.
Capitalize on Renewable Energy
Installing localized renewable power is one of the most critical measures in bolstering the grid amid rising demand. Wind and solar can provide grid-independent electricity to minimize rising consumption and prevent overloading the aging system.
Powering data centers through renewables is the best way forward. When these facilities can generate their own clean electricity, their consumption won’t translate into a demand spike on public energy infrastructure. While this strategy will entail high upfront costs, the money saved from outage prevention will make up for it in the long term.
Many areas have already seen success in moving to renewables, showcasing their potential as an alternate power source. Iceland gets nearly 100% of its electricity from hydropower and geothermal energy, and the U.K. has relied on renewables enough to phase out coal entirely.
Deploy Smart Grid Technology
Utility providers must also focus on using grid-improving AI applications. Smart transformers and related devices are the most impactful, so they should get attention and investment before other use cases.
Deploying smart transformers in outage-prone regions or those with particularly variable consumption trends will mitigate voltages to offset the impact of rising demand. Similar AI-powered technologies communicate maintenance data to users, which can inform faster, more effective repairs when necessary to prevent outages.
Like renewables, this strategy may be expensive upfront. Consequently, it’s best to apply smart grid technology to areas that need it most before expanding it.
Use AI for Targeted Grid Improvements
AI is a useful tool when planning these upgrades or other infrastructure improvements, too. Staving off the worst effects of an overloaded grid requires an understanding of which areas need which fixes and at what time. Such planning can be complicated, but AI makes it easier.
The National Renewable Energy Lab highlights how AI can run high-fidelity scenarios in digital models of the grid. This simulation reveals which upgrades may yield the best results in a given area. Using the insight to influence decision-making can help utility companies make the biggest changes in a shorter time.
This application removes the need for costly, time-consuming trial and error. Without that level of streamlining, large-scale optimizations may be too slow to account for skyrocketing electrical demands.
Embrace Long-Term Forecasting
Similarly, energy businesses should deploy AI to predict long-term usage and generation trends. Preventing excessive grid strain requires an understanding of future circumstances so organizations know how they must prepare. Machine learning can provide the necessary insight.
Researchers have found that AI is 12 times faster than conventional methods when calculating next-day energy demands and the most cost-effective way to dispatch energy. The same concept can apply to a longer-term scale as utilities collect more usage data. Over time and with more information, these predictions become increasingly accurate.
Regularly simulating future needs will inform any capacity upgrades, including which service areas need the higher generation most urgently. It can also reveal which regions may become prone to reliability issues, highlighting opportunities for large-scale upgrades. Conditions may change over time, so performing these analyses regularly is crucial.
AI’s Effect on the Grid Depends on Its Usage
How AI will impact the electrical grid hinges on how businesses use it. Varied, untargeted deployment of AI without any consideration of its unique energy needs will result in excessive strain and outages. By contrast, using it mainly as a tool to understand the grid and enable needed improvements will offset its high power demands.
As AI grows, so does the need to follow these steps. Adaptation and long-term planning are essential to ensure this technology does not cause widespread electrical problems