In the digital era, artificial intelligence (AI) technology is profoundly changing the way we live and work with its powerful data processing capabilities and intelligent applications. However, behind the rapid development of this technology, there are huge energy challenges. Recently, the North American Electric Reliability Corporation (NERC) warned that the rapid growth of AI applications could lead to large-scale power shortages and even blackouts in the United States and Canada by 2025. This warning has aroused widespread concern, and China Exportsemi will analyze the root cause of the problem and possible solutions from multiple perspectives such as AI power consumption growth, grid carrying capacity, and renewable energy utilization.
The surge in electricity demand driven by AI growth
The widespread application of AI is inseparable from huge data centers and high-performance computing equipment. According to the International Energy Agency (IEA), the global data center consumed about 500 TWh of electricity in 2022, accounting for about 2% of global electricity demand. With the explosive growth of generative AI, machine learning and other technologies, this number is expected to double to 1,000 TWh by 2026.
It is predicted that AI training and inference tasks require much more electricity than traditional computing tasks. For example, large language models, such as ChatGPT, need to process massive amounts of data and respond to user needs in real time. The data shows that ChatGPT responds to about 200 million requests a day and consumes more than 500,000 kilowatt hours of electricity, which is equivalent to the average daily electricity consumption of 17,000 American households. Such high energy requirements are putting unprecedented pressure on the power grid.
The reliability crisis faced by the North American power grid
The North American power grid has long been known for its wide coverage and technological advancement, but in recent years its reliability problems have become increasingly apparent. In its latest Long-Term Reliability Assessment report, NERC notes that the carrying capacity of the grid is being overtaken by rapidly increasing demand for electricity, especially during peak summer demand.
1. The Midwest bears the brunt: NERC predicts that the U.S. Midwest could experience the worst power outages in the next two years due to aging grid equipment and dwindling backup power reserves.
2. Pressure across North America: Even in more developed regions, such as California and Texas, there have been several outages in recent years triggered by extreme weather or surges in demand, which have sounded alarm bells for power supply across North America.
In addition, the peak power demand (i.e., instantaneous power demand) of AI tasks further exacerbates the instability of the power grid. Traditional grid designs are not designed for such frequent and large power fluctuations, making grid load management a significant technical challenge.
Figure: AI power consumption is soaring, and the North American power grid is in an emergency, and it may face a power outage crisis in 2025
The contradiction between supply and demand between fossil fuels and renewable energy
Another major challenge for the North American power grid is the transformation of the energy mix. In recent years, the United States and Canada have phased out fossil fuel power generation while accelerating the rollout of renewable energy. But this transition has not fully closed the power gap caused by the withdrawal of fossil fuels.
1. Rapid growth in electricity consumption: The U.S. Energy Information Administration (EIA) expects total U.S. electricity consumption to reach 4,086 billion kWh in 2024 and climb further to 4,165 billion kWh in 2025, both surpassing the all-time high of 4,067 billion kWh in 2022.
2. Insufficient new generation capacity: While solar and wind capacity continues to increase, their intermittent and unstable capacity limits the ability of renewables to contribute to the grid in an immediate manner. At the same time, the development of battery energy storage technology has not yet reached the level of maturity required for large-scale deployment.
In this context, there is a "time gap" between renewable energy and fossil fuels, that is, it is difficult for new renewable energy to quickly replace retired fossil fuel power generation facilities in the short term.
Technical details and data support for the increase in AI power consumption
The energy consumption of AI technology is not limited to data centers, but is also closely related to hardware usage and training processes. Here are some key data points:
1. GPU power consumption: The GPU (graphics processing unit) used for AI tasks consumes much more energy than traditional CPUs (central processing units). At present, the average power consumption of mainstream AI GPUs is 650 watts, and high-performance GPUs even reach more than 1000 watts.
2. Global AI industry electricity demand: It is estimated that by 2027, the annual electricity consumption of the entire AI industry will be between 85 and 134 TWh.
3. Energy consumption for a single model training: Taking GPT-3 as an example, its single training power consumption is as high as 1280 MWh, which is equivalent to the energy required by a fuel vehicle to drive 1.2 million kilometers continuously.
These data clearly show that the rapid development of AI is exacerbating the growth of electricity demand.
Solutions and sustainable development paths
Despite the severe power crisis in North America, multiple sectors are exploring solutions to alleviate the pressure caused by AI power consumption.
1. Optimize grid infrastructure
1. Enhance grid flexibility: Improve the grid's responsiveness to instantaneous power demand through the introduction of smart grid technology.
2. Improving transmission efficiency: Aging transmission lines are updated to reduce losses in the power transmission process.
2. Promote renewable energy and energy storage technologies
1. Increase investment: Accelerate the construction of clean energy projects such as solar and wind energy.
2. Deploy energy storage systems: Develop large-scale energy storage batteries to balance the impact of intermittent energy generation on the grid.
3. Improve the energy efficiency of AI technology
1. Hardware optimization: Develop AI-specific chips with lower energy consumption.
2. Algorithm optimization: Reduce the amount of computation and energy consumption by improving the model structure and training methods.
Global cooperation and experience sharing
The energy crisis is not unique to North America, and many parts of the world are exploring ways to cope with the development of energy-intensive technologies. Europe has a relatively mature experience in promoting clean energy and energy efficiency, such as its strict energy efficiency standards and strong support for green energy. These experiences can provide an important reference for the North American region.
At the same time, China has also made significant progress in the field of artificial intelligence and renewable energy in recent years, such as optimizing the layout of data centers through the "Eastern Data and Western Computing" project to reduce the burden of electricity in the east. This practice provides a valuable reference for how to balance AI development and energy structure adjustment in North America.
Conclusion
The rapid development of AI technology has promoted the progress of society, but it has also brought unprecedented energy challenges. The emergency of the North American power grid is not accidental, but the epitome of energy contradictions in the AI era. In the future, how to find a balance between technological innovation and energy sustainability will become a key issue for social development.
By improving grid efficiency, accelerating the clean energy transition, and improving the energy efficiency of AI technologies, we may be able to reap the dividends of AI without paying a heavy price from the energy crisis. The North American power crisis is not only a regional issue, but also a global issue, calling for all countries to work together to face the energy challenges in the AI era.
