The Need for a Strong Power Grid Infrastructure in the Age of AI

Artificial intelligence (AI) has quickly become a disruptive technology that various sectors are working overtime to incorporate. AI's benefits, such as increased productivity and reduced costs, have made it a popular technology for all business operations from healthcare to finance. Even consumers can enjoy greater personalization and assistance from AI applications outside enterprises, further fueling AI integration demand.

Over the next decade, AI is set to grow astonishingly. Statista's research reveals, “The market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024, a considerable jump of nearly 50 billion compared to 2023. This staggering growth will continue with the market racing past 826 billion U.S. dollars in 2030.”

AI will soon be integrated into various industries and market sectors. For example, AI laptops will dominate most of the market share in the coming years.  

Statista reports, "The PC market is expected to observe rapid growth in the proportion of PCs capable of handling artificial intelligence (AI) directly on the device. The share of AI-capable PCs among total PC shipments is forecast to grow from 19% in 2024 to 60% by 2027.”

With applications such as generative AI and other large language models (LLMs) sustaining the AI revolution, original component manufacturers (OCMs) and tech giants are pouring their attention into data centers to handle the necessary bandwidth. In a recent article by Goldman Sachs, AI is expected to cause a surge in data center power demand by 160% by 2030. The article notes, "On average, a ChatGPT query needs nearly ten times as much electricity to process as a Google search.”

Accurate estimates on the amount of energy required to power AI models are only “partial and contingent, offering only a glimpse of AI’s total energy usage. This is because machine learning models are incredibly variable, able to be configured in ways that dramatically alter their power consumption.”

Furthermore, the companies using AI the most, such as Meta, Microsoft, and OpenAI, aren’t exactly open with sharing this information. Most industry experts agree that the total amount is probably extremely high depending on the parameters a single model has. These numbers are likely to expand as greater demands are placed on the data centers supporting the workloads.

Now, where does that lead us? To support the growing energy demand required to run AI queries, there needs to be stable access to the energy powering these computational inquiries: electricity. With the ever-increasing demand for AI set to explode in the coming decade, power will see equivalent growth, which leaves the AI industry facing the same problem the electric vehicle (EV) market faces: unreliable power grid infrastructure.  

The Impact of AI on Power Demand

It is a simple fact that AI systems consume significant amounts of electricity, particularly those that utilize deep learning and large-scale data processing. Data centers require powerful servers, cooling systems, and power supplies to process AI's demands. The growing integration of AI innovations, including smart cities, autonomous vehicles, and Internet of Things (IoT) devices, will further strain an already overtaxed power grid.  

Power grid disruptions, or power outages, can contribute to operational losses, safety risks, increased costs, and data corruption. With the growing use of AI in different services, especially within high-reliability industries like healthcare, having a robust and resilient power grid infrastructure to support a developing AI ecosystem and other technology is pertinent for countries.

Unfortunately, as enterprises evolve, some industries have fallen behind. The electric grid, the backbone of a country’s economy, national security, and community health and safety, is growing more vulnerable to modern energy demands and weather in the, especially in the United States.  

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.

“Throw in resiliency benefits of stronger inter-regional grid connections so a region that’s struggling with an extreme event can call on its neighbors for help, and you’ve got even more reason to build a stronger, bigger grid.”

Furthermore, aging equipment can cause more damage than unreliability; the inability to incorporate renewable energy resources into the grid wastes potential power. As green initiatives rise, the unclean power grid will become more of a problem, especially when the emissions will double with the increased demands for electrical energy.

Building a solid power grid infrastructure must be done now while AI integration is still in the relatively early stages. Companies are leveraging AI to help optimize power consumption, reducing waste where possible, which can also be used for power grids. Likewise, the more modernized and smarter the power grid infrastructure becomes, the more efficient future updates and renewable energy scalability will be.

Industries must use the best components to maximize energy efficiency and relieve the strain on a developing power grid to ensure its optimization for the future.

5 Tools to Help Manage AI Demands on Power Infrastructure

Organizations must prioritize components and technologies that best aid power distribution and management to support the growing AI industry and power grid infrastructure. Optimizing energy usage and operations can reduce strain on data center demands, lessening the need for overwhelming amounts electricity. Cutting down on frivolous expenditures can take pressure off outdated power grids as the organizations work to modernize old systems.

It is a collaborative effort that requires participation from AI users and power grid operations. Several efficient power components can help manage these harsh demands and optimize energy demands.

1. High-Efficiency Power Converters

Power converters are electrical or electro-mechanical devices that convert alternative current (AC) into direct current (DC) and vice versa. High-efficiency power converters are crucial for transforming electrical energy into usable forms while reducing energy loss. The energy loss helps increase the overall efficiency of data centers and AI. 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. Several OCMs, including Wolfspeed, STMicroelectronics, and Infineon produce SiC components.

2. Uninterruptible Power Supplies (UPS)

AI applications, especially training models, often require a consistent power supply to maintain operation integrity. To prevent data loss or corruption if a power grid failure occurs, having a UPS system with backup power ensures AI processes can continue without interruption. Many modern UPS systems are integrated with advanced batteries and energy storage solutions to provide longer durations of backup power and better performance. A typical UPS system with battery replacements can last at least 10,000 hours or anywhere from 8 to 15 years. These systems are essential for modern-day operations, while current power grid infrastructure is fragile to severe weather and hackers.

3. Energy Storage Systems

A vital piece to balance supply and demand on power grids is energy storage systems (ESS). ESS enables energy transition and accelerates renewables for long-duration energy storage that is sustainable. Batteries are a type of ESS. Solar and thermal energy storage, compressed-air storage, flywheels, and more are other kinds of ESS. These technologies store excess energy during low-demand periods and release it during peak times. For AI applications, ESS can provide a buffer against power fluctuations, ensuring a stable power supply. Lithium-ion and emerging solid-state batteries offer high energy density and reliability, making them suitable for supporting AI infrastructure.

4. Smart Grid Technologies

An essential step in modernizing power grid infrastructure will be the integration of AI with the power grid itself. 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. Combining this with data centers that combine AI to handle energy demand efficiently will further optimize expenditures to prevent wasteful energy use.

5. Advanced Cooling Solutions

Have you ever heard your computer fan working overtime when running too many programs simultaneously? Data centers housing AI hardware generate substantial heat, necessitating efficient cooling solutions to maintain optimal performance and prevent overheating. Liquid cooling systems and advanced air-cooling technologies are essential for managing the thermal loads of high-performance computing environments. Likewise, plenty of sustainable options for cooling technologies can help reduce emissions used to cool systems, such as solar, geothermal-driven, and free cooling.  

Moving Forward

Investing in and upgrading power grid infrastructure is imperative to ensure a sustainable future where AI can thrive. Policymakers, industry leaders, and researchers must collaborate to develop and implement new systems that prioritize renewable energy and can adapt to future demand loads. This includes upgrading transmission lines, deploying innovative grid technologies, and investing in renewable energy sources to power AI data centers sustainably.

AI technology continues to advance and become more integral in various industries. As its influence expands, demand for a robust and reliable power grid infrastructure will only increase. By leveraging high-efficiency power components and innovative energy management solutions, companies can optimize the energy required by AI to put less stress on an already overtaxed power grid system.

Sourceability works with thousands of franchised, authorized, and qualified third parties that supply leading-edge components for numerous applications, including power. With our global team of experts, we can help you get the parts you need to create reliable power systems that reduce costs and manage energy efficiently. Our franchise partnerships offer design expertise on utilizing these components in your next product design, ensuring you get the most benefits from any product.  

Contact our experts today to start sourcing your power component of choice.