Google Advances Demand Response to Power AI-Driven Data Centers

As artificial intelligence (AI) technologies accelerate innovation and economic growth, meeting their substantial energy demands efficiently and reliably offers a unique chance to modernize the entire energy system, according to Google.

Google is pioneering flexible demand capabilities in its data center operations, allowing the company to shift or reduce power usage during critical hours or seasons, according to a statement on Google's site by Michael Terrell, head of Advanced Energy. This strategy, known as demand response, helps manage large electricity loads such as data centers more quickly and cost-effectively. It can reduce the need for new transmission lines and power plants, while enabling grid operators to maintain stability and efficiency amid rising electricity consumption across the U.S. and globally, Terrell wrote.

Google recently announced progress on this front through two new utility agreements with Indiana Michigan Power (I&M) and the Tennessee Valley Authority (TVA). These partnerships mark the first time Google is applying demand response specifically to machine learning (ML) workloads— a growing energy demand in AI operations. This initiative builds on a successful pilot with Omaha Public Power District (OPPD) last year, where Google reduced ML-related power demand during three grid events.

“As we add new large loads to our system, it is critical that we partner with our customers to effectively manage the generation and transmission resources necessary to serve them," said Steve Baker, president and COO of I&M. "Google’s ability to leverage load flexibility as part of the strategy to serve their load will be a highly valuable tool to meet their future energy needs.”

Google’s approach aligns with its 24/7 carbon-free energy goal, combining clean energy procurement with demand-side solutions that provide flexibility. By temporarily shifting non-urgent computing tasks, such as processing YouTube videos, during peak demand periods, Google has already collaborated with partners like Centrica Energy and Belgium’s Elia grid operator, as well as Taiwan Power Company, to help maintain grid reliability.

With AI adoption surging, Google sees expanding demand response to include ML workloads as crucial to managing substantial new energy loads. This strategy is particularly vital where power generation and transmission are constrained, offering a promising tool to support grid reliability while facilitating infrastructure investment and growth.

Despite its promise, demand flexibility remains in early stages and is location-specific. Google notes that high reliability requirements for critical services like Search, Maps, and healthcare cloud customers limit how much flexibility any data center can offer. Still, integrating ML workloads into demand response represents a significant step forward, enhancing grid stability and lowering costs.

Looking ahead, Google plans to work closely with utilities and system operators to embed demand flexibility into long-term resource planning and data center deployment. While managing data center load growth will require a diverse portfolio—including new generation and transmission—demand response is poised to play a key role.