How is Google Trialling Demand Response in its Data Centres?

Google is adapting how it runs AI tasks in its data centres in an effort to reduce the load on strained electricity grids and support clean energy integration. 

Through a strategy known as demand response, the company is shifting machine learning (ML) workloads to periods of lower demand or when clean energy is more readily available.

The approach is designed to meet the growing power needs of hyperscale AI infrastructure without requiring costly upgrades to transmission lines or generating capacity. 

In the US, the International Energy Agency (IEA) estimates that nearly half of the country's expected increase in electricity demand through to 2030 will come from data centres, largely driven by AI.

To avoid contributing further to this pressure, Google is rethinking its infrastructure operations and making AI workloads less demanding on power grids.

Making workloads flexible

Instead of relying on traditional peak capacity planning, Google is now targeting the flexibility of its own data centre operations. 

The aim is to adjust power usage dynamically in response to local grid conditions, particularly for energy-intensive AI training processes.

“Innovation isn’t just about developing brand new shiny things,” says Kate Brandt, Google’s Chief Sustainability Officer.

“In fact, some of the most important innovations come from collaborations to make existing systems more intelligent – and in this case, more flexible.

“We’re sharing our advancements with new flexible demand capabilities in our data centres, now for the first time by targeting ML workloads.

“This new approach can support AI growth and our grid partners at the same time – helping utilities reliably and cost-effectively meet the electricity needs of all their customers.

“While this is still early stages, we see demand response as a promising tool to shift or reduce our power demand, providing flexibility when the grid needs it most.”

Demand response typically involves large electricity users adjusting their consumption based on grid signals, price incentives or peak demand events. 

The technology has been applied to buildings and industry for years but has now become relevant to data centres and cloud infrastructure due to the surging power demands of AI.

Pilots with power utilities

Google has launched pilot projects with several US utilities to put the concept into action.

It has already run trials with Omaha Public Power District (OPPD), Tennessee Valley Authority (TVA) and Indiana Michigan Power (I&M), demonstrating the ability to reduce AI-related demand during critical grid events.

“I&M is excited to partner with Google to enable demand response capabilities at their new data centre in Fort Wayne, IN,” says Steve Baker, President and Chief Operating Officer of I&M.

“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.

“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.”

Dual benefits for grid and business

According to Google, this approach allows faster deployment of data centres, especially in regions where adding generation or transmission capacity is constrained. 

By matching compute cycles with grid availability, the company avoids costly infrastructure upgrades and reduces carbon emissions.

Michael Terrell, Google’s Head of Advanced Energy, explains: “By including load flexibility in our overall energy plan, we can manage AI-driven growth even where power generation and transmission are constrained.

“Incorporating ML workloads is an important step to enable larger scale demand flexibility, delivering grid reliability and cost-saving benefits in the places where these capabilities are deployed.

“By engaging in long-term resource planning with utility partners like I&M and TVA, we can integrate flexibility into future grid development alongside Google’s data centre infrastructure deployment.

“Looking forward, we remain committed to collaborating with system operators, utilities and industry partners to capture AI’s immense opportunity while supporting a clean, reliable and affordable energy system for everyone.”