Deciding where to build solar and wind farms is often left to individual developers, with little coordination across regions.

But a new study from MIT shows that better planning at a regional level, using detailed weather data and energy modeling, can make renewable energy systems much more efficient and cost-effective.

The research, published in Cell Reports Sustainability, highlights the importance of placing solar panels, wind turbines, and storage systems in locations that complement each other’s strengths.

By carefully coordinating where and how these energy systems are built, it’s possible to reduce costs, minimize the need for expensive energy storage, and make clean power more available when it’s needed.

Lead author Liying Qiu explains that different renewable energy sources can work together to balance energy supply and demand.

For example, solar panels produce energy during the day, while some wind farms are windier at night.

Combining these “complementary” resources makes the entire energy system more reliable and cost-efficient.

“We are using the natural variability of renewable energy sources to address the challenges of variability,” Qiu says.

The researchers focused on three U.S. regions—New England, Texas, and California—and analyzed more than 100,000 possible locations for solar and wind farms.

They used high-resolution weather data from the National Renewable Energy Laboratory, combined with their own energy system modeling, to guide their decisions.

Traditional planning methods often work at a broad scale, such as setting general goals like 30% wind power and 20% solar power for a region.

In contrast, the MIT team worked on a much finer scale—less than 10 kilometers (about 6 miles)—to figure out exactly where to build renewable energy plants for maximum efficiency.

For instance, in New England, the study revealed that wind farms should be located where nighttime winds are strongest to complement solar energy, which is only available during the day.

Similarly, in Texas, winds peak in the west in the morning and along the south coast in the afternoon, making these locations ideal for pairing wind farms with local energy needs.

By aligning energy production with demand, the researchers found that their high-resolution approach could significantly lower the total cost of renewable energy systems. One surprising finding was how much money could be saved by focusing on daily variations in weather patterns.

This method also reduced the need for costly storage systems like batteries, which are typically used to store energy for times when demand is high but renewable generation is low.

“This shows there’s a hidden cost-saving potential in using local weather patterns,” says Saurabh Amin, one of the study’s authors.

The researchers believe their planning method could be applied to any region, as it accounts for local weather patterns and energy needs. Co-author Rahman Khorramfar emphasizes that this data-driven approach “can drive system costs down and make the transition to renewable energy more affordable.”

By combining insights from fluid dynamics, atmospheric science, and energy engineering, the study provides a new way of thinking about renewable energy planning.

It’s not just about building the cheapest solar or wind farms but designing systems that work together to serve the grid efficiently and cost-effectively. This smarter approach could play a key role in the global transition to cleaner energy.