When it comes to deciding where to construct new solar or wind facilities, the process often hinges on individual developers or utility companies, leading to limited coordination. However, recent research indicates that regional-level planning—utilizing detailed weather data, energy consumption insights, and advanced energy system modeling—can significantly enhance the effectiveness and economic viability of these renewable installations.
The research demonstrates the advantages of strategically locating solar farms, wind turbines, and energy storage systems by considering local and temporal fluctuations in sunlight, wind, and energy demand. This comprehensive approach not only optimizes renewable resource use but also lessens the dependence on expensive storage solutions, thereby reducing overall system costs while ensuring the accessibility of clean energy when it is most needed.
The study, published today in Cell Reports Sustainability, was co-authored by postdoctoral researchers Liying Qiu and Rahman Khorramfar from MIT’s Department of Civil and Environmental Engineering, alongside professors Saurabh Amin and Michael Howland.
Liying Qiu, the lead author, emphasizes that their innovative approach allows for the harnessing of resource complementarity—where different types of renewable resources, like solar and wind, can mutually compensate for each other across time and geography. “This potential for spatial complementarity to improve system design has not been sufficiently recognized or measured in large-scale energy planning,” she points out.
As variable renewable energy sources increasingly contribute to the electricity grid, this complementarity becomes ever more crucial. By synchronizing production peaks and demand fluctuations, the team aims to utilize the natural variability of resources to effectively address energy supply challenges.
Traditionally, large-scale renewable energy planning tends to occur at a macro level. For instance, guidelines might specify that 30% of a country’s energy should derive from wind and 20% from solar. This study, however, examined localized factors on a scale of less than 10 kilometers (approximately 6 miles). “Our approach seeks to determine the exact locations for each renewable energy installation rather than merely estimating how many wind or solar farms a city might need,” explains Qiu.
To gather their data and support high-resolution planning efforts, the researchers integrated previously uncombined sources. They utilized high-resolution meteorological data from the National Renewable Energy Laboratory, which is often overlooked in planning models, but available at a fine 2-kilometer resolution. This data was fused with a customized energy system model to optimize placement decisions at an even finer scale, covering three U.S. regions—New England, Texas, and California—and analyzing up to 138,271 potential siting options within a single region.
By contrasting their high-resolution methodology against conventional siting practices, the researchers found that “resource complementarity significantly lowers system costs by aligning renewable generation with demand.” This insight is crucial for real-world decision-making. “If a developer simply targets the areas with the highest average wind or solar resources, it may not yield the best integration into a decarbonized energy framework,” Qiu cautions.
This challenge arises from the dynamic interactions between energy production and consumption, which change hourly and seasonally. “Our goal is to minimize the discrepancy between energy availability and demand, rather than just maximizing renewable energy output,” Qiu further explains. “Sometimes energy generation exceeds demand, while at other times there isn’t enough to meet consumer needs.”
For instance, in New England, the analysis recommends placing more wind farms in areas that capitalize on strong nighttime winds when solar options are unavailable. Certain regions may exhibit stronger wind resources at night, while others perform better during daylight.
The integration of high-resolution weather data and energy optimization revealed critical insights that are often obscured when relying on lower-resolution models, which are typically generated at a 30-kilometer scale. Such generalized planning diminishes the observed complementarity among renewable power sources, leading to substantially increased system costs. The enhanced modeling from high-resolution data allows for better representation of resource variability, thus promoting cost-effective energy solutions.
The framework devised by these researchers is highly adaptable, enabling application in various regions to reflect unique geographical and climactic conditions. For example, in Texas, peak winds occur in the morning in the west and in the afternoon along the southern coast, making them complementary.
Khorramfar highlights that this research underscores the essential role of data-driven strategies in energy sector planning. The findings illustrate that embracing high-resolution data along with tailored planning models can drive down costs, paving the way for more economically viable pathways to energy transition.
Amin, serving as a principal investigator in the MIT Laboratory of Information and Data Systems, notes the unexpected magnitude of the benefits derived from analyzing short-term variations over a single day. “The potential savings by leveraging daily resource complementarity were surprising,” he states.
Moreover, Amin points out the reduction in storage requirements that this modeling approach can yield, revealing a hidden cost-saving opportunity in optimizing local weather patterns, ultimately leading to a reduction in storage expenses.
Howland adds that this system-level analysis and planning shifts our perspective on renewable plant siting and design, ensuring they serve the energy grid more effectively. “It’s not just about reducing costs for individual wind or solar farms. These new insights can only be unlocked through interdisciplinary collaboration, combining expertise in fluid dynamics, atmospheric science, and energy engineering,” he states.
This groundbreaking research received support from the MIT Climate and Sustainability Consortium and MIT Climate Grand Challenges.
Photo credit & article inspired by: Massachusetts Institute of Technology
