So you want to build a solar or wind farm? Here’s how to decide where
The decision of where to build new solar or wind facilities is often left to individual developers or utilities, with limited overall coordination. But a new study shows that regional-level planning using fine-grained weather data, energy consumption information and energy system modeling can make a big difference in the design of such renewable energy installations. This also leads to more efficient and economically viable operations.
The findings demonstrate the benefits of coordinating the location of solar farms, wind farms and storage systems, taking into account local and temporal variations in wind, sunlight and energy demand to maximize the use of renewable resources. This approach can reduce the need for significant storage investments, and therefore overall system costs, while maximizing the availability of clean energy when it is needed, the researchers found.
The study, which appeared in the journal Cell Reports Sustainability, was co-authored by Liying Qiu and Rahman Khorramfar, postdocs in MIT’s Department of Civil and Environmental Engineering, and professors Saurabh Amin and Michael Howland.
Qiu, the lead author, says that with the team’s new approach “we can exploit resource complementarity, which means that renewable resources of different types, such as wind and solar energy, or different locations can compensate each other in time and space . This potential for spatial complementarity to improve system design has not been emphasized and quantified in existing large-scale planning.”
Such complementarity will become increasingly important as variable renewables account for a greater share of the energy entering the grid, she says. By more smoothly coordinating the peaks and troughs of production and demand, she says, “we’re actually trying to use the natural variability itself to address the variability.”
When planning large-scale renewable energy installations, Qiu says, some typically work at the country level, for example saying that 30 percent of the energy should be wind energy and 20 percent solar energy. That is very general. For this study, the team looked at both weather data and energy system planning models at a scale of less than 10 kilometers (about 6 miles) resolution. “It’s a way to determine where exactly we should build each renewable energy plant, rather than just saying this city should have this many wind or solar farms,” she explains.
To collect their data and enable high-resolution planning, the researchers relied on a variety of sources that had not been previously integrated. They used high-resolution meteorological data from the National Renewable Energy Laboratory, which is publicly available at 2-kilometer resolution but rarely used in a planning model at such a fine scale. This data was combined with an energy system model they developed to optimize the site with a resolution of less than 10 kilometers. To get a sense of how the small-scale data and model made a difference in different regions, they focused on three US regions – New England, Texas and California – analyzing up to 138,271 possible settlement locations simultaneously for a single region.
By comparing the results of location determination based on a typical method with their high-resolution approach, the team showed that “resource complementarity really helps us reduce system costs by matching renewable energy generation with demand”, which should translate directly into practice. decision-making, says Qiu. “If an individual developer wants to build a wind or solar farm and simply goes to where there is the most wind or solar energy on average, this does not necessarily guarantee that it is the best fit for a carbon-free energy system.”
This is due to the complex interactions between production and electricity demand, as both vary from hour to hour and month to month as the seasons change. “What we are trying to do is minimize the difference between energy supply and demand rather than simply providing as much renewable energy as possible,” Qiu says. “Sometimes your generation cannot be utilized by the system, while other times you don’t have enough to meet demand.”
In New England, for example, the new analysis shows that there should be more wind farms in locations where there is strong wind force at night, when solar energy is not available. Some locations tend to be windier at night, while others have more wind during the day.
These insights were revealed through the integration of high-resolution weather data and energy system optimization used by the researchers. When planning with lower resolution weather data, which was generated globally at a resolution of 30 kilometers and is more commonly used in energy system planning, there was much less complementarity between renewable energy plants. The total system costs were therefore much higher. The complementarity between wind and solar farms was enhanced by the high-resolution modeling due to an improved representation of the variability of renewable resources.
The researchers say their framework is highly flexible and can be easily adapted to any region to take into account local geophysical and other conditions. In Texas, for example, peak winds in the west occur in the morning, while along the south coast they occur in the afternoon, so the two naturally complement each other.
Khorramfar says this work “highlights the importance of data-driven decision making in energy planning.” The work shows that using such high-resolution data in combination with a carefully formulated energy planning model “can reduce system costs and ultimately provide more cost-effective pathways for the energy transition.”
One thing that was surprising about the findings, says Amin, principal investigator at the MIT Laboratory of Information and Data Systems, is how big the gain was from analyzing relatively short-term variations in inputs and outputs that occur in a 24-hour process. -hour period. “The kind of cost-saving potential of trying to leverage complementarities within a day was not something you would have expected before this study,” he says.
Additionally, Amin says, it was also surprising how much these types of models could reduce the need for storage as part of these energy systems. “This study shows that there is actually a hidden cost-saving potential in exploiting local weather patterns, which can result in a monetary reduction in storage costs.”
The system-level analysis and planning proposed in this study, Howland says, “will change the way we think about where we put renewable power plants and how we design those renewable plants so they can maximize service to the energy grid. It must go further than just reducing the energy costs of individual wind or solar farms. And these new insights can only be realized if we continue to collaborate across traditional research boundaries, integrating expertise in fluid dynamics, atmospheric science and energy engineering.
Research report:Low-carbon energy system planning with high-resolution spatial representation of renewable energy sources reduces costs
