Low-level sea clouds can make offshore wind farms more efficient, say researchers at the University of Twente in the Netherlands. This increase in efficiency is due to atmospheric mixing effects that shrink the wake of slower air behind nearby turbines, and the team say it could help wind farm designers make the most of ambient conditions.

Scientists have known for decades that multiple wind farms can be significantly less efficient than a single, isolated one. This is because air slows down as it moves through turbine blades, creating a wake that impairs the performance of wind farms downstream. This slow-air wake may extend for tens of kilometres, and in extreme cases it can even stretch beyond 100 km. “How quickly the wake recovers depends on how effectively atmospheric mixing pulls air from the stronger wind above and on the sides,” explains Davide Selvatici, a physicist who co-led the research with his colleague Richard Stevens.

In their latest study, which appears in PRX Energy, Selvatici and Stevens show that the presence of low-level stratocumulus clouds can enhance this mixing via a process called radiative cooling. This occurs when air at the top of a cloud radiates heat into space and becomes cooler, much like the ground does on a clear night. Because cooler air is denser than the surrounding air, it sinks, generating an enhanced downward motion that intensifies atmospheric turbulence. This ongoing mixing helps sustain the cloud by drawing moisture up from the surface below.

Fully three-dimensional simulations

To piece together this picture, Selvatici and Stevens needed to accurately model the turbulence in cloud tops and the way that air flows through a wind farm. They used large-eddy simulations to study how low-lying clouds impact the length of a wind farm’s wake. They also performed high-resolution simulations of stratocumulus-topped boundary layers across a range of atmospheric conditions: clear skies, cloudy skies without radiative cooling; and cloudy skies with radiative cooling.

These simulations are fully three-dimensional, and Selvatici explains that creating them meant solving equations within the model to determine how wind moves in all three directions, while also incorporating changes in temperature, water vapour and liquid water suspended within the cloud. For this, they relied on the Navier-Stokes equations (the standard equations used to describe the movement of fluids such as air) with some modifications. “We use an extended set of equations to include the effects of heat and moisture and to represent the energy and momentum extracted by the wind turbines under local wind conditions,” Selvatici explains.

“Effects can be significant”

The pair’s results indicate that the effects of cloud-top cooling can be significant. When these effects are present, the mean wind speed within a wake returns to approximately 90% of the incoming wind speed about 17 km beyond the end of the wind farm. In simulations without cloud-top cooling, however, the mean wind speed remained below 90% even 32 km beyond the end of the farm.

“Clouds are inherently coupled to the atmosphere, making it difficult to isolate their effects, but by comparing simulations with and without cloud-top cooling, we identified cloud-top cooling as an important process for wind farm wake recovery,” Selvatici explains.

Since wind farm power production increases with the cube of wind speed, even a modest reduction in wind speed can have a considerable effect, he adds. The problem is that most existing wind-farm models either simplify cloud-related processes or exclude them altogether. “Our results demonstrate that these should be taken into account,” says Stevens, “particularly in regions with frequent low-level clouds as over the North Sea, where many wind farms are found and where the annual cloud coverage is higher than 30%.”

Stevens adds that a better understanding of these effects could improve wind farm design and inform how far individual wind farms should be placed from each other. However, Selvatici notes that they’re not yet able to offer definite practical advice. “We cannot conclude whether wind farms should indeed be placed closer together in cloudy regions because our simulations represented idealized conditions,” he says.

The post Low-level clouds could make wind farms more efficient appeared first on Physics World.

Oracle’s proposed investment in 2 gigawatts of renewable energy projects for New Mexico comes as local opposition could delay development of the Project Jupiter data center that Oracle is building for OpenAI.

The two tech companies are developing the $165 billion Project Jupiter data center in Santa Teresa, New Mexico, as part of the broader Stargate AI infrastructure project announced by President Donald Trump in 2025. But Project Jupiter faces local protests and court battles over concerns about its environmental impacts—and the latest Oracle announcement on September 8 seeking proposals for renewable energy projects does not change the fact that the data center will be powered by fuel cells that consume natural gas.

“Like all matching programs, this would be synthetic in the sense that 2 GW of renewables wouldn't directly power the data center,” wrote Michael Thomas, CEO of the Cleanview data platform that tracks renewable energy and data center projects, in a LinkedIn post.

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Just when it looked like Net Zero sceptics were winning the argument, the green lobby hit back. But David Turver says scratch beneath the headlines and the data simply doesn't stack up.

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“Solar towers” that convert sunlight into heat could play a useful role in the renewable energy transition, but high installation costs and complex requirements limit their attractiveness to commercial operators. A team of researchers from Germany’s Karlsruher Institut für Technologie (KIT) and the German Aerospace Center (DLR) is now taking steps to change that. By creating a freely accessible database of information about how these plants work, they aim to accelerate research on solar thermal energy and drive improvements that could allow the technology to be more widely deployed in the future.

Unlike photovoltaic panels, concentrating solar power (CSP) plants do not convert sunlight directly into electricity. Instead, they generate heat by using an array of movable mirrors called heliostats to funnel sunlight onto a receiver located at the top of a tower. This heat can be used to drive a turbine to produce electricity immediately, but it can also be stored and used to generate electricity at night or on cloudy days. Depending on the plant’s location and setup, the heat could even be used directly for industrial applications and district heating.

The problem, explains study leader Kaleb Phipps, is that operating solar power tower plants safely and efficiently is a complex and expensive task. “CSP technologies have the potential to help the transition to a renewable energy system,” says Phipps, a research fellow at KIT’s Scientific Computing Center. “However, they are still not financially viable and there are operating challenges associated with them.”

Before researchers can develop and reliably test new, improved processes, Phipps explains that they need access to real-world operational CSP data. “We believe that modern data-driven machine learning methods like ours can help solve a lot of these problems,” he says.

849 gigabytes of operational data

To construct their database, which is known as PAINT, researchers collected data from the CSP tower near Jülich, Germany. These data include the exact positions of the tower’s 2014 mirrors, their dimensions, how they are warped and the way they rotate and tilt. The team also obtained fine-grained weather data for the entire 2021-2024 period under study, plus a further 218 000 images to help determine whether the mirrors were efficiently directing sunlight precisely to the top of the tower.

All in all, the PAINT database contains 849 gigabytes of operational data organized using the SpatioTemporal Asset Catalog (STAC) standard. To make these data FAIR (findable, accessible, interoperable and reusable), the researchers developed software to simplify access to the database and then published the data in a way that is freely available to all users.

Their goal, they say, is for other researchers to use this FAIR database to develop “digital twins”, or virtual replicas, of real-world CSP plants. When combined with machine-learning models, such twins can be used to answer questions such as whether the mirrors are aligned for maximal light absorption (one of the most important parameters for efficient operation) in near real time.

Community reaction

“The fact that we are receiving many positive responses regarding the data and that people seem to appreciate it is our biggest success,” Phipps says. Such responses are especially gratifying, he adds, because constructing databases like PAINT can be an arduous task.

“The main difficulties were about collecting the data, cleaning and unifying it into standard formats and parsing and organising all the metadata, which was originally complete chaos,” Phipps recalls. “We therefore put a lot of time and effort into creating standardized and automated workflows so that the process is as transparent as possible and can be reused for future data.”

The team also faced challenges when trying to convince peers that this work counted as “research”, he adds. “The fact that the data itself didn’t directly result in a new algorithm or method meant that lots of people didn’t believe we would be able to publish the results or achieve anything with them,” says Phipps. “Luckily, we were able to prove the doubters wrong.”

While the PAINT database currently contains data from only one CSP, the KIT researchers, who report their work in Nature Energy, are hoping to expand it. “We really need contributions from other CSP power plants around the world to transform the database into a truly international and diverse source for researchers,” Phipps says. “As data from different facilities is added in the future, it will be possible to develop a common standard for open operational data in solar tower research. This would speed up the development and promote a widespread adoption of this technology.”

The post Solar tower power plants get a data boost appeared first on Physics World.

Thousands of electric vehicles in Waymo’s autonomous robotaxi fleet may eventually give up their used batteries for a very different purpose—contributing up to hundreds of megawatt-hours of stationary energy storage to local power grids.

That prospect comes from a “strategic supply agreement” announced by Waymo and B2U Storage Solutions on June 4. B2U has been repurposing thousands of used batteries from various electric vehicles by installing them in large stationary energy storage projects. Such energy storage facilities can capture excess renewable energy during low demand periods and release such energy when local power grids are experiencing peak demand periods.

“Our business is getting the full residual value out of electric vehicle batteries after they're no longer suitable for automotive use,” Freeman Hall, CEO of B2U Storage Solution, told Ars. “Waymo puts a lot of miles on EVs and their model is expanding rapidly, and so we're just very pleased and honored to be able to work with them.”

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