Researchers have secured $3.3 million in funding to develop an AI-powered subsurface sensing system aimed at improving the safety and efficiency of underground power line installation. Photo via Getty Images

Researchers from the University of Houston — along with a Hawaiian company — have received $3.3 million in funding to explore artificial intelligence-backed subsurface sensing system for safe and efficient underground power line installation.

Houston's power lines are above ground, but studies show underground power is more reliable. Installing underground power lines is costly and disruptive, but the U.S. Department of Energy, in an effort to find a solution, has put $34 million into its new GOPHURRS program, which stands for Grid Overhaul with Proactive, High-speed Undergrounding for Reliability, Resilience, and Security. The funding has been distributed across 12 projects in 11 states.

“Modernizing our nation’s power grid is essential to building a clean energy future that lowers energy costs for working Americans and strengthens our national security,” U.S. Secretary of Energy Jennifer M. Granholm says in a DOE press release.

UH and Hawaii-based Oceanit are behind one of the funded projects, entitled “Artificial Intelligence and Unmanned Aerial Vehicle Real-Time Advanced Look-Ahead Subsurface Sensor.”

The researchers are looking a developing a subsurface sensing system for underground power line installation, potentially using machine learning, electromagnetic resistivity well logging, and drone technology to predict and sense obstacles to installation.

Jiefu Chen, associate professor of electrical and computer engineering at UH, is a key collaborator on the project, focused on electromagnetic antennas installed on UAV and HDD drilling string. He's working with Yueqin Huang, assistant professor of information science technology, who leads the geophysical signal processing and Xuqing Wu, associate professor of computer information systems, responsible for integrating machine learning.

“Advanced subsurface sensing and characterization technologies are essential for the undergrounding of power lines,” says Chen in the release. “This initiative can enhance the grid's resilience against natural hazards such as wildfires and hurricanes.”

“If proven successful, our proposed look-ahead subsurface sensing system could significantly reduce the costs of horizontal directional drilling for installing underground utilities,” Chen continues. “Promoting HDD offers environmental advantages over traditional trenching methods and enhances the power grid’s resilience.”

University of Houston professor Xiaonan Shan and the rest of his research team are celebrating fresh funding from a federal grant. Photo via UH.edu

Houston scientists land $1M NSF funding for AI-powered clean energy project

A team of scientists from the University of Houston, in collaboration with Howard University in Washington D.C., has received a $1 million award from the National Science Foundation for a project that aims to automate the discovery of new clean-energy catalysts.

The project, dubbed "Multidisciplinary High-Performance Computing and Artificial Intelligence Enabled Catalyst Design for Micro-Plasma Technologies in Clean Energy Transition," aims to use machine learning and AI to improve the efficiency of catalysts in hydrogen generation, carbon capture and energy storage, according to UH.

“This research directly contributes to these global challenges,” Jiefu Chen, the principal investigator of the project and associate professor of electrical and computer engineering, said in a statement. “This interdisciplinary effort ensures comprehensive and innovative solutions to complex problems.”

Chen is joined by Lars Grabow, professor of chemical and biomolecular engineering; Xiaonan Shan, associate professor of electrical and computing engineering; and Xuquing Wu, associate professor of information science technology. Su Yan, an associate professor of electrical engineering and computer science at Howard University, is collaborating on the project.

The University of Houston team: Xiaonan Shan, associate professor electrical and computing engineering, Jiefu Chen, associate professor of electrical and computer engineering, Lars Grabow, professor of chemical and biomolecular engineering, and Xuquing Wu, associate professor of information science technology. Photo via UH.edu

The team will create a robotic synthesis and testing facility that will automate the experimental testing and verification process of the catalyst design process, which traditionally is slow-going. It will implement AI and advanced, unsupervised machine learning techniques, and have a special focus on plasma reactions.

The project has four main focuses, according to UH.

  1. Using machine learning to discover materials for plasma-assisted catalytic reactions
  2. Developing a model to simulate complex interactions to better understand microwave-plasma-assisted heating
  3. Designing catalysts supports for efficient microwave-assisted reactions
  4. Developing a bench scale reactor to demonstrate the efficiency of the catalysts support system

Additionally, the team will put the funding toward the development of a multidisciplinary research and education program that will train students on using machine learning for topics like computational catalysis, applied electromagnetics and material synthesis. The team is also looking to partner with industry on related projects.

“This project will help create a knowledgeable and skilled workforce capable of addressing critical challenges in the clean energy transition,” Grabow added in a statement. “Moreover, this interdisciplinary project is going to be transformative in that it advances insights and knowledge that will lead to tangible economic impact in the not-too-far future.”

This spring, UH launched a new micro-credential course focused on other applications for AI and robotics in the energy industry.

Around the same time, Microsoft's famous renowned co-founder Bill Gates spoke at CERAWeek to a standing-room-only crowd on the future of the industry. Also founder of Breakthrough Energy, Gates addressed the topic of AI.

ExxonMobil and Intel are working to design, test, research and develop new liquid cooling technologies to optimize data center performance and help customers meet their sustainability goals. Photo via Getty Images

ExxonMobil, Intel eye sustainable solutions within data center innovation

the view from heti

Two multinational corporations have announced a new collaboration to create energy-efficient and sustainable solutions for data centers as the market experiences significant growth.

ExxonMobil and Intel are working to design, test, research and develop new liquid cooling technologies to optimize data center performance and help customers meet their sustainability goals. Liquid cooling solutions serve as an alternative to traditional air-cooling methods in data centers.

“Our partnership with ExxonMobil to co-develop turnkey solutions for liquid cooling will enable significant energy and water savings for data center and network deployments,” said Jen Huffstetler, Chief Product Sustainability Officer, Intel.

According to consulting firm McKinsey, “a hyperscaler’s data center can use as much power as 80,000 households do,” and that demand is expected to keep surging. Power consumption by the U.S. data center market is forecasted “to reach 35 gigawatts (GW) by 2030, up from 17 GW in 2022,” according to a McKinsey analysis. Artificial intelligence and machine learning, and other advanced computing techniques are increasing computational workloads, and in return, increasing electricity demand. Therefore, companies are searching for solutions to support this growth.

ExxonMobil launched its full portfolio of data center immersion fluid products last year. The partnership with Intel will allow them to further advance their efforts in this market.

“By integrating ExxonMobil’s proven expertise in liquid cooling technologies with Intel’s long legacy of industry leadership in world-changing computing technologies, together we will further the industry’s adoption and acceptance as it transitions to liquid cooling technologies,” said Sarah Horne, Vice President, ExxonMobil.

Learn more about this collaboration here.

———

This article originally ran on the Greater Houston Partnership's Houston Energy Transition Initiative blog. HETI exists to support Houston's future as an energy leader. For more information about the Houston Energy Transition Initiative, EnergyCapitalHTX's presenting sponsor, visit htxenergytransition.org.

The UH team is developing ways to use machine learning to ensure that power systems can continue to run efficiently when pulling their energy from wind and solar sources. Photo via Getty Images

Houston researcher wins competitive NSF award for work tying machine learning to the power grid

grant funding

An associate professor at the University of Houston received the highly competitive National Science Foundation CAREER Award earlier this month for a proposal focused on integrating renewable resources to improve power grids.

The award grants more than $500,000 to Xingpeng Li, assistant professor of electrical and computer engineering and leader of the Renewable Power Grid Lab at UH, to continue his work on developing ways to use machine learning to ensure that power systems can continue to run efficiently when pulling their energy from wind and solar sources, according to a statement from UH. This work has applications in the events of large disturbances to the grid.

Li explains that currently, power grids run off of converted, stored kinetic energy during grid disturbances.

"For example, when the grid experiences sudden large generation losses or increased electrical loads, the stored kinetic energy immediately converted to electrical energy and addressed the temporary shortfall in generation,” Li said in a statement. “However, as the proportion of wind and solar power increases in the grid, we want to maximize their use since their marginal costs are zero and they provide clean energy. Since we reduce the use of those traditional generators, we also reduce the power system inertia (or stored kinetic energy) substantially.”

Li plans to use machine learning to create more streamlined models that can be implemented into day-ahead scheduling applications that grid operators currently use.

“With the proposed new modeling and computational approaches, we can better manage grids and ensure it can supply continuous quality power to all the consumers," he said.

In addition to supporting Li's research and model creations, the funds will also go toward Li and his team's creation of a free, open-source tool for students from kindergarten up through their graduate studies. They are also developing an “Applied Machine Learning in Power Systems” course. Li says the course will help meet workforce needs.

The CAREER Award recognizes early-career faculty members who “have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization,” according to the NSF. It's given to about 500 researchers each year.

Earlier this year, Rice assistant professor Amanda Marciel was also granted an NSF CAREER Award to continue her research in designing branch elastomers that return to their original shape after being stretched. The research has applications in stretchable electronics and biomimetic tissues.
The new course will provide participants with insights on how to use robotics to enhance efficiency in data collection, AI data analysis tools for industry, risk management with AI, and more. Photo courtesy of UH

Houston university launches latest micro-credential course focused on AI, robotics for the energy industry

coming soon

The University of Houston will launch its latest micro-credential course next month that focuses on how AI and robotics can be used in inspection processes for the energy industry.

Running from March 22 through April 22, the course is open to "engineers, technicians and industry professionals with advanced knowledge in the dynamic fields of robotics and AI," according to a statement from UH. It will combine weekly online lectures and in-person hands-on demonstrations and provide participants with insights on how to use robotics to enhance efficiency in data collection, AI data analysis tools for industry, risk management with AI, and more.

“By blending theoretical knowledge with practical applications and hands-on experience, the course aims to empower participants with the skills needed to evaluate and adopt these advanced technologies to address real-world challenges in asset management,” Vedhus Hoskere, assistant professor at the UH Cullen College of Engineering, said in a statement. “We hope that upskilling and knowledge gained from this course will help accelerate the adoption of AI and robotics and contribute to the advancement of safer and more resource-efficient energy infrastructure systems.”

Hoskere will teach the course module titled “Computer Vision and Deep Learning for Inspections.” He also recently received a $500,000 grant from the Texas Department of Transportation (TxDOT) to look at how to use drones, cameras, sensors and AI to support Texas' bridge maintenance programs.

Other leaders of the UH Energy course will include:

  • Kimberley Hayes, founder of Valkim Technologies: Lead speaker who will provide an overview and introduction of AI applications, standards and certification
  • Gangbing Song, Moores Professor of Mechanical Engineering at UH: Machine learning hands-on exercises
  • Pete Peterson, head of product management and marketing with XaaS Lab: Computer vision technology in the oil and gas industry
  • Matthew Alberts, head of project management with Future Technologies Venture Venture LLC: Use cases, workflow and optimizing inspections with AI and drones
  • Suchet Bargoti, chief technology officer at Abyss Solutions: AI and robots for integrity management.

Registration accepted up to the first day of the course and can be completed online.

The world can't keep on with what it's doing and expect to reach its goals when it comes to climate change. Radical innovations are needed at this point, writes Scott Nyquist. Photo via Getty Images

Only radical innovation can get the world to its climate goals, says this Houston expert

guest column

Almost 3 years ago, McKinsey published a report arguing that limiting global temperature rises to 1.5 degrees Celsius above pre-industrial levels was “technically achievable,” but that the “math is daunting.” Indeed, when the 1.5°C figure was agreed to at the 2015 Paris climate conference, the assumption was that emissions would peak before 2025, and then fall 43 percent by 2030.

Given that 2022 saw the highest emissions ever—36.8 gigatons—the math is now more daunting still: cuts would need to be greater, and faster, than envisioned in Paris. Perhaps that is why the Intergovernmental Panel on Climate Change (IPCC) noted March 20 (with “high confidence”) that it was “likely that warming will exceed 1.5°C during the 21st century.”

I agree with that gloomy assessment. Given the rate of progress so far, 1.5°C looks all but impossible. That puts me in the company of people like Bill Gates; the Economist; the Australian Academy of Science, and apparently many IPCC scientists. McKinsey has estimated that even if all countries deliver on their net zero commitments, temperatures will likely be 1.7°C higher in 2100.

In October, the UN Environment Program argued that there was “no credible pathway to 1.5°C in place” and called for “an urgent system-wide transformation” to change the trajectory. Among the changes it considers necessary: carbon taxes, land use reform, dietary changes in which individuals “consume food for environmental sustainability and carbon reduction,” investment of $4 trillion to $6 trillion a year; applying current technology to all new buildings; no new fossil fuel infrastructure. And so on.

Let’s assume that the UNEP is right. What are the chances of all this happening in the next few years? Or, indeed, any of it? President Obama’s former science adviser, Daniel Schrag, put it this way: “ Who believes that we can halve global emissions by 2030?... It’s so far from reality that it’s kind of absurd.”

Having a goal is useful, concentrating minds and organizing effort. And I think that has been the case with 1.5°C, or recent commitments to get to net zero. Targets create a sense of urgency that has led to real progress on decarbonization.

The 2020 McKinsey report set out how to get on the 1.5°C pathway, and was careful to note that this was not a description of probability or reality but “a picture of a world that could be.” Three years later, that “world that could be” looks even more remote.

Consider the United States, the world’s second-largest emitter. In 2021, 79 percent of primary energy demand (see chart) was met by fossil fuels, about the same as a decade before. Globally, the figures are similar, with renewables accounting for just 12.5 percent of consumption and low-emissions nuclear another 4 percent. Those numbers would have to basically reverse in the next decade or so to get on track. I don’t see how that can happen.

No alt text provided for this image

Credit: Energy Information Administration

But even if 1.5°C is improbable in the short term, that doesn’t mean that missing the target won’t have consequences. And it certainly doesn’t mean giving up on addressing climate change. And in fact, there are some positive trends. Many companies are developing comprehensive plans for achieving net-zero emissions and are making those plans part of their long-term strategy. Moreover, while global emissions grew 0.9 percent in 2022, that was much less than GDP growth (3.2 percent). It’s worth noting, too, that much of the increase came from switching from gas to coal in response to the Russian invasion of Ukraine; that is the kind of supply shock that can be reversed. The point is that growth and emissions no longer move in lockstep; rather the opposite. That is critical because poorer countries are never going to take serious climate action if they believe it threatens their future prosperity.

Another implication is that limiting emissions means addressing the use of fossil fuels. As noted, even with the substantial rise in the use of renewables, coal, gas, and oil are still the core of the global energy system. They cannot be wished away. Perhaps it is time to think differently—that is, making fossil fuels more emissions efficient, by using carbon capture or other technologies; cutting methane emissions; and electrifying oil and gas operations. This is not popular among many climate advocates, who would prefer to see fossil fuels “stay in the ground.” That just isn’t happening. The much likelier scenario is that they are gradually displaced. McKinsey projects peak oil demand later this decade, for example, and for gas, maybe sometime in the late 2030s. Even after the peak, though, oil and gas will still be important for decades.

Second, in the longer term, it may be possible to get back onto 1.5°C if, in addition to reducing emissions, we actually remove them from the atmosphere, in the form of “negative emissions,” such as direct air capture and bioenergy with carbon capture and storage in power and heavy industry. The IPCC itself assumed negative emissions would play a major role in reaching the 1.5°C target; in fact, because of cost and deployment problems, it’s been tiny.

Finally, as I have argued before, it’s hard to see how we limit warming even to 2°C without more nuclear power, which can provide low-emissions energy 24/7, and is the largest single source of such power right now.

None of these things is particularly popular; none get the publicity of things like a cool new electric truck or an offshore wind farm (of which two are operating now in the United States, generating enough power for about 20,000 homes; another 40 are in development). And we cannot assume fast development of offshore wind. NIMBY concerns have already derailed some high-profile projects, and are also emerging in regard to land-based wind farms.

Carbon capture, negative emissions, and nuclear will have to face NIMBY, too. But they all have the potential to move the needle on emissions. Think of the potential if fast-growing India and China, for example, were to develop an assembly line of small nuclear reactors. Of course, the economics have to make sense—something that is true for all climate-change technologies.

And as the UN points out, there needs to be progress on other issues, such as food, buildings, and finance. I don’t think we can assume that such progress will happen on a massive scale in the next few years; the actual record since Paris demonstrates the opposite. That is troubling: the IPCC notes that the risks of abrupt and damaging impacts, such as flooding and crop yields, rise “with every increment of global warming.” But it is the reality.

There is one way to get us to 1.5°C, although not in the Paris timeframe: a radical acceleration of innovation. The approaches being scaled now, such as wind, solar, and batteries, are the same ideas that were being discussed 30 years ago. We are benefiting from long-term, incremental improvements, not disruptive innovation. To move the ball down the field quickly, though, we need to complete a Hail Mary pass.

It’s a long shot. But we’re entering an era of accelerated innovation, driven by advanced computing, artificial intelligence, and machine learning that could narrow the odds. For example, could carbon nanotubes displace demand for high-emissions steel? Might it be possible to store carbon deep in the ocean? Could geo-engineering bend the curve?

I believe that, on the whole, the world is serious about climate change. I am certain that the energy transition is happening. But I don’t think we are anywhere near to being on track to hit the 1.5°C target. And I don’t see how doing more of the same will get us there.

------

Scott Nyquist is a senior advisor at McKinsey & Company and vice chairman, Houston Energy Transition Initiative of the Greater Houston Partnership. The views expressed herein are Nyquist's own and not those of McKinsey & Company or of the Greater Houston Partnership. This article originally ran on LinkedIn.

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Houston venture firm invests in Virginia fusion power plant company in collaboration with TAMU

fusion funding

Houston-based climate tech venture firm Ecosphere Ventures has partnered with Virginia Venture Partners and Virginia Innovation Partnership Corporation’s venture capital program to invest in Virginia-based NearStar Fusion Inc., which develops fusion energy power plants.

NearStar aims to use its proprietary plasma railgun technology to safely and affordably power baseload electricity on and off the power grid through a Magnetized Target Impact Fusion (MTIF) approach, according to a news release from the company.

NearStar’s power plants are designed to retrofit traditional fossil fuel power plants and are expected to serve heavy industry, data centers and military installations.

“Our design is well-suited to retrofit coal-burning power plants and reuse existing infrastructure such as balance of plant and grid connectivity, but I’m also excited about leveraging the existing workforce because you won’t need PhDs in plasma physics to work in our power plant,” Amit Singh, CEO of NearStar Fusion, said in a news release.

NearStar will also conduct experiments at the Texas A&M Hypervelocity Impact Laboratory (HVIL) in Bryan, Texas, on prototype fuel targets and evolving fuel capsule design. The company plans to publish the results of the experiments along with a concept paper this year. NearStar will work with The University of Alabama in Huntsville (UAH) to develop computer performance models for target implosions.

NearStar’s MTIF approach will utilize deuterium, which is a common isotope of hydrogen found in water. The process does not use tritium, which NearStar believes will save customers money.

“While avoiding tritium in our power plant design reduces scientific gain of the fusion process, we believe the vastly reduced system complexity and cost savings of eliminating complicated supply chains, regulatory oversight, and breeding of tritium allows NearStar to operate power plants more profitably and serve more customers worldwide, ”Douglas Witherspoon, NearStar founder and chief scientist, said in a news release.

Houston’s Ecosphere Ventures invests in climate tech and sustainability innovations from pre-seed to late-seed stages in the U.S. Ecosphere also supports first-time entrepreneurs and technical founders.

Solar farms are booming and putting thousands of hungry sheep to work

Solar Power

On rural Texas farmland, beneath hundreds of rows of solar panels, a troop of stocky sheep rummage through pasture, casually bumping into one another as they remain committed to a single task: chewing grass.

The booming solar industry has found an unlikely mascot in sheep as large-scale solar farms crop up across the U.S. and in the plain fields of Texas. In Milam County, outside Austin, SB Energy operates the fifth-largest solar project in the country, capable of generating 900 megawatts of power across 4,000 acres.

How do they manage all that grass? With the help of about 3,000 sheep, which are better suited than lawnmowers to fit between small crevices and chew away rain or shine.

The proliferation of sheep on solar farms is part of a broader trend — solar grazing — that has exploded alongside the solar industry.

Agrivoltaics, a method using land for both solar energy production and agriculture, is on the rise with more than 60 solar grazing projects in the U.S., according to the National Renewable Energy Laboratory. The American Solar Grazing Association says 27 states engage in the practice.

"The industry tends to rely on gas-powered mowers, which kind of contradicts the purpose of renewables," SB Energy asset manager James Hawkins said.

A sunny opportunity
Putting the animals to work on solar fields also provides some help to the sheep and wool market, which has struggled in recent years. The inventory of sheep and lamb in Texas fell to 655,000 in January 2024, a 4% drop from the previous year, according to the most recent figures from the U.S. Department of Agriculture.

Because solar fields use sunny, flat land that is often ideal for livestock grazing, the power plants have been used in coordination with farmers rather than against them.

Sheepherder JR Howard accidentally found himself in the middle of Texas' burgeoning clean energy transition. In 2021, he and his family began contracting with solar farms — sites with hundreds of thousands of solar modules — to use his sheep to eat the grass.

What was once a small business has turned into a full-scale operation with more than 8,000 sheep and 26 employees.

"Just the growth has been kind of crazy for us," said Howard, who named his company Texas Solar Sheep. "It's been great for me and my family."

Following the herd
Some agriculture experts say Howard's success reflects how solar farms have become a boon for some ranchers.

Reid Redden, a sheep farmer and solar vegetation manager in San Angelo, Texas, said a successful sheep business requires agricultural land that has become increasingly scarce.

"Solar grazing is probably the biggest opportunity that the sheep industry had in the United States in several generations," Redden said.

The response to solar grazing has been overwhelmingly positive in rural communities near South Texas solar farms where Redden raises sheep for sites to use, he said.

"I think it softens the blow of the big shock and awe of a big solar farm coming in," Redden said.

Fielding more research
Agrivoltaics itself isn't new. Solar farms are land-intensive and require a lot of space that could be used for food production. Agrivoltaics compensates by allowing the two to coexist, whether growing food or caring for livestock.

There is a lot still unknown about the full effects of solar grazing, said Nuria Gomez-Casanovas, an assistant professor in regenerative system ecology at Texas A&M University.

Not enough studies have been done to know the long-term environmental impacts, such as how viable the soil will be for future agriculture, although Gomez-Casanovas suspects solar grazing may improve sheep productivity because the panels provide shade and can be more cost-efficient than mowing.

"We really have more questions than answers," Gomez-Casanovas said. "There are studies that show that the land productivity is not higher versus solar alone or agriculture alone, so it's context-dependent."

As one of Texas' largest solar sheep operators, Howard has more clients than he can handle. He expects to add about 20 more employees by the end of this year, which would nearly double his current workforce. As for the sheep, he has enough already.

Chevron and partners to develop innovative power plants to support AI-focused data centers

power partners

Houston-based Chevron U.S.A. Inc., San Francisco investment firm Engine No. 1, and Boston electric service company GE Vernova have announced a partnership to create natural gas power plants in the United States. These plants support the increased demand for electricity at data centers, specifically those developing artificial intelligence solutions.

“The data centers needed to scale AI require massive amounts of 24/7 power. Meeting this demand is forecasted to require significant investment in power generation capacity, while managing carbon emissions and mitigating the risk of grid destabilization,” Chevron CEO Mike Wirth, shared in a LinkedIn post.

The companies say the plants, known as “power foundries,” are expected to deliver up to four gigawatts, equal to powering 3 million to 3.5 million U.S. homes, by the end of 2027, with possible project expansion. Their design will allow for the future integration of lower-carbon solutions, such as carbon capture and storage and renewable energy resources.

They are expected to leverage seven GE Vernova 7HA natural gas turbines, which will serve co-located data centers in the Southeast, Midwest and West. The exact locations have yet to be specified.

“Energy is the key to America’s AI dominance, “ Chris James, founder and chief investment officer of investment firm Engine No. 1, said in a news release. “By using abundant domestic natural gas to generate electricity directly connected to data centers, we can secure AI leadership, drive productivity gains across our economy and restore America’s standing as an industrial superpower. This partnership with Chevron and GE Vernova addresses the biggest energy challenge we face.”

According to the companies, the projects offer cost-effective and scalable solutions for growth in electrical demand while avoiding burdening the existing electrical grid. The companies plan to also use the foundries to sell surplus power to the U.S. power grid in the future.