There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”

The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.

Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”

© Image: Igor Borisenko/iStock

As AI Energy Demands Grow, Improved Power Electronics Will Be Needed As AI becomes integrated into daily processes and data centers are built...

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Emissions from buildings make up about two-thirds of the greenhouse gas footprint of Indianapolis. So when the city committed to slash emissions, in its 2019 climate action plan and then as part of the Bloomberg American Cities Climate Challenge in 2020, leaders knew where they had to start.

A 2021 ordinance requires all buildings over 50,000 square feet and publicly-owned buildings over 25,000 square feet to do energy benchmarking and report results to the city, to be made publicly available by 2026. 

The deadline to comply was July 1, 2024. But at year’s end, only about 20% of the 1,500 buildings covered had complied — even though the process can be done in a matter of hours using EPA’s ENERGYSTAR Portfolio manager software. The city also hosted workshops to help walk building managers through the process.

Now the city’s challenge is to boost benchmarking compliance. The penalties for failing to comply are low: fines of $100 the first year and $250 yearly after that. Chicago’s 2013 benchmarking ordinance, by comparison, includes fines of $100 for the first day of a violation and up to $25 each day thereafter, with a maximum fine of $9,200 per year — and the city has a much higher compliance rate.

Lindsay Trameri, community engagement manager for the Indianapolis Office of Sustainability, said the office is continuing outreach, including sending postcards to all relevant building managers and owners. 

“We’re not assessing fines yet, but we’re making sure they’re aware this isn’t a city program that’s going away, it is indeed local law,” Trameri said. “And there are benefits to be gleaned from participating. It might cost hundreds of dollars not to participate, but you could save thousands if you participate and take it seriously.”

Trameri said 27 publicly-owned buildings in the consolidated city and county government must be benchmarked, and the city is planning to use about $800,000 worth of federal Department of Energy funding to hire an energy manager “who will be solely focused on looking at city-owned buildings and how to make them more energy efficient.” 

In Indiana, reducing buildings’ electricity use is particularly urgent since the state got about 45% of its power from coal in 2023. The benchmarking mandate doesn’t require buildings to take any action based on their energy results, but benchmarking often motivates building owners and municipalities to invest in savings, experts say. 

Cities participating in the Bloomberg program saw 3% to 8% energy reductions and millions in savings, with nearly 400 million square feet now covered by benchmarking policies and over 37,000 energy audits completed, according to Kelly Shultz, who leads Bloomberg Philanthropies” sustainable cities initiative. 

Success stories

Though overall compliance is low, some major public and private entities have completed benchmarking in Indianapolis, including the airport, convention center, the Indianapolis Museum of Art, Target and JC Penney. 

Phil Day, facilities director for the museum, noted that it’s crucial for museums to keep consistent levels of humidity and temperature. That means high energy use, and also vulnerability to blackouts or energy price spikes. Benchmarking has helped him develop plans for reducing natural gas and electricity use with smaller boilers and heat pumps distributed throughout the facilities, a possible geothermal chilling system, and better insulation. These innovations should save money and make the museum more resilient to energy disruptions.

“Museums aren’t typically known as an energy efficient facility, but it is always high on my priority list in everything we program or replace,” Day said.

The firm Cenergistic has done benchmarking since 2017 for Indianapolis Public Schools, and identified more than $1 million in wasteful energy costs that could be cut across 71 schools. Under Cenergistic’s contract, it is paid half of the energy savings it secures. Seventeen school buildings have obtained EPA Energy Star status based on their energy efficiency improvements, Cenergistic CEO Dennis Harris said. 

“Benchmarking provided a clear starting point by identifying high-energy-consuming facilities and systems,” Harris said. “Cenergistic energy specialists track energy consumption at all campuses with the company’s software platform, identifying waste and driving conservation. By consistently reviewing this data, Cenergistic continues to work with IPS to make data-driven decisions, set measurable goals, and continually refine its strategy for maximum impact.” 

Trameri said the schools’ success is “a great message to point to. If they can do it, we can do it. Of course, we want those millions to go back into classrooms and teachers and students versus out the door for utility costs.”

Learning by example

Trameri said in developing its benchmarking program and ordinance, Indianapolis has relied on guidance and lessons from other cities including Columbus, Ohio and Chicago, both fellow participants in the Bloomberg challenge. 

In Chicago, about 85% of the 3,700 buildings covered by the ordinance are in compliance, said Amy Jewel, vice president of programs at Elevate, the organization that oversees Chicago’s program. She said nine out of 10 buildings complied even right after the ordinance took effect, thanks to years of organizing by city leaders and NGOs like the Natural Resources Defense Council.

“A large number of building owners recognized this was coming. They engaged in the process, and saw their fingerprints within the ordinance,” said Lindy Wordlaw, director of climate and environmental justice initiatives for the city of Chicago. 

Chicago passed an additional ordinance creating an energy rating program, where buildings receive a score of 0 to 4 based on their energy benchmarking results. An 11-by-17-inch placard with the score and explanation must be publicly posted, “similar to a food safety rating for a restaurant,” Wordlaw said.

In 2021, Chicago reported that median energy use per square foot had dropped by 7% over the past three years, and greenhouse gas emissions had dropped 37% since 2016 in buildings subject to the ordinance. City public housing and buildings owned by the Archdiocese were among those to do early benchmarking and investments.

Along with Philadelphia, New York and Washington D.C., Chicago was among the nation’s first major cities to institute benchmarking. Jewel said they hope to keep sharing lessons learned.

For example, “it’s actually pretty hard to come up with the covered buildings list,” Jewel noted, since there is no central list of all buildings in a city but rather various records “all used for slightly different purposes — the property tax database, different sources tracking violations. It took a bit of time to get that list together, and it takes time to maintain it as buildings are constructed or demolished.”

In Indianapolis, Trameri said they are hopeful more buildings will get with the program as awareness grows about the requirement.

“There has always been evidence that you can’t manage what you don’t measure,” said Trameri. “It’s a market-based strategy. Truly once a facilities owner or manager is able to look at their energy usage over a month, 12 months, or multiple years and make evidence-based decisions based on that data, it will affect your bottom line, and those savings you can reinvest into whatever your organization’s mission is.”

Correction: An earlier version of this story misattributed performance information about Bloomberg Philanthropies’ sustainable cities initiative.

Indianapolis grapples with low compliance on energy benchmarking requirement for large buildings is an article from Energy News Network, a nonprofit news service covering the clean energy transition. If you would like to support us please make a donation.

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.

© Credit: Courtesy of Mohamed Elrefaie

Charlotte, North Carolina, may soon get access to a new tool to deploy in its push toward 100% clean power: data.

The Tar Heel state’s largest city aims to power all government operations with carbon-free electricity by the end of the decade, including the city-owned Charlotte-Douglas International Airport, one of the busiest in the world. 

But the hub is a big question mark for the city’s climate target. Officials don’t actually know how much energy it uses — or how much renewable energy they need to offset it — because the utility bills for the five-terminal airport are paid by dozens of individual customers, from Cinnabon to Jamba Juice to airline club lounges.

Now, after a decade of urging by Charlotte and others, Duke Energy has a proposal to change that: an eight-page plan for improved data access that has sign-off from the North Carolina Sustainable Energy Association; Public Staff, the state-sanctioned customer advocate; and Dominion Energy, which serves the northeast corner of the state.

Filed last month with regulators for approval, Duke’s proposed rules could have wide application, said Ethan Blumenthal, regulatory counsel for the North Carolina Sustainable Energy Association. 

“For municipalities applying for federal grants, large customers pursuing energy efficiency, and homeowners and solar companies that are trying to right-size solar installations,” Blumenthal said, “this access to data is essential.”

Avoiding a ‘laborious process’

The Charlotte airport is a prime example of one hurdle facing local communities with climate goals. Today, getting total energy usage data for government-owned buildings with multiple meters means reaching out to individual tenants to get permission to access their accounts.  

“It would be a very laborious process to do that at the airport and anywhere else we have tenants,” said Aaron Tauber, Charlotte’s sustainability analyst.

The problem extends to private building owners who aim to reduce their carbon footprints or improve efficiency but don’t have insight into their renters’ energy consumption. Honeywell, for instance, is a partner in the city’s “Power Down the Crown” initiative, whereby building managers look to reduce energy use by optimizing efficiency. 

“They don’t own all of the data,” Tauber said. “They have tenants in their properties. So, they don’t have visibility to the entire building’s energy use.” 

The new rule will allow a large user, from Honeywell to Charlotte, to access aggregated data for a large building with multiple tenants by request to Duke, so long as at least 15 individual accounts are involved, and none consumes more than 15% of the building’s energy use. 

“Being a larger city, we do have a lot of large buildings with multiple tenants,” said Tauber. “I’m just really excited for these building owners to really — for the first time — gain an understanding of how their buildings are using energy.”

That understanding, he said, is critical for commercial properties to access a new law that allows them to borrow public money for energy efficiency upgrades and pay it back on their property tax bills.  

“Being able to unlock a financing mechanism based on this data will really go a long way for the city to be able to meet our strategic energy action goal of being a low-carbon community,” said Tauber.

Not just for big buildings

The data access rule also applies to a census block, zip code, or other area with at least 15 accounts, which will help local governments meet community-wide climate goals. 

“You can use the aggregated data to make good decisions for program design, and where you might want to target,” said Ann Livingston, senior executive and director of programs with the Southeast Sustainability Directors Network. “You can assess: is this particular block or neighborhood really using a lot more energy per house per square foot than others?” 

Durham County, for instance, together with neighboring Granville and Orange counties, has a $1.5 million federal grant to help low-income homeowners cut their energy use through weatherization and other upgrades.  

“We want to focus in areas where there’s a higher energy use or higher energy burden,” said Tobin Freid, the county’s sustainability manager. “We’d like information at a more granular level than just the county.”

If the new Duke rule is approved, it will also help county officials better tailor the program to individual households and assess its impacts. The proposal would ease the approval process for allowing third-party access to data and ensure that at least two years of prior energy use is included.

“For every home that we work on, we would need historic data to see: what was your energy use before?” Freid said.

Both the aggregated data and third-party access provisions will also be critical for federal programs like Solar for All, aimed at deploying rooftop solar on low-income households. 

“Often, those federal funding opportunities require you to assess and report on energy impact,” said Livingston. “Solar for All will be a very clear example of this, where you need to report energy savings for individual participants.”

Growing interest in local impact

Apart from the sustainability goals, government officials also have a commitment to manage public dollars efficiently, Livingston noted. That’s especially pertinent for large energy users like Durham County, who may pay a higher “demand charge” for a single 30-minute spike in energy use. Large customers with net-metered solar power also pay more during times of peak demand. 

The proposed rules will help solve these challenges by allowing third parties access to machine-readable, easily analyzed data for customers of all sizes. The format would essentially meet national “Green Button” standards, one familiar to the many companies around the country dedicated to managing building energy performance.

The Green Button initiative, a project of the U.S. Department of Energy that originated in Canada, has been around for over a decade – about as long as the Sustainable Energy Association has been advocating for improved customer data access, along with counties like Durham.

But the issue seems to have gained new steam in recent months, as local governments look to take advantage of new federal grants and laws aimed at reducing climate pollution.

What’s more, Blumenthal said, Duke has pledged to implement the rules within 18 months of their approval and help expedite any data requests in the interim.

“There is a commitment to doing everything they can, essentially, to provide data for federal funding purposes up until [the proposal] is fully implemented,” Blumenthal said. “A commitment to try to bridge the gap.”

Asked what prompted the agreement with Blumenthal’s group and others after all this time, Duke spokesperson Logan Stewart said over email: 

“A lot has changed in the last decade from a technology, cybersecurity, and customer engagement perspective that made this stipulation possible. Duke Energy is always looking for ways to collaborate with stakeholders to achieve outcomes that benefit customers.”

Duke Energy data access rules poised to help North Carolina communities meet climate goals is an article from Energy News Network, a nonprofit news service covering the clean energy transition. If you would like to support us please make a donation.

Offshore wind faces intensified operational and maintenance challenges from bigger turbines and aging fleets, risking project efficiencies, durability, and profitability   Shoreline Wind’s new white paper underscores an urgent need to review and update existing O&M strategies, as the industry enters new territories and matures globally   Esbjerg, Denmark, 16th September 2024 – With the offshore wind …

The post Larger turbines and aging assets pose fresh challenges for offshore wind O&M appeared first on Alternative Energy HQ.

Climate anxiety affects nearly half of young people aged 16-25. Students like second-year Rachel Mohammed find hope and inspiration through her involvement in innovative climate solutions, working alongside peers who share her determination. “I’ve met so many people at MIT who are dedicated to finding climate solutions in ways that I had never imagined, dreamed of, or heard of. That is what keeps me going, and I’m doing my part,” she says.

Hydrogen-fueled engines

Hydrogen offers the potential for zero or near-zero emissions, with the ability to reduce greenhouse gases and pollution by 29 percent. However, the hydrogen industry faces many challenges related to storage solutions and costs.

Mohammed leads the hydrogen team on MIT’s Electric Vehicle Team (EVT), which is dedicated to harnessing hydrogen power to build a cleaner, more sustainable future. EVT is one of several student-led build teams at the Edgerton Center focused on innovative climate solutions. Since its founding in 1992, the Edgerton Center has been a hub for MIT students to bring their ideas to life.

Hydrogen is mostly used in large vehicles like trucks and planes because it requires a lot of storage space. EVT is building their second iteration of a motorcycle based on what Mohammed calls a “goofy hypothesis” that you can use hydrogen to power a small vehicle. The team employs a hydrogen fuel cell system, which generates electricity by combining hydrogen with oxygen. However, the technology faces challenges, particularly in storage, which EVT is tackling with innovative designs for smaller vehicles.

Presenting at the 2024 World Hydrogen Summit reaffirmed Mohammed’s confidence in this project. “I often encounter skepticism, with people saying it’s not practical. Seeing others actively working on similar initiatives made me realize that we can do it too,” Mohammed says.

The team’s first successful track test last October allowed them to evaluate the real-world performance of their hydrogen-powered motorcycle, marking a crucial step in proving the feasibility and efficiency of their design.

MIT’s Sustainable Engine Team (SET), founded by junior Charles Yong, uses the combustion method to generate energy with hydrogen. This is a promising technology route for high-power-density applications, like aviation, but Yong believes it hasn’t received enough attention. Yong explains, “In the hydrogen power industry, startups choose fuel cell routes instead of combustion because gas turbine industry giants are 50 years ahead. However, these giants are moving very slowly toward hydrogen due to its not-yet-fully-developed infrastructure. Working under the Edgerton Center allows us to take risks and explore advanced tech directions to demonstrate that hydrogen combustion can be readily available.”

Both EVT and SET are publishing their research and providing detailed instructions for anyone interested in replicating their results.

© Photo: Adam Glanzman