TransCanada to buy National Grid plant

By Reuters


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TransCanada has agreed to buy a large New York power plant from utility National Grid for around $2.9 billion.

National Grid is divesting the 2,480 megawatt gas-fired Ravenswood plant to satisfy regulators after its takeover of New York utility KeySpan.

"National Grid have received a price of $1,160 per kilowatt, which is a good price - slight premium to re-investment cost," said analysts at UBS.

Ravenswood is valuable as it supplies over 20 percent of the electricity for New York City, where it is difficult to find land on which to build new capacity and demand for power is always high.

The deal, which is expected to complete this summer, will reduce TransCanada earnings for the first two years but boost profit thereafter.

The sale will generate a gain for National Grid, which said KeySpan had recorded a carrying value of $1.2 billion for the plant.

Analysts at Credit Suisse estimated National Grid would gain $2 billion in proceeds after capital gains tax and accounting for fuel stocks and lease prepayment, which would give 7 pence a share of upside to their 880 pence target price.

"We estimate the sale converts into a gross $1,170 per kilowatt valuation, slightly above our estimated newbuild cost of circa $900-$1100 per kilowatt for U.S. Combined Cycle Gas Turbines and for other recent transactions in the U.S.," they added.

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Purdue: As Ransomware Attacks Increase, New Algorithm May Help Prevent Power Blackouts

Infrastructure Security Algorithm prioritizes cyber defense for power grids and critical infrastructure, mitigating ransomware, blackout risks, and cascading failures by guiding utilities, regulators, and cyber insurers on optimal security investment allocation.

 

Key Points

An algorithm that optimizes security spending to cut ransomware and blackout risks across critical infrastructure.

✅ Guides utilities on optimal security allocation

✅ Uses incentives to correct human risk biases

✅ Prioritizes assets to prevent cascading outages

 

Millions of people could suddenly lose electricity if a ransomware attack just slightly tweaked energy flow onto the U.S. power grid, as past US utility intrusions have shown.

No single power utility company has enough resources to protect the entire grid, but maybe all 3,000 of the grid's utilities could fill in the most crucial security gaps if there were a map showing where to prioritize their security investments.

Purdue University researchers have developed an algorithm to create that map. Using this tool, regulatory authorities or cyber insurance companies could establish a framework for protecting the U.S. power grid that guides the security investments of power utility companies to parts of the grid at greatest risk of causing a blackout if hacked.

Power grids are a type of critical infrastructure, which is any network - whether physical like water systems or virtual like health care record keeping - considered essential to a country's function and safety. The biggest ransomware attacks in history have happened in the past year, affecting most sectors of critical infrastructure in the U.S. such as grain distribution systems in the food and agriculture sector and the Colonial Pipeline, which carries fuel throughout the East Coast, prompting increased military preparation for grid hacks in the U.S.

With this trend in mind, Purdue researchers evaluated the algorithm in the context of various types of critical infrastructure in addition to the power sector, including electricity-sector IoT devices that interface with grid operations. The goal is that the algorithm would help secure any large and complex infrastructure system against cyberattacks.

"Multiple companies own different parts of infrastructure. When ransomware hits, it affects lots of different pieces of technology owned by different providers, so that's what makes ransomware a problem at the state, national and even global level," said Saurabh Bagchi, a professor in the Elmore Family School of Electrical and Computer Engineering and Center for Education and Research in Information Assurance and Security at Purdue. "When you are investing security money on large-scale infrastructures, bad investment decisions can mean your power grid goes out, or your telecommunications network goes out for a few days."

Protecting infrastructure from hacks by improving security investment decisions

The researchers tested the algorithm in simulations of previously reported hacks to four infrastructure systems: a smart grid, industrial control system, e-commerce platform and web-based telecommunications network. They found that use of this algorithm results in the most optimal allocation of security investments for reducing the impact of a cyberattack.

The team's findings appear in a paper presented at this year's IEEE Symposium on Security and Privacy, the premier conference in the area of computer security. The team comprises Purdue professors Shreyas Sundaram and Timothy Cason and former PhD students Mustafa Abdallah and Daniel Woods.

"No one has an infinite security budget. You must decide how much to invest in each of your assets so that you gain a bump in the security of the overall system," Bagchi said.

The power grid, for example, is so interconnected that the security decisions of one power utility company can greatly impact the operations of other electrical plants. If the computers controlling one area's generators don't have adequate security protection, as seen when Russian hackers accessed control rooms at U.S. utilities, then a hack to those computers would disrupt energy flow to another area's generators, forcing them to shut down.

Since not all of the grid's utilities have the same security budget, it can be hard to ensure that critical points of entry to the grid's controls get the most investment in security protection.

The algorithm that Purdue researchers developed would incentivize each security decision maker to allocate security investments in a way that limits the cumulative damage a ransomware attack could cause. An attack on a single generator, for instance, would have less impact than an attack on the controls for a network of generators, which sophisticated grid-disruption malware can target at scale, rather than for the protection of a single generator.

Building an algorithm that considers the effects of human behavior

Bagchi's research shows how to increase cybersecurity in ways that address the interconnected nature of critical infrastructure but don't require an overhaul of the entire infrastructure system to be implemented.

As director of Purdue's Center for Resilient Infrastructures, Systems, and Processes, Bagchi has worked with the U.S. Department of Defense, Northrop Grumman Corp., Intel Corp., Adobe Inc., Google LLC and IBM Corp. on adopting solutions from his research. Bagchi's work has revealed the advantages of establishing an automatic response to attacks, and analyses like Symantec's Dragonfly report highlight energy-sector risks, leading to key innovations against ransomware threats, such as more effective ways to make decisions about backing up data.

There's a compelling reason why incentivizing good security decisions would work, Bagchi said. He and his team designed the algorithm based on findings from the field of behavioral economics, which studies how people make decisions with money.

"Before our work, not much computer security research had been done on how behaviors and biases affect the best defense mechanisms in a system. That's partly because humans are terrible at evaluating risk and an algorithm doesn't have any human biases," Bagchi said. "But for any system of reasonable complexity, decisions about security investments are almost always made with humans in the loop. For our algorithm, we explicitly consider the fact that different participants in an infrastructure system have different biases."

To develop the algorithm, Bagchi's team started by playing a game. They ran a series of experiments analyzing how groups of students chose to protect fake assets with fake investments. As in past studies in behavioral economics, they found that most study participants guessed poorly which assets were the most valuable and should be protected from security attacks. Most study participants also tended to spread out their investments instead of allocating them to one asset even when they were told which asset is the most vulnerable to an attack.

Using these findings, the researchers designed an algorithm that could work two ways: Either security decision makers pay a tax or fine when they make decisions that are less than optimal for the overall security of the system, or security decision makers receive a payment for investing in the most optimal manner.

"Right now, fines are levied as a reactive measure if there is a security incident. Fines or taxes don't have any relationship to the security investments or data of the different operators in critical infrastructure," Bagchi said.

In the researchers' simulations of real-world infrastructure systems, the algorithm successfully minimized the likelihood of losing assets to an attack that would decrease the overall security of the infrastructure system.

Bagchi's research group is working to make the algorithm more scalable and able to adapt to an attacker who may make multiple attempts to hack into a system. The researchers' work on the algorithm is funded by the National Science Foundation, the Wabash Heartland Innovation Network and the Army Research Lab.

Cybersecurity is an area of focus through Purdue's Next Moves, a set of initiatives that works to address some of the greatest technology challenges facing the U.S. Purdue's cybersecurity experts offer insights and assistance to improve the protection of power plants, electrical grids and other critical infrastructure.

 

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The Impact of AI on Corporate Electricity Bills

AI Energy Consumption strains corporate electricity bills as data centers and HPC workloads run nonstop, driving carbon emissions. Efficiency upgrades, renewable energy, and algorithm optimization help control costs and enhance sustainability across industries.

 

Key Points

AI Energy Consumption is the power used by AI compute and data centers, impacting costs and sustainability.

✅ Optimize cooling, hardware, and workloads to cut kWh per inference

✅ Integrate on-site solar, wind, or PPAs to offset data center power

✅ Tune models and algorithms to reduce compute and latency

 

Artificial Intelligence (AI) is revolutionizing industries with its promise of increased efficiency and productivity. However, as businesses integrate AI technologies into their operations, there's a significant and often overlooked impact: the strain on corporate electricity bills.

AI's Growing Energy Demand

The adoption of AI entails the deployment of high-performance computing systems, data centers, and sophisticated algorithms that require substantial energy consumption. These systems operate around the clock, processing massive amounts of data and performing complex computations, and, much like the impact on utilities seen with major EV rollouts, contributing to a notable increase in electricity usage for businesses.

Industries Affected

Various sectors, including finance, healthcare, manufacturing, and technology, rely on AI-driven applications for tasks ranging from data analysis and predictive modeling to customer service automation and supply chain optimization, while manufacturing is influenced by ongoing electric motor market growth that increases electrified processes.

Cost Implications

The rise in electricity consumption due to AI deployments translates into higher operational costs for businesses. Corporate entities must budget accordingly for increased electricity bills, which can impact profit margins and financial planning, especially in regions experiencing electricity price volatility in Europe amid market reforms. Managing these costs effectively becomes crucial to maintaining competitiveness and sustainability in the marketplace.

Sustainability Challenges

The environmental impact of heightened electricity consumption cannot be overlooked. Increased energy demand from AI technologies contributes to carbon emissions and environmental footprints, alongside rising e-mobility demand forecasts that pressure grids, posing challenges for businesses striving to meet sustainability goals and regulatory requirements.

Mitigation Strategies

To address the escalating electricity bills associated with AI, businesses are exploring various mitigation strategies:

  1. Energy Efficiency Measures: Implementing energy-efficient practices, such as optimizing data center cooling systems, upgrading to energy-efficient hardware, and adopting smart energy management solutions, can help reduce electricity consumption.

  2. Renewable Energy Integration: Investing in renewable energy sources like solar or wind power and energy storage solutions to enhance flexibility can offset electricity costs and align with corporate sustainability initiatives.

  3. Algorithm Optimization: Fine-tuning AI algorithms to improve computational efficiency and reduce processing times can lower energy demands without compromising performance.

  4. Cost-Benefit Analysis: Conducting thorough cost-benefit analyses of AI deployments to assess energy consumption against operational benefits and potential rate impacts, informed by cases where EV adoption can benefit customers in broader electricity markets, helps businesses make informed decisions and prioritize energy-saving initiatives.

Future Outlook

As AI continues to evolve and permeate more aspects of business operations, the demand for electricity will likely intensify and may coincide with broader EV demand projections that increase grid loads. Balancing the benefits of AI-driven innovation with the challenges of increased energy consumption requires proactive energy management strategies and investments in sustainable technologies.

Conclusion

The integration of AI technologies presents significant opportunities for businesses to enhance productivity and competitiveness. However, the corresponding surge in electricity bills underscores the importance of proactive energy management and sustainability practices. By adopting energy-efficient measures, leveraging renewable energy sources, and optimizing AI deployments, businesses can mitigate cost impacts, reduce environmental footprints, and foster long-term operational resilience in an increasingly AI-driven economy.

 

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Consumer choice has suddenly revolutionized the electricity business in California. But utilities are striking back

California Community Choice Aggregators are reshaping electricity markets with renewable energy, solar and wind sourcing, competitive rates, and customer choice, challenging PG&E, SDG&E, and Southern California Edison while advancing California's clean power goals.

 

Key Points

Local governments that buy power, often cleaner and cheaper, while utilities handle delivery and billing.

✅ Offer higher renewable mix than utilities at competitive rates

✅ Utilities retain transmission and billing responsibilities

✅ Rapid expansion threatens IOU market share across California

 

Nearly 2 million electricity customers in California may not know it, but they’re part of a revolution. That many residents and businesses are getting their power not from traditional utilities, but via new government-affiliated entities known as community choice aggregators. The CCAs promise to deliver electricity more from renewable sources, such as solar and wind, even as California exports its energy policies across Western states, and for a lower price than the big utilities charge.

The customers may not be fully aware they’re served by a CCA because they’re still billed by their local utility. But with more than 1.8 million accounts now served by the new system and more being added every month, the changes in the state’s energy system already are massive.

Faced for the first time with real competition, the state’s big three utilities have suddenly become havens of innovation. They’re offering customers flexible options on the portion of their power coming from renewable energy, amid a broader review to revamp electricity rates aimed at cleaning the grid, and they’re on pace to increase the share of power they get from solar and wind power to the point where they are 10 years ahead of their deadline in meeting a state mandate.

#google#

But that may not stem the flight of customers. Some estimates project that by late this year, more than 3 million customers will be served by 20 CCAs, and that over a longer period, Pacific Gas & Electric, Southern California Edison, and San Diego Gas & Electric could lose 80% of their customers to the new providers.

Two big customer bases are currently in play: In Los Angeles and Ventura counties, a recently launched CCA called the Clean Power Alliance is hoping by the end of 2019 to serve nearly 1 million customers. Unincorporated portions of both counties and 29 municipalities have agreed in principle to join up.

Meanwhile, the city of San Diego is weighing two options to meet its goal of 100% clean power by 2035, as exit fees are being revised by the utilities commission: a plan to be submitted by SDG&E, or the creation of a CCA. A vote by the City Council is expected by the end of this year. A city CCA would cover 1.4 million San Diegans, accounting for half SDG&E’s customer demand, according to Cody Hooven, the city’s chief sustainability officer.

Don’t expect the big companies to give up their customers without a fight. Indeed, battle lines already are being drawn at the state Public Utilities Commission, where a recent CPUC ruling sided with a community energy program over SDG&E, and local communities.

“SDG&E is in an all-out campaign to prevent choice from happening, so that they maintain their monopoly,” says Nicole Capretz, who wrote San Diego’s climate action plan as a city employee and now serves as executive director of the Climate Action Campaign, which supports creation of the CCA.

California is one of seven states that have legalized the CCA concept, even as regulators weigh whether the state needs more power plants to ensure reliability. (The others are New York, New Jersey, Massachusetts, Ohio, Illinois and Rhode Island.) But the scale of its experiment is likely to be the largest in the country, because of the state’s size and the ambition of its clean-power goal, which is for 50% of its electricity to be generated from renewable sources by 2030.

California created its system via legislative action in 2002. Assembly Bill 117 enabled municipalities and regional governments to establish CCAs anywhere that municipal power agencies weren’t already operating. Electric customers in the CCA zones were automatically signed up, though they could opt out and stay with their existing power provider. The big utilities would retain responsibility for transmission and distribution lines.

The first CCA, Marin Clean Energy, began operating in 2010 and now serves 470,000 customers in Marin and three nearby counties.

The new entities were destined to come into conflict with the state’s three big investor-owned utilities. Their market share already has fallen to about 70%, from 78% as recently as 2010, and it seems destined to keep falling. In part that’s because the CCAs have so far held their promise: They’ve been delivering relatively clean power and charging less.

The high point of the utilities’ hostility to CCAs was the Proposition 16 campaign in 2009. The ballot measure was dubbed the “Taxpayers Right to Vote Act,” but was transparently an effort to smother CCAs in the cradle. PG&E drafted the measure, got it on the ballot, and contributed all of the $46.5 million spent in the unsuccessful campaign to pass it.

As recently as last year, PG&E and SDG&E were lobbying in the legislature for a bill that would place a moratorium on CCAs. The effort failed, and hasn’t been revived this year.

Rhetoric similar to that used by PG&E against Marin’s venture has surfaced in San Diego, where a local group dubbed “Clear the Air” is fighting the CCA concept by suggesting that it could be financially risky for local taxpayers and questioning whether it will be successful in providing cleaner electricity. Whether Clear the Air is truly independent of SDG&E’s parent, Sempra Energy, is questionable, as at least two of its co-chairs are veteran lobbyists for the company.

SDG&E spokeswoman Helen Gao says the utility supports “customers’ right to choose an energy provider that best meets their needs” and expects to maintain a “cooperative relationship” with any provider chosen by the city.

 

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China to build 2,000-MW Lawa hydropower station on Jinsha River

Lawa Hydropower Station approved on the Jinsha River, a Yangtze tributary, delivers 2,000 MW via four units; 784 ft dam, 12 sq mi reservoir, Sichuan-Tibet site, US$4.59b investment, Huadian stake, renewable energy generation.

 

Key Points

A 2,000 MW dam project on the Jinsha River with four units, a 784 ft barrier, and 8.36 billion kWh annual output.

✅ Sichuan-Tibet junction on the Jinsha River

✅ 2,000 MW capacity; four turbine-generator units

✅ 8.36 bn kWh/yr; US$4.59b total; Huadian 48% stake

 

China has approved construction of the 2,000-MW Lawa hydropower station, a Yangtze tributary hydropower project on the Jinsha River, multiple news agencies are reporting.

Lawa, at the junction of Sichuan province and the Tibet autonomous region, will feature a 784-foot-high dam and the reservoir will submerge about 12 square miles of land. The Jinsha River is a tributary of the Yangtze River, and the project aligns with green hydrogen development in China.

The National Development and Reform Commission of the People’s Republic of China, which also guides China's nuclear energy development as part of national planning, is reported to have said that four turbine-generator units will be installed, and the project is expected to produce about 8.36 billion kWh of electricity annually.

Total investment in the project is to be US$4.59 billion, and Huadian Group Co. Ltd. will have a 48% stake in the project, reflecting overseas power infrastructure activity, with minority stakes held by provincial firms, according to China Daily.

In other recent news in China, Andritz received an order in December 2018 to supply four 350-MW reversible pump-turbines and motor-generators, alongside progress in compressed air generation technologies, for the 1,400-MW ZhenAn pumped storage plant in Shaanxi province.

 

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British Columbia Halts Further Expansion of Self-Driving Vehicles

BC Autonomous Vehicle Ban freezes new driverless testing and deployment as BC develops a regulatory framework, prioritizing safety, liability clarity, and road sharing with pedestrians and cyclists while existing pilot projects continue.

 

Key Points

A moratorium pausing new driverless testing until a safety-first regulatory framework and clear liability rules exist.

✅ Freezes new AV testing and deployment provincewide

✅ Current pilot shuttles continue under existing approvals

✅ Focus on safety, liability, and road-user integration

 

British Columbia has halted the expansion of fully autonomous vehicles on its roads. The province has announced it will not approve any new applications for testing or deployment of vehicles that operate without a human driver until it develops a new regulatory framework, even as it expands EV charging across the province.


Safety Concerns and Public Questions

The decision follows concerns about the safety of self-driving vehicles and questions about who would be liable in the event of an accident. The BC government emphasizes the need for robust regulations to ensure that self-driving cars and trucks can safely share the road with traditional vehicles, pedestrians, and cyclists, and to plan for infrastructure and power supply challenges associated with electrified fleets.

"We want to make sure that British Columbians are safe on our roads, and that means putting the proper safety guidelines in place," said Rob Fleming, Minister of Transportation and Infrastructure. "As technology evolves, we're committed to developing a comprehensive framework to address the issues surrounding self-driving technology."


What Does the Ban Mean?

The ban does not affect current pilot projects involving self-driving vehicles that already operate in BC, such as limited shuttle services and segments of the province's Electric Highway that support charging and operations.


Industry Reaction

The response from industry players working on autonomous vehicle technology has been mixed, amid warnings of a potential EV demand bottleneck as adoption ramps up. While some acknowledge the need for clear regulations, others express concern that the ban could stifle innovation in the province.

"We understand the government's desire to ensure safety, but a blanket ban risks putting British Columbia behind in the development of this important technology," says a spokesperson for a self-driving vehicle start-up.


Debate Over Self-Driving Technology

The BC ban highlights a larger debate about the future of autonomous vehicles. While proponents point to potential benefits such as improved safety, reduced traffic congestion, and increased accessibility, and national policies like Canada's EV goals aim to accelerate adoption, critics raise concerns about liability, potential job losses in the transportation sector, and the ability of self-driving technology to handle complex driving situations.


BC Not Alone

British Columbia is not the only jurisdiction grappling with the regulation of self-driving vehicles. Several other provinces and states in both Canada and the U.S. are also working to develop clear legal and regulatory frameworks for this rapidly evolving technology, even as studies suggest B.C. may need to double its power output to fully electrify road transport.


The Road Ahead

The path forward for fully autonomous vehicles in BC depends on the government's ability to create a regulatory framework that balances safety considerations with fostering innovation, and align with clean-fuel investments like the province's hydrogen project to support zero-emission mobility.  When and how that framework will materialize remains unclear, leaving the future of self-driving cars in the province temporarily uncertain.

 

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A new approach finds materials that can turn waste heat into electricity

Thermoelectric Materials convert waste heat into electricity via the Seebeck effect; quantum computations and semiconductors accelerate discovery, enabling clean energy, higher efficiency, and scalable heat-to-power conversion from abundant, non-toxic, cost-effective compounds.

 

Key Points

Thermoelectric materials turn waste heat into electricity via the Seebeck effect, improving energy efficiency.

✅ Convert waste heat to electricity via the Seebeck effect

✅ Quantum computations rapidly identify high-performance candidates

✅ Target efficient, low-thermal-conductivity, non-toxic, abundant compounds

 

The need to transition to clean energy is apparent, urgent and inescapable. We must limit Earth’s rising temperature to within 1.5 C to avoid the worst effects of climate change — an especially daunting challenge in the face of the steadily increasing global demand for energy and the need for reliable clean power, with concepts that can generate electricity at night now being explored worldwide.

Part of the answer is using energy more efficiently. More than 72 per cent of all energy produced worldwide is lost in the form of heat, and advances in turning thermal energy into electricity could recover some of it. For example, the engine in a car uses only about 30 per cent of the gasoline it burns to move the car. The remainder is dissipated as heat.

Recovering even a tiny fraction of that lost energy would have a tremendous impact on climate change. Thermoelectric materials, which convert wasted heat into useful electricity, can help, especially as researchers pursue low-cost heat-to-electricity materials for scalable deployment.

Until recently, the identification of these materials had been slow. My colleagues and I have used quantum computations — a computer-based modelling approach to predict materials’ properties — to speed up that process and identify more than 500 thermoelectric materials that could convert excess heat to electricity, and help improve energy efficiency.


Making great strides towards broad applications
The transformation of heat into electrical energy by thermoelectric materials is based on the “Seebeck effect.” In 1826, German physicist Thomas Johann Seebeck observed that exposing the ends of joined pieces of dissimilar metals to different temperatures generated a magnetic field, which was later recognized to be caused by an electric current.

Shortly after his discovery, metallic thermoelectric generators were fabricated to convert heat from gas burners into an electric current. But, as it turned out, metals exhibit only a low Seebeck effect — they are not very efficient at converting heat into electricity.

In 1929, the Russian scientist Abraham Ioffe revolutionized the field of thermoelectricity. He observed that semiconductors — materials whose ability to conduct electricity falls between that of metals (like copper) and insulators (like glass) — exhibit a significantly higher Seebeck effect than metals, boosting thermoelectric efficiency 40-fold, from 0.1 per cent to four per cent.

This discovery led to the development of the first widely used thermoelectric generator, the Russian lamp — a kerosene lamp that heated a thermoelectric material to power a radio.


Are we there yet?
Today, thermoelectric applications range from energy generation in space probes to cooling devices in portable refrigerators, and include emerging thin-film waste-heat harvesters for electronics as well. For example, space explorations are powered by radioisotope thermoelectric generators, converting the heat from naturally decaying plutonium into electricity. In the movie The Martian, for example, a box of plutonium saved the life of the character played by Matt Damon, by keeping him warm on Mars.

In the 2015 film, The Martian, astronaut Mark Watney (Matt Damon) digs up a buried thermoelectric generator to use the power source as a heater.

Despite this vast diversity of applications, wide-scale commercialization of thermoelectric materials is still limited by their low efficiency.

What’s holding them back? Two key factors must be considered: the conductive properties of the materials, and their ability to maintain a temperature difference, as seen in nighttime electricity from cold concepts, which makes it possible to generate electricity.

The best thermoelectric material would have the electronic properties of semiconductors and the poor heat conduction of glass. But this unique combination of properties is not found in naturally occurring materials. We have to engineer them, drawing on advances such as carbon nanotube energy harvesters to guide design choices.

Searching for a needle in a haystack
In the past decade, new strategies to engineer thermoelectric materials have emerged due to an enhanced understanding of their underlying physics. In a recent study in Nature Materials, researchers from Seoul National University, Aachen University and Northwestern University reported they had engineered a material called tin selenide with the highest thermoelectric performance to date, nearly twice that of 20 years ago. But it took them nearly a decade to optimize it.

To speed up the discovery process, my colleagues and I have used quantum calculations to search for new thermoelectric candidates with high efficiencies. We searched a database containing thousands of materials to look for those that would have high electronic qualities and low levels of heat conduction, based on their chemical and physical properties. These insights helped us find the best materials to synthesize and test, and calculate their thermoelectric efficiency.

We are almost at the point where thermoelectric materials can be widely applied, but first we need to develop much more efficient materials. With so many possibilities and variables, finding the way forward is like searching for a tiny needle in an enormous haystack.

Just as a metal detector can zero in on a needle in a haystack, quantum computations can accelerate the discovery of efficient thermoelectric materials. Such calculations can accurately predict electron and heat conduction (including the Seebeck effect) for thousands of materials and unveil the previously hidden and highly complex interactions between those properties, which can influence a material’s efficiency.

Large-scale applications will require themoelectric materials that are inexpensive, non-toxic and abundant. Lead and tellurium are found in today’s thermoelectric materials, but their cost and negative environmental impact make them good targets for replacement.

Quantum calculations can be applied in a way to search for specific sets of materials using parameters such as scarcity, cost and efficiency, and insights can even inform exploratory devices that generate electricity out of thin air in parallel fields. Although those calculations can reveal optimum thermoelectric materials, synthesizing the materials with the desired properties remains a challenge.

A multi-institutional effort involving government-run laboratories and universities in the United States, Canada and Europe has revealed more than 500 previously unexplored materials with high predicted thermoelectric efficiency. My colleagues and I are currently investigating the thermoelectric performance of those materials in experiments, and have already discovered new sources of high thermoelectric efficiency.

Those initial results strongly suggest that further quantum computations can pinpoint the most efficient combinations of materials to make clean energy from wasted heat and the avert the catastrophe that looms over our planet.

 

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