�AI First Scenarios - Energy

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Digital Science Center

Digital Science Center

Clean energy technologies threaten to overwhelm the grid. Here’s how it can adapt.

https://www.vox.com/energy-and-environment/2018/11/30/17868620/renewable-energy-power-grid-architecture

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Digital Science Center

Three clean-energy trends are shaking up the Grid

1. The rise of renewables. Wind and solar complicate management of the grid because they are variable — they come and go with the weather. You can’t ramp them up and down at will like you can fossil fuel plants. The sun comes up, you get a flood of power from all those solar panels; the sun goes down, you get none.
2. The rise of distributed energy resources (DERs): small-scale energy resources often (though not always) found “behind the meter,” on the customer side. Some DERs generate energy, like solar panels, small wind turbines, or combined heat-and-power (CHP) units.
1. Some DERs store energy, like batteries, fuel cells, or thermal storage like water heaters. And some DERs monitor and manage energy, like smart thermostats, smart meters, smart chargers, and whole-building energy management systems. (The oldest and still most common DER is diesel generators, which are obviously not ideal from a climate standpoint.)
2. DERs are sometimes known as “grid edge” technologies because they exist at the bottom edge of the grid, near or behind customer meters
3. The increasing sophistication and declining cost of information and communication technology (ICT). As sensors and processors continue to get cheaper, it is increasingly possible to see exactly what is going on in a distribution grid down to the individual device, and to share that knowledge in real-time over the web. More information can be generated, and with artificial intelligence and machine learning, information and energy can both be more intelligently managed.

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Digital Science Center

Distributed and Centralized Energy

• Power is generated at large power plants and fed into high-voltage transmission lines, which can carry it over long distances. At various points along the way, power is dumped from the transmission system into local distribution areas (LDAs) via substations, where transformers lower the voltage. Local distribution grids then carry the electricity to customers.
• Distributed energy is different from the conventional model in that its origin lies within an LDA. That’s where it is generated, stored, and managed; no transmission lines are involved.
• The “TD interface” is the point where the transmission and distribution systems meet

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• The choice between distributed and centralized grids is laid out in “A Tale of Two Visions: Designing a Decentralized Transactive Electric System,” published in 2016 in IEEE Power and Energy Magazine by Kristov, Paul De Martini of the California Institute of Technology, and Jeffrey Taft of the Pacific Northwest National Laboratory.
• Compare to Cloud and Edge computing

Digital Science Center

Wildfires and blackouts mean Californians need solar panels and microgrids

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• https://www.vox.com/energy-and-environment/2019/10/28/20926446/california-grid-distributed-energy
• A rapid scale-up of “vehicle-to-grid” (V2G) technology, which makes EVs (electric vehicles) into bidirectional energy-storage and demand-shifting resources that can serve the microgrid (or the larger grid). EVs will further increase the reliability and capabilities of behind-the-meter systems.

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Community Solar

After the Kincade Fire

Digital Science Center

Microgrids

There is an in-depth explainer on microgrids if you want to dive in, but here’s the capsule version: any system that can island off from the grid is a microgrid, a miniature, semi-independent grid of its own. Technically, a single building, even a single room could be a microgrid, but more often, when people refer to microgrids they are talking about groups of buildings and facilities — a campus, a neighborhood, or even a whole community.�Distributed energy can make the grid more stable and resilient, even when there’s no blackout

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Digital Science Center

Economic value of microgrids

A recent McKinsey research report on the effects of energy storage on the power system found that “partial grid defection” — which it defines as generating 80 to 90 percent of your own energy — could become economical for most customers within a decade.

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Digital Science Center

Clean Energy Investment

• Venture capital and private equity investors flooded clean energy with $10.5 billion in 2019, according to a new report by BloombergNEF. It's the largest annual investment since 2010. https://www.businessinsider.com/top-10-most-active-clean-energy-investors-in-2019-bnef-2020-1

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• Business Insider highlighted the 10 most active venture capital and private equity investors, based on the number of deals that closed in 2019, using data from BloombergNEF.
• The list shows that oil and gas giants are among the most active private investors, alongside the Bill Gates-led investor coalition, Breakthrough Energy Ventures.
• Clean energy" refers to "renewable energy excluding large hydro-electric projects, but including equity-raising by companies in smart grid, digital energy, energy storage, and electric vehicles”

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Digital Science Center

Batteries for Power Grids

• https://www.businessinsider.com/form-energy-battery-startup-disrupting-the-industry-2020-1
• As more renewable energy is added to the grid, utilities and other electricity providers will need big batteries that last for tens or hundreds of hours. They'll count on those batteries to kick in when there's a lull in power, like during a stretch of cloudy days in a region that relies on solar energy. And at that demand, Li-ion is just too expensive.
• In 2018, lithium-ion (Li-ion) made up 95% of stationary energy-storage technologies, according to the consulting firm Wood Mackenzie. Since 2010, the cost of a Li-ion battery pack has fallen by 85%, according to Wood Mackenzie, and will undoubtedly continue to fall, though by only about 5 to 6% in the coming years.
• Leader in new batteries is a startup called Form Energy, which has raised about $50 million and has a post-money valuation of $110 million, according to PitchBook.
• One of Form Energy’s projects is an aqueous-sulfur flow battery, which Form has been developing in partnership with the US Department of Energy. Jaramillo said its duration would be on the scale of tens of hours.
• Flow batteries store an electrical charge in two tanks of liquid electrolyte — in this case, one of them is likely a chemical mixture containing sulfur. When the battery is plugged in, charged atoms called ions flow between the two tanks, generating an electric current.
• That value of grid batteries is tied to what portion of power is generated by renewables in a given region. Once it hits 70 to 80% and beyond, the intermittency issues become more pronounced. At that point, power providers will all need grid-scale batteries.

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• https://www.technologyreview.com/s/614467/how-a-new-class-of-startups-are-working-to-solve-the-grid-storage-puzzle/
• https://www.greentechmedia.com/articles/read/form-fenergy-series-b-long-duration-energy-storage

Digital Science Center

Fusion: One form of Clean Energy

• Space and Fusion used to be government-only activities
• Industry revolutionized Space; what about fusion?
• https://www.businessinsider.com/fusion-startup-tokamak-energy-raises-87-million-2020-1

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• British startup Tokamak Energy just raised $87 million from investors including billionaire Hans-Peter Wild, who owns the juice brand Capri Sun.
• The company's chief executive says the funds will help Tokamak Energy put clean fusion electricity on the grid by 2030.
• The recent investment brings the company's total funding to well over $100 million, according to PitchBook, putting Tokamak on par with other leading fusion startups including US-based Commonwealth Fusions Systems and the Canadian company, General Fusion.
• Jonathan Carling, the company's chief executive, said Tokamak Energy will use the funds to generate temperatures of 100 million degrees Celsius — which is more than six times hotter than the center of the sun — inside its prototype reactor.

Digital Science Center

Fusion at a High Level

• At a basic level, fusion is the process that powers the sun: The nuclei of hydrogen atoms crash into each under extreme heat and pressure, causing them to fuse together and form the nuclei of another, heavier element — helium. This results in a loss of mass and a huge surplus of energy.
• The most common approach involves using two forms of hydrogen atoms, called deuterium and tritium. They fuse together relatively easily, but the process still requires a tremendous amount of pressure and a temperature as high as 150 million degrees Celsius.
• To achieve this, most companies use an obscure device, shaped like a hollow bagel, called a tokamak. Inside a typical tokamak is a superheated hydrogen gas containing deuterium and tritium, known as plasma, that's surrounded by superconducting magnets. Those magnets heat and compress the gas until fusion occurs.
• Tokamak Energy — which operates a spherical tokamak that many consider more efficient than the traditional donut-shaped ones — has made considerable progress since it was founded in 2009. In 2018, the company announced that it had achieved plasma temperatures of 15 million degrees.
• Traditional Tokomak’s have a nontrivial problem to keep plasma stable and deep learning has been used to identify the patterns that are harbingers of instability; some instability can damage the tokamak
• “Predicting disruptive instabilities in controlled fusion plasmas through deep learning” https://www.nature.com/articles/s41586-019-1116-4
• Princeton’s Fusion Recurrent Neural Network code (FRNN) uses convolutional & recurrent neural network components to integrate both spatial and temporal information for predicting disruptions in tokamak plasmas with unprecedented accuracy and speed on top supercomputers
• https://www.energy.gov/science/fes/articles/artificial-intelligence-and-deep-learning-accelerate-efforts-develop-clean
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Digital Science Center

Princeton Deep Learning

System overview and disruption-prediction workflow. a–e, The top image shows an interior view of the JET tokamak, with a nondisruptive plasma on the left and a disruptive plasma on the right. Diagnostics (a) provide streams of sensory data (b) which are fed to the RNN-based deep learning algorithm (c) every 1 ms, producing a corresponding ‘disruptivity’ output at every time step (d). If the output crosses a preset threshold value (dashed horizontal line), a disruption alarm is called (red star). This alarm triggers mitigation action, such as gas injection (e) into the tokamak, to reduce the deleterious effects of the impending disruption. f, A detailed schematic of our deep-learning model. The input data consist of scalar zero-dimensional (0D) signals and 1D profiles. N layers of convolutional (containing NF filters each) and downsampling (max-pooling) operations reduce the dimensionality of the profile data and extract salient low-dimensional representations (features; here, 1D features).

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These features are concatenated with the 0D signals and fed into a multilayer long/short-term memory network (LSTM) with M layers, which also receives its internal state from the last time step (T = t − 1) as input.

The resulting final feature vector ideally contains salient information from the past temporal evolution (T ≤ t − 1) and the present state of all signals (T = t). This vector is fed through a fully connected layer to produce the output. Panel a has been modified from an image of the interior of JET obtained from the EUROfusion media library at www. euro-fusion.org/media-library. Ip,target, plasma current target; Ii, internal inductance; LM, locked-mode amplitude; Ip, plasma current; Pin, input power; Prad,core, core radiated power; β, normalized plasma pressure; ne, electron density; WMHD, plasma energy; Prad, total radiated power; Te (ρ), electron-temperature profile; ne (ρ), electron-density profile.

Digital Science Center

Artificial Intelligence in the Energy Industry I

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence undated article from a German energy company building virtual power plants (VPP)

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Digital Science Center

Artificial Intelligence in the Energy Industry II

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence
• Artificial Intelligence in the Power Grid with Sector Coupling for intelligent networking of electricity consumers and generators across sector boundaries. With the increasing decentralization and digitalization of the power grid, it is becoming more difficult to manage the large number of grid participants and keep the grid in balance.
• Smart grids are another area of application. The power networks transport not only electricity but also data. Especially with an increasing number of volatile power generation plants such as solar and wind, it is becoming more and more important for power generation to react intelligently to consumption (and vice versa). AI can help evaluate, analyze, and control the data of the various participants (consumers, producers, storage facilities) connected to each other via the grid.

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Digital Science Center

Artificial Intelligence in the Energy Industry III: Grid (contd)

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence
• Electro mobility. An increase in e-cars offers opportunities and challenges. The charging of electric cars must be coordinated, but at the same time, they offer the possibility of storing electricity and stabilizing the grid, for example by adjusting the charging demand to price signals and availability. AI can help with all this by monitoring and coordinating.
• Grid Stability AI can stabilize the power grid by, for example, detecting anomalies in generation, consumption, or transmission in near real time, and then develop suitable solutions. Initial research projects in this field, such as at the Fraunhofer Institute, are already underway.
• Monitoring and Maintenance AI can help coordinate maintenance work and determine optimal times for the maintenance of networks or individual systems. This helps minimize costs and loss of profit as well as disturbances of the network operation.

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Digital Science Center

Artificial Intelligence in the Energy Industry V

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence
• Virtual Power Plant for which much data are processed and forecasts are made. The AI algorithms help generate increasingly accurate forecasts as well as coordinate various participants in the Virtual Power Plant.
• This happens, for example, when it is necessary to coordinate which plant generates or consumes how much electricity and when. The basis of the analyses are, among other data, live feed-in data, historical data, data from electricity trading centers, and weather forecasts.
• With AI, it is simpler to evaluate systematically the large amount of data in electricity (VPP) management, such as weather data or historical data. Better forecasts also increase grid stability and thus supply security. Especially in the field of forecasts, AI can help facilitate and speed up the integration of renewables. Machine Learning and Neural Networks play an important role in improving forecasts in the energy industry.
• Developments in forecasting quality in recent years have shown the potential of AI in this area: There is already a reduction in the demand for control reserve, even though the share of volatile power generators in the market has increased.
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Digital Science Center

Artificial Intelligence in the Energy Industry VI

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence
• Power Consumption: Consumers, intelligently connected in the electricity system, can contribute to a stable and green electricity grid. Smart home solutions and smart meters already exist, but they are not yet widely used.
• In a smart networked home, the networked devices react to prices on the electricity market and adapt to household usage patterns in order to save electricity and reduce costs. One example is smart networked air conditioning systems. They react to prices on the electricity market by boosting their output when electricity is abundant and cheap. By analyzing user data, they can also include information about user preferences and time windows in their calculations.

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Digital Science Center

Artificial Intelligence in the Energy Industry IV

• https://www.next-kraftwerke.com/knowledge/artificial-intelligence
• Electricity Trading between providers and consumers: Artificial Intelligence in power trading helps improve forecasts.
• Some AI algorithms are already sufficiently intelligent that they can trade on their own. This is what they call algorithmic trading, algo trading, or automated trading.
• AI can also help to automatically monitor and analyze trading on the electricity market. This makes it possible to detect and prevent deviations from the norm, such as the abuse of market power, more quickly and specifically

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Digital Science Center

�Energy Investments by Bill Gates

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Digital Science Center

Digital Science Center

Investments by Bill Gates

• Gates advocates for zero emissions, but emphasizes that such a goal won't be possible simply by flying and driving less, and by limiting consumption of other forms of energy, especially in less-wealthy regions that are energy-poor.Instead, he says, we should invest in clean energy.
• "Of course, cutting back is a good thing for those who can afford to do it, as I can," he wrote. "But overall, the world should be using more energy, not less — as long as it is clean."
• Getting clean energy technologies to market has been a core part of Gates' investment strategy.
• His most public venture vehicle, Breakthrough Energy Ventures (BEV), has more than $1 billion in assets under management and involves a coalition of extremely high-net-worth individuals. Richard Branson, Michael Bloomberg, Ray Dalio, and Vinod Khosla are all investors in BEV, one of the world's most active clean-energy venture funds.
• Gates funds a wide range of technologies, but many of them fall within the categories of long-duration storage, nuclear energy, and carbon capture.
• Business Insider compiled a list of the 26 clean-energy companies that Gates has backed through one of his investment vehicles, using data from PitchBook.
• Business Insider September 10 2020

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https://www.businessinsider.com/list-of-clean-energy-startups-bill-gates-is-backing-2020-4#quantumscape-996-million-26

Digital Science Center

Bluefield Technologies — $2 million

Year founded: 2017

The startup measures methane emissions using imagery gathered by backpack-sized satellites. "Our sensor detects the spectral signature of methane in sunlight that is reflected off the ground. We then use machine vision algorithms to further enhance the optically rich data our sensor captures," the company's website states.

https://bluefield.co/ �https://pitchbook.com/profiles/company/185199-13

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Digital Science Center

• Seaborg — $6 million https://www.seaborg.co/ https://pitchbook.com/profiles/company/265280-86
• Year founded: 2014
• What it is: Denmark-based Seaborg is developing compact molten salt nuclear reactors designed to limit waste. The startup is planning to have commercial reactors by 2027.
• Total raised: About $6 million in equity. That doesn't include an additional $2 million in grants, the company said in April
• Fervo Energy — $11 million https://www.fervoenergy.com/ https://pitchbook.com/profiles/company/228101-95
• Year founded: 2017
• What it is: California-based Fervo Energy is a geothermal energy startup. Geothermal energy typically refers to using the heat stored in the Earth's crust to power turbines or heat homes.
• Lilac Solutions — $24 million http://www.lilacsolutions.com/ https://pitchbook.com/profiles/company/181403-11
• Year founded: 2016
• What it is: Lilac developed a technique to extract the element lithium — used to make lithium-ion batteries, which power electric cars — in a more efficient and cost-effective way, the company says
• Malta — $26 million https://www.maltainc.com/ https://pitchbook.com/profiles/company/244267-75
• Year founded: 2018
• What it is: Incubated at Alphabet's secretive X, known formerly as Google X, Malta develops a long-duration energy storage technology. The startup turns surplus electricity into thermal energy — both heat and cold, which are stored separately — which it can then convert back into electricity
• Varentec — $37 million https://varentec.com/ https://pitchbook.com/profiles/company/14339-35
• Year founded: 2009
• What it is: The Silicon Valley startup sells hardware and software to optimize the electric grid. The technology can reduce the "voltage volatility" by as much as 72% and prevent watt loss, the company says.
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Digital Science Center

Heliogen — $9 million

Year founded: 2013

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What it is: The startup's system uses a mosaic of mirrors to concentrate sunlight, which generates extreme heat necessary for industrial processes like cement or steel manufacturing. The technology could eventually replace fossil fuels in those processes, as Business Insider previously reported.

https://pitchbook.com/profiles/company/97707-16

https://heliogen.com/

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Digital Science Center

Arnergy — $9 million

Year founded: 2013

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Nigeria-based Arnergy sells distributed energy solutions, such as rooftop solar and storage, in emerging markets.

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https://pitchbook.com/profiles/company/143644-15

https://arnergy.com/

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Digital Science Center

CarbonCure — $11 million

Year founded: 2007

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What it is: CarbonCure sells a technology that enhances concrete using recycled carbon dioxide, or CO2. The company pumps CO2 into wet concrete while it's being mixed, at which point the gas reacts with water and calcium ions in the cement, forming solid limestone. The carbon is stuck in the limestone indefinitely. What's more, is that this mineralization process makes ready-mix concrete slightly stronger than some alternatives, according to the company

https://pitchbook.com/profiles/company/59883-49

https://www.carboncure.com/

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Digital Science Center

SparkMeter — $14 million

Year founded: 2013

What it is: SparkMeter designs and sells smart meters and other equipment to utilities in developing markets. The startup's technology "enables utilities operating in remote locations to access a range of features — prepaid billing, customer communications, and remote monitoring and control — that improve their operations and help them achieve financial sustainability," the company's website says.

https://pitchbook.com/profiles/company/145239-85

https://www.sparkmeter.io/

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Digital Science Center

Quidnet Energy — $18 million

A pumped hydro plant in the Czech Republic Adam Pemble/ASSOCIATED PRESS

Year founded: 2013

What it is: A startup that develops a pumped-hydro energy storage technology. Water is pumped into an underground cavern, where it becomes pressurized, and then released to power energy-generating turbines.

https://pitchbook.com/profiles/company/162975-79

http://www.quidnetenergy.com/

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Digital Science Center

75F — $25 million

Year founded: 2012

What it is: 75F is a smart-building startup that sells various tools to make commercial buildings more energy-efficient. The company says it can cut costs by up to 50%.

https://pitchbook.com/profiles/company/119465-74

https://www.75f.io/

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Digital Science Center

Boston Metal — $30 million

Year founded: 2012

What it is: MIT spinout Boston Metal uses a process called molten oxide electrolysis to turn raw metals into molten products used by various industries, such as steel production. The startup says the process produces far less carbon dioxide emissions.

https://www.bostonmetal.com/

https://pitchbook.com/profiles/company/234100-27

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Digital Science Center

Redwood Materials — $39 million Founded 2017

• Unclear activities centered on “elements”. One effort is recycling batteries: What tends to be overlooked as the EV revolution gains momentum is how to recycle the billions of battery cells that will be powering all those cars and trucks.
• Nearly 11 million tons of lithium-ion batteries are expected to reach the end of their useful life by 2030. The elements inside them — nickel, lithium, and cobalt, among others — don’t disappear when that happens.
• They can be recovered and used to make new battery cells, reducing the need to extract, transport, and refine new supplies of them.
• https://pitchbook.com/profiles/company/180447-85 http://www.redwoodmaterials.com/

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Digital Science Center

Chemicals in Redwood Materials

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Digital Science Center

• Natel Energy — $48 million
• Year founded: 2009 https://pitchbook.com/profiles/company/97489-54 http://www.natelenergy.com/
• What it is: California-based Natel Energy is a hydropower startup that is developing small, fish-friendly turbines, which the company says are cheaper to build and easier to permit. Unlike traditional hydropower dams, which are massive, expensive, and generally considered environmentally destructive, Natel's turbines are designed to benefit the river ecosystem and fit within a structure closer to the size of a beaver dam. As a result, they require minimal excavation. ��
• Form Energy — $75 million
• Year founded: 2011 http://www.formenergy.com/ https://pitchbook.com/profiles/company/186688-63
• What it is: Secretive but working on a handful of different battery chemistries for long term storage. One is aqueous-sulfur flow battery, which Form has been developing in partnership with the US Department of Energy with a duration of tens of hours.
• Flow batteries store an electrical charge in two tanks of liquid electrolyte — in this case, one of them is likely a chemical mixture containing sulfur. When the battery is plugged in, charged atoms called ions flow between the two tanks, generating an electric current. The big benefit to flow batteries is that they scale, and are cheaper for grid-scale storage.�
• Sierra Energy — $63 million
• Year founded: 2004 http://www.sierraenergy.com/ https://pitchbook.com/profiles/company/161817-76
• What it is: Sierra Energy is commercializing a gasification technology that can turn municipal waste �into valuable products like diesel and hydrogen gas. Total raised: About $63 million, including �$30 million the startup received through its parent company, �Sierra Railroad Company, the company said in April

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Digital Science Center

ESS Inc. — $47 million

Year founded: 2011

What it is: ESS manufactures flow batteries. Unlike a traditional cell, flow batteries store an electrical charge in two tanks of liquid electrolyte. In this case, the electrolyte contains a mixture of iron, salt, and water. When the battery is plugged in, charged atoms called ions flow between the two tanks, generating an electric current.

https://pitchbook.com/profiles/company/64649-26

http://www.essinc.com/

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Digital Science Center

enVerid Systems— $60 million

Year founded: 2010

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What it is: Based outside of Boston, enVerid Systems developed a high-efficiency heating and cooling technology that uses as much as 30% less energy than traditional systems. While traditional HVAC systems rely on bulky machines that continuously suck in large amounts of air into a building, as a way to keep it clean, enVerid's device recycles indoor air, instead, by running it through an advanced filter that can remove molecular pollutants, including carbon dioxide.

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https://pitchbook.com/profiles/company/61962-40

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http://www.enverid.com/

Digital Science Center

Ambri — $80 million

Year founded: 2010

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What it is: MIT spinout Ambri is developing a liquid-metal battery for long-duration storage.

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http://www.ambri.com/

https://pitchbook.com/profiles/company/54537-94

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Digital Science Center

Carbon Engineering — $84

A rendering of Carbon Engineering's technology. At this scale, the company says it could capture 1 million tons of CO2 per year.

Year founded: 2009

What it is: Carbon Engineering designed a direct-air capture technology for removing carbon dioxide from the atmosphere.

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https://pitchbook.com/profiles/company/108045-46

http://www.carbonengineering.com/

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Digital Science Center

Commonwealth Fusion Systems — $115 million

Year founded: 2018

What it is: MIT spinout Commonwealth Fusion Systems is developing a compact reactor to generate nuclear fusion, a source of clean energy.

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Total raised: More than $200 million, according to the company

http://www.cfs.energy/

https://pitchbook.com/profiles/company/226641-88

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Digital Science Center

Mainspring Energy — $133 million

Mainspring generator in picture

Year founded: 2010

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What it is: Formerly known as EtaGen, Mainspring Energy develops a technology called a linear generator, which uses a reaction between air and fuel to move magnets through copper coils and generate electricity. The company says it's efficient and produces "near-zero" nitrogen oxide emissions.

https://pitchbook.com/profiles/company/56368-63

http://www.mainspringenergy.com/

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Digital Science Center

TerraPower — $178 million

A TerraPower facility in picture

Year founded: 2006

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What it is: Launched by Bill Gates himself, TerraPower is developing various nuclear technologies including a modular reactor that uses molten chloride instead of water as a coolant, as Business Insider previously reported. TerraPower believes the design will be safer and more efficient than today's reactors.

http://www.terrapower.com/

https://pitchbook.com/profiles/company/43074-28

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Digital Science Center

1366 Technologies — $107 million

Year founded: 2007

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What it is: The Boston-based startup makes wafers for solar panels directly from molten silicon.

https://pitchbook.com/profiles/company/51151-96

http://www.1366tech.com/

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Digital Science Center

QuantumScape — $296 million

QuantumScape partnered with Volkswagen. In the picture, the carmaker's e-Bulli concept. Volkswagen

Year founded: 2010

What it is: Stanford spinout QuantumScape is trying to develop solid-state batteries — which, unlike lithium-ion batteries, rely on solid-not-liquid electrolytes — in partnership with Volkswagen. Volkswagen says solid-state batteries would more than double the range of its electric e-Golf car.

Total raised: $496 million, according to PitchBook

http://www.quantumscape.com/

https://pitchbook.com/profiles/company/54764-56

For QuantumScape, Fixing for Explosions Is Only the First Step Toward a Superbattery Very recent

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Digital Science Center