Amnesty program promotes energy efficient LEDs

Hybrid Electric Ships leverage marine batteries, LNG engines, and clean propulsion to cut emissions in shipping. From ferries to cargo vessels, electrification and sustainability meet IMO regulations, Corvus Energy systems, and dockside fast charging.

Key Points

Hybrid electric ships use batteries with diesel or LNG engines to cut fuel and emissions and meet stricter IMO rules.

✅ LNG or diesel gensets recharge marine battery packs.

✅ Cuts CO2, NOx, and particulate emissions in port and at sea.

✅ Complies with IMO standards; enables quiet, efficient operations.

The river is running strong and currents are swirling as the 150-metre-long Seaspan Reliant slides gently into place against its steel loading ramp on the shores of B.C.'s silty Fraser River.

The crew hustles to tie up the ship, and then begins offloading dozens of transport trucks that have been brought over from Vancouver Island.

While it looks like many vessels working the B.C. coast, below decks, the ship is very different. The Reliant is a hybrid, partly powered by electricity, and joins BC Ferries' hybrid ships in the region, the seagoing equivalent of a Toyota Prius.

Down below decks, Sean Puchalski walks past a whirring internal combustion motor that can run on either diesel or natural gas. He opens the door to a gleaming white room full of electrical cables and equipment racks along the walls.

"As with many modes of transportation, we're seeing electrification, from electric planes to ferries," said Puchalski, who works with Corvus Energy, a Richmond, B.C. company that builds large battery systems for the marine industry.

In this case, the batteries are recharged by large engines burning natural gas.

"It's definitely the way of the future," said Puchalski.

The 10-year-old company's battery system is now in use on 200 vessels around the world. Business has spiked recently, driven by the need to reduce emissions, and by landmark projects such as battery-electric high-speed ferries taking shape in the U.S.

"When you're building a new vessel, you want it to last for, say, 30 years. You don't want to adopt a technology that's on the margins in terms of obsolescence," said Puchalski. "You want to build it to be future-proof."

Dirty ships

For years, the shipping industry has been criticized for being slow to clean up its act. Most ships use heavy fuel oil, a cheap, viscous form of petroleum that produces immense exhaust. According to the European Commission, shipping currently pumps out about 940 million tonnes of CO2 each year, nearly three per cent of the global total.

That share is expected to climb even higher as other sectors reduce emissions.

When it comes to electric ships, Scandinavia is leading the world. Several of the region's car and passenger ferries are completely battery powered — recharged at the dock by relatively clean hydro power, and projects such as Kootenay Lake's electric-ready ferry show similar progress in Canada.

Tougher regulations and retailer pressure

The push for cleaner alternatives is being partly driven by worldwide regulations, with international shipping regulators bringing in tougher emission standards after a decade of talk and study, while financing initiatives are helping B.C. electric ferries scale up.

At the same time, pressure is building from customers, such as Mountain Equipment Co-op, which closely tracks its environmental footprint. Kevin Lee, who heads MEC's supply chain, said large companies are realizing they are accountable for their contributions to climate change, from the factory to the retail floor.

"You're hearing more companies build it into their DNA in terms of how they do business, and that's cool to see," said Lee. "It's not just MEC anymore trying to do this, there's a lot more partners out there."

In the global race to cut emissions, all kinds of options are on the table for ships, including giant kites being tested to harvest wind power at sea, and ports piloting hydrogen-powered cranes to cut dockside emissions.

Modern versions of sailing ships are also being examined to haul cargo with minimal fuel consumption.

But in practical terms, hybrids and, in the future, pure electrics are likely to play a larger role in keeping the propellers turning along Canada's coast, with neighboring fleets like Washington State Ferries' upgrade underscoring the shift.

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CO2 Removal Technologies address climate change via negative emissions, including carbon capture, reforestation, soil carbon, biochar, BECCS, DAC, and mineralization, helping meet Paris Agreement targets while managing costs, land use, and infrastructure demands.

Key Points

Methods to extract or sequester atmospheric CO2, combining natural and engineered approaches to limit warming.

✅ Includes reforestation, soil carbon, biochar, BECCS, DAC, mineralization

✅ Balances climate goals with costs, land, energy, and infrastructure

✅ Key to Paris Agreement targets under 1.5-2.0 °C warming

The world is, on average, 1.1 degrees Celsius warmer today than it was in 1850. If this trend continues, our planet will be 2 – 3 degrees hotter by the end of this century, according to the Intergovernmental Panel on Climate Change (IPCC).

The main reason for this temperature rise is higher levels of atmospheric carbon dioxide, which cause the atmosphere to trap heat radiating from the Earth into space. Since 1850, the proportion of CO2 in the air has increased, with record greenhouse gas concentrations documented, from 0.029% to 0.041% (288 ppm to 414 ppm).

This is directly related to the burning of coal, oil and gas, which were created from forests, plankton and plants over millions of years. Back then, they stored CO2 and kept it out of the atmosphere, but as fossil fuels are burned, that CO2 is released. Other contributing factors include industrialized agriculture and slash-and-burn land clearing techniques, and emissions from SF6 in electrical equipment are also concerning today.

Over the past 50 years, more than 1200 billion tons of CO2 have been emitted into the planet's atmosphere — 36.6 billion tons in 2018 alone, though global emissions flatlined in 2019 before rising again. As a result, the global average temperature has risen by 0.8 degrees in just half a century.

Atmospheric CO2 should remain at a minimum
In 2015, the world came together to sign the Paris Climate Agreement which set the goal of limiting global temperature rise to well below 2 degrees — 1.5 degrees, if possible.

The agreement limits the amount of CO2 that can be released into the atmosphere, providing a benchmark for the global energy transition now underway. According to the IPCC, if a maximum of around 300 billion tons were emitted, there would be a 50% chance of limiting global temperature rise to 1.5 degrees. If CO2 emissions remain the same, however, the CO2 'budget' would be used up in just seven years.

According to the IPCC's report on the 1.5 degree target, negative emissions are also necessary to achieve the climate targets.

Using reforestation to remove CO2
One planned measure to stop too much CO2 from being released into the atmosphere is reforestation. According to studies, 3.6 billion tons of CO2 — around 10% of current CO2 emissions — could be saved every year during the growth phase. However, a study by researchers at the Swiss Federal Institute of Technology, ETH Zurich, stresses that achieving this would require the use of land areas equivalent in size to the entire US.

Young trees at a reforestation project in Africa (picture-alliance/OKAPIA KG, Germany)
Reforestation has potential to tackle the climate crisis by capturing CO2. But it would require a large amount of space

More humus in the soil
Humus in the soil stores a lot of carbon. But this is being released through the industrialization of agriculture. The amount of humus in the soil can be increased by using catch crops and plants with deep roots as well as by working harvest remnants back into the ground and avoiding deep plowing. According to a study by the German Institute for International and Security Affairs (SWP) on using targeted CO2 extraction as a part of EU climate policy, between two and five billion tons of CO2 could be saved with a global build-up of humus reserves.

Biochar shows promise
Some scientists see biochar as a promising technology for keeping CO2 out of the atmosphere. Biochar is created when organic material is heated and pressurized in a zero or very low-oxygen environment. In powdered form, the biochar is then spread on arable land where it acts as a fertilizer. This also increases the amount of carbon content in the soil. According to the same study from the SWP, global application of this technology could save between 0.5 and two billion tons of CO2 every year.

Storing CO2 in the ground
Storing CO2 deep in the Earth is already well-known and practiced on Norway's oil fields, for example. However, the process is still controversial, as storing CO2 underground can lead to earthquakes and leakage in the long-term. A different method is currently being practiced in Iceland, in which CO2 is sequestered into porous basalt rock to be mineralized into stone. Both methods still require more research, however, with new DOE funding supporting carbon capture, utilization, and storage.

Capturing CO2 to be held underground is done by using chemical processes which effectively extract the gas from the ambient air, and some researchers are exploring CO2-to-electricity concepts for utilization. This method is known as direct air capture (DAC) and is already practiced in other parts of Europe.  As there is no limit to the amount of CO2 that can be captured, it is considered to have great potential. However, the main disadvantage is the cost — currently around €550 ($650) per ton. Some scientists believe that mass production of DAC systems could bring prices down to €50 per ton by 2050. It is already considered a key technology for future climate protection.

The inside of a carbon capture facility in the Netherlands (RWE AG)
Carbon capture facilities are still very expensive and take up a huge amount of space

Another way of extracting CO2 from the air is via biomass. Plants grow and are burned in a power plant to produce electricity. CO2 is then extracted from the exhaust gas of the power plant and stored deep in the Earth, with new U.S. power plant rules poised to test such carbon capture approaches.

The big problem with this technology, known as bio-energy carbon capture and storage (BECCS) is the huge amount of space required. According to Felix Creutzig from the Mercator Institute on Global Commons and Climate Change (MCC) in Berlin, it will therefore only play "a minor role" in CO2 removal technologies.

CO2 bound by rock minerals
In this process, carbonate and silicate rocks are mined, ground and scattered on agricultural land or on the surface water of the ocean, where they collect CO2 over a period of years. According to researchers, by the middle of this century it would be possible to capture two to four billion tons of CO2 every year using this technique. The main challenges are primarily the quantities of stone required, and building the necessary infrastructure. Concrete plans have not yet been researched.

Not an option: Fertilizing the sea with iron
The idea is use iron to fertilize the ocean, thereby increasing its nuturient content, which would allow plankton to grow stronger and capture more CO2. However, both the process and possible side effects are very controversial. "This is rarely treated as a serious option in research," concludes SWP study authors Oliver Geden and Felix Schenuit.

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Electricity Grid Flow Prediction leverages big data, machine learning, and weather analytics to forecast power flows across smart grids, enhancing reliability, reducing blackouts and curtailment, and optimizing renewable integration under EU Horizon 2020 innovation.

Key Points

Short-term forecasting of power flows using big data, weather inputs, and machine learning to stabilize smart grids.

✅ Uses big data, weather, and ML for 6-hour forecasts

✅ Improves reliability, cuts blackouts and energy waste

✅ Supports smart grids, renewables, and grid balancing

Three European prediction specialists have won prizes worth €2 million for developing the most accurate predictions of electricity flow through a grid

The three winners of the Big Data Technologies Horizon Prize received their awards at a ceremony on 12th November in Austria.

The first prize of €1.2 million went to Professor José Vilar from Spain, while Belgians Sofie Verrewaere and Yann-Aël Le Borgne came in joint second place and won €400,000 each.

The challenge was open to individuals groups and organisations from countries taking part in the EU’s research and innovation programme, Horizon 2020.

Carlos Moedas, Commissioner for Research, Science and Innovation, said: “Energy is one of the crucial sectors that are being transformed by the digital grid worldwide.

“This Prize is a good example of how we support a positive transformation through the EU’s research and innovation programme, Horizon 2020.

“For the future, we have designed our next programme, Horizon Europe, to put even more emphasis on the merger of the physical and digital worlds across sectors such as energy, transport and health.”

The challenge for the applicants was to create AI-driven software that could predict the likely flow of electricity through a grid taking into account a number of factors including the weather and the generation source (i.e. wind turbines, solar cells, etc).

Using a large quantity of data from electricity grids, EU smart meters, combined with additional data such as weather conditions, applicants had to develop software that could predict the flow of energy through the grid over a six-hour period.

Commissioner for Digital Economy and Society Mariya Gabriel said: “The wide range of possible applications of these winning submissions could bring tangible benefits to all European citizens, including efforts to tackle climate change with machine learning across sectors.”

The decision to focus on energy grids for this particular prize was driven by a clear market need, including expanding HVDC technology capabilities.

Today’s energy is produced at millions of interconnected and dispersed unpredictable sites such as wind turbines, solar cells, etc., so it is harder to ensure that electricity supply matches the demand at all times.

This complexity means that huge amounts of data are produced at the energy generation sites, in the grid and at the place where the energy is consumed.

Being able to make accurate, short-term predictions about power grid traffic is therefore vital to reduce the risks of blackouts or, by enabling utilities to use AI for energy savings, limit waste of energy.

Reliable predictions can also be used in fields such as biology and healthcare. The predictions can help to diagnose and cure diseases as well as to allocate resources where they are most needed.

Ultimately, the winning ideas are set to be picked up by the energy sector in the hopes of creating smarter electricity infrastructure, more economic and more reliable power grids.

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Muskrat Falls rate mitigation progresses as Newfoundland and Labrador and Ottawa align under the updated Atlantic Accord, targeting affordable electricity rates through federal involvement, PUB input, and potential financing solutions with Nalcor, Emera, and lenders.

Key Points

An initiative by NL and Ottawa to keep electricity rates affordable via federal support, PUB input, and financing options.

✅ Federal-provincial talks under the updated Atlantic Accord

✅ PUB process integrated for independent oversight

✅ Possible roles for Nalcor, Emera, and project lenders

At the announcement of an updated Atlantic Accord between the provincial and federal governments, Newfoundland and Larbrador Premier Dwight Ball gave notice federal Finance Minister Bill Morneau will be in St. John’s to talk about the cost of Muskrat Falls and how Labrador power flows through Quebec to market.

“We look forward to welcoming Minister Morneau and his team to advance discussions on federal financing and rate mitigation,” read a statement from the premier’s office Tuesday, in response to questions about that coming meeting and federal-provincial work on rate mitigation.

At the announcement, Ball specifically said the plan is to “finalize federal involvement for making sure electricity rates remain affordable,” such as shielding ratepayers from overruns through federal-provincial measures, with Ball and MP Seamus O’Regan trumpeting the provincial-federal relationship.

The provincial and federal governments are not the only two parties involved in provincial power rates and handling of Muskrat Falls, even as electricity users have started paying for the project across Newfoundland and Labrador, but The Telegram is told details of meetings on rate mitigation are not being released, down to the list of attendees.

The premier’s office was asked specifically about the involvement of Nalcor Energy, including a recent financial update during the pandemic, Emera, Goldman, TD or any others involved in project financing. The response was that the plan is not to indicate what is being explored and who might be involved, until there is something more concrete to speak about.

The government’s plan is to have something to feed into the ongoing work of the Public Utilities Board, to develop a more complete response for rate mitigation, including lump-sum credits on electricity bills and other tools, for the PUB’s final report, due in 2020, even as regulators in Nova Scotia weigh a 14% rate hike in a separate proceeding.

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Polycrystalline Tin Selenide Thermoelectrics enable waste heat recovery with ZT 3.1, matching single crystals while cutting costs, powering greener car engines, industrial furnaces, and thermoelectric generators via p-type and emerging n-type designs.

Key Points

Low-cost tin selenide devices that turn waste heat into power, achieving ZT 3.1 and enabling p-type and n-type modules.

✅ Oxygen removal prevents heat-leaking tin oxide grain skins.

✅ Polycrystalline ingots match single-crystal ZT 3.1 at lower cost.

✅ N-type tin selenide in development to pair with p-type.

So-called thermoelectric generators turn waste heat into electricity without producing greenhouse gas emissions, providing what seems like a free lunch. But despite helping power the Mars rovers, the high cost of these devices has prevented their widespread use. Now, researchers have found a way to make cheap thermoelectrics that work just as well as the pricey kind. The work could pave the way for a new generation of greener car engines, industrial furnaces, and other energy-generating devices.

“This looks like a very smart way to realize high performance,” says Li-Dong Zhao, a materials scientist at Beihang University who was not involved with the work. He notes there are still a few more steps to take before these materials can become high-performing thermoelectric generators. However, he says, “I think this will be used in the not too far future.”

Thermoelectrics are semiconductor devices placed on a hot surface, like a gas-powered car engine or on heat-generating electronics using thin-film converters to capture waste heat. That gives them a hot side and a cool side, away from the hot surface. They work by using the heat to push electrical charges from one to the other, a process of turning thermal energy into electricity that depends on the temperature gradient. If a device allows the hot side to warm up the cool side, the electricity stops flowing. A device’s success at preventing this, as well as its ability to conduct electrons, feeds into a score known as the figure of merit, or ZT.

 Over the past 2 decades, researchers have produced thermoelectric materials with increasing ZTs, while related advances such as nighttime solar cells have broadened thermal-to-electric concepts. The record came in 2014 when Mercouri Kanatzidis, a materials scientist at Northwestern University, and his colleagues came up with a single crystal of tin selenide with a ZT of 3.1. Yet the material was difficult to make and too fragile to work with. “For practical applications, it’s a non-starter,” Kanatzidis says.

So, his team decided to make its thermoelectrics from readily available tin and selenium powders, an approach that, once processed, makes grains of polycrystalline tin selenide instead of the single crystals. The polycrystalline grains are cheap and can be heated and compressed into ingots that are 3 to 5 centimeters long, which can be made into devices. The polycrystalline ingots are also more robust, and Kanatzidis expected the boundaries between the individual grains to slow the passage of heat. But when his team tested the polycrystalline materials, the thermal conductivity shot up, dropping their ZT scores as low as 1.2.

In 2016, the Northwestern team discovered the source of the problem: an ultrathin skin of tin oxide was forming around individual grains of polycrystalline tin selenide before they were pressed into ingots. And that skin acted as an express lane for the heat to travel from grain to grain through the material. So, in their current study, Kanatzidis and his colleagues came up with a way to use heat to drive any oxygen away from the powdery precursors, leaving pristine polycrystalline tin selenide, whereas other devices can generate electricity from thin air using ambient moisture.

The result, which they report today in Nature Materials, was not only a thermal conductivity below that of single-crystal tin selenide but also a ZT of 3.1, a development that echoes nighttime renewable devices showing electricity from cold conditions. “This opens the door for new devices to be built from polycrystalline tin selenide pellets and their applications to be explored,” Kanatzidis says.

Getting through that door will still take some time. The polycrystalline tin selenide the team makes is spiked with sodium atoms, creating what is known as a “p-type” material that conducts positive charges. To make working devices, researchers also need an “n-type” version to conduct negative charges.

Zhao’s team recently reported making an n-type single-crystal tin selenide by spiking it with bromine atoms. And Kanatzidis says his team is now working on making an n-type polycrystalline version. Once n-type and p-type tin selenide devices are paired, researchers should have a clear path to making a new generation of ultra-efficient thermoelectric generators. Those could be installed everywhere from automobile exhaust pipes to water heaters and industrial furnaces to scavenge energy from some of the 65% of fossil fuel energy that winds up as waste heat. 

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AI Load Forecasting for Utilities leverages machine learning, smart meters, and predictive analytics to balance energy demand during COVID-19 disruptions, optimize grid reliability, support demand response, and stabilize rates for residential and commercial customers.

Key Points

AI predicts utility demand with ML and smart meters to improve reliability and reduce costs.

✅ Adapts to rapid demand shifts with accurate short term forecasts

✅ Optimizes demand response and distributed energy resources

✅ Reduces outages risk while lowering procurement and operating costs

The spread of the novel coronavirus that causes COVID-19 has prompted state and local governments around the U.S. to institute shelter-in-place orders and business closures. As millions suddenly find themselves confined to their homes, the shift has strained not only internet service providers, streaming platforms, and online retailers, but the utilities supplying power to the nation’s electrical grid, which face longer, more frequent outages as well.

U.S. electricity use on March 27, 2020 was 3% lower than it was on March 27, 2019, a loss of about three years of sales growth. Peter Fox-Penner, director of the Boston University Institute for Sustainable Energy, asserted in a recent op-ed that utility revenues will suffer because providers are halting shutoffs and deferring rate increases. Moreover, according to research firm Wood Mackenzie, the rise in household electricity demand won’t offset reduced business electricity demand, mainly because residential demand makes up just 40% of the total demand across North America.

Some utilities are employing AI and machine learning for the energy transition to address the windfalls and fluctuations in energy usage resulting from COVID-19. Precise load forecasting could ensure that operations aren’t interrupted in the coming months, thereby preventing blackouts and brownouts. And they might also bolster the efficiency of utilities’ internal processes, leading to reduced prices and improved service long after the pandemic ends.

Innowatts
Innowatts, a startup developing an automated toolkit for energy monitoring and management, counts several major U.S. utility companies among its customers, including Portland General Electric, Gexa Energy, Avangrid, Arizona Public Service Electric, WGL, and Mega Energy. Its eUtility platform ingests data from over 34 million smart energy meters across 21 million customers in more than 13 regional energy markets, while its machine learning algorithms analyze the data to forecast short- and long-term loads, variances, weather sensitivity, and more.

Beyond these table-stakes predictions, Innowatts helps evaluate the effects of different rate configurations by mapping utilities’ rate structures against disaggregated cost models. It also produces cost curves for each customer that reveal the margin impacts on the wider business, and it validates the yield of products and cost of customer acquisition with models that learn the relationships between marketing efforts and customer behaviors (like real-time load).

Innowwatts told VentureBeat that it observed “dramatic” shifts in energy usage between the first and fourth weeks of March. In the Northeast, “non-essential” retailers like salons, clothing shops, and dry cleaners were using only 35% as much energy toward the end of the month (after shelter-in-place orders were enacted) versus the beginning of the month, while restaurants (excepting pizza chains) were using only 28%. In Texas, conversely, storage facilities were using 142% as much energy in the fourth week compared with the first.

Innowatts says that throughout these usage surges and declines, its clients took advantage of AI-based load forecasting to learn from short-term shocks and make timely adjustments. Within three days of shelter-in-place orders, the company said, its forecasting models were able to learn new consumption patterns and produce accurate forecasts, accounting for real-time changes.

Innowatts CEO Sid Sachdeva believes that if utility companies had not leveraged machine learning models, demand forecasts in mid-March would have seen variances of 10-20%, significantly impacting operations.

“During these turbulent times, AI-based load forecasting gives energy providers the ability to … develop informed, data-driven strategies for future success,” Sachdeva told VentureBeat. “With utilities and energy retailers seeing a once-in-a-lifetime 30%-plus drop in commercial energy consumption, accurate forecasting has never been more important. Without AI tools, utilities would see their forecasts swing wildly, leading to inaccuracies of 20% or more, placing an enormous strain on their operations and ultimately driving up costs for businesses and consumers.”

Autogrid
Autogrid works with over 50 customers in 10 countries — including Energy Australia, Florida Power & Light, and Southern California Edison — to deliver AI-informed power usage insights. Its platform makes 10 million predictions every 10 minutes and optimizes over 50 megawatts of power, which is enough to supply the average suburb.

Flex, the company’s flagship product, predicts and controls tens of thousands of energy resources from millions of customers by ingesting, storing, and managing petabytes of data from trillions of endpoints. Using a combination of data science, machine learning, and network optimization algorithms, Flex models both physics and customer behavior, automatically anticipating and adjusting for supply and demand patterns through virtual power plants that coordinate distributed assets.

Autogrid also offers a fully managed solution for integrating and utilizing end-customer installations of grid batteries and microgrids. Like Flex, it automatically aggregates, forecasts, and optimizes capacity from assets at sub-stations and transformers, reacting to distribution management needs while providing capacity to avoid capital investments in system upgrades.

Autogrid CEO Dr. Amit Narayan told VentureBeat that the COVID-19 crisis has heavily shifted daily power distribution in California, where it’s having a “significant” downward impact on hourly prices in the energy market. He says that Autogrid has also heard from customers about transformer failures in some regions due to overloaded circuits, which he expects will become a problem in heavily residential and saturated load areas during the summer months (as utilities prepare for blackouts across the U.S. when air conditioning usage goes up).

“In California, [as you’ll recall], more than a million residents faced wildfire prevention-related outages in PG&E territory in 2019,” Narayan said, referring to the controversial planned outages orchestrated by Pacific Gas & Electric last summer. “The demand continues to be high in 2020 in spite of the COVID-19 crisis, as residents prepare to keep the lights on and brace for a similar situation this summer. If a 2019 repeat happens again, it will be even more devastating, given the health crisis and difficulty in buying groceries.”

AI making a difference
AI and machine learning isn’t a silver bullet for the power grid — even with predictive tools at their disposal, utilities are beholden to a tumultuous demand curve and to mounting climate risks across the grid. But providers say they see evidence the tools are already helping to prevent the worst of the pandemic’s effects — chiefly by enabling them to better adjust to shifted daily and weekly power load profiles.

“The societal impact [of the pandemic] will continue to be felt — people may continue working remotely instead of going into the office, they may alter their commute times to avoid rush hour crowds, or may look to alternative modes of transportation,” Schneider Electric chief innovation officer Emmanuel Lagarrigue told VentureBeat. “All of this will impact the daily load curve, and that is where AI and automation can help us with maintenance, performance, and diagnostics within our homes, buildings, and in the grid.”

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