Fears fade on hybrid batteries

Russian Utility Grid Cyberattacks reveal DHS findings on Dragonfly/Energetic Bear breaching control rooms and ICS/SCADA via vendor supply-chain spear-phishing, threatening blackouts and critical infrastructure across U.S. power utilities through stolen credentials and reconnaissance.

Key Points

State-backed ops breaching utilities via vendors to reach ICS/SCADA, risking grid disruption and control-room access.

✅ Spear-phishing and watering-hole attacks on vendor networks

✅ Stolen credentials used to reach isolated ICS/SCADA

✅ Potential to trigger localized blackouts and service disruptions

Hackers working for Russia were able to gain access to the control rooms of US electric utilities last year, allowing them to cause blackouts, federal officials tell the Wall Street Journal.

The hackers -- working for a state-sponsored group previously identified as Dragonfly or Energetic Bear -- broke into utilities' isolated networks by hacking networks belonging to third-party vendors that had relationships with the power companies, the Department of Homeland Security said in a press briefing on Monday.

Officials said the campaign had claimed hundreds of victims and is likely continuing, the Journal reported.

"They got to the point where they could have thrown switches" to disrupt the flow power, Jonathan Homer, chief of industrial-control-system analysis for DHS, told the Journal.

"While hundreds of energy and non-energy companies were targeted, the incident where they gained access to the industrial control system was a very small generation asset that would not have had any impact on the larger grid if taken offline," the DHS said in a statement Tuesday. "Over the course of the past year as we continued to investigate the activity, we learned additional information which would be helpful to industry in defending against this threat."

Organizations running the nation's energy, nuclear and other critical infrastructure have become frequent targets for cyberattacks in recent years due to their ability to cause immediate chaos, whether it's starting a blackout or blocking traffic signals. These systems are often vulnerable because of antiquated software and the high costs of upgrading infrastructure.

The report comes amid heightened tension between Russia and the US over cybersecurity, alongside US condemnation of power grid hacking in recent months. Earlier this month, US special counsel Robert Mueller filed charges against 12 Russian hackers tied to cyberattacks on the Democratic National Committee.

Hackers compromised US power utility companies' corporate networks with conventional approaches, such as spear-phishing emails and watering-hole attacks as seen in breaches at power plants across the US that target a specific group of users by infecting websites they're known to visit, the newspaper reported. After gaining access to vendor networks, hackers turned their attention to stealing credentials for access to the utility networks and familiarizing themselves with facility operations, officials said, according to the Journal.

Homeland Security didn't identify the victims, the newspaper reports, adding that some companies may not know they had been compromised because the attacks used legitimate credentials to gain access to the networks.

Cyberattacks on electrical systems aren't an academic matter. In 2016, Ukraine's grid was disrupted by cyberattacks attributed to Russia, which is engaged in territorial disputes with the country over eastern Ukraine and the Crimean peninsula. Russia has denied any involvement in targeting critical infrastructure.

President Donald Trump signed an executive order in May designed to bolster the United States' cybersecurity by protecting federal networks, critical infrastructure and the public online. One section of the order focuses on protecting the grid like electricity and water, as well as financial, health care and telecommunications systems.

The Department of Homeland Security didn't respond to a request for comment.

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Rattlesnake Ridge Wind Project delivers 117.6 MW in southeast Alberta for BHE Canada, a Berkshire Hathaway Energy subsidiary, using 28 turbines near Medicine Hat under a long-term PPA, supplying renewable power to 79,000 homes.

Key Points

A 117.6 MW Alberta wind farm by BHE Canada supplying 79,000 homes via 28 turbines and a long-term PPA.

✅ 28 turbines near Medicine Hat, 117.6 MW capacity

✅ Long-term PPA with a major Canadian corporate buyer

✅ Developed with RES; no subsidies; competitive pricing

A company linked to U.S. investor Warren Buffett says it will break ground on a $200-million, 117.6-megawatt wind farm in southeastern Alberta next year.

In a release, Calgary-based BHE Canada, a subsidiary of Buffett's Berkshire Hathaway Energy, says its Rattlesnake Ridge Wind project will be located southwest of Medicine Hat and will produce enough energy to supply the equivalent of 79,000 homes.

"We felt that it was time to make an investment here in Alberta," said Bill Christensen, vice-president of corporate development for BHE Canada, in an interview with the Calgary Eyeopener.

"The structure of the markets here in Alberta, including frameworks for selling renewable energy, make it so that we can invest, and do it at a profit that works for us, and at a price that works for the off-taker," Christensen explained.

Berkshire Hathaway Energy also owns AltaLink, the regulated transmission company that supplies electricity to more than 85 per cent of the Alberta population.

BHE Canada says an unnamed large Canadian corporate partner has signed a long-term power purchase agreement, similar to RBC's solar purchase arrangements, for the majority of the energy output generated by the 28 turbines at Rattlesnake Ridge.

"If you look at just the raw power price that power is going for in Alberta right now, it's averaged around $55 a megawatt hour, or 5.5 cents a kilowatt hour. And we're selling the wind power to this customer at substantially less than that, reflecting wind power's competitiveness in the market, and there's been no subsidies," Christensen said.

Positive energy outlook

Christensen said he sees a good future for Alberta's renewable energy industry, not just in wind but also in solar power growth, particularly in the southeast of the province.

But he says BHE Canada is interested in making investments in traditional energy in Alberta, too, as the province is a powerhouse for both green energy and fossil fuels overall.

"It's not a choice of one or the other. I think there is still opportunity to make investments in oil and gas," he said.

"We're really excited about having this project and hope to be able to make other investments here in Alberta to help support the economy here, amid a broader renewable energy surge across the province."

The project is being developed by U.K.-based Renewable Energy Systems, part of a trend where more energy sources make better projects for developers, which is building two other Alberta wind projects totalling 134.6 MW this year and has 750 MW of renewable energy installed or currently under construction in Canada.

BHE Canada and RES are also looking for power purchase partners for the proposed Forty Mile Wind Farm in southeastern Alberta. They say that with generation capacity of 398.5 MW, it could end up being the largest wind power project in Canada.

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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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USAID GE Mobile Power Plant Ukraine supplies 28MW of emergency power and distributed generation to bolster energy security, grid resilience, and critical infrastructure reliability across cities and regions amid ongoing attacks.

Key Points

A 28MW GE gas turbine from USAID providing mobile, distributed power to strengthen Ukraine's grid resilience.

✅ 28MW GE gas turbine; power for 100,000 homes

✅ Mobile deployment to cities and regions as needed

✅ Supports hospitals, schools, and critical infrastructure

Deputy U.S. Administrator Isobel Coleman announced during her visit to Kyiv that the U.S. Agency for International Development (USAID) has provided the Government of Ukraine with a mobile gas turbine power plant purchased from General Electric (GE), as discussions of a possible agreement on power plant attacks continue among stakeholders.

The mobile power plant was manufactured in the United States by GE’s Gas Power business and has a total output capacity of approximately 28MW, which is enough to provide the equivalent electricity to at least 100,000 homes. This will help Ukraine increase the supply of electricity to homes, hospitals, schools, critical infrastructure providers, and other institutions, as the country has even resumed electricity exports in recent months. The mobile power plant can be operated in different cities or regions depending on need, strengthening Ukraine’s energy security amid the Russian Federation’s continuing strikes against critical infrastructure.   

Since the February 2022 full-scale invasion of Ukraine, and particularly since October 2022, the Russian Federation has deliberately targeted critical civilian heating, power, and gas infrastructure in an effort to weaponize the winter, raising nuclear risks to grid stability noted by international monitors. Ukraine has demonstrated tremendous resilience in the wake of these attacks, with utility workers routinely risking their lives to repair the damage, often within hours of air strikes, even as Russia builds power lines to reactivate the Zaporizhzhia plant to influence the energy situation.

The collaboration between USAID and GE reflects the U.S. government’s emphasis on engaging American private sector expertise and procuring proven and reliable equipment to meet Ukraine’s needs. Since the start of Putin’s full-scale war against Ukraine, USAID has both directly procured equipment for Ukraine from American companies and engaged the private sector in partnerships to meet Ukraine’s urgent wartime needs, with U.S. policy debates such as a proposal on Ukraine’s nuclear plants drawing scrutiny.

This mobile power plant is the latest example of USAID assistance to Ukraine’s energy sector since the start of the Russian Federation’s full-scale invasion, during which Ukraine has resumed electricity exports as conditions improved. USAID has already delivered more than 1,700 generators to 22 oblasts across Ukraine, with many more on the way. These generators ensure electricity and heating for schools, hospitals, accommodation centers for internally-displaced persons, district heating companies, and water systems if and when power is knocked out by the Russian Federation’s relentless, systematic and cruel attacks against critical civil infrastructure. USAID has invested $55 million in Ukraine’s heating infrastructure to help the Ukrainian people get through winter. This support will benefit up to seven million Ukrainians by supporting repairs and maintenance of pipes and other equipment necessary to deliver heating to homes, hospitals, schools, and businesses across Ukraine. USAID’s assistance builds on over two decades of support to Ukraine to strengthen the country’s energy security, complementing growth in wind power that is harder to destroy.

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US SF6 Emissions Decline as NOAA analysis and EPA mitigation show progress, with atmospheric measurements and Greenhouse Gas Reporting verifying reductions from the electric power grid; sulfur hexafluoride's extreme global warming potential underscores inventory improvements.

Key Points

A documented drop in US sulfur hexafluoride emissions, confirmed by NOAA atmospheric data and EPA reporting reforms.

✅ NOAA towers and aircraft show 2007-2018 decline

✅ EPA reporting and utility mitigation narrowed inventory gaps

✅ Winter leaks and servicing signal further reduction options

A new NOAA analysis shows U.S. emissions of the super-potent greenhouse gas sulfur hexafluoride (SF6) have declined between 2007-2018, likely due to successful mitigation efforts by the Environmental Protection Agency (EPA) and the electric power industry, with attention to SF6 in the power industry across global markets. 

At the same time, significant disparities that existed previously between NOAA’s estimates, which are based on atmospheric measurements, and EPA’s estimates, which are based on a combination of reported emissions and industrial activity, have narrowed following the establishment of the EPA's Greenhouse Gas Reporting Program. The findings, published in the journal Atmospheric Chemistry and Physics, also suggest how additional emissions reductions might be achieved. 

SF6 is most commonly used as an electrical insulator in high-voltage equipment that transmits and distributes electricity, and its emissions have been increasing worldwide as electric power systems expand, even as regions hit milestones like California clean energy surpluses in recent years. Smaller amounts of SF6 are used in semiconductor manufacturing and in magnesium production. 

SF6 traps 25,000 times more heat than carbon dioxide over a 100-year time scale for equal amounts of emissions, and while CO2 emissions flatlined in 2019 globally, that comparison underscores the potency of SF6. That means a relatively small amount of the gas can have a significant impact on climate warming. Because of its extremely large global warming potential and long atmospheric lifetime, SF6 emissions will influence Earth’s climate for thousands of years.

In this study, researchers from NOAA’s Global Monitoring Laboratory, as record greenhouse gas concentrations drive demand for better data, working with colleagues at EPA, CIRES, and the University of Maryland, estimated U.S. SF6 emissions for the first time from atmospheric measurements collected at a network of tall towers and aircraft in NOAA’s Global Greenhouse Gas Reference Network. The researchers provided an estimate of SF6 emissions independent from the EPA’s estimate, which is based on reported SF6 emissions for some industrial facilities and on estimated SF6 emissions for others.

“We observed differences between our atmospheric estimates and the EPA’s activity-based estimates,” said study lead author Lei Hu, a Global Monitoring Laboratory researcher who was a CIRES scientist at the time of the study. “But by closely collaborating with the EPA, we were able to identify processes potentially responsible for a significant portion of this difference, highlighting ways to improve emission inventories and suggesting additional emission mitigation opportunities, such as forthcoming EPA carbon capture rules for power plants, in the future.” 

In the 1990s, the EPA launched voluntary partnerships with the electric power, where power-sector carbon emissions are falling as generation shifts, magnesium, and semiconductor industries to reduce SF6 emissions after the United States recognized that its emissions were significant. In 2011, large SF6 -emitting facilities were required to begin tracking and reporting their emissions under the EPA Greenhouse Gas Reporting Program. 

Hu and her colleagues documented a decline of about 60 percent in U.S. SF6 emissions between 2007-2018, amid global declines in coal-fired power in some years—equivalent to a reduction of between 6 and 20 million metric tons of CO2 emissions during that time period—likely due in part to the voluntary emission reduction partnerships and the EPA reporting requirement. A more modest declining trend has also been reported in the EPA’s national inventories submitted annually under the United Nations Framework Convention on Climate Change. 

Examining the differences between the NOAA and EPA independent estimates, the researchers found that the EPA’s past inventory analyses likely underestimated SF6 emissions from electrical power transmission and distribution facilities, and from a single SF6 production plant in Illinois. According to Hu, the research collaboration has likely improved the accuracy of the EPA inventories. The 2023 draft of the EPA’s U.S. Greenhouse Gas Emissions and Sinks: 1990-2021 used the results of this study to support revisions to its estimates of SF6 emissions from electrical transmission and distribution. 

The collaboration may also lead to improvements in the atmosphere-based estimates, helping NOAA identify how to expand or rework its network to better capture emitting industries or areas with significant emissions, according to Steve Montzka, senior scientist at GML and one of the paper’s authors.

Hu and her colleagues also found a seasonal variation in SF6 emissions from the atmosphere-based analysis, with higher emissions in winter than in summer. Industry representatives identified increased servicing of electrical power equipment in the southern states and leakage from aging brittle sealing materials in the equipment in northern states during winter as likely explanations for the enhanced wintertime emissions—findings that suggest opportunities for further emissions reductions.

“This is a great example of the future of greenhouse gas emission tracking, where inventory compilers and atmospheric scientists work together to better understand emissions and shed light on ways to further reduce them,” said Montzka.

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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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