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Navigating the complex SF6 road

A year from now, under EU Regulation (EU) 2024/573 and in line with the Paris Agreement, the use of sulphur hexafluoride (SF6) will be banned in medium voltage equipment rated up to 24kV across the EU-27. In 2030, the ban will be extended to all devices with an insulation voltage of up to 52kV and, […]

A year from now, under EU Regulation (EU) 2024/573 and in line with the Paris Agreement, the use of sulphur hexafluoride (SF6) will be banned in medium voltage equipment rated up to 24kV across the EU-27.

In 2030, the ban will be extended to all devices with an insulation voltage of up to 52kV and, in 2032, high voltage switchgear with a “global warming potential” (GWP) of one or more and a short circuit current of more than 50kA will be banned.

GWP is based on SF6, a potent, synthetic greenhouse gas that has been used as an insulating and circuit breaking medium in switchgear for a long time, especially since the production of an earlier and also highly pernicious insulator, polychlorinated biphenyl (PCB) was supposedly banned more than 40 years ago under the Stockholm Convention.

SF6 has a GWP that is 22,800 to 25,000 times higher than carbon dioxide (CO2).

Whether the UK will follow the timeframe set out by the EU appears to be in flux. There has been speculation that our power distribution networks (grid) favour a split.

However, the change of government last summer and efforts now being made by the Starmer administration to rebuild bridges with the EU-27 could force the UK into line.

Meanwhile, the regulation, which includes measures for the management of existing SF6-containing equipment to ensure safe handling and eventual disposal, is of huge importance to the energy industry; particularly wind power off and onshore, generating plants on North Sea oil and gas installations and power grids, directly and indirectly.

Across the EU where there is a huge onshore industry and where the offshore build-out is now leaping forward, it appears that the first phase will be highly significant, especially onshore as much of the generating and related grid infrastructure is now mature and based on relatively small turbines.

Offshore is a different matter; the sector is younger and much of it is based on large and, increasingly super-large turbines. The bulk of North Sea developments to date are in the UK sector.

While wind turbines were producing 0.5MW of power at a height of 40m in 1990, today’s turbines produce 10 MW and more, and so need higher voltage levels and innovative technologies.

A transmission voltage of 66 kV has now become standard for offshore wind projects.

Broadly, the electrical system for an offshore wind farm presently comprises a medium-voltage electrical collection grid within the asset and a high-voltage electrical transmission system to deliver the power to an onshore transmission line.

The following data should help provide a basic means by which the scale of the SF6 transition challenge becomes obvious.

Europe installed 18.3GW of new wind power capacity in 2023. The EU-27 installed 16.2GW of this; a record amount but only half of what is needed to meet its 2030 climate and energy targets. 79% of the wind capacity built in Europe last year was onshore.

The volume of new offshore installations is growing – last year it was a record 3.8GW in Europe. But two-thirds of the new wind installations up to 2030 will continue to be onshore.

The expectation is that Europe will install 260GW of new wind power capacity over 2024-2030. The EU-27 should install 200GW of this so, 29GW a year on average.

To meet its 2030 climate and energy targets the EU now needs to build 33GW a year on average.

The expectation is that the EU-27 will fall around 30GW short of its overall 425GW ambition needed to meet the 42.5% renewable energy target.

Closer to home, the UK has passed the 30GW milestone, split virtually equally onshore: offshore. Trade association Renewable UK currently records a new project pipeline of around 45GW, primarily offshore.

The current government’s dream is to double onshore wind and quadruple offshore wind by 2030 to 30GW and 60GW respectively.

Fortunately, from a greenhouse gas compliance perspective, the build-out of new generating capacity offers the opportunity to incorporate necessary changes in a relatively straightforward way.

As the oil and gas industry knows only too well, modifying existing infrastructure is complex, costly and may hit revenue.

However, Big Wind is already proving itself equal to challenges thrown at it to date including upgrading existing wind farms via repowering. It helps that there is a lot of relevant North Sea oil and gas experience to tap.

The greatest challenges may turn out to be onshore as it is estimated that, in the UK alone, five times more power infrastructure needs to be constructed by 2030 than in the past three decades to deliver a net zero grid.

According to Europe’s Environmental Coalition on Standards (ECOS): “In the UK, one million kg of SF6 is installed across the electricity network. This is increasing by 30 to 40 tonnes per year.”

SF6 use is also rising in the EU

“In 2017, total SF6 emissions in the EU and UK combined amounted to 6.73 million tonnes of CO2, which equals the emission of 1.3million extra cars on the road,” adds ECOS.

The UK’s National Grid has been on the case for at least a decade and, in 2017, set a precedent.

April 13 that year, it passed a major milestone with the energisation of the first SF6-free 420kV gas-insulated line in its south-east England network. The new Sellindge substation was deployed in an operationally critical part of the UK’s transmission network.

In October this year, news broke that the company had started work with DNV and others on a project aimed at managing SF6 out of the UK’s electricity transmission network to facilitate the shift to clean alternatives, including for retrofit of existing switchgear.

Partners involved in this collaborative include National Grid as lead; University of Manchester, ScottishPower Transmission, Scottish Hydro Electric Transmission, DILO Armaturen und Anlagen and WIKA Instruments.

Staying onshore for a few more paragraphs, last year, UK Power Networks (UKPN) installed its first ‘clean air’ gas-insulated switchgear (GIS) operating at 132,000 volts, which is free of SF6.

In its place is a mix of dehumidified oxygen, and nitrogen, which has zero global warming potential.

UKPN worked with Siemens Energy to manufacture the system which is apparently doing its job efficiently as part of a substation upgrade in Lewes, East Sussex.

UKPN is the first UK distribution network operator (DNO) with an officially ratified science based target (SBT) committing to reduce all its Scope 1, 2 and 3 emissions by 25% by 2028 from a 2018/19 baseline.

The firm has also set a net zero target by 2028 on its controllable Scope 1 and 2 emissions, which include SF6 emissions.

Back to the North Sea and offshore wind where discovering who is actively trying to eliminate SF6 proved surprisingly hard; even working with EIAs (environmental impact assessments) presented to government for interrogation and approval by developers.

The most eye-opening criticism discovered was the EIA for Dogger Bank D submitted in June 2023 by a 50/50 joint venture between SSE Renewables and Equinor to the Planning Inspectorate of the UK Government.

It is tough: “The inspectorate does not consider that the scoping report has provided a sufficient amount of detail to justify scoping out the impacts of emissions from plant and machinery on human health and ecological sites during operation.

“Additionally, limited information has been provided with regard to hydrogen, oxygen and sulphur hexafluoride (SF6) emissions associated with operation of the hydrogen production facility (HPF).

“The inspectorate considers that an assessment of emissions associated with operational plant and machinery and the HPF should be included in the ES where likely significant effects could occur, or for the ES to provide a reasoned justification supported by evidence to demonstrate why a detailed assessment is not required.”

Turning to the 1,283-page Moray West Offshore Windfarm EIA dated 2018 and submitted to Marine Scotland for approval, there are just two cursory references to SF6.

Page 7 of ‘Description of Development’; listed under ‘Oils and Fluids’. No description.

Page 22/23, this time under ‘Oils, Fluids and Effluents’. The notation reads: “SF6 (insulating gas for preventing electrical discharge)”.

That’s all that developers EDP Renewables (EDPR) and ENGIE in JV (but with a minor shareholding by Ignitis Group) accorded to the intended use of the world’s most dangerous and forever greenhouse gas.

Notwithstanding, at least one developer is aware and it is ScottishPower with its East Anglia One project, possibly the first UK offshore North Sea windfarm to eliminate the use of SF6 offshore at the outset.

It was 2009 when ScottishPower Renewables and Vattenfall Wind Power began working on an equal collaborative partnership 2009 to develop capacity for offshore wind generation off the East Anglian coast.

£ 2.5 billion East Anglia One (714MW), was first out of the gate, securing development in 2014 followed by the award of a contract for difference (CfD) by the government in 2015.

In February 2015, ScottishPower went solus on the project, basing it on 102 Siemens Gamesa turbines with SF6-free, gas-insulated 8VM1 high-voltage switchgear (GIS) sets.

Offshore construction began in 2018 and the project was commissioned in July 2020.

Two further phases, two and three are in hand but no information about their SF6 status was forthcoming from ScottishPower despite repeated approaches.

To close, in its brief ‘Worst in Class, Phasing-out Sulphur Hexafluoride’, ECOS advises that SF6 used in renewables projects is not necessarily accounted for in official statistics, and that “many developers of renewable energy installations are not aware of the problems related to SF6, nor the existing alternatives”.

Renewable energy associations claim that the emission savings from replacing fossil fuels with renewable energy are still important despite the use of SF6.

ECOS warns: “However, given the long lifetime of SF6-based switchgear, it is important to transition towards clean and renewable energy sources, and switch to SF6-free alternatives as soon as the renewable energy facilities are installed.”

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Russian Crude Output Rose Last Month

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Gunvor Scraps Lukoil Deal

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Ship With Russia Oil Makes Rare Move Offshore India

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Petrobras Ramps Up Production at Major Oilfield

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Southwest Power Pool to develop 765-kV regional transmission ‘backbone’

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Designing the AI Century: 7×24 Exchange Fall ’25 Charts the New Data Center Industrial Stack

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs

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Top network and data center events 2025 & 2026

Denise Dubie is a senior editor at Network World with nearly 30 years of experience writing about the tech industry. Her coverage areas include AIOps, cybersecurity, networking careers, network management, observability, SASE, SD-WAN, and how AI transforms enterprise IT. A seasoned journalist and content creator, Denise writes breaking news and in-depth features, and she delivers practical advice for IT professionals while making complex technology accessible to all. Before returning to journalism, she held senior content marketing roles at CA Technologies, Berkshire Grey, and Cisco. Denise is a trusted voice in the world of enterprise IT and networking.

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Google’s cheaper, faster TPUs are here, while users of other AI processors face a supply crunch

Opportunities for the AI industry LLM vendors such as OpenAI and Anthropic, which still have relatively young code bases and are continuously evolving them, also have much to gain from the arrival of Ironwood for training their models, said Forrester vice president and principal analyst Charlie Dai. In fact, Anthropic has already agreed to procure 1 million TPUs for training and its models and using them for inferencing. Other, smaller vendors using Google’s TPUs for training models include Lightricks and Essential AI. Google has seen a steady increase in demand for its TPUs (which it also uses to run interna services), and is expected to buy $9.8 billion worth of TPUs from Broadcom this year, compared to $6.2 billion and $2.04 billion in 2024 and 2023 respectively, according to Harrowell. “This makes them the second-biggest AI chip program for cloud and enterprise data centers, just tailing Nvidia, with approximately 5% of the market. Nvidia owns about 78% of the market,” Harrowell said. The legacy problem While some analysts were optimistic about the prospects for TPUs in the enterprise, IDC research director Brandon Hoff said enterprises will most likely to stay away from Ironwood or TPUs in general because of their existing code base written for other platforms. “For enterprise customers who are writing their own inferencing, they will be tied into Nvidia’s software platform,” Hoff said, referring to CUDA, the software platform that runs on Nvidia GPUs. CUDA was released to the public in 2007, while the first version of TensorFlow has only been around since 2015.

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Cisco launches AI infrastructure, AI practitioner certifications

“This new certification focuses on artificial intelligence and machine learning workloads, helping technical professionals become AI-ready and successfully embed AI into their workflows,” said Pat Merat, vice president at Learn with Cisco, in a blog detailing the new AI Infrastructure Specialist certification. “The certification validates a candidate’s comprehensive knowledge in designing, implementing, operating, and troubleshooting AI solutions across Cisco infrastructure.” Separately, the AITECH certification is part of the Cisco AI Infrastructure track, which complements its existing networking, data center, and security certifications. Cisco says the AITECH cert training is intended for network engineers, system administrators, solution architects, and other IT professionals who want to learn how AI impacts enterprise infrastructure. The training curriculum covers topics such as: Utilizing AI for code generation, refactoring, and using modern AI-assisted coding workflows. Using generative AI for exploratory data analysis, data cleaning, transformation, and generating actionable insights. Designing and implementing multi-step AI-assisted workflows and understanding complex agentic systems for automation. Learning AI-powered requirements, evaluating customization approaches, considering deployment strategies, and designing robust AI workflows. Evaluating, fine-tuning, and deploying pre-trained AI models, and implementing Retrieval Augmented Generation (RAG) systems. Monitoring, maintaining, and optimizing AI-powered workflows, ensuring data integrity and security. AITECH certification candidates will learn how to use AI to enhance productivity, automate routine tasks, and support the development of new applications. The training program includes hands-on labs and simulations to demonstrate practical use cases for AI within Cisco and multi-vendor environments.

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Chip-to-Grid Gets Bought: Eaton, Vertiv, and Daikin Deals Imply a New Thermal Capital Cycle

This week delivered three telling acquisitions that mark a turning point for the global data center supply chain; and more specifically, for the high-density liquid cooling mega-play now unfolding across the power-thermal continuum. Eaton is acquiring Boyd Thermal for $9.5 billion from Goldman Sachs Asset Management. Vertiv is buying PurgeRite for about $1 billion from Milton Street Capital. And Daikin Applied has moved to acquire Chilldyne, one of the most proven negative-pressure direct-to-chip pioneers. On paper, they’re three distinct transactions. In reality, they’re chapters in the same story: the acceleration of strategic vertical integration around thermal infrastructure for AI-class compute. The Equity Layer: Private Capital Builds, Strategics Buy From an equity standpoint, these are classic handoff moments between private-equity construction and corporate consolidation. Goldman Sachs built Boyd Thermal into a global platform spanning cold plates, CDUs, and high-density liquid loop design, now sold to Eaton at an enterprise multiple north of 5× 2026E revenue. Milton Street Capital took PurgeRite from a specialist contractor in fluid flushing and commissioning into a nationwide services platform. And Daikin, long synonymous with chillers and air-side thermal, is crossing the liquid Rubicon by buying its way into the D2C ecosystem. Each deal crystallizes a simple fact: liquid cooling is no longer an adjunct; it’s core infrastructure. Private equity did its job scaling the parts. Strategic players are now paying up for the system. Eaton’s Bid: The Chip-to-Grid Thesis For Eaton, Boyd Thermal is the final missing piece in its “chip-to-grid” thesis. The company already owns the electrical side of the data center: UPS, busway, switchgear, and monitoring. Boyd plugs the thermal gap, allowing Eaton to market full rack-to-substation solutions for AI loads in the 50–100 kW+ range. It’s a statement acquisition that places Eaton squarely against Schneider Electric, Vertiv and ABB in the race to

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Space: The final frontier for data processing

There are, however, a couple of reasons why data centers in space are being considered. There are plenty of reports about how the increased amount of AI processing is affecting power consumption within data centers; the World Economic Forum has estimated that the power required to handle AI is increasing at a rate of between 26% and 36% annually. Therefore, it is not surprising that organizations are looking at other options. But an even more pressing reason for orbiting data centers is to handle the amount of data that is being produced by existing satellites, Judge said. “Essentially, satellites are gathering a lot more data than can be sent to earth, because downlinks are a bottleneck,” he noted. “With AI capacity in orbit, they could potentially analyze more of this data, extract more useful information, and send insights back to earth. My overall feeling is that any more data processing in space is going to be driven by space processing needs.” And China may already be ahead of the game. Last year, Guoxing Aerospace  launched 12 satellites, forming a space-based computing network dubbed the Three-Body Computing Constellation. When completed, it will contain 2,800 satellites, all handling the orchestration and processing of data, taking edge computing to a new dimension.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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