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UK government urged to ‘make a decision’ quickly on zonal pricing

Delays to a decision on zonal electricity pricing are an “albatross around the neck” of UK renewable energy developers, a parliamentary committee has heard. Energy UK director of policy and advocacy Adam Berman said the review of electricity market arrangements (REMA) process has “gone on endlessly now”. Berman said industry developers want to see a […]

Delays to a decision on zonal electricity pricing are an “albatross around the neck” of UK renewable energy developers, a parliamentary committee has heard.

Energy UK director of policy and advocacy Adam Berman said the review of electricity market arrangements (REMA) process has “gone on endlessly now”.

Berman said industry developers want to see a quick decision on locational pricing reforms as well as more information on “crucial elements” of how the scheme may be grandfathered in for existing projects.

He told the Energy Security and Net Zero committee hearing that “no one knows which way the government’s going to go” and called for clarity as soon as possible.

Launched in 2022 under the previous Conservative government, the REMA process is exploring reform options which could split the UK into regional electricity markets.

In December, the Department for Energy Security and Net Zero (DESNZ) said no decision has been taken between zonal pricing or “reformed national pricing”.

However, DESNZ confirmed the current status quo is “not an option”.

The issue of zonal pricing has divided the sector amid concerns over the potential impact on renewable energy investment, particularly in Scotland.

AR7 and zonal pricing concerns

Berman told Westminster MPs that unless a decision is made on zonal pricing soon, it could impact bids in the seventh renewable auction round (AR7) later this year.

The UK government is aiming to achieve the “biggest and most successful” renewables auction round ever in 2025, but Berman warned developers could simply walk away without clarity on zonal pricing.

“The government is not considering implementing any of this until after 2030, but nonetheless we have a lot of investment we need [up] until 2030,” he said.

Installation of the first turbine at Moray West. © Supplied by Ocean Winds
Installation of the first turbine at the Moray West offshore wind farm in Scotland.

“I think there’s every chance that if  the government makes a decision in favour of quite substantial market change so close to an auction round, that the auction round could well not go the way the government wanted.”

Berman said the Contracts for Difference (CfD) process leaves developers with a very short window of time to submit bids after receiving the auction parameters.

“What essentially the government is saying is we’re going to radically transform the market and we’ll give you a month to figure out whether a 25 year asset is doable and at what price,” he added.

Berman said the government has yet to produce a “single detailed policy paper from the government as to how this would work”.

“Ultimately, we are undertaking a Herculean task to decarbonise the power sector by 2030,” he said.

“Anything that casts doubt or uncertainty in the mind of private business… I think is not a welcome distraction at this point in time.”

Zonal pricing debate

Industry bodies like RenewableUK, Offshore Energies UK, Scottish Renewables and Make UK have urged DESNZ to rule out zonal pricing.

They argue the “radical change” will lead to increased volatility and higher capital costs, while making UK manufacturing less competitive.

Similarly, major firms like SSE and ScottishPower say it’s “time to move on” from the “distraction” of the zonal pricing debate.

Meanwhile, proponents like Octopus Energy argue zonal pricing will correct inefficiencies in the system and save consumers billions.

The Energy Systems Catapult says zonal pricing is the “only option” that can efficiently integrate renewables onto the grid and “build the foundation for net zero”.

With the UK set to build significant offshore wind capacity off the coast of Scotland, transporting the electricity to the south will require huge investments in infrastructure.

Introducing locational incentives could encourage firms to build generation in areas where they can receive a higher price for electricity, mostly in the south of England.

This would reduce the need for future investments in overhead transmission lines and subsea cables, potentially avoiding protracted planning battles.

In addition, zonal pricing could lead to Scotland having the lowest power prices in all of Europe, facilitating energy intensive industries such as green hydrogen production.

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HSE report finds offshore industry “stepped up” to safety leadership challenge

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Shell Restarts Production at UK North Sea Penguins Field

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ADNOC Gas Posts Record $5 Billion Annual Profit Driven by Domestic Demand

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Ørsted steered steady course through troubled waters in 2024

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UK forecast to miss 2035 emissions-reduction targets

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Linux containers in 2025 and beyond

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Blue Owl Swoops In As Major Backer of New, High-Profile, Sustainable U.S. Data Center Construction

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Global Data Center Operator Telehouse Launches Liquid Cooling Lab in the UK to Meet Ongoing AI and HPC Demand

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Flexential Partners with Lonestar to Support First Lunar Data Center

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Why DeepSeek Is Great for AI and HPC and Maybe No Big Deal for Data Centers

In the rapid and ever-evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), the emergence of DeepSeek’s R1 model has sent ripples across industries. DeepSeek has been the data center industry’s topic of the week, for sure. The Chinese AI app surged to the top of US app store leaderboards last weekend, sparking a global selloff in technology shares Monday morning.  But while some analysts predict a transformative impact within the industry, a closer examination suggests that, for data centers at large, the furor over DeepSeek might ultimately be much ado about nothing. DeepSeek’s Breakthrough in AI and HPC DeepSeek, a Chinese AI startup, this month unveiled its R1 model, claiming performance on par with, or even surpassing, leading models like OpenAI’s ChatGPT-4 and Anthropic’s Claude-3.5-Sonnet. Remarkably, DeepSeek developed this model at a fraction of the cost typically associated with such advancements, utilizing a cluster of 256 server nodes equipped with 2,048 GPUs. This efficiency has been attributed to innovative techniques and optimized resource utilization. AI researchers have been abuzz about the performance of the DeepSeek chatbot that produces results similar to ChatGPT, but is based on open-source models and reportedly trained on older GPU chips. Some researchers are skeptical of claims about DeepSeek’s development costs and means, but its performance appears to challenge common assumptions about the computing cost of developing AI applications. This efficiency has been attributed to innovative techniques and optimized resource utilization.  Market Reactions and Data Center Implications The announcement of DeepSeek’s R1 model led to significant market reactions, with notable declines in tech stocks, including a substantial drop in Nvidia’s valuation. This downturn was driven by concerns that more efficient AI models could reduce the demand for high-end hardware and, by extension, the expansive data centers that house them. For now, investors are re-assessing the

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

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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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