PacifiCorp aims to add 4.7 GW renewables, 1.7 GW storage by 2031

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PacifiCorp aims to add 4.7 GW renewables, 1.7 GW storage by 2031

Dive Brief: PacifiCorp expects to procure through requests for proposals about 2,400 MW of solar, 2,270 MW of wind, 1,680 MW of four-hour storage and 510 MW of 100-hour iron air battery storage by the end of 2030, according to the utility company’s integrated resource plan, filed with state regulators on Monday. By the end […]

Dive Brief:

PacifiCorp expects to procure through requests for proposals about 2,400 MW of solar, 2,270 MW of wind, 1,680 MW of four-hour storage and 510 MW of 100-hour iron air battery storage by the end of 2030, according to the utility company’s integrated resource plan, filed with state regulators on Monday.
By the end of this year, PacifiCorp also plans to finalize a 500-MW agreement to buy electricity from TerraPower’s Natrium advanced nuclear project in Wyoming, which is set to be operating by the end of 2031, the utility company said. The cost of the project is confidential.
Also, if, as expected, the Biden administration’s greenhouse gas emissions rule for power plants doesn’t take effect, PacifiCorp plans to continue operating some coal-fired generation it had previously expected to retire. The plan calls for converting 562 MW of coal-fired generation to gas, exiting 386 MW of minority-owned coal-fired capacity, and adding carbon capture equipment to two coal-fired units at the Jim Bridger power plant in Wyoming that total 700 MW.

Dive Insight:

PacifiCorp utilities serve about 2 million customers in California, Idaho, Oregon, Utah, Washington and Wyoming. The IRP, filed every two years with stakeholder input, provides a roadmap for supplying power to those customers over the next 20 years.

The utility owns or contracts for about 5,150 MW of wind, 4,530 MW of coal, 3,850 MW of gas, 3,620 MW of solar coupled with 550 MW of storage, 1,200 MW of hydroelectric capacity and 525 MW of stand-alone storage, according to the IRP.

Under its resource plan, PacifiCorp expects that coal- and gas-fired generation will account for 16% and 10% of its energy mix in 2031, down from 35% and 19% this year, respectively. Wind and solar will make up 32% and 25%, up from 24% and 10%, respectively, in the same period.

Western Resource Advocates supported PacifiCorp’s continued shift towards clean energy. “Renewable energy brings greater price stability to customers and decreases harmful greenhouse gas emissions,” Sophie Hayes, Utah clean energy manager and senior attorney at WRA, said Tuesday in a press release.

PacifiCorp expects its annual carbon dioxide emissions will fall to about 11 million metric tons in 2032, down from about 55 MMT in 2013, according to the IRP. However, the utility company forecasts that its carbon emissions will slowly climb to about 17 MMT by 2045, partly due to increased wholesale market purchases.

The company’s resource plan calls for adding about $2.3 billion in transmission by the end of 2032. The transmission would allow about 4,220 MW to interconnect to the grid, according to the plan.

Federal and state tax credits continue to encourage wind and solar procurement, which will likely dominate U.S. capacity additions for the next decade, PacifiCorp said. However, Pacificorp said its plans could be affected by changing federal policies.

A repeal of the Inflation Reduction Act is unlikely, but the Trump administration could slow the payment of grants and loans or change regulations and guidance issued on how to implement the IRA, according to PacifiCorp.

“This action would make it difficult for companies and individuals to plan with certainty with respect to claiming tax credits for investments in new renewable and non-emitting technologies including [electric vehicles] and offshore wind,” Pacificorp said. The utility company in mid-January received a $3.5 billion conditional loan guarantee from the U.S. Department of Energy to help finance transmission projects in Idaho, Oregon and Utah.

A shift away from federal climate initiatives could increase China’s global dominance in clean energy industries and new import tariffs could hinder the deployment technologies supported by the IRA, the utility company said.

“Competing state and federal policy are ultimately driving PacifiCorp’s decisions to retire coal units or keep them running,” the WRA’s Hayes said. “PacifiCorp makes it clear that changes in policy may impair its ability to reduce greenhouse gas emissions, placing serious cost risks onto the backs of customers. In the face of changing federal regulations, states have an important role to play — for good or ill — in addressing the challenges of climate change.”

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UK government to profit from nuclear fusion fund

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EIP Raises Stake in Eni Renewables Arm to 10 Percent

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New MLCommons benchmarks to test AI infrastructure performance

The latest release also broadens its scope beyond chatbot benchmarks. A new graph neural network (GNN) test targets datacenter-class hardware and is designed for workloads like fraud detection, recommendation engines, and knowledge graphs. It uses the RGAT model based on a graph dataset containing over 547 million nodes and 5.8 billion edges. Judging performance Analysts suggest that these benchmarks will make it easier to judge the performance of various hardware chips and clusters based on documented models. “As every chipmaker seeks to prove that its hardware is good enough to support AI, we now have a standard benchmark that shows the quality of question support, math, and coding skills associated with hardware,” said Hyoun Park, CEO and Chief Analyst at Amalgam Insights. Chipmakers can now compete not just on traditional speeds and feeds, but in mathematical skill and informational accuracy. This benchmark provides a rare opportunity to add new performance standards on cross-vendor hardware, Park added. “The latency in terms of how quickly tokens are delivered and the time for the user to see the response is the deciding factor,” said Neil Shah, partner and co-founder at Counterpoint Research. “This is where players such as NVIDIA, AMD, and Intel have to get the software right to help developers optimize the models and bring out the best compute performance.” Benchmarking and buying decisions Independent benchmarks like those from MLCommons play a key role in helping buyers evaluate system performance, but relying on them alone may not provide the full picture.

Potential Nvidia chip shortage looms as Chinese customers rush to beat US sales ban

Will it lead to shortages? The US first placed export controls on chips sent to China in October 2022 as a means to slow the country’s technological advances. It blocked the sale of Nvidia’s A100 and H100 chips, leading the company to develop the less powerful A800 and H800 chips for the market; they were also subsequently banned. There was a surge in demand for the H20 following the arrival of Chinese startup DeepSeek’s ultra low-cost, open-source AI model in January. And while the H20 is reported to be 15 times slower than Nvidia’s newest Blackwell chips sold elsewhere in the world, it was designed specifically by Nvidia to comply with the further US export controls introduced in October 2023. It is being used by Chinese companies for training, although it’s billed as an inference chip, explained Matt Kimball, VP and principal analyst for datacenter compute and storage at Moor Insights & Strategy. Should Nvidia choose to focus its efforts on manufacturing more of the chips, Kimball said he doesn’t think it will impact supply in the US and Europe, as Blackwell is the main product sold in those markets and H20 is an N-1 Hopper architecture chip. “If you take this a step further and ask whether this large order slows down the production of chips destined for the US and Europe, I’d say the answer is no, as the Hopper family is built on a different process node than the Blackwell family,” he said. Still, Kimball noted, “supply chain management is difficult, especially for smaller organizations that are put to the back of the line as hyperscalers with multibillion dollar orders are first in line for the newest [chips].”

European cloud group invests to create what it dubs “Trump-proof cloud services”

But analysts have questioned whether the Microsoft move truly addresses those European business concerns. Phil Brunkard, executive counselor at Info-Tech Research Group UK, said, commenting on last month’s announcement of the EU Data Boundary for the Microsoft Cloud, “Microsoft says that customer data will remain stored and processed in the EU and EFTA, but doesn’t guarantee true data sovereignty.” And European companies are now rethinking what data sovereignty means to them. They are moving beyond having it refer to where the data sits to focusing on which vendors control it, and who controls them. Responding to the new Euro cloud plan, another analyst, IDC VP Dave McCarthy, saw the effort as “signaling a growing European push for data control and independence.” “US providers could face tougher competition from EU companies that leverage this tech to offer sovereignty-friendly alternatives. Although €1 million isn’t a game-changer on its own, it’s a clear sign Europe wants to build its own cloud ecosystem—potentially at the expense of US market share,” McCarthy said. “For US providers, this could mean investing in more EU-based data centers or reconfiguring systems to ensure European customers’ data stays within the region. This isn’t just a compliance checkbox. It’s a shift that could hike operational costs and complexity, especially for companies used to running centralized setups.” Adding to the potential bad news for US hyperscalers, McCarthy said that there was little reason to believe that this trend would be limited to Europe. “If Europe pulls this off, other regions might take note and push for similar sovereignty rules. US providers could find themselves adapting to a patchwork of regulations worldwide, forcing a rethink of their global strategies,” McCarthy said. “This isn’t just a European headache, it’s a preview of what could become a broader challenge.”

Talent gap complicates cost-conscious cloud planning

The top strategy so far is what one enterprise calls the “Cloud Team.” You assemble all your people with cloud skills, and your own best software architect, and have the team examine current and proposed cloud applications, looking for a high-level approach that meets business goals. In this process, the team tries to avoid implementation specifics, focusing instead on the notion that a hybrid application has an agile cloud side and a governance-and-sovereignty data center side, and what has to be done is push functionality into the right place. The Cloud Team supporters say that an experienced application architect can deal with the cloud in abstract, without detailed knowledge of cloud tools and costs. For example, the architect can assess the value of using an event-driven versus transactional model without fixating on how either could be done. The idea is to first come up with approaches. Then, developers could work with cloud providers to map each approach to an implementation, and assess the costs, benefits, and risks. Ok, I lied about this being the top strategy—sort of, at least. It’s the only strategy that’s making much sense. The enterprises all start their cloud-reassessment journey on a different tack, but they agree it doesn’t work. The knee-jerk approach to cloud costs is to attack the implementation, not the design. What cloud features did you pick? Could you find ones that cost less? Could you perhaps shed all the special features and just host containers or VMs with no web services at all? Enterprises who try this, meaning almost all of them, report that they save less than 15% on cloud costs, a rate of savings that means roughly a five-year payback on the costs of making the application changes…if they can make them at all. Enterprises used to build all of

Lightmatter launches photonic chips to eliminate GPU idle time in AI data centers

“Silicon photonics can transform HPC, data centers, and networking by providing greater scalability, better energy efficiency, and seamless integration with existing semiconductor manufacturing and packaging technologies,” Jagadeesan added. “Lightmatter’s recent announcement of the Passage L200 co-packaged optics and M1000 reference platform demonstrates an important step toward addressing the interconnect bandwidth and latency between accelerators in AI data centers.” The market timing appears strategic, as enterprises worldwide face increasing computational demands from AI workloads while simultaneously confronting the physical limitations of traditional semiconductor scaling. Silicon photonics offers a potential path forward as conventional approaches reach their limits. Practical applications For enterprise IT leaders, Lightmatter’s technology could impact several key areas of infrastructure planning. AI development teams could see significantly reduced training times for complex models, enabling faster iteration and deployment of AI solutions. Real-time AI applications could benefit from lower latency between processing units, improving responsiveness for time-sensitive operations. Data centers could potentially achieve higher computational density with fewer networking bottlenecks, allowing more efficient use of physical space and resources. Infrastructure costs might be optimized by more efficient utilization of expensive GPU resources, as processors spend less time waiting for data and more time computing. These benefits would be particularly valuable for financial services, healthcare, research institutions, and technology companies working with large-scale AI deployments. Organizations that rely on real-time analysis of large datasets or require rapid training and deployment of complex AI models stand to gain the most from the technology. “Silicon photonics will be a key technology for interconnects across accelerators, racks, and data center fabrics,” Jagadeesan pointed out. “Chiplets and advanced packaging will coexist and dominate intra-package communication. The key aspect is integration, that is companies who have the potential to combine photonics, chiplets, and packaging in a more efficient way will gain competitive advantage.”

Silicon Motion rolls SSD kit to bolster AI workload performance

The kit utilizes the PCIe Dual Ported enterprise-grade SM8366 controller with support for PCIe Gen 5 x4 NVMe 2.0 and OCP 2.5 data center specifications. The 128TB SSD RDK also supports NVMe 2.0 Flexible Data Placement (FDP), a feature that allows advanced data management and improved SSD write efficiency and endurance. “Silicon Motion’s MonTitan SSD RDK offers a comprehensive solution for our customers, enabling them to rapidly develop and deploy enterprise-class SSDs tailored for AI data center and edge server applications.” said Alex Chou, senior vice president of the enterprise storage & display interface solution business at Silicon Motion. Silicon Motion doesn’t make drives, rather it makes reference design kits in different form factors that its customers use to build their own product. Its kits come in E1.S, E3.S, and U.2 form factors. The E1.S and U.2 forms mirror the M.2, which looks like a stick of gum and installs on the motherboard. There are PCI Express enclosures that hold four to six of those drives and plug into one card slot and appear to the system as a single drive.

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.

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

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2025 playbook for enterprise AI success, from agents to evals

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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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