nuclear-news

More Renewables – or more nuclear?

May 24, 2025, https://renewextraweekly.blogspot.com/2025/05/more-renewables-or-more-nuclear.html

In my last post, I looked at how, despite renewable expansion, emissions were still rising. I focussed mainly on coal, but clearly it’s wider than that: multi-billion fossil fuel investment continues. In this post I will look at what is arguably another big issue- the attempt, to rebrand nuclear as a solution.  Certainly some people in the UK and elsewhere think that there is a case for nuclear as part of a low carbon answer to climate change, although others do not agree. Even leaving aside the safety, security and waste issues, they say it’s too expensive and takes too long to build compared with renewables.  

That debate continues, but in terms of what’s actually happening on the ground, the battle has arguably been won by renewables – they are expanding very rapidly around the world, leaving nuclear mostly stalled. Even China’s nuclear programme, currently at around 60 GW, is tiny compared with its renewables capacity, which hit 1.82 TW last year and is still expanding fast. 

However, nuclear is still in the game in some locations, with, for example, Russia trying to export its nuclear technology and fuel services.  And more generally, while the nuclear industry may mostly have to accept a lesser role globally, as renewables expand to high percentages of overall power, that in fact may be seen by them as a new opportunity- on the argument that nuclear will be needed to back up variable renewables. 

The latest example seems to be Denmark, famed for its anti-nuclear ‘Atomkraft Nein Danke’ stance, with renewables now supplying over 80% of its power and aiming to get to 100% of all energy by 2050.  That will require new grid balancing capacity, the most obvious being storage- with excess renewable output being stored in batteries or converted to hydrogen for use when there is renewable supply lull or a peak in demand.  But evidently there is also now government interest in nuclear- and the idea of small modular reactors (SMR). It’s hard to see how this would be viable for occasional backup. Large conventional nuclear plants are expensive to build and inflexible to run, and current designs for SMRs are no better – and trying to make them flexible is likely add even more to the cost. So it seems very odd. 

Spain also has a high renewable percentage, Portugal too, though, unlike Denmark and Portugal, Spain does have some nuclear plants. But it is planning to phase them out.  Certainly they were no use in avoiding the recent large-scale total Iberian blackout- Spain’s nuclear plants evidently can’t run competitively when renewables are at peak. So, as it seems happens regularly, they were throttled back- most of the power was coming from wind and solar.  We still don’t know what exactly went wrong, but it does seem that the remaining nuclear plants tripped out due to a grid overload signal, and PV solar also cut out for some reason.  Perhaps a bit prematurely, the Spanish prime minister said that ‘there is no empirical evidence that the incident was caused by a surplus of renewables or a lack of nuclear power plants in Spain.’

He added ‘we are not going to deviate a single millimetre from the energy road map we have planned since 2018. Not only are renewables our country’s energy future, they are our only and best option. They are the only way to re-industrialise Spain.’ 

It may turn out have been a simple line fault, but it’s also possible that it was due to lack of synchronous inertia on the grid system. Large conventional fossil fuel fired or nuclear plants have large heavy spinning turbo-generators that help balance short-term output perturbations. PV solar has none, wind turbines only a small amount. But it would be crazy to build large expensive fossil or nuclear plants to provide rotational inertia for usually rare events.

There has been talk in the past of using just the turbo-generator sets of old closed plants, unpowered except for grid power, to provide ‘spinning reserve’ rotational inertia. However, if its needed, there may be easier and cheaper ways to do this electronically, via fast response storage and inverter systems- virtual synchronous inertia. Tidal lagoons and barrages are another option- they offer large scale energy storage capacity along with significant rotational inertia. So too do hydro projects. So inertia issues are not necessarily a major problem. 

Needless to say though, despite there being no poof as to what actually happened as yet, there was a rush to early judgement and speculation by some of the media – along the lines that ‘you can’t rely on renewables’ and ‘this is what you get with net zero’!  So far though, most of this invective has bounced off. Anti-nuclear Germany, currently gets around 63% of its power from renewables, and aims to get to net zero carbon with no nuclear. Occasionally there has ben some pressure to go back to nuclear but it has been resisted and certainly the ‘baseload for grid balancing’ case for nuclear seems very weak. 

Meanwhile, the UK is getting 50% of its power from renewables, but is struggling to fund its proposed new nuclear plant at Sizewell: EdF evidently is no longer able to help out- it has enough financial and operational problems with its troubled EPR programme in France. It has also halted its initial SMR programme. The UK however is still  keen to promote SMRs, although Westinghouse has pulled out of the race. The basic problem with nuclear technology, old or new, small or large, is cost – renewables like wind and solar are far cheaper, and storage backup is also now getting cheap.

The UK does need to get moving on hydrogen storage for the longer term, and also heat storage, which some see as better than heat pumps in some locations, possibly with green AD biogas as a storable energy source. Tidal lagoon power, though still a bit too costly, is also beginning to be talked up again. All of these are arguably a lot more promising for balancing than new nuclear. 

So what’s likely to happen? There’s no question that the nuclear lobby has been pushing hard to get back in the game after the 2011 Fukushima disaster in Japan, which had led to nuclear programme reversals around the world, but in Asia especially. There were huge public demonstrations of opposition. But, in time, the phase out or cut back programmes planned by some Asian governments (including Japan, Taiwan and Thailand) faded away and nuclear is back in favour- although renewables are now seen as the main energy supply options in most cases. Not least since, even there, though lower than in the West, the constructions costs and investment risks of new nuclear are high, compared with solar and wind. 

However, some also see carbon removal and biomass carbon capture playing a carbon negative role, especially in countries where there are a lot of agricultural carbon emissions- and also land.  But as I noted in my last post, although there may be some exceptions, the economic and ecological viability of that approach on a large scale is debatable- expanding renewables even faster looks like a better bet. Though, as with nuclear, the debate continues…

May 26, 2025 Posted by Christina Macpherson | ENERGY | Leave a comment

We did the math on AI’s energy footprint. Here’s the story you haven’t heard

Tallies of AI’s energy use often short-circuit the conversation—either by scolding individual behavior, or by triggering comparisons to bigger climate offenders. Both reactions dodge the point: AI is unavoidable, and even if a single query is low-impact, governments and companies are now shaping a much larger energy future around AI’s needs.

“It’s not clear to us that the benefits of these data centers outweigh these costs,”

Tallies of AI’s energy use often short-circuit the conversation—either by scolding individual behavior, or by triggering comparisons to bigger climate offenders. Both reactions dodge the point: AI is unavoidable, and even if a single query is low-impact, governments and companies are now shaping a much larger energy future around AI’s needs.

“It’s not clear to us that the benefits of these data centers outweigh these costs,”

The emissions from individual AI text, image, and video queries seem small—until you add up what the industry isn’t tracking and consider where it’s heading next.

James O’Donnell, Casey Crownhart, MIT Technology Review, May 20, 2025

AI’s integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. But what’s powering all of that?

Today, new analysis by MIT Technology Review provides an unprecedented and comprehensive look at how much energy the AI industry uses—down to a single query—to trace where its carbon footprint stands now, and where it’s headed, as AI barrels towards billions of daily users.

This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution.

We spoke to two dozen experts measuring AI’s energy demands, evaluated different AI models and prompts, pored over hundreds of pages of projections and reports, and questioned top AI model makers about their plans. Ultimately, we found that the common understanding of AI’s energy consumption is full of holes.

We started small, as the question of how much a single query costs is vitally important to understanding the bigger picture. That’s because those queries are being built into ever more applications beyond standalone chatbots: from search, to agents, to the mundane daily apps we use to track our fitness, shop online, or book a flight. The energy resources required to power this artificial-intelligence revolution are staggering, and the world’s biggest tech companies have made it a top priority to harness ever more of that energy, aiming to reshape our energy grids in the process.

Meta and Microsoft are working to fire up new nuclear power plants. OpenAI and President Donald Trump announced the Stargate initiative, which aims to spend $500 billion—more than the Apollo space program—to build as many as 10 data centers (each of which could require five gigawatts, more than the total power demand from the state of New Hampshire). Apple announced plans to spend $500 billion on manufacturing and data centers in the US over the next four years. Google expects to spend $75 billion on AI infrastructure alone in 2025.

This isn’t simply the norm of a digital world. It’s unique to AI, and a marked departure from Big Tech’s electricity appetite in the recent past. From 2005 to 2017, the amount of electricity going to data centers remained quite flat thanks to increases in efficiency, despite the construction of armies of new data centers to serve the rise of cloud-based online services, from Facebook to Netflix. In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023. The latest reports show that 4.4% of all the energy in the US now goes toward data centers.

the US average.

Given the direction AI is headed—more personalized, able to reason and solve complex problems on our behalf, and everywhere we look—it’s likely that our AI footprint today is the smallest it will ever be. According to new projections published by Lawrence Berkeley National Laboratory in December, by 2028 more than half of the electricity going to data centers will be used for AI. At that point, AI alone could consume as much electricity annually as 22% of all US households.

Meanwhile, data centers are expected to continue trending toward using dirtier, more carbon-intensive forms of energy (like gas) to fill immediate needs, leaving clouds of emissions in their wake. And all of this growth is for a new technology that’s still finding its footing, and in many applications—education, medical advice, legal analysis—might be the wrong tool for the job or at least have a less energy-intensive alternative.

Tallies of AI’s energy use often short-circuit the conversation—either by scolding individual behavior, or by triggering comparisons to bigger climate offenders. Both reactions dodge the point: AI is unavoidable, and even if a single query is low-impact, governments and companies are now shaping a much larger energy future around AI’s needs.

We’re taking a different approach with an accounting meant to inform the many decisions still ahead: where data centers go, what powers them, and how to make the growing toll of AI visible and accountable.

That’s because despite the ambitious AI vision set forth by tech companies, utility providers, and the federal government, details of how this future might come about are murky. Scientists, federally funded research facilities, activists, and energy companies argue that leading AI companies and data center operators disclose too little about their activities. Companies building and deploying AI models are largely quiet when it comes to answering a central question: Just how much energy does interacting with one of these models use? And what sorts of energy sources will power AI’s future?

This leaves even those whose job it is to predict energy demands forced to assemble a puzzle with countless missing pieces, making it nearly impossible to plan for AI’s future impact on energy grids and emissions. Worse, the deals that utility companies make with the data centers will likely transfer the costs of the AI revolution to the rest of us, in the form of higher electricity bills.

It’s a lot to take in. To describe the big picture of what that future looks like, we have to start at the beginning.

ning.

Part One: Making the model|…………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

At each of these centers, AI models are loaded onto clusters of servers containing special chips called graphics processing units, or GPUs, most notably a particular model made by Nvidia called the H100.

This chip started shipping in October 2022, just a month before ChatGPT launched to the public. Sales of H100s have soared since, and are part of why Nvidia regularly ranks as the most valuable publicly traded company in the world.

Other chips include the A100 and the latest Blackwells. What all have in common is a significant energy requirement to run their advanced operations without overheating.

A single AI model might be housed on a dozen or so GPUs, and a large data center might have well over 10,000 of these chips connected together.

Wired close together with these chips are CPUs (chips that serve up information to the GPUs) and fans to keep everything cool.

Some energy is wasted at nearly every exchange through imperfect insulation materials and long cables in between racks of servers, and many buildings use millions of gallons of water (often fresh, potable water) per day in their cooling operations.

Depending on anticipated usage, these AI models are loaded onto hundreds or thousands of clusters in various data centers around the globe, each of which have different mixes of energy powering them.

They’re then connected online, just waiting for you to ping them with a question.

Part Two: A Query……………………………

Part Three: Fuel and emissions………………………………………………………

Part four: The future ahead|……………………………………………………………………………………..

The Lawrence Berkeley researchers offered a blunt critique of where things stand, saying that the information disclosed by tech companies, data center operators, utility companies, and hardware manufacturers is simply not enough to make reasonable projections about the unprecedented energy demands of this future or estimate the emissions it will create. They offered ways that companies could disclose more information without violating trade secrets, such as anonymized data-sharing arrangements, but their report acknowledged that the architects of this massive surge in AI data centers have thus far not been transparent, leaving them without the tools to make a plan.

“Along with limiting the scope of this report, this lack of transparency highlights that data center growth is occurring with little consideration for how best to integrate these emergent loads with the expansion of electricity generation/transmission or for broader community development,” they wrote. The authors also noted that only two other reports of this kind have been released in the last 20 years.

We heard from several other researchers who say that their ability to understand the emissions and energy demands of AI are hampered by the fact that AI is not yet treated as its own sector. The US Energy Information Administration, for example, makes projections and measurements for manufacturing, mining, construction, and agriculture, but detailed data about AI is simply nonexistent.

Individuals may end up footing some of the bill for this AI revolution, according to new research published in March. The researchers, from Harvard’s Electricity Law Initiative, analyzed agreements between utility companies and tech giants like Meta that govern how much those companies will pay for power in massive new data centers. They found that discounts utility companies give to Big Tech can raise the electricity rates paid by consumers. In some cases, if certain data centers fail to attract the promised AI business or need less power than expected, ratepayers could still be on the hook for subsidizing them. A 2024 report from the Virginia legislature estimated that average residential ratepayers in the state could pay an additional $37.50 every month in data center energy costs.

“It’s not clear to us that the benefits of these data centers outweigh these costs,” says Eliza Martin, a legal fellow at the Environmental and Energy Law Program at Harvard and a coauthor of the research. “Why should we be paying for this infrastructure? Why should we be paying for their power bills?”

When you ask an AI model to write you a joke or generate a video of a puppy, that query comes with a small but measurable energy toll and an associated amount of emissions spewed into the atmosphere. Given that each individual request often uses less energy than running a kitchen appliance for a few moments, it may seem insignificant.

But as more of us turn to AI tools, these impacts start to add up. And increasingly, you don’t need to go looking to use AI: It’s being integrated into every corner of our digital lives.

Crucially, there’s a lot we don’t know; tech giants are largely keeping quiet about the details. But to judge from our estimates, it’s clear that AI is a force reshaping not just technology but the power grid and the world around us.

We owe a special thanks to Jae-Won Chung, Mosharaf Chowdhury, and Sasha Luccioni, who shared their measurements of AI’s energy use for this project. https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/?utm_source=Global+Energy+Monitor&utm_campaign=689b47e840-EMAIL_CAMPAIGN_2025_05_19_12_14&utm_medium=email&utm_term=0_-689b47e840-621514978

May 23, 2025 Posted by Christina Macpherson | ENERGY | Leave a comment