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What happens to the grid when the sun goes away?

Early April is typically a kind of goldilocks moment for solar power. Days are getting longer but the weather is still mild, meaning that higher solar power generation isn’t entirely eaten up by increased demand due to air conditioning.
But that all depends on the sun actually shining.
Monday’s solar eclipse took a big chunk of power off the grid. Since 2017’s eclipse, solar power generation has increased substantially, both locally (think rooftops) and at utility scale (think massive fields of solar panels). In 2017, the U.S. had around 35 gigawatts of utility-scale solar capacity, a figure that had increased to an estimated 95 gigawatts by the end of 2023.
While total solar eclipses are rare (the next one to hit the lower 48 isn’t expected until 2044), the challenges they present to grid operators may be part of the new normal. With vastly expanded renewable energy generation comes a greater degree of unpredictability, as a growing a portion of the generation fleet can drop off the grid due to weather and climate conditions — like, say, clouds of smoke from a wildfire — that cannot be precisely predicted by 17th century science.
Grid operators were confident they’d be able to manage through the eclipse without any reliability issues, and what actually transpired mostly confirmed their forecasts. In Texas, solar power production shrunk from around 13.5 gigawatts at noon, making up 27% of the grid’s electricity supply, to a mere 0.8 gigawatts at 1:30 p.m. Things did not go as well for the Midcontinent Independent System Operator, however, which includes a swath of the middle of the country from Minnesota to Indiana to Louisiana. Solar output was estimated to drop from around 4 gigawatts at 1 p.m. Central time to 2 gigawatts an hour later, according to Grid Status. Instead, output dropped to around 300 megawatts, causing real-time prices for power on the grid to spike.

Overall, the U.S. Energy Information Administration estimated that some 6,500 megawatts of solar generation capacity would be fully obscured during the eclipse, which would “partially block sunlight to facilities with a combined 84.8 GW of capacity in an even larger swath of the United States around peak solar generating time.” Some 40 gigawatts may have come off the grid, enough power for about 28 million homes, according to a release from Solcast, a solar forecasting company.
By comparison, during the 2017 eclipse, solar power loss at its peak was between 4 and 6.5 gigawatts and the total loss of power was around 11 gigawatts, according to the National Renewable Energy Laboratory.
In states like Texas, the main effect was on utility-scale production of solar, but in the Northeast and parts of the mid-Atlantic and Midwest, there was also a related problem: Behind-the-meter solar fell off, too, thus requiring the homes and businesses that generate power for themselves in the middle of the day to get more power from the grid, increasing demand on the grid at a time of low supply.
New England has seen immense growth in rooftop solar, and solar production was expected to fall by “thousands” of megawatts, according to ISO New England, while the New York Independent System Operators expected to lose 700 megawatts of behind the meter solar.
During the 2017 eclipse, the National Renewable Energy Laboratory found that “the burden of compensating for the lost energy from solar generators fell to the thermal fleet,” i.e. natural gas, along with some increases in coal and hydropower production.
Since then, the coal fleet has shrunk, thus putting more of the burden of responding to Monday’s eclipse onto gas and hydro, but the basic logic still applies. “Grid operators are expected to rely on natural gas to ensure stability and meet the household demand spike across national grids, as was done during the previous eclipse in 2023 in California and Texas,” according to Solcast. As the sun was dimming in Texas, natural gas generation rose from 18.7 gigawatts to 27.5 gigawatts.
Something else that’s changed since 2017: batteries. By the end of 2023, Texas had installed 5.6 gigawatts of grid storage, most of it providing so-called “ancillary services,” power sources that can respond quickly to immediate needs. ERCOT, the electricity market that covers most of Texas, said in a presentation back in February that it would rely on these ancillary tools to get through the eclipse, and once again, it was right. Power from batteries on the grid got up 1.4 gigawatts during the eclipse.
Editor’s note: This story has been updated to reflect the actual effects of the eclipse on U.S. power generation.
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation. What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.