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Maybe you’ve never heard of it. Maybe you know it too well. But to a certain type of clean energy wonk, it amounts to perhaps the three most dreaded words in climate policy: the interconnection queue.
The queue is the process by which utilities decide which wind and solar farms get to hook up to the power grid in the United States. Across much of the country, it has become so badly broken and clogged that it can take more than a decade for a given project to navigate.
On this week’s episode of Shift Key, Jesse and Rob speak with two experts about how to understand — and how to fix — what is perhaps the biggest obstacle to deploying more renewables on the U.S. power grid. Tyler Norris is a doctoral student at Duke University’s Nicholas School of the Environment. He was formerly vice president of development at Cypress Creek Renewables, and he served on North Carolina Governor Roy Cooper’s Carbon Policy Working Group. Claire Wayner is a senior associate at RMI’s carbon-free electricity program, where she works on the clean and competitive grids team. Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap, and Jesse Jenkins, a professor of energy systems engineering at Princeton University.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
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Here is an excerpt from our conversation:
Robinson Meyer: Can I interject and just ask why, over the past decade, the interconnection queue got much longer — but also over the past decade, 15 years, the U.S. grid did change in character and in fuel type a lot, right? We went from burning a lot of coal to a lot of natural gas. And that transition is often cited as one of the model transitions, one of the few energy transitions to happen globally that happened at the speed with which we would need to decarbonize. Obviously, switching coal to gas is not decarbonizing, but it is a model — it happened fast enough that it is a good model for what decarbonizing would look like in order to meet climate goals.
Evidently, that did not run into these kind of same interconnection queue problems. Why is that? Is that because we were swapping in within individual power plants? We were just changing the furnace from a coal furnace to a gas furnace? Is that because these were larger projects and so it didn’t back up in the queue in the same way that a lot of smaller solar or wind farms do?
Claire Wayner: I would say all the reasons you just gave are valid, yeah. The coal to gas transition involved, likely, a lot of similar geographic locations. With wind and solar, we’re seeing them wanting to build on the grid and in a lot of cases in new, rather remote locations that are going to require new types of grid upgrades that the coal to gas transition just doesn’t have.
Jesse Jenkins: Maybe it is — to use a metaphor here — it’s a little bit like traffic congestion. If you add a generator to the grid, it’s trying to ship its power through the grid, and that decision to add your power mix to the grid combines with everyone else that’s also generating and consuming power to drive traffic jams or congestion in different parts of the grid, just like your decision to hop in the car and drive to work or to go into the city for the weekend to see a show or whatever you’re doing. It’s not just your decision. It’s everyone’s combined decisions that affects travel times on the grid.
Now, the big difference between the grid and travel on roads or most other forms of networks we’re used to is that you don’t get to choose which path to go down. If you’re sending electricity to the grid, electricity flows with physics down the path of least resistance or impedance, which is the alternating current equivalent of resistance. And so it’s a lot more like rivers flowing downhill from gravity, right? You don’t get to choose which branch of the river you go down. It’s just, you know, gravity will take you. And so you adding your power flows to the grid creates complicated flows based on the physics of this mesh network that spans a continent and interacts with everyone else on the grid.
And so when you’re going from probably a few dozen large natural gas generators added that operate very similarly to the plants that they’re replacing to hundreds of gigawatts across thousands of projects scattered all over the grid with very complicated generation profiles because they’re weather-dependent renewables, it’s just a completely different challenge for the utilities.
So the process that the regional grid operators developed in the 2000s, when they were restructuring and taking over that role of regional grid operator, it’s just not fit for purpose at all for what we face today. And I want to highlight another thing you mentioned, which is the software piece of it, too. These processes, they are using software and corporate processes that were also developed 10 or 20 years ago. And we all know that software and computing techniques have gotten quite a bit better over a decade or two. And rarely have utilities and grid operators really kept pace with those capabilities.
Wayner: Can I just say, I’ve heard that in some regions, interconnection consists of still sending back and forth Excel files. To Tyler’s point earlier that we only just now are getting data on the interconnection queue nationwide and how it stands, that’s one challenge that developers are facing is a lack of data transparency and rapid processing from the transmission providers and the grid operators.
And so, to use an analogy that my colleague Sarah Toth uses a lot, which I really love: Imagine if we had a Domino’s pizza tracker for the interconnection queue, and that developers could just log on and see how their projects are doing in many, if not most regions. They don’t even have that visibility. They don’t know when their pizza is going to get delivered, or if it’s in the oven.
This episode of Shift Key is sponsored by …
Watershed’s climate data engine helps companies measure and reduce their emissions, turning the data they already have into an audit-ready carbon footprint backed by the latest climate science. Get the sustainability data you need in weeks, not months. Learn more at watershed.com.
As a global leader in PV and ESS solutions, Sungrow invests heavily in research and development, constantly pushing the boundaries of solar and battery inverter technology. Discover why Sungrow is the essential component of the clean energy transition by visiting sungrowpower.com.
Antenna Group helps you connect with customers, policymakers, investors, and strategic partners to influence markets and accelerate adoption. Visit antennagroup.com to learn more.
Music for Shift Key is by Adam Kromelow.
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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.