On April 8, 2024 the contiguous United States experienced its second total solar eclipse of the 21st century. The first happened in 2017; the next won’t happen for another two decades. No shortage of digital ink was spent covering the run-up to — and post-mortem analysis of — the eclipse, and especially how it impacted solar PV generation across the country.

Coverage ranged from the measured (“Darkness from April's eclipse will briefly impact solar power in its path. Experts say there's no need to worry,” noted USA Today) to the dramatic (“The solar eclipse is a critical test for the US power grid,” declared Vox) to outright fear-mongering (the New York Times and many others debunked myths that the eclipse would cause the grid to fail).

In practice, grid operators as well as government agencies such as US EIA and NREL were well-prepared for this year’s Great North American Eclipse, as it’s become known. But exactly how the nation’s grid operators handled the predicted drop in solar power generation differed significantly, which is what we’re examining more closely in this blog post.

In California, batteries that charged on excess renewable energy backfilled solar’s slump

Across the Western Interconnection (WECC) — which includes all or part of 14 U.S. states — the percent of solar obscuration ranged from 20% in the Pacific Northwest (farthest from the path of totality) to 80% in the southeast corner of New Mexico. Across all of WECC, NREL estimated that the maximum reduction in solar PV generation would reach 45%, although that varied significantly by proximity to the eclipse path.

In California, the impact ranged from ~30% for utility-scale solar farms in the central part of the state to 50+% for solar in southern California. Statewide on April 8, CAISO reported that solar generation peaked that morning at close to 14.5 GW, plateaued around 12.4 GW through most of mid-morning, then fell a further ~27%, bottoming out at ~9.1 GW around 11:15 am. By 12:15 pm — with the eclipse over — solar generation had rebounded to 14+ GW.

That much of the story has already been well-reported, but at least two other interesting things happened in tandem.

First, through the hours of the eclipse, solar curtailment on CAISO’s grid all but disappeared. In the hour before the eclipse, California discarded more than 2.5 GWh of solar energy while simultaneously charging energy storage.

Second, battery energy storage — which normally charges during daytime periods of solar excess generation in preparation for California’s evening peak — flipped from charging at nearly 2.6 GW into discharging at 2.7 GW in less than an hour. In doing so, storage almost entirely backfilled the midday solar slump from the eclipse. Meanwhile, natural gas — which usually sleeps during the day awaiting the evening ramp — barely registered a change in generation. After the eclipse, energy storage resumed charging in preparation for the evening peak.

In Texas, natural gas illuminated the darkness

The dark path of this year’s eclipse passed straight through the heart of ERCOT solar country, where NREL forecasted up to a 93% drop in peak solar PV output. ERCOT data confirm that reality matched expectations: solar generation plummeted from ~13.8 GW at 12:15 pm local time to just 0.8 GW a short 45 minutes later at 1:30 pm, a 94% reduction. By 2:45 pm, solar was back up to 13.7 GW. Solar’s generation profile that day looked like a narrow-waisted hourglass tipped on its side, going from 27.6% of ERCOT generation to 1.7% and back up to 27% in the span of just two hours.

But unlike in CAISO — where batteries were the chief responding resource — in ERCOT natural gas stepped in to meet demand, ramping up from ~19 GW to 27+ GW, then quickly tapering back to ~18 GW. Energy storage made a smaller, incremental contribution of ~1.4 GW during the peak of the eclipse, but gas-fired generators dominated the response.

Across the Eastern Interconnection, the story was much the same as in Texas. In PJM — where totality passed through Ohio and then western Pennsylvania — natural gas backfilled solar’s temporary dip. That motif repeated in NYISO, and then ISO New England. In New York and New England, behind-the-meter solar — rather than utility-scale solar — was the protagonist. In each case, though, the grid response followed suit, with natural gas stepping in.

Conclusion

The response to the eclipse can be seen as a microcosm of how grids are managing the transition to renewables and their predictable variability.

Places like California are using energy storage (usually charged on excess renewable energy) to fill the gaps in the fluctuations of wind and solar energy (not to mention sudden disruptions in fossil-fueled thermal power plants). In grids like Texas and the Northeast, where there is not yet considerable excess renewable energy or sufficient energy storage, fossil natural gas plants are used to make up the difference.

Maintaining grid reliability while also minimizing electricity-related emissions requires a detailed understanding of how power plants, energy storage, and load flexibility can all participate in a choreographed dance to support the grid’s real-time needs for supply / demand balance.

Hero image of the 2024 solar eclipse passing over the Washington Monument in Washington, DC, by NASA/Bill Ingalls. Used with permission via CC BY-NC-ND 2.0 DEED.

As computation has exploded — whether for AI, Bitcoin, or general use — data center energy use is projected to double over just the next two years. In response, load shifting has emerged as a simple yet powerful strategy to unlock myriad benefits.

This focus on load flexibility has garnered more attention of late, from a New York Times investigative piece last year digging into whether Bitcoin mining operations truly modulate their load to soak up more renewables, to a recent Bloomberg article about the growing electricity consumption of the world’s data centers and their attempts to reduce the associated emissions and use more renewable energy through various forms of load shifting.

Load shifting can potentially drive many benefits, for example:

1. Load shifting away from times of extreme peak demand can alleviate strain on the grid, supporting greater reliability, reducing the risk of blackouts, and potentially lowering costs.
2. Similarly, load shifting away from times of dirtier electricity, such as when a more-polluting fossil peaker plant is the responding generator, can lower overall grid emissions.
3. Load shifting toward times of excess wind or solar generation that’s being curtailed (AKA thrown away), can both reduce emissions and also boost renewable energy’s grid integration. 

But whenever you see a story about load shifting, the key question is, which times is the organization’s electricity use shifting to and from? Or, in a question so critical we named our whole nonprofit after it: “Watt” time is the load being shifted to?

The promise (and pitfalls) of load shifting

As we just noted, load shifting is often touted for the beneficial things it can do. But load shifting is only good if it does do those things. And it only does those things if it shifts the load to the times that are best for a specific objective.

In fact, experts have long known — even since the late 2000s from research like this 2008 study — that many cases of load shifting that people thought helped the environment actually increased emissions, not decreased them. Then further research found it happening again and again. Why? Because whether load shifting helps or hurts any particular goal depends totally on what times you are shifting load to and from. 

This is because load shifting isn’t necessarily good or bad. It is simply a technique that can be leveraged toward various ends, to varying degrees of success (or not). Energy journalist David Roberts summed this up well in a 2019 article for Vox. His article was focused on battery energy storage, but the perspective applies equally to load shifting overall:

“It’s a mistake to deploy batteries, or energy storage in general, as though they will inevitably reduce emissions. They might or might not. Indeed, it’s probably a mistake to think of them as emissions-reducing technologies at all. Rather, it’s better to think of storage as akin to transmission lines. Wires can carry both clean and dirty energy; their impact on emissions depends on local circumstances. Their primary purpose is not to reduce emissions, though, but to make the grid run more smoothly. They’re a grid tech, not a decarbonization tech. The same applies to batteries.”

For load shifting to reduce emissions, the software intelligence driving the load shifting needs to be optimized (or co-optimized) for doing that: reducing emissions. And you need to use the right signals to do so.

Load shifting based on marginal emissions and system-level impacts

Make no mistake: load shifting — by time, by location, or both — can indeed help sop up excess renewables and reduce grid emissions. But what does it really mean to do those things?

For many years, people tended to assume that shifting load to times when wholesale electricity prices were lower must reduce emissions. But all three studies linked earlier in this article showed that the opposite is often true. 

Then, for many years people assumed that shifting load to times of low average emissions rates — rather than low marginal emissions rates — must reduce emissions. Then study after study after study proved that that’s wrong, too.

With load shifting, more than a decade and a half of peer-reviewed studies has clearly established that what affects emissions and excess renewables are the marginal generator(s). Which power plant(s) respond by turning on or off, or ramping up or down, in response to changes in demand from load shifting? That’s how you appropriately measure the real impact on the grid system and its emissions.

If you’re perhaps thinking about load shifting data center computing to minimize emissions, you might think that shifting to a time and location where the sun is shining and solar PV is cranking out clean energy would help. But if that grid’s overall demand is already using all the solar that’s being generated, then adding new demand via your load shifting could cause a polluting fossil-fueled peaker plant to respond. Oops!

Load shifting to soak up surplus renewables that would otherwise be curtailed thus requires looking at the marginal emissions rate and the marginal generators. When and where are wind and solar on the margin? When and where are they being curtailed, such that shifted load could help absorb more of that clean electricity for zero increase in overall grid system emissions? That’s what affects the environmental aspects of load shifting.

We’re excited to see software practitioners increasingly thinking about the best times and locations for their software to consume electricity — and developing approaches to turn theory into practice. Called Carbon Awareness by the Green Software Foundation, software developers can build these capabilities into their operations.

It’s undoubtedly an exciting time. Software and computing are often the “brains” behind load shifting other technologies’ electricity use to reduce its associated emissions, from smart thermostats to EV charging. More than ever, practitioners are also looking at how computing itself can tap into these same load shifting opportunities.

As ever, we’re strong proponents of load shifting as an emissions-reduction solution with gigatons of potential at scale. To get there, we just have to do it right.

At WattTime, we’re excited to see an increasingly large number of organizations asking, “When we take an action on the grid — whether that’s building a renewable energy project, shifting load to different times, or adding new load — what are the ways that action affects real-world carbon emissions?”

One way to think about that question is to break it into two parts: 1) What are the short-run effects of that project on real-world carbon emissions in the near term? 2) Will the long-run effect be pretty similar to that short-term effect, or somehow systematically different on a longer time frame?

Both of these questions can be answered by examining the marginal emissions rates of power grids, albeit through two different lenses: short-run marginal vs. long-run marginal. There are reasons to believe they might be systematically different.

The energy transition is causing near-term operational and long-term structural changes to electricity generation & emissions

Amidst the energy transition, power grids are changing in diverse and profound ways.

Renewable energy projects built today will be in operation decades from now, when the world — and the grid’s supply-demand interactions — may work differently. Or, it could be that the effect of an action now — such as the proliferation of data centers, electric vehicles, and industrial and residential electrification — also drives structural change in how power grids evolve in response, causing effects that might not show up until a long time later.

For example, in the short term, introducing a large load like a new data center or hydrogen electrolyzer in a given grid region will cause marginal generators to immediately ramp up and meet that new load. But over the longer term, this and other durable new demand might also nudge the grid operator to eventually build additional generating capacity. If that new capacity is much cleaner (or dirtier) than the generators that respond in the near term, the long-term change in emissions could be lower (or higher) than the short-term effect.

The challenge of long-run marginal insights

Long-run marginal emissions are not a topic that WattTime has previously weighed in on, as it’s different from our primary expertise in one key way. At WattTime, everything we do is rooted in scientifically validated, empirical, data-driven approaches that can be easily verified. We spent a lot of time comparing the predictions of different models to what actually happened in the real world, and rejecting models that failed to correctly predict real-world behavior.

We’ve had good success doing that for short-run marginal emissions. But for long-term models, that’s hard to do. How do you verify the accuracy of a model that makes predictions about 20 years in the future… without waiting 20 years to find out if you were right? That’s why in the past we have stayed out of debates about long-run marginal emissions rates, and left it to others who were more comfortable making estimates that are harder to verify.

But just because something is hard to measure doesn’t mean it’s not important. Long run effects may be significant, they may be systematically different from short-run marginal emissions, and we applaud those arguing that it’s smart to consider long-run effects as well as short-run effects when trying to make decisions about how to best reduce emissions. 

The importance (and opportunity) of long-run marginal insights

Given the growing willingness of companies and governments to actually make different decisions based on what experts like us say would be most impactful, we think this topic is becoming more important than ever. So, we’re starting to explore the existing and emerging research in this area from many different experts, and try to ascertain what we as a society can know with confidence about long-run effects.

Examples of projects we’re looking into are: seeing whether models can at least predict changes 5 years out successfully; whether models can predict structural change that happens quickly, but then lasts a long time; or gauging whether models applied to data from 20 years ago can reasonably predict successfully what’s going on today (without “cheating” and being fed the answer indirectly). 

Our hypothesis going in is that long-run models will rarely predict the future exactly, but often may give clear, robust directional evidence that certain decisions are almost certainly more impactful than others. But that’s a hypothesis; we’ll know more once we actually study the evidence. 

This is a new area of research for us and we’re very conscious we don’t have all the answers. We also don’t want to reinvent the wheel if others have already solved some aspects of this problem. If you’re looking at these topics too, we would be thrilled to collaborate with you. We’re looking forward to collaborating with other researchers in this area!

Here at WattTime we’re more accustomed to looking forward, rather than backward, with a focus on further impact we can help to catalyze. But today is a special date in our history. It’s our 10th birthday! February 21, 2024 marks a decade to the day since our official incorporation in 2014. And so in this article we’re going to be unusually introspective, taking a look back at some of the pivotal milestones and accomplishments of these past 10 years — and what we’re most excited about in the years ahead.

1. Behavioral economics academic research around choice.

 In the early 2010s, many of the first eventual WattTimers were grad students at UC Berkeley. We were behavioral economists, software programmers, data scientists. And we all shared a fundamental intellectual curiosity: What happens on the power grid when you flip on a light switch?

It seemed crazy that we, as everyday consumers, did not know. It was equally infuriating that we had no power over whether our electricity use caused more or less pollution. Yet we turned that sort of righteous indignation into opportunity via hackathons to try and figure out the answer.

2. Officially born in 2014 as a mission-centric nonprofit… with a software tech startup DNA.

As initial hackathons progressed and we rolled up our proverbial sleeves further, we soon discovered — to our surprise — that everyone else had this righteous indignation about it, too. They wanted the opportunity to voluntarily go green, if only given the choice to do so. A/B consumer testing strongly confirmed this hypothesis. (Subsequent consumer sentiment and behavior research, such as with our partners at the Great Lakes Protection Fund, have further affirmed our initial findings.) All of which prompted us to found WattTime as a mission-driven nonprofit, even though the solutions taking shape would have a high-tech software aspect to them.

3. Pioneering the idea of AER, powered by v1 MOERs.

Those first hackathons eventually evolved and matured into our first flagship solution: Automated Emissions Reduction (AER). AER provides a signal for smart devices to schedule their electricity use for times when they will cause less emissions and pollution.

We began with direct-to-consumer ideas such as smart plugs. The first adoption by an external user was four golf carts at UC Merced. Then things started to snowball with major tech companies and automakers, spanning technologies such as smart thermostats, battery energy storage systems, EVs (and their charging), and beyond.

v1 of our marginal operating emissions rate (MOER) powered this capability. We upgraded to v3 MOERs in 2021, also now available in a new-and-improved v3 API, including expanding geographic coverage for power grids around the world.

4. Championing the importance of marginal emissions.

When we started out with AER, as academics we knew that the best way to measure the impact of interventions (i.e., academic speak for things like load shifting) was to use marginal emissions, such as our MOER signal. This built upon the established, peer-reviewed literature that came before us.

More recently, though, we have found ourselves in important industry discussions (and sometimes, heated debates) about using average vs. marginal emissions rates. We didn’t set out with any expectation of getting involved in such debates; it has simply come with the job description.

The commercial tides are now turning in favor of the long-established academic findings. The likes of Microsoft, TimberRock, Brainbox AI, and others building WattTime and other marginal emissions signals into their energy and carbon intelligence platforms. Now there’s also, VERACI-T, a cross-industry collaborative group validating marginal emissions datasets.

5. 2017–2018: WattTime’s “Oscars party” collective moment.

For any idea or solution, there’s a time when it starts to gain real traction and recognition in the market. For us, these years were that moment — both for WattTime as an organization and for individual members of our team.

Our co-founder and executive director Gavin McCormick was named a climate “fixer” in the 2017 edition of the Grist 50, an annual list of emerging green leaders and bold problem solvers. One year later in 2018, he was named a finalist to the Pritzker Emerging Environmental Genius Award at the UCLA Institute of the Environment & Sustainability, which focuses on “uncovering promising young innovators and boosting their careers as champions for the environment.”

That same year, ‘emissionality’ was recognized as a finalist in the 2018 Shorty Impact Awards and AER was recognized as a finalist in the Emerging Technology of the Year category of S&P Global Platts’ annual Global Energy Awards. 2018 became an even bigger year when AER was named a winner of the 2018 Keeling Curve Prize, an initiative that recognizes and rewards the most promising projects that effectively reduce greenhouse gas emissions or increase carbon uptake.

6. An emissions signal for battery energy storage.

A different level of credibility came into play when government agencies and programs began incorporating some of our emissions signal work.

In California, for example, battery energy storage systems under the Public Utility Commission’s Self-Generation Incentive Program (SGIP) were supposed to help the state’s grid reduce its carbon emissions. That wasn’t happening — until SGIP began using WattTime to develop their program signal, ensuring battery energy storage programs achieved their actual emissions-reduction goals.

Now other states and jurisdictions are exploring similar approaches, using more direct measurement of the target metric (e.g., marginal emissions), rather than proxy signals and assumptions (e.g., price or roundtrip BESS efficiency).

7. A shift toward Impact Accounting.

Carbon accounting standards — especially the GHG Protocol’s prevalent Scope 2 guidance around the indirect emissions associated with electricity use — have motivated sweeping clean energy investments from corporations and institutions worldwide.

But best practices evolve with the times. Which is why we’ve teamed up with companies such as REsurety and written joint position papers with organizations such as Electricity Maps. It’s why we cheer on our corporate partners at the Emissions First Partnership and why we’ve written our own insight brief on the idea of Impact Accounting.

These and other efforts all aim to help better align corporate actions with true real-world impact and authentic emissions reductions, and to combat a rise in greenwashing concerns and skepticism around hollow actions that don’t achieve their proclaimed benefits.

8. Expanding from climate to health damages. 

Although we started our work years ago focused primarily on carbon emissions, we also recognize the importance of mercury and other forms of power plant air pollution — including their impacts on human health and environmental justice. So after much hard work, we unveiled a new health damages signal, which ties electricity use (and its associated grid emissions) to human harm.

9. Surpassing 1 billion watts of emissionality. 

Toward the end of the previous decade, we popularized emissionality as a next evolution of and complement to additionality.

As a strategy for clean energy procurement, the idea behind emissionality is simple: Not all renewable energy is created equal. The avoided emissions of a new wind or solar farm can vary, by a lot, depending on where that project gets built and what power plants its generation displaces. The size of the prize is literally gigatons of avoided emissions opportunity on the table.

Boston University was one of the first organizations to adopt the strategy. Others soon followed: steelmaker Nucor, tech giant Salesforce, solar developer Clearloop, advisory Edison Energy, and others have also leaned into an emissionality strategy for their clean energy procurement.

Toward that end, last year we were thrilled to surpass 1 GW of renewables procured via this strategy. Less than 6 months later, we’re already closing in on the next gigawatts of wind and solar procured in part with emissionality in mind.

10. Co-founding Climate TRACE and incorporating satellite-based emissions monitoring.

In 2019 we announced a new project to measure emissions of the world’s power plants from space, launched with grant support from Google.org’s AI Impact Challenge and covered by the likes of Vox. By 2020, that initial effort had expanded in a big way into Climate TRACE, a global coalition of NGOs, tech companies, universities, and climate leaders including Al Gore using satellites and AI to measure human-caused GHG emissions from essentially all of the major sources on the planet.

Across the three years since then, Climate TRACE’s data have progressed by leaps and bounds, rapidly advancing from country-level annual data to facility-level data for 350+ million assets in the world’s most-comprehensive and granular such dataset, which we unveiled in December 2023 on the mainstage at COP28.

Along the way, Climate TRACE has been named to Fast Company’s “most innovative” list and TIME’s “100 best inventions.” We received the Sierra Club’s Earthcare Award and our executive director Gavin McCormick gave a TED talk on Climate TRACE that’s been viewed nearly 1.8 million times.

But it’s the use of the data for faster, deeper decarbonization that makes us most proud. From national, regional, and local governments to major companies such as Tesla, GM, Polestar, and Boeing. 

What’s next: scaling further impact together

Whew! It’s been a busy (and positively impactful) 10 years. But after today’s celebration of our official 10th birthday, that’ll be enough reminiscing in the rearview mirror. We’re far more excited and motivated about the work ahead of us, and the even greater impact we can achieve together. Won’t you join us?