UNFCCC marginal emissions data show that building renewables in the Global South has greatest benefit

For companies and other organizations investing in new renewable energy projects, two main strategies guide their procurement:

  1. 24/7 Carbon-Free Energy (24/7 CFE): focuses on hour-by-hour megawatt-hour (MWh) matching of renewable generation’s timing and a corporation’s electricity demand load profile, with the clean energy procured from the same grid region where the electricity load is located
  2. Emissionality: an emissions-first approach that targets the dirtiest grids globally, procuring clean energy from wherever the new renewable capacity will have the greatest avoided emissions benefit by displacing generation from the most-polluting fossil-fueled power plants

These two strategies have important implications for where clean energy investment will flow and where new renewable capacity will get built, and consequently, on how much (or how little) climate benefit those projects will ultimately have.

In this analysis, we use marginal emissions data from the United Nations Framework Convention on Climate Change (UNFCCC) to gain insights into these questions, especially: Where should new renewable energy projects get built to have the greatest overall climate benefit?

Tapping the UNFCCC’s marginal emissions data

To better understand the beneficial impact of new renewable projects across the globe, the UNFCCC — the UN body that oversees the Paris Agreement — developed a methodology for estimating the long-term impact on grid emissions in each country around the world. UNFCCC’s combined margin emissions factors take into account both operating margin and build margin, giving a sense for renewable energy’s climate benefit in the nearer and longer terms.

Using data from the IEA’s Global Energy and Climate Model (previously known as the World Energy Model) — which underpins IEA’s annual World Energy Outlook — UNFCCC experts calculated the marginal emissions rates resulting from predicted new generation across both traditional firm energy sources (e.g., fossil fuels, nuclear, geothermal) as well as clean energy technologies (e.g., solar PV, wind, tidal). The IEA model incorporates information on existing energy sources, economics, and policies in 26 large countries and regions, with additional regression modeling for other countries, making it comprehensive in breadth and scale.

Mapping renewable energy’s avoided emissions potential

Looking at a global heat map of marginal emissions rates for new renewable energy sources, the greatest avoided emissions potential based on UNFCCC data is primarily located in the Global South, in countries spanning Asia, Africa, and Eastern Europe (red shades on the map). These countries’ grids tend to rely on heavier-polluting sources of generation, such as coal-fired power plants.

Conversely, the lowest avoided emissions potential is primarily in the Global North, in countries spanning the EU and North America, as well as select countries elsewhere around the world where hydropower (and sometimes, nuclear) provides a dominant share of electricity generation (blue shades on the map).

Power grid combined marginal emissions factor by country map of the world

Multiplying the avoided emissions benefit of renewable energy investment

For any organization deciding where to invest in new renewable capacity, using marginal emissions estimates like these from UNFCCC can lead to much larger reductions in overall global emissions.

In Annex I countries (which largely overlaps with the Global North), the average avoided emissions rate (weighted by total electricity generation) is 345 g CO2/kWh. Meanwhile, countries in the top 50% of most-polluting power grids have an avoided emissions potential of 702 g CO2/kWh, and countries among the top 10% of most-polluting electricity generation have an avoided emissions rate of 979 g CO2/kWh. These heavier-polluting power grids are predominantly throughout the Global South.

In other words, investing in renewable energy projects across the Global South can yield 2x to nearly 3x greater climate benefit vs. renewables projects in the Annex I countries of the Global North. Organizations considering siting renewable energy — whether bilateral national agreements now being drafted under the revised Article 6 framework of the Paris Agreement, or voluntary corporate actors using the GHG Protocol — may want to consider UNFCCC’s data when deciding where to invest in renewable energy projects.

Avoided emissions rate of renewable energy projects by global countries category column chart

How procurement approach influences renewable energy’s potential

24/7 CFE proponents argue that it encourages the buildout of renewables that would generate during “off” hours, helping power grids move closer to 100% clean energy around the clock. This may be partly true. But it also amounts to massive investment aimed at “squeezing the last drop” of emissions from grids that have already significantly decarbonized. This misses opportunities for major larger global decarbonization by building renewables in other places where they’d have greater avoided emissions benefits and where coal-fired generation still dominates the grid mix.

Moreover, some of 24/7 CFE’s biggest proponents are major tech companies whose operations and data centers are overwhelmingly located in the EU, US, and other Global North locations. These are regions that have already seen large investment in new wind and solar capacity, especially. Meanwhile, Global South locations — the same places where UNFCCC marginal emissions data show there are the greatest avoided emissions opportunities — have seen chronic underinvestment in clean energy technologies, according to IEA data.

From a global climate action perspective, it’s far less impactful to inch California or Texas (where wind and solar have already made huge gains) closer to 100% carbon-free energy than it is to invest in new renewables in a place such as India, where coal still contributes more than 70% of the nation’s electricity generation and clean energy investment in 2024 was just one-fifth of what it was in the US.

On top of this compelling climate argument, there’s also the crucially important humanitarian component, too. Investing in renewable energy in Global South countries will also bring economic and health benefits by expanding energy access and reducing air pollution in the places that also have the worst air quality. Globally, 1 in 8 deaths are now attributed to air pollution, predominantly in countries with the most-polluting electric generation, since the same power plants spew both carbon dioxide and PM2.5.

Conclusion

The UNFCCC model is not the only model for estimating marginal emissions rates. In terms of long-run build margin in particular, it lacks many of the more-detailed features of other models such as Cambium, GenX, and PyPSA. In particular, it does not consider variance in emissions rates within a country, which can be great in large countries such as the US or China. But it is one of the only existing models that covers the entire globe, which is a critical consideration when evaluating emissions reductions. 

More research is needed to evaluate these different modeling approaches and to develop more detailed models across the globe, so that renewable energy investments can be targeted at the location where they have the most impact. For now, one thing is clear: data is increasingly pointing to the Global South as a critical focus for the world’s future renewable investment.

image source: iStock | rvimages

The methodology behind our latest global data expansion

This week we’re excited to announce a major, global geographic expansion of our flagship Marginal Operating Emissions Rate (MOER) carbon data signal — growing from 40 to 210 total countries and territories. You can read the full press release here. With this expansion to ~170 new geographies, WattTime now offers actionable marginal emissions data — available with 5-minute granularity and a combination of historical, real-time, and rolling 3-day forecast perspectives — for 99% of the world's electricity consumption.

MOER coverage map

Accurate, location-specific, timely, and granular MOER signals are central to a trio of solutions — carbon-aware load shifting, emissionality-based renewables siting and procurement, and supply chain decarbonization — that can save more than 9 gigatons of emissions every year. That’s equal to nearly 20% of total global carbon emissions. These solutions also accelerate the reduction of harmful air pollution that disproportionately affects the developing countries that often are last to gain access to technological solutions that improve their lives.

Adopting those three solutions at scale (and unlocking those 9+ gigatons of emissions reductions) depends on a truly global MOER signal. Which is why this week’s announcement represents an enormous step-change for what’s possible.

In this article, we’ll take a closer look at the foundational methodology driving our MOER models thus far, as well as the new methodologies that allowed our team to expand to substantially global data coverage.

The science behind WattTime’s current MOERs

When electricity demand rises or falls, or new wind or solar capacity gets built, which generators respond varies. Namely, a certain generator (or generators) ramp up or down — or turn on or off — in response to changes in load or new renewables added to the grid mix. These power plants on the edge of the dispatch stack order are what’s known as marginal generators, and their associated emissions are what’s described by our MOER signal. Sometimes polluting, fossil-fueled peaker plants might set the marginal emissions rate; at other times, surplus renewables being curtailed might be on the margin.

Scientists agree that such a marginal signal is the best way to guide (and measure the impact of) interventions such as cleaner EV charging to reduce emissions or the avoided emissions of building a new wind farm on a coal-heavy grid.

Our current-best MOER — with foundations in academic literature and iterated on by WattTime for over ten years — is based on causal, empirical modeling. In the grid regions of North America and Europe, we use robust, detailed, generator-level data inputs to refine and train our algorithms. For example, we incorporate generation and emissions data from individual power plants via the US EPA’s Continuous Emissions Monitoring System (CEMS). We get demand, interchange, and generation by fuel type data from the US Energy Information Administration (EIA) and European Network of Transmission System Operators for Electricity (ENTSO-E). We also integrate myriad other data sources into our overall modeling. For regions where these data are available, we feed it all into a binned regression model. The result is today considered WattTime's highest-quality methodology for the MOER signal type.

In regions where we have access to partial real-time and forecasted information about the status of the grid (such as demand or energy prices), but are lacking specific ground truth time series data such as emissions or generation by fuel type required to train a binned regression model, we use a proxy regression model where we use machine learning to identify a data-rich grid with similar characteristics to use as a proxy for the grid with partial data. The power plants for the region of interest are characterized by Climate TRACE and assembled into a supply curve that resembles economic dispatch for the proxy region, which is then used to estimate the MOER based on the real-time demand in the region. MOER data from this “Proxy Regression” model is available in countries such as Brazil, India, Chile, and Turkey. (Longtime WattTime partner Microsoft helped fund development of this method.)

But after deploying these higher-quality MOER signals for the grids of all countries with the necessary data, we still had not covered about 170 countries of the world. Yet many of these are the same countries where emissions are not yet falling, and it’s not good enough to just ignore them. Our latest MOER expansion fixes that in big ways, thanks to a novel modeling approach from our team.

Inside the methodology powering our global MOER expansion

With the goal of making MOER data truly global, we’ve employed a synthetic demand model for countries where we lack historical and real-time grid information. It extends and improves upon the 2021 work of Mattsson et al.

In lieu of actual real-time demand data (like those used in our proxy regression models), the Mattsson framework uses atmospheric estimations derived from ERA5 climate and weather data, along with demographic information, to model synthetic power demand with a mean absolute percentage error of only 8% when averaged by month and hour.

To model the MOER, we expanded upon Mattsson’s academic model to account for more geographic and economic diversity. This enabled us to produce estimates of real-time and forecasted demand. To productionize this, we needed to build a sophisticated weather data modeling pipeline, ingesting gigabytes of global weather data four times a day, thus producing timely synthetic demand estimates. Currently, we’re using these synthetic demand estimates as inputs to our existing proxy regression models in order to produce MOERs using a model we are calling the “Synthetic Demand Proxy Regression” model, or “Synthetic Proxy” for short.

While the MOER signals resulting from this method are inherently less accurate than those that we can derive using the binned regression model, they’re nevertheless useful for both carbon-aware load shifting and renewables siting — which unlocks significant emissions-reduction potential. Of course, data transparency will only increase in regions across the globe. As it does, we will vigilantly be upgrading our MOER signals according to higher-quality binned regression methodologies.

Putting global MOER data into action 

The MOER signals generated via our synthetic proxy regression models unlock potential to enable much more strategic emissions reductions decisions and automations, and in regions that have never before enjoyed access to such actionable data. Historically, granular and accessible emissions data have been far more accessible for countries in the Global North. This release is a major step in closing this gap, and opens up opportunities to drive emissions reductions and new solution possibilities across the Global South.

All WattTime data can be accessed through our API. Basic access is available for free to all users. Partners on our Pro data plan get full access to historical and forecast data, including premium support. Our partners with a global data license will automatically gain access to these new grid regions. Contact our team to learn more.

Is battery energy storage (finally) living up to its promise of enabling a net-zero grid?

From the World Economic Forum to utility industry magazines to the US Department of Energy, in recent years there’s been a growing refrain: how batteries can enable a net-zero electricity grid. Implicit in that statement is the idea that batteries can (and should) help lower grid emissions, increase the integration of zero-emissions renewable energy sources, and support overall power sector decarbonization. Yet battery energy storage is sometimes finding itself in the hot seat for exactly the opposite reason.

Earlier this year, a University of Michigan study focused on the PJM market (the large regional transmission organization covering all or part of 13 U.S. states plus Washington, D.C.) found that batteries sometimes increased grid emissions. While the U-M study was based on older data (from 2012 to 2014), its takeaways echo concerns we’ve heard before. 

In the early 2010s, California’s Self-Generation Incentive Program (SGIP) — a major driver of the state’s behind-the-meter battery energy storage market — shifted its focus to specifically prioritize greenhouse gas reductions for the Golden State’s power grid. But then circa 2018 and 2019, analysis found that batteries were often increasing, rather than decreasing, grid emissions.

Batteries are only as clean as the electricity used to charge them

For the better part of a decade, batteries have been described as a Swiss Army knife of the power grid, capable of performing myriad functions — from customer-centric services such as backup power, peak shaving, solar self-consumption, and time-of-use energy arbitrage to grid-centric services such as frequency and voltage regulation, demand response, and mitigating renewables curtailment.

Ultimately, doing all of that involves software algorithms that dictate when a battery energy storage system charges and discharges. Those algorithms typically co-optimize around various price signals. But it’s the marginal emissions of the power grid at the times a battery is charging vs. discharging that determines whether the battery causes a net decrease (or increase) in grid emissions.

Unless energy storage considers emissions in their control approach, there’s no guarantee that they’ll help decarbonize power grids. Energy journalist David Roberts summed it up well: “It’s a mistake to deploy batteries … as though they will inevitably reduce emissions. They’re a grid tech, not a decarbonization tech,” more akin to transmission lines that can equally carry dirty or clean power, agnostic to the electricity’s generation source and the associated carbon emissions. So, too, with batteries in the absence of the right signals.

California’s battery emissions success story

To address the emissions increase caused by energy storage participating in SGIP, the rules of the program were revised with the goal of enabling the state’s participating behind-the-meter commercial and residential batteries to live up to their emissions-reducing promise. Almost immediately after the rule change, we started to see positive outcomes. A detailed impact evaluation published earlier this year by CPUC with analysis by Verdant gives a longer-term view of SGIP’s turnaround story.

Between 2018 and 2022 (the period covered by Verdant’s analysis), battery systems in California’s SGIP fully reversed course, flipping from causing a net increase in grid emissions to causing a significant net decrease in a resounding decarbonization success.

Now, energy storage has cemented its central role supporting California’s goal of achieving 100% carbon-free electricity by 2045. The state boasts more than 10 GW of installed battery capacity, and earlier this year, batteries became the single largest contributor to the state’s grid briefly during the evening peak. Grid-scale batteries charged on excess daytime solar are starting to displace natural gas power plants. And during this year’s solar eclipse, batteries charged on excess renewable energy carried California’s power sector through the temporary slump in solar PV generation.

Net GHG emissions of battery energy storage in CA's SGIP

A cautionary tale for other states

California may be the country’s most-prominent example, but it’s hardly the only US state setting combinations of both emissions-reduction / net-zero emissions targets as well as energy storage goals. For just four examples, Connecticut, Massachusetts, New Jersey, and New York — all members of the Regional Greenhouse Gas Initiative (RGGI) — each have robust energy storage targets tied to 100% clean energy and GHG reduction goals. So does Michigan.

For energy storage to help these and other states achieve their clean energy goals, it will be crucial to learn from California’s SGIP growing pains — and using a true marginal emissions GHG signal, rather than a proxy metric, to inform batteries’ duty cycles. Just look at what has transpired in Texas and the ERCOT market.

The Lone Star State has been called “the hottest grid battery market in the country.” But analysis from Tierra Climate published in June 2024 in collaboration with REsurety, Grid Status, Modo Energy, and WattTime found that 92% of batteries in ERCOT increased grid emissions in 2023. This is largely because those batteries are not co-optimizing their operation in coordination with a carbon signal like SGIP’s GHG signal. That same report found that co-optimization with a carbon signal (or a carbon price) would move these battery energy storage assets from carbon increasing to carbon decreasing.

The US energy storage market is growing fast, with record-setting capacity additions in Q1 2024 and a staggering 75 GW of cumulative new capacity forecasted to come online during the period 2024–2028. If battery energy storage is to continue living up to its promise of enabling a net-zero grid, it’s more important than ever that state policies and battery control algorithms include a marginal emissions signal as part of their intelligence under the hood.

Inside Texas’s power sector paradox

The United States’ clean energy leader is also its number-one source of electricity emissions. Welcome to the Lone Star State, aka the China of the U.S. in terms of fossil fuel historical dominance — as well as record-setting wind and solar.

In March, Texas published its first-ever greenhouse gas (GHG) inventory, joining more than 20 other U.S. states in cataloging annual statewide emissions. This inventory, which covers the state’s 2021 GHG emissions, revealed the Lone Star State as the United States’ top emitter overall by state. Per both the U.S. EPA and Climate TRACE data from 2022, Texas’s overall, economy-wide GHG emissions were more than double that of America’s second-place emitter, California. 

According to its inaugural self-assessment, in 2021 Texas released more than 873 million tonnes (Mt) of GHG emissions. To put that in context, if Texas were its own country, it would rank 11th on a global scale — just ahead of Mexico and behind Saudi Arabia, according to Climate TRACE 2022 data.

There’s more to this story than meets the eye. Pop culture portrayals of the Lone Star State have long made ample use of oil barons and rigs dipping into dusty prairie, and for good reason… at least historically. While that fossil-fuel-happy reputation still applies — Texas remains America’s top producer of crude oil and natural gas — it’s also become America’s clean energy leader.

So let’s take a closer look at what WattTime knows best (electricity) and unpack Texas’s power sector energy and emissions data to better understand where it has been — and where it might be going.

Everything’s bigger in Texas — including appetite for renewables

The self-proclaimed “Energy Capital of the World,” Houston is fast becoming a clean hydrogen hub and currently ranks #1 on the EPA’s Green Power Partnership list — a program ranking organizations by their voluntary clean energy procurement — in the local government category. In fact, the top six slots nationwide include five Texan entities, including Dallas, DFW airport, Austin, and Harris County. Austin, Dallas, Houston, and San Antonio are all officially working toward net-zero-by-2050 goals. 

Since the early 2000s, Texas has famously led America’s wind energy pack, comfortably sitting in the top spot for installed wind capacity, according to U.S. DOE WINDExchange data. With more than 41 GW, the state is responsible for more than a quarter of all U.S. wind energy capacity, tripling silver-medal Iowa’s contribution of 13 GW. 

Solar PV is growing, too. Late last year, Texas overtook longtime solar leader California to capture the top spot among U.S. states for installed utility-scale solar capacity. And in early 2024 solar generation passed coal-fired electricity generation for the first time in Texas history. 
Although natural gas still leads the generation stack for ERCOT — the independent system operator (ISO) that balances supply and demand for 90% of Texas’s electricity — as of May 2024, wind and solar together are closing in on gas’s lead, with 38.4% of the state’s electric generating capacity, compared with natural gas at 44.3%.

Texas power sector emissions 2021

Texas’s fossil-burning power plants in focus

So with clean energy scaling rapidly, where exactly are all Texas’s electricity generation emissions coming from?

For starters, Texas has some 180 combustion power plants, per Climate TRACE and WattTime data. Most of those are gas–fired power plants. But data from the Texas Comptroller shows that 15 coal-fired plants are currently operational; a third of them are slated for retirement by 2030.
Among those 180 fossil-burning plants identified in Climate TRACE data, the biggest power sector culprit is the dual gas/coal-fired WA Parish Generating Station. With 3.9 GW capacity, the #5 emitter for power plants across the U.S. is notorious among environmental advocates and Texas media outlets, which have been raising alarms about the pollution and harm caused by the WA Parish plant.

power plant satellite image

However, while coal-fired power plants might be Texas’s dirtiest on a per-MWh basis, the sheer size (and policy support) of the state’s gas-fired fleet matters, too. In recent years, the Lone Star State has been digging in its spurs — or, at least, its heels — to prop up the gas fleet.

For instance, in early 2021 Winter Storm Uri infamously caused widespread blackouts across Texas and $195 billion in damages. The Texas PUC set an astronomical system-wide price cap of $9,000 ​​per MWh in a bid to bring more generation online (market-clearing prices were closer to $1,200 per MWh). In the wake of that catastrophe, Texas legislators introduced bills to bolster gas-fired generating resources and keep more fossil-burning power plants online, even though multiple Uri post-mortems found that gas infrastructure was the biggest failure during the winter storm; nearly twice as much gas-fired capacity went offline as wind capacity.

More recently, during April 2024’s total solar eclipse, gas proved Texas’s electricity generation fuel of choice, when ERCOT ramped up gas to make up for solar’s temporary dip.

Austin also illustrated this “can’t quit fossil” dynamic when it approved a plan in 2020 to shut down its greatest source of carbon emissions, the coal-fired Fayette Power Project plant. The decision was a boon to Austinites’ goal of producing wholly emissions-free electricity by 2035; however, the closure never came to pass and the Fayette Plant remains operational today. 

An hour south in San Antonio, CPS Energy — America’s largest municipally-owned electric and gas utility — has already acknowledged it won’t meet the Climate Action and Adaptation Plan the city adopted in late 2019, now that its customers owe $200 million-plus in late bills for natural gas purchased at elevated rates during winter 2021. And in late 2023, ERCOT asked CPS Energy to bring a coal plant it had recently shuttered back into operation in an effort to secure more reserve power ahead of winter.

America’s China?

The energy landscape within America’s second-largest GDP after California in many ways parallels that of the world’s second-largest economy — one similarly marked by massive ongoing power sector emissions, clean energy leadership, heavy industrial growth, surging populations, and uncertainty about how the future will unfold.

Like China, Texas’s key contributors to new emissions stem from the power and industrial sectors. Despite China’s role as the world’s leading deployer of renewable energy in electricity generation — it’s the only entity in the world that tops Texas in installed wind capacity — the production of fossil fuels continues to grow strongly in China, which is the world’s #1 emitter of GHGs. Still, coal’s share within China’s electricity generation mix has steadily declined — as it has in Texas — and the long-term plan is to phase it out. But over the near term, coal will retain its pivotal role within China’s generation mix, which could translate to bumps in its coal-fired emissions. 

Texas is a space to watch for that same phenomenon, especially this summer, as the window spanning May through August historically marks Texas’s high point for power generation and demand. And given the heat wave that slammed Texas over Memorial Day weekend, this summer looks to be a heat demand doozy, requiring fast-response power resources. 

Don’t mess with Texas’s clean energy leadership

On a more hopeful note, in addition to its greenhouse gas inventory, the Texas Commission on Environmental Quality used EPA grant funds to create an emissions reduction plan for Texas. According to its estimates, implementation of suggested measures — divvied into buckets tailored to each of the state’s highest-emitting sectors: industry, transportation, and electric power — could reduce GHG emissions in the Lone Star State by 174 Mt from 2025 through 2030 and 592 Mt from 2025 through 2050.

The plan spells out precise priority measures — voluntary, yet incentivized ones, created with extensive input from a variety of Texan stakeholders. And in 2027, the TCEQ will publish a status report detailing implementation progress, priority analyses, next steps, and future budget and staffing needs to continue deployment of the measures. So it seems Texas is taking its emissions reduction plan seriously.

A shining example of a power grid in the midst of a massive transition — wherein wind, solar, and battery energy storage are poised to together become the dominant wedge of the power generation pie, supplanting natural gas’s piece — Texas provides a valuable example for how grids across the country can tap wind, scale up solar, utilize existing energy infrastructure to generate clean hydrogen, and ultimately, decarbonize the power sector. Especially as coal-fired generation retires in the years ahead, Texas’s model, from a clean energy leadership perspective, is one not to be messed with.

A tale of two grids: how CA and TX generation responded differently to the April 2024 solar eclipse

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.

Inside the post-pandemic power sector’s emissions ups and downs

Electricity generation annual emissions for G20 countries graph

This story is already familiar to most, and for many, already feels like a distant memory: in March 2020, much of the world went into lockdown as COVID-19 raged. Everyday life paused and economic activity slowed. In tandem, air pollution and carbon emissions both dropped noticeably.

But then, as life resumed and the global economy returned closer to normal in 2021 and 2022, emissions predictably rebounded. This was true across more or less every sector of the economy, including power sector emissions. The United States — the world’s #2 source of carbon emissions, both overall and for electricity generation in particular — is a good example of this general trend. So is the United Kingdom.

Here at WattTime, we dug deeper into G20 countries’ pre-, during-, and post-pandemic electricity emissions — all cataloged in the detailed Climate TRACE data — and found some interesting alternate trends that deviated from the “standard” pandemic emissions trajectories seen in the U.S. and other countries.

They largely fell into three buckets: 1) countries whose power sector emissions climbed straight through the pandemic and have continued rising, 2) countries whose emissions fell but didn’t rebound, and which have continued falling, and 3) countries whose electricity emissions underwent sharp booms and busts. Why these trends happened in any given country is especially interesting.

Countries where electricity emissions climbed straight throughout the pandemic — and beyond

electricity emissions increase for China and India during pandemic

Across the 19 individual countries of the G20 (the G20 currently also includes the European Union and African Union), most saw their power sector emissions slump during the 2020 pandemic and about half of the G20 hit all-time lows that year. But for a select few, emissions from their country’s electricity generation didn’t blink. It rose during the pandemic and has continued climbing higher since.

China’s power sector emissions march upward: China is the world’s #1 source of greenhouse gas pollution, and the power sector is the country’s single largest source of carbon emissions, according to Climate TRACE data. Those emissions rose in 2020 vs. 2019, then again in 2021 and yet again in 2022 to a new all-time high. Despite rapidly expanding clean energy generation (China installed about as much new solar in 2022 as the rest of the world combined), ongoing expansion of the country’s coal-fired generation and a drought that impacted its sizable hydro fleet have resulted in power sector emissions still creeping upward.

India’s emissions ascent continues: Although India’s rising power sector emissions briefly stalled during the pandemic, they’ve since reached an all-time high in 2022. In fact, India is one of only three countries (behind China and the United States) whose annual emissions from electricity generation exceed 1 billion tonnes — and India’s electricity emissions at #3 globally equals countries 4, 5, and 6 combined. Coal-fired generation comprises more than 70% of the nation’s power mix. Ironically, summer heat waves intensifying from climate change prompted the country’s leaders to mandate that coal-fired generation operate at full capacity to meet surging electricity demand, further contributing to the climate-induced problem. Early this year, India announced plans to further expand its coal-fired capacity.

Countries where power sector emissions have stayed on the down slope

Australia, Japan, and South Africa emissions declined during and after the pandemic

Emissions in the Land Down Under keep declining: In sunny Australia, power sector emissions have been on a five-year run of annual declines since at least 2017. They fell 3.8% during the 2020 pandemic year vs. 2019, then 5.1% in 2021 and a further 4.1% in 2022, totaling an 18.7% drop from 2017 levels. Large declines in the country’s coal-fired generation — and, in parallel, a meteoric rise of new solar capacity, plus some new wind — have driven down overall electricity emissions. These trends are expected to continue, with AEMO forecasting that coal could all but disappear from the nation’s generation mix within a decade.

Falling emissions in the Land of the Rising Sun: As many will recall, Japan largely relied on nuclear power until the 2011 earthquake and subsequent Fukushima accident. In response, the country shuttered its nuclear reactors and pivoted to fossil-fueled generation, including hefty LNG imports, raising the nation’s power sector emissions in the short term. But those emissions have been declining since at least 2015, reaching lows in 2021 not seen since before the Fukushima incident. In 2022, Japan’s power sector emissions bumped up slightly, driven by increased coal-fired generation as a reaction against higher natural gas prices. However, growing renewable generation and offshore wind ambition are keeping the country on an overall downward emissions trajectory.

Coal-dependent South Africa turns the corner: Thanks to coal’s 85% dominance of South Africa’s electricity generation mix, the nation boasts the highest power sector carbon intensity of any country in the G20. There are signs that the situation may now be changing, as evidenced by sharp declines in the country’s electricity emissions in 2022. In recent years new solar installs have been booming, reports BNEF, while state-owned utility Eskom grapples with an ongoing energy crisis and charts a pathway that decommissions much of the nation’s coal-fired power plants as part of a just energy transition plan.

Countries on an electricity emissions roller coaster

Brazil and Mexico emissions have been variable

Drought hurts hydro in Brazil: Hydro comprises nearly two-thirds of Brazil’s electricity generation. It’s one big, wet reason why the country ranks 6th overall globally for GHG emissions, yet sits outside the top 30 for electricity generation emissions in particular. Consequently, Brazil has one of the cleanest power sectors of any major economy. But across the years 2020–2022, a curious thing happened amidst the nation’s power sector emissions. They predictably slumped during the 2020 pandemic, then skyrocketed 68.8% higher in 2021, before falling massively to all-time lows in 2022. Why? As it turns out, in 2021 drought hit the country hard, suppressing hydro generation and prompting elevated LNG imports to compensate. By 2022, the rains returned while wind and solar expanded.

Mexican manufacturing and the growth of natural gas generation: After years of declining power sector emissions — through the pandemic and into 2021 — Mexico’s electricity emissions rebounded massively in 2022, to near an all-time high. At least three concurrent factors contributed: 1) a rise in Mexico’s manufacturing sector (partly in response to nearshoring trends), 2) drought that reduced the country’s hydro generation to a 20-year low, and 3) a significant bump in natural gas-fired electricity generation. Meanwhile, the nation’s lawmakers eliminated its Climate Change Fund and have put the future of clean energy development into question.

Conclusion

Looking back across these examples, it becomes clear that specific causes in each country’s power sector are driving the macro trends for annual electricity emissions: 1) Where wind and solar are scaling and capturing a great portion of a nation’s generation mix, fossil-fueled electricity emissions are falling. 2) In countries where the buildout of coal-fired generating capacity continues, electricity emissions are still rising, too. 3) For countries with a notable slice of hydro power in their electricity mix, they are backfilling drought-reduced hydro generation with natural gas, causing electricity emissions to yo-yo.

Later this year, WattTime and Climate TRACE will update our data with 2023 numbers, too. It will be interesting to see how these and other countries continue to track.

Load shifting of computing can lower emissions and soak up surplus renewables. Except when it doesn’t.

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.

Extending our view to long-run marginal emissions

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!

10 years of impact: on WattTime’s 10th birthday, a look back… and forward.

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?

Announcing New API, New Regions, New Data Signals

As WattTime continues to ‘bend the curve’ of emissions reductions, we’re excited to announce the release of our upgraded API (version 3 or v3), which includes new regions and data signals in addition to a more refined and intuitive schema. By expanding to new countries and regions, we’re enabling our partners to bring emissions-reducing technology to a greater global audience. With additional grid signals, we’re able to maximize human health benefits in addition to greenhouse gas (GHG) reductions.  

New API

The v3 API brings many improvements, including more intuitive and descriptive data delivery, error handling, and more. We don't undertake changes to our API lightly. We think the upgrades we've made in API v3 will be well worth the effort, as they will unlock greater opportunities for emissions reductions. We're here to support our partners as they begin using the new API.

New Countries and Regions

We have also released data for 12 new countries, which will only be available in API v3: 

  1. Mexico
  2. Japan (10 regions)
  3. South Korea
  4. Brazil
  5. India
  6. Chile
  7. Peru
  8. Turkey
  9. Malaysia
  10. Nicaragua
  11. Philippines
  12. Singapore

Check out our coverage map to see our full coverage, now with unique map layers for each data signal we offer through the API.

New Data Signals

In addition to CO2, the new API now offers our health damage data signal, which estimates the damage to human life and health caused by emissions from electricity generation based on the time and place that electricity is used. While currently only available in the US, this signal can be used to make decisions that reduce negative impacts on human life and health. IoT and EV companies have already begun using it as an input signal to device scheduling optimization, or to create a UI element advising users when to run appliances or plug in an EV. It can be used in tandem with the marginal operating emissions rate (MOER) to co-optimize device operation to reduce GHG emissions and damage to human health.

We’ve also added an average operating emissions rate (AOER), which is the average emissions rate (in lbs of CO2 per MWh) of all the generators operating at a particular time, weighted by their energy output. Using this signal for load shifting wouldn’t reduce emissions, but many companies find the data helpful for calculating total annual footprint for GHGP Corporate Standard, Scope 2.

To learn more about the different signals we provide, visit our data signals page.

Refined Handling of Real-time and Historical Data

Two of the biggest changes between our v2 and v3 API are our handling of real-time and historical data. 

“Real-time” data (formerly found in both the /v2/data and /v2/index endpoints), used to vary in recency, typically from five minutes old up to six hours. Now, all real-time data is always available within five minutes (in the /v3/forecast endpoint, the first data point applies to the current five-minute period). This provides a single, more reliable place to look for the data that apply to right now.

“Historical” data (formerly found in both the /v2/data and /v2/historical endpoints) used to be created typically within five minutes to six hours, but was never changed or updated after that. Now we’ve designed v3 such that we can still deliver historical data within a few hours (in the /v3/historical endpoint), but we can update those data later if more or better source data for a particular data point become available (data points are not overwritten, but additional points for the same timestamp become available). This allows us to maintain a historical database of emissions data that is more representative of the best available source information.

Transition Resources

We want this transition to be as easy as possible and worth the effort to upgrade. We’ve prepared a number of resources to guide our partners through the transition and help with getting acquainted with the new API, new regions, and new signals. 

  1. Transition Guide for APIv2 -> APIv3
  2. APIv3 documentation
  3. Release notes related to the API, data models, and methodology
  4. Data Signals Overview to explain each of the data types we offer
  5. Methodology & Validation have been updated and expanded

Support Webinar

WattTime will host a Q&A webinar about the new API and new features on Tuesday, January 23, at 11:30 a.m. PST / 2:30 p.m. EST. Learn more and sign up for the webinar here, and if you miss the webinar, the recording will be accessible on-demand using the same page after the event concludes.

API Version 2 Support

API v2 will continue to be supported until June 2024. While your upgrade to API v3 will be optional for approximately the next six months, we encourage you to proactively plan for your transition so that we can support you along the way if needed.