All of this means that the bottleneck has become communications as much as anything else. This blog post is going to try to rectify that problem and share a bit more about what we’ve been working on. If you’re at all interested in a behind-the-scenes view of what it looks like to work on solving the problem of turning DERs into grid resources, keep reading.

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One way to visualize how much AI is changing product velocity is by looking at Github commits. This isn’t a perfect proxy, but the scale is indicative of the increase.

For our team, the jump is largely attributable to a decision to create devboxes for each of our team members (both engineers and non-engineers) and integrate Claude and Codex into the dev environment. That setup allowed each team member to use our existing codebase to create rapid prototypes, shareable over Tailscale, and to turn those prototypes into tested, deployable code with virtually no friction. Combined with the release of models that could write high-enough quality code (and a bunch of safeguards internally that prevent most slop from reaching production), we essentially went dark factory starting around May.

So what have we been working on? There are three main groups of features:

• Agentic data science

• Grid integration

• Universal M&V

I. Agentic Data Science

AI is magical in many ways, but one of the biggest benefits is serving as a translation layer to highly complex subject matter. M&V is complicated enough that figuring out why certain results happened requires a relatively sophisticated understanding of data science. The amount of data to sift through also creates a paralyzing challenge of where to start.

We call our M&V agent “Aristotle” and we’ve been teaching it how to serve as an interface between our users and our data science tools underneath. A lot of this work is about building a “harness” that directs the agent to respond to inquiries in the right way and prevents answers that stray from what we would have intended. Every time that we add new functionality, the harness has to be tested to make sure that its new instructions don’t interfere with the quality of its existing knowledge base.

Practically speaking, when a user logs into their account, an Ask Aristotle chat allows them to use natural language to interrogate their data, e.g., what’s working, where are the poor performers, what do the outliers mean, how do the savings compare to what was expected? With many of our users negotiating deals with utilities, hyperscalers, and building owners, being able to show how their current projects are performing has been very important for proving the impact of their projects.

Agentic chat is table stakes for most companies these days, and while it’s been a big unlock for us, the bigger value will come from combining data science with reporting. This is a much bigger technical lift - data provenance and auditability are essential here - but the implications are enormous.

Imagine being able to run a continuous integrated resource planning analysis with live inputs from every end point on the grid, every distributed energy resource, real-time price inputs, and integrated grid modeling. Then changing the formatting and layout of your report with simple chat inputs and exporting to key stakeholders in the form of a custom PDF or a live external website via HTML.

Or consider how valuable it would be to model the impacts of a portfolio of batteries, knowing exactly what the existing load profiles of a particular area’s buildings were, what transmission constraints were found in the area, and the deferral benefits for the local distribution grid.

Or let’s say you have contracted for a portfolio of demand-side energy resources and you want to know how they are performing. Instead of waiting around for the utility to update your data, you can just stream them to a personalized dashboard with the metrics that matter to you, whether it’s capacity, energy, emissions, or bill savings (or something else entirely).

At the rate we are moving, it’s highly likely that these use cases are just the tip of the iceberg in terms of what chat-enabled data science will unlock. It’s not improbable that within a year there will be no need to ever commission a third party “expert” study. You’ll just be able to ask the question. Every day we get new use cases that we can load into our engine and every day we get closer to this reality.

II. Grid Integration

A few months ago we released GridSolver, a visualization of a dataset we started to assemble that would make it easier to understand the grid value of DERs. Since then, the only major visible upgrade was a “Predict” function, which gave a load forecast for every individual transmission substation in the country (this is not the congestion value, but rather a representation of how much energy demand is forecast for the surrounding area over the next six days).

But behind this front layer, we’ve been building a much larger data model of the U.S. grid from the point of view of DERs so that we can integrate site level M&V with the contextual data that defines its value.

This is probably the most ambitious and far-reaching undertaking we’ve tried. We’re effectively taking every possible source of data about the grid and organizing it into a relational database keyed on location so that the grid-DER relationship can be understood in multiple dimensions.

DERs are inherently bi-directional, even the ones that don’t actually push power back to the grid. The way to think about this is that a DER both responds to grid signals and is the source of grid signals. Most data out there is oriented to one or the other of these concepts, but not both at the same time. To hold both in a dataframe at the same time requires an entirely different way of thinking about the grid-DER relationship.

Generation, transmission, and distribution are all downstream of energy consumption. As demand rises and falls, the grid responds accordingly. But depending on how the grid responds, demand-side energy resources may actually counter-respond. The tail wags the dog.

Consider a heat wave. Energy consumption rises because HVAC use rises. Generators respond to price signals to increase production. Transmission grids send congestion price signals to optimize power flows. Distribution grids ensure that electrons reach their intended end uses. But as these various price signals are sent, HVAC controllers, embedded batteries, and other devices might respond in turn by decreasing demand, reducing the need for generation, and making congestion less of a problem.

Policymakers have decided that these DERs can be compensated, sometimes directly as if they were a generator, sometimes indirectly based on the costs of transmission and distribution, and sometimes just for being available in case grid resources are insufficient for the amount of power being demanded.

As we map this in our platform it means that not only do we need to consume the obvious signals - wholesale energy prices, power system metadata, capacity prices, etc., - but also develop entirely new ways to understand grid dynamics. This is most apparent in the forecasting and modeling of demand, both short run and long run, as well as the value of DERs in different market contexts.

Consider the fact that grid stress is almost entirely due to one of two factors. Either the weather is extreme, driving up energy consumption, or one or more generators or important pieces of infrastructure is offline for maintenance or because of a malfunction. If it’s weather that is causing the stress, we can predict the amount of stress by looking at the individual energy use patterns of end users. If it’s maintenance or failure, it’s up to the utility to send out an SOS for relief.

From a modeling standpoint it means that we can build bottoms-up models for predictable sources of grid stress, and top-down models for unpredictable events. So we’ve created weather-normalized models of every building in the United States, with the capability of layering any future weather or energy intervention scenario on top, and we’ve made these aggregable so that we can estimate the impacts of DERs on knowable grid stress events. At the same time, the top-down view gives us visibility into how and when utilities value DERs as well as how DERs roll up from premise level to distribution grid to transmission grid to generators and regional authorities.

This functional combination is important because of the way energy markets are evolving. Increasingly, data centers and other large loads are being asked to “bring your own capacity” which is largely a function of forecastable grid stress events. At the same time, because grids are being run at higher utilization rates, there is less margin for error when it comes to unforeseen events or when utilities need to bring capacity offline for maintenance. In these situations, DERs can help bolster their long-term cash flows by agreeing to availability when otherwise their value to the grid would be very low.

To put this in more concrete terms, and something that GridSolver shows, weather/demand-driven stress is common in the summer and winter, while systemic stress is common in the spring and fall. The point of the data model is to understand how these interact with each other so that the full lifecycle value of a DER can be underwritten.

III. Universal M&V

By the end of next decade, upwards of 50% of all energy will flow through a DER, and most of this will be transactable, whether as energy, capacity, emission reductions, or something else. All of the work we are doing in support of agentic workflows, and data model intelligence is in support of the broader vision of a true demand-side energy market.

Today, DER settlement is largely an exercise in trust. Utility programs tend to use nameplate capacity or stipulated savings based on technical resource manuals to estimate savings. Wholesale energy markets use load-impact protocols that vary in sophistication, but are subject to gaming and abuse (witness recent scandals in PJM and MISO). New technologies are often frozen out of these markets by virtue of the fact that measurement protocols are inflexible and outdated.

For the past few years, WattCarbon has run the OpenEAC Alliance, an informal collective of DER developers, M&V professionals, and other stakeholders collaborating to define a consistent set of methodologies that allow all types of DERs to be transacted.

The challenge facing a company like ours is the vast sea of complexity that makes it difficult to cover all of the different potential types of M&V methodologies and the different permutations within each type. Normally this is handled by one-off spreadsheet applications, which are neither scalable nor particularly auditable, and so we’ve built software to automate M&V while maintaining the flexibility and auditability to extend to all types of DER assets.

One of the lessons we’ve learned in working with AI is that you definitely don’t want your AI bots doing your M&V for you. You get a different answer every time and you have no idea why. So if you are going to use AI it has to be in service of writing the deterministic code that executes the measurement process.

The infrastructure unlock that makes this possible in our platform is the concept of a “measurement job” which breaks down all of the individual steps and creates a reproducible set of outputs that are delivered alongside the savings value and are readable both by humans as well as our Aristotle chat agent. This allows the user to ask “how did you get this answer” and for the answer to be based on the actual outputs of the measurement job. The rule underneath Aristotle is that the AI never does the math. It only explains and navigates the results. When the user asks for the provenance of an answer, by definition there is always something behind it.

Where this has been magical is using these measurements to actually diagnose performance shortcomings in projects that would otherwise have never been noticed. Heat pumps with miscalibrated settings, solar panels that malfunction, batteries dispatching at the wrong time, etc.; these insights are actually saving as much energy in some cases as the original project. And because the measurements are automated, the AI agent can take a scan through the entire portfolio on a nightly basis looking for potential issues.

The reason to invest so heavily in universal M&V is because it unlocks procurement. The way that DERs will finally scale is through Power Purchase Agreements (PPAs) that direct large sums of capital to the deployment of different forms of energy efficiency, demand flexibility, and decarbonization. PPAs are fundamentally outcome-based procurements, meaning that you are not underwriting the asset itself, but rather the production of the asset, and the proof of impact that it delivers.

The reason that utility programs are so broken is that they are more or less agnostic to outcomes, instead choosing to prioritize politics and process. Audits are a classic example of do-nothing utility programs, where payment goes to the auditor simply for showing up, not for any realized savings. A PPA agreement anchors on the outcome, which in turn incentivizes audits and other work to maximize the value of the project. If we want DERs to turn into grid assets, they’ll need to be procured based on performance, and settlement will be critical to the way in which these performance contracts are underwritten.

Hopefully you’ve stuck with me here this far. We’re excited to keep pushing on the frontier of AI and energy, unlocking the value of DERs as grid resources, as decarbonization tools, and as a way to lower the costs of energy for everyone. If you’d like to learn more, feel free to reach out, or just create an account at www.wattcarbon.com and start playing around with sample assets and the chat tools that we’ve enabled. It’s endlessly fascinating!

I floated this idea in late August, as the crescendo of public outrage over data centers was cresting over the nation. Unsurprisingly, nearly every conversation at NY Climate Week this year touched on this idea in one form or another.

“Why don’t we get data centers to pay for [XXX]” was the refrain, and data center companies seem to be obliging. One developer tried direct cash payments to local homeowners. Amazon and Meta separately announced $1b for community investment funds. Google has a $17m Water Restoration Fund on top of many other initiatives. The list is long, and at times, bewildering.

Politicians smell opportunity. For example, in New York, Governor Kathy Hochul has suggested that new large loads should pay $1 million per megawatt into a government piggybank. This money would fund affordable housing, childcare, roads, and a lot of other programs that Democrats care about. Nearly every state - Red or Blue - has a proposal like this now. We might call this the Great AI Shakedown.

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The idea behind these reparations is that data centers are making so much money that they should compensate the general public, given that their massive revenue is enabled by their use of public energy resources. Estimates range, but it’s safe to assume that a data center will earn roughly $1.25 in revenue for each kilowatt hour that it consumes (typically costing them around $0.20 in energy bills). These are money-printing machines, and most of that money will end up in Silicon Valley and Wall Street and not in the communities where the power grids are laboring to keep up with demand.

But while the allure of booty leads all of us to consider what nirvana might unfold, these untold riches are far from guaranteed, and poorly designed programs threaten to waste what is otherwise a spectacular moment for community reinvestment into the energy transition.

Why this Matters

Nearly 40% of carbon emissions are associated with buildings and upwards of 75% of carbon emission reductions will be delivered through investments in heating, transportation, and clean energy generation. We probably shouldn’t evaluate data center impact solely in terms of carbon emissions, but the link between data center energy use and the fact that what’s driving global warming is also largely related to energy use shouldn’t be lost either.

The reasoning behind a Community Portfolio Standard is more than “make them pay their fair share.” To be clear, there is every reason to expect a data center to pay for its power, its allocation of capacity, and infrastructure investments required to transport power to its site. A data center’s GHG emissions should be fully mitigated and other externalities should be addressed, just like any other new construction.

But the bit-watt spread, the margin that is earned on the consumption of electricity, means that any other form of extracting concessions from a data center except for those that enable it to consume more electricity will be suboptimal and result in a missed opportunity for the community. Left to traditional tax and spend politics, we’ll likely see a repeat of California’s Cap and Trade debacle, where billions of dollars in taxes were levied on polluters for their GHG emissions only to be poured down the drain of a high-speed rail boondoggle.

Rather than filling the coffers of government agencies, data centers should be investing directly into their local communities. These investments should be tied to the thing that data centers care about the most - watts. Again, these data centers will earn around $30/day for using the amount of electricity that most people use in their homes on a daily basis. Some of these data centers are the equivalent of a large city in terms of their energy use, which means that they’ll be earning upwards of $30 million per day.

What is Possible

Imagine instead of a few thousand dollars here or there for a playground and some empty promises, that a data center had to make a firm commitment to unlock new capacity on the grid by paying for solar panels on every roof, a battery in every garage, and a new efficient heat pump in every back yard. What if free energy efficiency upgrades were available for anyone who wanted them and low-income weatherization wasn’t an underfunded state program but a ubiquitous feature?

Tying the allocation of watts for the data center to their investment in watts in the community and making their ability to make money contingent on successfully delivering on these community benefits aligns incentives in a way that leads to spirited competition, rather than shirking or avoidance. A data center that shows up to a city council meeting with a well-formed community benefit plan tied to its own consumption of power, where lower bills, warmer showers, and colder beers are the payoff for sharing in the public power supply is a better outcome for our communities, is a better way to approach the politics of data center development, and eliminates most of the pork barrel cynicism that accompanies climate-oriented policies today.

Looking around the country, there are many more initiatives to tax and regulate data centers than there are proposals to incentivize community energy development. Some states are leaning the right direction, but we won’t get there by piling more money into traditional utility rebate programs. A fresh approach that ties the insatiable desire for more power to investment into clean, community-based energy infrastructure unlocks far more value and earns data centers the right to use public power resources.

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As someone who spent many of his formative years holding a protest sign and learning about how the workers, united, will never be defeated, I can appreciate both the general anxiety as well as the feeling of empowerment that comes along with political mobilization. This is part of what makes America a special place and we should treasure this freedom.

I’ve also written a book on the origins of political advocacy organizations, and when there’s not a clear ask and no room for a genuine solution, the movement will eventually fade and the status quo will return. Banning new data centers is a good negotiating tactic, but the ultimate goal should be bringing new large electrical loads onto the system in a positive way. Some existing sleight-of-hand tactics, like non-disclosure agreements and secret negotiations should be done away with ASAP. But negotiations with the community over how to distribute the benefits of a data center are where the most equitable solutions will be found.

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Why a Community Energy Portfolio Standard is the Best Solution to Data Center Growth

The one thing every town, city, and state could require today that would solve the data center problem is to implement a Community Energy Portfolio Standard (CPS). Think of a CPS like an RPS (Renewable Portfolio Standard), where a certain amount of energy has to be procured in a certain way. States require utilities to procure a certain amount of their power from renewable energy, which has helped grow the solar and wind industry. Data centers could be required to abide by CPS requirements as a condition of their operational permits.

Here’s how a CPS would work. Every data center that wants to begin operating must track its power consumption, even if it is self-generating its own power. In order to receive permission to operate, it must show that it is procuring a minimum percentage of its energy from the surrounding community (the eligibility radius should allow for enough capacity). A good number to start with would be 25%. Let’s say a large data center uses 750,000 MWh of electricity per year. In order for it to operate at that level, it would have to procure 187,500 MWh from the local community. That would be about 18,000 homes with solar on their roofs, for example.

This procurement should also include energy efficiency projects like insulation and heat pumps, more efficient lights and pool pumps. Basically, as long as the total MWh threshold was hit, the data center would be in business. That’s it. We don’t need to overcomplicate things and introduce a bunch of weird regulatory constructs. And we definitely don’t want to put utilities in charge of procuring this energy. Just let the market work it out and don’t let a datacenter operate unless it can prove that it hit its CPS requirements on an annual basis.

Yeah, but what about…

One objection to solar panels is that they only produce energy when the sun is shining, and so you’d need to find a way to solve for the other hours of the day. In fact, there are certain hours of the day where the grid is already maxed out and more solar won’t help at all! In California, the biggest issues are in the evening when the sun has set and air conditioners are still running. Other places are seeing winter morning peak demand and still others see seasonal fluctuations in the fall and spring.

Every grid has certain locations and certain times of the day that need help. The best way to get help where the grid needs it is to allow the CPS to give these hours extra credit. It would be pretty easy to say that any hour that’s already a problem for the grid should earn 2x the rate of non-stressed hours. The same could hold for any location that is stressed. If you set up the CPS so that a data center got extra credit for procuring energy when and where it was needed most, not only would you help the grid, but the people living in those areas would get an even better deal.

It might be hard to fathom 18,000 homes getting solar in an area, but this is actually a pretty realistic number. Doing solar installations at scale would also bring down the costs. Rather than sending people out ringing doorbells trying to make a one-off sale, imagine if community members could just sign up for a free installation, pick their preferred time, and be assured that a certified contractor would show up and guarantee the work. Remember, if the solar panels don’t produce, the data center doesn’t get to operate. So there’s a clear incentive to make sure that 100% of the savings are delivered.

If data centers had to make these local investments, the money that’s now going to fund new fossil fuel gas and coal plants would be spent instead hiring local workers to build clean energy. The boost to the local economy and the reduction in pollution, along with massively lower bills and a more resilient grid would make this a much easier sell in any community. There are issues with noise that still need to be addressed, and there are likely other land and water-use restrictions that local communities will want to impose. But fundamentally, if we can require new data centers to offset their energy impacts by investing in community energy resources, the end result will be beneficial for both the companies that are making these investments in the first place as well as the local communities who are sharing their resources to make it possible.

Farther into the policy weeds, regional transmission congestion is also a problem that data centers are making worse. Strategically located DERs can help, but current rules make it difficult for their contributions to count. CPS resources should automatically be enrolled into wholesale markets to be valued for their contributions to congestion relief. PJM and other independent system operators have been dragging their feet on implementing FERC order 2222, making it difficult for small systems to participate in wholesale energy markets. Some quick wins would be to give credit to batteries for pushing energy back into the grid and making sure that these types of resources qualify for Bring Your Own New Capacity (BYONC) requirements.

A final benefit to the CPS is that it would reduce the amount of speculative projects in the interconnection queue. Companies that were serious about data center construction would start working well ahead of interconnection to build out the infrastructure to meet CPS requirements. If these data centers were built and then abandoned, the worst thing that would happen is that a bunch of people would have gotten free solar panels. Without CPS, there’s a good chance that communities will be paying data center bills for generations to come.

So that’s it. Solve the data center problem with a Community Energy Portfolio Standard. The amount of capital flowing into PPAs for VPPs will be unlike anything we’ve ever seen and the mobilized opposition to data center construction will have achieved a tangible result, improving the lives of hundreds of thousands of families around the country.

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Part 1 - What’s Different Now?

For a long time, the value of a distributed energy resource like solar panels or an energy efficiency upgrade was measured in the bill savings a customer would see. The benefits could also include comfort (i.e., cold beer and warm showers) and health, but more or less the cost/benefit would be established relative to the customer experience.

Utility programs introduced new concepts like Total System Benefits that expanded cost-benefit analysis to include non-customer benefits like infrastructure savings. Renewable energy credits widened the lens even further by explicitly valuing the environmental benefits from a select subset of energy generation resources.

Because bill savings are generally the most straightforward to model, customer-centric business models drove most of the first wave of DER deployments. Energy Services Companies (ESCOs) developed performance contracting models that shared savings with their customers. Solar companies invented power purchase agreements to defray the upfront costs of new systems.

Utility programs spawned an entire ecosystem of rebate-centric firms that pushed technology-specific solutions subsidized by utility ratepayers. The concept of a system benefit also launched Non-Wires Alternatives programs that specifically targeted the deferral of certain infrastructure upgrades. These types of investments led to standardization of DER value in regulatory proceedings. For example, in California, the system benefits of DERs are defined by an official Avoided Cost Calculator developed under the guidance of the California Public Utilities Commission, which is used to determine how cost-effectively public resources are spent on incentive programs.

Now that data centers are looking to DERs to offset their grid impacts, new questions are arising that expose gaps in existing DER valuation frameworks. How would we know if a battery in someone’s home actually helped reduce transmission costs for the power required by a new data center? Would a distribution substation and a transmission substation receive the same benefit from EV load shifting? How would we compare two different locations on the same grid? How would a utility quantify the benefits? How would a data center claim credit?

These types of questions led us to question some of our own assumptions. We had been working on a “GridScore” concept, reflecting the degree to which DERs delivered load reductions while demand was at its highest for that balancing authority. Was that the right number? What about distribution grid impacts? Was the system peak the same as the local peak? How would we think about transmission costs? If transmission costs are high, but the energy is mostly clean, do we care that we have to pay a little more to get it to where it needs to go?

Out of these questions we started looking for data that could help. There is plenty of information out there about generation and transmission systems. The major ISOs publish varying degrees of detailed maps and pricing and other information. But information becomes much scarcer the farther down into the stack you go. Some utilities publish distribution grid data, but there is nowhere that you can look that would tell you what the value of a DER would be at any given point on the grid.

It seems like being able to answer this question will be essential if we are truly going to move to robust demand-side energy markets where the counterparty is a data center trying to justify its capacity allocation. If we get the number wrong because it’s based on a 5-year old study, the consequences are severe!

Last month we released GridSolver to illustrate how widely variable the different needs across the country are and how different DERs are going to be differently valuable in different places. Since then we’ve layered in a prediction algorithm that allows us to predict the hourly load carried by every transmission substation in the country. And we’ve continued to build out the mapping of distribution infrastructure across utilities that publish this information. The result is a unique dataset that pulls together in a mostly comprehensive way all of the information that you would need to be able to answer the question: what is a DER worth here?

We’ve identified 7 major categories of value that could be captured by a DER. Some of these are mutually exclusive and some are limited to certain types of DERs, but generally this the universe of value that might be available to DER developers.

1. Resource adequacy/capacity - at any given time, the grid needs to be able to deliver power to meet aggregate demand. The cheaper and cleaner the better.

2. Transmission congestion - some areas need power that cannot be supplied adequately by local power resources and must be carried over longer distance power lines or rerouted to reach the desired destination

3. Energy arbitrage - energy supply and demand can be mismatched during the day and the ability to shift one side or the other can result in lower costs

4. Ancillary services - voltage regulation is an important part of grid stability and can be facilitated by DERs

5. Line loss reduction - generally the farther energy has to travel, the more is lost along the way and DERs that can shorten the distance can keep our system more efficient

6. Infrastructure deferral - when grids are sized for peak loads and those peaks are significantly higher than normal operations, cost recovery is difficult. Reducing peaks and increasing usage during off peak hours helps to lower infrastructure spending.

7. Bill savings - ultimately customers pay the bill for their energy consumption and DERs can help reduce exposure to high demand charges, time-of-use rates, and reduce wasteful spending.

Given these categories of value, the challenge comes in mapping these to any given DER. How do you simultaneously measure the benefits that might accrue?

This was the challenge we set out for ourselves when working on GridSolver. Could we actually gather all of these bits of information for any given DER anywhere on the grid in the United States?

Part 2 of this series will deep dive into all of the different sources of information and how we pulled them together into a single data model.

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The truth is, you don’t really want AI M&V. You want to be able to trust the numbers, to replicate them, to audit them. AI makes all of this difficult. The potential for hallucinations makes it even worse.

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That said, when Claude Opus came out at the end of last year, I went on a vibe-coding bender and built a prototype of what I thought a real AI M&V tool might look like. I built a new version of our website at the same time and teased with this hero image:

I handed it off to the team a couple of months ago, and this week, among other things, we released it to our users. I thought it might be useful to describe where we landed with this product and why we think it will be useful for bridging what has historically been a fairly wide gap between the business of energy and the data science behind it.

Why AI complements M&V

The hard part of M&V isn’t really in the math. Even our most sophisticated algorithms are barely above high school level statistics. Where the trouble starts is when the numbers don’t add up. M&V is as much a process of patching holes as it is anything else. Missing data, erratic data, incomplete data, corrupted data, time zones, corrected data, government shutdowns, buildings changing owners, devices getting bricked, these are the stories of M&V.

So when it comes to M&V the hard part isn’t the happy path; it’s understanding the edge cases, how things go wrong, how wrong things get fixed, and whether to intervene in the project sooner rather than later.

I’ve spent the past ten years staring at more M&V data than pretty much any other person on this planet, and I feel like I have a pretty good intuition now for these nuances. But as I started working on what would become our Aristotle AI, what I really wanted was a chance to go deeper. To ask more questions. To better understand patterns in the data that were too hard to see with the naked eye.

This is where we pointed our Aristotle AI agent: at the layer underneath the raw data. Should I be concerned with the underperformance of this asset? What trends do you see in my portfolio that stand out as different than what I would have expected? Are there chronic errors that suggest underlying issues with my data? What does it mean to have a high CVRMSE?

The best way to think about Aristotle is as a sherpa, and for those who are new to the world of M&V, once you start asking Aristotle questions, it’s hard to stop. It’s like unlocking a new world of insight that had been obscured by a thick fog of numbers before.

WattCarbon provides free access to its M&V tools (including Aristotle AI) to anyone who wants to kick the tires (the fees kick in when you want to use the API, create EACs, etc.). So I’d encourage anyone who has projects where energy savings are being calculated to give this a try. But for those who are underwriting performance contracts, investing in battery portfolios, and otherwise accountable to actual outcomes, what Aristotle unlocks is essential to your success.

And of course, now that we’ve released the first version, the floodgates are open. I’m pretty sure that next we’ll release AI-assisted data onboarding tools (those pesky Excel spreadsheets with awful formatting are child’s play for Claude these days). The roadmap has us extending the chat to include our grid data, DER grid planning and forecasting, and there will be new tools for data analysis that leverage the structured datasets that sit underneath our M&V.

In short, life just got a lot easier for teams on both sides of the M&V aisle, as the most annoying parts of the job are solved by the AI and the most fun and rewarding parts are centered in the user experience. Most importantly, the underlying calculations are still done in Python, meaning that we can be confident that the numbers coming out are supposed to look the way they do (even if we need to dig into why they came out that way). This is the essence of the modern automated M&V stack: transparent, replicable, and scalable data infrastructure that provides an end-to-end settlement layer for emerging demand-side energy markets.

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Flying under the radar a bit is a surge in utility mandates to expand VPP programs, in particular in Virginia where Dominion Energy is being asked to allow large loads who can offset their grid impacts with distributed energy resources to be given priority in the interconnection queue, and in Nevada where Microsoft and NV Energy released their framework for how to quantify grid impacts from data centers as well as corresponding mitigation efforts.

Across all of these (and plenty of others too numerous to detail) is a question that bedevils regulators, grid operators, data center developers, public interest advocates, and DER developers. What is the value of a DER?

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What is the Value of a DER?

Typically, the way we’ve valued DERs in the past has been either as a function of bill savings or on an avoided cost basis relative to grid alternatives. For example, a battery at a commercial site might be used to lower peak demand, thus reducing energy bills. A utility incentive for this battery might be sized based on avoided energy generation costs, reduced infrastructure costs, or lower GHG emissions.

The grid side of this equation is opaque. Lore has it that when you ask utility grid operators what the value of a DER is to them, you get answers ranging from “absolutely nothing” to “we hate this idea” to “we just do what our regulators require us to do” to “go look at appendix 35b on page 384 of this obscure PDF that we release once every three years and cross reference it with a different PDF that uses a completely different framework and do the math.”

You’ll have better luck with wholesale markets, which sometimes recognize DERs as capacity resources (PJM is a good example) and sometimes allow some DERs to participate in ancillary services markets, but where the value of a DER varies widely from market to market, and this value is based less on what the DER actually contributes and more on the particular rules of that market operator. More often than not, the value of DERs is based on vibes more than any concrete empirical framework.

But as the examples at the top demonstrate, the vibe era is over. If we’re going to allow data centers to mitigate their grid impacts with DERs, whether on a KW basis, a kWh basis, or a dollar value basis, we need to be able to consistently and accurately quantify what DERs are delivering to the grid.

Introducing GridSolver

Today we are releasing, through the OpenEAC Alliance, a new tool called GridSolver. The purpose of this tool is to start a conversation around how to value DERs as grid resources. If demand-side energy markets are going to scale, there must be a methodological framework that can be used to value the impact of DERs in a consistent way across different jurisdictions.

I encourage you to go visit the GridSolver page and click around a bit to get a feel for the data that we’ve surfaced, but I’ll also describe it a bit here. This work began as an exploratory exercise to look at what data might be available (inspired in part by the good work our friends at GridStatus have done on this front), then transitioned into asking the question whether it would even be possible to connect the dots from readily available data to the granular impacts of DERs.

Consider, for example, the challenge of differentiating between how DERs might solve transmission constraints versus how they might solve distribution constraints. A transmission cost is incurred when energy has to be moved long distances from where it is generated to where it is consumed. The more power that gets consumed from far away generators, the greater the transmission costs. But these costs might actually be a good thing! Consider the recent transmission lines that were completed in New York and California, which will allow hundreds of megawatts of clean energy to reach some of the most energy intensive regions of the country. In Texas, transmission congestion pricing peaks during periods of high renewable generation, when highly polluting fossil fuel power plants are priced out of the market and power is delivered over hundreds of miles of transmission lines from wind farms out in the plains.

On the other hand, distribution challenges reflect the limits of local grid infrastructure. Your local substation is built to provide a certain maximum amount of power at any given time to the surrounding neighborhoods. When everyone wants to charge their car at the same time as they are running their air conditioner, the substation reaches its thermal limit to absorb power from the transmission line and serve it to the local distribution network. If you install batteries in homes to flatten the load curve of the local neighborhood, does this translate to relief for the transmission grid? How would we know?

In developing GridSolver, we ended up building a vertically integrated data hierarchy of the grid, starting at the individual building level and going all the way up to the transmission grid. We framed it as a way to answer the following question:

given both the transmission and distribution challenges faced by any given substation anywhere in the country, what would be the optimal type of DER to deploy in that area?

The data is instructive, directional more than conclusive, but we’ve been able to pull together a couple of useful pieces of the puzzle in a novel way that I’ll describe below.

The GridSolver Stack

Generation

For any given point on the grid, we can start by asking how much power is needed from available power plants at any given hour of the day. For each balancing authority, we identify the net demand of the grid (total demand minus wind and solar) to know when the most stressed hours of the year occur. The best way to understand the peakiness of the grid is to look at the drop between the most stressed hour of the year and the 90th percentile most stressed hour of the year. The larger the gap between those two points, the more amenable that grid is to flexible resources that can shift load into less constrained hours. As the difference between the highest constrained hours and the median constrained hour grows smaller - i.e., the utilization rate of the grid improves - the DER solutions that will make the greatest impact become longer duration energy efficiency and solar projects.

Transmission

Some balancing authorities are home to an Independent System Operator (ISO), like PJM, CAISO, ERCOT, MISO, etc., and these ISOs publish market prices for energy, congestion, and line loss. These price signals are helpful because the add a different dimension to total net load. They price the cost of producing electricity in the first place, as well as moving the electricity from the point where it is produced to the points where it is consumed. ISO energy prices clear in a uniform fashion, but the cost of congestion and the loss rate vary by subregion within an ISO (down to individual price nodes in some places). Isolating the congestion component of the Locational Marginal Price (LMP) helps narrow down the places where adding DERs can significantly reduce the cost of bringing electricity in from far-away generation. Typically, these high costs of transmission are seasonal and concentrated in particular times of day, so we’ve isolated this congestion metric in the map and provided both a national view of where congestion is particularly concentrated (larger gap between the most congested hours and the 90th percentile) and we’ve made this metric available at the smallest geographic increment possible.

Distribution

The step down from transmission systems to distribution systems is deceptively challenging to build a data model around. One way to think of this is to imagine that the transmission system is like the railroads, while the distribution system is like subways. Sometimes the tracks overlap (like Penn Station in New York), but for the most part the systems are operated with minimal coordination. It may be convenient for Amtrak to make a single stop at Penn Station, even if it means that the subway gets overcrowded due to the influx of new riders at that stop.

Further challenging is the fact that distribution grid data is housed within utility data systems and generally not made publicly available. So while we might know where a substation is located, and we might know what its rated capacity might be, we don’t really know how much power actually flows through or where it might most benefit from the addition of new DERs. So we have to get creative.

For GridSolver, we’ve decided to anchor the map on the substation location, so every dot on the map represents a substation, but the data connected to that dot has very little to do with the actual substation. Generation data is sourced from the balancing authority, transmission data is sourced from the ISO. For the substation itself, we go in a different direction.

In the absence of reliable utility data, we have instead constructed a ground-up model of the entire US grid. The way we did this was by mapping every structure in the country and classifying it according to its end use (e.g., residential, commercial, industrial). Based on the size and location of the structure, we used NRL End Use Load Profile data to build an hourly load profile for each structure. Then for each substation, we drew a 7.5 mile radius around the substation and added up all of the buildings located within that radius.

This gave us two useful pieces of information. First, it helped estimate the likely peak load on any given substation (what hour of day, which days of the year) and second, what the likelihood is that any given grid constraint could be solved for with DERs. If there simply aren’t very many buildings surrounding a substation, it’s unlikely that anything more than a trivial grid constraint would be a good bet for a DER-first strategy. On the other hand, more dense population centers can more than support even large data center projects. One limitation of the NREL data is that it combines electricity and natural gas energy use, so winter electrical loads are inflated where there is a significant amount of natural gas or fuel oil heating. This data also doesn’t account for the presence of rooftop solar or onsite storage, which means that in solar heavy states like California, the typical loadshape will be significantly lower during the middle of the day.

Still, combined, this data provides us with unique insights into the dynamics of grids across the country. In some areas, high congestion and high energy use variability overlap. Other areas see neither high congestion nor high energy use variability and some only see one or the other.

DER Optimization

As a final step, we asked the question, “what type of DER would be best suited for the dynamics of any particular grid region?” To answer this question, we built an algorithm that evaluated the peakiness of energy and transmission constraints relative to the availability of local buildings to serve as DER hosts. Assigned to each substation, this GridScore provides a consistent framework for evaluating potential DER value anywhere in the country. In areas like the Southeast without an ISO market to price congestion we have to simply rely on the energy portion of the score, and because the WECC has barely begun to price congestion regionally, we are limited in the conclusions we can draw from those prices in that region as of today.

However, there are strong patterns that stand out. We see most of the country impacted by either peaky energy use, peaky congestion pricing, or both, but these happen during different times of the year. The upper Midwest is dominated by winter and summer peaks. The Pacific Northwest’s large hydro reserves and mild climate keep loads flat and congestion low. The Southeast’s load curve spikes in all seasons.

While there are likely many combinations of DERs that would make sense in each of these areas, the biggest bang for the buck will come from those that directly address the dominant costs to the grid. That means in places like Minnesota, where spikes are related to heating and cooling requirements, DERs that directly address HVAC related loads will be the most impactful. On the contrary, in San Antonio, where in all four seasons there are spikes, DERs like batteries that can respond in highly flexible ways will be the most useful. This is not to say that a battery wouldn’t also be useful in Minnesota, or that HVAC optimization won’t help in San Antonio, but rather the utilization rate of the DER might not be optimized in those areas.

DER ELCC

This brings us to a final concept: Effective Load Carrying Capacity (ELCC). ISO markets discount the capacity value of DERs by the likelihood that they will be available to respond to dispatch requests at designated times. This is an entirely discretionary value - as evidenced by very disparate values assigned by PJM and MISO for demand response. It’s also an incomplete concept. Time and locational impact of DERs is foundational to their value, but the use case is going to matter. If a behind-the-meter battery is deployed for the purposes of eliminating non-coincident peak demand charges it is almost certainly going to be dispatched at times of the day when the grid does not need it. Similarly, a lighting retrofit will only save energy when the lights are on - which could be at the exact wrong time of day to be beneficial to the grid.

GridSolver does not provide this ELCC metric. But it does point us to the importance of a new settlement layer for DER markets. The most interesting of these markets are being formed at the utility level, not the ISO level, and the value of a DER is now less associated with a utility avoided cost and more with efforts to increase grid utilization to support large load growth without raising customer rates.

As we contemplate leveraging DERs to offset the grid impacts of data centers, it will be incredibly important that we have clear and open methodologies for determining the grid value delivered by DERs, so that commitments being made on the part of data centers (or being required by regulators) to fully mitigate the costs to other users of the grid can be quantified and transacted in ways that inspire confidence to all sides of the market and that can be trusted by the public. The worst thing possible right now would be to treat these DER commitments like carbon offsets and wind up ten years down the road with a bunch of fake DERs that were never really installed, never really delivered any value to the grid, and only wound up lining the pockets of those with the worst of intentions.

GridScore and the OpenEAC Alliance

WattCarbon has been leading an effort to create transparent methodologies for measuring the impacts of distributed energy resources through the OpenEAC Alliance. GridSolver is being released as a collaboration with Resilience Energy under the auspices of the OpenEAC Alliance. We are excited to welcome them to the OpenEAC Alliance Board and look forward to future partnerships with other organizations who share our vision for demand-side energy markets that truly value DERs as climate-positive energy and grid resources. Over the next few years, as long-term offtake agreements for DERs become commonplace, the settlement layer for DERs will play an important role in unlocking this market. We invite all participants in these transactions, from DER developers to utilities to large load offtakers to join us in developing and refining these methodologies.

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As someone who falls on the AI optimist side of things, I find myself defending against the more outrageous claims of the fear-mongers. But at the same time, there is a reality out there that validates much of the pushback. AI data centers are being approved under the cover of darkness; AI energy requirements are being bridged (if not fully embraced) by fossil fuel resources that at this late stage of the global warming arc we should be phasing out, not doubling down on; and regardless of whether or not energy bills are rising as a result of data centers, the very entities that are bending over backward to serve data centers are also the ones sending out monthly bills that pinch already extended family budgets.

Trust and AI

Society can be built around any number of forces, but the foundation of a free society is trust. Trust is earned through civic, political, and economic institutions that abide by received (often unwritten) rules and norms. Local controversies surrounding data centers, and the national mood souring on AI in general, point to a profound unraveling of institutional trust. Just this past week we saw a commencement speaker lustily booed by students at a Florida university - hardly known for its anti-establishment radicalism - for the singular sin of mentioning AI’s transformational impacts.

The convulsive reaction to AI suggests that institutional trust is severely eroded. When the data center comes to town, residents smell deception. When AI gets introduced at work, we all wonder if our job is next. When our inflated power bill comes at the end of the month, we assume that it’s these exotic forces of disruption that are profiting from our misfortune.

AI and the shadow of climate

When we started WattCarbon, our idealistic hope was that we could drive billions of dollars of investment into decarbonization projects by providing a more trustworthy pathway into climate action. The solution, we thought, was trust: more transparency, more granularity, and more traceability from the source to the sink. We thought that better measurement and verification and a system of record would eliminate much of the greenwashing and double-counting that had come to discredit much of the corporate climate movement. Instead, while that legacy system collapses under its own weight, we’re now in this Rumspringa of emissionsmaxxing in which there are essentially no repercussions for even the most egregious backsliding on previous commitments. As a case in point, the organization formerly known as the Clean Energy Buyers Association, but now rebranded as the Corporate Energy Buyers Association, recently announced that Exxon/Mobil would be the title sponsor of their annual conference. So much for idealism.

As we now watch utilities squirm under the spotlight, once again the question returns to trust. How can we be sure that we aren’t being taken for a ride when a deal is announced? How can we be sure that promises to mitigate consumer pain aren’t as empty as the climate commitments of the past decade? How can we be sure that the path followed is as good for the community as it is for shareholders?

There is no question that AI technology will continue be both a source of wonder and of ennui as we push the boundaries of what is possible and where the human fits into this technology equation. It is also likely that the combination of community pushback, power realities, and economies of scope will mean that the gargantuan data centers that have been the center of most public attention will give way to smaller, more decentralized homes for AI computers.

But the most important actor in the coming decade will be the utility. The utility sits at the intersection of failed climate goals and a perilous dystopian future and faces a deeply skeptical public. It’s not clear that utilities have prepared themselves for this moment, nor that they know how to build the type of trust that’s required. While they’ve wildly succeeded in making sure that the lights stay on, there’s no question that they’ve whiffed on the data center spectacle thus far.

Why we remain optimists

It might be misplaced idealism yet again, but I believe that bonds of trust can be rebuilt. For the same reasons that it seemed possible to turn corporate climate commitments into real investments into decarbonization projects, I believe that the energy investments required to power data centers, electric trucks, decarbonized industrial processes, and new factories can unlock billions in new capital for solar panels, batteries, heat pumps, and energy efficiency. The same community energy resources that would allow us to reduce the rate of global warming can also meet the energy needs of these new energy consumers. It’s not a tradeoff.

Trust comes in many forms, but to the extent that utilities default to community energy resources when developing their plans to power data centers, to the extent that these types of resources are prioritized when allocating capital and pushing for favorable rulings from regulatory bodies, and to the extent that we build transparency into these important decisions, we will take what is admittedly a very difficult moment in our history and make it possible to imagine an inclusively prosperous future for all of us. -MY

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OpenEAC Alliance Meeting this Week

This Thursday the OpenEAC Alliance will celebrate its two year anniversary by digging into GridScore and CarbonScore metrics and launching a new methodology spec for smart thermostat load shifting. WattCarbon hosts OpenEAC Alliance meetings on a quarterly basis in an effort to build a community of M&V practitioners that share best practices for calculating hourly energy savings from demand-side energy resources. This is not an official standards body, but rather a way to go beyond the standard to build rigor into M&V processes so that we can build trust amongst all stakeholders.

OpenEAC Alliance

Q2 2026 OpenEAC Alliance Meeting

Next week (Thursday, 8am PT) is the next quarterly meeting of the OpenEAC Alliance. Following up on last quarter’s rollout of demand response and load shifting methodologies, we have introduced two new metrics for calculating the impacts of demand-side energy resources…

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5 months ago · WattCarbon

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We’re at a critical juncture right now where real choices are being made by data centers between investing in grid capacity and defecting to off-grid solutions. Bring your Own Capacity (BYOC) can mean building a new single-cycle gas power plant or it can mean installing thousands of batteries in the local community. Done right, investing in local community resources will reduce costs for everyone. But the overwhelming odds right now point to scenarios in which data centers drive up demand and energy prices continue to skyrocket. Where utilities once feared DERs because of the loss of revenue, they are now finding that DERs may be the best hope for solving near term load growth challenges and affordability at the same time.

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Aristotle and WEATS are unlocking new demand-side energy markets by creating trust on all sides. As part of that effort, we’ve revamped our Registry to focus on better understanding how DER portfolios contribute to grid resilience and carbon reductions. Here’s what’s new.

GridScore and CarbonScore: Now Live in the DER Registry

Our redesigned DER Registry is live, and two new metrics sit at its center.

GridScore answers a simple question: is this asset helping when the grid needs it most? It measures the share of a DER’s energy savings that occur during the top 10% of demand hours. A rooftop solar system that produces steadily all afternoon scores differently than a battery that dispatches precisely at peak. GridScore captures that difference.

CarbonScore answers the emissions question with the same precision. Rather than relying on annual average grid intensity, CarbonScore calculates the actual kg of CO2 avoided per MWh of energy displaced, using the carbon intensity of the grid at the hour the savings occurred and the related carbon savings associated with primary fuel consumption. For buyers making procurement or sustainability claims, this is the defensible number auditors and regulators expect.

Every enrolled asset is independently measured and scored. The data is open to market participants, transparent, and backed by meter-level verification. Explore the registry to see how different types of projects stack up.

We’re also getting ready to introduce Aristotle, our conversational interface for the platform. Tell it about the energy program you want to build, and it will help you design and set it up. We’ll have more to share soon.

Us at SF Climate Week: April 21

We’re hosting a panel during SF Climate Week, co-sponsored by AECOM:

Unlocking a Greener Grid: DERs, Electrification, and the Future of Energy Tuesday, April 21 | 5:00 to 6:30 PM | AECOM HQ San Francisco

The conversation will focus on what it takes to make distributed energy resources bankable: transparent measurement, credible certification, and transaction systems that work.

Our panelists:

• Ari Matusiak, CEO of Rewiring America

• Michael Lee, former CEO of Octopus Energy, North America

• Sarah Millar (Moderator), former VP at Renew Home

• McGee Young, CEO of WattCarbon

If you work in DER development, utility program design, clean energy procurement, or electrification policy, this is your room. The event is free and in-person. Register here.

Community Energy Standards for Data Centers

In March, we joined a panel at BayREN’s Codes and Standards Forum on Powering Data Centers: Balancing Growth with Sustainability and Affordability, alongside speakers from PG&E, the cities of San Jose and Santa Clara, Energy Solutions, and Caliber Strategies. (Full session recordings and slides are available on BayREN’s site.)

One number from PG&E’s presentation stood out: 7,250 MW of data center load is currently in their interconnection pipeline, with 3,550 MW already in final engineering. To put that in perspective, PG&E’s entire system peak is around 30,000 MW.

PG&E’s proposed Electric Rule 30 focuses almost entirely on transmission-level interconnection, cost responsibility, minimum demand charges, and 15-year service commitments. What’s missing from that framework is any requirement for these new loads to invest in local energy resources. A 100 MW data center in San Jose will pay for its transmission upgrades, but there’s no mechanism asking it to support rooftop solar, storage, or efficiency in the community whose grid it now shares. Cities like San Jose and Santa Clara are starting to set their own standards (San Jose requires carbon neutrality depending on energy provider), but there’s no consistent measurement infrastructure behind those requirements.

That’s the gap we’re working to fill. Demand-side energy markets need two things to be enforceable: independent measurement of what distributed resources actually deliver, and certificates that prove it. We’re building both. If your organization is working on data center energy policy or community energy programs, we’d welcome the conversation. You can reach me directly at mcgee@wattcarbon.com.

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Early on this journey, we found ourselves dealing with a bunch of novel situations, where the path to an hourly savings value was unclear and we wanted to make sure that we were doing things in a way that made sense to the rest of the community. Fundamentally, M&V is a trust-based project, and we wanted to be as open as possible about our approach. We recruited a small group of experts from within the community to serve as an advisory board and started writing methodologies that reflected how different organizations were trying to measure their impacts. Some of this work was related to carbon accounting, but mostly it dealt with the question of energy savings. Today, these methods are found at https://methods.openeac.org/ and reflect a mix of different types of interventions, data types, and outputs.

The purpose of the OpenEAC Alliance is not to serve some sort of gatekeeping function, but rather to create a shared space where best practices can be identified, complicated questions can be discussed, and ultimately where the core methodological approaches to valuing demand-side energy resources are hosted.

Demand Efficiency

As I described in an earlier blog post, the basic premise of energy efficiency is being challenged by a new era of load growth paired with renewable energy. Energy (how much) is cheap. Capacity (how fast) is expensive. Energy pays for capacity, but too much energy demanded at the wrong time means that more capacity will be required. This is a delicate balance for utilities to strike, and most don’t have the resources required to do so in an effective way.

Even so, if there is one core tenet of this new era, it’s that “demand efficiency” has superseded “energy efficiency” as the prime directive of demand-side energy policy. The value of demand side energy resources is a function of how well they help manage the balance of supply and demand on the grid. So for a given asset, whether it is a dispatchable battery, a non-dispatchable energy efficiency project, or a heat pump that combines both functions, the fundamental question is not how much energy is being saved volumetrically, but rather what time of day is the energy being saved and how does that line up with what the grid needs?

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On Thursday of this week, the OpenEAC Alliance will hold its quarterly meeting. The above linked slides represent a starting place for thinking more specifically about M&V as it relates to Demand Efficiency. I’m sharing them out ahead of the meeting in an effort to engage in a broader dialog with the community. Is this the right way to think about the problem in the first place? How should we go about understanding core concepts like capacity factor as they relate to DERs? How should we proxy what value would be delivered to the grid?

These questions will have to be answered if DERs are going to function as grid assets. We are long past the days where all kilowatts are created equal and it remains to be seen whether utilities or third parties will control most of these devices. In either case, it will have to be transparent to all market participants where the value of their assets comes from, which means that even where the utility is owning and operating DERs, their planning teams will have to answer these same questions.

If this is an interesting topic to you or your colleagues, please feel free to attend the OpenEAC Alliance meeting on Thursday. If timing is challenging, feel free to reach out to us with your thoughts as we continue to drive forward on these questions.

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The response, so far, has been a combination of slow-rolling interconnection queues and reallocating costs to data center developers. Ohio now requires data centers to pay for 85 percent of their subscribed capacity regardless of actual usage. California’s SB 887 would make data centers pay full infrastructure costs in exchange for expedited permitting. These measures are fundamentally defensive. They slow the problem without solving it.

Like everyone else in the energy industry, we have been doing a lot of thinking about this issue. As energy consumers, we’ve seen our rates increase, even before data centers starting adding stress on the grid. As domain experts, we know that there are elegant solutions possible. We also know that existing regulatory and market pathologies prevent many of these solutions from getting deployed in the first place.

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A few months ago, our friends at Piclo Energy started talking about ACE - Accelerated Community Energy. The basic idea builds on a lot of the work we’ve been doing with our Repowering Communities initiative, and with other partners like Rewiring America, to figure out how to source new energy capacity by deploying distributed energy resources.

After a few conversations with the team at Piclo, we realized that our two platforms would actually complement each other quite nicely. Piclo’s marketplace enables procurement and price discovery for DERs. Aristotle and WEATS provide the measurement and verification and system of record required for robust demand-side energy markets.

Community Energy

We decided to collaborate around our shared vision for Community Energy. We wondered what it would look like if utilities and regulators could require data centers to invest in local clean energy development. Could they implement a sort of “Community Portfolio Standard” in which the data center would procure a certain amount of its energy from local distributed energy resources, and receive credit for the new capacity available to the grid?

Today we are excited to release the first version of that vision in the form of a white paper that we authored together. Linked below, the paper explores how utilities can mobilize new investments into community-based energy projects so that, even as they say yes to new data center development, utilities and regulators are also protecting their customers and ratepayers from increased rates, and ensuring that jobs will be created within their communities.

Accelerated Community Energy White Paper (link)

Obviously, a white paper is just a first step. We are looking forward to rolling out this vision over the next few months with our early partners. But the choice for utilities is now clear - do you trade away your customers’ future in exchange for quick profits? Or do you ensure long-term investments in your communities? The next few months will reveal where they really stand on this question.

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1) Energy Efficiency is dead, but not for the reason you think it is

Last week, the Ad Hoc Group and Alliance to Save Energy published a report outlining how utilities could look to demand-side energy resources to solve their capacity challenges for siting new large loads. The most important takeaway from their report, aside from the obvious fact that demand-side energy resources are the cheapest, easiest and fastest to deploy in most parts of the country, is that energy efficiency isn’t actually the desired goal.

The purpose of deploying demand-side energy resources is not energy efficiency, but rather demand efficiency. In the era of cheap renewables and high load growth, reductions in energy consumption really only matter insofar as they coincide with peak demand.

The implications for the energy efficiency world are profound. First and foremost, it means that all purported savings must be calculated using real world data, and done so on an hourly basis. Any sort of deemed measures, annual reporting, or adjustments based on what other people are “already” doing are simply a non-starter in the world of demand efficiency. Second, I expect to see energy efficiency programs generally start to be phased out. The image below (thanks Claude) highlights the problem. If the problem is grid capacity, we’re only talking about certain hours in a year that need to be tackled. Otherwise, adding load is a net positive for the grid (assuming that we’re also bringing more renewables online at the same time).

Energy “efficiency” assumes that all energy savings are created equal. But if your energy savings happen at noon and mine happen at 6pm, the grid is going to value mine a lot more.

Here’s how to think about demand efficiency. Imagine that for any given grid, there’s a finite amount of capacity to deliver the power we need (roughly analogous to a water pipe). There are a bunch of fixed costs that we have to pay to maintain the grid. We pay for these fixed costs through the electricity that’s delivered through the grid (think water flowing through a water pipe). The more electricity that we can send through the existing grid and charge people for on the other end, the lower the fixed costs per unit of electricity (we all pay a little bit less for infrastructure). However, if we have to build new capacity (get larger pipes for those peak days), our fixed costs go up and we have to pay more, even if the total amount of power that goes through the new wires is the same as what came before.

To put it in other terms, every additional kilowatt (kW) of capacity that we have to add ends up costing us money. Every additional kilowatt-hour (kWh) of electricity that we serve ends up saving us money. Energy efficiency might end up costing us money if it targets the wrong time of day! But energy efficiency when the grid is otherwise constrained saves us money, because it helps prevent the need to build new capacity.

Demand efficiency maximizes the number of kWh served by the grid while minimizing the amount of kW capacity that needs to be added to serve that kWh.

2) Demand Flexibility is valuable, but not for the reason that you think it is

Naming conventions are not a strong suit of our industry. While I will defend “PPAs for VPPs” until the bitter end, ask any random stranger to explain any of the acronyms that litter our industry white papers and you’ll be left with blank stares and baffled bemusement. Over the past five years or so, in an effort to not turn off the general public, our industry has coalesced around “demand flexibility” as the way to signal the value of demand-side energy resources. Do your laundry a little later, plug your car in earlier, activate your battery when called upon, etc. This “flexibility” is meant to resemble a giant coordinated grid-balancing machine. It’s not just a virtual power plant, it’s a virtual “peaker” plant, orchestrating thousands of different devices in an effort to optimize power flows!

This type of flexibility is cute until a 50 MW data center comes to town. Then it’s irrelevant. The problem at hand is not fiddling with marginal resources, but rather identifying the main drivers of peak energy use, and finding permanent, scalable solutions to reduce load during peaks without sacrificing customer comfort or further raising bills.

Another thing to consider is that depending on what part of the country you are in, your peak may be totally different than somewhere else in the country. These peaks are determined by power resources, historical energy use patterns, industrial clusters, and more. There is no one-size-fits-all solution.

But this is precisely why demand flexibility is an important, and valuable concept. Not because of the implied temporal shifting (though this can be helpful sometimes), but because you can mix and match demand-side energy resources depending on the particular peak challenges you face. Demand-side energy resources are diverse, by their very nature, and will most often be the most widely available precisely when local grid peaks are at their most severe.

Rather than stipulating ahead of time what type of interventions are needed, a truly flexible demand-side energy resource strategy should focus on finding the peaks and sending the signal to the market for savings when the grid needs them most. Let the fact that there are diverse demand-side energy resources available be a feature, not a limitation of new demand-side energy markets. As the Ad Hoc group’s paper shows, a typical peak could be solved for with any combination of resources that are able to deliver during the required time frame.

3) Utility monopolies are bad, but not for the reason you think they are

Most Americans hate their utility. Over the past five years, favorability scores for utilities have dropped by nearly 50%. Three in four Americans believe that some sort of consumer choice for utility services would be preferable to a natural monopoly. Political candidates are riding a wave of dissatisfaction, stemming from rising rates and declining service, winning statewide elections and promising reforms. Very few economists or pundits stand up for utility monopoly rights and even those who do caveat their support with a litany of criticisms.

There are probably lots of valid points to be made, and its hard to blame people for being frustrated when their bills continue to go up, but the monopoly that we should worry more about isn’t the utility that’s selling you power; the monopoly we should pay attention to is the utility that is buying energy services from you.

The utility monopoly, in this case, is a monopsony - a single buyer rather than a single seller. They are buying energy services - everything from the solar on your roof to the managed EV charging in your garage to the energy efficiency in your attic. When they buy these services from you, they don’t have to spend money buying them elsewhere. Utilities use a tool called an Avoided Cost Calculator to determine how much they should spend on buying these services from you. You are, in essence, competing with a natural gas power plant for utility payments for your energy services.

If you choose not to accept a utility payment for your energy services, they will instead turn around and buy more or less the same service from a traditional power plant. There’s no real incentive for a utility to pay more for demand side energy resources, even if it means the grid gets cleaner, infrastructure costs go down, or consumer bills go down. You have basically zero bargaining leverage.

Imagine, though, what would happen if there were multiple buyers for your energy services! What if your rooftop solar or your battery or your attic insulation could be sold to another energy customer? If these energy customers competed for your services, the price that you could command would go up. I’m going to throw in an economics graph below to show how this works. Economists have figured out that competitive markets improve the bargaining power of sellers so that buyers can no longer capture monopoly rents from the market.

So what does this mean for demand-side energy markets? With all the drama over data centers needing to BYOC (Bring Your Own Capacity) and BYONCE (Bring Your Own New Clean Energy), perhaps it’s time to let them become buyers in the market for demand-side energy resources too. As our friends at the Ad Hoc Group pointed out (and before them Rewiring America and ACEEE), demand-side energy resources are the perfect complement to data centers. The main thing lacking is a market mechanism that allows them to efficiently procure this capacity and make a legitimate claim on it.

What we need are new open demand-side energy markets, embedded with measurement and verification, that support all types of distributed energy resources, and that come with systems of record that prevent double counting, that allow data centers and other large loads to procure these resources alongside traditional PPAs for generation. The utility still has a role to play here, of course. They need to specify the hours that matter and certify the savings calculations. They will have visibility into distribution networks that allow for better targeting of demand-side energy resources on a time and locational basis. Most importantly, utilities are custodians of economic growth and consumer welfare in our communities. Our utilities are essential participants in the well-being of our communities. They show up when the storms arrive and make sure that when the hottest and coldest days come that we all have the power we need to survive.

But it’s an all-hands-on-deck moment, where load growth threatens to overwhelm our capacity to manage it. If costs start to spiral, expect the political winds to blow in favor of complete restructuring of the utility ecosystem. If we don’t choose this moment to invest in our communities, keep costs in check, and minimize the new fossil fuel infrastructure we build, we should expect to pay the price for the next half-century or more.

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Consensus is emerging that the deployment of demand-side energy resources is going to be the primary way in which large new electrical loads are going to be integrated into the grid. Even the current administration is asking grid operators to allow for expedited review for data centers that can accommodate requests for flexibility. The reason for this is simple. There are a handful of hours on a handful of days in which the expected demand for power is going to exceed the capacity of the grid to supply it. Something has to give, and since a single data center can use the same amount of power as a million homes, the data center is the obvious choice for curtailment.

However, the amount of capacity that needs to be made available can vary significantly. It may be that just a few megawatts need to be curtailed, in which case, even though the data center may be the largest single contributor to demand, the best available flexible resources may be found outside of the data center. A fleet of distributed batteries, controllable EV charging loads, or, on a more permanent basis, more efficient air-conditioners, are all capable of delivering the required capacity savings to the grid. Because there are a variety of resources that might be tapped, grid planners, DER developers, and policymakers are excited about the future of distributed capacity markets.

Unfortunately, current market solutions are proving inadequate. Wholesale capacity markets discriminate against DERs, and in large swaths of the country utilities are vertically integrated, meaning that they control both generation and distribution. Demand response programs are notorious for paying pennies on the dollar and even the most successful demand response programs, like DSGS in California, suffer from the unpredictability of political winds.

It’s time for a new approach to demand side energy markets. We need a system that prioritizes lower rates, more resilience, and a cleaner grid. To date, we’ve seen no evidence that the current system will yield any of these outcomes - in fact, utility bills are rising, the grid seems ever more fragile, and carbon emissions are back on the rise. We can do better.

PPAs for VPPs

Today, we’re excited to announce a first-of-its-kind program that will catalyze investments into distributed clean energy, bring lower electricity rates for all customers, and build resilience in our communities. We call it Repowering California.

While this initiative is designed to work within California’s unique system (more on this below), it can be deployed anywhere in the country and in most places on the planet. We are truly on the verge of a transcendent ecosystem of distributed energy resources enabling the grid to absorb the largest influx of new demand since the 1950s.

The mechanism is simple and takes most of its inspiration from the procurement of renewable energy on the supply side. We imagine a power purchase agreement (PPA) in which a large load, like a datacenter, can directly procure “negawatts” or “flexiwatts” from distributed energy resources on its same grid. In aggregate, this procurement would be thought of as a distributed capacity contract, or more commonly a “virtual power plant” (VPP). Load-shifting heat pumps, energy efficiency projects, and controlled devices will be deployed so that energy demand goes down when spare capacity on the grid is needed. The PPA contract will pay these resources for their ability to reduce demand during critical peak hours.

The goal is to allow California to double the utilization rate of its electrical grid while reducing rates on consumers in the process. This happens by more efficiently using existing grid resources (more energy consumption in the middle of the day when solar is abundant and at night when plenty of wind and hydro can be utilized) and using the additional revenue from serving new loads more efficiently to fund infrastructure investments. We want our utilities to invest in the infrastructure needed to power the electrified economy, but to do so without raising the costs of electrification for consumers. This is possible if we use our grids more efficiently.

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How the Program Works

Repowering California invites large energy users, utilities, and DER developers to work together to provide the capacity needed to serve the entire state’s energy needs at lower cost with an eye towards resilience in the face of climate change and other challenges. It builds on California’s existing demand-side energy markets - collectively known as Market Access Programs - to recreate the same fundamental economic principles that allowed large-scale renewable projects to flourish in this state decades ago. And it uses WattCarbon’s unique data infrastructure - Aristotle and WEATS - to make sure that the savings are real and that they aren’t being double counted.

Here are the key pieces:

• Corporate PPAs for VPPs:

  • A PPA for a VPP allows a large energy user to provide financial support to the deployment of distributed energy resources. The animating principle of a PPA is the advance market commitment, which allows projects to be financed at lower interest rates. In essence, the large energy user is committing to buy the savings at a guaranteed rate. This guarantee allows the project developer to secure financing at favorable rates and lowers costs for consumers.

• Market Access Program Integration:

  • California currently operates a set of Market Access Programs (MAPs) in which energy efficiency savings are paid out to market aggregators on the basis of the “system benefit” value of energy efficiency projects - effectively the cost of generation, transmission, distribution, and overhead. These programs provide the critical connective tissue between corporate PPAs and the capacity credits that they need to be able to claim. Each one of the state’s investor-owned utilities plus several of its CCAs and RENs runs a Market Access Program. As a result, every project enrolled first gets credited by the state for its system benefit and certified by the utility for its capacity savings. This leaves the PPA offtaker with certified capacity credits that can be used as part of an agreement to site a new load or expand an existing service. In short, the Market Access Programs provide a framework where private companies can fund demand flexibility projects, submit them to their local utility, and have the capacity savings certified so that they can ensure no net new grid impacts.

• Customer Dividends:

  • Consumers are the ultimate winners here. Instead of just receiving the single stream of payments from the Market Access Programs, now consumers have the opportunity to monetize their investment through a PPA agreement as well. In some cases, the payment for the capacity might exceed the cost of the equipment in the first place! WattCarbon has been trialing this concept with its partners for the past few months. For the first time, these contractors and DER developers have access to automated measurement and verification of all of their projects. With access to a new revenue stream, projects that were formerly out of reach, suddenly become possible (see, e.g., link).

Program Vision & Next Steps

We are now opening up Repowering California to additional corporate partners, utilities, DER developers, and contractors who want to be part of the reimagining of our state’s energy future. Of course we couldn’t have gotten this far without our early partners like Harvest, QuitCarbon, Rewiring America, Northern Pacific Power, Powderwatts, Electrify My Home, Atrium, or the work done by so many to bring the market access programs to life. California is blessed with abundant natural resources that give us access to solar, geothermal, hydro and other forms of clean energy. But we also have aging infrastructure and fire risks that are being paid for through higher electricity bills. If our bills continue to rise, our economy will falter and our state will suffer. Giving large energy users the option to procure distributed capacity creates a financial incentive to better utilize existing grid resources, effectively reducing the burden on consumers. Plus, giving consumers another avenue for monetizing the investments they are making in their homes and businesses blows tailwinds into the state’s economy and unlocks the energy we need to stay competitive.

If you are a contractor or DER developer, learn more about how to participate at https://dividend.wattcarbon.com.

If you are interested in setting up a PPA for a VPP, please reach out directly.

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If tapping into DERs happens at scale, the focus will inevitably shift to markets. To date, markets for demand-side energy resources have mostly been limited to 1) ESCO agreements, in which a company offers to install new energy saving equipment in exchange for a share of the bill savings; 2) utility or wholesale energy market programs where a third party aggregator delivers resources to a single buyer (in economics, this is called a monopsony).

In a world in which data centers and other large loads are buying demand flexibility capacity from other DERs, a new wrinkle is introduced to existing markets. Suddenly you have multiple buyers in the market at the same time, and you may have the same DER sell to more than one buyer at a time. For example, if I have a battery that can dispatch daily, I may have one buyer that needs my capacity twice a year, and other that needs it twice a month, and yet another that needs it twice a week. I should be able to sell to each of them, but not at the same time.

Emerging DER markets need standardized Measurement and Verification (M&V) - we need to know how much was delivered; and a System of Record - we need to make sure that the same capacity isn’t being sold to multiple different offtakers at the same time. Standardized M&V means that we are measuring DERs using a consistent, transparent set of methodologies; a system of record means that we need to assign a unique serial number to each watt-hour that is transacted. The serial number must match up to the source and to the M&V. This is the settlement layer that DERs need to be able to scale to meet the moment.

Our Big Product Announcement

Since performance-based contracts were introduced in the late 1980s, M&V has always been tacked on as an engineering exercise that was performed periodically to validate the models used to estimate potential savings. This months-long exercise required data collection, an agreement on methodology, various bespoke adjustments, and if the numbers didn’t add up, it would often end up in a lawsuit. Companies like Recurve figured out how to perform M&V at scale, but being able to perform M&V on-demand has heretofore remained impossible.

When we launched Aristotle AI back in April, we got close to this goal. And over the past few months, as we’ve refined the platform, it has become more and more a reality.

Here’s why it’s important to be able to do M&V on demand. If you are a DER developer, the thing that’s most important for you is the performance of your asset. And if you are trying to close a customer, get financing, or sell your capacity to an offtaker, there is nothing more powerful than being able to point to independent, revenue-grade M&V that shows exactly what your assets are delivering.

The challenge - to date - has been a chicken and egg problem. Historically, this M&V has been expensive. Again, it’s been something that engineers do, and never at scale. If you’ve deployed a hundred DERs, you might end up spending tens of thousands of dollars on third party audits before you are able to sign an offtake agreement. (As an aside, this is how the carbon offset world works and part of the reason why it is so broken).

Because Aristotle AI is now able to provide end-to-end, automated M&V for any DER asset, we are extremely excited to announce that we can offer FREE M&V for any company that needs to prove the value of their energy resources.

We believe that the motive force of decarbonization will come from unlocking markets for DERs. As the “assayers” of the energy transition, WattCarbon is uniquely positioned to enable the thousands of companies who are creating, financing, and deploying the energy assets that will be foundational to the energy transition. Providing them with a way to prove their worth will be the most impactful thing we can do as a company.

If you give away your M&V for free, how will you make money?

I know what you’re thinking, and it’s a good question. We lay out the answer on our pricing page, but to save you a click, the short answer is that many of our customers use our API outputs in their own tools and others want custom reports based on the savings results. Additionally, we provide a settlement-grade Registry (WEATS) that’s used for transactions. In our view, it’s fine if all you want to do is show off your impacts. Once you want to use that data for official purposes, it’s seamless to make the transition.

Most companies take inspiration from previous innovators (aka, the “X for X”). We don’t see a direct antecedent to WattCarbon in that regard, but there is a similarity between the rise of DERs and the way that e-commerce emerged in the early 2010s. Where once PayPal and Braintree stood as innovators, Stripe allowed e-commerce to be programmed directly into an app. Today, ESCOs and market aggregators plug DERs into legacy systems the same way that PayPal and Braintree did for early e-commerce companies. Automated M&V changes the value proposition of DERs the way that programmable financial infrastructure changed e-commerce. You can now directly program your DER to enable it with revenue-grade M&V and a settlement-grade system of record. The potential is limitless. This is an exciting time and WattCarbon is looking forward to working with those companies on the vanguard of the energy transition.

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To begin, it’s worth looking back a little farther at the WattCarbon trajectory over the past few years. We started out trying to figure out how to quantify emissions impacts from demand-side energy resources. This drew on our previous experience at Recurve building measurement and verification tools for utility programs. We knew that hourly attribution was really important and wanted to bring together supply-side carbon accounting with demand-side energy flexibility.

We ran into headwinds from multiple directions. To begin with, the carbon-offset industrial complex was more formidable than expected. The overwhelming opposition to considering decarbonization (it has to be a carbon “removal”) as a plausible pathway to net zero and the variety of arbitrary and self-serving obstacles placed in the way of companies trying to get recognized for their impacts ruled out any headway on that front.

As an alternative approach, we tried to get organizations like the WRI (author of the GHG Protocols) and newer entities like EnergyTag to recognize demand flexibility as a clean energy resource. But these groups insisted that only energy generation would qualify as a Scope 2 offsetting energy credit. The debates between 24/7 energy matching and “emissionality” camps have grown increasingly disconnected from the realities on the ground.

While we were proud of our work to launch the first demand-side energy EAC market last year - and even today you can see in the WEATS Registry that these projects are yielding carbon reductions - we took a step back in December to build out our measurement and verification stack. In April, we launched Aristotle AI, our next-generation M&V platform that provided automated, hourly demand-side energy savings calculations.

We haven’t given up on decarbonization - in fact, it’s more of an imperative than ever before. But the reality is that the constructs that we have used in the past - carbon offsets, net-zero, environmental attributes, etc., - are now much less influential in driving investments into clean energy resources.

AI and Energy

The dominant theme of this year’s NYC Climate Week was AI and Energy. The consensus seems to be that around 100 GW of new data center load will arrive on our grids over the next 5-10 years. “Load growth” has breathed new life into the energy sector and is intersecting with resilience and lingering (if somewhat tenuous) commitments to lowering carbon emissions from the sector.

Secular load growth is manageable for utilities - but the ferocity of new interconnection requests has induced panic amongst utility executives facing the twin challenges of rising prices and cascading infrastructure failures. Something has to give.

Enter demand flexibility. The story goes something like this. Our current energy systems are sized to be able to provide power on the hottest day of the year. Adding a data center pushes the system beyond its limits (requiring both new generation and larger wires). Before a data center interconnection agreement can be approved, new generation must be constructed and upgrades to the system to carry the power to the data center must be completed. If these costs are passed along to ratepayers, bills will continue to rise.

The thing is, outside of really hot days, the current grid is actually underutilized. We actually have plenty of spare capacity most of the time. What we really need is to figure out how to shift current peak load to non-peak times of the day.

As it turns out, there are plenty of solutions lining up to provide this flexibility capacity. Batteries, EV chargers, smart thermostats, Bitcoin miners, and many other technologies are available to perform this service. And for those times of the year where demand is consistently high, there are energy efficiency measures like replacing old space heaters with modern heat pumps that stand to permanently reduce load during these peak periods.

Interestingly, the challenge of accounting for a demand-side energy resource is no different if you are trying to determine its carbon impact versus its energy impact. The same measurement and verification and system of record is required for both. Perhaps ironically, the very infrastructure that we had built at WattCarbon to bring carbon transparency to demand-side energy resources is also core to unlocking the value of these same resources to solve the problem of load growth.

Why this is all exciting

Over the past three months, we have been deploying our Aristotle AI platform across the entire energy sector for the purposes of providing measurement and verification for demand flexibility and energy efficiency. In addition to previously announced customers like Prologis and Rewiring America (check out their new report here), we are excited to be working with companies like Siemens, R-Zero, Peachtree Infrastructure, Elevate Energy, PowderWatts, Northern Pacific Power, and more to provide settlement-grade M&V via our platform.

Even more exciting is work that with organizations like NYSERDA and BayREN to develop transactional demand-side energy markets that is now extending across utilities and hyperscalers to unlock what we have been calling PPAs for VPPs. Once we make demand-side energy resources procurable - again, the basic construct is the same, the EAC just quantifies capacity in this case in addition to carbon - we will unlock what will likely become the largest energy market on the planet.

So as we take stock of NYC climate week, a couple of things are clearer than ever before. First, the pace and scope of decarbonization is increasing, not because of the moral imperative, but rather because of the economic imperative. The value of clean energy will now be unlocked through conventional markets, rather than driven by the value of the environmental attribute itself. That is, we will see the energy transaction be the primary driver of value with the environmental benefit as a secondary, though still relevant attribute of the transaction, rather than the other way around.

Second, demand-side energy markets are still mostly organized around the prerogatives of regulatory bodies, and need to substantially evolve if they are going to rise to the challenge. Without load growth, energy efficiency and demand response are performative. But in an era where falling short means triggering a blackout, “good enough” can no longer be good enough. Settlement-grade M&V is required for markets and systems of record like WEATS will be critical to ensuring that no double counting happens.

Collectively, these shifts mean that these are exciting times for us at WattCarbon. It’s been quite the journey from the earliest days of figuring out how to think about hourly carbon accounting as it relates to demand response to the realization that the underlying technology that we have built will be instrumental to the growth of a giant new clean energy market.

I’ll conclude here by noting that amidst everything else last week, ACEEE released a great report on how demand side energy resources can be part of a decarbonization strategy. Check it out and get in touch with us if this is something you’ve been thinking about as well.

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1. Update from Travis Sikes on the OpenDSM (OpenEEmeter). Last month, a new version was released that will be incorporated by reference into the OpenEAC methodology.

2. A new methodology for calculating the savings from controls that turn equipment on and off.

3. A deep dive with Harvest on the methodology for calculating savings from grid-aware heat pumps.

4. A discussion on “automated additionality” for carbon credit certification.

Because we had trouble with Zoom last time, the meeting has been upgraded to a Google Meet link. Join here: https://meet.google.com/sjw-hkbj-bwf

Thank you!

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But the team buried themselves to get Aristotle released back in April, and we’ve been fortunate to work with really great partners who believe in what we’re building - companies like Prologis and organizations like Rewiring America, and friends from throughout the industry who have stepped up in ways that will forever be part of our story. We’re able to continue to imagine a world in which demand-side energy markets scale to unlock the $10 trillion in investment we need to decarbonize the built environment. Today we are able to keep going on this journey because Matthew Stotts and Jahed Momand at Cerulean Ventures, Josh Cohen at City Light Capital, Tom O’Keefe, and Priscilla Tyler and the rest of the team at True Ventures also believe in this vision and were willing to back us to support our work.

Those of you close to the startup world know that this is a challenging time to raise capital. I’m particularly grateful for all of the intros, all of the meetings, and all of the advice that we have received over the past several months as we’ve tried to navigate these unsettled waters. Now it’s time to repay the favors by rolling up our sleeves and getting back to work. I want to launch some VPPAs for VPPs, see more carbon credits sold for installing heat pumps, automate M&V for new ESCO business models, deploy Green Equity Finance to replace all of the expiring tax credits, and help all of the other companies pushing on the energy transition find traction to accomplish their own goals. The stakes have never been higher and the alternatives never more stark. Let’s go!

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San Francisco, CA – July 17, 2025 – WattCarbon, the industry-leading measurement and verification (M&V) platform for distributed energy resources, today announced the successful completion of a Seed Extension round featuring new investors Cerulean Ventures, City Light Capital, and Tom O'Keefe, alongside existing investor True Ventures. This additional funding will accelerate WattCarbon's mission to become the authoritative "weights and measures" system for the energy transition, providing the transparent, auditable measurement infrastructure that unlocks new markets and maximizes value for clean energy investments.

Transforming Energy Markets Through Rigorous Measurement

As the energy sector rapidly transitions toward distributed clean resources, the lack of standardized, transparent measurement has created a critical market gap. WattCarbon's AI-powered platform, Aristotle, addresses this challenge by serving as the definitive "system of record" for hourly energy attributes, tracking every kilowatt-hour saved and every gram of carbon avoided with unprecedented granularity and transparency.

Unlocking New Value Through Precision Measurement

WattCarbon's measurement and verification capabilities are creating entirely new markets and value streams for distributed energy resources:

Automated Measurement and Verification (M&V): Processes that have historically taken months are now completed in seconds, as WattCarbon combines state-of-the-art data pipeline infrastructure with automated algorithms for calculating hourly energy savings. Any type of energy asset - from whole buildings, to EV chargers, to solar and batteries, are tracked in real-time so that they can be leveraged individually and collectively as functional energy assets.

Energy Attribute Certificates (EACs): WattCarbon's platform generates hourly EACs for every asset, creating tradeable certificates that represent verified energy savings and carbon reductions. This granular approach enables building owners, utilities, and corporations to monetize their decarbonization efforts in ways previously impossible.

Portfolio-Level Intelligence: The platform consolidates data from entire building portfolios into a single, intuitive interface, enabling sophisticated analytics that turn raw information into actionable insights. Users can identify top-performing assets, pinpoint optimization opportunities, and benchmark performance across their entire portfolio.

Strategic Investors Bring Deep Market Expertise

The addition of Cerulean Ventures, City Light Capital, and Tom O'Keefe brings significant strategic value beyond capital:

Cerulean Ventures

Cerulean invests at the intersection of AI, data, and climate, backing founders who create exponential solutions for nature and the built environment.

City Light Capital

City Light Capital is an early-stage impact VC firm. They partner with experienced teams building category-defining solutions in the areas of education, care, and climate where more revenue means more impact at scale, every time..

Tom O’Keefe

Tom O’Keefe is a climate-tech angel investor and policy organizer focused on accelerating capital flows toward decarbonization and climate resilience.

“WattCarbon is solving one of the most fundamental challenges in the clean energy transition – how do we accurately measure and verify the impact of distributed resources at scale," said Matthew Stotts from Cerulean Ventures. "Their platform creates the measurement infrastructure necessary to unlock trillions of dollars in clean energy investments, and drive the energy transition at Earth-scale.”

Addressing Critical Market Needs

The energy transition is creating unprecedented demand for accurate, real-time measurement of distributed resources. Traditional measurement approaches cannot keep pace with the complexity and scale of modern clean energy deployments. WattCarbon's platform addresses several critical market needs:

Utility Scale Grid Management: Providing utilities with real-time visibility into distributed energy resource performance, enabling better forecasting, planning, and integration of customer-owned assets.

Corporate Decarbonization: Helping enterprises develop innovative strategies for accelerating the energy transition and supporting ambitious net-zero commitments with credible data.

Program Optimization: Enabling program administrators to identify high-performing interventions and optimize resource allocation based on verified outcomes rather than projections.

Investment Validation: Providing investors and lenders with transparent, auditable data on clean energy project performance, reducing investment risk and enabling new financing models.

Platform Capabilities Drive Market Transformation

WattCarbon's Aristotle platform represents a fundamental advancement in measurement and verification technology:

• Automated M&V: AI-powered engine tracks entire portfolios of buildings and energy assets automatically in real-time

• Transparent Methodologies: All calculations follow published, peer-reviewed methodologies with full audit trails

• Granular Integration: Ingests data from smart meters, IoT devices, and building management systems at device-level granularity

• Real-Time Analytics: Provides immediate feedback to enhance project optimization opportunities

• Regulatory Compliance: Designed to meet evolving regulatory requirements with defensible, approved methods

Looking Forward: Scaling the Infrastructure for the Energy Transition

With this additional funding, WattCarbon will accelerate platform development and expand its customer base across utilities, enterprises, and program administrators. The company is positioned to become the standard measurement infrastructure that enables the next phase of clean energy market evolution.

"We're not just measuring the energy transition – we're providing the measurement infrastructure that makes the energy transition possible," said Young. "Every successful market transformation requires trusted measurement standards. We're building that foundation for the clean energy economy."

The funding will support continued platform development, expanded market penetration, and the advancement of open-source measurement methodologies through the OpenEAC Alliance. WattCarbon's platform is the only solution capable of measuring and reporting savings hourly across both energy and environmental attributes, positioning the company at the forefront of the energy transition.

About WattCarbon

WattCarbon is the first fully automated M&V platform for demand-side energy resources. Its Aristotle™ engine ingests utility, sub-meter, and device telemetry; applies statistically robust baselines; and delivers auditable savings and carbon-impact calculations in near-real time. For more information, visit www.wattcarbon.com.

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What’s left are local, distributed efforts. It won’t be easy to decarbonize this way. Resources are limited and opposition is real. But as I explain in the podcast, these are the foundational principles of the United States, inherited from thinkers like Adam Smith, Max Weber, Alexander Hamilton, James Madison, and others. National innovation has always followed local innovation in the U.S. We should continue to advocate for national policies that accelerate the energy transition away from fossil fuels. But in the meantime, we should also be rolling up our sleeves and working in our own backyards on positive change we can realize today.

https://modoenergy.com/research/channels/transmission?wchannelid=vtx0dp52da&wmediaid=o3sfcnvi7f

• Virtual Power Plants

• Automated M&V

• Attribute-based Accounting

• Competitive Demand-Side Energy Markets

Below I’ll preview a few themes related to each of these topics.

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Understanding Virtual Power Plants

"Virtual power plant" is a term frequently used in energy circles, though interpretations vary. Broadly, a VPP refers to a coordinated network of demand-side energy assets that collectively deliver value to the energy grid. This value can manifest as generation (e.g., rooftop solar), capacity (e.g., load shifting), emission reductions (e.g., heat pumps), or really any attribute of energy that the market finds valuable.

The core value proposition of a VPP comes from the fact that it changes previous energy consumption patterns. Rooftop solar reduces demand in the middle of the day. Load shifting can increase or decrease demand in different parts of the day. An electrification project changes the fuel that is used to heat a building. In other words, a VPP's value is found in the difference that it creates between "old-business-as-usual" and "new-business-as-usual."

The Role of Automated Measurement and Verification (M&V)

Determining if a VPP genuinely delivers its intended value, whether in the form of carbon reductions, increased capacity, or added generation, requires rigorous M&V. Without clearly establishing a business-as-usual scenario, accurately measuring impact is impossible, akin to dividing without knowing the denominator.

However, defining the business-as-usual scenario can be complex. Heating and air-conditioning loads heavily depend on weather; EV charging patterns fluctuate significantly; even permanent load shifts like solar panels will vary based on the type of building and interactive effects with residents. Automated, real-time M&V, as introduced with Aristotle, makes addressing these complexities feasible.

Beyond Price-Volume Thinking

When discussing VPP value, it’s helpful to move beyond the traditional price-times-volume metric (e.g., dollars per kilowatt-hour). Unlike conventional demand-side energy accounting, a VPP’s value isn’t purely about the volume of energy delivered (or saved); it also hinges on key attributes like timing, location, and the type of energy it displaces. For instance, distributed solar is more valuable in areas where there is lower overall solar penetration, while flexible loads are most valuable when the grid faces supply-demand imbalances. Similarly, a heat pump’s emission reduction value increases as as the electricity it consumes gets cleaner.

VPPs can be thought of as the energy system’s equivalent to accrual accounting for businesses, offering a sophisticated approach to measure and manage demand-side transactions, while factoring in time and temporality.

Competitive Demand-Side Energy Markets

The rapid growth of renewables in Texas, facilitated by ERCOT's open-market policies, provides inspiration for a big idea we have been kicking around: applying Texas-style markets to demand-side energy resources. Imagine if utilities set price signals for VPPs and allowed any demand-side resource aggregation to compete freely. Call it FERC 2222 for the distribution grid. Such an approach could spur innovation, significantly increase grid capacity, reduce consumer costs, and accelerate electrification needed to decarbonize the economy.

Historically, utilities have run programs that trace back to the era of decoupling, when regulators forced utilities to fund energy efficiency. These programs typically offer customers one-time incentives for adopting certain technologies or behaviors. As we know, these command-and-control approaches often discourage long-term investments, leading to inefficiencies, boom-and-bust cycles, and susceptibility to gaming.

By contrast, demand-side markets would operate by sending clear price signals rather than prescribing specific solutions. Utilities could sign sleeved Virtual Power Purchase Agreements (VPPAs) for VPP aggregations on behalf of large customers (like data centers). This more open approach, aimed at the attributes that matter, would encourage market participants to develop organic solutions that incorporate new technology and meet the needs of the local community. Rather than mandating prescribed (often outdated) technology, these markets would incentivize private investment by rewarding good outcomes directly.

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Hopefully these topics seem interesting. Feel free to leave comments below on any of these issues. I’m looking forward to having the conversation as we work through these weighty concepts. More to come soon!

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The provisional agenda is as follows:

1) Welcome and review of existing resources

• www.openeac.org

• methods.openeac.org

• https://github.com/wattcarbon/open-eac-alliance

2) Quarterly update

• New approval process

• Review of existing methodologies

• Deep dive on electrification methodology

• Utility meter data

• Device data

• Building/project only data

3) Q3 roadmap

• Demand response/flexibility methods

• Battery (FIFO)

• Generator fuel switching

4) Discussion

Looking forward to seeing everyone!

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Made possible by an advanced purchase of Energy Attribute Certificates (EACs), representing certified third-party emissions reductions, this partnership is enabling Harvest to bring advanced heat pump technology to underserved communities and shows how smart, strategic funding can accelerate decarbonization efforts, creating tangible benefits for communities and the climate.

Why Heat Pumps Need an Upgrade

Heat pumps have rightfully been celebrated as heroes in building decarbonization. But there's been a challenge: peak heating times—typically morning and evening—coincide with the periods when electricity is most expensive and carbon-intensive.

Harvest’s approach tackles this head-on by pairing hyper-efficient heat pumps with advanced thermal storage. Imagine capturing clean, affordable solar energy midday, storing that energy effectively in a thermal battery, and then releasing it precisely when it’s needed most. That’s what Harvest’s innovative Smart Thermal Battery™ achieves, combining intelligent software, thermal storage, and efficient heat pump technology.

Smarter Storage, Bigger Impact

Harvest doesn't just store energy—it selects the cleanest and cheapest energy available, ensuring maximum carbon reduction. The results speak for themselves: up to 30% energy bill reductions, 90% emissions reductions compared to traditional gas furnaces and an impressive 40% reduction compared to conventional heat pumps.

Harvest’s first collaboration with WattCarbon is centered in a multifamily affordable housing complex in Truckee, California. The Tahoe-adjacent town regularly experiences subzero temperatures. Harvest’s implementation in this geography is a testament to the system’s ability to perform even in cold climates.

But it's not just about cleaner fuel; it's about supporting a flexible, resilient grid. As California’s grid reaches over 54% renewable generation, Harvest’s technology ensures homes tap into abundant solar power, enhancing grid stability and paving the way for broader electrification without additional strain.

Investing Ahead for Maximum Impact

What truly sets this collaboration apart is how companies have stepped up to purchase the Energy Attribute Certificates (EACs) from Harvest ahead of time, ensuring their investment makes a real and tangible difference. By providing critical upfront funding, these forward-thinking companies are closing essential financing gaps, enabling the installation of Harvest’s decarbonization projects in buildings where gas would otherwise be the default choice, and directly driving measurable, verified carbon reductions.

For the initial set of projects, True Ventures, an investor in early stage startups, and the creative force behind the Climate Compass Report and the Climate Action Guide, purchased the EACs from Harvest’s projects in advance so that the money spent on EACs would unlock new projects within the Truckee complex that otherwise wouldn’t have had the funding necessary to get done.

Accurate, Transparent Carbon Tracking

WattCarbon is at the heart of verifying and tracking these reductions. Leveraging Aristotle AI, its robust automated Measurement and Verification (M&V) platform, WattCarbon meticulously tracks every gram of carbon saved through Harvest's installations. This precise accounting ensures that every carbon reduction claim is accurate, verifiable, and impactful.

"Electrification is crucial, but it must be smart electrification," says Jane Melia, co-founder of Harvest. "Our collaboration with WattCarbon allows organizations to directly support high-impact decarbonization projects, ensuring their investments yield real, measurable emissions reductions."

WattCarbon CEO McGee Young emphasizes the value of this innovative model: “Traditional methods of purchasing clean energy credits often fail to achieve real-world impact. Harvest’s technology, paired with our transparent tracking and upfront investments from committed companies, provides a new, effective way to invest in genuine decarbonization.”

A New Era of Climate Action

Harvest and WattCarbon’s partnership illustrates a powerful shift toward smarter, more accountable decarbonization strategies. By aligning cutting-edge thermal storage technology with precise carbon accounting and forward-looking investment models, we’re unlocking a scalable solution that makes buildings cleaner, electricity smarter, and our climate future brighter.

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