Post 5 of 10: The Potential Structural Transformation of the U.S. Electric Utility Industry
͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Forwarded this email? Subscribe here for more
I’m Bullish On DERs. I’m Bearish On the Infrastructure Around Them.
Distributed Grid
Mar 18
READ IN APP
There are now more than five million distributed solar installations in the United States. Millions more EV chargers, residential batteries, and smart thermostats are being added each year. The flexibility potential is real — I’ve seen it first hand.
But the layers connecting those resources to the grid are broken, in ways that stay largely invisible to the people building the resources and the customers funding them.
The last post documented why the distribution grid is underutilized and overbuilt, and why cost-of-service regulation actively rewards that outcome. This post is about the assets sitting on that grid that could change the economics of it, and why they are not yet delivering that value. The barriers are partly regulatory, partly technical, and partly a product of a utility business model that has limited financial interest in fixing any of them.
The Market Has Already Started Building
The evidence that distributed flexibility has real commercial value is already in plain sight.
Companies like Base Power and David Energy have built operating businesses around it. Base Power installs large residential batteries, 25 to 50 kWh per home, at near-zero customer cost, then dispatches that fleet against wholesale price signals in the Texas market. The company has raised over $1.3 billion in institutional capital and by early 2026 had signed agreements with multiple Texas electric cooperatives to deliver distributed battery capacity at utility scale. David Energy operates as a software-enabled retail electricity provider in New York, New Jersey, Massachusetts, and Texas, integrating with customer thermostats, batteries, and EV chargers and automatically co-optimizing demand response and capacity tag management against real-time wholesale prices.
These are real businesses with paying customers, not pilot programs. The model works where the market signals are.
And that last sentence is the key distinction.
Both companies are optimizing for wholesale and capacity market signals. Not because distribution signals are less valuable. Because distribution signals do not exist in a form that software can act on.
Where there are price signals, the assets will find a way to support. And much of our grid is locked without any signals, instead this information is monopolized to justify building more copper wire and steel.
What the Market Cannot See
Wholesale markets publish prices. Real-time locational marginal prices are transparent, liquid, standardized, and machine-readable. DER operators build software around them because the rules are clear and the compensation is reliable.
Distribution congestion is different. Thermal constraints on feeders and transformers are real, and they bind regularly. They are rarely measured in real time, never priced, and almost never shared with third parties. When a feeder approaches its thermal limit, the utility’s response is to build a business case for capital investment, not to pay a flexible resource down the street on that feeder to discharge for four hours.
The customer with a battery one block from a congested transformer has no idea the transformer is stressed. Neither does the software controlling the battery. There is no market signal to respond to because no one has built the market.
Post 4 documented this from the infrastructure investment side. The point here is what it costs at the DER level. A resource that cannot see a localized constraint cannot respond to it. And a market that does not price local relief cannot fund the assets and software capable of providing it. So the distribution layer stays chronically underserved, and assets that could be delivering locational value instead sit idle during the hours they would matter most.
This flexibility competes with justifying new ratebase, so we likely won’t see flexibility solve these problems at scale until COSR is solved. (Post 1)
The Data Problem, and Why It Is An Outcome Of Cost of Service Regulation (COSR)
There is a related but distinct problem that deserves its own section. Utilities hold enormous amounts of data that third-party DER operators would need to deliver locational grid services: customer interval usage, load profiles, asset locations mapped to specific feeders and phases, real-time hosting capacity. In most states there is no obligation to share any of it in a usable form or on a usable timeline. Aggregators are optimizing blind, not just because locational price signals are absent, but because the customer-level distribution data that might substitute for those signals is locked inside utility systems.
This is not primarily a privacy problem or a cybersecurity problem, though utilities invoke both. The deeper reason is structural.
Under cost-of-service regulation, the utility has no financial interest in enabling third-party participation on its network. Third-party DER aggregators, if successful, reduce the need for utility-owned infrastructure. They defer capital projects. They compete with utility-owned demand response programs. Every dollar of value a third party captures on the distribution network is a dollar that did not justify a rate-based investment. When utilities are financially rewarded for inputs (rate base) and not outcomes (lowering costs while increasing reliability) the current model treats third-party DER participation as a threat, not a complement.
When utilities do invest in data infrastructure under COSR, it is because the investment goes into rate base and earns a regulated return. The question of whether the resulting data is useful to third parties is secondary. A utility can spend tens of millions on an AMI rollout or a customer data platform, recover the full cost from ratepayers, earn a return on it for 40 years, and still not produce data in a format that aggregators can act on. The expenditure is justified by the investment, not by the outcome.
Under a performance-based model tied to reliability at lower costs, this calculus flips completely. One of the cheapest ways a utility creates those outcomes would be accelerating distributed resources into grid operations. Transparent hosting capacity maps, real-time feeder loading, customer usage profiles available to consenting aggregators: these become inputs to the utility’s own revenue, not threats to it. The data sharing problem under PBR largely solves itself because the utility’s interests and the aggregator’s interests (and most importantly, customer interests) finally point in the same direction. Utilities will want to radically accelerate into modern technology platforms, not because of the money they make by kicking off the project (ratebase, Totex, etc), but because of the actual outcomes that digital modernization unlocks.
That shift is about 180 degrees from where the industry sits today. And until then third parties will continue to struggle with getting clean, comprehensive, and timely data sets from utilities because it’s just not in their financial interest.
The DER Hardware Gap
Even where wholesale signals exist, the hardware and software to act on them is immature.
Texas’s ADER program is the clearest evidence. ADER is designed to let aggregated distributed resources participate in ERCOT’s wholesale market as a single dispatchable unit. The technical requirements are serious: real-time telemetry at two-second intervals, device-level validation within ±10%, 5-minute basepoints, and qualification tests confirming the aggregation can actually deliver the energy and reserves it commits. These are standards closer to what is expected of a conventional power plant than of a home appliance.
The pilot launched in 2022 with an 80 MW cap and spent its first two years falling well short of it. By late 2024, only Tesla Electric and Bandera Electric Cooperative had qualified, accounting for roughly 15 MW. The barrier was not customer interest, the barrier was technical.
The telemetry pipelines, control latency, and device-level confirmation protocols ERCOT requires did not exist at scale in the residential device ecosystem. Base Power qualified in summer 2025, rapidly hit the per-participant cap, and pushed for expansion. ERCOT raised the system-wide cap twice -- first to 160 MW, then to 200 MW -- in the second half of 2025 alone. By December, seven ADERs were participating. The program went from near-stagnation to rapid expansion in six months, driven almost entirely by one company that built its hardware to grid-grade standards from the start.
Author ’s note: I do agree that a 5 kWh residential battery should not face identical technical standards as an 800 MW thermal plant. But the OEM and aggregator community needs to engage seriously with grid operators on what workable standards actually look like, and utilities need to come to that conversation in good faith rather than using the absence of agreed standards as a quiet veto. A dynamic grid is operated in real time. Behind-the-meter assets that want to be dispatched as grid resources need real investment by OEMs and a credible pathway to perform as real-time grid assets.
Consumer-Grade Devices on a Grid-Grade Network
The thermostat and battery APIs that most aggregators work with today were designed for consumer convenience, not grid dispatch. That is not a criticism of the manufacturers. They built for the market that existed. The problem is that consumer requirements and grid requirements have diverged, and the gap has not closed.
A device API that is usable for real grid services has a few basic properties:
Latency: It can operate at low enough latency to be meaningful within a dispatch window
Validation: It confirms whether a command was executed, in near real time, because a resource that cannot confirm dispatch cannot be reliably bid
Specificity: It can reach individual assets, not just cohorts, because a grid constraint is locational and the relevant device might be one specific battery on one specific phase. Further, each customer has individual preferences and devices need to be co-optimized for each individual customer preference and how it interacts with the grid’s needs.
Most residential thermostat and battery APIs do not meet this bar. The architecture reflects how demand response programs were designed decades ago: the utility sends a signal to a cloud platform, the platform issues a setpoint adjustment, and a large block of load is nominally reduced. That was adequate when demand response meant pushing a single button to shave 50 MW across a territory. (By the way, this is the most consumer UNFRIENDLY way of creating flexibility). The programs were never redesigned because the utilities running them never needed anything more sophisticated. Nor did they care about the customer experience. The APIs were never upgraded because there was no market pressure to do so.
The result is weak single-device visibility, delayed or absent confirmation of response, and in some major platforms, no individual device control at all. This matters disproportionately for HVAC because cooling loads are among the most valuable flexible resources on the grid during peak demand events. The grid is often in stress because of excessive HVAC usage.
There is also a structural problem that goes beyond API design. Most major OEMs process device commands through centralized cloud infrastructure. That infrastructure is sized for normal operating conditions. Flexibility events, by definition, are not normal operating conditions. The handful of hours per year when demand response has the most value are precisely the hours when every aggregator on every platform is issuing commands simultaneously. OEM servers that handle routine scheduling and comfort adjustments throughout the year are not engineered for the processing spike that occurs during those specific peak minutes. Commands queue up. Dispatch is delayed. The grid needed the response at 4:47 PM and the thermostat changed its setpoint at 4:52 PM. At the wholesale level, that latency has real financial consequences.
This is what consumer-grade looks like in practice. The hardware is fine for its original purpose. The backend infrastructure was never built for grid-grade dispatch at scale.
The OEM lock-in problem compounds this challenge.
When a customer buys a battery or a smart thermostat, they are generally unaware that their hardware purchase comes with a set of constraints on what third parties can do with it. Some major manufacturers charge significant fees for API access. Others maintain fully private APIs with no third-party access at all. Some void product warranties if a third party dispatches the device outside the OEM’s own platform. The customer paid for the hardware. The OEM controls the flexibility value embedded in it, especially the monetization of it, even if the hardware is owned by the everyday customer.
This creates a high-friction, high-cost environment for aggregators trying to build grid services businesses. Whether an OEM wants to actively participate in flexibility markets or not, API lock-in means that aggregators must negotiate commercial agreements with each manufacturer, maintain integrations across a fragmented landscape of proprietary interfaces, and accept terms that may limit what they can do with the assets even when the customer has consented. The market structure rewards the OEM for controlling access to the asset’s grid value.
Customers deserve to know this when they make a hardware decision. A residential battery purchased today may or may not be available for third-party grid dispatch tomorrow, depending on the manufacturer’s commercial strategy. That is not disclosed anywhere near the front of a very expensive purchase decision.
The Solar and DER Community Need To Get Better At Grid Physics
The industry has proven it can deploy BTM solar and DERs at scale. Except for a few firms, it has not proven that it can operate these assets as grid assets.
A big part of the reason is that the financial products paying BTM resources were never designed to reward grid-aware behavior, and in some cases actively penalize it.
Net Energy Metering was designed to get the first solar projects built. It worked. As a bootstrapping tool for an industry that did not yet exist at scale, it was defensible. As a permanent compensation architecture, it has become a problem.
NEM credits solar production against retail consumption, netting across monthly billing periods regardless of when the electrons were produced or where on the system they arrived. A kilowatt-hour generated at noon on a low-demand Sunday in October earns the same credit as one generated at peak on a hot Tuesday afternoon in August. The grid values those two electrons differently by an order of magnitude. NEM treats them the same. The result is a financial product that actively discourages customers and DER operators from caring about time or location, which are the two dimensions that determine actual grid value.
Community solar extends the same problem one layer further. Most community solar programs allocate bill credits based on subscribed share of generation output, with no clear relationship between when and where the production occurred and the value of the credit given to the customer. People learn from this compensation structure that grid physics is irrelevant to the value of their participation.
These are not arguments against solar or against customers who installed systems under the rules in place. They are arguments against locking in immature compensation structures that create the wrong habits at the exact moment when the grid most needs DERs to develop the right ones. There is a lot of money in the status quo, and regulators have a hard time sunsetting these immature financial products. Just look at the multiple renewals of NY community solar or the bruising battle of NEM 3 in CA.
The Utility Culture Gap
Utility control rooms were built around a discrete operating model. A generator runs or it does not. A line flows or it trips. Staffing, tooling, and institutional culture all reflect that, and for the grid those control rooms were built to operate, the model was correct.
Distributed resources do not work that way.
A fleet of 10,000 enrolled thermostats controlling A/C looks like a reliable demand response resource on paper, but the available flexibility at any moment is shaped by forces the utility cannot directly observe. Some homes are occupied and the occupant has already pushed the setpoint back down. Some have tight thermal envelopes that hold a pre-cooled temperature for hours without the compressor cycling back on; others are older construction where a two-degree setpoint change produces no flexibility because the house is leaky. Some customers are traveling and the house is empty, delivering more avoided load than the model assumed. Some customers work from home and the house is already pre-cooled.
There is no single customer profile, there is a variety of customers and the key is using data to understand them. Operating that kind of resource requires probabilistic forecasting, A/B testing across customer segments and temperature bands. It’s a vastly different skillset than what utilities are used to.
The distrust of distributed assets in control rooms is not irrational. An operations team with no real-time visibility into assets it did not build and cannot directly control has good reasons to be skeptical. But the grid is already probabilistic. Wind output is a range. Solar is a range. Load is a range.
The distributed transition adds probabilistic flexibility at every node in the system. The same shift in thinking already required on the generation side is now required on the demand and storage side, at finer spatial granularity.
This is good for system resilience and lowering costs, provided the operator has enough redundancy across the probabilistic scenarios to maintain reliability. Getting there requires operators who have managed through this before, regulatory frameworks that reward it, and organizations that have rebuilt their analytics infrastructure around probabilistic dispatch. None of that is free or easy, and the current financial model does not create urgency to invest in it.
Interconnection as a Dynamic Signal
The same logic applies to how we manage the interconnection queue.
Static hosting capacity analysis, published periodically and updated slowly, tells developers where the grid can absorb new resources as of some recent snapshot. It does not tell them where the grid needs resources or when. Resources connect where they can get cheap and fast access, not where they would be most useful.
A better model publishes dynamic, real-time hosting capacity information and structures the queue around it. Developers who want to connect where capacity is readily available today move quickly and at low cost. Those who want to connect where the grid is already constrained face a genuine choice: fund the upgrade to get a firm, unconditional interconnection, or accept a lower-cost, faster connection with curtailment rights during surplus conditions.
That second option is not a consolation prize. For a battery operator or a managed load, being curtailed during surplus hours is not a meaningful sacrifice. Surplus on the distribution system typically coincides with low demand and high renewable generation, the hours when storage would normally be charging anyway. Curtailment in those hours costs very little. What the operator gains is a fast interconnection path and a built-in alignment between their asset’s operating profile and the hours when the grid actually needs relief. The incentive structure produces the right behavior without the regulator having to mandate it.
Australia has moved furthest toward this model. Network operators there are implementing flexible export limits that send dynamic signals to customer inverters to adjust exports based on real-time network conditions. The framework is called Dynamic Operating Envelopes: each customer’s import and export limits are recalculated based on current network state rather than fixed at connection. Australian distribution operators are now working on objective functions for allocating spare network capacity among DER customers as conditions shift in near real time. However, one item that will need to be worked out is curtailment priority order since assets and load growth come into the grid over time.
The Topology Floor
There is a physical constraint underneath all of the above that has no software solution.
To use distributed resources for localized grid services, an operator needs to know, with high confidence and in real time, which substation, feeder, and phase every asset is connected to. That mapping changes continuously as switches operate and load shifts. Current ADMS implementations provide substation-level awareness. AMI mesh networks provide some aggregate feeder data. Neither delivers the device-level, real-time topology knowledge needed to dispatch a battery and know with confidence that its discharge will relieve the right segment of wire.
Two customers 50 feet apart may or may not be on the same substation-feeder-phase combination. Without high-confidence real-time topology, you cannot guarantee that one battery’s discharge offsets the thermal stress the other’s EV is putting on shared infrastructure. The physics of the grid requires that specificity.
AMI 2.0 does not close this gap. The mesh network architecture carries inherent latency and coverage limitations that make it unsuitable for real-time distribution operations at 60 Hz. Regardless of the comms latency issue, AMI 2.0 still doesn’t solve the substation-phase-feeder challenge. Closing it requires genuine investment in the next generation of low-voltage distribution technology.
The Incentive Running Through All of It
There is a thread connecting every barrier in this post:
Customer-level data locked inside utility systems with no obligation to share
OEM APIs that are private, expensive, or simply not built for grid dispatch