Tag: Battery Storage

  • TAR’s $1B Series A Prices Off-Grid Power for AI Data Centers as Its Own Category

    TAR’s $1B Series A Prices Off-Grid Power for AI Data Centers as Its Own Category

    TL;DR · 30-second read

    The Short Version

    TAR, a Texas company started only this year, has raised $120 million from investors who now value it at $1 billion.

    It builds private power plants for the giant computer warehouses behind artificial intelligence tools. The plants use renewable energy and huge batteries, and never plug into the public electricity grid.

    Why it matters: these warehouses use so much electricity that many wait years to be connected. TAR says its approach lets them get power without competing with nearby homes and businesses for it.

    Austin-based TAR has raised $120 million in Series A funding at a $1 billion post-money valuation, Pulse 2.0 reported on September 11, 2026. Spark Capital led the round. TAR was founded in 2026 by Pat Becker and Leonhard (Lenny) Soenke. It builds self-contained, modular power systems that combine renewable generation with battery storage and run independently of the electric grid, aimed at AI data centers at gigawatt scale.

    The company says the money will expand its Austin headquarters and its San Francisco engineering office. It will also grow manufacturing and logistics operations in West Texas and speed up deployments already underway. Those deployments include a utility-scale project with one of the largest neoclouds, a dedicated project campus, and TAR Terminal One, a manufacturing and logistics center in West Texas.

    Executive Summary

    TAR’s raise is one of the clearest signs yet that investors see off-grid power for AI as a business category in its own right. It is no longer just a temporary fix for data centers waiting on a utility hookup. A company founded this year has reached unicorn status on its first institutional round by promising dedicated renewable-plus-battery generation that it designs, builds and operates itself.

    The thesis rests on two claims. The first is that power, not chips, is now the binding constraint on AI computing. The second is that the fastest way to supply it is to own the whole project lifecycle and automate construction. TAR pairs energy veterans from Hut 8, AES and Vistra with robotics engineers from Zipline, GrayMatter Robotics and Lucid Motors, which suggests an attempt to treat power plant construction partly as a manufacturing problem.

    What the company has not yet made public matters as much as the valuation. TAR has not disclosed installed megawatts, its customer’s name, contract terms or project timelines. The central engineering question for any off-grid system serving round-the-clock computing is how it maintains reliable supply, and TAR has not yet explained its approach.

    Off-Grid Power Gets Its Own Venture Price Tag

    For much of the data center boom, on-site generation has been framed as a bridge. It energizes a campus while the project waits in a utility’s interconnection queue, the line of projects awaiting approval to connect to the grid. A $1 billion post-money valuation for a company founded in 2026 suggests Spark Capital sees something more durable: a standalone category in which generation is built and run as a permanent, dedicated supply for computing.

    The arithmetic is instructive. A $120 million raise at $1 billion post-money implies a pre-money valuation of roughly $880 million. TAR reached that figure without publicly disclosing completed megawatts, contracted revenue or a named customer. At that level, investors are paying for team, speed and position in a market where, as Spark’s Will Reed put it, “power is becoming the main bottleneck to scaling compute.” It is a bet on the size of the problem as much as on a track record.

    The deal also points to a shift in where AI infrastructure capital goes. Energy assets have traditionally been funded by utilities, infrastructure funds and project lenders, who price them for steady, bond-like returns. A venture-style valuation for a power developer implies that some investors now treat the speed of energy delivery, and not only its cost, as a source of technology-like upside.

    Owning the Whole Stack: Faster Delivery, Concentrated Risk

    TAR’s strategy is vertical integration. It handles site selection, detailed engineering, procurement, logistics, civil construction, commissioning and ongoing operations itself, rather than coordinating a chain of outside contractors. The reasoning is that schedules tend to slip at the handoffs between firms. Co-founder Pat Becker said gigawatt-scale deployments (a gigawatt is a thousand megawatts, comparable to the output of a large power plant) “in tight time windows necessitate owning the full stack end-to-end.”

    The company’s purpose-built deployment automation stack is meant to install generation faster, at larger scale and with less field labor than conventional construction. If it works, the limit on how fast power can be built shifts away from the supply of skilled field crews and toward factory and logistics throughput. TAR Terminal One in West Texas appears designed to supply that throughput.

    The trade-off is concentration of risk. A contractor ecosystem spreads execution risk across many firms, while an integrated developer carries all of it. TAR must scale engineering, robotics, manufacturing, logistics and field operations at the same time. That is capital-intensive and operationally demanding. TAR has not yet disclosed completed projects that show its automation claims working at scale.

    The Reliability Question for Renewables Plus Batteries

    AI data centers run around the clock, and their customers expect very high uptime. A grid connection offers a backstop when on-site generation falls short, and an off-grid system has no such backstop. TAR describes its systems as combining renewable generation with battery storage and operating independently of the grid. The design therefore has to cover periods of weak sun or wind entirely on its own.

    That problem can be solved in several ways: oversizing generation, adding longer-duration storage, or including firm backup generation. Each adds cost, equipment and land. How TAR balances these choices will largely decide whether its power competes on price and reliability with grid electricity or with gas-fired on-site plants, the other common off-grid option.

    TAR’s community argument does resonate. Its systems do not draw on shared grid capacity, so the company says data center projects can add generation without competing with surrounding communities. That matters in siting debates, where the effect of very large facilities on local electricity supply and prices is a recurring concern.

    Who Stands to Gain if the Model Works

    Neoclouds are natural early customers. These are cloud providers that specialize in renting out GPU computing for AI, as opposed to the broad hyperscale clouds. They compete heavily on how quickly they can bring capacity online, which makes long grid timelines especially costly for them. TAR says it is already executing a utility-scale deployment with one of the largest neoclouds, a sign that its offer has found at least one buyer with urgent demand.

    For utilities and grid operators, the effects are mixed. Off-grid campuses ease pressure on congested transmission lines, but they also mean large new electricity users that never become utility customers. Suppliers of batteries and generation equipment, along with West Texas manufacturing and logistics workforces, stand to benefit from any sustained buildout. TAR’s competition includes other on-site power developers and the traditional engineering and construction firms whose multi-contractor model it is explicitly positioning against.

    Background

    AI data centers use far more electricity than traditional facilities, and in many regions connecting a new large user to the grid means joining a long interconnection queue and waiting for new transmission and generation. That has pushed developers toward “behind-the-meter” power: generation built on or next to the site that serves the data center directly. Such projects have often been treated as temporary bridges until a grid connection arrives.

    TAR, founded in 2026 in Austin, is pursuing the off-grid approach as a permanent model. It uses modular renewable generation and battery storage and manages projects end to end. Its lead investor, Spark Capital, is a venture capital firm. The company’s leadership draws on experience at energy and infrastructure companies including Hut 8, AES and Vistra, as well as robotics and hardware companies Zipline, GrayMatter Robotics and Lucid Motors.

    Sources

    Source: TAR Raises $120 Million Series A At $1 Billion Valuation To Build Off-Grid Power For AI Data Centers (Pulse 2.0), covering TAR’s Spark Capital-led round to expand off-grid renewable and battery power systems for AI data centers.

  • A 2% Premium Shows the Inverter Switch for AI-Era Power Hinges on Time, Not Price

    A 2% Premium Shows the Inverter Switch for AI-Era Power Hinges on Time, Not Price

    TL;DR · 30-second read

    The Short Version

    Every solar panel and battery farm needs a box called an inverter, which turns its power into the kind your sockets use. Many are connected to the internet, so a hacker could in theory take control of them.

    China has the factories to make about four in every five of the world’s inverters. Europe plans to stop funding big solar and battery farms that use inverters from countries it considers risky, including China.

    The International Energy Agency found switching adds only about 2% to a large solar farm’s cost. The harder part is time, as factories retool while artificial intelligence data centers need ever more power.

    In a commentary published July 29, 2026, the International Energy Agency (IEA) reported that China accounted for around 80% of global manufacturing capacity for the inverters used in solar and battery installations in 2025, and assessed the cost of moving away from suppliers that governments now classify as security risks. The analysis follows a European Commission decision to restrict EU funding, including through the European Investment Bank and European Investment Fund, for solar, wind and storage projects that use inverters from “high-risk” countries: China, Russia, Iran and North Korea.

    The IEA estimates that buying a utility-scale inverter from a manufacturer headquartered in the EU or another non-high-risk country costs about USD 13/kW more, roughly 75% above Chinese pricing, but adds just under 2% to total project cost. Europe’s inverter factories, at around 95 GW a year, already exceed its roughly 90 GW of annual shipments, though the IEA warns the models built locally may not match the models in demand.

    Executive Summary

    Inverters are the control layer of modern renewable power: the electronics that convert direct current from solar panels, batteries and many wind turbines into grid-ready alternating current, and that manage power flow, monitoring and grid safety. Because most are networked and remotely controllable, governments increasingly treat them as a cybersecurity exposure, with risks ranging from compromised passwords to back doors planted during manufacturing or malicious software updates.

    The policy response is spreading. Beyond the new EU funding restrictions, the US National Defense Authorization Act passed in December 2025 bars its funds from buying inverters from designated Foreign Countries of Concern, including China; Lithuania restricts remote control of inverters and now applies those rules to existing installations; and India requires inverters in a subsidised rooftop scheme to send data only to servers in India.

    The IEA’s central finding is that the price of de-risking is modest at the project level. That matters for anyone financing or buying power from new solar and storage, including the large electricity buyers behind AI data center growth: the binding constraint looks less like cost and more like whether the right inverter models can be produced in time.

    The Premium Is Real, but It Shrinks at Project Scale

    On a per-unit basis, the gap between Chinese and non-Chinese inverters is large. The IEA puts the average global inverter shipment price at around USD 28/kW, with high-power three-phase units near USD 25/kW and microinverters near USD 180/kW. For the systems typically used in utility-scale projects, shipments in key EU markets carried a premium of about USD 13/kW (EUR 12/kW) over Chinese prices, roughly 75% higher. The IEA attributes China’s lower prices to economies of scale, higher automation and intense price competition.

    Measured against the whole project, though, that premium is small. Utility-scale solar in the EU cost around USD 760/kW in 2024, so a USD 13/kW inverter premium works out to just under 2%. Small residential and commercial systems are more exposed: at around USD 1,000/kW installed, the IEA estimates a 3-4% premium for string inverters and about 20% for hybrid inverters and microinverters. In other words, the cost burden of the security push falls most heavily on rooftop segments the EU rules are not expected to cover, and least heavily on the utility-scale projects they are.

    Why the Constraint for AI-Era Power Is Timing, Not Price

    The headline aggregate looks comfortable: Europe could make around 95 GW of inverters a year in 2025, against total shipments of about 90 GW. Inverters are the one step of Europe’s solar supply chain where capacity exceeds demand, compared with just 33 GW of module capacity, 10 GW of cells and 1.5 GW of wafers against roughly 70 GW of module demand. Even adding around 17 GW of stationary battery deployment, which needs bidirectional power conversion, the IEA judges domestic capacity to be in excess of demand.

    The catch is fit. The IEA expects a likely mismatch between the models Europe produces and the models its market wants, noting that string and microinverters are often imported from China, and that retooling production lines to close those gaps is possible but would take some time. It says the mismatch could lead to project delays and cost increases. For utility-scale solar and storage developers, that is the operational risk: a sub-2% cost line is easy to absorb in a financial model, but a procurement delay on a power-conversion package can push back energisation dates.

    That sequencing matters to the buyers of that power. Data center operators expanding capacity for AI workloads are among the largest contracted buyers of new utility-scale solar and battery output, and utility-scale projects are expected to make up around half of global capacity additions over the coming decade in the IEA’s exploratory scenario projections. Where those projects rely on EU financing, the practical question for a data center’s power team is not whether compliant inverters cost more, but whether suppliers can deliver the specific models on schedule. The IEA points to partial relief: redistributing European output between exports and domestic demand, and higher imports from non-high-risk producers, notably in Southeast Asia.

    Security Rules Are Landing Where Controls Are Already Strongest

    The IEA describes how an attacker who gains access to an inverter could control and alter the equipment connected to it, with consequences ranging from localised disruption to lengthy blackouts. The threat can come through weak online connections and passwords, through back doors built in at the factory, or through malicious software updates from the manufacturer.

    The anticipated scope of the EU rules raises a fair question about fit between risk and remedy. Based on currently available information, the restrictions are widely expected to apply directly only to EU-funded utility-scale projects, which the IEA notes are professionally operated under stringent cybersecurity protocols. Most smaller rooftop installations in homes and businesses are not expected to be covered. Existing EU instruments, including the NIS2 directive, the Cyber Resilience Act and the Network Code on Cybersecurity, already address equipment security; how the new funding rule adds to them will depend on details the Commission has yet to publish.

    A Trade Dispute Layered on an Engineering Problem

    China is the only major inverter supplier among the four countries the EU designates as high-risk, and on May 8, 2026, China’s Ministry of Commerce criticised the restrictions, warning they could damage trade relations, supply chain security and Europe’s energy transition. Other markets face sharper exposure than Europe: Korean inverter manufacturers have raised concerns about the country’s more than 90% dependence on Chinese suppliers.

    The policy tools differ. The US approach restricts federally funded procurement, the EU’s restricts funding eligibility, Lithuania’s governs remote control and requires retrofits of some existing installations, and India’s targets where data is stored. For developers and equipment buyers operating across several markets, the likely outcome is not one standard but a patchwork of compliance regimes, each with its own implications for vendor selection and project timelines.

    Background

    Solar power has become one of the largest sources of new electricity worldwide: the IEA counts almost 3 TW of installed solar capacity supplying nearly 9% of global electricity, none of which could function without inverters. Battery storage and many modern wind turbines depend on the same power-conversion electronics, and inverters increasingly coordinate with connected loads such as electric car chargers, heat pumps and air conditioners.

    Chinese manufacturers have come to dominate inverter production through scale, automation and price competition, offering what the IEA describes as typically the lowest-cost option in most major markets with high performance and reliability. Europe is the second-largest producer, with about 8% of global capacity, and inverters are the one stage of its solar supply chain where domestic manufacturing exceeds demand.

    Sources

    Source: Inverter supply chains and cybersecurity – Analysis (International Energy Agency), an IEA commentary on inverter supply concentration, new EU sourcing restrictions and the cost of switching suppliers.

  • PJM’s 3.1 GW Data Center Dropout Shows AI’s Other Grid Risk: Load That Leaves

    PJM’s 3.1 GW Data Center Dropout Shows AI’s Other Grid Risk: Load That Leaves

    TL;DR · 30-second read

    The Short Version

    When one power line failed near Washington, DC, a wave of data centers (the warehouse-sized buildings full of computers that run the internet and AI) unplugged from the power grid within seconds and switched to their own backup power.

    Together they had been using about 3% of the electricity on a grid that serves 67 million people. Losing that much demand at once threw the system off balance. Lights flickered from Virginia to Chicago, and it took more than 10 minutes to settle.

    It was twice as big as a similar scare in 2024, and data centers keep multiplying. The proposed fix is to make them stay connected through brief hiccups, often by using giant batteries.

    A single power line failure outside Washington, DC, this past week caused a wave of Northern Virginia data centers to switch to backup power at nearly the same moment, TechCrunch reported. According to PJM data, about 3.1 gigawatts of demand disappeared from the PJM Interconnection grid in roughly 30 seconds. After a partial recovery, more facilities dropped off, and the grid’s surplus peaked at 3.49 gigawatts. Stabilizing took another 11 minutes. A line fault like this one normally takes only a few seconds to recover from.

    According to Reuters, the disconnected load was about 3% of PJM’s demand at the time. Sensor data from the startup Ting Labs showed voltage spiking across the grid from Northern Virginia to Chicago. There was no blackout, but lights flickered across the region. The event was roughly twice the size of a 2024 PJM incident in which 60 data centers disconnected at once and removed 1.5 gigawatts.

    Executive Summary

    Most discussion of data centers and the power grid asks whether there is enough electricity to feed them. This event shows the opposite failure: a cluster of very large customers that all leave the grid at once when they sense trouble. Each facility’s protective logic behaved as designed and switched to on-site backup power when voltage dipped. Together, those individually sensible decisions turned a routine line fault into a regional disturbance that lasted more than ten minutes.

    Two remedies are being proposed. The first is coordination. Ali Zain Banatwala, a senior market models specialist at the Independent Electricity System Operator, argues that neighboring loads need a way to disconnect and reconnect one after another rather than all together. The second is ride-through, which means keeping facilities connected through brief disturbances. Ricardo de Azevedo, CTO of ON.Energy, whose company sells campus-scale battery systems built for that purpose, said ERCOT, the Texas grid operator, will require large loads to ride through disruptions.

    The stakes rise with the load. Synapse Energy Economics put data centers at about 6% of PJM’s load in 2024 and projects 24% by 2040. The potential size of a synchronized dropout grows with that share unless the facilities are built to behave differently.

    AI’s Other Grid Problem Is Load That Leaves

    An electric grid has to keep supply and demand in near-perfect balance from moment to moment. When a line goes down, the grid normally absorbs the loss and rebalances within seconds. This time, the voltage dip from the fault reached many data centers sitting close together. Their protection systems are designed to make split-second decisions to safeguard the computers inside, and they moved the facilities onto backup power within a few seconds of each other. The grid lost about 3.1 gigawatts of demand in roughly 30 seconds. Generators were suddenly producing more electricity than customers were using, and voltage jumped.

    The second wave is the detail grid planners should focus on. PJM’s grid appeared to recover partway before more loads dropped off, pushing the surplus to a peak of 3.49 gigawatts. In that pattern, the disturbance triggers disconnections, and the disconnections deepen the disturbance. That feedback is what stretched a seconds-long event into an 11-minute one. A gigawatt is roughly the output of a large power plant. In effect, the grid had to absorb the sudden departure of several power plants’ worth of customers.

    That is why the grid risk from the data center buildout is not only a question of supply. Operators are used to planning around the sudden loss of a large generator. Clustered data centers create a mirror-image contingency: a large block of customers lost all at once because of a single triggering event. Everyone on the grid is exposed to that. Households saw flickering lights, generators must ramp down fast, and the operator must hold capability in reserve for a contingency that grows as data center load grows.

    Concentration Turns a Local Fault Into a Regional Event

    Northern Virginia has the highest concentration of data centers in the world. That density is what made the disconnection synchronized, because one voltage dip reached many facilities almost at once. The effect traveled well beyond Virginia. Ting Labs runs a sensor network through household electrical outlets, and it recorded the voltage spike as far away as Chicago. PJM covers the territory from New Jersey to Illinois, serves 67 million customers, and is the largest grid operator in the United States.

    The trend line gives the event its weight. In 2024, 60 data centers disconnected together and removed 1.5 gigawatts. This time the figure was about 3.1 gigawatts, roughly double. Data centers were about 6% of PJM’s load then, and Synapse Energy Economics projects 24% by 2040. Two events are not a statistical series, and nothing here shows the next one will double again. The arithmetic still matters: if protective behavior stays the same while the share of load it governs roughly quadruples, the ceiling on a synchronized dropout rises with it.

    De Azevedo called the event “the canary in the coal mine” and said incidents involving large loads are “happening more and more.” That claim comes from a vendor of the remedy. The two PJM incidents, with their measured sizes, are the documented reference points.

    Two Fixes: Choreograph the Exit, or Don’t Leave

    The first approach accepts that data centers will disconnect but asks them to do it in order. “We need to figure a way for these loads that are located next to each other to sequentially either disconnect or reconnect,” Banatwala said. Staggered behavior would let grid operators write procedures in advance instead of reacting to a cliff. The hard part is coordination. The facilities belong to different owners, each has settings tuned to protect its own uptime, and they would need shared thresholds or a shared signal.

    The second approach is ride-through, meaning the facility stays connected through brief disturbances. ON.Energy’s version is an uninterruptible power supply, the battery backup that keeps servers running through outages, sized for an entire campus, including chillers and other equipment as well as servers. Batteries and power-conversion equipment sit between the grid and the facility, so the grid sees one steady, predictable load. The company says the system:

    • charges its batteries when the grid has surplus power;
    • discharges to the servers when supply dips;
    • follows the grid within milliseconds;
    • smooths the swings of AI training workloads that ramp power up and down.

    It says it is installing 3 gigawatts of these systems across four campuses.

    Those claims are specific enough to test. The test is whether a campus behind such a system stays connected and grid-friendly during a real fault like this one. The economics matter too. Sizing batteries and power electronics for a whole campus adds capital cost that the developer bears, while most of the benefit goes to the grid. Mandates may be what shifts that calculation, such as ERCOT’s ride-through requirement, which de Azevedo described.

    What Developers and Buyers Should Expect

    From a tenant’s point of view, the July event worked: the data centers switched to backup, and the computers presumably kept running. The cost of that decision fell on the grid and everyone else connected to it. This asymmetry is what makes the event a policy question rather than just an engineering one. Individual facilities have little reason to change behavior that protects their own uptime unless interconnection rules require it.

    For developers, the likely direction is toward grid connection terms that specify how a facility behaves during a disturbance, not just how much power it draws. ERCOT is moving that way, according to de Azevedo. PJM, where both incidents occurred, is where the question is now most visible. For suppliers of storage, power conversion and switchgear, the grid-facing layer of a campus becomes part of the design requirement. For colocation buyers, it is reasonable to ask providers how their facilities respond to voltage dips and whether ride-through requirements could change facility design or pricing.

    Background

    PJM Interconnection is a regional transmission organization, the independent body that runs the high-voltage grid and wholesale electricity market for an area stretching from New Jersey to Illinois. It serves 67 million customers and is the largest grid operator in the United States. Its territory includes Northern Virginia, which has the highest concentration of data centers in the world.

    Data centers are built never to lose power. When grid voltage wavers, they switch to on-site backup, such as uninterruptible power supplies and generators, in a split second. That design protects the computers inside. When many facilities share the same stretch of grid, however, it also means they can react to the same disturbance at the same moment. PJM saw this in 2024, when 60 data centers disconnected simultaneously, and saw it again at twice the scale this July. ERCOT, the operator of most of Texas’s grid, is among the grid managers moving to require large loads to ride through disturbances.

    Sources

    Source: One fallen power line exposed a growing AI data center problem — here’s how to fix it (TechCrunch): a report on the roughly 3-gigawatt data center disconnection on the PJM grid and proposed ride-through fixes.