Week of Sep 18, 2026 Batch SaaS

Utility interconnection & grid-queue software

Startup Idea Brief Published Week of Sep 18, 2026

Software that digitizes and automates utility interconnection-queue processing so new generation and large loads connect to the grid in months, not years.

Utilities and regional grid operators are drowning in interconnection requests from data centers, solar and storage developers, and other large loads, and most of that intake, study, and approval process still runs on manual review, spreadsheets, and disconnected legacy tools. This product is a workflow and data platform for utility and ISO/RTO interconnection teams: it ingests interconnection applications, automates the technical screening and impact-study steps that currently take engineers months to do by hand, and gives utilities a real-time queue dashboard instead of a black box that developers wait years to hear back from.

The customer is the utility's or ISO's interconnection and planning group, sold as enterprise software with a long sales cycle but very sticky usage once adopted, because interconnection processing is a regulatory-mandated function utilities cannot skip. The wedge is speed and transparency: a utility that can process a queue in a fraction of the current time reduces its own liability for delay complaints and unlocks the generation and load growth its region needs, while developers on the other side get visibility into where their project actually stands.

This is infrastructure software sold into a slow-moving, highly regulated buyer, which is a much harder sale than a typical SaaS motion but comes with very high switching costs and long contract terms once won, because once a utility's queue data and workflows live inside the platform, ripping it out means disrupting an active regulatory process.

Why Now

AI data-center demand and broader electrification are projected to push US electricity demand up roughly 25 percent by 2030, arriving at the same time transmission and generation interconnection queues are already clogged with a backlog measured in thousands of gigawatts and multi-year wait times. Grid hardware, new transmission lines and generation, takes years to permit and build regardless of software, but the queue-processing and study workflow inside utilities is a pure information and workflow problem that software can compress dramatically, which is why Google's Tapestry unit is now working directly with PJM to build a unified AI-assisted model of its network specifically to speed up interconnection review. That a hyperscaler is investing its own engineering resources into fixing a grid operator's internal software stack is a strong signal that the queue itself, not just physical grid capacity, is now recognized as the binding constraint.

Demand Signal

The US interconnection queue backlog has swollen to roughly 2,200 to 2,600 gigawatts of proposed generation and storage awaiting approval, and transmission congestion cost the US grid an estimated $17.1 billion in 2025 alone, up 39 percent from $12.3 billion the year before, with the ten-year peak at $20.8 billion in 2022. Median wait times for a project to reach commercial operation are approaching five years, with some data-center interconnection cases facing delays up to 12 years, and most utilities are still running pilots rather than production deployments of any automated queue-management system. PJM's decision to partner directly with Google's Tapestry team to build a unified AI-assisted model of its interconnection process, and to deploy an agentic tool called HyperQ to auto-verify applications, is direct evidence that even the largest, most sophisticated grid operator in the US considers its current queue software inadequate for the scale of demand it now faces.

Defensibility

Once a utility or ISO's interconnection workflow and historical queue data live inside the platform, migrating away means disrupting an active, regulator-scrutinized process, which makes contracts extremely sticky regardless of typical SaaS competitive pressure. Deep expertise in the specific technical study methodologies each ISO/RTO uses (PJM, MISO, ERCOT, and others all have different processes) is slow to build and hard for a generalist software company to replicate quickly. Proprietary data on queue status, grid topology, and historical study outcomes accumulated across multiple utility customers compounds into a modeling advantage that improves the product's forecasting and automation accuracy in ways a new entrant starting from zero data cannot match, and the regulatory relationships required to even get in the room with a utility's interconnection team are themselves a barrier to entry.

Market Signals

Queue backlog: The US interconnection queue backlog reached roughly 2,600 GW in 2026, with median project wait times approaching five years and some data-center cases facing delays up to 12 years. (RMI / Ascend Analytics grid interconnection research)

Congestion cost: US grid transmission congestion cost an estimated $17.1 billion in 2025, up 39% from $12.3 billion in 2024. mgrid.org

Hyperscaler-backed fix: PJM partnered with Google's Tapestry unit in a multi-year effort to build a unified AI model of its grid network and deployed HyperQ, an agentic tool that auto-verifies interconnection applications, specifically to speed up queue processing. Utility Dive

How I'd Build This

MVP Scope

A queue-visibility and application-screening tool that ingests a utility's existing interconnection application data and automates the initial technical-screening checklist (the step that currently takes engineers weeks of manual review), surfaced as a dashboard the utility's interconnection team and outside developers can both see into, without touching the full impact-study modeling yet.

First Customers

Target mid-size municipal or cooperative utilities and smaller ISO member utilities first rather than PJM or CAISO directly, since they face the same backlog pressure but have far less internal engineering capacity to build custom tools and shorter procurement cycles than the largest grid operators; find them through NARUC and state utility regulatory conferences and through developer complaints about specific utilities' queue delays, which point to where the pain is sharpest.

Moat Sequence

Build the queue-visibility and screening automation first to get a foothold and real usage data; layer in automated technical impact-study modeling next, since that is the highest-value, hardest-to-replicate step; then aggregate anonymized queue and study data across customers to build a cross-utility benchmarking and forecasting layer that becomes progressively harder for a new entrant to match.

90-Day Timeline

Months 1-3: land one mid-size utility or co-op as a pilot design partner and build the queue-visibility dashboard against their real data; months 4-6: ship automated technical screening for that pilot customer, prove measurable time savings, and use that case study to start a second utility sales cycle, which given typical procurement timelines will likely still be in progress at month 6.

Sources

PJM, Google partner to speed grid interconnection using AI US Grid Congestion Hit $17.1 Billion in 2025 Energize Capital: Grid Software insights