Colorado’s clash with Xcel over “totally useless” grid data shows why bad interconnection information is killing clean energy projects—and how AI can fix it.
Most clean energy projects don’t fail because the tech doesn’t work. They fail because the grid says “no” — or worse, gives no meaningful answer at all.
Colorado regulators recently blasted Xcel Energy’s grid hosting capacity data as “totally useless” for developers trying to connect distributed energy resources (DERs) like solar, batteries, EV chargers, and demand response. Whether you’re building projects in Colorado or planning green technology deployments anywhere else, this should be a wake-up call.
Here’s the thing about the clean energy transition: the bottleneck has shifted from hardware to information. Panels, batteries, and inverters are ready. The missing piece is accurate, transparent, machine-readable grid data that tells you where and how you can connect.
This article breaks down what’s going wrong in Colorado, why it matters for every utility territory, and how better data and AI-driven grid planning can turn interconnection from a roadblock into a growth engine for green technology.
The Colorado–Xcel conflict: what’s actually happening?
The core issue is simple: Colorado regulators say Xcel’s grid maps and data are so low-quality that developers can’t rely on them to plan projects.
In the context of green technology, that’s a serious problem. Developers of rooftop solar, community solar, battery storage, and EV infrastructure depend on hosting capacity data — information showing how much additional load or generation a particular feeder or substation can handle without upgrades.
When that data is wrong, incomplete, or out of date, three things happen:
- Projects die early. Developers walk away after wasting months, because every potential site is “too risky” without real numbers.
- Interconnection queues explode. Everyone files speculative applications to “test” where they can connect, overloading utility and regulator staff.
- Costs rise for everyone. Grid upgrades become reactive and expensive instead of proactive and strategic.
Colorado’s Public Utilities Commission (PUC) has been pushing Xcel to provide usable, transparent interconnection information. When regulators start calling data “totally useless” in public, it’s not just a technical disagreement — it’s a trust problem. And trust is the foundation for any serious grid modernization effort.
Why bad grid data kills green technology at the edge
For the broader Green Technology series, this matters because distributed energy resources live and die on local grid conditions.
If you’re working on:
- Commercial rooftop solar plus storage
- Community solar gardens
- Behind-the-meter batteries participating in virtual power plants
- EV fast-charging hubs
- Smart building controls or flexible loads
…your business model depends on two questions:
- Can I connect here?
- What will it cost and how long will it take?
Without good grid data, the answer to both is: “We don’t really know.”
The hidden consequences of poor interconnection data
Here’s how that uncertainty shows up in practice:
- Overbuilt projects – Developers design smaller projects than the site can actually host, just to stay safe, leaving clean energy potential on the table.
- Underpriced risk – EPCs and investors underestimate upgrade costs that appear late in the interconnection process, blowing up project economics.
- Equity impacts – Low-income and rural communities get skipped because the data to build a confident business case simply doesn’t exist.
Meanwhile, regulators and utilities get blamed for being “slow” or “anti-solar,” when the real issue is that their grid models aren’t ready for the volume and complexity of modern DER portfolios.
The reality? It’s simpler than most people think: if you want more DERs, you must treat grid data like critical infrastructure.
From ‘totally useless’ to actionable: what good grid data looks like
Good grid data isn’t just a nicer-looking map. It’s an operational tool that lets developers, utilities, and regulators make aligned decisions.
A useful hosting capacity or interconnection map should provide at least:
- Feeder-level hosting capacity (kW/MW) for new generation and load
- Thermal, voltage, and protection constraints that are actually limiting capacity
- Update frequency (e.g., monthly or quarterly), not a static snapshot from years ago
- Data export (CSV/JSON), so developers and consultants can run their own analyses
- Scenario-based guidance, e.g. “BESS up to 1 MW is likely acceptable here with limited upgrades”
And for regulators, “compliance” shouldn’t mean “we posted a PDF to a website.” It should mean:
- Data is accurate enough that reality matches what the map suggests within a reasonable range
- Data is transparent enough that third parties can validate and challenge it
- Data is granular enough to be useful for site selection, not just academic curiosity
Most companies get this wrong because they treat hosting capacity as a checkbox requirement rather than a product that real users — developers, cities, fleet operators — depend on to make six- and seven-figure investment decisions.
How AI and advanced modelling can fix interconnection bottlenecks
Here’s where green technology and AI start to shift the picture.
Modern grids are too complex to manage interconnection with spreadsheets and static power-flow studies. AI-driven grid analytics and automated modelling can turn the “totally useless” label into a solvable engineering problem.
1. Automated, high-frequency hosting capacity analysis
Instead of running a handful of studies per feeder per year, AI-powered tools can:
- Continuously update hosting capacity using real-time or near-real-time SCADA and AMI data
- Run thousands of scenarios (solar only, storage only, solar+storage, EV load growth) overnight
- Flag low-regret upgrades (e.g., reconductoring a single span or adjusting protection settings) that unlock the most capacity per dollar
For developers, that means hosting capacity maps that actually reflect the grid you’re connecting to in 2025, not 2020.
2. Predictive interconnection timelines and costs
Using historical interconnection data, an AI model can estimate, for a specific feeder segment:
- Likely study time in weeks or months
- Probability of needing line upgrades, new transformers, or recloser changes
- A cost range for those upgrades, based on past projects
That transforms interconnection from a black box into a probabilistic business input, which investors and project finance teams know how to work with.
3. Proactive hosting capacity planning
Instead of waiting for developers to knock on the door, utilities can combine:
- Load forecasts (including EV adoption and electrification)
- DER adoption scenarios
- Policy targets (net-zero, building codes, EV mandates)
…to identify where to harden and upgrade the grid in advance.
This is where green technology and AI start reinforcing each other: smart infrastructure planning makes it cheaper and faster to deploy more renewable energy, which in turn justifies more investment in the data and modelling stack.
What regulators, utilities, and developers should do next
The Colorado–Xcel tension is a preview of the conversations every jurisdiction will have over the next few years. The ones who handle it well will attract capital and projects. The ones who don’t will see queues, frustration, and missed climate targets.
Here’s a practical roadmap for each side.
For regulators and policymakers
Regulators shouldn’t try to design grid models themselves — but they can set clear expectations:
- Require standardized hosting capacity data formats that are machine-readable
- Define minimum accuracy and refresh requirements (e.g., updated at least quarterly)
- Tie performance incentives or penalties to interconnection timelines and data quality
- Encourage or approve grid modernization budgets that explicitly include data and AI tooling, not just wires and transformers
I’ve found that when commissions tie rate recovery to measurable interconnection improvements, utilities suddenly discover they can modernize their data pipelines.
For utilities
Utilities that get ahead of this will become magnets for DER investment instead of targets for criticism.
Key moves:
- Treat grid models (
CYME,Synergi,PSSE, etc.) as living systems, not one-off studies - Invest in integration between GIS, outage management, AMI, and planning tools
- Pilot AI-based hosting capacity with a limited number of feeders, then scale
- Co-design tools with developers: get real feedback on what data fields they actually use
Utilities don’t have to be perfect; they just have to be predictably improving. Transparency about limitations beats silent, outdated maps every time.
For developers and green tech companies
On the market side, companies building solar, storage, EV, and flexibility projects can respond in three ways:
- Build internal grid literacy. Even a basic understanding of feeder constraints, voltage limits, and protection schemes can save you months.
- Use third-party grid analytics. There’s a growing ecosystem of tools that ingest utility data (where available) and help screen sites before you file.
- Engage early with regulators. Trade groups and coalitions can push for better interconnection standards, and regulators usually want credible, data-backed input from the market.
If you’re developing at scale, you can’t treat grid access as an afterthought anymore. It’s a core part of your product and financial model.
Turning interconnection from a barrier into a strategic advantage
The Colorado story isn’t just about one utility and one commission. It’s a snapshot of a global pattern: our grid institutions are being asked to support a distributed, data-heavy, AI-optimized clean energy system using tools designed for a centralized, analog past.
This matters because the whole Green Technology ecosystem — smart buildings, DERs, AI-driven energy management, EV fleets, flexible manufacturing — sits on top of the same foundation: a grid that knows itself well enough to say “yes” quickly, and “no” with reasons and alternatives.
The better way to approach this is straightforward:
- Treat grid data as critical infrastructure
- Use AI and advanced modelling to keep that data current and actionable
- Align regulatory expectations, utility incentives, and developer needs around transparency and speed
If you’re building or investing in green technology today, your next step is simple: ask, “What does interconnection look like in the territories I care about — and how can better data and AI give me an edge?”
The companies that can answer that clearly will win the next decade of clean energy growth.