Charge More Electric Trucks Without Grid Upgrades

A single distribution feeder peak hour can derail an entire electric truck depot.

That’s the part most people outside utility planning don’t see: your site might only hit its maximum demand for one hour a few days a year, but traditional interconnection studies often treat that hour like it’s guaranteed—and permanent. The result is a familiar pattern across North America: long queue times, expensive make-ready work, and fleets delaying electrification plans while they wait for capacity that’s already sitting idle 95% of the year.

Flexible service connections flip that logic. They let fleets energize sooner by agreeing to automatically curtail or shift charging during the small number of constrained hours that actually matter. And when you pair that flexibility with AI-driven grid optimization—forecasting feeder peaks, orchestrating managed charging, and verifying compliance—you get something utilities desperately need in 2026 planning cycles: more load served, faster, without triggering immediate upgrades.

This post is part of our AI for Energy & Utilities: Grid Modernization series, where we focus on practical ways utilities can add electrification load while keeping reliability metrics intact.

Why “one peak hour” blocks truck charging projects

The core problem is worst-case planning applied to a system that’s usually not at its worst. Distribution planning and interconnection processes often evaluate whether a new depot could coincide with the feeder’s annual peak. If the combined load crosses a safe operating limit, the utility may require upgrades before allowing service at the requested capacity.

That approach is understandable—utilities are accountable for safety and reliability. But it creates a predictable failure mode for heavy-duty EV charging:

• Truck depots are lumpy loads. A batch of trucks arriving after routes can create a steep evening ramp.
• Feeder peaks are time-specific. In many regions, peaks cluster into a narrow window (often early evening).
• The “conflict window” is small. The depot’s high-demand period doesn’t always overlap the feeder’s critical hours.

Here’s the practical consequence: a fleet gets told to fund upgrades for a constraint that might only happen for a handful of hours per year—sometimes at times the fleet wouldn’t even charge if they had price signals or managed charging controls.

Flexible service connections: the simplest way to find hidden capacity

A flexible service connection is an interconnection agreement that trades guaranteed access during peak constraints for faster, cheaper access the rest of the time.

Instead of waiting years for construction, the fleet connects now under a rule: when the local feeder is constrained, the site will curtail or shift load within a predefined flexibility window.

A few things make this especially attractive for heavy-duty truck depots:

• Depots already have natural “flex time” because trucks are parked for long stretches.
• Charging doesn’t have to start the moment a truck arrives; it just has to finish by the next dispatch.
• Compliance can be automated with managed charging software, so operations teams don’t babysit chargers.

Snippet-worthy reality: Flexible service connections turn “capacity” from a construction project into an operational constraint you manage with software.

What the Northern California data shows

RMI analyzed truck driving patterns and public feeder data from Northern California’s utility territory and found a powerful rule of thumb:

• Shifting just 1 MW of charging load away from peak hours by a few hours (on the limited days required) often allows a feeder to support about 10 more heavy-duty trucks than business-as-usual planning would permit.

Scaled across approximately 2,000 feeders, that’s the potential to support roughly 25,000–70,000 additional heavy-duty trucks per year, depending on duty cycle—with zero grid upgrades required.

That’s not a marginal improvement. That’s the difference between “EV trucks are stalled by interconnection queues” and “EV trucks can scale while upgrades catch up.”

Where AI fits: flexible connections don’t work at scale without optimization

Flexibility isn’t magic. It’s coordination. If you want hundreds (or thousands) of flexible connections across a service territory, you need systems that can forecast constraints, dispatch curtailments intelligently, and validate performance. That’s where AI and advanced analytics become more than buzzwords.

AI-driven load forecasting: knowing when the feeder will bind

Most feeder constraints are predictable when you combine:

• weather (especially heat waves)
• seasonal patterns
• DER output (solar drop-off timing)
• localized demand growth
• historical feeder peak behavior

AI-assisted forecasting improves two things that matter operationally:

1. Precision: fewer “false alarm” curtailment calls that annoy customers
2. Lead time: better day-ahead (or hour-ahead) planning for fleets and aggregators

Northern California peaks frequently occur 4–9 p.m. in summer, when solar generation falls and residential load rises. Forecasting that ramp—feeder by feeder—is exactly the kind of pattern-recognition problem modern grid analytics handles well.

Managed charging optimization: meeting fleet needs with fewer kilowatts at the wrong time

For fleets, the goal isn’t “charge whenever.” The goal is “be ready by dispatch.” AI-enabled managed charging systems can schedule charging to satisfy:

• state-of-charge targets
• route departure times
• charger power limits and sharing constraints
• demand charge thresholds
• flexibility requirements from the utility

RMI’s prior work found that heavy-duty trucks with daily mileages below 300 miles are typically parked between 4 p.m. and 5 a.m., and that average charging needs can fit into less than half of that dwell time at 75 kW (many depots use higher-power charging). Translation: most fleets already have the operational slack needed to shift charging—software just makes it reliable.

Measurement & verification: the quiet requirement nobody budgets for

Utilities can’t run a flexibility program on trust. The scalable model includes:

• telemetry (site power, charger status, maybe transformer loading)
• event logging (when a curtailment signal was sent and received)
• settlement logic (compliance, exceptions, penalties/credits)

AI can help detect anomalies (equipment failures, communication dropouts, unexpected load spikes) so flexibility is dependable enough to be treated like a grid resource—not a pilot.

How much flexibility is “enough”? The window size matters

More flexibility hours equals more trucks per feeder. RMI modeled different flexibility windows and found that expanding the window from 2 hours to 6 hours dramatically increases how many additional trucks can fit on a constrained feeder.

On the median feeder in the modeled territory:

• Regional heavy-duty trucks: additional trucks increase from 3 (2-hour window) to 22 (6-hour window)
• Urban heavy-duty trucks: additional trucks increase from 9 (2-hour window) to 64 (6-hour window)

When scaled territory-wide, the modeled potential grows to:

• 8,000–41,000 extra regional trucks
• 22,000–112,000 extra urban trucks

This is a key planning insight for utilities designing programs:

• A small flexibility commitment can deliver broad adoption quickly.
• A larger flexibility window produces bigger capacity gains, but may require better controls, better forecasts, and sometimes on-site storage.

Seasonality: why July can be the whole story

A detail that should reshape how utilities pitch these programs: for around 60% of feeders in the modeled territory, a two-hour flexibility requirement would only be triggered during July.

That means many fleets would operate normally for most of the year, with limited curtailment events concentrated in a short seasonal period. That’s an easier sell internally (operations) and externally (customers) than “you’ll be curtailed all the time.”

Real-world proof: what a leading utility is doing now

One utility has already shown flexible connections can work beyond a small pilot. Its program uses grid monitoring and forecasting to notify fleets day-ahead when high-load conditions require shifting away from peak hours.

A notable early outcome: a large fleet operator reportedly added 20 additional electric trucks to a depot under this approach—consistent with the “10+ trucks per MW shifted” concept seen in the modeling.

Other utilities are moving in this direction with pilots and seasonal approaches, but the bigger point is this: the operational playbook is known. The remaining challenge is scaling it without creating a customer service nightmare or a reliability risk. That’s a software + analytics problem, not a physics problem.

What utilities and fleets should do next (practical playbook)

If you’re a utility: treat flexible service connections as a grid modernization product, not a special contract.

Utility checklist: build a program that scales

1. Target the right feeders first

   • Start with feeders where constraint hours are narrow and predictable.
   • Prioritize locations with freight activity (ports, logistics hubs, industrial zones).

2. Invest in feeder-level visibility

   • If you can’t see peaks at the right granularity, you’ll over-curtail.
   • Over-curtailment kills participation.

3. Standardize the contract + signal

   • Make the flexibility event definition clear (hours, seasons, notification rules).
   • Provide a simple API or integration pathway for managed charging providers.

4. Operationalize M&V and exception handling

   • Define what happens when fleets can’t curtail due to emergencies.
   • Automate compliance scoring so it doesn’t become manual overhead.

5. Align incentives with grid value

   • If flexibility saves capex and accelerates load growth, share value with participants.
   • Consider credits, expedited timelines, or reduced make-ready contributions.

If you’re a fleet or depot developer: don’t wait for the utility to guess your flexibility—quantify it.

Fleet checklist: come prepared to win interconnection negotiations

• Build a charging load profile by route type (urban vs regional) and season.
• Document dwell time and “ready by” constraints per vehicle group.
• Deploy (or procure) managed charging that can:
  • accept utility curtailment signals
  • enforce site power limits
  • prove performance with logs and reports
• Evaluate battery storage if your duty cycle is tight or your utility requires longer flexibility windows.

A strong stance I’ll take: fleets that treat energy like a managed input—not a fixed bill—will electrify faster and cheaper. The operational discipline pays back quickly, even before you count emissions benefits.

The bigger grid modernization point: flexibility buys time for the right upgrades

Flexible service connections aren’t an excuse to avoid distribution investment. They’re a way to sequence investment intelligently.

• They accelerate electrification load now.
• They give utilities time to plan and build upgrades where they’re truly needed.
• They create a pathway for AI-driven grid optimization to become standard operations: forecasting, dispatch, and verification.

This matters in late 2025 because load growth isn’t theoretical anymore—between EV adoption, data centers, and electrified heating, distribution constraints are showing up in interconnection backlogs everywhere. Utilities that can turn flexibility into a repeatable program will connect more customers faster, grow the rate base responsibly, and keep reliability performance stable.

The forward-looking question for the next phase of grid modernization is simple: Will your territory scale electrification by building first—or by optimizing first and building smarter?