Charge More Electric Trucks Without Grid Upgrades

Northern California’s distribution grid could support 25,000–70,000 additional electric heavy-duty trucks per year—without a single feeder upgrade—if charging depots agree to shift about 1 MW out of a handful of local peak hours. That number isn’t a thought experiment. It comes from feeder-level analysis using real grid data and real truck-duty-cycle behavior.

Most companies get this wrong: they assume fleet electrification is blocked mainly by hardware—more chargers, bigger transformers, more construction. Hardware matters, but the bigger bottleneck is often process: the way utilities evaluate new loads using conservative “worst hour of the year” assumptions. The fix isn’t magic. It’s flexibility, formalized.

This post breaks down flexible service connections (sometimes called flexible interconnections), why they’re suddenly the fastest path to electrifying truck depots, and where AI-driven grid optimization fits in. And because this is part of our AI in Agriculture: Precision Farming for Modern Growers series, I’ll also connect the dots to agricultural operations—packing houses, cold storage, grain handling, and the fast-growing world of electric ag logistics.

Flexible service connections: the fastest capacity you already have

A flexible service connection is simple: the utility allows a new charging site to connect sooner and often cheaper, as long as the customer agrees to curtail or shift load during specific local feeder constraints.

Here’s the key point: a feeder can be “full” on paper but still have plenty of usable capacity most of the year. Traditional interconnection studies often focus on the single most constrained hour, then treat a new depot as if it will hit its maximum demand during that same hour. That’s how you end up with a “no” until upgrades are built—even if the depot would rarely (or never) charge during that specific bottleneck hour.

Flexible service connections flip the logic:

• Utilities define constrained hours (often a small set of summer evenings).
• Fleets agree to avoid those hours or reduce power during them.
• In return, fleets get energized sooner, with fewer (or deferred) upgrades.

One-line definition: A flexible service connection is a utility agreement that trades limited, pre-defined load curtailment for faster interconnection and lower upgrade costs.

For utilities, this isn’t charity. It’s smart asset utilization: more customers on existing infrastructure while deferring capital spend.

Why truck depots can shift load without breaking operations

Most heavy-duty depots have something that looks a lot like “built-in flexibility”: long dwell times.

Representative behavior analyses show many heavy-duty trucks with daily mileage under ~300 miles are parked at their depot roughly late afternoon through early morning. That window overlaps with common distribution peaks (often 4–9 p.m. in summer). But here’s the operational reality: a truck might be parked for 12+ hours and only need a fraction of that time to recharge—especially at higher charging power.

So the practical move is not “charge less.” It’s “charge smarter.” In many cases, fleets can:

• Delay charging by 1–6 hours
• Stagger start times across trucks
• Reduce coincident demand (peak kW) while delivering the same kWh overnight

Even with no special utility program, fleets often do this already to cut bills under time-of-use rates. Analyses have shown up to ~30% electricity cost savings from shifting load away from peak pricing periods.

The 1 MW shift that changes everything

In modeling based on Northern California feeder constraints, shifting just 1 MW out of constrained peak hours by a few hours enabled the majority of constrained feeders to support about 10 additional heavy-duty trucks compared with business-as-usual planning assumptions.

Scale that logic across roughly 2,000 feeders, and the numbers jump quickly:

• 25,000–70,000 additional heavy-duty trucks per year could be supported, depending on duty cycle
• With a broader flexibility window (2 to 6 hours), the median feeder could support:
  • +3 to +22 regional-haul trucks
  • +9 to +64 urban delivery trucks

Those are big swings from a relatively small behavioral adjustment.

Where AI fits: from “managed charging” to grid-aware charging

Flexible service connections work best when everyone trusts two things:

1. The utility can identify and communicate real constraints accurately.
2. The fleet can comply automatically without dispatchers babysitting chargers.

That’s exactly where AI in energy & utilities earns its keep.

AI for feeder constraint forecasting (utility side)

Utilities running flexible programs need to know when a feeder is likely to bind. Doing that well requires more than static studies.

AI models can improve this by combining:

• Hourly (or sub-hourly) feeder telemetry
• Weather and heat index forecasts (peak risk)
• Distributed energy resource output forecasts (solar drop-off timing)
• Customer load pattern clustering and anomaly detection

The result is a better answer to a simple question: “Which feeders will be constrained tomorrow, and during which hours?”

Some programs already provide day-ahead notifications to customers. AI helps make those signals less conservative and more precise—reducing unnecessary curtailment while keeping reliability intact.

AI for depot charging orchestration (fleet side)

On the fleet side, compliance should be invisible. You don’t want a driver coming in late, plugging in, and unknowingly triggering a violation.

Managed charging platforms increasingly use optimization to:

• Assign charging start times by route priority (first-out vehicles first)
• Enforce a depot “demand cap” (kW limit) during constraint periods
• Balance charger utilization (avoid bottlenecks)
• Adapt to real-world variance (late arrivals, partial SOC, cold weather efficiency)

A practical stance: if you’re planning a heavy-duty depot today and you’re not budgeting for software, you’re underestimating your infrastructure needs. Software is how you turn flexibility into bankable capacity.

Why this matters to the “AI in Agriculture” audience

Agriculture is becoming electrified at the edges first—often through logistics and processing.

If you run or serve ag operations, flexible service connections can accelerate electrification for:

• Produce and dairy distribution fleets serving regional routes
• Cold storage warehouses and packing houses with EV yard tractors
• Grain and feed logistics (urban delivery + regional haul)
• Electrified drayage tied to ports that move agricultural exports

And the conceptual overlap with precision agriculture is real: the same mindset that schedules irrigation around price and water constraints can schedule charging around grid constraints. It’s optimization under limits.

How to evaluate a flexible service connection for your depot

The technical idea is straightforward; execution is where projects win or stall. Here’s a field-tested checklist to bring to a utility conversation.

1) Map your “flexibility window” honestly

Answer first: If the utility asked you not to charge from 4–9 p.m. on a limited number of days, could you still hit daily energy targets?

To estimate:

• Total required energy per truck per day (kWh)
• Available dwell time at depot (hours)
• Charger power available (kW) and efficiency assumptions
• Fleet variability (late returns, early dispatch)

If you have 10–12 hours of dwell time and 4–6 hours of flexibility, you’re a strong candidate.

2) Decide if you need on-site storage (or can get by with software)

If your duty cycle is tight—say, trucks return late and leave early—then flexibility may require a buffer.

Options:

• Battery storage to cover constrained hours while continuing to charge vehicles
• On-site solar + storage to reduce grid draw during peaks
• Right-sizing chargers (more plugs at lower kW can outperform fewer high-kW units operationally)

A blunt truth: storage isn’t mandatory for every depot, but it’s the “get out of jail free” card for weird schedules.

3) Treat utility data as a strategic asset

Flexible programs depend on knowing when constraints happen. Some territories have strong public-facing grid data; many don’t.

Push for:

• Clear definitions of constraint triggers (seasonal vs day-ahead)
• Measurement methods (interval metering requirements)
• Penalty structures and cure periods
• A transparent path to convert from flexible to firm service after upgrades

The best deals are the ones you can operationalize without surprises.

4) Build a compliance plan you can automate

If you can’t automate it, you’ll break it.

A workable compliance plan includes:

• A managed charging controller that can enforce kW caps
• Charger and vehicle telemetry integration
• Exception handling (priority charging for must-run vehicles)
• A reporting layer that proves performance to the utility

This is where AI-driven optimization becomes more than a buzzword: it’s the tool that turns a contract into predictable operations.

Utility program design: what separates “pilot” from “scale”

Utilities exploring flexible service connections tend to get stuck in pilots because they fear reliability risk and customer backlash. The programs that scale do a few things well.

Make constraints specific, not vague

Customers can’t plan around “be flexible sometimes.” They can plan around “avoid these hours under these conditions.”

The most workable structures are:

• Day-ahead events (notification the prior day)
• Seasonal caps (e.g., summer evenings)
• Hybrid models (seasonal baseline + day-ahead tightening)

Invest in monitoring and analytics early

Flexible service is only as credible as the monitoring behind it. Analytics should identify:

• Which feeders are truly constrained
• How often constraints occur (many are clustered in a short season)
• Whether curtailment requests were actually necessary

The win for utilities is real: more electrification load served with existing assets, growing the rate base while deferring upgrades.

Practical FAQ (what decision-makers ask next)

Does this reduce total energy delivered to trucks?

No. Done right, it mainly reduces coincident peak demand during the feeder’s tightest hours. Trucks still receive the same kWh over the overnight window.

How many days per year are we talking about?

It depends on feeder conditions, but modeling in Northern California found that for a large share of feeders, load shifting was only required during July in a limited two-hour flexibility scenario.

What if our depot can’t shift because trucks are always moving?

Then flexibility can be created with on-site storage, alternative charging locations, or mixed strategies (partial curtailment plus battery discharge).

Is this only for California?

No. Summer evening peaks occur across much of the US due to air conditioning load and solar drop-off. The concept travels; the program design and data maturity vary by utility.

The bigger picture: electrification needs scheduling, not just steel

Flexible service connections are a reminder that the grid has two kinds of capacity: the capacity you build and the capacity you schedule. For heavy-duty charging, scheduling is often the faster win.

If you work in agriculture—on the farm, at a co-op, in processing, or in ag logistics—this is a familiar pattern. Precision farming succeeds when you manage constraints (water, labor, equipment time) with better data and better automation. Fleet electrification is headed the same direction: optimize around the constraint hours instead of overbuilding for them.

If you’re planning a depot or advising a utility, the next step is straightforward: identify candidate feeders, quantify your flexibility window, and put managed charging (with AI optimization) in the critical path. The question worth asking internally is simple: how much electrification are you postponing because you’re treating a handful of peak hours like they’re the whole year?