Mental models from software don't always make sense when applied to electrical grids. How should we think about energy and emissions?
Why Write This?
I keep having the same conversations... with smart engineers, genuinely trying to understand carbon-aware computing. But sometimes they raise concerns/objections that sound reasonable yet rest on flawed assumptions about how the grid actually works.
The objections sometime sound like:
- "If you use clean energy, someone else gets pushed to dirty energy"
- "Shifting load will destabilize the grid"
- "Marginal emissions are what matter, not average intensity"
- "This only works if we have 90%+ renewables. And then, does it even matter?"
- "Wait, you want my $$$$ GPUs to sit idle?"
These aren't dumb people. I think they're trying hard to understand. They're voicing sophisticated sounding concerns that happen to be wrong. Or at least, misguided in ways that would end up confounding practical decision-making.
The problem: technologists think about grids like they think about compute clusters. More-or-less fixed capacity. Zero-sum allocation. Deterministic scheduling.
But grids aren't clusters. They're economic systems with markets, slack, planning horizons and operators who adapt to ever-changing patterns.
Fallacy 1: Musical Chairs 🪑
The claim: "Clean energy is limited. When you take it, someone else gets pushed onto fossil power. Net effect: zero... or worse"
Why it sounds right: In a datacenter, if you take a CPU core, someone else can't use it. Fixed resources, zero-sum allocation.
Why it's wrong: Grids aren't fixed-size resource pools.
When you move load from Virginia's fossil heavy grid (~500 gCO2eq/kWh) to Quebec's hydro powered grid (~50 gCO2eq/kWh):
- You don't "take" someone's hydro electrons
- Quebec doesn't tell existing customers "sorry, you get coal now"
- Quebec responds by... continuing to run hydro, because that's what it always has
- At worst, Quebec's marginal intensity bumps slightly — maybe to 55 gCO2eq/kWh
- Your compute still runs at ~1/10th the intensity compared to Virginia
The better mental model: Markets, not resource pools.
Buying more EVs doesn't force someone else to buy a gas car. It shifts demand curves and production incentives over time. It's the same with clean electricity; sustained demand in clean regions signals investment in more clean capacity.
Fallacy 2: The Fragile Grid 🩹
The claim: "Sudden load shifts trigger fossil backup, raise prices and risk grid instability."
Why it sounds right: Grids must balance supply and demand in real-time. Imbalances cause blackouts. Therefore, moving load = dangerous.
Why it's wrong: Grids are designed for variable load. Your compute job is not even a rounding error.
Scale check — what actually stresses grids:
| Concern | Threshold | Your Job a concern? |
|---|---|---|
| Frequency response | 100+ MW in <1 second | No |
| Ramping stress | 1+ GW in <15 min | No |
| Forecast error | Unexpected GW-scale swings | Too small and relatively predictable |
Remember, here are just a few things the grid can support every day, without incident:
- Millions of air conditioners cycling on/off
- "it's half-time" → everyone opens their fridge and starts the [microwave] oven within a few seconds
- Cloud cover rolls across solar farms
- Morning demand ramps as cities wake up (or in the afternoon on solar-heavy grids)
Even if the largest data centers are relevant loads, individual compute runs/jobs hardly ever are. What is your batch job running over a couple hours? Even if it were meaningful in magnitude, grid operators would see it as predictable baseload. Practically a gift.
Rule of thumb: If your facility isn't comparable to an aluminum smelter (500MW+ constant draw), you're not a grid stability concern. You're a rather simple customer.
Fallacy 3: The Thundering Herd 🐏
The claim: "If everyone adopts carbon-aware scheduling, all load shifts to the same clean windows and crashes the grid."
Why it sounds right: Classic distributed systems problem. Everyone retries at the same time → system overload.
Why it's wrong: Three reasons.
1. Optimization spreads load, it doesn't synchronize it.
Carbon-aware schedulers don't fire every possible job in a single millisecond. They spread deferrable work across clean windows that are typically hours long. The "thundering herd" assumes everyone has identical deadlines and zero flexibility; but that's not the case for large swathes of deferrable compute.
2. The Invisible Hand steps in.
If too much load clusters in clean windows, wholesale prices rise and some load shifts back. This is how markets work. The equilibrium point isn't "everyone stampedes to 2pm." It's "2pm is more clean, but only carbon-sensitive loads may care."
3. Grid operators aren't NPCs.
Indepentent System Operators (ISOs) observe patterns and adapt. Load clustering at low-carbon hours? They schedule more generation for those hours. This is literally their job. The "optimization breaks everything" scenario assumes operators don't respond to changing demand; but theirs is a profession built on forecasting.
Fallacy 4: The Idle GPU 🤑
The claim: "Carbon-aware scheduling means expensive hardware sits unused during dirty windows."
Why it sounds right: If you defer work during high-carbon periods, the GPUs must not be earning their keep.
Why it's wrong: Deferral ≠ idleness. In practice, it becomes a prioritization signal and mechanism.
In a world with carbon-aware scheduling:
- Urgent, high-value work runs whenever it wants, including during dirty windows (at an implicit carbon premium)
- Deferrable, speculative work shifts to cleaner windows
- Total energy utilization may stay the same — but total system emissions drop!
Nobody's $$$$ GPUs are less than fully amortized! Market participants are simply influenced differently by the same signals. Think of congestion pricing on the roads. Rush hour doesn't mean empty highways. Discretionary trips shift; essential trips don't.
Fallacy 5: "Just Build More Renewables" 🌞
The claim: "Carbon-aware scheduling is a distraction. We just need more clean generation."
Why it sounds right: When the grid is 100% clean, timing shouldn't matter. Focus on the supply side.
Why it's incomplete: It's a classic "yes, and..." situation. Even with abundant clean generation, you need:
- Transmission capacity to move it
- Storage to buffer intermittency
- Grid stability services (frequency response, voltage support)
Flexible load (demand that can shift to match supply) is valuable infrastructure. It helps integrate renewables by absorbing variability. Carbon-aware compute isn't just about finding clean energy; it's about being a good grid citizen, facilitating more renewables coming online more quickly.
Clean capacity buildout and demand flexibility aren't competing strategies. They stack and complement each other.
Fallacy 6: Marginal Absolutism 💯
The claim: "Average grid intensity doesn't tell you your actual impact. Only marginal emissions matter."
Why it sounds right: Economically, marginal analysis is often correct. Your load is (mostly) served by whatever generator ramps up to meet it, not the grid average.
Why it's incomplete: We're still talking about meager loads. And especially when it comes to spatial shifting, marginal vs. average differences barely matter.
- Quebec's marginal source (hydro or imports from hydro-heavy neighbors) is (still much) cleaner than Virginia's marginal source (gas)
- The 10-15x intensity gaps between clean and dirty grids outweigh most marginal nuances
When it can matter more: Temporal shifting within a single grid with high renewable variability. The "cleanest" hour by average intensity might not be the hour with lowest marginal impact.
For most practical decisions: If you're choosing between Region A or B... Or you're choosing when, within a single grid, to run... average intensity is usually, at least, directionally correct. Don't let perfect be the enemy of good.
What Grid Operators Actually Care About ⚙️
They need to know:
- New large facilities (100+ MW sustained) — capacity planning
- Major industrial additions (smelting furnaces, fabs, large datacenters) — transmission planning
- Demand response program participation — operational planning
They like to know:
- Regional datacenter expansion trends
- EV charging pattern shifts
- Large commercial load forecasts
They care about:
- Reliability
- Reliability
- Reliability - this isn't a joke
They don't care about:
- Your 100 GPU cluster's schedule
- Whether your training job runs at 2am, 2pm or whenever
- Individual workload optimizations
But WE are allowed to care. Afterall, we all have lungs.🍃
The Bottom Line
Grids are resilient, adaptive, market-driven systems operated by the most responsible professionals you can possibly imagine. They've been balancing variable load for a century. The grid handles unexpected weather, sporting event demand synchs and large-scale industrial swings daily.
Your carbon-aware practices aren't going to break anything. ❤️ They will:
- Send demand signals toward clean regions → investment follows
- Shift flexible load to times/places with abundant clean supply → better utilize what's already clean
- Make you a more predictable, valuable customer → grid operators will like you 🎉💃🏽
Barriers to carbon-aware computing are real: operational complexity, data residency, latency requirements. "Grid stability" shouldn't be one of them.
Stop worrying about breaking the grid. Instead, optimize away carbon emissions today!
Let's figure out your carbon optimization opportunity →
Compute Gardener is an open-source project focused on making carbon-aware computing simpler. Join us in moving sustainable computing forward.