id stringlengths 18 21 | world dict | reference dict |
|---|---|---|
ERCO-2024-01-02-c909 | {
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ERCO-2024-01-02-c1336 | {
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ERCO-2024-01-05-c310 | {
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ERCO-2024-01-05-c888 | {
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ERCO-2024-01-08-c151 | {
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ERCO-2024-01-09-c536 | {
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ERCO-2024-01-09-c699 | {
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ERCO-2024-01-09-c688 | {
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... |
ERCO-2024-01-09-c762 | {
"ba": "ERCO",
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"units": [
{
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ERCO-2024-01-09-c1105 | {
"ba": "ERCO",
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ERCO-2024-01-09-c1366 | {
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... |
ERCO-2024-01-12-c9 | {
"ba": "ERCO",
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ERCO-2024-01-12-c60 | {
"ba": "ERCO",
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ERCO-2024-01-12-c145 | {
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ERCO-2024-01-12-c165 | {
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ERCO-2024-01-12-c287 | {
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ERCO-2024-01-12-c725 | {
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ERCO-2024-01-14-c236 | {
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ERCO-2024-01-18-c124 | {
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ERCO-2024-01-28-c272 | {
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ERCO-2024-01-28-c416 | {
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ERCO-2024-02-03-c136 | {
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ERCO-2024-02-03-c604 | {
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ERCO-2024-02-03-c928 | {
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Grid Dispatch tasks
500 tasks for the Grid Dispatch RL environment (Verifiers v1). An AI runs a power grid for one real day: it decides which of 17 power plants are on each hour, and when to use a battery, to keep the lights on at the lowest cost.
The day plays out in 4-hour steps. At each step the AI looks, tests ideas with simulate, gives orders, then advances
the clock. The real day happens as it goes: demand differs from the forecast, wind falls short, and plants break
down. Decisions can't be undone, and big plants need hours of notice, so a good dispatcher has to act before the problem
arrives.
| Split | Tasks | Real days |
|---|---|---|
train |
453 | 252 |
test |
47 | 28 |
The split is by real day: one day can appear several times with a different fleet and breakdowns, and no day is in both splits. Test rows don't include the solver's moves.
from datasets import load_dataset
test = load_dataset("dev-front/grid-dispatch-tasks", split="test")
How a task is made
- A real day: one 2024 day for a US grid area (SPP, ERCOT, MISO, PJM or ISNE) from EIA-930, giving hourly demand, its real day-ahead forecast, and wind and solar output. Days where actual net load beat the forecast most are preferred.
- A fleet: 17 plants sampled from the RTS-GMLC test system in PGLib-UC (nuclear, coal, combined cycle, steam, peakers), plus a battery. The day is scaled so its peak fills 85% of the fleet.
- Breakdowns: 0–2 big plants trip during the day, mostly in the hours before the evening peak.
- A reference dispatcher: a solver (HiGHS) that re-plans every 4 hours with exactly the information the AI has, keeping spare power for one breakdown or any two. Each day uses whichever version did better.
- Keep only hard days: following the midnight plan, and doing nothing, must both score ≤ 0.6 (on average they score 0.10 and 0.15), and the reference must lose at most 1% of the day's power (it loses none on 333 of 500).
Replaying the reference's moves reproduces its exact cost on every task.
Fields
| Field | Meaning |
|---|---|
id |
<area>-<date>-c<candidate> |
world.units |
The 17 plants: pmin, pmax (MW), ramp, min_up / min_down (hours), lead (hours of notice to start), startup_cost, curve (MW, $/h) |
world.battery |
power (MW), energy (MWh), eff (one-way efficiency), soc0 (starting charge share) |
world.demand, world.demand_da |
Actual and day-ahead forecast demand per hour (MW, scaled) |
world.renew, world.renew_da |
Actual wind+solar, and its forecast, per hour (MW, scaled) |
world.trips |
Plant → hour it breaks. Hidden from the AI until it happens |
world.init_on, world.init_p |
Which plants are on at midnight, and their output |
reference.policy |
rolling (spare power for one breakdown) or rolling2 (for any two) |
reference.cost, reference.shed_mwh, reference.demand_mwh |
The reference's total cost, unserved energy, and the day's demand: what the score is measured against |
reference.baselines |
Every policy played on this day (rolling, rolling2, hindsight, static, myopic, nothing) with its cost, unserved MWh, start-ups and score |
reference.actions |
The reference's moves step by step (train only) |
world holds the hidden truth (the actual demand, wind and trips). The environment only reveals it as the clock
passes. Keep it out of prompts.
Score
Score = reliability × economy, each against the reference:
- reliability =
e^(−excess unserved MWh / (0.5% of the day's demand)); - economy =
0.2 + 0.8·e^(−overspend / 10%), where overspend is fuel, no-load and start-up cost above the reference's.
Blackouts can't be bought off with fuel savings, and matching or beating the reference scores 1.0.
Baselines (the 5 hardest days, all from train; one attempt, no thinking cap)
| Model | Average score |
|---|---|
| DeepSeek V4.1 Flash | 0.39 (one day 1.00, one 0.64, three ≤ 0.15) |
| Nemotron Lightning 3.5 30B-A3B | 0.12 |
DeepSeek never overspends, but on hard days it keeps too little spare power and is caught by the second breakdown.
Licence and attribution
Released under CC BY 4.0. Built from:
- EIA-930 Hourly Electric Grid Monitor (US Energy Information Administration; US government data). In the 2024 files, PJM's forecast was shifted back one hour.
- PGLib-UC (CC BY 4.0), RTS-GMLC test system (GridMod/RTS-GMLC). Changes: units sampled, renamed and rounded, and given synthetic start-up notice times.
Synthetic parts: start-up notice times, the wind/solar forecast (actual plus half of yesterday's error), the trips, the battery, and the scaling.
The reward pattern (real records plus a solver reference) is inspired by PortSimEnv.
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