README.md

metadata

license: cc-by-4.0
pretty_name: Grid Dispatch tasks
tags:
  - reinforcement-learning
  - rl-environment
  - agents
  - tool-use
  - operations-research
  - power-systems
  - unit-commitment
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl
      - split: test
        path: data/test.jsonl

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

1. 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.
2. 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.
3. Breakdowns: 0–2 big plants trip during the day, mostly in the hours before the evening peak.
4. 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.
5. 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.