Datasets:
episode_id int32 0 1.17k | step int64 0 151k | norm_observation large listlengths 18 18 | action large listlengths 9 9 | reward float32 -21.78 257 | source_policy large_stringclasses 6
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End of preview. Expand in Data Studio
AEMO Simulated Trade — Impact-Aware Episodes
Impact-aware battery trading episodes from Australia's National Electricity Market (AEMO/NEM), generated under an endogenous market-impact model for retraining a Decision Transformer (DT) to avoid self-impact.
This is the Phase 4 companion dataset to
mrvictoru/AEMO_simulated_trade:
where the original is price-taking, every episode here is rolled out with a
piecewise-linear merit-order market-impact model enabled — the battery's own
energy dispatch and FCAS bids move the realized clearing prices.
Dataset summary
| Property | Value |
|---|---|
| Episodes | 1,169 |
| Rows | 29,270,943 |
| Regions | NSW1, QLD1, SA1, TAS1, VIC1 |
| Date range | 2021–2023 (per-region windows) |
| Battery sizes | 8 / 50 / 150 / 250 MWh |
| Horizons | 12-day, 8-week, 26-week, ~9-month |
| Degradation | real_world (LFP, 30 °C) |
| Market impact | piecewise-linear merit-order (energy + 8 FCAS services) |
| Action space | full_fcas (1 energy + 8 FCAS bids) |
Dataset structure
| Column | Type | Description |
|---|---|---|
episode_id |
int32 |
Episode index (0..1168) |
step |
int64 |
Step index within the episode |
norm_observation |
list<float32> (18) |
Normalized observation (see below) |
action |
list<float64> (9) |
full_fcas action: [energy dispatch, 8 FCAS bids] |
reward |
float32 |
Per-step reward (revenue − degradation cost) |
source_policy |
string |
Trajectory source policy (see below) |
Observation (18-dim, normalized): [hour_sin, hour_cos, day_sin, day_cos, is_peak, RRP_norm, DEMAND_norm, 8×FCAS_norm, GEN_solar_pct, GEN_wind_pct, SOC]
in the AEMOBatteryTradingEnv layout.
Source policies
| Policy | Rows | Description |
|---|---|---|
ppo |
11.0M | Existing SB3 PPO (online RL) under impact |
dt_v2 |
6.8M | Modern v2 DT (self-generated) under impact |
a2c |
5.6M | Existing SB3 A2C under impact |
fcas_rule |
3.0M | FCAS rule baseline under impact |
oracle_mi |
2.2M | Impact-aware optimal LP (Oracle_MI) over varied sub-windows |
oracle_pt |
0.7M | Price-taking optimal LP replayed under impact (failure-mode contrast) |
Episode counts by region / battery / horizon:
region: NSW1 232 · QLD1 232 · SA1 234 · TAS1 232 · VIC1 239
battery: b08 312 · b50 285 · b150 263 · b250 309
horizon: short 614 · medium 323 · long 163 · xlong 69
Intended use
Retrain the modern v2 Decision Transformer (8×768 GQA) on impact-aware trajectories so it learns to moderate dispatch to avoid self-impact and to account for degradation over long horizons. Evaluation targets: the Phase 3 market-impact surface (identity vs. impact, batteries 8/50/150/250 MWh).
Note on deterministic Oracle sources: oracle_mi/oracle_pt are
deterministic LP solves, so their diversity comes from solving over distinct
14-day (or 8-week) sub-windows per region — each is a different optimal
trajectory. They are restricted to short/medium horizons (LP size).
Generation
- Pipeline:
scripts/generate_impact_dataset.py(sources:oracle_mi,oracle_pt,ppo,a2c,dt_v2,fcas_rule) - Precomputed 2021–2023 supply curves + FCAS depth:
scripts/precompute_supply_curves.py - Observation reconstruction + assembly:
scripts/assemble_impact_dataset.py - Research context:
docs/market_impact_plan.md(Phase 4), branchfeature/market-impact-modelingin mrvictoru/energydecision
Related models
mrvictoru/energydecision-dt-v2— modern v2 pretrained DT (SOTA baseline)mrvictoru/energydecision-dt-v2-forecast— forecast-conditioned DT (negative result)
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