diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..27e4fca6e04b729787caac96a9258809bb850746 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +raw/packs/ercot-dam-v1-train.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9db1c8bc68f276552080242dc79317213c6b5a92 --- /dev/null +++ b/README.md @@ -0,0 +1,498 @@ +--- +license: other +license_name: ercot-website-user-agreement-eia-public-domain-apache-2.0 +license_link: https://github.com/earakely-scale/agentenv-gridsim-plugin/blob/v0.1.0/data/LICENSE +pretty_name: 'GridSimEnv v1: The Battery Desk' +language: +- en +task_categories: +- reinforcement-learning +- text-generation +size_categories: +- 10K + +GPT-6.1 Sol's run of a 160 MW battery at ERCOT's West hub on 15 March 2025, replayed in 3D at the $102 evening peak + +

GridSimEnv v1: The Battery Desk

+ +

Run a grid battery at a Texas hub for one day on the real day-ahead prices, graded exactly against the optimum

+ +

An AgentEnv environment built from ERCOT's public day-ahead market prices and EIA-860 battery records, with 21,250 battery-days, a public train split and no judge: every schedule is scored against a dynamic program that finds the best possible day.

+ +Replays +Code on GitHub +AgentEnv Framework + + + +--- + +## The task + +An agent operates one battery in the ERCOT day-ahead market (DAM) for one operating day at one of ERCOT's 7 trading +hubs or 8 load zones. It reads the battery and the day's 24 hourly DAM settlement point prices, then submits 24 +integers, one per hour ending (HE01 to HE24, Central Prevailing Time): how many energy units to sell (positive) or buy +(negative) that hour. The battery is a price taker, so each hour settles at that hour's price. The schedule is graded +once, exactly, against the best schedule a dynamic program finds for the same day. + +The battery moves energy in whole units of `unit_mwh` MWh and holds 0 to `soc_levels` units. The rules: + +1. Each hour, `-k_ch <= a <= k_dis`. +2. The state of charge stays between 0 and `soc_levels` after every hour; selling `a` units lowers it by `a`. +3. The units discharged over the day sum to at most `cycle_cap_units`. +4. At the first hour that breaks rule 1, 2 or 3, that hour and every later hour execute as 0; the hours before still + settle. +5. The state of charge after HE24 must be at least `s_end_min`. Each missing unit is bought back at `unit_mwh / eta` + MWh times the day's highest price, or $50/MWh if that is higher. + +Selling `a` units delivers `a * unit_mwh` MWh. Buying `a` units draws `a * unit_mwh / eta` MWh, so the losses fall on +the buy side. Each unit discharged costs `c_deg * unit_mwh` dollars of degradation. Profit is the sum of the hourly +cash less the end-of-day penalty. + +The env exposes three tools: `get_situation`, `check_schedule` (up to 3 dry runs that return feasibility, the state +of charge path, any end shortfall and, at the foresight tier, the profit) and `submit_schedule`, which ends the +episode. An episode allows 4 turns and 24 tool calls. The `oneshot` harness, which training uses, offers only +`submit_schedule`. + +**Two tiers.** At the **foresight** tier the agent sees the day's 24 prices. At the **blind** tier it sees the prices +at the same point for the previous day and for each of the previous 7 days, not the day's own, and its schedule +settles on the day's actual prices. Each row of `v1_tasks` is one battery-day at one tier; a battery-day with blind +inputs appears once per tier. + +### A worked day + +`dam-v1-2025-02-25-LZ_AEN-2h-o`, from `eval_2025`: the Austin Energy load zone on Tuesday 25 February 2025, with a +150 MW / 300 MWh battery (archetype `2h-o`): `unit_mwh` 75, `soc_levels` 4, `k_dis` = `k_ch` = 2 (150 MW), +`eta` 0.80, `c_deg` $4/MWh, `cycle_cap_units` 6 (1.5 cycles), starting empty and free to end empty. The prices, in +$/MWh: + +| HE | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| $/MWh | 22.49 | 22.77 | 23.97 | 25.86 | 31.00 | 41.90 | 49.57 | 46.44 | 27.87 | 21.10 | 18.18 | 17.00 | 14.61 | 14.45 | 12.61 | 10.79 | 16.16 | 29.37 | 49.59 | 34.00 | 27.89 | 23.32 | 21.26 | 20.23 | + +The day has two peaks, at HE07 and HE19. The optimum takes both, and its second round trip uses the last two units +of the cycle cap: + +| HE | Price | Action | MWh | Cash | State of charge after | +|---|---:|---:|---:|---:|---:| +| 01 | 22.49 | −2 (buy) | −187.5 | −$4,216.88 | 2 | +| 07 | 49.57 | +2 (sell) | 150 | $6,835.50 | 0 | +| 15 | 12.61 | −2 | −187.5 | −$2,364.38 | 2 | +| 16 | 10.79 | −2 | −187.5 | −$2,023.12 | 4 | +| 19 | 49.59 | +2 | 150 | $6,838.50 | 2 | +| 20 | 34.00 | +2 | 150 | $4,500.00 | 0 | + +Every other hour is idle. The day's profit is **$9,569.63** (P_opt; doing nothing earns $0), net of $1,800 of +degradation, and scores 1.0. The pack's references on the same day: the greedy rule, which pairs the cheapest hours +with the dearest later ones, makes one evening round trip for $4,815.38 (reward 0.603); the threshold rule $5,411.63 +(0.652); the clock rule, which charges HE02 to HE05 and sells HE18 to HE21, $1,880.25 (0.357). At the blind tier the +anchor is the DP schedule on the previous day's prices, settled on this day: $8,937.75, a hindsight capture (`r_opt`) +of 0.947. + +## Grading + +A schedule's reward is + + R = clip(0.2 + 0.8 * (P - P_idle) / (P_anchor - P_idle), 0, 1) + +where P is its profit, P_idle the profit of doing nothing (negative for the 12 of the 78 battery designs whose end +obligation is above their starting charge) and P_anchor the reference with the same information as the agent: + +- **foresight**: P_opt, the exact optimum. A dynamic program over the state of charge, the units discharged so far + and every allowed action finds it in under a millisecond, with ties broken deterministically. An LP relaxation + bound (`p_lp`) is reported beside it: over every candidate day, the bound exceeds the optimum by at most 4.2% of + its margin over idle, and by nothing up to the 99th percentile. +- **blind**: max(P_persist, P_ridge), the better of two schedules a desk could make from the same lagged prices, + each the DP schedule on a price forecast, settled on the actual day: persistence (the previous day's prices) and a + lagged ridge regression (`raw/packs/ridge-v1.json`). The blind headline is `r_opt`, the same formula against P_opt. + +Idle scores 0.2, the anchor 1.0. A run that never submits a parseable schedule scores 0; there is no format credit. +The hindsight capture `r_opt` (the reward against P_opt) is reported on every row at weight 0. The env, its +verifier and training all grade with the same code, `gridsim_core.grade`, and the plugin's tests check that the env +and gridsim_core agree to 1e-6. + +## Tables + +All parquet. The splits are `train`, `eval_2025`, `eval_iid` and `rtcb`; the showcase days are a config of their +own, so they never mix into a split. + +| Config | Split | Rows | What | +|---|---|---:|---| +| `v1_tasks` (default) | `train` | 26,000 | 20,000 battery-days of 2014 to 2024 at the foresight tier, and 6,000 of them again at the blind tier | +| | `eval_2025` | 800 | 500 held-out battery-days of January to November 2025, and 300 of them at the blind tier | +| | `eval_iid` | 500 | 500 battery-days on reserved dates of 2014 to 2024 (every 10th calendar day), foresight only | +| | `rtcb` | 452 | 250 battery-days after ERCOT's real-time co-optimization went live (5 December 2025 to 17 October 2026), and 202 of them at the blind tier | +| `v1_showcase` | `showcase` | 1,461 | 761 battery-days of famous events (Winter Storm Uri, Elliott, August 2023, 6 September 2023, Heather), 700 of them also blind: for display, never for scoring a claim | +| `v1_references` | each split above | 20,000 / 500 / 500 / 250 / 761 | per battery-day: P_opt and its schedule, P_idle, the LP bound, the clock, threshold, greedy and random-feasible schedules with their profits and rewards, and for blind battery-days the persistence and ridge schedules and the anchor | +| `v1_episodes` | `eval_2025`, `rtcb`, `showcase`, `train` (each episode's day) | 1,320 / 250 / 10 / 7 | recorded runs: the model or checkpoint, the tier and harness, the reward and hindsight capture, the schedule, every tool call and the transcript as chat messages. GPT-6.1 Sol's 1,550 board runs (1,300 on `eval_2025`, 250 on `rtcb`) with the agent's own transcripts, and 37 reference episodes the Space also replays | + + + +`raw/packs/` holds the five packs exactly as the env loads them, one JSON task per line (`.jsonl`), with each +pack's manifest (sources and their SHA-256, filters, counts, the battery table) and `stats.md`, the pack statistics. +`runs/` holds the Episode JSON (`gridsim.episode/v1`) the replay Space reads, with `runs/index.json` listing them, and +`results/` the eval card `eval-v1.json` and `board.md`. Each bundle published for `agent-env hf run` adds the bundle +under `bundles/` and agentenv-hf's `_tasks` config, and `_episodes` once it has recorded runs (see +[Run it yourself](#run-it-yourself)). A config with no rows yet is left off this card. + +## Use + +```python +import json +from datasets import load_dataset + +repo = "earakely-scale/GridSimEnv-AgentEnv" +days = load_dataset(repo, "v1_tasks", split="eval_2025", revision="v0.1.0") +day = days[0] +print(day["task_id"], day["tier"], day["settlement_point"], day["archetype"]) +# what the agent gets: the system prompt and the situation, as path-B training sends them +print(day["system_prompt"], day["situation"], sep="\n\n") +day["prompt"] # the same two messages, as a chat list +json.loads(day["tools"]) # the submit_schedule function schema +blind = days.filter(lambda r: r["tier"] == "blind") # the 300 blind battery-days + +refs = load_dataset(repo, "v1_references", split="eval_2025", revision="v0.1.0") +print(refs[0]["p_opt"], refs[0]["opt"], refs[0]["greedy_reward"]) +``` + +Grade any schedule with `gridsim_core`, which installs with the plugin: + +```python +from gridsim_core.grade import grade + +task = json.loads(day["task"]) # the battery-day as the env loads it +grade(task, [0] * 24, day["tier"])["reward"] # idle: 0.2 +grade(task, task["refs"]["foresight"]["opt"], "foresight")["reward"] # the DP optimum: 1.0 +``` + +For TRL, `v1_tasks` is a GRPO dataset as it stands: `prompt` is the conversational prompt, and +`gridsim_core.trl.reward_fn` takes the `task` and `tier` columns TRL passes to reward functions, reads the +`submit_schedule` call out of each completion and returns its reward. + +```python +from gridsim_core.trl import reward_fn +from trl import GRPOConfig, GRPOTrainer + +train = load_dataset(repo, "v1_tasks", split="train", revision="v0.1.0").filter(lambda r: r["tier"] == "foresight") +trainer = GRPOTrainer(model="Qwen/Qwen3-1.7B", reward_funcs=reward_fn, train_dataset=train, + args=GRPOConfig(output_dir="gridsim-grpo")) +``` + +This is the minimal wiring; the recipe behind the published runs (the chat template with the tool schema, thinking +off, LoRA, the C0 to C2 curriculum) lands with the training results ([Training](#training)). + +## Run it yourself + +You need Docker, [uv](https://docs.astral.sh/uv/) and git. Install the plugin, build and register the env and the +`gridsim-llm` agent, and check the wiring with a task that calls no model: + +```bash +uv tool install agentenv-framework \ + --with "agentenv-gridsim @ git+https://github.com/earakely-scale/agentenv-gridsim-plugin@v0.1.0" +# build the env and agent images and register them +agent-env gridsim setup --agent +# no model: submits the DP optimum of a fixture day, prints "passed" +agent-env run gridsim --task smoke +``` + +Then play a battery-day straight from this dataset with a model on your LiteLLM endpoint: + +```bash +export LITELLM_BASE_URL=https://your-litellm-proxy/v1 LITELLM_API_KEY=... +agent-env hf run earakely-scale/GridSimEnv-AgentEnv@v0.1.0 --bundle ercot-dam-v1-eval \ + --task dam-v1-2025-02-25-LZ_AEN-2h-o --model openai/gpt-6.1-sol +``` + + + +The plugin brings agentenv-hf with it. `hf run` downloads this card and `bundles/` at the tag's commit, checks that +the plugin the card's `agentenv` table names is installed, shows what it will run and asks first; `--dry-run` runs +nothing. To sweep a model over a pack and write the board, use the plugin's CLI: + +```bash +agent-env gridsim sweep run --name mine --models openai/gpt-6.1-sol --tasks 20 --cap-usd 5 +agent-env gridsim sweep report mine # results/eval-v1.json and results/board.md +agent-env gridsim episodes mine # Episode JSON under results/runs/mine/, each replayed and checked +agent-env gridsim view # the viewer on http://127.0.0.1:8239/ +``` + +## How the battery-days were picked + +- **Prices.** ERCOT's *Historical DAM Load Zone and Hub Prices* (report NP4-180-ER, report type 13060), the yearly + files for 2014 to 2026, with their SHA-256 in each manifest: the hourly DAM settlement point price at the hubs + HB_HOUSTON, HB_NORTH, HB_PAN (from 6 April 2019), HB_SOUTH, HB_WEST, HB_HUBAVG and HB_BUSAVG, and the load zones + LZ_AEN, LZ_CPS, LZ_HOUSTON, LZ_LCRA, LZ_NORTH, LZ_RAYBN, LZ_SOUTH and LZ_WEST. +- **Clean days.** Days of the daylight-saving changes are dropped (364 point-days), as is any day without exactly 24 + hourly prices (none). 67,810 point-days remain. Seven point-days of 17 February 2021 (Uri) above the era's + $9,000/MWh cap are kept for the showcase only. +- **Duplicates.** Within a date, a point whose 24 prices match a point already kept (correlation above 0.999 and no + hour more than $1 apart) is dropped: 19,733 point-days, most of them load zones that track their hub, and the two + averages. +- **Splits by date.** `eval_2025` is 1 January to 30 November 2025 (the first battery-day is 6 January); `rtcb` is 5 + December 2025, when real-time co-optimization plus batteries (RTC+B) went live, to 17 October 2026; `eval_iid` is + every 10th calendar day from 10 January 2014 through 2024; `train` is the rest of 2014 to 2024; the showcase + windows are 10 to 22 February 2021, 22 to 24 December 2022, August 2023, 6 September 2023 and 13 to 17 January + 2024. 1 to 4 December 2025 is unused. 2025 and later never reach `train`. +- **Batteries.** Each point-day gets one archetype battery, chosen by a stable hash of the date and point: the + duration class with the mix 1 h 40%, 2 h 45%, 4 h 15%, then one of 26 lettered designs in that class. Power, + `unit_mwh` and the rest come from a fixed table (in each manifest and in `stats.md`): 7.5 to 225 MW, round-trip + efficiency 0.80 to 0.90, degradation $0 to $8/MWh discharged, 1 to 2 cycles a day, varied start and end charge, and + up to 16 charge levels. The sizes follow EIA-860 2025 (Schedule 3_4, operable storage in the ERCO balancing + authority): 204 units, 13,115 MW, a mean duration of 1.645 h weighted by power, and 40% / 59% / 1% of the power in + the 1 h / 2 h / 4 h classes. 4 h batteries are over-weighted on purpose, to give long-duration days. The batteries + are archetypes, not real sites. +- **Filters.** A battery-day is dropped as low-spread when P_opt − P_idle is under $22 per MW of discharge power + (14.6% of train candidates, 5.4% of eval), and 80% of the days where the greedy rule already scores 0.97 or more + are dropped, chosen by hash (31,281 train candidates). The values were tuned on train and eval so that the greedy + mean lands near 0.75 to 0.80 and about 15% of train drops as low-spread; the tuning table is in `stats.md`. A day + joins the blind tier only if its anchor clears the same low-spread margin. +- **Sampling.** `train` is 20,000 of the 27,688 kept candidates by hash, and 6,000 of those with blind inputs form + its blind tier. `eval_2025`, `eval_iid` and `rtcb` are drawn round-robin over (point, duration class, month) to 500, + 500 and 250; `eval_2025`'s 300 blind battery-days are drawn the same way, and `rtcb` uses every blind-eligible + day (202). `eval_iid` has no blind tier. +- **Tags.** `binding` names the first of the cycle cap, the end obligation and the rate limit whose removal would + raise P_opt by more than 10% of P_opt − P_idle (`none` otherwise); `neg_hours` counts negative-price hours; `spike` + marks a day whose top price reaches the larger of $200 and 3 times its median. `difficulty` is 1 minus the greedy + reward. The curriculum is `C2` when the cycle cap or the end obligation binds or any hour is negative, else `C0` + when difficulty is at most 0.15, else `C1`. + +## Baselines + +Mean foresight reward per pack, from `stats.md`. Idle is 0.2 and the DP optimum 1.0 by construction. + +| Pack | Split | Battery-days | Blind | Clock | Threshold | Greedy | Random feasible | +|---|---|---:|---:|---:|---:|---:|---:| +| `ercot-dam-v1-train` | `train` | 20,000 | 6,000 | 0.573 | 0.606 | 0.761 | 0.064 | +| `ercot-dam-v1-eval` | `eval_2025` | 500 | 300 | 0.485 | 0.642 | 0.761 | 0.072 | +| `ercot-dam-v1-eval-iid` | `eval_iid` | 500 | 0 | 0.555 | 0.607 | 0.751 | 0.067 | +| `ercot-dam-v1-rtcb` | `rtcb` | 250 | 202 | 0.435 | 0.664 | 0.724 | 0.043 | +| `showcase` | `showcase` | 761 | 700 | 0.627 | 0.602 | 0.832 | 0.125 | + +- **Clock**: charge at full rate HE02 to HE05, sell at full rate HE18 to HE21. +- **Threshold**: charge at or below the day's 25th-percentile price, sell at or above its 75th while the spread + covers the losses and degradation. +- **Greedy**: pair the cheapest hours with the dearest later ones at full rate while each pair pays. +- **Random feasible**: uniform over the actions that keep every rule and leave the end obligation reachable. + +Each is clipped to the rules hour by hour and repaired to meet the end obligation. At the blind tier, the hindsight +capture (`r_opt`) of the anchor and of persistence alone: + +| Pack | Blind battery-days | Anchor r_opt | Persistence r_opt | +|---|---:|---:|---:| +| `ercot-dam-v1-train` | 6,000 | 0.904 | 0.858 | +| `ercot-dam-v1-eval` | 300 | 0.920 | 0.882 | +| `ercot-dam-v1-rtcb` | 202 | 0.888 | 0.844 | +| `showcase` | 700 | 0.894 | 0.882 | + +## Results + +GPT-6.1 Sol (`openai/gpt-6.1-sol`, reasoning effort high) played each battery-day once, under the board registered +before the first run ([`docs/prereg/v1-board.md`](https://github.com/earakely-scale/agentenv-gridsim-plugin/blob/v0.1.0/docs/prereg/v1-board.md)): +column A is the `full` harness with three checks at the foresight tier on the held-out `eval_2025` battery-days, B the +`oneshot` harness (the path-B training prompt) on the same days, C the `full` harness at the blind tier, and the +optional D the `full` harness at the foresight tier on the `rtcb` days. The 95% intervals are bootstrap intervals over +battery-days (10,000 resamples); optimal means R ≥ 0.99, valid submitted and feasible. Every run is in `v1_episodes` +with its transcript, and every recorded tool call replays on the env with the same output and reward. + + +| Column | Model | Pack | Tier | Harness | Battery-days | Mean reward (95% CI) | r_opt | Optimal | Valid | Beats anchor | $ per episode | +|---|---|---|---|---|---:|---|---:|---:|---:|---:|---:| +| A | GPT-6.1 Sol | `ercot-dam-v1-eval` | foresight | full | 500 | **0.9999** (0.9997 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | $0.0157 | +| B | GPT-6.1 Sol | `ercot-dam-v1-eval` | foresight | oneshot | 500 | **0.9999** (0.9998 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | $0.0117 | +| C | GPT-6.1 Sol | `ercot-dam-v1-eval` | blind | full | 300 | **0.9721** (0.9624 to 0.9804) | 0.9165 | 72.3% | 100.0% | 40.3% | $0.0193 | +| D | GPT-6.1 Sol | `ercot-dam-v1-rtcb` | foresight | full | 250 | **1.0000** (0.9999 to 1.0000) | 1.0000 | 100.0% | 100.0% | – | $0.0161 | + +Baselines graded on each column's battery-days (mean reward; r_opt in brackets at the blind tier): + +- A: idle 0.200, random 0.072, clock 0.485, threshold 0.642, greedy 0.761, persistence-DP 0.882 on 300, ridge-DP 0.799 on 300, blind anchor 0.920 on 300; the DP optimum 1.000. +- B: idle 0.200, random 0.072, clock 0.485, threshold 0.642, greedy 0.761, persistence-DP 0.882 on 300, ridge-DP 0.799 on 300, blind anchor 0.920 on 300; the DP optimum 1.000. +- C: idle 0.200 (0.200), random 0.076 (0.076), clock 0.516 (0.487), threshold 0.698 (0.655), greedy 0.821 (0.783), persistence-DP 0.957 (0.882), ridge-DP 0.861 (0.799), blind anchor 1.000 (0.920); the DP optimum 1.000. +- D: idle 0.200, random 0.043, clock 0.435, threshold 0.664, greedy 0.724, persistence-DP 0.844 on 202, ridge-DP 0.764 on 202, blind anchor 0.888 on 202; the DP optimum 1.000. + +The headline is the amended board. The pre-registered $0.10 episode cap stopped runs before they submitted (A 14, B 3, C 63, D 5): the agent's own pre-request cost guard, not the model. Those runs were re-run once at $0.40 and all scored. The pre-registered scoring, its scored runs only: A 0.9999 (486 of 500); B 0.9999 (497 of 500); C 0.9769 (237 of 300); D 1.0000 (245 of 250). Dropping the stopped runs would bias the blind column toward easy days, so the amended column is the headline. + + +Foresight is saturated for a frontier model: with the day's prices in view Sol finds the optimum. The blind tier is +where models separate, against the anchor's own hindsight capture on the same days. The plugin's +[`results/board.md`](https://github.com/earakely-scale/agentenv-gridsim-plugin/blob/v0.1.0/results/board.md) has +the full board: the amendment, the breakdowns by checks used, battery class, binding constraint and day type, cost and +time, the hypotheses against what was measured, and the unscored runs. + + + +## Training + +The point of GridSimEnv is to show training on an AgentEnv simulation end to end. Path B trains on the `train` +split with one prompt and one completion per battery-day: the prompt is exactly `v1_tasks`' `prompt` (the `oneshot` +harness's system prompt and situation), the completion ends in a `submit_schedule` call, and the reward is +`gridsim_core.trl.reward_fn`, the same `grade` the env's verifier uses, in under a millisecond. Path A proves the +policy in full AgentEnv episodes: the trained model plays the `oneshot` bundle on the env, so the two paths grade the +same schedule identically. Path-B evaluations write the same Episode JSON as the env's runs, so checkpoints appear in +`v1_episodes` with `trained` set to `grpo` and their `checkpoint_step`, and replay in the Space. + +**In progress:** the results land with the training lane. + + + +## Simplifications + +- **A price taker.** The battery's trades never move the price. +- **The DAM award is the physical schedule.** No real-time market, no deviations, no imbalance settlement. +- **Hourly steps.** One action per hour; `q_15` (a quarter of `unit_mwh`) is stored so a 15-minute version can + expand the same battery exactly. +- **Losses on the buy side.** Buying a unit draws `unit_mwh / eta`; selling delivers `unit_mwh`. +- **Linear degradation.** A flat `c_deg` per MWh discharged, with no depth-of-discharge or calendar ageing. +- **Archetype batteries, not real sites.** Sizes follow the ERCOT fleet in EIA-860; no task is any operator's asset. +- **Energy only at hubs and load zones.** No ancillary services, no real-time prices, no congestion at a resource + node, no outages. +- **Integer charge levels.** Energy moves in whole units, which is what makes the optimum exact. + +## Dataset structure + +**Tasks** (`v1_tasks`, `v1_showcase`), one row per battery-day and tier: + +- `task_id` (`dam-v1---`; the tier is not in the id), `tier` (`foresight` or `blind`), + `has_blind` (the battery-day also plays at the blind tier), `split`, `date`, `settlement_point`, `point_kind` + (`hub` or `load_zone`), `region` (the viewer's backdrop: west, panhandle, north, houston, south or central). +- The battery: `archetype`, `p_dis_mw`, `p_ch_mw`, `energy_mwh`, `unit_mwh`, `q_15`, `k_dis`, `k_ch`, `soc_levels`, + `eta`, `c_deg` ($/MWh discharged), `cycle_cap_units`, `s0`, `s_end_min`. +- `prices`: the day's 24 DAM prices in $/MWh, HE01 first, on which every tier settles (a blind row's agent never sees + them). `dam_prev_day` (24) and `dam_prev_7d` (7 × 24, oldest first): the blind inputs, when the battery-day has + them. +- `system_prompt`, `situation`: the system prompt and the opening user message exactly as the `oneshot` harness and + path-B training send them (`gridsim_core.prompt`); `prompt` is the same pair as a chat list, and `tools` the JSON + function schema of `submit_schedule`. The `full` harness's prompt differs only in describing `check_schedule` and + the check budget. +- `curriculum` (`C0`, `C1`, `C2`), `difficulty`, `season`, `binding`, `neg_hours`, `spike`, `era` (the market era: + price caps and products in force), and `tags`, all the tags as JSON. +- `task`: the full battery-day as JSON, as the env loads it, with its references. + +**References** (`v1_references`), one row per battery-day: `p_idle`, `p_opt`, `opt` (the DP schedule), `p_lp`; for +each of `clock`, `threshold`, `greedy` and `random`, its `_profit`, `_reward` and `_schedule`; and for blind +battery-days `p_persist` and `persist`, `p_ridge` and `ridge`, `p_anchor` and which one it is (`anchor`), +`r_opt_anchor`, and each forecast schedule's blind-tier `_reward` and `_r_opt`. + +**Episodes** (`v1_episodes`), one row per recorded run: `episode_id`, `task_id`, `split`, `tier`, `bundle` (`full`, +`oneshot` or `train`), `path` (`A`, a full AgentEnv episode; `B`, a training-path completion), `model` (the LiteLLM +id, or `reference/...` for the reference episodes the Space also replays: the DP optimum, the baselines, persistence-DP +and a deliberately flawed plan), `model_label` (the display name, e.g. GPT-6.1 Sol), `trained` +(`base`, `grpo` or `sft`), `checkpoint_step`, `seed`; `reward`, `r_opt`, `beats_anchor` (blind), `submitted`, +`feasible`, `truncated_at`, `violation`, `end_shortfall_units`, `profit_usd`, `p_opt`, `p_idle`, `p_anchor`; +`final_schedule`, `soc_path`, `checks_used`, `tool_calls`, `steps` (each call's arguments as sent and its output, as +JSON); `messages`, the transcript as chat messages in the shape TRL and transformers read (OpenAI roles, `tool_calls` +with `arguments` as objects, `tool_call_id` and `name` on tool messages; the `actions` argument kept as the model sent +it, a list or a string); `transcript` (`record`, the agent's own record, or `rebuilt` from the Episode JSON's calls +when that record isn't published); `assistant_text`; `input_tokens`, `output_tokens`, `usd`, `wall_seconds`; and +`file`, the episode's JSON under `runs/`. Every episode is graded again with `gridsim_core` before it is published, +and the build stops if any reward, profit or situation differs. + +## Licence and attribution + +The dataset carries several sets of terms, hence `license: other`; the plugin's +[data/LICENSE](https://github.com/earakely-scale/agentenv-gridsim-plugin/blob/v0.1.0/data/LICENSE) holds the ERCOT +terms in full and [NOTICE](https://github.com/earakely-scale/agentenv-gridsim-plugin/blob/v0.1.0/NOTICE) sets out +each source. + +**ERCOT prices, under ERCOT's terms.** The prices, and everything derived from them in this dataset, come from public +data of the Electric Reliability Council of Texas, Inc. (ERCOT): the report NP4-180-ER, *Historical DAM Load Zone and +Hub Prices*, at . ERCOT data is used under +ERCOT's Website User Agreement (last updated 20 July 2023), section 5, quoted from + as accessed on 24 October 2026: + +> 5. The information presented on this website has been compiled by or for ERCOT for general information purposes +> only. The publicly available contents of this website may be used, reproduced, and redistributed, provided that the +> contents are not modified and that you maintain all copyright and other notices contained in the contents, +> including this Agreement. Notwithstanding the foregoing, raw data provided in public portions of this website may +> be used, reproduced, and redistributed in compilations, charts, and analyses without maintaining such notices. +> ERCOT does not guarantee the accuracy of any such compilations, charts, or analyses. + +Automated access to ERCOT's reports is governed by ERCOT's Market Data Transparency Terms of Use (version 1.2, 11 +August 2020, linked from ), which limits how often a report may be queried; ERCOT +provides its data "as is", without warranties (Website User Agreement, section 2). data/LICENSE reproduces both. The +ERCOT data is not relicensed: it is not under CC BY or any other licence from us, and anyone using it is bound by +ERCOT's terms. This dataset is a compilation and analysis of ERCOT's raw data; ERCOT does not guarantee its accuracy. +Source: ERCOT. GridSimEnv is not affiliated with or endorsed by ERCOT, and uses no ERCOT logo or trademark beyond +naming the source. + +**EIA-860, public domain.** The battery archetypes are sized from the U.S. Energy Information Administration's Form +EIA-860 annual electric generator data (2025 release, Schedule 3_4, Energy Storage), at +, a work of the U.S. Government. Source: U.S. Energy Information +Administration. + +**Apache-2.0: the code.** The grader (`gridsim_core`), the verifier and bundle files under `bundles/` and the plugin +that builds this dataset are Apache-2.0. The prompts, references and transcripts are derived from the ERCOT prices +and carry ERCOT's terms with them. + +**Not in this dataset.** The replay Space can show each day's weather at a representative airport, from the Iowa +Environmental Mesonet's ASOS archive (Iowa State University), credited there. + +`scripts/hub_dataset.py` in the plugin builds this dataset, after agentenv-hf's scan of every file for keys and token +shapes. Neither ERCOT, the EIA nor the IEM is affiliated with it or endorses it. + +## Citation + +```bibtex +@misc{gridsimenv2026, + title = {{GridSimEnv}: run a grid battery on the real {Texas} day-ahead market, an {AgentEnv} environment}, + author = {Arakelyan, Edgar and Kolavi, Adithya S}, + year = {2026}, + howpublished = {\url{https://huggingface.co/datasets/earakely-scale/GridSimEnv-AgentEnv}}, + note = {Version v0.1.0. Prices: ERCOT NP4-180-ER. Code: https://github.com/earakely-scale/agentenv-gridsim-plugin} +} +``` diff --git a/episodes/v1/eval_2025.parquet b/episodes/v1/eval_2025.parquet new file mode 100644 index 0000000000000000000000000000000000000000..51111354ad1578f98fb379558063663e9217a08a --- /dev/null +++ b/episodes/v1/eval_2025.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d8bea1af6683c9c4ee61cffb5d70cbbcf4165dd9b7c23e9532effc7ae5e44ad +size 579048 diff --git a/episodes/v1/rtcb.parquet b/episodes/v1/rtcb.parquet new file mode 100644 index 0000000000000000000000000000000000000000..8703cb16efb686bf6e13d4671c694148f68c3710 --- /dev/null +++ b/episodes/v1/rtcb.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6541ff0b6a19c26eb25ee46b11b7a09266b58ae14fdd102b7a388abb4b71a078 +size 125578 diff --git a/episodes/v1/showcase.parquet b/episodes/v1/showcase.parquet new file mode 100644 index 0000000000000000000000000000000000000000..b5e0298560bd05e5794e19534041e6a3b0f88b5e --- /dev/null +++ b/episodes/v1/showcase.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:268ed17526e5c5d265134b58c0d87c3d339917a4605489a584c954fb6c9d99aa +size 24822 diff --git a/episodes/v1/train.parquet b/episodes/v1/train.parquet new file mode 100644 index 0000000000000000000000000000000000000000..d962b4c58682c9263e22fb9c0ab031d2fbfb3786 --- /dev/null +++ b/episodes/v1/train.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04d6a7fe6d150f885ab6dd278997b047ae3fc6e42fc7df77842cf458134f7901 +size 21544 diff --git a/raw/packs/ercot-dam-v1-eval-iid.jsonl b/raw/packs/ercot-dam-v1-eval-iid.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..26e5769b1ac4366eef815dbd2ab8f87eb32a6fc7 --- /dev/null +++ b/raw/packs/ercot-dam-v1-eval-iid.jsonl @@ -0,0 +1,500 @@ 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+{"task_id":"dam-v1-2023-05-23-LZ_WEST-2h-u","contract_version":"1.0","pack":"ercot-dam-v1-eval-iid","split":"eval_iid","date":"2023-05-23","settlement_point":"LZ_WEST","point_kind":"load_zone","region":"west","prices":[20.36,18.09,16.86,16.79,18.19,21.77,26.02,21.7,17.28,17.34,19.54,23.9,27.99,37.84,51.07,60.11,72.74,56.3,42.04,45.05,39.65,29.35,26.92,24.32],"blind":null,"battery":{"archetype":"2h-u","source":"archetype","p_dis_mw":225.0,"p_ch_mw":225.0,"energy_mwh":450.0,"unit_mwh":75.0,"q_15":18.75,"k_dis":3,"k_ch":3,"soc_levels":6,"eta":0.8,"c_deg":8.0,"cycle_cap_units":6,"s0":3,"s_end_min":3},"refs":{"foresight":{"p_idle":0.0,"p_opt":14729.0625,"opt":[0,0,0,-3,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,-3],"p_lp":14729.0625,"baselines":{"clock":{"profit":6598.6875,"reward":0.558404,"schedule":[0,-3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,-3]},"threshold":{"profit":9487.6875,"reward":0.715318,"schedule":[0,-3,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,0,-3]},"greedy":{"profit":14709.375,"reward":0.998931,"schedule":[0,0,-3,0,0,0,0,0,0,0,0,0,0,0,0,3,3,0,0,0,0,0,0,-3]},"random":{"profit":-12727.6875,"reward":0.0,"schedule":[2,0,0,0,-2,-3,0,3,-1,1,-2,0,0,-1,0,0,0,0,0,0,0,0,0,0]}}},"blind":null},"difficulty":0.001069,"curriculum":"C0","tags":{"neg_hours":0,"spike":false,"season":"spring","binding":"rate","era":"hcap-5000","blind_eval":false}} 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index 0000000000000000000000000000000000000000..1c62521fb89369947ca073814e96bf67ffe5dc72 --- /dev/null +++ b/raw/packs/ercot-dam-v1-rtcb.jsonl @@ -0,0 +1,250 @@ 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diff --git a/raw/packs/ercot-dam-v1-rtcb.manifest.json b/raw/packs/ercot-dam-v1-rtcb.manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1c46cb64eb5ed3c3d8f89cca1f66dcd1484fd535 --- /dev/null +++ b/raw/packs/ercot-dam-v1-rtcb.manifest.json @@ -0,0 +1,1897 @@ +{ + "pack": "ercot-dam-v1-rtcb", + "contract_version": "1.0", + "count": 250, + "count_blind": 202, + "blind_task_ids": [ + "dam-v1-2025-12-06-HB_HOUSTON-4h-o", + "dam-v1-2025-12-06-LZ_LCRA-4h-o", + "dam-v1-2025-12-06-LZ_WEST-2h-i", + "dam-v1-2025-12-11-HB_HUBAVG-4h-m", + "dam-v1-2025-12-12-HB_SOUTH-2h-s", + "dam-v1-2025-12-13-HB_HUBAVG-2h-j", + "dam-v1-2025-12-13-LZ_NORTH-2h-q", + "dam-v1-2025-12-15-HB_PAN-2h-c", + "dam-v1-2025-12-15-HB_SOUTH-4h-v", + "dam-v1-2025-12-16-HB_HOUSTON-2h-x", + "dam-v1-2025-12-17-HB_WEST-2h-e", + "dam-v1-2025-12-17-LZ_RAYBN-1h-n", + "dam-v1-2025-12-17-LZ_SOUTH-1h-o", + "dam-v1-2025-12-19-HB_NORTH-2h-q", + "dam-v1-2025-12-29-HB_WEST-1h-x", + 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diff --git a/raw/packs/showcase.manifest.json b/raw/packs/showcase.manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..721bbb7599da044b945fe03ca3ed3229668cddc9 --- /dev/null +++ b/raw/packs/showcase.manifest.json @@ -0,0 +1,2395 @@ +{ + "pack": "showcase", + "contract_version": "1.0", + "count": 761, + "count_blind": 700, + "blind_task_ids": [ + "dam-v1-2021-02-10-HB_HOUSTON-1h-e", + "dam-v1-2021-02-10-HB_HUBAVG-2h-s", + "dam-v1-2021-02-10-HB_PAN-2h-v", + "dam-v1-2021-02-10-HB_SOUTH-1h-p", + "dam-v1-2021-02-10-LZ_AEN-4h-c", + "dam-v1-2021-02-10-LZ_CPS-4h-x", + "dam-v1-2021-02-10-LZ_LCRA-1h-c", + "dam-v1-2021-02-10-LZ_RAYBN-2h-b", + "dam-v1-2021-02-10-LZ_SOUTH-2h-n", + "dam-v1-2021-02-10-LZ_WEST-4h-x", + "dam-v1-2021-02-11-HB_BUSAVG-2h-o", + "dam-v1-2021-02-11-HB_HOUSTON-1h-l", + "dam-v1-2021-02-11-HB_HUBAVG-2h-z", + "dam-v1-2021-02-11-HB_NORTH-1h-d", + "dam-v1-2021-02-11-HB_PAN-1h-n", + "dam-v1-2021-02-11-HB_SOUTH-1h-m", + 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"soc_levels": 12, + "eta": 0.8, + "c_deg": 8.0, + "cycles": 2.0, + "s0": 12, + "s_end_min": 0 + }, + { + "letter": "e", + "ep_h": 4, + "p_dis_mw": 225.0, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 75.0, + "soc_levels": 12, + "eta": 0.9, + "c_deg": 8.0, + "cycles": 2.0, + "s0": 6, + "s_end_min": 12 + }, + { + "letter": "f", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 25.0, + "soc_levels": 16, + "eta": 0.8, + "c_deg": 4.0, + "cycles": 1.0, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "g", + "ep_h": 4, + "p_dis_mw": 150.0, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 50.0, + "soc_levels": 12, + "eta": 0.8, + "c_deg": 4.0, + "cycles": 1.0, + "s0": 12, + "s_end_min": 12 + }, + { + "letter": "h", + "ep_h": 4, + "p_dis_mw": 10.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 5.0, + "soc_levels": 8, + "eta": 0.85, + "c_deg": 4.0, + "cycles": 1.5, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "i", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 25.0, + "soc_levels": 16, + "eta": 0.9, + "c_deg": 4.0, + "cycles": 1.0, + "s0": 16, + "s_end_min": 16 + }, + { + "letter": "j", + "ep_h": 4, + "p_dis_mw": 160.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 40.0, + "soc_levels": 16, + "eta": 0.8, + "c_deg": 8.0, + "cycles": 2.0, + "s0": 8, + "s_end_min": 8 + }, + { + "letter": "k", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 25.0, + "soc_levels": 16, + "eta": 0.9, + "c_deg": 0.0, + "cycles": 2.0, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "l", + "ep_h": 4, + "p_dis_mw": 225.0, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 75.0, + "soc_levels": 12, + "eta": 0.8, + "c_deg": 4.0, + "cycles": 1.0, + "s0": 6, + "s_end_min": 0 + }, + { + "letter": "m", + "ep_h": 4, + "p_dis_mw": 160.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 40.0, + "soc_levels": 16, + "eta": 0.85, + "c_deg": 0.0, + "cycles": 1.0, + "s0": 0, + "s_end_min": 8 + }, + { + "letter": "n", + "ep_h": 4, + "p_dis_mw": 7.5, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 2.5, + "soc_levels": 12, + "eta": 0.9, + "c_deg": 8.0, + "cycles": 1.0, + "s0": 6, + "s_end_min": 6 + }, + { + "letter": "o", + "ep_h": 4, + "p_dis_mw": 160.0, + "k_dis": 4, + "k_ch": 4, + "unit_mwh": 40.0, + "soc_levels": 16, + "eta": 0.85, + "c_deg": 8.0, + "cycles": 1.5, + "s0": 16, + "s_end_min": 16 + }, + { + "letter": "p", + "ep_h": 4, + "p_dis_mw": 150.0, + "k_dis": 2, + "k_ch": 3, + "unit_mwh": 75.0, + "soc_levels": 8, + "eta": 0.85, + "c_deg": 0.0, + "cycles": 1.5, + "s0": 8, + "s_end_min": 0 + }, + { + "letter": "q", + "ep_h": 4, + "p_dis_mw": 150.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 75.0, + "soc_levels": 8, + "eta": 0.85, + "c_deg": 4.0, + "cycles": 1.0, + "s0": 4, + "s_end_min": 4 + }, + { + "letter": "r", + "ep_h": 4, + "p_dis_mw": 225.0, + "k_dis": 3, + "k_ch": 2, + "unit_mwh": 75.0, + "soc_levels": 12, + "eta": 0.8, + "c_deg": 4.0, + "cycles": 1.5, + "s0": 12, + "s_end_min": 6 + }, + { + "letter": "s", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 50.0, + "soc_levels": 8, + "eta": 0.9, + "c_deg": 0.0, + "cycles": 1.5, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "t", + "ep_h": 4, + "p_dis_mw": 10.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 5.0, + "soc_levels": 8, + "eta": 0.85, + "c_deg": 4.0, + "cycles": 1.5, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "u", + "ep_h": 4, + "p_dis_mw": 10.0, + "k_dis": 2, + "k_ch": 3, + "unit_mwh": 5.0, + "soc_levels": 8, + "eta": 0.8, + "c_deg": 0.0, + "cycles": 1.0, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "v", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 4, + "k_ch": 3, + "unit_mwh": 25.0, + "soc_levels": 16, + "eta": 0.9, + "c_deg": 0.0, + "cycles": 2.0, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "w", + "ep_h": 4, + "p_dis_mw": 100.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 50.0, + "soc_levels": 8, + "eta": 0.9, + "c_deg": 4.0, + "cycles": 2.0, + "s0": 0, + "s_end_min": 0 + }, + { + "letter": "x", + "ep_h": 4, + "p_dis_mw": 10.0, + "k_dis": 2, + "k_ch": 2, + "unit_mwh": 5.0, + "soc_levels": 8, + "eta": 0.85, + "c_deg": 0.0, + "cycles": 1.5, + "s0": 4, + "s_end_min": 0 + }, + { + "letter": "y", + "ep_h": 4, + "p_dis_mw": 150.0, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 50.0, + "soc_levels": 12, + "eta": 0.8, + "c_deg": 0.0, + "cycles": 1.5, + "s0": 6, + "s_end_min": 12 + }, + { + "letter": "z", + "ep_h": 4, + "p_dis_mw": 90.0, + "k_dis": 3, + "k_ch": 3, + "unit_mwh": 30.0, + "soc_levels": 12, + "eta": 0.8, + "c_deg": 0.0, + "cycles": 2.0, + "s0": 0, + "s_end_min": 0 + } + ] + }, + "eia_fit": { + "source": "eia8602025.zip", + "schedule": "3_4_Energy_Storage_Y2025.xlsx", + "ba": "ERCO", + "units": 204, + "total_mw": 13115.0, + "mw_weighted_mean_duration_h": 1.645, + "p_mw_quantiles": { + "10": 9.9, + "25": 9.9, + "50": 10.0, + "75": 100.0, + "90": 174.7 + }, + "p_mw_quantiles_mw_weighted": { + "10": 50.0, + "25": 100.0, + "50": 150.0, + "75": 200.0, + "90": 257.7 + }, + "ep_h_quantiles_mw_weighted": { + "10": 1.0, + "25": 1.0, + "50": 2.0, + "75": 2.0, + "90": 2.17 + }, + "ep_h_quantiles": { + "10": 1.0, + "25": 1.0, + "50": 1.1, + "75": 2.0, + "90": 2.21 + }, + "ep_bucket_share_by_count": { + "1h": 0.5588, + "2h": 0.4216, + "4h": 0.0196 + }, + "ep_bucket_share_by_mw": { + "1h": 0.4024, + "2h": 0.5871, + "4h": 0.0105 + }, + "ep_bucket_share_by_mw_since_2023": { + "1h": 0.3529, + "2h": 0.6355, + "4h": 0.0116 + }, + "buckets": "1h: E/P < 1.5 h, 2h: 1.5-3 h, 4h: >= 3 h", + "archetype_mix_used": { + "1h": 0.4, + "2h": 0.45, + "4h": 0.15 + } + }, + "build_command": "python scripts/build_pack.py --workers 12", + "parse": { + "point_days_raw": 68174, + "negative_hours": 13912, + "days_with_negative": 1913, + "days_max_le_0": 45, + "dst_dropped": 364, + "era_flagged": 7, + "points_seen": [ + "HB_BUSAVG", + "HB_HOUSTON", + "HB_HUBAVG", + "HB_NORTH", + "HB_PAN", + "HB_SOUTH", + "HB_WEST", + "LZ_AEN", + "LZ_CPS", + "LZ_HOUSTON", + "LZ_LCRA", + "LZ_NORTH", + "LZ_RAYBN", + "LZ_SOUTH", + "LZ_WEST" + ], + "era_violations": [ + "2021-02-17 HB_PAN", + "2021-02-17 HB_WEST", + "2021-02-17 LZ_AEN", + "2021-02-17 LZ_CPS", + "2021-02-17 LZ_LCRA", + "2021-02-17 LZ_NORTH", + "2021-02-17 LZ_WEST" + ], + "clean_point_days": 67810, + "first_date": { + "HB_HOUSTON": "2014-01-01", + "HB_NORTH": "2014-01-01", + "HB_PAN": "2019-04-06", + "HB_SOUTH": "2014-01-01", + "HB_WEST": "2014-01-01", + "LZ_AEN": "2014-01-01", + "LZ_CPS": "2014-01-01", + "LZ_HOUSTON": "2014-01-01", + "LZ_LCRA": "2014-01-01", + "LZ_NORTH": "2014-01-01", + "LZ_RAYBN": "2014-01-01", + "LZ_SOUTH": "2014-01-01", + "LZ_WEST": "2014-01-01", + "HB_HUBAVG": "2014-01-01", + "HB_BUSAVG": "2014-01-01" + }, + "last_date": "2026-10-17" + }, + "dedup": { + "dropped": 19733, + "by_point": { + "HB_NORTH": 696, + "HB_SOUTH": 668, + "HB_WEST": 1009, + "LZ_CPS": 1157, + "LZ_HOUSTON": 2978, + "LZ_LCRA": 2083, + "LZ_NORTH": 2466, + "LZ_RAYBN": 2377, + "LZ_SOUTH": 525, + "LZ_WEST": 629, + "HB_HUBAVG": 1646, + "HB_BUSAVG": 2317, + "LZ_AEN": 1043, + "HB_PAN": 139 + }, + "pairs": { + "LZ_HOUSTON->HB_HOUSTON": 2957, + "LZ_NORTH->HB_NORTH": 1786, + "LZ_RAYBN->HB_NORTH": 1613, + "HB_BUSAVG->HB_HOUSTON": 839, + "LZ_LCRA->LZ_AEN": 788, + "HB_HUBAVG->HB_HOUSTON": 737, + "HB_NORTH->HB_HOUSTON": 696, + "HB_BUSAVG->HB_NORTH": 619, + "LZ_LCRA->HB_HOUSTON": 617, + "HB_SOUTH->HB_HOUSTON": 601, + "LZ_NORTH->HB_HOUSTON": 598, + "LZ_AEN->HB_HOUSTON": 565, + "HB_WEST->HB_NORTH": 554, + "LZ_CPS->HB_SOUTH": 543, + "LZ_RAYBN->HB_HOUSTON": 511, + "HB_BUSAVG->HB_HUBAVG": 494, + "LZ_CPS->HB_HOUSTON": 426, + "LZ_LCRA->HB_SOUTH": 405, + "HB_WEST->HB_HOUSTON": 393, + "HB_HUBAVG->HB_NORTH": 338 + } + } +} diff --git a/raw/packs/stats.md b/raw/packs/stats.md new file mode 100644 index 0000000000000000000000000000000000000000..ad5b6b19fe907906d041ae5d226a8e31ea265dfb --- /dev/null +++ b/raw/packs/stats.md @@ -0,0 +1,290 @@ +# GridSimEnv v1 pack statistics + +Built by `scripts/build_pack.py` (contract 1.0). All numbers are measured on the packs. + +## Packs + +| pack | tasks | blind | clock | threshold | greedy | random | LP-DP gap median | p95 | +|---|---|---|---|---|---|---|---|---| +| ercot-dam-v1-train | 20000 | 6000 | 0.5734 | 0.6061 | 0.7607 | 0.0644 | 0.0 | 0.0 | +| ercot-dam-v1-eval | 500 | 300 | 0.4849 | 0.6418 | 0.7611 | 0.0716 | 0.0 | 0.0 | +| ercot-dam-v1-eval-iid | 500 | 0 | 0.5547 | 0.6073 | 0.7514 | 0.0665 | 0.0 | 0.0 | +| ercot-dam-v1-rtcb | 250 | 202 | 0.4352 | 0.6638 | 0.724 | 0.0433 | 0.0 | 0.0 | +| showcase | 761 | 700 | 0.627 | 0.6015 | 0.8316 | 0.1247 | 0.0 | 0.0 | +| fixtures | 20 | 4 | 0.4495 | 0.5809 | 0.6647 | 0.095 | 0.0 | 0.0009 | + +Baseline columns are mean foresight rewards (idle is 0.2 by construction). The LP-DP gap is (P_lp - P_opt) / (P_lp - P_idle). + +## Blind tier + +| pack | blind tasks | anchor r_opt | persistence r_opt | +|---|---|---|---| +| ercot-dam-v1-train | 6000 | 0.9039 | 0.8578 | +| ercot-dam-v1-eval | 300 | 0.9204 | 0.882 | +| ercot-dam-v1-rtcb | 202 | 0.8878 | 0.844 | +| showcase | 700 | 0.8937 | 0.882 | +| fixtures | 4 | 0.9285 | 0.7684 | + +## Filters + +| split | candidates | low-spread dropped | share | greedy-easy dropped | kept | blind candidates | blind anchor dropped (share) | +|---|---|---|---|---|---|---|---| +| train | 69032 | 10063 | 0.1458 | 31281 | 27688 | 26305 | 3068 (0.1166) | +| eval | 4421 | 237 | 0.0536 | 1605 | 2579 | 2442 | 220 (0.0901) | +| eval_iid | 3870 | 529 | 0.1367 | 1835 | 1506 | 1435 | 174 (0.1213) | +| rtcb | 4191 | 626 | 0.1494 | 1228 | 2337 | 2268 | 342 (0.1508) | + +Low-spread: drop when P_opt - P_idle < $22 per MW of p_dis. Greedy-easy: drop 80% of tasks with greedy >= 0.97 (hash-chosen). Blind: a task leaves the blind tier when P_anchor - P_idle is below the low-spread margin. + +### Tuning table + +| split | $/MW | greedy-easy drop | low-spread share | kept | greedy mean | +|---|---|---|---|---|---| +| train | 8.0 | 0.0 | 0.03 | 66962 | 0.8801 | +| eval | 8.0 | 0.0 | 0.0095 | 4379 | 0.8289 | +| train | 8.0 | 0.5 | 0.03 | 45132 | 0.8233 | +| eval | 8.0 | 0.5 | 0.0095 | 3323 | 0.7759 | +| train | 8.0 | 0.8 | 0.03 | 31885 | 0.751 | +| eval | 8.0 | 0.8 | 0.0095 | 2708 | 0.7257 | +| train | 15.0 | 0.0 | 0.0786 | 63609 | 0.8848 | +| eval | 15.0 | 0.0 | 0.0258 | 4307 | 0.8308 | +| train | 15.0 | 0.5 | 0.0786 | 42682 | 0.8295 | +| eval | 15.0 | 0.5 | 0.0258 | 3266 | 0.7781 | +| train | 15.0 | 0.8 | 0.0786 | 30018 | 0.7586 | +| eval | 15.0 | 0.8 | 0.0258 | 2657 | 0.728 | +| train | 20.0 | 0.0 | 0.1256 | 60365 | 0.8866 | +| eval | 20.0 | 0.0 | 0.0434 | 4229 | 0.8315 | +| train | 20.0 | 0.5 | 0.1256 | 40431 | 0.8319 | +| eval | 20.0 | 0.5 | 0.0434 | 3212 | 0.7794 | +| train | 20.0 | 0.8 | 0.1256 | 28373 | 0.7616 | +| eval | 20.0 | 0.8 | 0.0434 | 2609 | 0.7292 | +| train | 22.0 | 0.0 | 0.1458 | 58969 | 0.8872 | +| eval | 22.0 | 0.0 | 0.0536 | 4184 | 0.8325 | +| train | 22.0 | 0.5 | 0.1458 | 39485 | 0.8329 | +| eval | 22.0 | 0.5 | 0.0536 | 3176 | 0.7806 | +| train | 22.0 | 0.8 | 0.1458 | 27688 | 0.7628 | +| eval | 22.0 | 0.8 | 0.0536 | 2579 | 0.7306 | +| train | 25.0 | 0.0 | 0.1779 | 56748 | 0.8886 | +| eval | 25.0 | 0.0 | 0.0735 | 4096 | 0.8345 | +| train | 25.0 | 0.5 | 0.1779 | 37912 | 0.8345 | +| eval | 25.0 | 0.5 | 0.0735 | 3109 | 0.7833 | +| train | 25.0 | 0.8 | 0.1779 | 26549 | 0.7648 | +| eval | 25.0 | 0.8 | 0.0735 | 2523 | 0.7338 | +| train | 30.0 | 0.0 | 0.2333 | 52926 | 0.8902 | +| eval | 30.0 | 0.0 | 0.1083 | 3942 | 0.8375 | +| train | 30.0 | 0.5 | 0.2333 | 35297 | 0.8367 | +| eval | 30.0 | 0.5 | 0.1083 | 2985 | 0.7867 | +| train | 30.0 | 0.8 | 0.2333 | 24626 | 0.7671 | +| eval | 30.0 | 0.8 | 0.1083 | 2411 | 0.7367 | + +## Counts by point + +| point | ercot-dam-v1-train | ercot-dam-v1-eval | ercot-dam-v1-eval-iid | ercot-dam-v1-rtcb | showcase | +|---|---|---|---|---|---| +| HB_BUSAVG | 877 | 32 | 29 | 10 | 50 | +| HB_HOUSTON | 1823 | 34 | 37 | 21 | 53 | +| HB_HUBAVG | 1180 | 35 | 32 | 15 | 51 | +| HB_NORTH | 1563 | 35 | 35 | 22 | 51 | +| HB_PAN | 1492 | 33 | 37 | 19 | 52 | +| HB_SOUTH | 1597 | 34 | 35 | 16 | 53 | +| HB_WEST | 1749 | 34 | 40 | 16 | 51 | +| LZ_AEN | 1363 | 33 | 37 | 16 | 53 | +| LZ_CPS | 1379 | 33 | 35 | 17 | 51 | +| LZ_HOUSTON | 609 | 28 | 27 | 13 | 38 | +| LZ_LCRA | 1001 | 34 | 27 | 19 | 51 | +| LZ_NORTH | 778 | 33 | 28 | 19 | 51 | +| LZ_RAYBN | 830 | 33 | 29 | 16 | 51 | +| LZ_SOUTH | 1618 | 33 | 35 | 15 | 53 | +| LZ_WEST | 2141 | 36 | 37 | 16 | 52 | + +## Counts by archetype class + +| archetype class | ercot-dam-v1-train | ercot-dam-v1-eval | ercot-dam-v1-eval-iid | ercot-dam-v1-rtcb | showcase | +|---|---|---|---|---|---| +| 1h | 7038 | 172 | 171 | 81 | 310 | +| 2h | 9268 | 166 | 184 | 92 | 329 | +| 4h | 3694 | 162 | 145 | 77 | 122 | + +## Counts by season + +| season | ercot-dam-v1-train | ercot-dam-v1-eval | ercot-dam-v1-eval-iid | ercot-dam-v1-rtcb | showcase | +|---|---|---|---|---|---| +| fall | 5112 | 138 | 127 | 44 | 15 | +| spring | 5383 | 139 | 131 | 76 | 0 | +| summer | 3280 | 131 | 108 | 64 | 460 | +| winter | 6225 | 92 | 134 | 66 | 286 | + +## Counts by curriculum + +| curriculum | ercot-dam-v1-train | ercot-dam-v1-eval | ercot-dam-v1-eval-iid | ercot-dam-v1-rtcb | showcase | +|---|---|---|---|---|---| +| C0 | 8342 | 202 | 214 | 88 | 487 | +| C1 | 5588 | 116 | 141 | 69 | 154 | +| C2 | 6070 | 182 | 145 | 93 | 120 | + +## Counts by binding constraint + +| binding constraint | ercot-dam-v1-train | ercot-dam-v1-eval | ercot-dam-v1-eval-iid | ercot-dam-v1-rtcb | showcase | +|---|---|---|---|---|---| +| cycle_cap | 2761 | 103 | 64 | 38 | 87 | +| end_soc | 2529 | 65 | 67 | 33 | 33 | +| none | 7814 | 169 | 180 | 110 | 365 | +| rate | 6896 | 163 | 189 | 69 | 276 | + +## Prices + +- Clean point-days after parsing: 67810 (DST dropped 364, not-24 dropped 0, above the era cap/floor 7, kept for showcase only: 2021-02-17 HB_PAN, 2021-02-17 HB_WEST, 2021-02-17 LZ_AEN, 2021-02-17 LZ_CPS, 2021-02-17 LZ_LCRA, 2021-02-17 LZ_NORTH, 2021-02-17 LZ_WEST). +- Within-date dedup dropped 19733 point-days: {'HB_NORTH': 696, 'HB_SOUTH': 668, 'HB_WEST': 1009, 'LZ_CPS': 1157, 'LZ_HOUSTON': 2978, 'LZ_LCRA': 2083, 'LZ_NORTH': 2466, 'LZ_RAYBN': 2377, 'LZ_SOUTH': 525, 'LZ_WEST': 629, 'HB_HUBAVG': 1646, 'HB_BUSAVG': 2317, 'LZ_AEN': 1043, 'HB_PAN': 139}. +- Candidates with any negative hour: 3018 of 82275 (3.7%); negative hours: 22356 of 1974600 (1.13%); days with max price <= 0: 80. +- First date per point: {'HB_HOUSTON': '2014-01-01', 'HB_NORTH': '2014-01-01', 'HB_PAN': '2019-04-06', 'HB_SOUTH': '2014-01-01', 'HB_WEST': '2014-01-01', 'LZ_AEN': '2014-01-01', 'LZ_CPS': '2014-01-01', 'LZ_HOUSTON': '2014-01-01', 'LZ_LCRA': '2014-01-01', 'LZ_NORTH': '2014-01-01', 'LZ_RAYBN': '2014-01-01', 'LZ_SOUTH': '2014-01-01', 'LZ_WEST': '2014-01-01', 'HB_HUBAVG': '2014-01-01', 'HB_BUSAVG': '2014-01-01'}. + +## LP-DP gap (all candidates) + +| quantile | p50 | p75 | p90 | p95 | p99 | max | +|---|---|---|---|---|---|---| +| (P_lp - P_opt)/(P_lp - P_idle) | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0417 | + +## Battery archetypes (EIA-860 fit) + +```json +{ + "source": "eia8602025.zip", + "schedule": "3_4_Energy_Storage_Y2025.xlsx", + "ba": "ERCO", + "units": 204, + "total_mw": 13115.0, + "mw_weighted_mean_duration_h": 1.645, + "p_mw_quantiles": { + "10": 9.9, + "25": 9.9, + "50": 10.0, + "75": 100.0, + "90": 174.7 + }, + "p_mw_quantiles_mw_weighted": { + "10": 50.0, + "25": 100.0, + "50": 150.0, + "75": 200.0, + "90": 257.7 + }, + "ep_h_quantiles_mw_weighted": { + "10": 1.0, + "25": 1.0, + "50": 2.0, + "75": 2.0, + "90": 2.17 + }, + "ep_h_quantiles": { + "10": 1.0, + "25": 1.0, + "50": 1.1, + "75": 2.0, + "90": 2.21 + }, + "ep_bucket_share_by_count": { + "1h": 0.5588, + "2h": 0.4216, + "4h": 0.0196 + }, + "ep_bucket_share_by_mw": { + "1h": 0.4024, + "2h": 0.5871, + "4h": 0.0105 + }, + "ep_bucket_share_by_mw_since_2023": { + "1h": 0.3529, + "2h": 0.6355, + "4h": 0.0116 + }, + "buckets": "1h: E/P < 1.5 h, 2h: 1.5-3 h, 4h: >= 3 h", + "archetype_mix_used": { + "1h": 0.4, + "2h": 0.45, + "4h": 0.15 + } +} +``` + +The E/P mix used for tasks is the design mix (1 h 40%, 2 h 45%, 4 h 15%); the EIA ERCO fit above is the check. Letters per class (manifest `letters`): + +| code | P MW | k_dis | k_ch | q MWh | N | eta | c_deg | cycles | s0 | s_end_min | +|---|---|---|---|---|---|---|---|---|---|---| +| 1h-a | 10 | 2 | 3 | 5 | 2 | 0.8 | 4 | 1.0 | 1 | 1 | +| 1h-b | 200 | 4 | 4 | 50 | 4 | 0.85 | 4 | 2.0 | 0 | 0 | +| 1h-c | 100 | 2 | 2 | 50 | 2 | 0.9 | 0 | 1.5 | 2 | 2 | +| 1h-d | 90 | 3 | 3 | 30 | 3 | 0.85 | 8 | 1.0 | 1 | 0 | +| 1h-e | 90 | 3 | 3 | 30 | 3 | 0.85 | 8 | 1.0 | 0 | 1 | +| 1h-f | 150 | 2 | 2 | 75 | 2 | 0.85 | 4 | 1.0 | 0 | 1 | +| 1h-g | 150 | 3 | 3 | 50 | 3 | 0.9 | 8 | 1.0 | 3 | 3 | +| 1h-h | 10 | 2 | 2 | 5 | 2 | 0.85 | 8 | 1.5 | 1 | 0 | +| 1h-i | 225 | 3 | 3 | 75 | 3 | 0.8 | 0 | 1.5 | 0 | 0 | +| 1h-j | 10 | 4 | 4 | 2.5 | 4 | 0.8 | 0 | 2.0 | 0 | 0 | +| 1h-k | 150 | 2 | 2 | 75 | 2 | 0.8 | 4 | 1.0 | 0 | 0 | +| 1h-l | 100 | 4 | 3 | 25 | 4 | 0.8 | 4 | 1.5 | 2 | 0 | +| 1h-m | 150 | 3 | 3 | 50 | 3 | 0.8 | 0 | 2.0 | 3 | 3 | +| 1h-n | 100 | 2 | 2 | 50 | 2 | 0.9 | 8 | 1.5 | 2 | 1 | +| 1h-o | 10 | 4 | 4 | 2.5 | 4 | 0.8 | 0 | 1.5 | 0 | 0 | +| 1h-p | 150 | 3 | 3 | 50 | 3 | 0.85 | 0 | 1.0 | 0 | 1 | +| 1h-q | 225 | 3 | 3 | 75 | 3 | 0.85 | 8 | 2.0 | 1 | 1 | +| 1h-r | 10 | 4 | 4 | 2.5 | 4 | 0.9 | 4 | 2.0 | 0 | 0 | +| 1h-s | 10 | 2 | 3 | 5 | 2 | 0.8 | 8 | 1.0 | 1 | 1 | +| 1h-t | 160 | 4 | 4 | 40 | 4 | 0.9 | 4 | 1.5 | 4 | 0 | +| 1h-u | 100 | 2 | 2 | 50 | 2 | 0.85 | 0 | 2.0 | 0 | 0 | +| 1h-v | 160 | 4 | 3 | 40 | 4 | 0.9 | 8 | 2.0 | 0 | 0 | +| 1h-w | 7.5 | 3 | 3 | 2.5 | 3 | 0.8 | 4 | 1.5 | 1 | 1 | +| 1h-x | 150 | 3 | 2 | 50 | 3 | 0.85 | 4 | 1.5 | 1 | 0 | +| 1h-y | 100 | 2 | 2 | 50 | 2 | 0.9 | 0 | 1.0 | 2 | 2 | +| 1h-z | 200 | 4 | 3 | 50 | 4 | 0.9 | 0 | 2.0 | 2 | 4 | +| 2h-a | 90 | 3 | 3 | 30 | 6 | 0.85 | 4 | 2.0 | 0 | 0 | +| 2h-b | 100 | 2 | 2 | 50 | 4 | 0.9 | 4 | 1.0 | 4 | 2 | +| 2h-c | 10 | 4 | 3 | 2.5 | 8 | 0.85 | 0 | 1.5 | 8 | 8 | +| 2h-d | 200 | 4 | 3 | 50 | 8 | 0.8 | 0 | 1.0 | 4 | 0 | +| 2h-e | 90 | 3 | 3 | 30 | 6 | 0.9 | 0 | 2.0 | 3 | 3 | +| 2h-f | 150 | 2 | 2 | 75 | 4 | 0.9 | 8 | 1.5 | 4 | 2 | +| 2h-g | 225 | 3 | 3 | 75 | 6 | 0.8 | 4 | 2.0 | 6 | 6 | +| 2h-h | 100 | 4 | 4 | 25 | 8 | 0.8 | 4 | 1.0 | 0 | 0 | +| 2h-i | 150 | 3 | 2 | 50 | 6 | 0.8 | 4 | 1.5 | 3 | 3 | +| 2h-j | 160 | 4 | 4 | 40 | 8 | 0.8 | 8 | 1.5 | 0 | 4 | +| 2h-k | 200 | 4 | 4 | 50 | 8 | 0.8 | 0 | 1.0 | 4 | 4 | +| 2h-l | 7.5 | 3 | 3 | 2.5 | 6 | 0.85 | 0 | 2.0 | 6 | 0 | +| 2h-m | 225 | 3 | 3 | 75 | 6 | 0.9 | 8 | 1.5 | 6 | 6 | +| 2h-n | 10 | 2 | 2 | 5 | 4 | 0.9 | 4 | 1.5 | 0 | 2 | +| 2h-o | 150 | 2 | 2 | 75 | 4 | 0.8 | 4 | 1.5 | 0 | 0 | +| 2h-p | 100 | 2 | 2 | 50 | 4 | 0.9 | 8 | 1.5 | 0 | 2 | +| 2h-q | 7.5 | 3 | 3 | 2.5 | 6 | 0.85 | 4 | 1.0 | 6 | 6 | +| 2h-r | 150 | 2 | 3 | 75 | 4 | 0.85 | 0 | 1.5 | 4 | 2 | +| 2h-s | 10 | 2 | 2 | 5 | 4 | 0.85 | 4 | 2.0 | 0 | 0 | +| 2h-t | 10 | 4 | 4 | 2.5 | 8 | 0.85 | 0 | 2.0 | 0 | 4 | +| 2h-u | 225 | 3 | 3 | 75 | 6 | 0.8 | 8 | 1.0 | 3 | 3 | +| 2h-v | 100 | 4 | 3 | 25 | 8 | 0.9 | 0 | 2.0 | 4 | 4 | +| 2h-w | 150 | 2 | 2 | 75 | 4 | 0.85 | 8 | 1.0 | 2 | 2 | +| 2h-x | 100 | 2 | 3 | 50 | 4 | 0.85 | 0 | 1.0 | 0 | 0 | +| 2h-y | 160 | 4 | 4 | 40 | 8 | 0.9 | 8 | 2.0 | 4 | 4 | +| 2h-z | 7.5 | 3 | 3 | 2.5 | 6 | 0.8 | 8 | 1.0 | 3 | 3 | +| 4h-a | 225 | 3 | 3 | 75 | 12 | 0.85 | 8 | 2.0 | 12 | 12 | +| 4h-b | 10 | 4 | 4 | 2.5 | 16 | 0.9 | 8 | 1.5 | 8 | 16 | +| 4h-c | 10 | 2 | 3 | 5 | 8 | 0.85 | 8 | 1.0 | 4 | 4 | +| 4h-d | 150 | 3 | 2 | 50 | 12 | 0.8 | 8 | 2.0 | 12 | 0 | +| 4h-e | 225 | 3 | 3 | 75 | 12 | 0.9 | 8 | 2.0 | 6 | 12 | +| 4h-f | 100 | 4 | 4 | 25 | 16 | 0.8 | 4 | 1.0 | 0 | 0 | +| 4h-g | 150 | 3 | 3 | 50 | 12 | 0.8 | 4 | 1.0 | 12 | 12 | +| 4h-h | 10 | 2 | 2 | 5 | 8 | 0.85 | 4 | 1.5 | 0 | 0 | +| 4h-i | 100 | 4 | 4 | 25 | 16 | 0.9 | 4 | 1.0 | 16 | 16 | +| 4h-j | 160 | 4 | 4 | 40 | 16 | 0.8 | 8 | 2.0 | 8 | 8 | +| 4h-k | 100 | 4 | 4 | 25 | 16 | 0.9 | 0 | 2.0 | 0 | 0 | +| 4h-l | 225 | 3 | 3 | 75 | 12 | 0.8 | 4 | 1.0 | 6 | 0 | +| 4h-m | 160 | 4 | 4 | 40 | 16 | 0.85 | 0 | 1.0 | 0 | 8 | +| 4h-n | 7.5 | 3 | 3 | 2.5 | 12 | 0.9 | 8 | 1.0 | 6 | 6 | +| 4h-o | 160 | 4 | 4 | 40 | 16 | 0.85 | 8 | 1.5 | 16 | 16 | +| 4h-p | 150 | 2 | 3 | 75 | 8 | 0.85 | 0 | 1.5 | 8 | 0 | +| 4h-q | 150 | 2 | 2 | 75 | 8 | 0.85 | 4 | 1.0 | 4 | 4 | +| 4h-r | 225 | 3 | 2 | 75 | 12 | 0.8 | 4 | 1.5 | 12 | 6 | +| 4h-s | 100 | 2 | 2 | 50 | 8 | 0.9 | 0 | 1.5 | 0 | 0 | +| 4h-t | 10 | 2 | 2 | 5 | 8 | 0.85 | 4 | 1.5 | 0 | 0 | +| 4h-u | 10 | 2 | 3 | 5 | 8 | 0.8 | 0 | 1.0 | 0 | 0 | +| 4h-v | 100 | 4 | 3 | 25 | 16 | 0.9 | 0 | 2.0 | 0 | 0 | +| 4h-w | 100 | 2 | 2 | 50 | 8 | 0.9 | 4 | 2.0 | 0 | 0 | +| 4h-x | 10 | 2 | 2 | 5 | 8 | 0.85 | 0 | 1.5 | 4 | 0 | +| 4h-y | 150 | 3 | 3 | 50 | 12 | 0.8 | 0 | 1.5 | 6 | 12 | +| 4h-z | 90 | 3 | 3 | 30 | 12 | 0.8 | 0 | 2.0 | 0 | 0 | diff --git a/references/v1/eval_2025.parquet b/references/v1/eval_2025.parquet new file mode 100644 index 0000000000000000000000000000000000000000..4017cf5504b008abb2a840955494b30273289132 --- /dev/null +++ b/references/v1/eval_2025.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..3171ca5deaaa9b48df671ccd3cd72cb2052c35ed --- /dev/null +++ b/references/v1/showcase.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e958ced062529585613aa115aa9a85ff6c78d433c5d07b08d1a5756c70dc1db +size 130567 diff --git a/references/v1/train.parquet b/references/v1/train.parquet new file mode 100644 index 0000000000000000000000000000000000000000..0a11ce5f8ae0efc0fcaae8a583f538dcd3c3f739 --- /dev/null +++ b/references/v1/train.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a34235d54e8e2ac46c6eb198c7006dadf43cb90ee55c36c8cd0eb22e842b10f +size 2432291 diff --git a/results/board.md b/results/board.md new file mode 100644 index 0000000000000000000000000000000000000000..42ab67563b6f8925352e892f9f22eba3d2044150 --- /dev/null +++ b/results/board.md @@ -0,0 +1,271 @@ +# GridSim v1 board + +On 24 October 2026 GPT-6.1 Sol (`openai/gpt-6.1-sol`, reasoning effort `high`, through LiteLLM) played the v1 board once +per battery-day, as [pre-registered](../docs/prereg/v1-board.md) before the first episode: column **A**, the `full` +harness (3 checks) at the foresight tier, on the 500 held-out days of January to November 2025 (`ercot-dam-v1-eval`); +**B**, the `oneshot` harness (submit only, the training prompt) on the same 500; **C**, `full` at the blind tier on that +pack's 300 blind days; and the optional **D**, `full` at the foresight tier on the 250 days of `ercot-dam-v1-rtcb`, +after ERCOT's real-time co-optimization went live. The overnight v1 build ran the four sweeps for Edgar Arakelyan with +`agent-env gridsim sweep run` on one Linux cloud box, the env and agent as local containers, from 06:58 to 07:22 UTC +(first attempts' starts), and the amendment's four re-run sweeps from 07:25 to 07:30 UTC. The run files don't record the +commit: main's head when the sweeps started was `1f0c24c`, and gridsim_core, the env, the verifier and the agent are the +same from it to the commit that publishes this board. The model spend was $25.88 over 1,635 attempts, 1,550 of them +scored. + +Every scored run replays: `agent-env gridsim episodes` played each of the 2,634 recorded tool calls of the 1,550 scored +runs (1,550 submits, 1,051 checks, 33 `get_situation`) again on a fresh env and got the recorded output every time, and +every reward equals both the verifier's and gridsim_core's grade of the schedule (within 1e-6). No run mismatched. The +runs are in the dataset's `v1_episodes` config with their transcripts and replay in 3D in the Space ([links](#links)). + +**Headline: the amended columns.** Foresight is saturated: Sol scores 0.9999 on A (99.6% of days optimal), 0.9999 on B +and 1.0000 on D, with no rule broken in any of the 1,550 scored runs. The blind tier separates: R 0.9721 (0.962 to +0.980), r_opt 0.9165 against the blind anchor's own 0.9204 on the same days, beating its anchor on 40.3%. + +## The amendment + +The board ran under one amendment, made after the sweeps and before this report, recorded as the main session wrote it: + +> The pre-registered per-episode cap of $0.10 (4× the smoke median, rounded up) collided with the agent's pessimistic +> pre-request cost guard. It estimates about $0.08-0.11 worst-case per request. So some episodes stopped as unscored +> "cost_cap" failures, all of them guard trips and none model failures: A 14, B 3, C 63, D 5. Retries at the same cap +> failed again. Those exact tasks were re-run once at a $0.40 episode cap, as sweeps `*-amend`. All scored. Both are +> reported: the pre-registered column (scored only) and the amended column (pre-reg scored + amend). The amended column +> is the headline, with this footnote, because dropping 21% of blind days biases toward easy days. + +What the files show: + +- **The cap.** The smoke sweeps (`smoke-a`, `-b`, `-c`: 10 train days each at full/foresight, oneshot/foresight and + full/blind, all 30 scored) had median episodes of $0.0126, $0.0073, $0.0148; 4× the largest is $0.0593, rounded up to + the registered $0.10. +- **The stops.** 85 pre-registered runs stopped with `cost_cap` (A 14, B 3, C 63, D 5), each after 1 or 2 turns and + $0.0208 to $0.0448 of spend, when the agent's guard put its next request's worst case at $0.0777 to $0.1081, past the + cap. None had submitted. Each has one attempt in its sweep's `results.jsonl`: `sweep run` counts a cost-cap stop as + final, so the same-cap retries are not in these files. +- **The re-runs.** The `-amend` sweeps re-ran exactly those 85 tasks once each at a $0.40 episode cap; all 85 scored, + for $2.46, the dearest episode $0.0539. +- **The bias.** The stops fell on the days that make Sol work hardest: C's 63 re-run days score R 0.9539 (r_opt 0.8865) + against 0.9769 (0.9245) on the 237 that scored under the cap, so the pre-registered C (0.9769) overstates the full + column (0.9721). The 22 stopped foresight runs (A, B and D) scored 1.0000 on average on their re-runs. + +## Columns + +Reward R = clip(0.2+0.8(P-P_idle)/(P_anchor-P_idle),0,1): foresight against P_opt, blind against max(P_persist, P_ridge); r_opt is R against P_opt. Mean R over battery-days, its CI a 95% bootstrap over tasks, 10,000 resamples. Optimal: R >= 0.99 (at the blind tier, at or within 1% of the anchor's scale). Valid: submitted and feasible. An amended row adds the amendment's re-runs to the pre-registered sweep's scored runs. + +| Column | Scoring | Model | Pack | Tier | Harness | Scored/planned | Mean R (95% CI) | r_opt | Optimal | Valid | Beats anchor | Checks used | +|---|---|---|---|---|---|---:|---|---:|---:|---:|---:|---:| +| A | amended | GPT-6.1 Sol | ercot-dam-v1-eval | foresight | full | 500/500 | 0.9999 (0.9997 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | 1.00 | +| A | pre-registered | GPT-6.1 Sol | ercot-dam-v1-eval | foresight | full | 486/500 | 0.9999 (0.9997 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | 1.00 | +| B | amended | GPT-6.1 Sol | ercot-dam-v1-eval | foresight | oneshot | 500/500 | 0.9999 (0.9998 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | 0.00 | +| B | pre-registered | GPT-6.1 Sol | ercot-dam-v1-eval | foresight | oneshot | 497/500 | 0.9999 (0.9998 to 1.0000) | 0.9999 | 99.6% | 100.0% | – | 0.00 | +| C | amended | GPT-6.1 Sol | ercot-dam-v1-eval | blind | full | 300/300 | 0.9721 (0.9624 to 0.9804) | 0.9165 | 72.3% | 100.0% | 40.3% | 1.00 | +| C | pre-registered | GPT-6.1 Sol | ercot-dam-v1-eval | blind | full | 237/300 | 0.9769 (0.9678 to 0.9846) | 0.9245 | 75.1% | 100.0% | 39.2% | 1.00 | +| D | amended | GPT-6.1 Sol | ercot-dam-v1-rtcb | foresight | full | 250/250 | 1.0000 (0.9999 to 1.0000) | 1.0000 | 100.0% | 100.0% | – | 1.00 | +| D | pre-registered | GPT-6.1 Sol | ercot-dam-v1-rtcb | foresight | full | 245/250 | 1.0000 (0.9999 to 1.0000) | 1.0000 | 100.0% | 100.0% | – | 1.00 | + +## Errors + +Scored runs whose submitted schedule broke a rule (it executes up to that hour), ended short of the end-of-day state of charge, or idled all day. + +| Column | overfill | sell_before_charge | rate | cycle_cap | end_short | all_idle | +|---|---:|---:|---:|---:|---:|---:| +| A eval foresight full | 0 | 0 | 0 | 0 | 0 | 0 | +| B eval foresight oneshot | 0 | 0 | 0 | 0 | 0 | 0 | +| C eval blind full | 0 | 0 | 0 | 0 | 0 | 0 | +| D rtcb foresight full | 0 | 0 | 0 | 0 | 0 | 0 | + +## Checks + +Runs by the number of `check_schedule` calls, with their mean R (and r_opt at the blind tier); the count of runs in brackets. + +| | A eval foresight full | B eval foresight oneshot | C eval blind full | D rtcb foresight full | +|---|---:|---:|---:|---:| +| 0 checks | – | 0.9999 (500) | 0.9978 / 0.9602 (1) | – | +| 1 check | 0.9999 (499) | – | 0.9720 / 0.9164 (299) | 1.0000 (249) | +| 2 checks | 1.0000 (1) | – | – | 1.0000 (1) | + +## By battery class, binding constraint and day type + +Mean R (and r_opt at the blind tier) over the battery-days in each group, the count in brackets. The binding constraint is the pack's tag for what limits the optimum; a day with negative-price hours and a spike counts in both. + +| | A eval foresight full | B eval foresight oneshot | C eval blind full | D rtcb foresight full | +|---|---:|---:|---:|---:| +| 1 h battery | 1.0000 (172) | 1.0000 (172) | 0.9698 / 0.9303 (102) | 1.0000 (81) | +| 2 h battery | 1.0000 (166) | 1.0000 (166) | 0.9653 / 0.9083 (105) | 0.9999 (92) | +| 4 h battery | 0.9997 (162) | 0.9997 (162) | 0.9822 / 0.9106 (93) | 1.0000 (77) | + +| | A eval foresight full | B eval foresight oneshot | C eval blind full | D rtcb foresight full | +|---|---:|---:|---:|---:| +| No constraint binds | 0.9999 (169) | 1.0000 (169) | 0.9604 / 0.9149 (94) | 1.0000 (110) | +| Rate | 1.0000 (163) | 0.9999 (163) | 0.9810 / 0.9162 (93) | 0.9999 (69) | +| Cycle cap | 0.9996 (103) | 0.9999 (103) | 0.9714 / 0.9050 (70) | 1.0000 (38) | +| End-of-day charge | 1.0000 (65) | 0.9998 (65) | 0.9793 / 0.9395 (43) | 1.0000 (33) | + +| | A eval foresight full | B eval foresight oneshot | C eval blind full | D rtcb foresight full | +|---|---:|---:|---:|---:| +| Negative-price hours | 0.9983 (27) | 0.9998 (27) | 0.9794 / 0.8228 (12) | 1.0000 (25) | +| Price spike | 1.0000 (14) | 1.0000 (14) | 0.9790 / 0.9662 (9) | 1.0000 (3) | +| Neither | 1.0000 (459) | 0.9999 (459) | 0.9715 / 0.9189 (279) | 1.0000 (222) | + +## Cost, tokens and time + +Per scored run: model spend at the agent's price table, tokens and wall time of the whole `agent-env run`. Spend counts every attempt, the unscored ones included. + +| Column | $/episode (median) | Spend (unscored) | Input tokens (cached) | Output tokens | Wall s, median (max) | Attempts | Unscored attempts | +|---|---:|---:|---:|---:|---:|---:|---| +| A eval foresight full | $0.0157 ($0.0145) | $8.24 ($0.39) | 3,012 (51) | 976 | 27.7 (88.1) | 514 | cost_cap 14 | +| B eval foresight oneshot | $0.0117 ($0.0104) | $5.98 ($0.13) | 883 (0) | 994 | 26.8 (96.5) | 503 | cost_cap 3 | +| C eval blind full | $0.0193 ($0.0185) | $7.52 ($1.73) | 5,362 (2,461) | 1,324 | 36.5 (103.1) | 363 | cost_cap 63 | +| D rtcb foresight full | $0.0161 ($0.0140) | $4.15 ($0.13) | 3,072 (68) | 1,005 | 28.6 (94.5) | 255 | cost_cap 5 | + +## Baselines on the same tasks + +Each reference policy graded by `gridsim_core` at the column's tier on the column's battery-days: mean R, and r_opt at the blind tier; persistence-DP, ridge-DP and the blind anchor need the blind inputs, so on foresight columns they cover only the days that have them (count in brackets). Threshold and greedy read the day's own prices, so at the blind tier they are not policies a blind desk could run. + +| | A eval foresight full | B eval foresight oneshot | C eval blind full | D rtcb foresight full | +|---|---:|---:|---:|---:| +| Idle | 0.200 | 0.200 | 0.200 / 0.200 | 0.200 | +| Random feasible | 0.072 | 0.072 | 0.076 / 0.076 | 0.043 | +| Clock | 0.485 | 0.485 | 0.516 / 0.487 | 0.435 | +| Threshold | 0.642 | 0.642 | 0.698 / 0.655 | 0.664 | +| Greedy | 0.761 | 0.761 | 0.821 / 0.783 | 0.724 | +| Persistence-DP | 0.882 (300) | 0.882 (300) | 0.957 / 0.882 | 0.844 (202) | +| Ridge-DP | 0.799 (300) | 0.799 (300) | 0.861 / 0.799 | 0.764 (202) | +| Blind anchor | 0.920 (300) | 0.920 (300) | 1.000 / 0.920 | 0.888 (202) | +| DP optimum | 1.000 | 1.000 | 1.000 / 1.000 | 1.000 | +| **Model** | **1.000** | **1.000** | **0.972** / **0.916** | **1.000** | + +LP bound (the convex relaxation, in $) against the exact DP optimum on each column's days: + +- A eval foresight full: mean P_opt $11,726.75, mean P_lp $11,726.75; P_lp exceeds P_opt on 0 of 500 days (largest gap $0.00). +- B eval foresight oneshot: mean P_opt $11,726.75, mean P_lp $11,726.75; P_lp exceeds P_opt on 0 of 500 days (largest gap $0.00). +- C eval blind full: mean P_opt $11,417.64, mean P_lp $11,417.64; P_lp exceeds P_opt on 0 of 300 days (largest gap $0.00). +- D rtcb foresight full: mean P_opt $8,699.23, mean P_lp $8,699.23; P_lp exceeds P_opt on 0 of 250 days (largest gap $0.00). + +## Hypotheses + +The pre-registration's hypotheses, from the spec before any run, against the amended columns. + +| Row | Hypothesis | Measured | Reading | +|---|---|---|---| +| Sol, full, foresight (A) | ~0.90-0.95 | 0.9999 (0.9997 to 1.0000) | above the range | +| Sol, oneshot, foresight (B) | below full, by an amount not predicted | 0.9999; B − A +0.0000 over the 500 days (B lower on 4, higher on 5) | not below: no difference | +| Greedy (filtered pack) | ~0.78 (tuned to 0.75-0.80) | 0.761 on A's days | in the tuned band, under 0.78 | +| Threshold | ~0.62 | 0.642 on A's days | near | +| Sol, blind (C) | no prior; first measurement | R 0.9721 (0.9624 to 0.9804), r_opt 0.9165, beats the anchor on 40.3% | first measurement | + +## Reading + +- **Foresight is saturated for Sol,** as FD-4332 warned it would be for a frontier model: A's mean is 0.9999, 99.6% of + its 500 days score R ≥ 0.99 (the 2 others 0.968, 0.986), B matches A, and D's 250 post-RTC+B days all score 1.0000. + Every submitted schedule was valid: no overfill, no sell-before-charge, no rate or cycle-cap break, no end-of-day + shortfall and no all-idle day in any column. With 24 prices in view, Sol finds the DP's optimum; the foresight tier + stays the training headline for small models and the check that the harness works, not a way to rank frontier models. +- **The blind tier carries the discrimination.** Sol's r_opt is 0.9165 against the blind anchor's own 0.9204 on the same + 300 days (persistence-DP 0.8820, ridge-DP 0.7988): it sits above persistence-DP and just under the better of the two + forecasters, which a desk can build from the same inputs. It beats its anchor on 40.3% of the days and scores R ≥ 0.99 + on 72.3%; 21.7% of its blind days score r_opt ≥ 0.99. It does worst on days with negative-price hours (r_opt 0.823 on + 12 days, against 0.919 on the 279 days with neither negative prices nor a spike), and 1 blind day scored below idle. +- **Checking: one check, almost always.** Sol used exactly one of its three checks on 499 of A's 500 runs, 299 of C's + 300 and 249 of D's 250, never three; on 1,046 of the 1,049 runs that checked, the last schedule checked is the one it + submitted. B, with no checks, scores the same as A, so at the foresight tier the check confirms rather than repairs. +- **Cost.** $25.88 on the model for the whole board, the amendment included: A $0.0157, B $0.0117, C $0.0193, D $0.0161 + a scored episode on average; the median episode took 28 s (A), 27 s (B), 36 s (C), 29 s (D) from container start to + grade. + +## Links + +- Every run with its transcript: the dataset's + [`v1_episodes`](https://huggingface.co/datasets/earakely-scale/GridSimEnv-AgentEnv/viewer/v1_episodes). +- The replays in 3D: the [Space](https://huggingface.co/spaces/earakely-scale/GridSimEnv-AgentEnv). +- The code: [agentenv-gridsim-plugin](https://github.com/earakely-scale/agentenv-gridsim-plugin); the eval card: + [`eval-v1.json`](eval-v1.json). + +## Unscored runs + +| Sweep | Task | Rep | Attempts | Last outcome (spend) | Amendment | Amended outcome | +|---|---|---:|---:|---|---|---| +| v1-board-sol-full | dam-v1-2025-09-20-HB_HUBAVG-2h-v | 1 | 1 | cost_cap ($0.0230) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-02-08-LZ_CPS-4h-y | 1 | 1 | cost_cap ($0.0234) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-10-13-HB_WEST-4h-k | 1 | 1 | cost_cap ($0.0234) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-05-22-HB_BUSAVG-4h-v | 1 | 1 | cost_cap ($0.0286) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-07-31-LZ_CPS-4h-y | 1 | 1 | cost_cap ($0.0281) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-02-08-HB_HUBAVG-4h-v | 1 | 1 | cost_cap ($0.0286) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-08-22-LZ_LCRA-4h-v | 1 | 1 | cost_cap ($0.0382) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-03-17-HB_PAN-4h-w | 1 | 1 | cost_cap ($0.0364) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-02-19-HB_WEST-4h-e | 1 | 1 | cost_cap ($0.0239) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-08-22-HB_SOUTH-4h-v | 1 | 1 | cost_cap ($0.0340) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-06-30-HB_NORTH-4h-v | 1 | 1 | cost_cap ($0.0286) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-07-17-HB_HOUSTON-4h-y | 1 | 1 | cost_cap ($0.0234) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-08-20-LZ_HOUSTON-4h-d | 1 | 1 | cost_cap ($0.0270) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-full | dam-v1-2025-03-13-LZ_LCRA-4h-y | 1 | 1 | cost_cap ($0.0234) | v1-board-sol-full-amend | scored 1.0000 | +| v1-board-sol-oneshot | dam-v1-2025-05-22-HB_BUSAVG-4h-v | 1 | 1 | cost_cap ($0.0427) | v1-board-sol-oneshot-amend | scored 1.0000 | +| v1-board-sol-oneshot | dam-v1-2025-08-22-LZ_LCRA-4h-v | 1 | 1 | cost_cap ($0.0427) | v1-board-sol-oneshot-amend | scored 1.0000 | +| v1-board-sol-oneshot | dam-v1-2025-08-22-HB_SOUTH-4h-v | 1 | 1 | cost_cap ($0.0427) | v1-board-sol-oneshot-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-03-05-LZ_WEST-1h-u | 1 | 1 | cost_cap ($0.0243) | v1-board-sol-blind-amend | scored 0.8837 | +| v1-board-sol-blind | dam-v1-2025-01-24-LZ_CPS-4h-e | 1 | 1 | cost_cap ($0.0279) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-04-09-HB_HOUSTON-2h-b | 1 | 1 | cost_cap ($0.0237) | v1-board-sol-blind-amend | scored 0.7723 | +| v1-board-sol-blind | dam-v1-2025-05-06-LZ_RAYBN-2h-v | 1 | 1 | cost_cap ($0.0226) | v1-board-sol-blind-amend | scored 0.8415 | +| v1-board-sol-blind | dam-v1-2025-11-25-HB_BUSAVG-4h-s | 1 | 1 | cost_cap ($0.0354) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-25-LZ_WEST-4h-y | 1 | 1 | cost_cap ($0.0292) | v1-board-sol-blind-amend | scored 0.9328 | +| v1-board-sol-blind | dam-v1-2025-06-17-HB_BUSAVG-4h-v | 1 | 1 | cost_cap ($0.0233) | v1-board-sol-blind-amend | scored 0.9978 | +| v1-board-sol-blind | dam-v1-2025-11-17-HB_PAN-2h-p | 1 | 1 | cost_cap ($0.0238) | v1-board-sol-blind-amend | scored 0.9524 | +| v1-board-sol-blind | dam-v1-2025-04-26-HB_HUBAVG-2h-c | 1 | 1 | cost_cap ($0.0244) | v1-board-sol-blind-amend | scored 0.9738 | +| v1-board-sol-blind | dam-v1-2025-11-10-LZ_SOUTH-2h-k | 1 | 1 | cost_cap ($0.0283) | v1-board-sol-blind-amend | scored 0.9077 | +| v1-board-sol-blind | dam-v1-2025-02-12-LZ_LCRA-4h-p | 1 | 1 | cost_cap ($0.0250) | v1-board-sol-blind-amend | scored 0.9987 | +| v1-board-sol-blind | dam-v1-2025-06-23-HB_HUBAVG-4h-k | 1 | 1 | cost_cap ($0.0304) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-08-14-LZ_RAYBN-4h-r | 1 | 1 | cost_cap ($0.0448) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-07-15-LZ_WEST-1h-z | 1 | 1 | cost_cap ($0.0227) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-10-14-HB_SOUTH-4h-w | 1 | 1 | cost_cap ($0.0216) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-05-10-LZ_WEST-4h-a | 1 | 1 | cost_cap ($0.0293) | v1-board-sol-blind-amend | scored 0.9136 | +| v1-board-sol-blind | dam-v1-2025-03-17-HB_BUSAVG-2h-t | 1 | 1 | cost_cap ($0.0212) | v1-board-sol-blind-amend | scored 0.9753 | +| v1-board-sol-blind | dam-v1-2025-01-16-HB_BUSAVG-1h-p | 1 | 1 | cost_cap ($0.0243) | v1-board-sol-blind-amend | scored 0.9970 | +| v1-board-sol-blind | dam-v1-2025-04-22-HB_WEST-4h-p | 1 | 1 | cost_cap ($0.0224) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-02-08-HB_HUBAVG-4h-v | 1 | 1 | cost_cap ($0.0448) | v1-board-sol-blind-amend | scored 0.9796 | +| v1-board-sol-blind | dam-v1-2025-11-18-HB_HOUSTON-2h-c | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-05-03-HB_HUBAVG-4h-h | 1 | 1 | cost_cap ($0.0280) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-04-02-HB_PAN-2h-c | 1 | 1 | cost_cap ($0.0304) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-26-LZ_CPS-4h-j | 1 | 1 | cost_cap ($0.0312) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-07-04-HB_WEST-4h-r | 1 | 1 | cost_cap ($0.0235) | v1-board-sol-blind-amend | scored 0.9515 | +| v1-board-sol-blind | dam-v1-2025-05-27-LZ_LCRA-4h-y | 1 | 1 | cost_cap ($0.0277) | v1-board-sol-blind-amend | scored 0.9809 | +| v1-board-sol-blind | dam-v1-2025-05-03-LZ_NORTH-2h-i | 1 | 1 | cost_cap ($0.0242) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-02-19-HB_WEST-4h-e | 1 | 1 | cost_cap ($0.0365) | v1-board-sol-blind-amend | scored 0.9978 | +| v1-board-sol-blind | dam-v1-2025-05-25-LZ_WEST-1h-g | 1 | 1 | cost_cap ($0.0216) | v1-board-sol-blind-amend | scored 0.9954 | +| v1-board-sol-blind | dam-v1-2025-04-24-HB_PAN-4h-h | 1 | 1 | cost_cap ($0.0273) | v1-board-sol-blind-amend | scored 0.9942 | +| v1-board-sol-blind | dam-v1-2025-04-09-LZ_AEN-2h-b | 1 | 1 | cost_cap ($0.0220) | v1-board-sol-blind-amend | scored 0.8434 | +| v1-board-sol-blind | dam-v1-2025-04-12-LZ_SOUTH-4h-w | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-05-09-LZ_CPS-4h-j | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-02-01-LZ_WEST-2h-y | 1 | 1 | cost_cap ($0.0322) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-03-05-LZ_HOUSTON-4h-e | 1 | 1 | cost_cap ($0.0301) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-05-06-HB_HUBAVG-2h-o | 1 | 1 | cost_cap ($0.0282) | v1-board-sol-blind-amend | scored 0.9245 | +| v1-board-sol-blind | dam-v1-2025-04-09-HB_HUBAVG-4h-w | 1 | 1 | cost_cap ($0.0337) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-06-16-HB_WEST-4h-b | 1 | 1 | cost_cap ($0.0225) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-10-27-LZ_RAYBN-4h-e | 1 | 1 | cost_cap ($0.0314) | v1-board-sol-blind-amend | scored 0.9780 | +| v1-board-sol-blind | dam-v1-2025-05-15-HB_PAN-2h-a | 1 | 1 | cost_cap ($0.0260) | v1-board-sol-blind-amend | scored 0.8319 | +| v1-board-sol-blind | dam-v1-2025-11-28-HB_SOUTH-4h-y | 1 | 1 | cost_cap ($0.0284) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-05-10-HB_HOUSTON-2h-z | 1 | 1 | cost_cap ($0.0208) | v1-board-sol-blind-amend | scored 0.1972 | +| v1-board-sol-blind | dam-v1-2025-01-07-HB_PAN-4h-b | 1 | 1 | cost_cap ($0.0227) | v1-board-sol-blind-amend | scored 0.9972 | +| v1-board-sol-blind | dam-v1-2025-10-06-HB_HUBAVG-4h-v | 1 | 1 | cost_cap ($0.0268) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-09-07-HB_BUSAVG-4h-j | 1 | 1 | cost_cap ($0.0215) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-30-HB_HUBAVG-2h-f | 1 | 1 | cost_cap ($0.0285) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-18-LZ_WEST-2h-r | 1 | 1 | cost_cap ($0.0286) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-11-HB_WEST-2h-m | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 0.9311 | +| v1-board-sol-blind | dam-v1-2025-04-17-LZ_LCRA-4h-b | 1 | 1 | cost_cap ($0.0268) | v1-board-sol-blind-amend | scored 0.9837 | +| v1-board-sol-blind | dam-v1-2025-01-13-HB_WEST-4h-b | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-03-04-LZ_NORTH-2h-n | 1 | 1 | cost_cap ($0.0268) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-01-13-LZ_LCRA-4h-j | 1 | 1 | cost_cap ($0.0290) | v1-board-sol-blind-amend | scored 0.9950 | +| v1-board-sol-blind | dam-v1-2025-06-16-LZ_WEST-2h-r | 1 | 1 | cost_cap ($0.0250) | v1-board-sol-blind-amend | scored 0.9932 | +| v1-board-sol-blind | dam-v1-2025-04-20-LZ_LCRA-2h-v | 1 | 1 | cost_cap ($0.0277) | v1-board-sol-blind-amend | scored 0.9862 | +| v1-board-sol-blind | dam-v1-2025-01-27-LZ_RAYBN-4h-z | 1 | 1 | cost_cap ($0.0278) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-01-26-HB_PAN-2h-r | 1 | 1 | cost_cap ($0.0252) | v1-board-sol-blind-amend | scored 0.7164 | +| v1-board-sol-blind | dam-v1-2025-05-04-LZ_RAYBN-4h-r | 1 | 1 | cost_cap ($0.0381) | v1-board-sol-blind-amend | scored 0.9473 | +| v1-board-sol-blind | dam-v1-2025-11-23-LZ_AEN-2h-v | 1 | 1 | cost_cap ($0.0246) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-01-10-HB_NORTH-4h-b | 1 | 1 | cost_cap ($0.0304) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-11-30-HB_BUSAVG-2h-f | 1 | 1 | cost_cap ($0.0239) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-02-07-HB_BUSAVG-4h-y | 1 | 1 | cost_cap ($0.0304) | v1-board-sol-blind-amend | scored 1.0000 | +| v1-board-sol-blind | dam-v1-2025-04-03-LZ_RAYBN-4h-o | 1 | 1 | cost_cap ($0.0262) | v1-board-sol-blind-amend | scored 0.9299 | +| v1-board-sol-blind | dam-v1-2025-11-10-LZ_LCRA-1h-c | 1 | 1 | cost_cap ($0.0369) | v1-board-sol-blind-amend | scored 0.7927 | +| v1-board-sol-rtcb | dam-v1-2026-01-25-LZ_AEN-4h-m | 1 | 1 | cost_cap ($0.0234) | v1-board-sol-rtcb-amend | scored 1.0000 | +| v1-board-sol-rtcb | dam-v1-2026-10-12-LZ_SOUTH-4h-v | 1 | 1 | cost_cap ($0.0278) | v1-board-sol-rtcb-amend | scored 1.0000 | +| v1-board-sol-rtcb | dam-v1-2026-01-16-HB_HUBAVG-4h-s | 1 | 1 | cost_cap ($0.0286) | v1-board-sol-rtcb-amend | scored 1.0000 | +| v1-board-sol-rtcb | dam-v1-2026-04-15-HB_NORTH-4h-y | 1 | 1 | cost_cap ($0.0280) | v1-board-sol-rtcb-amend | scored 1.0000 | +| v1-board-sol-rtcb | dam-v1-2026-09-12-HB_PAN-4h-b | 1 | 1 | cost_cap ($0.0257) | v1-board-sol-rtcb-amend | scored 1.0000 | diff --git a/results/eval-v1.json 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"k": 1, + "episode_cap_usd": 0.1, + "tier": "foresight", + "bundle": "full", + "pack": "ercot-dam-v1-rtcb" + }, + { + "name": "v1-board-sol-full-amend", + "models": [ + "openai/gpt-6.1-sol" + ], + "tasks": 14, + "k": 1, + "episode_cap_usd": 0.4, + "tier": "foresight", + "bundle": "full", + "pack": "ercot-dam-v1-eval", + "amends": "v1-board-sol-full" + }, + { + "name": "v1-board-sol-oneshot-amend", + "models": [ + "openai/gpt-6.1-sol" + ], + "tasks": 3, + "k": 1, + "episode_cap_usd": 0.4, + "tier": "foresight", + "bundle": "oneshot", + "pack": "ercot-dam-v1-eval", + "amends": "v1-board-sol-oneshot" + }, + { + "name": "v1-board-sol-blind-amend", + "models": [ + "openai/gpt-6.1-sol" + ], + "tasks": 63, + "k": 1, + "episode_cap_usd": 0.4, + "tier": "blind", + "bundle": "full", + "pack": "ercot-dam-v1-eval", + "amends": "v1-board-sol-blind" + }, + { + "name": "v1-board-sol-rtcb-amend", + "models": [ + "openai/gpt-6.1-sol" + ], + "tasks": 5, + "k": 1, + "episode_cap_usd": 0.4, + "tier": "foresight", + "bundle": "full", + "pack": "ercot-dam-v1-rtcb", + "amends": "v1-board-sol-rtcb" + } + ], + "links": { + "runs": "https://huggingface.co/datasets/earakely-scale/GridSimEnv-AgentEnv/viewer/v1_episodes", + "replays": "https://huggingface.co/spaces/earakely-scale/GridSimEnv-AgentEnv", + "code": "https://github.com/earakely-scale/agentenv-gridsim-plugin" + }, + "date": "2026-10-24", + "prereg": "docs/prereg/v1-board.md", + "commit": "1f0c24c", + "amendment": { + "text": "The pre-registered per-episode cap of $0.10 (4\u00d7 the smoke median, rounded up) collided with the agent's pessimistic pre-request cost guard. It estimates about $0.08-0.11 worst-case per request. So some episodes stopped as unscored \"cost_cap\" failures, all of them guard trips and none model failures: A 14, B 3, C 63, D 5. Retries at the same cap failed again. Those exact tasks were re-run once at a $0.40 episode cap, as sweeps `*-amend`. All scored. Both are reported: the pre-registered column (scored only) and the amended column (pre-reg scored + amend). The amended column is the headline, with this footnote, because dropping 21% of blind days biases toward easy days.", + "headline": "amended", + "sweeps": { + "v1-board-sol-full": "v1-board-sol-full-amend", + "v1-board-sol-oneshot": "v1-board-sol-oneshot-amend", + "v1-board-sol-blind": "v1-board-sol-blind-amend", + "v1-board-sol-rtcb": "v1-board-sol-rtcb-amend" + }, + "episode_cap_usd": { + "prereg": 0.1, + "amended": 0.4 + }, + "stopped": { + "A": 14, + "B": 3, + "C": 63, + "D": 5 + } + }, + "spend_usd": 25.8802, + "hypotheses": [ + { + "row": "Sol, full, foresight", + "hypothesis": "~0.90-0.95", + "measured": 0.9999 + }, + { + "row": "Sol, oneshot, foresight", + "hypothesis": "below full, by an amount not predicted", + "measured": 0.9999, + "minus_full": 0.0 + }, + { + "row": "Greedy (filtered pack)", + "hypothesis": "~0.78 (tuned to 0.75-0.80)", + "measured": 0.7611 + }, + { + "row": "Threshold", + "hypothesis": "~0.62", + "measured": 0.6418 + }, + { + "row": "Sol, blind", + "hypothesis": "no prior; first measurement", + "measured": 0.9721, + "r_opt": 0.9165, + "beats_anchor": 0.4033 + } + ] +} diff --git a/runs/index.json b/runs/index.json new file mode 100644 index 0000000000000000000000000000000000000000..6494d67de43defa4487c092f7d1fa7452250448b --- /dev/null +++ b/runs/index.json @@ -0,0 +1,25394 @@ +[ + { + "path": "reference/dam-v1-2021-02-14-HB_WEST-2h-p-blind-persist.json", + "episode_id": "dam-v1-2021-02-14-HB_WEST-2h-p-blind-persist", + "task_id": "dam-v1-2021-02-14-HB_WEST-2h-p", + "tier": "blind", + "bundle": "reference", + "path_kind": "ref", + "model": "reference/persistence-dp", + "trained": null, + "reward": 1.0, + "r_opt": 0.900938, + "profit_usd": 724974.222222, + "date": "2021-02-14", + "settlement_point": "HB_WEST", + "region": "west" + }, + { + "path": "reference/dam-v1-2021-02-14-HB_WEST-2h-p-foresight-opt.json", + "episode_id": "dam-v1-2021-02-14-HB_WEST-2h-p-foresight-opt", + "task_id": "dam-v1-2021-02-14-HB_WEST-2h-p", + "tier": 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The checked schedule is feasible, ends empty with no shortfall, and earns **$3,517.99**.\n" + } + ], + "usage": { + "input_tokens": 3128, + "output_tokens": 1211, + "usd": 0.018366 + }, + "wall_seconds": 36.6, + "model_label": "GPT-6.1 Sol" +} diff --git a/runs/v1-board-sol-full/dam-v1-2025-07-09-LZ_CPS-1h-m-openai-gpt-6.1-sol-r1.json b/runs/v1-board-sol-full/dam-v1-2025-07-09-LZ_CPS-1h-m-openai-gpt-6.1-sol-r1.json new file mode 100644 index 0000000000000000000000000000000000000000..54269a467baca8452a0aecc650b36ba7632c31d3 --- /dev/null +++ b/runs/v1-board-sol-full/dam-v1-2025-07-09-LZ_CPS-1h-m-openai-gpt-6.1-sol-r1.json @@ -0,0 +1,657 @@ +{ + "schema": "gridsim.episode/v1", + "episode_id": "dam-v1-2025-07-09-LZ_CPS-1h-m-w51pxfod", + "env": "gridsim", + "version": "v1", + "task_id": "dam-v1-2025-07-09-LZ_CPS-1h-m", + "tier": "foresight", + "bundle": "full", + "path": "A", + "model": "openai/gpt-6.1-sol", + "trained": "base", + "checkpoint_step": null, + "seed": null, + "situation": { + 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Even the cheapest charging energy costs more than the highest sale price net of degradation.\n\nVerified profit: **$13,437**, with all constraints satisfied and no end shortfall." + } + ], + "usage": { + "input_tokens": 2383, + "output_tokens": 370, + "usd": 0.008466 + }, + "wall_seconds": 17.6, + "model_label": "GPT-6.1 Sol" +} diff --git a/runs/v1-board-sol-full/dam-v1-2025-11-30-HB_BUSAVG-2h-f-openai-gpt-6.1-sol-r1.json b/runs/v1-board-sol-full/dam-v1-2025-11-30-HB_BUSAVG-2h-f-openai-gpt-6.1-sol-r1.json new file mode 100644 index 0000000000000000000000000000000000000000..b0d9367617004233f2d24cb925de73327c35dd0c --- /dev/null +++ b/runs/v1-board-sol-full/dam-v1-2025-11-30-HB_BUSAVG-2h-f-openai-gpt-6.1-sol-r1.json @@ -0,0 +1,925 @@ +{ + "schema": "gridsim.episode/v1", + "episode_id": "dam-v1-2025-11-30-HB_BUSAVG-2h-f-q4tt3y1m", + "env": "gridsim", + "version": "v1", + "task_id": "dam-v1-2025-11-30-HB_BUSAVG-2h-f", + "tier": "foresight", + "bundle": "full", + "path": "A", + "model": 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All other hours are idle.\n\nProfit: **$33,363.38**, with no constraint violations or end-of-day shortfall." + } + ], + "usage": { + "input_tokens": 2679, + "output_tokens": 695, + "usd": 0.012308 + }, + "wall_seconds": 26.3, + "model_label": "GPT-6.1 Sol" +} diff --git a/runs/v1-board-sol-rtcb/dam-v1-2026-02-01-HB_SOUTH-2h-v-openai-gpt-6.1-sol-r1.json b/runs/v1-board-sol-rtcb/dam-v1-2026-02-01-HB_SOUTH-2h-v-openai-gpt-6.1-sol-r1.json new file mode 100644 index 0000000000000000000000000000000000000000..75f18103a7386d0b7aacd49e90eb3034c1c8384f --- /dev/null +++ b/runs/v1-board-sol-rtcb/dam-v1-2026-02-01-HB_SOUTH-2h-v-openai-gpt-6.1-sol-r1.json @@ -0,0 +1,925 @@ +{ + "schema": "gridsim.episode/v1", + "episode_id": "dam-v1-2026-02-01-HB_SOUTH-2h-v-2vmjhbul", + "env": "gridsim", + "version": "v1", + "task_id": "dam-v1-2026-02-01-HB_SOUTH-2h-v", + "tier": "foresight", + "bundle": "full", + "path": "A", + "model": "openai/gpt-6.1-sol", + "trained": "base", + "checkpoint_step": null, + 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