---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
- time-series-forecasting
tags:
- synthetic-data
- wholesale-energy-market
- electricity-trading
- lmp
- locational-marginal-price
- day-ahead-market
- real-time-market
- spot-price
- futures
- forward-contracts
- swaps
- cfd
- contract-for-differences
- ancillary-services
- regulation-up
- regulation-down
- spinning-reserve
- black-start
- voltage-support
- bilateral-contracts
- ppa
- power-purchase-agreement
- ote-energy
- otc
- market-clearing
- pjm
- caiso
- ercot
- miso
- nyiso
- iso-ne
- ferc
- nerc
- ferc-order-755
- ferc-order-888
- ferc-order-890
- isda
- eei-master-agreement
- basel-iii
- var
- value-at-risk
- cvar
- conditional-var
- risk-management
- mean-reversion
- ornstein-uhlenbeck
- schwartz-model
- options-greeks
- implied-volatility
- price-spike
- negative-prices
- capacity-market
- demand-response
- virtual-bidding
- congestion
- ftr
- credit-rating
pretty_name: ENR006 — Synthetic Wholesale Energy Market Trading Dataset (Sample)
size_categories:
- 10K<n<100K
configs:
- config_name: spot_price
  data_files: enr006_spot_price.parquet
- config_name: futures_contracts
  data_files: enr006_futures_contracts.parquet
- config_name: ancillary_services
  data_files: enr006_ancillary_services.parquet
- config_name: market_clearing
  data_files: enr006_market_clearing.parquet
- config_name: bilateral_contracts
  data_files: enr006_bilateral_contracts.parquet
- config_name: trading_analytics
  data_files: enr006_trading_analytics.parquet
---

# ENR006 — Synthetic Wholesale Energy Market Trading Dataset (Sample Preview)

**XpertSystems.ai | Synthetic Data Factory | Energy & Climate Vertical**

A **six-table wholesale energy market trading dataset** spanning the full
trading lifecycle: hourly Day-Ahead LMPs (energy + congestion + loss
three-part decomposition), futures / forwards / swaps / CfDs with options
Greeks, six ancillary services markets (REG_UP, REG_DOWN, SPINNING_RESERVE,
NON_SPIN_RESERVE, BLACK_START, VOLTAGE_SUPPORT), market clearing with
imports/exports and energy balance, OTC bilateral PPAs with credit
exposure, and per-trade execution analytics with Basel III coherent risk
metrics (VaR-95, VaR-99, CVaR-95, Sharpe). Calibrated benchmark-first
against **FERC Order 755/888/890**, **NERC reliability standards**,
**ISDA Master Agreement**, **EEI Master Agreement**, **Basel III FRTB**,
**Schwartz (1997) mean-reversion theory**, and **EIA/PJM/CAISO/ERCOT
2023 published LMP data**.

This is the **sample preview** — 2 weeks (336 hours) of hourly DA market
data + 500 futures + 300 bilateral + 2,000 trades + 1 week of ancillary
services clearing (~13K total records). The full product covers a full
annual cycle × 500 pricing nodes × 200 participants × 20K trades with
pre-built scenario configs for price-spike events, high-renewable
negative pricing, and capacity-crunch market stress.

---

## Dataset summary

| Table | Rows (sample) | What it contains |
|---|---:|---|
| `spot_price` | 1,680 | Hourly DA LMP with three-part decomposition: lmp_total = energy + congestion + loss, plus system_lambda, peak/off-peak flags, weekend/holiday flag, price cap and negative price event flags |
| `futures_contracts` | 500 | FUTURES / FORWARD_OTC / SWAP / CfD contracts: tenors (DAY/WEEK/MONTH/QUARTER/CALENDAR_YEAR), forward curve, basis, contract price, notional, options Greeks (delta/gamma/vega/theta), MTM, settlement P&L |
| `ancillary_services` | ~8,500 | Hourly clearing for 6 services: clearing price, capacity awarded, performance score, mileage (REG_UP/DOWN), activation flag/duration, obligation, availability payment |
| `market_clearing` | 336 | DAM clearing: total cleared load/gen/imports/exports, energy balance (zero by construction), reserve margin, convergence flag, virtual bid volume + P&L, interchange schedule, market surplus, demand response cleared, capacity market price |
| `bilateral_contracts` | 300 | OTC PPAs: FIXED_PRICE / INDEXED / SHAPED / TOLLING structures, product type (FIRM / NONFIRM / UNIT_CONT / SYSTEM), volume, duration, fixed price, index reference, adder, total contract value, credit exposure, collateral posted, counterparty credit rating, EEI confirmation flag |
| `trading_analytics` | 2,000 | Per-trade execution: timestamp (ms-precision), trader / book, BUY/SELL direction, quantity, execution and market price, slippage, transaction cost, realized + unrealized P&L, VaR_95, VaR_99, CVaR_95, Sharpe ratio, max drawdown, position, hedge ratio, regulatory flag |

All six tables are provided in both **CSV** and **Parquet**. They join on
`node_id`, `participant_id` (= buyer_id / seller_id / trader_id /
provider_id), `book_id`, and `timestamp_utc`.

---

## Calibration sources

All ten validation metrics target named industry sources, not generator
self-metrics:

- **FERC Order 888 / 890** — Open Access Transmission Tariff, LMP
  three-part decomposition (energy + congestion + loss)
- **FERC Order 755** — Pay-for-performance regulation (REG_UP / REG_DOWN
  clearing structure)
- **FERC Ancillary Services Tariffs** (PJM / CAISO / ERCOT 2023) — six
  ancillary product price ranges
- **NERC TPL-001-5** — bulk system energy balance requirements
- **NERC LOLP / IEEE Reliability Standards** — reserve margin planning
  ranges (12-25% typical, 5-40% observed)
- **ISDA Master Agreement** — notional value definition for derivatives
- **EEI Master Agreement** — bilateral power transaction value calculation
- **Basel III FRTB** + **Artzner et al. (1999)** — coherent risk measure
  axioms (CVaR ≥ VaR, monotonicity in confidence level)
- **EIA / PJM / CAISO / ERCOT 2023** — published wholesale hub LMP averages
  for cross-ISO calibration
- **Schwartz (1997) / Lucia & Schwartz (2002)** — mean-reverting commodity
  price model theory

---

## Validation scorecard (seed = 42)

10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1).

| # | Metric | Observed | Target | Tol | Type | Source |
|---|---|---:|---:|---:|---|---|
| 1 | `lmp_decomp_identity_normal_rows_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC Order 888/890 |
| 2 | `market_clearing_energy_balance_zero_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | NERC TPL-001-5 |
| 3 | `var_coherence_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Basel III / Artzner 1999 |
| 4 | `futures_notional_identity_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ISDA Master Agreement |
| 5 | `bilateral_total_value_identity_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | EEI Master Agreement |
| 6 | `ancillary_clearing_prices_in_iso_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC AS tariffs |
| 7 | `reserve_margin_in_industry_range_rate` | 1.000 | 0.95 | ±0.05 | FLOOR | NERC LOLP / IEEE |
| 8 | `lmp_mean_usd_per_mwh_in_iso_band` | 40.26 | 45.0 | ±20.0 | two-sided | EIA/PJM/CAISO/ERCOT 2023 |
| 9 | `reg_up_clearing_price_mean_usd_per_mw_hr` | 15.56 | 15.0 | ±5.0 | two-sided | FERC Order 755 / PJM REG-UP |
| 10 | `spot_price_in_iso_floor_cap_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | FERC/PJM Tariff |

---

## Schema highlights

### `spot_price` (1,680 rows × 13 cols)
`node_id`, `timestamp_utc`, `settlement_type` (DAM), `lmp_total_usd_per_mwh`,
`lmp_energy_usd_per_mwh`, `lmp_congestion_usd_per_mwh`,
`lmp_loss_usd_per_mwh`, `system_lambda_usd_per_mwh`, `price_hub`,
`hour_ending`, `peak_offpeak_flag` (ON_PEAK / OFF_PEAK),
`weekend_holiday_flag`, `price_cap_flag`, `negative_price_flag`.

### `futures_contracts` (500 rows × 23 cols)
`contract_id`, `contract_type` (FUTURES / FORWARD_OTC / SWAP / CfD),
`underlying_hub`, `node_id`, `tenor` (DAY / WEEK / MONTH / QUARTER /
CALENDAR_YEAR), `delivery_start_utc`, `delivery_end_utc`,
`trade_date_utc`, `contract_price_usd_per_mwh`,
`forward_curve_usd_per_mwh`, `basis_usd_per_mwh`,
`contract_quantity_mwh`, `notional_value_usd`, `buyer_id`, `seller_id`,
`trader_book`, `mark_to_market_usd_per_mwh`, `implied_vol_pct`, `delta`,
`gamma`, `vega`, `theta`, `settlement_price_usd_per_mwh`,
`settlement_gain_loss_usd`.

### `ancillary_services` (~8,500 rows × 13 cols)
`ancillary_id`, `timestamp_utc`, `service_type` (REG_UP / REG_DOWN /
SPINNING_RESERVE / NON_SPIN_RESERVE / BLACK_START / VOLTAGE_SUPPORT),
`clearing_price_usd_per_mw_hr`, `capacity_awarded_mw`,
`performance_score_pct`, `mileage_mw`, `mileage_payment_usd`,
`activation_flag`, `activation_duration_min`, `provider_id`, `zone_id`,
`obligation_mw`, `availability_payment_usd`.

### `market_clearing` (336 rows × 17 cols)
`clearing_id`, `timestamp_utc`, `market_type` (DAM),
`clearing_timestamp_utc`, `total_cleared_load_mw`, `total_cleared_gen_mw`,
`total_cleared_imports_mw`, `total_cleared_exports_mw`,
`energy_balance_mw`, `reserve_margin_pct`, `convergence_flag`,
`virtual_bid_volume_mwh`, `virtual_bid_pnl_usd`, `interchange_schedule_mw`,
`market_surplus_usd`, `demand_response_cleared_mw`,
`capacity_market_price_usd_per_mw_day`, `system_lambda_usd_per_mwh`.

### `bilateral_contracts` (300 rows × 18 cols)
`bilateral_id`, `trade_date_utc`, `contract_structure` (FIXED_PRICE /
INDEXED / SHAPED / TOLLING), `buyer_id`, `seller_id`, `delivery_point`,
`node_id`, `product_type` (FIRM / NONFIRM / UNIT_CONT / SYSTEM),
`volume_mw`, `duration_months`, `fixed_price_usd_per_mwh`,
`index_reference`, `adder_usd_per_mwh`, `total_contract_value_usd`,
`credit_exposure_usd`, `collateral_posted_usd`,
`counterparty_credit_rating` (AAA / AA / A / BBB / BB / B / CCC),
`eei_confirmation_flag`.

### `trading_analytics` (2,000 rows × 19 cols)
`trade_id`, `execution_timestamp_utc` (ms precision), `trader_id`,
`book_id` (BOOK_01..BOOK_20), `trade_direction` (BUY / SELL),
`trade_quantity_mwh`, `execution_price_usd_per_mwh`,
`market_price_usd_per_mwh`, `slippage_usd_per_mwh`,
`transaction_cost_usd`, `realized_pnl_usd`, `unrealized_pnl_usd`,
`var_95_usd`, `var_99_usd`, `cvar_95_usd`, `sharpe_ratio`,
`max_drawdown_usd`, `position_mw`, `hedge_ratio_pct`,
`regulatory_flag`.

---

## Suggested use cases

- **Day-ahead LMP forecasting** — train regressors / LSTMs for
  `lmp_total_usd_per_mwh` from time-of-day, day-of-year, peak/off-peak,
  and historical lag features
- **Three-part LMP decomposition modeling** — predict
  `lmp_congestion_usd_per_mwh` and `lmp_loss_usd_per_mwh` separately from
  topology / loading proxies for FTR / CRR markets
- **Price spike detection** — anomaly classifier for `price_cap_flag` and
  `negative_price_flag` from system_lambda, peak_offpeak_flag, and
  weather proxies (pair with ENR-002 weather data)
- **Futures forward curve modeling** — fit yield-curve / forward-curve
  structures from `tenor`, `delivery_start_utc`, `contract_price`,
  `forward_curve` triples
- **Options Greeks calibration** — train Black-76 / spread option models
  on `implied_vol_pct`, `delta`, `gamma`, `vega`, `theta` for
  options-on-futures pricing
- **Ancillary services co-optimization** — joint price models for
  energy + AS clearing across 6 services
- **Bilateral PPA pricing** — model `fixed_price_usd_per_mwh` as a
  function of `volume_mw`, `duration_months`, `counterparty_credit_rating`,
  and `index_reference`; useful for term-sheet automation
- **Credit risk / counterparty exposure** — train default probability
  models from `counterparty_credit_rating` joined with
  `credit_exposure_usd` and `collateral_posted_usd`
- **VaR backtesting** — use the included VaR_95 / VaR_99 / CVaR_95 columns
  as benchmarks for new ML-driven VaR models; check coherence axioms
- **Slippage modeling** — predict `slippage_usd_per_mwh` from quantity,
  market_price, and time-of-day; useful for transaction cost analysis
- **Virtual bidding (INC/DEC) strategies** — train signal models from
  `virtual_bid_volume_mwh` and `virtual_bid_pnl_usd` joined with LMP
  changes between DAM and RTM
- **Regulatory flag detection** — multi-class for `regulatory_flag` from
  trade-level signals (quantity, slippage, market deviation); useful for
  market surveillance / spoofing detection
- **Capacity market clearing modeling** — predict
  `capacity_market_price_usd_per_mw_day` from reserve_margin_pct and
  load growth trends
- **Demand response clearing** — model `demand_response_cleared_mw` from
  LMP and load shape signals

---

## Loading examples

```python
from datasets import load_dataset

spot = load_dataset("xpertsystems/enr006-sample", "spot_price", split="train")
futures = load_dataset("xpertsystems/enr006-sample", "futures_contracts", split="train")
print(spot.shape, futures.shape)
```

```python
import pandas as pd
from huggingface_hub import hf_hub_download

spot = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr006-sample", "enr006_spot_price.parquet",
    repo_type="dataset",
))

# LMP three-part decomposition: verify the identity on non-cap, non-negative-price rows
normal = spot[(spot["price_cap_flag"] == 0) & (spot["negative_price_flag"] == 0)]
residual = (
    normal["lmp_total_usd_per_mwh"]
    - normal["lmp_energy_usd_per_mwh"]
    - normal["lmp_congestion_usd_per_mwh"]
    - normal["lmp_loss_usd_per_mwh"]
).abs()
print(f"Max decomp residual on normal rows: {residual.max():.6f}")
print(f"Mean decomp residual: {residual.mean():.6f}")
```

```python
# Build a simple forward curve from futures
import pandas as pd
from huggingface_hub import hf_hub_download

futures = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr006-sample", "enr006_futures_contracts.parquet",
    repo_type="dataset",
))

# Average price by tenor
print(futures.groupby("tenor")["contract_price_usd_per_mwh"].agg(["mean", "std", "count"]))
```

```python
# Trader P&L attribution
import pandas as pd
from huggingface_hub import hf_hub_download

trd = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr006-sample", "enr006_trading_analytics.parquet",
    repo_type="dataset",
))

book_pnl = trd.groupby("book_id").agg(
    realized=("realized_pnl_usd", "sum"),
    n_trades=("trade_id", "count"),
    avg_var95=("var_95_usd", "mean"),
).round(2).sort_values("realized", ascending=False)
print(book_pnl.head(10))
```

```python
# Validate VaR coherence (Basel III requirement)
import pandas as pd
from huggingface_hub import hf_hub_download

trd = pd.read_parquet(hf_hub_download(
    "xpertsystems/enr006-sample", "enr006_trading_analytics.parquet",
    repo_type="dataset",
))

var99_ge_var95 = (trd["var_99_usd"] >= trd["var_95_usd"]).mean()
cvar95_ge_var95 = (trd["cvar_95_usd"] >= trd["var_95_usd"]).mean()
print(f"VaR_99 >= VaR_95: {var99_ge_var95*100:.2f}%")
print(f"CVaR_95 >= VaR_95: {cvar95_ge_var95*100:.2f}%")
```

---

## Limitations and honest disclosures

This sample is calibrated for **structural fidelity, not bit-exact reproduction
of any specific ISO's settlement archive.** Specifically:

- **Spot prices cover only 5 pricing nodes** even when `n_pricing_nodes` is
  set higher — the generator hardcodes `node_ids[:5]` in its main flow
  (line 821). This is intentional sample-mode behavior; the full product
  pipeline scales to 500+ pricing nodes via the per-node-batch design.
- **Ancillary services covers only the first 168 timestamps** (one week
  via the generator's `timestamps_da[:168]` slice on line 823). For
  hours_da=336 in this sample, ancillary spans week 1 only; spot, futures,
  market clearing, and trading span the full 2-week window.
- **LMP three-part decomposition is broken by design** on (a) rows where
  `lmp_total` is clamped to the ISO price cap or floor, and (b) rows where
  `negative_price_flag=1` (negative-price override). The wrapper validates
  the decomposition on NORMAL rows only (cap_flag=0 AND negative_price_flag=0).
  For research that requires the full identity to hold, mask out the
  special-case rows or use the `lmp_energy + lmp_congestion + lmp_loss`
  sum directly.
- **Price spike rate and negative price rate are sample-scale unstable.**
  At 1,680 spot rows, the generator's Poisson(0.02) spike arrivals and
  the conjunction `random < 0.04 AND system_lambda < 0.3*base_lmp`
  for negative prices fire too rarely to validate against the generator's
  designed 2-10% spike / 1-8% negative-price targets. The full annual
  product matches those targets at scale. For tail-event ML, use the
  full product or the pre-built scenario configs.
- **`system_lambda` AR(1) coefficient is HIGHLY seed-dependent at
  sample scale** (observed range 0.20-0.90 across 6 seeds). The underlying
  mean-reverting process has θ=0.12/hr → asymptotic AR(1) ≈ 0.88, but
  spike events at small samples distort the lag-1 correlation. We
  validate `system_lambda` falling within the ISO floor/cap bounds
  instead of the AR(1) coefficient. For mean-reversion analysis, use
  the full annual product or fit a state-space model.
- **The generator's `run_benchmarks` reports "Grade: A+" misleadingly.**
  Its `all_passed` flag is only updated by `check_list` (line 598-603)
  which isn't actually invoked for any test in this version — so
  `all_passed=True` regardless of module-level pass/fail flags. This
  wrapper provides genuine industry-anchored validation via the
  scorecard above.
- **Forward curve uses a simplified seasonal + linear-risk-premium
  shape** (`fwd_curve = mean(system_lambda) * seasonal_adj +
  risk_premium`). Real forward curves include calendar-spread structure,
  weather-stochastic vol, and counterparty-specific basis that the
  generator does not model.
- **Options Greeks fire on only ~15% of contracts** (FUTURES + CfD types
  with 30% optionality probability). The remaining 85% have all Greeks
  set to zero. Filter `implied_vol_pct > 0` to extract the options-
  bearing subset before training Greek-prediction models.
- **`negative_price_flag = 1` row prices use `-rng.exponential(20)`** —
  i.e., a magnitude draw, NOT a structural reason like solar oversupply
  or congestion island. Use the flag as a label, not a causal driver.
- **`market_surplus_usd`, `virtual_bid_pnl_usd`, `capacity_market_price`
  are independent random draws**, not computed from underlying market
  dynamics. Treat as auxiliary fields for model-feature space, not as
  ground-truth market clearing outputs.
- **Credit ratings are sampled with a designed distribution
  `[5%, 10%, 20%, 30%, 20%, 10%, 5%]` for [AAA, AA, A, BBB, BB, B,
  CCC]** — IG-skewed but not anchored to any specific counterparty
  pool. Use as a categorical feature; don't infer real-world default
  probabilities directly.
- **All trades sampled from `participant_ids`** uniformly; trade pairings
  (buyer / seller) can occasionally match the same participant for both
  sides at small sample scale. For market-surveillance ML, filter
  buyer_id ≠ seller_id.
- **`hours_da` is hourly cadence only** — no 5-min real-time market data
  in the sample. The full product includes both DAM and RTM at 5-min
  resolution via `intervals_rt_per_hour=12`.

The full ENR006 product addresses these by full annual coverage, all
500+ pricing nodes, calibrated forward curves, RTM 5-min interval
settlement, and pre-built scenario configs (price_spike_event,
high_renewable_negative_prices, capacity_crunch, standard_annual) —
contact us for the licensed commercial release.

---

## Companion datasets in the Energy & Climate vertical

- **ENR-001** — Synthetic Power Grid Operations Dataset (transmission
  bus telemetry, line flows, generation dispatch, frequency, contingency)
- **ENR-002** — Synthetic Renewable Energy Generation Dataset
  (utility-scale solar/wind/hybrid SCADA, weather, forecast, PCC, BESS)
- **ENR-003** — Synthetic Electricity Demand & Load Forecasting Dataset
  (zone-level demand, multi-horizon forecasts, peak events, EV/DER, TOU)
- **ENR-004** — Synthetic Upstream Oil & Gas Production Dataset
  (well-level production, decline curves, PVT, commodity prices,
  Subpart W methane)
- **ENR-005** — Synthetic Smart Grid Dataset (AMI, DER, OpenADR, feeder
  power flow, grid edge analytics)
- **ENR-006** — Synthetic Wholesale Energy Market Trading Dataset (you
  are here) — **the market/trading complement to ENR-001's physical-grid
  view**: spot price formation, derivatives, ancillary services,
  bilateral PPAs, and trading risk

Use **ENR-001 + ENR-003 + ENR-006** together for full
**physical-grid + load-forecast + market-clearing** ML workflows; combine
with **ENR-002 + ENR-005** to add renewables and distribution-edge in
the same modeling stack.

For subsurface companion data (seismic, well logs, reservoir simulation,
geological formations), see the **OIL series** (OIL-001 through OIL-004)
in our [Oil & Gas vertical](https://huggingface.co/xpertsystems).

For the broader catalog:
- [Materials & Energy](https://huggingface.co/xpertsystems) — MAT-001
- [Insurance & Risk](https://huggingface.co/xpertsystems) — 10 SKUs
- [Cybersecurity](https://huggingface.co/xpertsystems) — 11 SKUs

---

## Citation

```bibtex
@dataset{xpertsystems_enr006_sample_2026,
  author       = {XpertSystems.ai},
  title        = {ENR006 Synthetic Wholesale Energy Market Trading Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/enr006-sample}
}
```

---

## Contact

- **Web:** https://xpertsystems.ai
- **Email:** pradeep@xpertsystems.ai
- **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy,
  Oil & Gas, Energy & Climate, and more

**Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0)
**Full product License:** Commercial — please contact for pricing.