European Power Price Convergence & Congestion
How coupled or congested each European power border is, day by day: for ~55 neighbouring bidding-zone pairs, the mean absolute day-ahead price spread (EUR/MWh), the signed mean spread (zone A - zone B), and the share of hours the two zones cleared at an identical price (fully coupled). Interconnector congestion and market-decoupling events show up as a widening absolute spread and a falling coupled-hours share - the interconnector-value and congestion signal traders and TSOs watch, pre-computed from day-ahead prices, 2018-present. Licence-clean and AI-training-safe.
- Source
- derived
- Licence
- Redistributable open data
- Updates
- Daily
- Data through
- 2026-08-01
- Quality
- QC clean · 0 failures
- Format
- Apache Parquet + dictionary
Safe to train on
Built only from a named, redistributable official source under a documented open licence - not scraped web data. No copyright grey zone, no personal data. Ships machine-readable Croissant metadata (ML Commons - loads in Hugging Face / Kaggle / Google), an AI training-licence manifest documenting source, licence and provenance for your model's data governance, and a machine-readable data dictionary (drop it into an agent / RAG prompt so the model knows every column) - all inspectable before you buy.
What's included
- Mean absolute spread + signed spread + fully-coupled-hours % - per border pair × day
- ~55 neighbouring bidding-zone pairs across the coupled European market
- Zone = directed pair label 'A~B' (spread is A - B); one row per pair × day × metric
- Congestion / decoupling = wide absolute spread + low coupled-hours share
- Derived purely from day-ahead prices; licence-clean & AI-training-safe
- Source: ENTSO-E (CC-BY)
Schema
| Column | Type | Description | Filled | Distinct |
|---|---|---|---|---|
| zone | str | Neighbouring bidding-zone pair 'A~B' | 100.0% | 59 |
| ts | timestamp (UTC) | Day | 100.0% | 3,136 |
| variable | str | Mean absolute spread / signed spread A-B / Hours fully coupled (%) | 100.0% | 3 |
| value | float | EUR/MWh (spreads) or % (coupled hours) | 100.0% | 26,847 |
| unit | str | mixed - see the variable name | 100.0% | 2 |
| res_min | int | Native resolution (daily = 1440) | 100.0% | 1 |
Sample & preview
Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: derived (redistributable open data; attribution passes through - see our Licence).
Don't trust screenshots - drop the sample into your notebook right now.
import pandas as pd df = pd.read_parquet("european-price-convergence_sample.parquet") df.info() # typed columns, gap-aware, ready to join⬇ Download free Parquet sample or CSV for Excel
Coverage: 59 zones
AT~CH · AT~CZ · AT~HU · AT~IT_NORD · AT~SI · CZ~PL · CZ~SK · DE_LU~AT · DE_LU~CH · DE_LU~CZ · DE_LU~DK1 · DE_LU~FR · DE_LU~NL · DE_LU~PL · DK1~DK2 · DK1~NO2 · DK1~SE3 · DK2~SE4 · EE~LV · ES~PT · FI~EE · FR~BE · FR~CH · FR~ES · FR~IT_NORD · GR~BG · HU~HR · HU~RO · HU~SI · IT_CALA~IT_SICI · IT_CNOR~IT_CSUD · IT_CSUD~IT_SARD · IT_CSUD~IT_SUD · IT_NORD~IT_CNOR · IT_SUD~IT_CALA · LT~PL · LT~SE4 · LV~LT · NL~BE · NO1~NO2 · NO1~NO3 · NO1~NO5 · NO1~SE3 · NO2~NO5 · NO3~NO4 · NO3~NO5 · NO3~SE2 · NO4~SE1 · NO4~SE2 · PL~SK · RO~BG · SE1~FI · SE1~SE2 · SE2~SE3 · SE3~FI · SE3~SE4 · SI~HR · SI~IT_NORD · SK~HU
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