geoscope-analysis is an open-source Python toolkit for evaluating simulated day-ahead electricity market prices under varying geographic simulation scope. It is a results-evaluation package and not simulation software. It contains no market-clearing or dispatch model. The package reads the PowerACE-simulated price dataset published separately as "Simulated Day-Ahead Electricity Prices for 48 European Bidding Zones under Varying Geographic Simulation Scope" (https://doi.org/10.35097/f3urvb31dq6r5wd1) and reproduces the core analyses of the accompanying study: regressions of average price and intraday price variance on the simulated scope, a convergence analysis of how results approach the full-scope reference as the scope grows, and a classification of how consistently the ranking among bidding strategies holds across scope configurations. A reliability check shows for which market areas the observed effects are statistically defensible. The package supports independent reproduction of the study's results and can be reused for other analyses of geographic-scope sensitivity in simulated electricity markets.

geoscope-analysis

Content: geoscope-analysis (Python, MIT license) reads the dayahead_prices_<strategy>.parquet files published separately as the companion dataset "Simulated Day-Ahead Electricity Prices for 48 European Bidding Zones under Varying Geographic Simulation Scope" (https://doi.org/10.35097/f3urvb31dq6r5wd1) and reproduces the accompanying study's analyses: the effect of scope on average price and intraday price variance, a reliability gate, a convergence analysis, and a strategy-ranking-stability comparison.
Technical requirements: Python 3.10 or newer. Dependencies (installed automatically): numpy, pandas, pyarrow, scipy, statsmodels, networkx, plotly.
Installation and use:
pip install .
Use it either as a Python library (import geoscope_analysis as ga) or via the geoscope-analysis command-line tool. Subcommands are available for the reliability gate, the dispersion/convergence check, metric-vs-scope plots, and the strategy-ranking-stability comparison. A full-study orchestrator reproduces the paper's complete regression and convergence report end to end.
Input/output: Reads the published Parquet day-ahead price tables (see the companion dataset's documentation for the schema). Writes CSV result tables and interactive Plotly HTML figures. No proprietary software is needed to inspect either.
Reuse: The individual building blocks (panel construction, reliability gating, dispersion/convergence testing, strategy-ranking-stability comparison) can be used independently of the accompanying study, for example for other analyses of geographic-scope sensitivity in simulated electricity markets.
License: MIT (https://opensource.org/license/mit)
Copyright 2026, Thorsten Weiskopf, Jonathan Stelzer, Karlsruhe Institute of Technology
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Authors: Thorsten Weiskopf and Jonathan Stelzer, Karlsruhe Institute of Technology.