Contribution type: poster
Reproducing European Electricity Price Stability Estimates: A PyPSA-based Replication and Uncertainty Analysis
Presenter: Nick Herrmann (INATECH, University of Freiburg)
Description: This poster replicates the price estimates of Navia Simon & Diaz Anadon (2025, Nature Energy), who use GenX with 300 stochastic Monte Carlo runs to show that higher renewable shares lower both the mean 2030 European electricity price and its volatility. Using the same open ENTSO-E ERAA input data but PyPSA as an independent open-source tool and a deterministic setup, this work asks whether those price estimates hold under a different, fully open modelling approach and additionally, what makes this kind of study hard to reproduce exactly. The comparison shows that reproducing a stochastic study’s results requires matching not only its input data, but its underlying methodological design as well as full knowledge of the modelling decisions behind it.
Background: Reproducibility is a recognized challenge in energy system modelling: studies rarely share code, and even with open input data, matching a published result exactly depends on choices that are often undocumented.
This case is a practical test of that challenge: it combines the original study’s fully open input data (ENTSO-E ERAA) with an independent, widely-used open-source modelling tool (PyPSA), making an end-to-end replication feasible rather than only a partial or code-level check.
The project was carried out at the University of Freiburg as part of a Master’s program, ahead of the Master’s thesis.