PyPSA-Eur: An Open Optimisation Model of the European Transmission System

TL;DR

PyPSA-Eur is a comprehensive, open-source model of the European transmission network, integrating detailed data on infrastructure, power plants, and renewable resources for operational and planning studies.

Contribution

It introduces the first open, highly detailed European power system dataset with an automated pipeline, enabling easy updates and comparisons.

Findings

Contains 6001 lines and 3657 substations covering the full ENTSO-E area.

Includes renewable resource time series and capacity potentials based on re-analysis data.

Validates the dataset with a new methodology for comparing geo-referenced network data.

Abstract

PyPSA-Eur, the first open model dataset of the European power system at the transmission network level to cover the full ENTSO-E area, is presented. It contains 6001 lines (alternating current lines at and above 220 kV voltage level and all high voltage direct current lines), 3657 substations, a new open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. The model is suitable both for operational studies and generation and transmission expansion planning studies. The continental scope and highly resolved spatial scale enables a proper description of the long-range smoothing effects for renewable power generation and their varying resource availability. The restriction to freely available and open data encourages the open exchange of model data developments and eases the comparison of model results. A further novelty of the dataset is the publication of the full, automated software pipeline to assemble the load-flow-ready model from the original datasets, which enables easy replacement and improvement of the individual parts. This paper focuses on the description of the network topology, the compilation of a European power plant database and a top-down load time-series regionalisation. It summarises the derivation of renewable wind and solar availability time-series from re-analysis weather datasets and the estimation of renewable capacity potentials restricted by land-use. Finally, validations of the dataset are presented, including a new methodology to compare geo-referenced network datasets to one another.