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reneuro

A European energy system optimization model with an R interface.

r·en·euro provides a European energy system model built on energyRt. It was developed as teaching material for a modelling course, and as a standalone working model.

It currently reproduces PyPSA-Eur, an established open model of the European power system, at several spatial resolutions — which gives a reference solution the R implementation can be verified against.

Models

Eight models of the same European system, shipped ready to solve, from two different routes. All cover the full year (8,760 hourly snapshots) unless stated.

Built by PyPSA-Eur — reduced by PyPSA-Eur’s own cluster_network workflow and read straight into energyRt, so each is directly comparable to a PyPSA-Eur run at the same size.

model nodes clustered by period
pypsa_eur_5 5 k-means, Belgium only one week
pypsa_eur_5cp 5 as above, copperplate one week
pypsa_eur_41 41 k-means full year
pypsa_eur_250 250 k-means full year
pypsa_eur_nuts3 1,035 NUTS3 regions, not k-means full year

Aggregated in R — derived from pypsa_eur_nuts3 with aggregate_pypsa(), applying PyPSA’s own reduction strategies in seconds rather than a full rebuild from raw data.

model nodes aggregated to build period
pypsa_eur_nuts0 36 NUTS0, countries 16 s full year
pypsa_eur_nuts1 106 NUTS1 34 s full year
pypsa_eur_nuts2 289 NUTS2 75 s full year

The routes meet usefully: pypsa_eur_nuts2 (289 nodes, aggregated in R) and pypsa_eur_250 (PyPSA’s k-means) are the same system at nearly the same size by two independent methods, so one checks the other.

Nomenclature des Unités Territoriales Statistiques (NUTS), the Nomenclature of Territorial Units for Statistics — is Eurostat’s official hierarchy of European regions, the one national statistics are published against. Four nested levels, each a complete partition of the territory:

level what it is regions model nodes
NUTS0 countries 36 36
NUTS1 major socio-economic regions 109 106
NUTS2 basic regions for regional policy 296 289
NUTS3 small regions, for specific diagnoses 1,477 1,035

Nodes fall short of regions because a region containing no substation merges into a neighbour, keeping its demand and generation. Using an official classification rather than an algorithmic clustering is what lets a model be joined to published statistics — population, GDP, industrial output — without a crosswalk of one’s own.

pypsa_eur_250 is the recommended working model. pypsa_eur_nuts3 is a source model rather than one to solve whole — its purpose is to have a country or study area lifted out of it at full granularity.

Models are lazy-loaded and need neither Python nor a PyPSA-Eur clone. The About article covers how each was built, the four countries outside NUTS, and the region data that ships alongside.

Installation 

# install.packages("pak")
pak::pak("optimal2050/reneuro")

energyRt is not on CRAN; pak installs it from GitHub alongside reneuro. A solver is also required — GLPK is enough for the smallest models, while continental scale needs Julia/HiGHS or Pyomo/HiGHS:

Quick start 

library(reneuro)
library(energyRt)

scen <- interpolate_model(pypsa_eur_5, name = "be")
scen <- write_script(scen, solver = solver_options$glpk)
scen <- read_solution(solve_scenario(scen, wait = TRUE))

getData(scen, "vObjective", merge = TRUE)$value

The solved scenario also ships as be_solved, so the results can be explored without a solver installed.

Documentation

  • Getting started — a model end to end, and carving a local model out of NUTS3
  • The data — what ships, and what changing spatial resolution does to it
  • About — how the models were built, solver benchmarks, references and licences

Contributing

Contributions are welcome. Issues and pull requests may be opened at github.com/optimal2050/reneuro. The package follows the optimal2050 conventions.

Licence

reneuro’s sources are Apache-2.0. It imports energyRt, which is AGPL-3, so a distribution of the two together is conveyed under AGPL-3. The shipped models inherit the licences of their inputs, chiefly ODbL-1.0 for OpenStreetMap transmission topology. Full detail, including data provenance, is in the About article and in NOTICE.

If you use reneuro in research, please cite it with citation("reneuro") and also cite PyPSA-Eur — see References.