Release Notes

Future release


This unreleased version currently may require the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.

  • new feature

PyPSA-Eur-Sec 0.7.0 (16th February 2023)

This release includes many new features. Highlights include new gas infrastructure data with retrofitting options for hydrogen transport, improved carbon management and infrastructure planning, regionalised potentials for hydrogen underground storage and carbon sequestration, new applications for biomass, and explicit modelling of methanol and ammonia as separate energy carriers.

This release is known to work with PyPSA-Eur Version 0.7.0 and Technology Data Version 0.5.0.

Gas Transmission Network

  • New rule retrieve_gas_infrastructure_data that downloads and extracts the SciGRID_gas IGGIELGN dataset from zenodo. It includes data on the transmission routes, pipe diameters, capacities, pressure, and whether the pipeline is bidirectional and carries H-Gas or L-Gas.
  • New rule build_gas_network processes and cleans the pipeline data from SciGRID_gas. Missing or uncertain pipeline capacities can be inferred by diameter.
  • New rule build_gas_input_locations compiles the LNG import capacities (from the Global Energy Monitor’s Europe Gas Tracker, pipeline entry capacities and local production capacities for each region of the model. These are the regions where fossil gas can eventually enter the model.
  • New rule cluster_gas_network that clusters the gas transmission network data to the model resolution. Cross-regional pipeline capacities are aggregated (while pressure and diameter compatibility is ignored), intra-regional pipelines are dropped. Lengths are recalculated based on the regions’ centroids.
  • With the option sector: gas_network:, the existing gas network is added with a lossless transport model. A length-weighted k-edge augmentation algorithm can be run to add new candidate gas pipelines such that all regions of the model can be connected to the gas network. The number of candidates can be controlled via the setting sector: gas_network_connectivity_upgrade:. When the gas network is activated, all the gas demands are regionally disaggregated as well.
  • New constraint allows endogenous retrofitting of gas pipelines to hydrogen pipelines. This option is activated via the setting sector: H2_retrofit:. For every unit of gas pipeline capacity dismantled, sector: H2_retrofit_capacity_per_CH4 units are made available as hydrogen pipeline capacity in the corresponding corridor. These repurposed hydrogen pipelines have lower costs than new hydrogen pipelines. Both new and repurposed pipelines can be built simultaneously. The retrofitting option sector: H2_retrofit: also works with a copperplated methane infrastructure, i.e. when sector: gas_network: false.
  • New hydrogen pipelines can now be built where there are already power or gas transmission routes. Previously, only the electricity transmission routes were considered.

Carbon Management and Biomass

  • Add option to spatially resolve carrier representing stored carbon dioxide (co2_spatial). This allows for more detailed modelling of CCUTS, e.g. regarding the capturing of industrial process emissions, usage as feedstock for electrofuels, transport of carbon dioxide, and geological sequestration sites.
  • Add option for regionally-resolved geological carbon dioxide sequestration potentials through new rule build_sequestration_potentials based on CO2StoP. This can be controlled in the section regional_co2_sequestration_potential of the config.yaml. It includes options to select the level of conservatism, whether onshore potentials should be included, the respective upper and lower limits per region, and an annualisation parameter for the cumulative potential. The defaults are preliminary and will be validated the next release.
  • Add option to sweep the global CO2 sequestration potentials with keyword seq200 in the {sector_opts} wildcard (for limit of 200 Mt CO2).
  • Add option to include Allam cycle gas power plants (allam_cycle).
  • Add option for planning a new carbon dioxide network (co2network).
  • Separate option to regionally resolve biomass (biomass_spatial) from option to allow biomass transport (biomass_transport).
  • Add option for biomass boilers (wood pellets) for decentral heating.
  • Add option for BioSNG (methane from biomass) with and without carbon capture.
  • Add option for BtL (biomass to liquid fuel/oil) with and without carbon capture.

Other new features

  • Add regionalised hydrogen salt cavern storage potentials from Technical Potential of Salt Caverns for Hydrogen Storage in Europe. This data is compiled in a new rule build_salt_cavern_potentials.
  • Add option to resolve ammonia as separate energy carrier with Haber-Bosch synthesis, ammonia cracking, storage and industrial demand. The ammonia carrier can be nodally resolved or copperplated across Europe (see ammonia).
  • Add methanol as energy carrier, methanolisation as process, and option for methanol demand in shipping sector.
  • Shipping demand now defaults to methanol rather than liquefied hydrogen until 2050.
  • Demand for liquid hydrogen in international shipping is now geographically distributed by port trade volumes in a new rule build_shipping_demand using data from the World Bank Data Catalogue. Domestic shipping remains distributed by population.
  • Add option to aggregate network temporally using representative snapshots or segments (with tsam).
  • Add option for minimum part load for Fischer-Tropsch plants (default: 90%) and methanolisation plants (default: 50%).
  • Add option to use waste heat of electrolysis in district heating networks (use_electrolysis_waste_heat).
  • Add option for coal CHPs with carbon capture (see coal_cc).
  • In overnight optimisation, it is now possible to specify a year for the technology cost projections separate from the planning horizon.
  • New config options for changing energy demands in aviation (aviation_demand_factor) and HVC industry (HVC_demand_factor), as well as explicit ICE shares for land transport (land_transport_ice_share) and agriculture machinery (agriculture_machinery_oil_share).
  • It is now possible to merge residential and services heat buses to reduce the problem size (see cluster_heat_nodes).
  • Added option to tweak (almost) any configuration parameter through the {sector_opts} wildcard. The regional_co2_sequestration_potential is triggered by the prefix CF+ after which it is possible to pipe to any setting that does not contain underscores (_). Example: CF+sector+v2g+false disables vehicle-to-grid flexibility.
  • Option retrieve_sector_databundle to automatically retrieve and extract data bundle.
  • Removed the need to clone technology-data repository in a parallel directory. The new approach automatically retrieves the technology data from remote in the rule retrieve_cost_data.
  • Improved network plots including better legends, hydrogen retrofitting network display, and change to EqualEarth projection. A new color scheme for technologies was also introduced.
  • Add two new rules build_transport_demand and build_population_weighted_energy_totals using code previously contained in prepare_sector_network.
  • Rules that convert weather data with atlite now largely run separately for categories residential, rural and total.
  • Units are assigned to the buses. These only provide a better understanding. The specifications of the units are not taken into account in the optimisation, which means that no automatic conversion of units takes place.
  • Configuration file and wildcards are now stored under n.meta in every PyPSA network.
  • Updated data bundle that includes the hydrogan salt cavern storage potentials.
  • Updated and extended documentation in <>
  • Added new rule copy_conda_env that exports a list of packages with which the workflow was executed.
  • Add basic continuous integration using Github Actions.
  • Add basic rsync setup.


  • The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version) caused a failure to read in the shadow prices of other global constraints.
  • Correct capital cost of Fischer-Tropsch according to new units in technology-data repository.
  • Fix unit conversion error for thermal energy storage.
  • For myopic pathway optimisation, set optimised capacities of power grid expansion of previous iteration as minimum capacity for next iteration.
  • Further rather minor bugfixes for myopic optimisation code (see #256).

Many thanks to all who contributed to this release!

PyPSA-Eur-Sec 0.6.0 (4 October 2021)

This release includes improvements regarding the basic chemical production, the addition of plastics recycling, the addition of the agriculture, forestry and fishing sector, more regionally resolved biomass potentials, CO2 pipeline transport and storage, and more options in setting exogenous transition paths, besides many performance improvements.

This release is known to work with PyPSA-Eur Version 0.4.0, Technology Data Version 0.3.0 and PyPSA Version 0.18.0.

Please note that the data bundle has also been updated.


  • With this release, we change the license from copyleft GPLv3 to the more liberal MIT license with the consent of all contributors.

New features and functionality

  • Distinguish costs for home battery storage and inverter from utility-scale battery costs.
  • Separate basic chemicals into HVC (high-value chemicals), chlorine, methanol and ammonia [#166].
  • Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [#166].
  • Include energy demands and CO2 emissions for the agriculture, forestry and fishing sector. It is included by default through the option A in the sector_opts wildcard. Part of the emissions (1.A.4.c) was previously assigned to “industry non-elec” in the co2_totals.csv. Hence, excluding the agriculture sector will now lead to a tighter CO2 limit. Energy demands are taken from the JRC IDEES database (missing countries filled with eurostat data) and are split into electricity (lighting, ventilation, specific electricity uses, pumping devices (electric)), heat (specific heat uses, low enthalpy heat) machinery oil (motor drives, farming machine drives, pumping devices (diesel)). Heat demand is assigned at “services rural heat” buses. Electricity demands are added to low-voltage buses. Time series for demands are constant and distributed inside countries by population [#147].
  • Include today’s district heating shares in myopic optimisation and add option to specify exogenous path for district heating share increase under sector: district_heating: [#149].
  • Added option for hydrogen liquefaction costs for hydrogen demand in shipping. This introduces a new H2 liquid bus at each location. It is activated via sector: shipping_hydrogen_liquefaction: true.
  • The share of shipping transformed into hydrogen fuel cell can be now defined for different years in the config.yaml file. The carbon emission from the remaining share is treated as a negative load on the atmospheric carbon dioxide bus, just like aviation and land transport emissions.
  • The transformation of the Steel and Aluminium production can be now defined for different years in the config.yaml file.
  • Include the option to alter the maximum energy capacity of a store via the carrier+factor in the {sector_opts} wildcard. This can be useful for sensitivity analyses. Example: co2 stored+e2 multiplies the e_nom_max by factor 2. In this example, e_nom_max represents the CO2 sequestration potential in Europe.
  • Use JRC ENSPRESO database to spatially disaggregate biomass potentials to PyPSA-Eur regions based on overlaps with NUTS2 regions from ENSPRESO (proportional to area) (#151).
  • Add option to regionally disaggregate biomass potential to individual nodes (previously given per country, then distributed by population density within) and allow the transport of solid biomass. The transport costs are determined based on the JRC-EU-Times Bioenergy report in the new optional rule build_biomass_transport_costs. Biomass transport can be activated with the setting sector: biomass_transport: true.
  • Add option to regionally resolve CO2 storage and add CO2 pipeline transport because geological storage potential, CO2 utilisation sites and CO2 capture sites may be separated. The CO2 network is built from zero based on the topology of the electricity grid (greenfield). Pipelines are assumed to be bidirectional and lossless. Furthermore, neither retrofitting of natural gas pipelines (required pressures are too high, 80-160 bar vs <80 bar) nor other modes of CO2 transport (by ship, road or rail) are considered. The regional representation of CO2 is activated with the config setting sector: co2_network: true but is deactivated by default. The global limit for CO2 sequestration now applies to the sum of all CO2 stores via an extra_functionality constraint.
  • The myopic option can now be used together with different clustering for the generators and the network. The existing renewable capacities are split evenly among the regions in every country [#144].
  • Add optional function to use geopy to locate entries of the Hotmaps database of industrial sites with missing location based on city and country, which reduces missing entries by half. It can be activated by setting industry: hotmaps_locate_missing: true, takes a few minutes longer, and should only be used if spatial resolution is coarser than city level.

Performance and Structure

  • Extended use of multiprocessing for much better performance (from up to 20 minutes to less than one minute).
  • Handle most input files (or base directories) via snakemake.input.
  • Use of mock_snakemake from PyPSA-Eur.
  • Update solve_network rule to match implementation in PyPSA-Eur by using n.ilopf() and remove outdated code using pyomo. Allows the new setting to skip iterated impedance updates with solving: options: skip_iterations: true.
  • The component attributes that are to be overridden are now stored in the folder data/override_component_attrs analogous to pypsa/component_attrs. This reduces verbosity and also allows circumventing the n.madd() hack for individual components with non-default attributes. This data is also tracked in the Snakefile. A function helper.override_component_attrs was added that loads this data and can pass the overridden component attributes into pypsa.Network().
  • Add various parameters to config.default.yaml which were previously hardcoded inside the scripts (e.g. energy reference years, BEV settings, solar thermal collector models, geomap colours).
  • Removed stale industry demand rules build_industrial_energy_demand_per_country and build_industrial_demand. These are superseded with more regionally resolved rules.
  • Use simpler and shorter gdf.sjoin() function to allocate industrial sites from the Hotmaps database to onshore regions. This change also fixes a bug: The previous version allocated sites to the closest bus, but at country borders (where Voronoi cells are distorted by the borders), this had resulted in e.g. a Spanish site close to the French border being wrongly allocated to the French bus if the bus center was closer.
  • Retrofitting rule is now only triggered if endogeneously optimised.
  • Show progress in build rules with tqdm progress bars.
  • Reduced verbosity of Snakefile through directory prefixes.
  • Improve legibility of config.default.yaml and remove unused options.
  • Use the country-specific time zone mappings from pytz rather than a manual mapping.
  • A function add_carrier_buses() was added to the prepare_network rule to reduce code duplication.
  • In the prepare_network rule the cost and potential adjustment was moved into an own function maybe_adjust_costs_and_potentials().
  • Use matplotlibrc to set the default plotting style and backend.
  • Added benchmark files for each rule.
  • Consistent use of __main__ block and further unspecific code cleaning.
  • Updated data bundle and moved data bundle to (10.5281/zenodo.5546517).

Bugfixes and Compatibility

  • Compatibility with atlite>=0.2. Older versions of atlite will no longer work.
  • Corrected calculation of “gas for industry” carbon capture efficiency.
  • Implemented changes to n.snapshot_weightings in PyPSA v0.18.0.
  • Compatibility with xarray version 0.19.
  • New dependencies: tqdm, atlite>=0.2.4, pytz and geopy (optional). These are included in the environment specifications of PyPSA-Eur v0.4.0.

Many thanks to all who contributed to this release!

PyPSA-Eur-Sec 0.5.0 (21st May 2021)

This release includes improvements to the cost database for building retrofits, carbon budget management and wildcard settings, as well as an important bugfix for the emissions from land transport.

This release is known to work with PyPSA-Eur Version 0.3.0 and Technology Data Version 0.2.0.

Please note that the data bundle has also been updated.

New features and bugfixes:

  • The cost database for retrofitting of the thermal envelope of buildings has been updated. Now, for calculating the space heat savings of a building, losses by thermal bridges and ventilation are included as well as heat gains (internal and by solar radiation). See the section retro for more details on the retrofitting module.
  • For the myopic investment option, a carbon budget and a type of decay (exponential or beta) can be selected in the config.yaml file to distribute the budget across the planning_horizons. For example, cb40ex0 in the {sector_opts} wildcard will distribute a carbon budget of 40 GtCO2 following an exponential decay with initial growth rate 0.
  • Added an option to alter the capital cost or maximum capacity of carriers by a factor via carrier+factor in the {sector_opts} wildcard. This can be useful for exploring uncertain cost parameters. Example: solar+c0.5 reduces the capital_cost of solar to 50% of original values. Similarly solar+p3 multiplies the p_nom_max by 3.
  • Rename the bus for European liquid hydrocarbons from Fischer-Tropsch to EU oil, since it can be supplied not just with the Fischer-Tropsch process, but also with fossil oil.
  • Bugfix: The new separation of land transport by carrier in Version 0.4.0 failed to account for the carbon dioxide emissions from internal combustion engines in land transport. This is now treated as a negative load on the atmospheric carbon dioxide bus, just like aviation emissions.
  • Bugfix: Fix reading in of pypsa-eur/resources/powerplants.csv to PyPSA-Eur Version 0.3.0 (use column attribute name DateIn instead of old YearDecommissioned).
  • Bugfix: Make sure that Store components (battery and H2) are also removed from PyPSA-Eur, so they can be added later by PyPSA-Eur-Sec.

Thanks to Lisa Zeyen (KIT) for the retrofitting improvements and Marta Victoria (Aarhus University) for the carbon budget and wildcard management.

PyPSA-Eur-Sec 0.4.0 (11th December 2020)

This release includes a more accurate nodal disaggregation of industry demand within each country, fixes to CHP and CCS representations, as well as changes to some configuration settings.

It has been released to coincide with PyPSA-Eur Version 0.3.0 and Technology Data Version 0.2.0, and is known to work with these releases.

New features:

  • The Hotmaps Industrial Database is used to disaggregate the industrial demand spatially to the nodes inside each country (previously it was distributed by population density).
  • Electricity demand from industry is now separated from the regular electricity demand and distributed according to the industry demand. Only the remaining regular electricity demand for households and services is distributed according to GDP and population.
  • A cost database for the retrofitting of the thermal envelope of residential and services buildings has been integrated, as well as endogenous optimisation of the level of retrofitting. This is described in the paper Mitigating heat demand peaks in buildings in a highly renewable European energy system. Retrofitting can be activated both exogenously and endogenously from the config.yaml.
  • The biomass and gas combined heat and power (CHP) parameters c_v and c_b were read in assuming they were extraction plants rather than back pressure plants. The data is now corrected in Technology Data Version 0.2.0 to the correct DEA back pressure assumptions and they are now implemented as single links with a fixed ratio of electricity to heat output (even as extraction plants, they were always sitting on the backpressure line in simulations, so there was no point in modelling the full heat-electricity feasibility polygon). The old assumptions underestimated the heat output.
  • The Danish Energy Agency released new assumptions for carbon capture in October 2020, which have now been incorporated in PyPSA-Eur-Sec, including direct air capture (DAC) and post-combustion capture on CHPs, cement kilns and other industrial facilities. The electricity and heat demand for DAC is modelled for each node (with heat coming from district heating), but currently the electricity and heat demand for industrial capture is not modelled very cleanly (for process heat, 10% of the energy is assumed to go to carbon capture) - a new issue will be opened on this.
  • Land transport is separated by energy carrier (fossil, hydrogen fuel cell electric vehicle, and electric vehicle), but still needs to be separated into heavy and light vehicles (the data is there, just not the code yet).
  • For assumptions that change with the investment year, there is a new time-dependent format in the config.yaml using a dictionary with keys for each year. Implemented examples include the CO2 budget, exogenous retrofitting share and land transport energy carrier; more parameters will be dynamised like this in future.
  • Some assumptions have been moved out of the code and into the config.yaml, including the carbon sequestration potential and cost, the heat pump sink temperature, reductions in demand for high value chemicals, and some BEV DSM parameters and transport efficiencies.
  • Documentation on Supply and demand options has been added.

Many thanks to Fraunhofer ISI for opening the hotmaps database and to Lisa Zeyen (KIT) for implementing the building retrofitting.

PyPSA-Eur-Sec 0.3.0 (27th September 2020)

This releases focuses on improvements to industry demand and the generation of intermediate files for demand for basic materials. There are still inconsistencies with CCS and waste management that need to be improved.

It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69), PyPSA v0.17.1 and technology-data v0.1.0. Please note that the data bundle has also been updated.

New features:

  • In previous version of PyPSA-Eur-Sec the energy demand for industry was calculated directly for each location. Now, instead, the production of each material (steel, cement, aluminium) at each location is calculated as an intermediate data file, before the energy demand is calculated from it. This allows us in future to have competing industrial processes for supplying the same material demand.
  • The script determines the future industrial production of materials based on today’s levels as well as assumed recycling and demand change measures.
  • The energy demand for each industry sector and each location in 2015 is also calculated, so that it can be later incorporated in the pathway optimization.
  • Ammonia production data is taken from the USGS and deducted from JRC-IDEES’s “basic chemicals” so that it ammonia can be handled separately from the others (olefins, aromatics and chlorine).
  • Solid biomass is no longer allowed to be used for process heat in cement and basic chemicals, since the wastes and residues cannot be guaranteed to reach the high temperatures required. Instead, solid biomass is used in the paper and pulp as well as food, beverages and tobacco industries, where required temperatures are lower (see DOI:10.1002/er.3436 and DOI:10.1007/s12053-017-9571-y).
  • National installable potentials for salt caverns are now applied.
  • When electricity distribution grids are activated, new industry electricity demand, resistive heaters and micro-CHPs are now connected to the lower voltage levels.
  • Gas distribution grid costs are included for gas boilers and micro-CHPs.
  • Installable potentials for rooftop PV are included with an assumption of 1 kWp per person.
  • Some intermediate files produced by scripts have been moved from the folder data to the folder resources. Now data only includes input data, while resources only includes intermediate files necessary for building the network models. Please note that the data bundle has also been updated.
  • Biomass potentials for different years and scenarios from the JRC are generated in an intermediate file, so that a selection can be made more explicitly by specifying the biomass types from the config.yaml.

PyPSA-Eur-Sec 0.2.0 (21st August 2020)

This release introduces pathway optimization over many years (e.g. 2020, 2030, 2040, 2050) with myopic foresight, as well as outsourcing the technology assumptions to the technology-data repository.

It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69), PyPSA v0.17.1 and technology-data v0.1.0.

New features:

  • Option for pathway optimization with myopic foresight, based on the paper Early decarbonisation of the European Energy system pays off (2020). Investments are optimized sequentially for multiple years (e.g. 2020, 2030, 2040, 2050) taking account of existing assets built in previous years and their lifetimes. The script uses data on the existing assets for electricity and building heating technologies, but there are no assumptions yet for existing transport and industry (if you include these, the model will greenfield them). There are also some outstanding issues on e.g. the distribution of existing wind, solar and heating technologies within each country. To use myopic foresight, set foresight : 'myopic' in the config.yaml instead of the default foresight : 'overnight'. An example configuration can be found in config.myopic.yaml. More details on the implementation can be found in Myopic transition path.
  • Technology assumptions (costs, efficiencies, etc.) are no longer stored in the repository. Instead, you have to install the technology-data database in a parallel directory. These assumptions are largely based on the Danish Energy Agency Technology Data. More details on the installation can be found in Installation.
  • Logs and benchmarks are now stored with the other model outputs in results/run-name/.
  • All buses now have a location attribute, e.g. bus DE0 3 urban central heat has a location of DE0 3.
  • All assets have a lifetime attribute (integer in years). For the myopic foresight, a build_year attribute is also stored.
  • Costs for solar and onshore and offshore wind are recalculated by PyPSA-Eur-Sec based on the investment year, including the AC or DC connection costs for offshore wind.

Many thanks to Marta Victoria for implementing the myopic foresight, and Marta Victoria, Kun Zhu and Lisa Zeyen for developing the technology assumptions database.

PyPSA-Eur-Sec 0.1.0 (8th July 2020)

This is the first proper release of PyPSA-Eur-Sec, a model of the European energy system at the transmission network level that covers the full ENTSO-E area.

It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69) and PyPSA v0.17.0.

We are making this release since in version 0.2.0 we will introduce changes to allow myopic investment planning that will require minor changes for users of the overnight investment planning.

PyPSA-Eur-Sec builds on the electricity generation and transmission model PyPSA-Eur to add demand and supply for the following sectors: transport, space and water heating, biomass, industry and industrial feedstocks. This completes the energy system and includes all greenhouse gas emitters except waste management, agriculture, forestry and land use.

PyPSA-Eur-Sec was initially based on the model PyPSA-Eur-Sec-30 (Version 0.0.1 below) described in the paper Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system (2018) but it differs by being based on the higher resolution electricity transmission model PyPSA-Eur rather than a one-node-per-country model, and by including biomass, industry, industrial feedstocks, aviation, shipping, better carbon management, carbon capture and usage/sequestration, and gas networks.

PyPSA-Eur-Sec includes PyPSA-Eur as a snakemake subworkflow. PyPSA-Eur-Sec uses PyPSA-Eur to build the clustered transmission model along with wind, solar PV and hydroelectricity potentials and time series. Then PyPSA-Eur-Sec adds other conventional generators, storage units and the additional sectors.

PyPSA-Eur-Sec 0.0.2 (4th September 2020)

This version, also called PyPSA-Eur-Sec-30-Path, built on PyPSA-Eur-Sec 0.0.1 (also called PyPSA-Eur-Sec-30) to include myopic pathway optimisation for the paper Early decarbonisation of the European energy system pays off (2020). The myopic pathway optimisation was then merged into the main PyPSA-Eur-Sec codebase in Version 0.2.0 above.

This model has its own github repository and is archived on Zenodo.

PyPSA-Eur-Sec 0.0.1 (12th January 2018)

This is the first published version of PyPSA-Eur-Sec, also called PyPSA-Eur-Sec-30. It was first used in the research paper Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system (2018). The model covers 30 European countries with one node per country. It includes demand and supply for electricity, space and water heating in buildings, and land transport.

It is archived on Zenodo.

Release Process

  • Finalise release notes at doc/release_notes.rst.
  • Update version number in doc/ and *config.*.yaml.
  • Make a git commit.
  • Tag a release by running git tag v0.x.x, git push, git push --tags. Include release notes in the tag message.
  • Make a GitHub release, which automatically triggers archiving by zenodo.
  • Send announcement on the PyPSA mailing list.

To make a new release of the data bundle, make an archive of the files in data which are not already included in the git repository:

data % tar pczf pypsa-eur-sec-data-bundle.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson h2_salt_caverns_GWh_per_sqkm.geojson