Release Notes¶
Future release¶
Note
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 settingsector: 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 optionsector: H2_retrofit:
also works with a copperplated methane infrastructure, i.e. whensector: 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 sectionregional_co2_sequestration_potential
of theconfig.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 prefixCF+
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 ruleretrieve_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
andbuild_population_weighted_energy_totals
using code previously contained inprepare_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 <https://pypsa-eur-sec.readthedocs.io/en/latest/>
- 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.
Bugfixes
- 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.
General
- 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 thesector_opts
wildcard. Part of the emissions (1.A.4.c) was previously assigned to “industry non-elec” in theco2_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 viasector: 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 thee_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 settingsector: 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 anextra_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 settingindustry: 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 usingn.ilopf()
and remove outdated code usingpyomo
. Allows the new setting to skip iterated impedance updates withsolving: options: skip_iterations: true
. - The component attributes that are to be overridden are now stored in the folder
data/override_component_attrs
analogous topypsa/component_attrs
. This reduces verbosity and also allows circumventing then.madd()
hack for individual components with non-default attributes. This data is also tracked in the Snakefile. A functionhelper.override_component_attrs
was added that loads this data and can pass the overridden component attributes intopypsa.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
andbuild_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 theprepare_network
rule to reduce code duplication. - In the
prepare_network
rule the cost and potential adjustment was moved into an own functionmaybe_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 zenodo.org (10.5281/zenodo.5546517).
Bugfixes and Compatibility
- Compatibility with
atlite>=0.2
. Older versions ofatlite
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
andgeopy
(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 theplanning_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 thecapital_cost
of solar to 50% of original values. Similarlysolar+p3
multiplies thep_nom_max
by 3. - Rename the bus for European liquid hydrocarbons from
Fischer-Tropsch
toEU 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 nameDateIn
instead of oldYearDecommissioned
). - 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
andc_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
build_industrial_production_per_country_tomorrow.py
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 folderresources
. Nowdata
only includes input data, whileresources
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 theconfig.yaml
instead of the defaultforesight : 'overnight'
. An example configuration can be found inconfig.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. busDE0 3 urban central heat
has alocation
ofDE0 3
. - All assets have a
lifetime
attribute (integer in years). For the myopic foresight, abuild_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/conf.py
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