PyPSA 0.16.0 (20th December 2019)
This release contains major new features. It is also the first release to drop support for Python 2.7. Only Python 3.6 and 3.7 are supported going forward. Python 3.8 will be supported as soon as the gurobipy package in conda is updated.
A new version of the linear optimal power flow (LOPF) has been introduced that uses a custom optimization framework rather than Pyomo. The new framework, based on nomoypomo, uses barely any memory and is much faster than Pyomo. As a result the total memory usage of PyPSA processing and gurobi is less than a third what it is with Pyomo for large problems with millions of variables that take several gigabytes of memory (see this graphical comparison for a large network optimization). The new framework is not enabled by default. To enable it, use
network.lopf(pyomo=False). Almost all features of the regular
network.lopfare implemented with the exception of minimum down/up time and start up/shut down costs for unit commitment. If you use the
network.lopfyou will need to update your code for the new syntax. There is documentation for the new syntax as well as a Jupyter notebook of examples.
Distributed active power slack is now implemented for the full non-linear power flow. If you pass
distribute_slack=True, it will distribute the slack power across generators proportional to generator dispatch by default, or according to the distribution scheme provided in the argument
distribute_slack=Falseonly the slack generator takes up the slack. There is further documentation.
Unit testing is now performed on all of GNU/Linux, Windows and MacOS.
NB: You may need to update your version of the package
Special thanks for this release to Fabian Hofmann for implementing the nomopyomo framework in PyPSA and Fabian Neumann for providing the customizable distributed slack.
PyPSA 0.15.0 (8th November 2019)
This release contains new improvements and bug fixes.
The unit commitment (UC) has been revamped to take account of constraints at the beginning and end of the simulated
snapshotsbetter. This is particularly useful for rolling horizon UC. UC now accounts for up-time and down-time in the periods before the
snapshots. The generator attribute
initial_statushas been replaced with two attributes
down_time_beforeto give information about the status before
network.snapshots. At the end of the simulated
snapshots, minimum up-times and down-times are also enforced. Ramping constraints also look before the simulation at previous results, if there are any. See the unit commitment documentation for full details. The UC example has been updated with a rolling horizon example at the end.
Documentation is now available on readthedocs, with information about functions pulled from the docstrings.
The dependency on cartopy is now an optional extra.
PyPSA now works with pandas 0.25 and above, and networkx above 2.3.
A bug was fixed that broke the Security-Constrained Linear Optimal Power Flow (SCLOPF) constraints with extendable lines.
Network plotting can now plot arrows to indicate the direction of flow by passing
The objective sense (
maximize) can now be set (default remains
network.snapshot_weightingsis now carried over when the network is clustered.
Various other minor fixes.
We thank colleagues at TERI for assisting with testing the new unit commitment code, Clara Büttner for finding the SCLOPF bug, and all others who contributed issues and pull requests.
PyPSA 0.14.1 (27th May 2019)
This minor release contains three small bug fixes:
Documentation parses now correctly on PyPI
Python 2.7 and 3.6 are automatically tested using Travis
PyPSA on Python 2.7 was fixed
This will also be the first release to be available directly from conda-forge.
PyPSA 0.14.0 (15th May 2019)
This release contains a new feature and bug fixes.
Network plotting can now use the mapping library cartopy as well as basemap, which was used in previous versions of PyPSA. The basemap developers will be phasing out basemap over the next few years in favour of cartopy (see their end-of-life announcement). PyPSA now defaults to cartopy unless you tell it explicitly to use basemap. Otherwise the plotting interface is the same as in previous versions.
Optimisation now works with the newest version of Pyomo 5.6.2 (there was a Pyomo update that affected the opt.py expression for building linear sums).
A critical bug in the networkclustering sub-library has been fixed which was preventing the capital_cost parameter of conventional generators being handled correctly when networks are aggregated.
Network.consistency_check() now only prints necessary columns when reporting NaN values.
Import from pandapower networks has been updated to pandapower 2.0 and to include non-standard lines and transformers.
We thank Fons van der Plas and Fabian Hofmann for helping with the cartopy interface, Chloe Syranidis for pointing out the problem with the Pyomo 5.6.2 update, Hailiang Liu for the consistency check update and Christian Brosig for the pandapower updates.
PyPSA 0.13.2 (10th January 2019)
This minor release contains small new features and fixes.
Optimisation now works with Pyomo >= 5.6 (there was a Pyomo update that affected the opt.py LConstraint object).
New functional argument can be passed to Network.lopf: extra_postprocessing(network,snapshots,duals), which is called after solving and results are extracted. It can be used to get the values of shadow prices for constraints that are not normally extracted by PyPSA.
In the lopf kirchhoff formulation, the cycle constraint is rescaled by a factor 1e5, which improves the numerical stability of the interior point algorithm (since the coefficients in the constraint matrix were very small).
Updates and fixes to networkclustering, io, plot.
We thank Soner Candas of TUM for reporting the problem with the most recent version of Pyomo and providing the fix.
PyPSA 0.13.1 (27th March 2018)
This release contains bug fixes for the new features introduced in 0.13.0.
Export network to netCDF file bug fixed (components that were all standard except their name were ignored).
Import/export network to HDF5 file bug fixed and now works with more than 1000 columns; HDF5 format is no longer deprecated.
When networks are copied or sliced, overridden components (introduced in 0.13.0) are also copied.
Sundry other small fixes.
We thank Tim Kittel for pointing out the first and second bugs. We thank Kostas Syranidis for not only pointing out the third issue with copying overridden components, but also submitting a fix as a pull request.
For this release we acknowledge funding to Tom Brown from the RE-INVEST project.
PyPSA 0.13.0 (25th January 2018)
This release contains new features aimed at coupling power networks to other energy sectors, fixes for library dependencies and some minor internal API changes.
If you want to define your own components and override the standard functionality of PyPSA, you can now override the standard components by passing pypsa.Network() the arguments
override_component_attrs, see the section on Custom Components. There are examples for defining new components in the git repository in
examples/new_components/, including an example of overriding
network.lopf()for functionality for combined-heat-and-power (CHP) plants.
Linkcomponent can now be defined with multiple outputs in fixed ratio to the power in the single input by defining new columns
bus3, etc. (
busfollowed by an integer) in
network.linksalong with associated columns for the efficiencies
efficiency3, etc. The different outputs are then proportional to the input according to the efficiency; see sections Link with multiple outputs or inputs and Controllable branch flows: links and the example of a CHP with a fixed power-heat ratio.
Networks can now be exported to and imported from netCDF files with
network.import_from_netcdf(). This is faster than using CSV files and the files take up less space. Import and export with HDF5 files, introduced in PyPSA 0.12.0, is now deprecated.
The export and import code has been refactored to be more general and abstract. This does not affect the API.
The internally-used sets such as
pypsa.components.one_port_componentshave been moved from
network.one_port_components, since these sets may change from network to network.
For linear power flow, PyPSA now pre-calculates the effective per unit reactance
x_pu_efffor AC lines to take account of the transformer tap ratio, rather than doing it on the fly; this makes some code faster, particularly the kirchhoff formulation of the LOPF.
PyPSA is now compatible with networkx 2.0 and 2.1.
PyPSA now requires Pyomo version greater than 5.3.
We thank Russell Smith of Edison Energy for the pull request for the effective reactance that sped up the LOPF code and Tom Edwards for pointing out the Pyomo version dependency issue.
For this release we also acknowledge funding to Tom Brown from the RE-INVEST project.
PyPSA 0.12.0 (30th November 2017)
This release contains new features and bug fixes.
Support for Pyomo’s persistent solver interface, so if you’re making small changes to an optimisation model (e.g. tweaking a parameter), you don’t have to rebuild the model every time. To enable this,
network_lopfhas been internally split into
solveto allow more fine-grained control of the solving steps. Currently the new Pyomo PersistentSolver interface is not in the main Pyomo branch, see the pull request; you can obtain it with
pip install git+https://github.com/Pyomo/pyomo@persistent_interfaces
Lines and transformers (i.e. passive branches) have a new attribute
s_max_puto restrict the flow in the OPF, just like
p_max_pufor generators and links. It works by restricting the absolute value of the flow per unit of the nominal rating
abs(flow) <= s_max_pu*s_nom. For lines this can represent an n-1 contingency factor or it can be time-varying to represent weather-dependent dynamic line rating.
marginal_costattribute of generators, storage units, stores and links can now be time dependent.
When initialising the Network object, i.e.
network = pypsa.Network(), the first keyword argument is now
import_namePyPSA recognises whether it is a CSV folder or an HDF5 file based on the file name ending and deals with it appropriately. Example usage:
nw1 = pypsa.Network("my_store.h5")and
nw2 = pypsa.Network("/my/folder"). The keyword argument
csv_folder_nameis still there but is deprecated.
network.objectiveis now read from the Pyomo results attribute
Upper Boundinstead of
Lower Bound. This is because for MILP problems under certain circumstances CPLEX records the
Lower boundas the relaxed value.
Upper boundis correctly recorded as the integer objective value.
Bug fix due to changes in pandas 0.21.0: A bug affecting various places in the code, including causing
network.lopfto fail with GLPK, is fixed. This is because in pandas 0.21.0 the sum of an empty Series/DataFrame returns NaN, whereas before it returned zero. This is a subtle bug; we hope we’ve fixed all instances of it, but get in touch if you notice NaNs creeping in where they shouldn’t be. All our tests run fine.
Bug fix due to changes in scipy 1.0.0: For the new version of scipy,
csgraphhas to be imported explicit.
Bug fix: A bug whereby logging level was not always correctly being seen by the OPF results printout is fixed.
Bug fix: The storage unit spillage had a bug in the LOPF, whereby it was not respecting
We thank René Garcia Rosas, João Gorenstein Dedecca, Marko Kolenc, Matteo De Felice and Florian Kühnlenz for promptly notifying us about issues.
PyPSA 0.11.0 (21st October 2017)
This release contains new features but no changes to existing APIs.
There is a new function
network.iplot()which creates an interactive plot in Jupyter notebooks using the plotly library. This reveals bus and branch properties when the mouse hovers over them and allows users to easily zoom in and out on the network. See the SciGRID example for a showcase of this feature and also the (sparse) documentation Plotting Networks.
There is a new function
network.madd()for adding multiple new components to the network. This is significantly faster than repeatedly calling
network.add()and uses the functions
network.import_series_from_dataframe()internally. Documentation and examples can be found at Adding and removing multiple components.
There are new functions
network.import_from_hdf5()for exporting and importing networks as single files in the Hierarchical Data Format.
network.lopf()function the KKT shadow prices of the branch limit constraints are now outputted as series called
PyPSA 0.10.0 (7th August 2017)
This release contains some minor new features and a few minor but important API changes.
There is a new component Global Constraints for implementing constraints that effect many components at once (see also the LOPF subsection Global constraints). Currently only constraints related to primary energy (i.e. before conversion with losses by generators) are supported, the canonical example being CO2 emissions for an optimisation period. Other primary-energy-related gas emissions also fall into this framework. Other types of global constraints will be added in future, e.g. “final energy” (for limits on the share of renewable or nuclear electricity after conversion), “generation capacity” (for limits on total capacity expansion of given carriers) and “transmission capacity” (for limits on the total expansion of lines and links). This replaces the ad hoc
network.co2_limitattribute. If you were using this, instead of
network.co2_limit = my_capdo
network.add("GlobalConstraint", "co2_limit", type="primary_energy", carrier_attribute="co2_emissions", sense="<=", constant=my_cap). The shadow prices of the global constraints are automatically saved in
The LOPF output
network.buses_t.marginal_priceis now defined differently if
network.snapshot_weightingsare not 1. Previously if the generator at the top of the merit order had
marginal_costc and the snapshot weighting was w, the
marginal_pricewas cw. Now it is c, which is more standard. See also Nodal power balances.
network.pf()now returns a dictionary of pandas DataFrames, each indexed by snapshots and sub-networks.
convergedis a table of booleans indicating whether the power flow has converged;
errorgives the deviation of the non-linear solution;
n_iterthe number of iterations required to achieve the tolerance.
network.consistency_check()now includes checking for potentially infeasible values in
The PyPSA version number is now saved in
network.pypsa_version. In future versions of PyPSA this information will be used to upgrade data to the latest version of PyPSA.
extra_functionalityargument that behaves like that for
Component attributes which are strings are now better handled on import and in the consistency checking.
There is a new generation investment screening curve example showing the long-term equilibrium of generation investment for a given load profile and comparing it to a screening curve analysis.
There is a new logging example that demonstrates how to control the level of logging that PyPSA reports back, e.g. error/warning/info/debug messages.
Sundry other bug fixes and improvements.
All examples have been updated appropriately.
Thanks to Nis Martensen for contributing the return values of
network.pf() and Konstantinos Syranidis for contributing the
PyPSA 0.9.0 (29th April 2017)
This release mostly contains new features with a few minor API changes.
Unit commitment as a MILP problem is now available for generators in the Linear Optimal Power Flow (LOPF). If you set
committable == Truefor the generator, an addition binary online/offline status is created. Minimum part loads, minimum up times, minimum down times, start up costs and shut down costs are implemented. See the documentation at Generator unit commitment constraints and the unit commitment example. Note that a generator cannot currently have both unit commitment and capacity expansion optimisation.
Different mathematically-equivalent formulations for the Linear Optimal Power Flow (LOPF) are now documented in Passive branch flow formulations and the arXiv preprint paper Linear Optimal Power Flow Using Cycle Flows. The new formulations can solve up to 20 times faster than the standard angle-based formulation.
You can pass the
solver_ioargument for pyomo.
There are some improvements to network clustering and graphing.
API change: The attribute
network.nowhas been removed since it was unnecessary. Now, if you do not pass a
snapshotsargument to network.pf() or network.lpf(), these functions will default to
API change: When reading in network data from CSV files, PyPSA will parse snapshot dates as proper datetimes rather than text strings.
João Gorenstein Dedecca has also implemented a MILP version of the transmission expansion, see https://github.com/jdedecca/MILP_PyPSA, which properly takes account of the impedance with a disjunctive relaxation. This will be pulled into the main PyPSA code base soon.
PyPSA 0.8.0 (25th January 2017)
This is a major release which contains important new features and changes to the internal API.
Standard types are now available for lines and transformers so that you do not have to calculate the electrical parameters yourself. For lines you just need to specify the type and the length, see Line Types. For transformers you just need to specify the type, see Transformer Types. The implementation of PyPSA’s standard types is based on pandapower’s standard types. The old interface of specifying r, x, b and g manually is still available.
The transformer model has been substantially overhauled, see Transformer model. The equivalent model now defaults to the more accurate T model rather than the PI model, which you can control by setting the attribute
model. Discrete tap steps are implemented for transformers with types. The tap changer can be defined on the primary side or the secondary side. In the PF there was a sign error in the implementation of the transformer
phase_shift, which has now been fixed. In the LPF and LOPF angle formulation the
phase_shifthas now been implemented consistently. See the new transformer example.
There is now a rudimentary import function for pandapower networks, but it doesn’t yet work with all switches and 3-winding transformers.
The object interface for components has been completely removed. Objects for each component are no longer stored in e.g.
network.lines["obj"]and the descriptor interface for components is gone. You can only access component attributes through the dataframes, e.g.
Component attributes are now defined in CSV files in
pypsa/component_attrs/. You can access these CSVs in the code via the dictionary
network.components["Line"]["attrs"]will show a pandas DataFrame with all attributes and their types, defaults, units and descriptions. These CSVs are also sourced for the documentation in Components, so the documentation will always be up-to-date.
All examples have been updated appropriately.
PyPSA 0.7.1 (26th November 2016)
This release contains bug fixes, a minor new feature and more warnings.
The unix-only library
resourceis no longer imported by default, which was causing errors for Windows users.
Bugs in the setting and getting of time-varying attributes for the object interface have been fixed.
efficiencycan now be make time-varying so that e.g. heat pump Coefficient of Performance (COP) can change over time due to ambient temperature variations (see the heat pump example).
network.snapshotsis now cast to a
There are new warnings, including when you attach components to non-existent buses.
Thanks to Marius Vespermann for promptly pointing out the
PyPSA 0.7.0 (20th November 2016)
This is a major release which contains changes to the API, particularly regarding time-varying component attributes.
network.generators_tare no longer pandas.Panels but dictionaries of pandas.DataFrames, with variable columns, so that you can be flexible about which components have time-varying attributes; please read Time-varying data carefully. Essentially you can either set a component attribute e.g.
Generator, to be static by setting it in the DataFrame
network.generators, or you can let it be time-varying by defining a new column labelled by the generator name in the DataFrame
network.generators_t["p_max_pu"]as a series, which causes the static value in
network.generatorsfor that generator to be ignored. The DataFrame
network.generators_t["p_max_pu"]now only includes columns which are specifically defined to be time-varying, thus saving memory.
The following component attributes can now be time-varying:
Store.e_min_pu. This allows the demand-side management scheme of https://arxiv.org/abs/1401.4121 to be implemented in PyPSA.
StorageUnitare now removed, because the ability to make
p_min_pueither static or time-varying removes the need for this distinction.
All messages are sent through the standard Python library
logging, so you can control the level of messages to be e.g.
error. All verbose switches and print statements have been removed.
There are now more warnings.
You can call
network.consistency_check()to make sure all your components are well defined; see Troubleshooting.
All examples have been updated to accommodate the changes listed below.
PyPSA 0.6.2 (4th November 2016)
This release fixes a single library dependency issue:
pf: A single line has been fixed so that it works with new pandas versions >= 0.19.0.
We thank Thorben Meiners for promptly pointing out this issue with the new versions of pandas.
PyPSA 0.6.1 (25th August 2016)
This release fixes a single critical bug:
opf: The latest version of Pyomo (4.4.1) had a bad interaction with pandas when a pandas.Index was used to index variables. To fix this, the indices are now cast to lists; compatibility with less recent versions of Pyomo is also retained.
We thank Joao Gorenstein Dedecca for promptly notifying us of this bug.
PyPSA 0.6.0 (23rd August 2016)
Like the 0.5.0 release, this release contains API changes, which complete the integration of sector coupling. You may have to update your old code. Models for Combined Heat and Power (CHP) units, heat pumps, resistive Power-to-Heat (P2H), Power-to-Gas (P2G), battery electric vehicles (BEVs) and chained hydro reservoirs can now be built (see the sector coupling examples). The refactoring of time-dependent variable handling has been postponed until the 0.7.0 release. In 0.7.0 the object interface to attributes may also be removed; see below.
All examples have been updated to accommodate the changes listed below.
components, opt: A new
Storecomponent has been introduced which stores energy, inheriting the energy carrier from the bus to which it is attached. The component is more fundamental than the
StorageUnit, which is equivalent to a
Linkfor storing and dispatching. The
Generatoris equivalent to a
Storewith a lossy
Link. There is an example which shows the equivalences.
components, opt: The
Sourcecomponent and the
gen.sourcehave been renamed
gen.carrier, to be consistent with the
bus.carrierattribute. Please update your old code.
components, opt: The
link.s_nom*have been renamed
link.p_nom*to reflect the fact that the link can only dispatch active power. Please update your old code.
components, opt: The
Convertercomponents, which were deprecated in 0.5.0, have been now completely removed. Please update your old code to use
Downgrading object interface
The intention is to have only the pandas DataFrame interface for accessing component attributes, to make the code simpler. The automatic generation of objects with descriptor access to attributes may be removed altogether.
examples: Patterns of for loops through
network.components.objhave been removed.
components: The methods on
bus.loads()have been removed.
network.add()no longer returns the object.
components, opf: Unlimited upper bounds for e.g.
line.s_nom_maxwere previous set using
np.nan; now they are set using
float("inf")which is more logical. You may have to update your old code accordingly.
components: A memory leak whereby references to
component.networkwere not being correctly deleted has been fixed.
PyPSA 0.5.0 (21st July 2016)
This is a relatively major release with some API changes, primarily aimed at allowing coupling with other energy carriers (heat, gas, etc.). The specification for a change and refactoring to the handling of time series has also been prepared (see Time-varying data), which will be implemented in the next major release PyPSA 0.6.0 in the late summer of 2016.
An example of the coupling between electric and heating sectors can be
found in the GitHub repository at
pypsa/examples/coupling-with-heating/ and at
components: To allow other energy carriers, the attribute
current_typefur buses and sub-neworks (sub-networks inherit the attribute from their buses) has been replaced by
carrierwhich can take generic string values (such as “heat” or “gas”). The values “DC” and “AC” have a special meaning and PyPSA will treat lines and transformers within these sub-networks according to the load flow equations. Other carriers can only have single buses in sub-networks connected by passive branches (since they have no load flow).
components: A new component for a controllable directed link
Linkhas been introduced;
Converterare now deprecated and will be removed soon in an 0.6.x release. Please move your code over now. See Link for more details and a description of how to update your code to work with the new
Linkcomponent. All the examples in the GitHub repository in
pypsa/examples/have been updated to us the
graph: A new sub-module
pypsa.graphhas been introduced to replace most of the networkx functionality with scipy.sparse methods, which are more performant the the pure python methods of networkx. The discovery of network connected components is now significantly faster.
io: The function
network.export_to_csv_folder()has been rewritten to only export non-default values of static and series component attributes. Static and series attributes of all components are not exported if they are default values. The functionality to selectively export series has been removed from the export function, because it was clumsy and hard to use. See Export to folder of CSV files for more details.
plot: Plotting networks is now more performant (using matplotlib LineCollections) and allows generic branches to be plotted, not just lines.
test: Unit testing for Security-Constrained Linear Optimal Power Flow (SCLOPF) has been introduced.
PyPSA 0.4.2 (17th June 2016)
This release improved the non-linear power flow performance and included other small refactorings:
pf: The non-linear power flow
network.pf()now accepts a list of snapshots
network.pf(snapshots)and has been refactored to be much more performant.
nowargument anymore - for the power flow on a specific snapshot, either set
network.nowor pass the snapshot as an argument.
descriptors: The code has been refactored and unified for each simple descriptor.
opt: Constraints now accept both an upper and lower bound with
opf: Sub-optimal solutions can also be read out of pyomo.
PyPSA 0.4.1 (3rd April 2016)
This was mostly a bug-fixing and unit-testing release:
pf: A bug was fixed in the full non-linear power flow, whereby the reactive power output of PV generators was not being set correctly.
io: When importing from PYPOWER ppc, the generators, lines, transformers and shunt impedances are given names like G1, G2, …, L1, T1, S1, to help distinguish them. This change was introduced because the above bug was not caught by the unit-testing because the generators were named after the buses.
opf: A Python 3 dict.keys() list/iterator bug was fixed for the spillage.
test: Unit-testing for the pf and opf with inflow was improved to catch bugs better.
We thank Joao Gorenstein Dedecca for a bug fix.
PyPSA 0.4.0 (21st March 2016)
pypsa.contingencyfor contingency analysis and security-constrained LOPF
pypsa.geofor basic manipulation of geographic data (distances and areas)
Re-formulation of LOPF to improve optimisation solving time
New objects pypsa.opt.LExpression and pypsa.opt.LConstraint to make the bypassing of pyomo for linear problem construction easier to use
Deep copying of networks with
network.copy()(i.e. all components, time series and network attributes are copied)
Stricter requirements for PyPI (e.g. pandas must be at least version 0.17.1 to get all the new features)
Updated SciGRID-based model of Germany
Various small bug fixes
We thank Steffen Schroedter, Bjoern Laemmerzahl and Joao Gorenstein Dedecca for comments and bug fixes.
PyPSA 0.3.3 (29th February 2016)
network.lpfcan be called on an iterable of
network.lpf(snapshots), which is more performant that calling
network.lpfon each snapshot separately.
Bug fix on import/export of transformers and shunt impedances (which were left out before).
Refactoring of some internal code.
Better network clustering.
PyPSA 0.3.2 (17th February 2016)
In this release some minor API changes were made:
The Newton-Raphson tolerance
network.nr_x_tolwas moved to being an argument of the function
network.pf(x_tol=1e-6)instead. This makes more sense and is then available in the docstring of
Following similar reasoning
network.opf_keep_fileswas moved to being an argument of the function
PyPSA 0.3.1 (7th February 2016)
In this release some minor API changes were made:
Optimised capacities of generators/storage units and branches are now written to p_nom_opt and s_nom_opt respectively, instead of over-writing p_nom and s_nom
The p_max/min limits of controllable branches are now p_max/min_pu per unit of s_nom, for consistency with generation and to allow unidirectional HVDCs / transport links for the capacity optimisation.
network.remove() and io.import_series_from_dataframe() both take as argument class_name instead of list_name or the object - this is now fully consistent with network.add(“Line”,”my line x”).
The booleans network.topology_determined and network.dependent_values_calculated have been totally removed - this was causing unexpected behaviour. Instead, to avoid repeated unnecessary calculations, the expert user can call functions with skip_pre=True.
PyPSA 0.3.0 (27th January 2016)
In this release the pandas.Panel interface for time-dependent variables was introduced. This replaced the manual attachment of pandas.DataFrames per time-dependent variable as attributes of the main component pandas.DataFrame.
Update version in
git commitand put release notes in commit message
git tag v0.x.0
git push --tags
To upload to PyPI, run
python setup.py sdist, then
twine check dist/pypsa-0.x.0.tar.gzand
twine upload dist/pypsa-0.x.0.tar.gz
To update to conda-forge, check the pull request generated at the feedstock repository.
Upload a zip to zenodo (this should also be possible automatically via a github hook).
Inform the PyPSA mailing list.