Tags: aodenweller/PyPSA
Tags
PyPSA 0.19.1 (18th February 2022) Full release notes: https://pypsa.readthedocs.io/en/latest/release_notes.html
PyPSA 0.19.0 (11th February 2022) Full release notes: https://pypsa.readthedocs.io/en/latest/release_notes.html
PyPSA 0.18.1 (15th October 2021) Full release notes: https://pypsa.readthedocs.io/en/latest/release_notes.html
PyPSA Version 0.18.0 Hyperlinked release notes can be found here: https://pypsa.readthedocs.io/en/latest/release_notes.html#pypsa-0-18-0-12th-august-2021
PyPSA Version 0.17.1 Hyperlinked release notes can be found here: https://pypsa.readthedocs.io/en/latest/release_notes.html#pypsa-0-17-1-15th-july-2020
PyPSA Version 0.17.0 Hyperlinked release notes can be found here: https://pypsa.readthedocs.io/en/latest/release_notes.html#pypsa-0-17-0-23rd-march-2020
PyPSA Version 0.16.1 Hyperlinked release notes can be found here: https://pypsa.readthedocs.io/en/latest/release_notes.html#pypsa-0-16-1-10th-january-2020 This release contains a few minor bux fixes from the introduction of nomopyomo in the previous release, as well as a few minor features. * When using the nomopyomo formulation of the LOPF with network.lopf(pyomo=False), PyPSA was not correcting the bus marginal prices by dividing by the network.snapshot_weightings, as is done in the pyomo formulation. This correction is now applied in the nomopyomo formulation to be consistent with the pyomo formulation. (The reason this correction is applied is so that the prices have a clear currency/MWh definition regardless of the snapshot weighting. It also makes them stay roughly the same when snapshots are aggregated: e.g. if hourly simulations are sampled every n-hours, and the snapshot weighting is n.) * The status, termination_condition that the network.lopf returns is now consistent between the nomopyomo and pyomo formulations. The possible return values are documented in the LOPF docstring, see also the documentation. Furthermore in the nomopyomo formulation, the solution is still returned when gurobi finds a suboptimal solution, since this solution is usually close to optimal. In this case the LOPF returns a status of warning and a termination_condition of suboptimal. * For plotting with network.plot() you can override the bus coordinates by passing it a layouter function from networkx. See the docstring for more information. This is particularly useful for networks with no defined coordinates. * For plotting with network.iplot() a background from mapbox can now be integrated. Please note that we are still aware of one implementation difference between nomopyomo and pyomo, namely that nomopyomo doesn’t read out shadow prices for non-extendable branches, see the github issue.
PyPSA Version 0.16.0 Hyperlinked release notes can be found here: https://pypsa.readthedocs.io/en/latest/release_notes.html#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.lopf are implemented with the exception of minimum down/up time and start up/shut down costs for unit commitment. If you use the extra_functionality argument for network.lopf you 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 network.pf() the argument 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 slack_weights. If distribute_slack=False only 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 six. 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 Version 0.15.0 Hyperlinked release notes can be found here: file:///home/tom/fias/lib/pypsa/doc/_build/html/release_notes.html#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 snapshots better. This is particularly u seful for rolling horizon UC. UC now accounts for up-time and down-time in the periods before the snapshots. The generator attribute initial_status has been replaced with two attributes up_time_before and down_time_before to 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. * The objective sense (minimize or maximize) can now be set (default remains minimize). * The network.snapshot_weightings is 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 Version 0.14.1 Hyperlinked release notes can be found here: https://pypsa.org/doc/release_notes.html#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.
PreviousNext