Release Notes

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_components_from_dataframe() and network.import_series_from_dataframe() internally. Documentation and examples can be found at Adding multiple components.
  • There are new functions network.export_to_hdf5() and network.import_from_hdf5() for exporting and importing networks as single files in the Hierarchical Data Format.
  • In the network.lopf() function the KKT shadow prices of the branch limit constraints are now outputted as series called mu_lower and mu_upper.

We thank Bryn Pickering for introducing us to plotly and helping to hack together the first working prototype using PyPSA.

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_limit attribute. If you were using this, instead of network.co2_limit = my_cap do 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 network.global_constraints.mu.
  • The LOPF output network.buses_t.marginal_price is now defined differently if network.snapshot_weightings are not 1. Previously if the generator at the top of the merit order had marginal_cost c and the snapshot weighting was w, the marginal_price was 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. converged is a table of booleans indicating whether the power flow has converged; error gives the deviation of the non-linear solution; n_iter the number of iterations required to achieve the tolerance.
  • network.consistency_check() now includes checking for potentially infeasible values in generator.p_{min,max}_pu.
  • 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.
  • network.sclopf() has an extra_functionality argument that behaves like that for network.lopf().
  • 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 improved network.consistency_check().

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 == True for 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.
  • Generator ramping limits have also been implemented for all generators. See the documentation at Generator ramping constraints and the unit commitment example.
  • 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 network.lopf function the solver_io argument for pyomo.
  • There are some improvements to network clustering and graphing.
  • API change: The attribute network.now has been removed since it was unnecessary. Now, if you do not pass a snapshots argument to network.pf() or network.lpf(), these functions will default to network.snapshots rather than network.now.
  • 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_shift has 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. network.lines.
  • 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, e.g. 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 resource is 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.
  • The Link attribute efficiency can 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.snapshots is now cast to a pandas.Index.
  • There are new warnings, including when you attach components to non-existent buses.

Thanks to Marius Vespermann for promptly pointing out the resource bug.

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_t are 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. p_max_pu of 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.generators for 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: Link.p_max_pu, Link.p_min_pu, Store.e_max_pu and Store.e_min_pu. This allows the demand-side management scheme of https://arxiv.org/abs/1401.4121 to be implemented in PyPSA.
  • The properties dispatch, p_max_pu_fixed and p_min_pu_fixed of Generator and StorageUnit are now removed, because the ability to make p_max_pu and p_min_pu either 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. debug, info, warning or 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.

Sector coupling

  • components, opt: A new Store component 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 Store and two Link for storing and dispatching. The Generator is equivalent to a Store with a lossy Link. There is an example which shows the equivalences.
  • components, opt: The Source component and the Generator attribute gen.source have been renamed Carrier and gen.carrier, to be consistent with the bus.carrier attribute. Please update your old code.
  • components, opt: The Link attributes 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 TransportLink and Converter components, which were deprecated in 0.5.0, have been now completely removed. Please update your old code to use Link instead.

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.obj have been removed.
  • components: The methods on Bus like bus.generators() and bus.loads() have been removed.
  • components: network.add() no longer returns the object.

Other

  • components, opf: Unlimited upper bounds for e.g. generator.p_nom_max or line.s_nom_max were 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.network were 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 http://www.pypsa.org/examples/lopf-with-heating.html.

  • components: To allow other energy carriers, the attribute current_type fur buses and sub-neworks (sub-networks inherit the attribute from their buses) has been replaced by carrier which 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 Link has been introduced; TransportLink and Converter are 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 Link component. All the examples in the GitHub repository in pypsa/examples/ have been updated to us the Link.
  • graph: A new sub-module pypsa.graph has 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.
  • pf: Neither network.pf() nor network.lpf() accept the now argument anymore - for the power flow on a specific snapshot, either set network.now or 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)

Additional features:

  • New module pypsa.contingency for contingency analysis and security-constrained LOPF
  • New module pypsa.geo for 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)

Additional features:

  • network.lpf can be called on an iterable of snapshots i.e. network.lpf(snapshots), which is more performant that calling network.lpf on 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_tol was 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 network.pf.
  • Following similar reasoning network.opf_keep_files was moved to being an argument of the function network.lopf(keep_files=False).

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.