####################### Release Notes ####################### Upcoming Release ================ .. warning:: The features listed below are not released yet, but will be part of the next release! To use the features already you have to install the ``master`` branch, e.g. ``pip install git+https://github.com/pypsa/pypsa#egg=pypsa``. * new feature or bugfix PyPSA 0.29.0 (31st July 2024) ============================= * Removed ``n.lopf()`` pyomo-based and nomopyomo-based optimisation modules. Use linopy-based optimization with ``n.optimize()`` instead. (https://github.com/PyPSA/PyPSA/pull/884) * HiGHS becomes the new default solver for ``n.optimize()``. (https://github.com/PyPSA/PyPSA/pull/884) * Changes to the ``statistics`` module: - The statistics functions ``n.statistics.capex()``, ``n.statistics.installed_capex()``, and ``expanded_capex`` now have an optional ``cost_attribute`` argument, which defaults to `capital_cost`. The default behavior of the functions is not changed. (https://github.com/PyPSA/PyPSA/pull/989) - The functions ``n.statistics.optimal_capacity()`` and ``n.statistics.expanded_capacity()`` now return positive and negative capacity values if a ``bus_carrier`` is selected. Positive values correspond to production capacities, negative values to consumption capacities. (https://github.com/PyPSA/PyPSA/pull/885) - The statistics module now supports the ``nice_name`` argument for bus carriers. Previously, nice names were only supported for components carriers. (https://github.com/PyPSA/PyPSA/pull/991) - The statistics module now features functionality to set global style parameters (e.g. ``nice_names``, ``drop_zero`` and ``round``) which is then applied to all statistics methods without the need to set them individually. To set parameters one can run ``n.statistics.set_parameters(nice_names=False, round=2)`` and to view current parameters setting ``n.statistics.parameters``. (https://github.com/PyPSA/PyPSA/pull/886) * Changes to the ``clustering`` module: - Add attribute-based exemptions for clustering lines and links. With the argument ``custom_line_groupers`` in the function ``aggregatelines()`` one can specify additional columns besides ``bus0`` and ``bus1`` to consider as unique criteria for clustering. This is useful, for example, to avoid the aggregation of lines/links with different ``build_year`` or ``carrier``. (https://github.com/PyPSA/PyPSA/pull/982) * Changes to the ``plot```module: - Add option to add semicircle legends by running ``pypsa.plot.add_legend_semicircle(ax, sizes=[1000/scaling_factor], labels=["1 GWh"])``. (https://github.com/PyPSA/PyPSA/pull/986) - Add functionality to provide list of colors in ``add_legend_lines()``. (https://github.com/PyPSA/PyPSA/pull/902) * Bugfixes: - The security-constrained optimization via ``n.optimize.optimize_security_constrained()`` was fixed to correctly handle multiple subnetworks. (https://github.com/PyPSA/PyPSA/pull/946) - The global constraint on the total transmission costs now includes the weight of the investment periods and persistence of investment costs of active assets in multi-horizon optimisations. - Retain investment periods and weightings when clustering networks. (https://github.com/PyPSA/PyPSA/pull/891) - Removed performance regression of ``statistics`` module. (https://github.com/PyPSA/PyPSA/pull/990) - When adding bus ports on the fly with `add` methods, the dtype of the freshly created column is now fixed to `string`. (https://github.com/PyPSA/PyPSA/pull/893) - Using timezone information in `n.snapshots` raises an error now, since it leads to issues with `numpy`/ `xarray`. (https://github.com/PyPSA/PyPSA/pull/976) * Improvements to consistency checks and model debugging: - When adding components with bus ports greater than 1, e.g. `bus2`, pypsa checks if the bus exists and prints a warning if it does not. (https://github.com/PyPSA/PyPSA/pull/893) - Also check for missing values of default attributes in the `n.consistency_check()` function. (https://github.com/PyPSA/PyPSA/pull/903) - Restructure ``n.consistency_check()``. (https://github.com/PyPSA/PyPSA/pull/903,https://github.com/PyPSA/PyPSA/pull/918, https://github.com/PyPSA/PyPSA/pull/920) - Add option `n.optimize(compute_infeasibilities=True)` to compute Irreducible Inconsistent Subset (IIS) in case an infeasibility was encountered and Gurobi is installed. (https://github.com/PyPSA/PyPSA/pull/978) - Improved error messages. (https://github.com/PyPSA/PyPSA/pull/897) * Add functionality to compare two networks for equality via equality operator (``==``). (https://github.com/PyPSA/PyPSA/pull/924) * Add single-node electricity-only and sector-coupled capacity expansion example. (https://github.com/PyPSA/PyPSA/pull/904) * Added new line type "Al/St 490/64 4-bundle 380.0". (https://github.com/PyPSA/PyPSA/pull/887) * Use ``ruff``. (https://github.com/PyPSA/PyPSA/pull/900, https://github.com/PyPSA/PyPSA/pull/901) * Improve CI and auto-release process. (https://github.com/PyPSA/PyPSA/pull/907, https://github.com/PyPSA/PyPSA/pull/921) * Restructured API reference. (https://github.com/PyPSA/PyPSA/pull/960) * Compatibility with ``numpy>=2.0``. (https://github.com/PyPSA/PyPSA/pull/932) * See full list of changes `here `__. PyPSA 0.28.0 (8th May 2024) ================================= * When using iterative optimisation functionality ``n.optimize_transmission_expansion_iteratively()``, add option to discretize optimised line and link capacities in the final iteration based on new keyword arguments ``line_unit_size``, ``link_unit_size``, ``line_threshold`` and ``link_threshold``. This allows to round the optimised capacities to a multiple of the unit size based on the threshold. (https://github.com/PyPSA/PyPSA/pull/871) * A new function ``n.merge()`` was added allowing the components and time-dependent data of one network to be added to another network. The function is also available via ``n + m`` with default settings. The function requires disjunct component indices and identical snapshots and snapshot weightings. (https://github.com/PyPSA/PyPSA/pull/783) * New features in the statistics module (https://github.com/PyPSA/PyPSA/pull/860): - The statistics module introduces a new keyword argument ``at_port`` to all functions. This allows considering the port of a component when calculating statistics. Depending on the function, the default of ``at_port`` is set to ``True`` or ``False``, for example for the dispatch all ports are considered. - The statistics module now supports an optional ``port`` argument in ``groupby`` functions. This allows to group statistics while considering the port of a component. - The ``statistics.revenue`` function introduces a new keyword argument ``kind`` to optionally calculate the revenue based on the ``input`` commodity or the ``output`` commodity of a component. - The ``statistics.energy_balance`` function introduces a new keyword argument ``kind`` to optionally calculate the ``supply`` and ``withdrawal`` of a component. - Deprecation warnings are added to the statistics module for the functionalities that will be removed in the next major release. * Updated ``environment_doc.yml`` to include the latest required ``pip`` dependencies for the documentation environment. (https://github.com/PyPSA/PyPSA/pull/862) * Bugfix: calling ``n.create_model()`` or ``n.optimize()`` when a global operational limit is defined will no longer set the carrier attribute of stores to the carrier of the bus they are attached to. (https://github.com/PyPSA/PyPSA/pull/880) * Added warning to ``plot.py`` with instructions to handle the case where the ``requests`` dependency is missing. (https://github.com/PyPSA/PyPSA/pull/882) * Bugfix: calling ``n.optimize.*`` functions (e.g. ``n.optimize.optimize_mga``) now correctly returns each functions return values. (https://github.com/PyPSA/PyPSA/pull/871) PyPSA 0.27.1 (22nd March 2024) ================================= * Fixed sometimes-faulty total budget calculation for single-horizon MGA optimisations. * Fixed assignment of active assets in multi-horizon optimisation with ``n.optimize``. * Fixed setting of investment periods when copying a multi-horizon network. * Always use name and mask keys in variable and constraint assignment to protect against future changes in argument order. * Rewrite function ``get_switchable_as_dense`` so that it consumes less memory when calling it with large dataframes. * Fix of the capex description in the attribute CSV files. PyPSA 0.27.0 (18th February 2024) ================================= * Bugfix: If plotting a network map with split buses (``n.plot(bus_split_circles=True)``), the bus sizes are now scaled by factor 2 to account for the fact that the bus sizes are split into half circles. This makes the area scaling of the buses consistent with the area of non-split buses. * The global constraint ``define_tech_capacity_expansion_limit`` now also takes branch components into account. If defined per bus, the ``bus0`` of the branch is considered as a reference bus. * Bugfixes in building of global constraints in multi-horizon optimisations. * Fixed total budget calculation for MGA on multi-horizon optimisations. * The ``extra_functionality`` argument is now also supported in ``solve_model`` accessor. * ``optimize_mga`` now returns the solver termination status and condition. * The deprecated functions ``_make_consense``, ``aggregategenerators``, ``get_buses_linemap_and_lines`` and ``get_clustering_from_busmap`` were removed. * The minimum ``networkx`` version was bumped from ``1.10`` to ``2``. * ``pyomo`` is no longer supported for Python 3.12 or higher. PyPSA 0.26.3 (25th January 2024) ================================= * Bugfix: With line transmission losses there was a sign error in the calculation of the line capacity constraints. * Approximated transmission losses of lines are now stored after optimisation as the difference between ``n.lines_t.p0`` and ``n.lines_t.p1`` so that they appear in the energy balance (e.g. ``n.statistics.energy_balance()``) and when calculating losses with ``n.lines_t.p0 + n.lines_t.p1``. PyPSA 0.26.2 (31st December 2023) ================================= * Bugfix in the definition of spillage variables for storage units. Previously, the spillage variable creation was skipped in some cases due to a wrong condition check even though there was a positive inflow. PyPSA 0.26.1 (29th December 2023) ================================= * The output attribute ``n_mod`` introduced in the previous version was removed since it contains duplicate information. Calculate the number of expanded modules with ``p_nom_opt / p_nom_mod`` instead. * Bugfix in MGA function to correctly parse the ``sense`` keyword argument. * Fix strict type compatibility issues with ``pandas>=2.1`` causing problems for clustering. * Removed ``numexpr`` version constraint. PyPSA 0.26.0 (4th December 2023) ================================ **New Features** * The ``Network`` class has a **new component for geometric shapes** under ``n.shapes``. It consists of a ``geopandas`` dataframe which can be used to store network related geographical data (for plotting, calculating potentials, etc). The dataframe has the columns `geometry`, `component`, `idx` and `type`. The columns `component`, `idx` and `type` do not require specific values, but allow for storing information about which components the shapes belong to. The coordinate reference system (CRS) of the shapes can be accessed and set via a new attribute ``n.crs``. For a transition period, the attribute ``n.srid``, which independently refers to the projection of the bus coordinates, is kept. * Improvements to the network **statistics module**: * The statistics module now supports the consideration of multi-port links. An additional argument `bus_carrier` was added to the statistics functions to select the components that are attached to buses of a certain carrier. * The statistics module now supports the consideration of multiple investment periods. As soon as ``n.snapshots`` is a MultiIndex, the network statistics are calculated separately for each investment period. * A new function ``transmission`` was added to the statistics accessor. This function considers all lines and links that connect buses of the same carrier. * The statistics functions now support the selection of single components in the ``comps`` argument. * The plotting function ``n.plot()`` now supports **plotting of only a subset of network components** by allowing that arguments like ``bus_sizes``, ``link_widths`` or ``link_colors`` do no longer require to contain the full set of indices of a component. * Add option to specify **time-varying ramp rates** for generators and links (``ramp_limit_up`` and ``ramp_limit_down``, e.g. under ``n.links_t.ramp_limit_up``). * Added attributes ``p_nom_mod``, ``e_nom_mod``, and ``s_nom_mod`` to components to consider capacity modularity. When this attribute is non-zero and the component is extendable, the component's capacity can only be extended in multiples of the capacity modularity. The optimal number of components is stored as ``n_mod`` (such that ``p_nom_mod * n_mod == p_nom_opt``). The default is kept such that extendable components can be expanded continuously. **Bugfixes and Compatibiliity** * Bugfix: In rolling horizon optimisation with unit commitment constraints, the generator status of the previously optimized time step is now considered. * Bugfix: Allow optimising the network for just subset of investment periods by using ``n.optimize(multi_investment_periods=True, snapshots=...)``. * Bugfix: The function ``n.import_from_netcdf()`` failed when trying to import data from an ``xarray`` object. * Bugfix: Fix global constraints for primary energy and transmission volume limits for networks with multiple investment periods. * Bugfix: Fix stand-by-costs optimization for latest ``linopy`` version. * Resolve performance regression for multi-decade optimisation in highly meshed networks. * Compatibility with ``pandas==2.1``. * Added Python 3.12 to CI and supported Python versions. PyPSA 0.25.2 (30th September 2023) ================================== * Add option to enable or disable nice carrier name in the statistics module, e.g. ``n.statistics(nice_name=False)``. * Add example in documentation for the statistics module. * Add example for stochastic optimization with PyPSA to the documentation. * Extended documentation for multi-decade optimization. * Bugfix: Use of ``nice_names`` keyword argument in ``n.statistics.energy_balance()``. * Bugfix: Correctly handle ``p_nom`` or ``p_nom_opt`` in power flow distributed slack. * Bugfix: After the optimization the right-hand side and sign of global constraints were previously overwritten by altered values. * Bugfix: In netCDF export, typecasting to float32 after setting the compression encoding led to ignored compression encodings. * Bugfix: Handle solver options for CBC and GLPK for ``n.lopf(pyomo=False)``. * Bugfix: Handle cases with multi-decade optimisation, activated transmission limit and an empty list of lines or DC links. PyPSA 0.25.1 (27th July 2023) ============================= **New Features** * The function ``get_clustering_from_busmap`` has a new argument ``line_strategies``. * The ``n.optimize()`` function gets a new keyword argument ``assign_all_duals=False`` which controls whether all dual values or only those that already have a designated place in the network are assigned. (https://github.com/PyPSA/PyPSA/pull/635) **Changes** * The function ``get_buses_linemap_and_lines`` was deprecated, in favor of direct use of ``aggregatebuses`` and ``aggregate_lines``. * Improve logging printout for rolling horizon optimization. (https://github.com/PyPSA/PyPSA/pull/697, https://github.com/PyPSA/PyPSA/pull/699) * The CI environment handling was migrated to ``micromamba`` (https://github.com/PyPSA/PyPSA/pull/688). **Bugfixes** * The aggregation functions in the clustering module were adjusted to correctly handle infinity values (see https://github.com/pandas-dev/pandas/issues/54161 for more details). (https://github.com/PyPSA/PyPSA/pull/684) * The unit commitment formulation with a rolling horizon horizon was fixed in case of non-committable and committable generators with ramp limits. (https://github.com/PyPSA/PyPSA/pull/686) * The clustering functionality was fixed in case of passing a subset of carriers that should be aggregated. (https://github.com/PyPSA/PyPSA/pull/696) * When clustering, allow safe clustering of component attributes which are both static and dynamic. (https://github.com/PyPSA/PyPSA/pull/700) * When assigning a new user-defined variable to the underlying optimization model, the assignment of the solution resulted in an error if the variable name did not match the pattern ``{Component}-{Varname}``. This has been fixed by ignoring variables that do not match the pattern during solution assignment. (https://github.com/PyPSA/PyPSA/pull/693) * Multilinks are now also handled automatically when importing a network from file. (https://github.com/PyPSA/PyPSA/pull/702) * Multilink default efficiencies are always set to 1.0. (https://github.com/PyPSA/PyPSA/pull/701) * For linearized unit commitment relaxation, some tightening additional constraints are only valid if start-up and shut-down costs are equal. These constraints are now skipped if this is not the case and a warning message is printed. (https://github.com/PyPSA/PyPSA/pull/690) * Fix division in capacity factor calculation in statistics module when not aggregating in the time dimension. (https://github.com/PyPSA/PyPSA/pull/687) PyPSA 0.25.0 (13th July 2023) ============================= **New Features** * **Stand-by costs:** PyPSA now supports stand-by cost terms. A new column ``stand_by_cost`` was added to generators and links. The stand-by cost is added to the objective function when calling ``n.optimize()``. (https://github.com/PyPSA/PyPSA/pull/659) * **Rolling horizon function:** The ``n.optimize`` accessor now provides functionality for rolling horizon optimisation using ``n.optimize.optimize_with_rolling_horizon()`` which splits whole optimization of the whole time span into multiple subproblems which are solved consecutively. This is useful for operational optimizations with a high spatial resolution. (https://github.com/PyPSA/PyPSA/pull/668) * **Modelling-to-generate-alternatives (MGA) function** The ``n.optimize`` accessor now provides functionality for running modelling-to-generate-alternatives (MGA) on previously solved networks using ``n.optimize.optimize_mga(slack=..., weights=...)``. This is useful for exploring the near-optimal feasible space of the network. (https://github.com/PyPSA/PyPSA/pull/672) **Changes** * **Multilinks by default:** Links with multiple inputs/outputs are now supported by default. The Link component attributes are automatically extended if a link with ``bus2``, ``bus3``, etc. are added to the network. Overriding component attributes at network initialisation is no longer required. (https://github.com/PyPSA/PyPSA/pull/669) * **Spatial clustering refactored:** The spatial clustering module was refactored. The changes lead to performance improvements and a more consistent clustering API. (https://github.com/PyPSA/PyPSA/pull/673) * The network object has a new accessor ``cluster`` which allows accessing clustering routines from the network itself. For example, ``n.cluster.cluster_spatially_by_kmeans`` returns a spatially clustered version of the network. * The default clustering strategies were refined. Per default, columns like ``efficiency`` and ``p_max_pu`` are now aggregated by the capacity weighted mean. * The clustering module now applies the custom strategies to time-dependant data. * The function ``pypsa.clustering.spatial.get_clustering_from_busmap`` and ``pypsa.clustering.spatial.aggregategenerators`` now allows the passing of a list of buses for which aggregation of all carriers is desired. Generation from a carrier at a bus is aggregated now if: It is either in the passed list of aggregated carriers, or in the list of aggregated buses. * Take generator strategies for time-series into account. Before, time-series would always be aggregated by summing. (https://github.com/PyPSA/PyPSA/pull/670) * The deprecated ``networkclustering`` module was removed. (https://github.com/PyPSA/PyPSA/pull/675) * A new function `get_country_and_carrier` was added to the statistics module in order to group statistics by country and carrier. (https://github.com/PyPSA/PyPSA/pull/678) * NetCDF file compression is now disabled by default when exporting networks. (https://github.com/PyPSA/PyPSA/pull/679) **Breaking Changes** * The ``Clustering`` class no longer contains a positive and negative linemap. * Outdated examples were removed. (https://github.com/PyPSA/PyPSA/pull/674) **Bugfixes** * In the statistics module, the calculation of operational costs of storage units was corrected. (https://github.com/PyPSA/PyPSA/pull/671) PyPSA 0.24.0 (27th June 2023) ================================= * PyPSA now supports quadratic marginal cost terms. A new column `marginal_cost_quadratic` was added to generators, links, stores and storage units. The quadratic marginal cost is added to the objective function when calling ``n.optimize()``. This requires a solver that is able to solve quadratic problems, for instance, HiGHS, Gurobi, Xpress, or CPLEX. * The statistics function now allows calculating energy balances ``n.statistics.energy_balance()`` and dispatch ``n.statistics.dispatch()``, as well as time series (e.g. ``n.statistics.curtailment(aggregate_time=False)``). The energy balance can be configured to yield energy balance time series for each bus. * The statistics function ``n.statistics()`` now also supports the calculation of the market values of components. * The function ``n.set_snapshots()`` now takes two optional keyword arguments; ``default_snapshot_weightings`` to change the default snapshot weightings, and ``weightings_from_timedelta`` to compute the weights if snapshots are of type ``pd.DatetimeIndex``. * The function ``n.lopf()`` is deprecated in favour of the linopy-based implementation ``n.optimize()`` and will be removed in PyPSA v1.0. We will have a generous transition period, but please start migrating your ``extra_functionality`` functions, e.g. by following our `migration guide `_. * The module ``pypsa.networkclustering`` was moved to ``pypsa.clustering.spatial``. The module ``pypsa.networkclustering`` is now deprecated but all functionality will continue to be accessible until PyPSA v0.25. * Bug fix in linearized unit commitment implementation correcting sign. * The minimum required version of ``linopy`` is now ``0.2.1``. * Dropped support for Python 3.8. The minimum required version of Python is now 3.9. PyPSA 0.23.0 (10th May 2023) ================================= * Transmission losses can now be represented during optimisation with ``n.optimize()`` or ``n.lopf()`` using a piecewise linear approximation of the loss parabola as presented in `this paper `_. The number of segments can be chosen with the argument ``n.optimize(transmission_losses=3)``. The default remains that transmission losses are neglected with ``n.optimize(transmission_losses=0)``, and analogously for ``n.lopf(pyomo=True)`` and ``n.lopf(pyomo=False)``. [`#462 `_] * Efficiencies and standing losses of stores, storage units and generators can now be specified as time-varying attributes (``efficiency``, ``efficiency_dispatch``, ``efficiency_store``, ``standing_loss``). For example, this allows specifying temperature-dependent generator efficiencies or evaporation in hydro reservoirs. [`#572 `_] * Unit commitment constraints (ramp limits, start up and shut down costs) can now also be applied to links in addition to generators. This is useful to model the operational restrictions of fuel synthesis plants. [`#582 `_] * Added implementation for a linearized unit commitment approximation (LP-based) that can be activated when calling ``n.optimize(linearized_unit_commitment=True)``. The implementation follows Hua et al. (2017), `10.1109/TPWRS.2017.2735026 `_. This functionality is not implemented for ``n.lopf()``. [`#472 `_] * NetCDF (``.nc``) and HDF5 (``.h5``) network files can now be read directly from URL: ``pypsa.Network("https://github.com/PyPSA/PyPSA/raw/master/examples/scigrid-de/scigrid-with-load-gen-trafos.nc")`` [`#569 `_] * Networks are now compressed when exporting the NetCDF ``n.export_to_netcdf(...)`` step using the native compression feature of netCDF files. Additionally, a typecasting option from float64 to float 32 was added. Existing network files are not affected. To also compress existing networks, load and save them using ``xarray`` with compression specified, see `the xarray documentation `_ for details. The compression can be disabled with ``n.export_to_netcdf(compression=None)``. Use ``n.export_to_netcdf(float32=True, compression={'zlib': True, 'complevel': 9, 'least_significant_digit': 5})`` for high compression. [`#583 `_, `#614 `_] * Time aggregation for OPEX, curtailment, supply, withdrawal, and revenue now default to 'sum' rather than 'mean'. * A new type of ``GlobalConstraint`` called `operational_limit` is now supported through the ``n.optimize()`` function. It allows to limit the total production of a carrier analogous to `primary_energy_limit` with the difference that it applies directly to the production of a carrier rather than to an attribute of the primary energy use. [`#618 `_] * The attributes ``lifetime`` and ``build_year`` are now aggregated with a capacity-weighted mean when clustering the network. Previously, these attributes had to carry identical values for components that were to be merged. [`#571 `_] * To enable better backwards compatibility with the ``n.lopf()`` function, the ``n.optimize()`` functions has now the explicit keyword argument ``solver_options``. It takes a dictionary of options passed to the solver. Before, these were passed as keyword arguments to the ``n.optimize()`` function. Note that both functionalities are supported. [`#595 `_] * Fixed interference of io routines with linopy optimisation [`#564 `_, `#567 `_] * Fix a bug where time-dependant generator variables could be forgotten during aggregation in a particular case. [`#576 `_] * A new type of ``GlobalConstraint`` called `operational_limit` is now supported through the `Network.optimize` function. It allows to limit the total production of a carrier analogous to `primary_energy_limit` with the difference that it applies directly to the production of a carrier rather than to an attribute of the primary energy use. * Fix an issue appeared when processing networks which were reduced to a set of isolated nodes in course of clustering. Previously, an empty ``Line`` component has lead to problems when processing empty lines-related dataframes. That has been fixed by introducing special treatment in case a lines dataframe is empty. [`#599 `_] PyPSA 0.22.1 (15th February 2023) ================================= * The model creation for large, sector-coupled models is now much quicker. * The FICO Xpress solver interface now skips loading a basis if there is an error associated with the basis function and continues without it. * The colors of borders and coastlines can now be controlled with ``n.plot(color_geomap=dict(border='b', coastline='r'))``. * Plotting multiple legends was fixed for applying a tight layout with ``matplotlib>=3.6``. * The plotting function now supports plotting negative and positive values separately per bus using the argument ```n.plot(bus_split_circles=...)``. This results in drawing separate half circles for positive and negative values. PyPSA 0.22.0 (3rd February 2023) ================================ * Python 3.11 is now tested. The support of Python 3.7 was dropped. The minimum supported python version is now 3.8. * The linopy based optimization (``n.optimize()``) now allows to limit the carrier's growth by an additional linear term, so that one can limit an expansion growth by multiples of what was installed in the preceding investment period. * The linopy based optimization now requires ``linopy`` version 0.1.1 or higher. The new version eases the creation of custom constraint through a better display of linear expression and variables. * Wrapped functions defined by the ``Network.optimize`` accessor are now wrapping meta information of the original functions more coherently. This enables better feedback in interactive sessions. * Checking of datatypes in the ``consistency_check`` is now deactivated by default. Set ``n.consistency_check(check_dtypes=True)`` to activate it. * The plotting functionality ``n.plot()`` now supports setting alpha values on the branch components individually. * The plotting functionality ``n.plot()`` now allows independent control of arrow size and branch width using ``line_widths`` and ``flow`` in conjunction. * The documentation shines in a new look using the ``sphinx-book-theme``. Limit ``sphinx`` to versions below 6. * Address various deprecation warnings. PyPSA 0.21.3 (16th December 2022) ================================= * Bugfix: Time-varying marginal cost of a component were removed if at least one of its value was zero. * Bugfix: Due to xarray's ``groupby`` operation not fully supporting multi-indexes in recent version (see https://github.com/pydata/xarray/issues/6836), parts of the multi investment optimization code was adjusted. * Update HiGHS parsing function in linopt for HiGHS version 1.4.0. Minimum version of HiGHS is v1.3.0. Older versions have not been tested. * Update of gas boiler example to ``linopy``. * New standard line types for DC lines. * Included code of conduct. PyPSA 0.21.2 (30th November 2022) ================================= * Compatibility with ``pyomo>=6.4.3``. PyPSA 0.21.1 (10th November 2022) ================================= * Default of ``n.lopf()`` changed to ``n.lopf(pyomo=False)``. * Bugfix in calculating statistics of curtailment. * Bugfix in IO of netCDF network files for datetime indices. * Bugfix for warning about imports from different PyPSA versions. * Add linopy and statistics module to API reference. PyPSA 0.21.0 (7th November 2022) ================================ * A new optimization module `optimization` based on `Linopy `_ was introduced. It aims at being as fast as the in-house optimization code and as flexible as the optimization with ``Pyomo``. A introduction to the optimization can be found at the `examples section `_ a migration guide for extra functionalities can be found at `here `_ * A new module for a quick calculation of system relevant quantities was introduced. It is directly accessible via the new accessor `Network.statistics` which returns a table of values often calculated manually. At the same time `Network.statistics` allows to call individual functions, as `capex`, `opex`, `capacity_factor` etc. * Add reference to `Discord server `_ for support and discussion. * Restore import of pandapower networks. Issues regarding the transformer component and indexing as well as missing imports for shunts are fixed. [`#332 `_] * The import performance of networks was improved. With the changes, the import time for standard netcdf imports decreased by roughly 70%. PyPSA 0.20.1 (6th October 2022) =============================== * The representation of networks was modified to show the number of components and snapshots. * The performance of the consistency check function was improved. The consistency check was extended by validating the capacity expansion limits as well as global constraint attributes. * When applying network clustering algorithms, per unit time series are now aggregated using a capacity-weighted average and default aggregation strategies were adjusted. * The value of ``n.objective`` is now set to NaN for failed optimisation runs. * Added example notebook on how to model redispatch with PyPSA. * Added new network plotting example. * Bugfix for non-pyomo version of ``n.sclopf()``. * Accept ``pathlib.Path`` objects when importing networks with ``pypsa.Network()``. * Addressed ``.iteritems()`` deprecations. PyPSA 0.20.0 (26th July 2022) ============================== This release contains new features for plotting and storing metadata with Network objects. * A new attribute ``n.meta`` was added to the Network object. This can be an arbitrary dictionary, and is used to store meta data about the network. * Improved support for individually normed colorbars in ``n.plot()`` for buses, lines, links, transformers with keyword arguments ``bus_norm``, ``line_norm``, ``link_norm``, ``transformer_norm``. .. code-block:: python :caption: Colorbar plotting example import pypsa import matplotlib.pyplot as plt n = pypsa.examples.ac_dc_meshed() norm = plt.Normalize(vmin=0, vmax=10) n.plot( bus_colors=n.buses.x, bus_cmap='viridis', bus_norm=norm ) plt.colorbar(plt.cm.ScalarMappable(cmap='viridis', norm=norm)) * New utility functions to add legends for line widths (:func:`pypsa.plot.add_legend_lines`), circles and pie chart areas (:func:`pypsa.plot.add_legend_circles`), and patch colors (:func:`pypsa.plot.add_legend_patches`). See the following example: .. code-block:: python :caption: Legend plotting example import pypsa import matplotlib.pyplot as plt import cartopy.crs as ccrs from pypsa.plot import add_legend_circles n = pypsa.examples.ac_dc_meshed() fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) n.plot(ax=ax, bus_sizes=1) add_legend_circles( ax, [1, 0.5], ["reference size", "reference size 2"], legend_kw=dict(frameon=False, bbox_to_anchor=(1,0.1)) ) * When iterating over components of a Subnetwork, only a those assets are included in the dataframes which are included in the subnetwork. * In ``n.plot()``, compute boundaries in all cases for consistent circle sizes. This is realised by setting a new default margin of 0.05. * Compatibility with pyomo 6.4.1. * Removed ``pypsa.stats`` module. * Extended defaults for the clustering of attributes in ``pypsa.networkclustering``. * Removed deprecated clustering algorithms in ``pypsa.networkclustering``. * Improved documentation and README. * Fix a few deprecations. * Improved test coverage, e.g. when copying networks. * Testing: ``pypower`` is not importable with newest numpy versions. Skip test if import fails. Special thanks for this release to @Cellophil, @txelldm and @rockstaedt for improving test coverage and documentation. PyPSA 0.19.3 (22nd April 2022) ============================== * Apply pre-commit formats to support development (incl. black formatting, jupyter cleanup, import sorting, preventing large file uploads). This will distort ``git blame`` functionality, which can be fixed by running ``git config blame.ignoreRevsFile .git-blame-ignore-revs`` inside the PyPSA repository. Run ``pre-commit install`` to set up locally. * Change message when exporting and importing networks without a set ``network_name``. Fixes [`#381 `_]. * Greedy Modularity Maximisation was introduced as new spatial clustering method [`#377 `_]. PyPSA 0.19.2 (7th March 2022) ============================= * Add standard line type for 750 kV transmission line. PyPSA 0.19.1 (18th February 2022) ================================= * When setting ramp limits for links and calling ``Network.lopf`` with ``pyomo=False``, an unexpected KeyError was raised. This was fixed by correctly accessing the data frame referring to the power dispatch of links. PyPSA 0.19.0 (11th February 2022) ================================= This release contains new features for ramping constraints in link components, hierarchical network clustering functionality, and an interface to the open-source HiGHS solver. **New Features** * Ramp limits for ``Links``. The ``Link`` component has two new attributes, :code:`ramp_limit_up` and :code:`ramp_limit_down`, which limits the marginal power increase equivalent to the implementation for generators. The new attributes are only considered when running ``network.lopf(pyomo=False)``. * Hierarchical Agglomerative Clustering (HAC) was introduced as new spatial clustering method [`#289 `_]. * Clustering networks now also supports the clustering of time-series associated to lines. * Add open-source `HiGHS solver `_. * A new convenience function ``Network.get_committable_i`` was added. This returns an index containing all committable assets of component ``c``. In case that component ``c`` does not support committable assets, it returns an empty dataframe. * A warning message is shown if a network contains one or more links with an :code:`efficiency` smaller than 1 and a negative value for :code:`p_min_pu` [`#320 `_]. * New example for spatial clustering. * Speed-up of ``network.plot()`` by only plotting buses with non-zero size. * Increased test coverage. **Changes** * The names of the indexes in static dataframes are now set to the component names. So, the index of ``n.generators`` has the name 'Generator'. The same accounts for the columns of the timeseries. * The snapshot levels of a multi-indexed snapshot were renamed to ['period', 'timestep'], the name of the index was set to 'snapshot'. This makes the snapshot name coherent for single and multi-indexed snapshots. **Bugs and Compatibility** * Compatibility with ``pandas>=1.4``. * Drop support for Python 3.6 in accordance with its [end-of-life](https://endoflife.date/python). * Use ``nx.Graph`` instead of ``nx.OrderedGraph`` which guarantees order is preserved for Python 3.7 and above. * Add assert: CBC solver does not work with '>' and '<'. * When running ``network.lopf(pyomo=False)``, the ramp limits did not take the time step right before the optimization horizon into account (relevant for rolling horizon optimization). This is now fixed. * Fix bug when multi-links are defined but the network has no links. Special thanks for this release to Samuel Matthew Dumlao (@smdumlao) for implementing the ramp limits for Links in PyPSA, Martha Frysztacki (@martacki) for implementing the hierarchical network clustering, and Max Parzen (@pz-max) for implementing the HiGHS solver interface. PyPSA 0.18.1 (15th October 2021) ================================ * Compatibility with ``pyomo>=6.1``. * Bugfix: specifying the ``solver_logfile`` is no longer mandatory with CPLEX for ``n.lopf(pyomo=False)``. * The distance measures for the network clustering functions ``busmap_by_spectral()`` and ``busmap_by_louvain()`` were adapted to use electrical distance (``s_nom/|r+i*x|``) (before: ``num_parallel``). * Deprecations: The functions ``busmap_by_linemask()``, ``busmap_by_length()``, ``length_clustering()``, ``busmap_by_spectral_clustering()``, ``spectral_clustering()``, ``busmap_by_louvain()``, ``louvain_clustering()``, ``busmap_by_rectangular_grid()``, ``rectangular_grid_clustering()`` and ``stubs_clustering()`` were deprecated and will be removed in v0.20. * Distance measures for function ``busmap_by_spectral()`` and ``busmap_by_louvain()`` were adapted to electrical distance (``s_nom/|r+i*x|``) (before: ``num_parallel``) * In ``pypsa.networkclustering``, strip the string of the clustered component name. Not doing this had caused troubles for components with an empty carrier column. * Various documentation updates. PyPSA 0.18.0 (12th August 2021) =============================== This release contains new features for pathway optimisation, improvements of the documentation's examples section as well as compatibility and bug fixes. **Licensing** * With this release, we have changed the licence from the copyleft GPLv3 to the more liberal MIT licence with the consent of all contributors (for the reasoning why, see the `pull request `_). **New features** * Added support for the optimisation of multiple investment periods, also known as pathway optimization. With this feature, snapshots can span over multiple years or decades which are divided into investment periods. Within each investment period, assets can be added to the network. The optimization only works with ``pyomo=False``. For more information see the documentation at :ref:`multi-horizon` and the `example notebook `_. Endogenous learning curves can be applied as ``extra_functionality``. * ``n.snapshot_weightings`` is now a ``pandas.DataFrame`` rather than a ``pandas.Series`` with weightings now subdivided into weightings for the objective function, generators and stores/storage units. This separation of weightings is relevant for temporal snapshot clustering, where the weight in the objective function may differ from the number of hours represented by each snapshot for storage purposes. * Objective weightings determine the multiplier of the marginal costs in the objective function of the LOPF. * Generator weightings specify the impact of generators in a ``GlobalConstraint`` (e.g. in a carbon dioxide emission constraint). * Store weightings define the elapsed hours for the charge, discharge, standing loss and spillage of storage units and stores in order to determine the current state of charge. PyPSA still supports setting ``n.snapshot_weightings`` with a ``pandas.Series``. In this case, the weightings are uniformly applied to all columns of the new ``n.snapshot_weightings`` ``pandas.DataFrame``. * All functionalities except for optimisation with ``pyomo=True`` now work with multi-indexed snapshots. * Many example notebooks are now also integrated in the documentation. See :doc:`/getting-started/examples-basic`, :doc:`/examples-index/lopf`, :doc:`/examples-index/sector-coupling` and :doc:`/examples-index/other`. * A new module ``examples`` was added which contains frontend functions for retrieving/loading example networks provided by the PyPSA project. * When solving ``n.lopf(pyomo=False)``, PyPSA now supports setting lower and upper capacity bounds per bus and carrier. These are specified in the columns ``n.buses['nom_min_{carrier}']`` and ``n.buses['nom_max_{carrier}']`` respectively. For example, if multiple generators of carrier ``wind`` are at bus ``bus1``, the combined capacity is limited to 1000 MW by setting ``n.buses.loc['bus1', 'nom_max_wind'] = 1000`` (a minimal capacity is forced by setting ``n.buses.loc['bus1', 'nom_min_wind']``). In the same manner the combined ``p_nom`` of components ``StorageUnit`` and ``e_nom`` of components ``Store`` can be limited. * Add new attribute ``carrier`` to the components ``Line``, ``Link``, ``Store`` and ``Load``, defining the energy carrier of the components. Its default is an empty string. When calling ``n.calculate_dependent_values()``, empty carriers are replaced by the carriers of the buses to which the components are attached. * Add new descriptive attribute ``unit`` to ``bus`` component. * Automated upload of code coverage reports for pull requests. **Changes** * When using iterative LOPF with ``n.ilopf()`` to consider impedance updates of reinforced transmission lines, the attributes ``p_nom`` and ``s_nom`` of lines and links are reset to their original values after final iteration. * ``n.snapshots`` are now a property, hence assigning values with ``n.snapshots = values`` is the same as ``n.set_snapshots(values)``. * Remove deprecated function ``geo.area_from_lon_lat_poly``. **Deprecations** * The function ``geo.area_from_lon_lat_poly()`` was deprecated and will be removed in v0.19. * The deprecated argument ``csv_folder_name`` in ``pypsa.Network`` was removed. * The deprecated column names ``source``, ``dispatch``, ``p_max_pu_fixed``, ``p_min_pu_fixed`` for the class ``Generator``, ``current_type`` for the class ``Bus`` and ``s_nom`` for the class ``Link`` were removed. **Bugs and Compatibility** * Added support for ``pandas`` version 1.3. * Adjust log file creation for CPLEX version 12.10 and higher. * ``n.snapshot_weightings`` is no longer copied for ``n.copy(with_time=False)``. * Bugfix in ``n.ilopf()`` where previously all links were fixed in the final iteration when it should only be the HVDC links. * Fix setting ``margin`` and ``boundaries`` when plotting a network with ``geomap=False``. Special thanks for this release to Lisa Zeyen (@lisazeyen) for implementing the multi-horizon investment in PyPSA and to Fabian Hofmann (@FabianHofmann) for thoroughly reviewing it and adding the example notebooks to the documentation. PyPSA 0.17.1 (15th July 2020) ============================= This release contains bug fixes and extensions to the features for optimization when not using Pyomo. * N-1 security-constrained linear optimal power flow is now also supported without pyomo by running ``network.sclopf(pyomo=False)``. * Added support for the FICO Xpress commercial solver for optimization without pyomo, i.e. ``pyomo=False``. * There was a bug in the LOPF with ``pyomo=False`` whereby if some Links were defined with multiple outputs (i.e. bus2, bus3, etc. were defined), but there remained some Links without multiple outputs (bus2, bus3, etc. set to ``""``), then the Links without multiple outputs were assigned erroneous non-zero values for p2, p3, etc. in the LOPF with ``pyomo=False``. Now p2, p3, etc. revert to the default value for Links where bus2, bus3, etc. are not defined, just like for the LOPF with ``pyomo=True``. * Handle double-asterisk prefix in ``solution_fn`` when solving ``n.lopf(pyomo=False)`` using CBC. * When solving ``n.lopf(pyomo=False, store_basis=True, solver_name="cplex")`` an error raised by trying to store a non-existing basis is caught. * Add compatibility for Pyomo 5.7. This is also the new minimum requirement. * Fixed bug when saving dual variables of the line volume limit. Now using dual from the second last iteration in ``pypsa.linopf``, because last iteration returns NaN (no optimisation of line capacities in final iteration). * Added tracking of iterations of global constraints in the optimisation. * When solving ``n.lopf(pyomo=False)``, PyPSA now constrains the dispatch variables for non extendable components with actual constraints, not with standard variable bounds. This allows retrieving shadow prices for all dispatch variables when running ``n.lopf(pyomo=False, keep_shadowprices=True)``. * Can now cluster lines with different static ``s_max_pu`` values. Time-varying ``s_max_pu`` are not supported in clustering. * Improved handling of optional dependencies for network clustering functionalities (``sklearn`` and ``community``). Thanks to Pietro Belotti from FICO for adding the Xpress support, to Fabian Neumann (KIT) and Fabian Hofmann (FIAS) for all their hard work on this release, and to all those who fixed bugs and reported issues. PyPSA 0.17.0 (23rd March 2020) ================================ This release contains some minor breaking changes to plotting, some new features and bug fixes. * For plotting geographical features ``basemap`` is not supported anymore. Please use ``cartopy`` instead. * Changes in the plotting functions ``n.plot()`` and ``n.iplot()`` include some **breaking changes**: * A set of new arguments were introduced to separate style parameters of the different branch components: ``link_colors``, ``link_widths``, ``transformer_colors``, ``transformer_widths``, ``link_cmap``, ``transformer_cmap`` * ``line_widths``, ``line_colors``, and ``line_cmap`` now only apply for lines and can no longer be used for other branch types (links and transformers). Passing a pandas.Series with a pandas.MultiIndex will raise an error. * Additionally, the function `n.iplot()` has new arguments ``line_text``, ``link_text``, ``transformer_text`` to configure the text displayed when hovering over a branch component. * The function ``directed_flow()`` now takes only a pandas.Series with single pandas.Index. * The argument ``bus_colorscale`` in ``n.iplot()`` was renamed to ``bus_cmap``. * The default colours changed. * If non-standard output fields in the time-dependent ``network.components_t`` (e.g. ``network.links_t.p2`` when there are multi-links) were exported, then PyPSA will now also import them automatically without requiring the use of the ``override_component_attrs`` argument. * Deep copies of networks can now be created with a subset of snapshots, e.g. ``network.copy(snapshots=network.snapshots[:2])``. * When using the ``pyomo=False`` formulation of the LOPF (``network.lopf(pyomo=False)``): * It is now possible to alter the objective function. Terms can be added to the objective via ``extra_functionality`` using the function `pypsa.linopt.write_objective`. When a pure custom objective function needs to be declared, one can set ``skip_objective=True``. In this case, only terms defined through ``extra_functionality`` will be considered in the objective function. * Shadow prices of capacity bounds for non-extendable passive branches are parsed (similar to the ``pyomo=True`` setting) * Fixed `pypsa.linopf.define_kirchhoff_constraints` to handle exclusively radial network topologies. * CPLEX is now supported as an additional solver option. Enable it by installing the `cplex `_ package (e.g. via ``pip install cplex`` or ``conda install -c ibmdecisionoptimization cplex``) and setting ``solver_name='cplex'`` * When plotting, ``bus_sizes`` are now consistent when they have a ``pandas.MultiIndex`` or a ``pandas.Index``. The default is changed to ``bus_sizes=0.01`` because the bus sizes now relate to the axis values. * When plotting, ``bus_alpha`` can now be used to add an alpha channel which controls the opacity of the bus markers. * The argument ``bus_colors`` can a now also be a pandas.Series. * The ``carrier`` component has two new columns 'color' and 'nice_name'. The color column is used by the plotting function if ``bus_sizes`` is a pandas.Series with a MultiIndex and ``bus_colors`` is not explicitly defined. * The function `pypsa.linopf.ilopf` can now track the intermediate branch capacities and objective values for each iteration using the ``track_iterations`` keyword. * Fixed unit commitment: * when ``min_up_time`` of committable generators exceeds the length of snapshots. * when network does not feature any extendable generators. * Fixed import from pandapower for transformers not based on standard types. * The various Jupyter Notebook examples are now available on the `binder `_ platform. This allows new users to interactively run and explore the examples without the need of installing anything on their computers. * Minor adjustments for compatibility with pandas v1.0.0. * After optimizing, the network has now an additional attribute ``objective_constant`` which reflects the capital cost of already existing infrastructure in the network referring to ``p_nom`` and ``s_nom`` values. Thanks to Fabian Hofmann (FIAS) and Fabian Neumann (KIT) for all their hard work on this release, and to all those who reported issues. 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 weightings. 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 `LOPF 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 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 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 useful 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 by passing ``network.plot`` an ``flow`` argument. * 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 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_components`` and ``override_component_attrs``, see the section on :ref:`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. * The ``Link`` component can now be defined with multiple outputs in fixed ratio to the power in the single input by defining new columns ``bus2``, ``bus3``, etc. (``bus`` followed by an integer) in ``network.links`` along with associated columns for the efficiencies ``efficiency2``, ``efficiency3``, etc. The different outputs are then proportional to the input according to the efficiency; see sections :ref:`components-links-multiple-outputs` and :ref:`opf-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.export_to_netcdf()`` and ``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.all_components`` and ``pypsa.components.one_port_components`` have been moved from ``pypsa.components`` to ``network``, i.e. ``network.all_components`` and ``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_eff`` for 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. * PyPSA now uses the `Travis CI `_ continuous integration service to test every commit in the `PyPSA GitHub repository `_. This will allow us to catch library dependency issues faster. 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_lopf`` has been internally split into ``build_model``, ``prepare_solver`` and ``solve`` to 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_pu`` to restrict the flow in the OPF, just like ``p_max_pu`` for 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. * The ``marginal_cost`` attribute 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_name`` instead of ``csv_folder_name``. With ``import_name`` PyPSA 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_name`` is still there but is deprecated. * The value ``network.objective`` is now read from the Pyomo results attribute ``Upper Bound`` instead of ``Lower Bound``. This is because for MILP problems under certain circumstances CPLEX records the ``Lower bound`` as the relaxed value. ``Upper bound`` is 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.lopf`` to 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, ``csgraph`` has 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 ``network.snapshot_weightings`` properly. 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 (sparse) documentation :doc:`/user-guide/plotting`. * 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 :ref:`madd`. * 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 :ref:`global-constraints` for implementing constraints that effect many components at once (see also the LOPF subsection :ref:`global-constraints-opf`). 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 :ref:`nodal-power-balance`. * ``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 :ref:`unit-commitment` 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 :ref:`ramping` and the `unit commitment example `_. * Different mathematically-equivalent formulations for the Linear Optimal Power Flow (LOPF) are now documented in :ref:`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 ``_, 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 :ref:`line-types`. For transformers you just need to specify the type, see :ref:`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 :ref:`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 :doc:`/user-guide/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 :ref:`time-varying` 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 ``_ 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 :doc:`/contributing/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 :ref:`time-varying`), 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_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 :ref:`controllable-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 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 :ref:`export-csv` 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. Release process =============== * Update ``doc/references/release-notes.rst`` * You don't need to update the version number anywhere, this is done automatically. * ``git commit`` and put release notes in commit message * ``git tag vx.x.x`` * ``git push`` and ``git push --tags`` * The upload to `PyPI `_ is automated in the Github Action ``deploy.yml``, which is triggered by pushing a tag. To upload manually, run ``python setup.py sdist``, then ``twine check dist/pypsa-0.x.0.tar.gz`` and ``twine upload dist/pypsa-0.x.0.tar.gz`` * The `GitHub release `_ is also automated in the Github Action. Making a GitHub release will also trigger `zenodo `_ to archive the release with its own DOI. * To update to conda-forge, check the pull request generated at the `feedstock repository `_. * Inform the PyPSA mailing list.