Hyperlinked release notes can be found here:

https://pypsa.org/doc/release_notes.html#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 2.0.0: 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.