As of 18 January 2023, version 0.6.10 is packaged up and ready for download on PyPI and conda-forge! As of now, the 0.6.x branch of Calliope is mostly in bugfix mode, while work has started on 0.7, the next major version of Calliope with several wide-ranging improvements. Version 0.6.10, as well as 0.6.8 and 0.6.9 before, focus on fixing various smaller bugs and keeping up with newer versions of Python and other packages. → Read more...

Version 0.6.7 is packaged up and ready for download on PyPI and conda-forge! Key additional features are: Support for Pyomo’s solver interface with gurobi_persistent has been enabled. When working with the Gurobi solver and looking to rerun a model several times, it can be done without the overhead of sending the model across to Gurobi. This update entails a new backend interface method to send backend model updates to the Gurobi model instance (model. → Read more...

Version 0.6.61 is packaged up and ready for download on PyPI and conda-forge! Key additional features are: This release expands yet again on the model-wide group constraints added in 0.6.5, namely by addition of the carrier_con constraints. A full list of available constraints can be found in the documentation. There is a new run configuration: spores. ‘SPORES’ refers to Spatially-explicit Practically Optimal REsultS. This run mode allows a user to generate any number of alternative results which are within a certain range of the optimal cost. → Read more...

Version 0.6.5 is packaged up and ready for download on PyPI and conda-forge! This release improves and expands on the model-wide group constraints added in 0.6.4, further increasing the flexibility they make available. There is also a new storage_discharge_depth constraint, which allows setting a minimum stored-energy level to be preserved by a storage technology. This version is also fully Python 3.8 compatible. In the process of updating dependencies for Python 3. → Read more...

Version 0.6.4 is packaged up and ready for download on PyPI and conda-forge! Highlights of this version are new group constraints that add a range of flexibility. For example, the cost_var_max group constraint allows flexible emissions limits on a per-region basis (assuming emissions are modelled as a cost class). The demand_share_max or demand_share_per_timestep_max group constraints allow limiting groups of technologies (e.g., variable renewables, or legacy generators) to a maximum on a model-wide basis or for groups of locations, on average or in each individual time step, respectively. → Read more...