News on Calliope

Major new features: pre-release of version 0.7.0

Wed, 26 Mar 2025 08:00:00 +0100

For quite a while now we have been working on a major new version of Calliope - v0.7.0 – packed with powerful new features and major usability upgrades.

A pre-release version of v0.7.0 is already available through both PyPI and Conda-Forge. For new projects, we recommend using this new version already as it introduces a range of backwards-incompatible changes. The Calliope team is using it productively in several projects.

Some of the major new features include:

Release v0.6.10

Wed, 18 Jan 2023 16:00:00 +0100

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. In particular, Calliope now runs on Apple Silicon devices.

Release v0.6.7

Wed, 30 Jun 2021 16:00:00 +0100

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.backend.regenerate_persistent_solver(...)).

Release v0.6.6

Mon, 12 Oct 2020 14:00:00 +0100

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. This run mode has been recently applied to the Italian energy system More information on using this run mode can be found in the documentation.

Release v0.6.5

Wed, 15 Jan 2020 10:00:00 +0100

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 3.8, we updated to the most recent version of scikit-learn (0.22), which fixes a bug in how k-means clustering reacts to specifying a random seed. This may result in models running Calliope 0.6.5 and setting a random seed seeing different k-means clusters than Calliope 0.6.4 and older.

Release v0.6.4

Mon, 27 May 2019 16:00:00 +0200

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. Many kinds of policy constraints on system planning can easily be modelled through group constraints.

New year, new website

Mon, 07 Jan 2019 15:00:02 +0200

We have a new logo and website!

Replacing the dusty old static site with little informational value and little visual appeal, the new website is the first landing point for information about Calliope and its applications.

In particular, we now have a model gallery with on existing Calliope models (which are also a good place to start learning when building a new model). More models will be added to the gallery in the near future.