We added a new oemof repository aiming at collecting all kinds of examples on how to use oemof and its various libraries. You are welcome to help us fill the new repo by contributing your example via a pull request or by sending us an e-mail (see here for contact information).
There was a little difference that made the examples run in the last release if you installed oemof locally but not if you installed it from pypi. Thus, they did not work with version 0.1.2.
Now we learned how to test them properly, applied a hotfix and released two versions again, due to superstitions (1.3 is almost a 13). The actual version is now v.0.1.4. It is not true but a nice story and due to the open philosophy errors like these fortunately show up really quick
Have a look at the 0.1.2 release to see the real changes and stay tuned!
The winter is gone, spring cleaning is done and we come up with a new release…
A revision of the installation guidelines and the examples will make it easier for new users to enter the world of oemof. Additionally, some new features have been imlemented besides a cleaned up code base:
Continue reading “The oemof springtime release (v0.1.2)”
A new version of the oemof database extension package oemof.db is now available.
This week, the first version of the oemof-application renpassG!S has been released!
renpassG!S is an easy-to-use application designed to model the cost-minimal dispatch of energy supply systems. Technically speaking, it is a so-called numerical partial equilibrium model of a liberalised electricity market often referred to as fundamental model.
More information and some screenshots of possible outcomes can be found here: https://github.com/znes/renpass_gis
We proudly present the totally revised version of oemof!
Now oemof is more flexible, better documented and ready to join for new contributors and users.
The framework provides the basis for a great range of different energy system model types, ranging from LP bottom-up (power and heat) economic dispatch models with optional investment to MILP operational unit commitment models.