POMMES goes public

We are happy to announce the initial public release of pommesdispatch which is now also available on PyPI.

This is POMMES, A bottom-up fundamental power market model for the German electricity sector

Features:

• pommesdispatch is the dispatch variant of POMMES that allows to simulate dispatch decisions and power prices for Germany in hourly resolution for one year (or shorter time frames).
• pommesdispatch allows for negative power prices due to its in-depth representation of renewable plants in the market premium scheme.
• Consistent input data sets for POMMES models can be obtained from pommesdata, supporting years between 2017 and 2030 and taking into account various open data sources.
• All POMMES models are easy to adjust and extend because it is build on top of oemof.solph

Stay tuned for upcoming releases as well as the data preparation package pommes-data and the investment variant pommes-invest!

We have included quite some improvements to our code. Just to name a few:

• pommesdispatch can now be configured via a yaml file and run in a console.
• Tests have been integrated achieving a code coverage of almost 90%.
• pommesdispatch models can now be run for any year between 2017 and 2030.

POMMES using yaml and sphinx

In this release, we included:

• a more comfortable and clean option to control the model settings via a config.yml file using the human-readable YAML format.
• a draft for a sphinx documentation with still some open work to do.
• Some improvements in architecture and a console script for running pommes_dispatch (run_pommes_dispatch).

The upcoming release for v0.0.3 is already on its way and will include the open bugfixes as well as a first time PyPI hosting of pommes_dispatch. Stay tuned and hungry for more POMMES. ;-)

POMMES's very first release

This is POMMES, A bottom-up fundamental power market model for the German electricity sector

Features:

• pommes-dispatch is the dispatch variant of POMMES that allows to simulate dispatch decisions and power prices for Germany in hourly resolution for one year (or shorter time frames).
• pommes-dispatch allows for negative power prices due to its in-depth representation of renewable plants in the market premium scheme.
• Consistent input data sets for POMMES models can be obtained from pommes-data, supporting years between 2017 and 2030 and taking into account various open data sources.
• All POMMES models are easy to adjust and extend because it is build on top of oemof.solph

Stay tuned for upcoming releases as well as the data preparation package pommes-data and the investment variant pommes-invest!