<a href="https://www.e2m.energy/"><img src="https://user-images.githubusercontent.com/8255114/148765040-975650b6-1db2-4537-aac4-0840f28bf678.png" alt="e2m logo" title="e2m" height="50" align="right"></a>
# aFRR remuneration
A tool to calculate the aFRR remuneration for the european energy market.
## About
This project was initiated with the start of aFRR remuneration in temporal resolution of seconds on October 1st 2021
which is one further step to fulfill the EU target market design.
The motivation for creating this python package is to provide a tool for evaluating remuneration of aFRR activation
events by TSOs.
Therefore, it provides an implementation of the calculation procedure described in the
[model description](https://www.regelleistung.net/ext/download/Modellbeschreibung_aFRR-Abrechnung_ab_01.10.2021) as
python code.
## Installation
We aim to release a package on PyPi soonish. Until then, please see in
[development installation](#Development-installation) how to install the package from sources.
## Usage
Here is some example code that shows how use functionality of this package.
Make sure you have a file at hand with data about setpoints and actual values of an aFRR activation event. See the
example files from
[regelleistung.net](https://www.regelleistung.net/ext/download/Beispieldateien_aFRR-Abrechnung_ab_01.10.2021) to
understand the required file format.
Note, you have to make sure that data starts at the beginning of an aFRR activation event.
````python
from afrr_renumeration.aFRR import calc_acceptance_tolerance_band, calc_underfulfillment_and_account
from afrr_renumeration.data import parse_tso_data
# load the setpoint and the measured value for example by reading the tso data
file = "20211231_aFRR_XXXXXXXXXXXXXXXX_XXX_PT1S_043_V01.csv"
tso_df = parse_tso_data(file)
# calculate the tolerance band
band_df = calc_acceptance_tolerance_band(
setpoint=tso_df["setpoint"], measured=tso_df["measured"]
)
# calculate acceptance values and other relevant serieses like the under-/overfulfillment
underful_df = calc_underfulfillment_and_account(
setpoint=band_df.setpoint,
measured=band_df.measured,
upper_acceptance_limit=band_df.upper_acceptance_limit,
lower_acceptance_limit=band_df.lower_acceptance_limit,
lower_tolerance_limit=band_df.lower_tolerance_limit,
upper_tolerance_limit=band_df.upper_tolerance_limit,
)
````
## Next Steps
We plan to
- [ ] Add a testfile with artificial data
- [ ] Add an example with a valid MOL
Feel free to help us here!
## Contributing
Contributions are highly welcome. For more details, please have a look in to
[contribution guidelines](https://github.com/energy2market/afrr-remuneration/blob/main/CONTRIBUTING.md).
### Development installation
For installing the package from sources, please clone the repository with
```bash
git clone git@github.com:energy2market/afrr-remuneration.git
```
Then, in the directory `afrr-remuneration` (the one the source code was cloned to), execute
```bash
poetry install
```
which creates a virtual environment under `./venv` and installs required package and the package itself to this virtual environment.
Read here for more information about <a href="https://python-poetry.org/">poetry</a>.