Elmada: electricity market data for energy system modeling

The open-source Python package Elmada provides carbon emission factors and wholesale prices of the national electricity supply system for the modeling of distributed energy systems. Elmada stands for electricity market data. It is part of the Draf Project but can be used as a standalone package.

Features

• Carbon emission factors are calculated depending on country and year in up to quarter-hourly resolution. You can choose between

  • grid mix emission factors (XEFs) from fuel type-specific ENTSO-E electricity generation data (method="XEF_EP")
  • and approximations using merit order based simulations which allow also for the calculation of marginal emission factors (MEFs). The according Power Plant method (PP) and Piecewise Linear method (PWL) are described in this open-access paper. The data used depends on the method chosen, see scheme below.

• Electrcity prices are provided for European national electricity grids. You can choose between the real historical ENTSO-E data or the simulation results of PP/PWL method.

• Other interesting market data such as the merit order list, fuel-specific generation data, and power plant lists are provided as a by-product of the CEF calculations.

Methodology

This scheme from the paper shows an overview of the methods PP, PWL, and PWLv:

Data

Data modes

You can use Elmada in two data modes which can be set with elmada.set_mode(mode=<MODE>):

• mode="safe" (default):
  • Pre-cached data for 4 years and 20 countries are used. The data are described in the paper.
  • The years are 2017 to 2020 and the countries AT, BE, CZ, DE, DK, ES, FI, FR, GB, GR, HU, IE, IT, LT, NL, PL, PT, RO, RS, SI.
  • The data is available in the space-saving and quick-to-read Parquet format under .../safe_cache.
• mode="live":
  • Up-to-date data are retrieved on demand and are cached to an OS-specific directory, see elmada.paths.CACHE_DIR. A symbolic link to it can be conveniently created by executing elmada.make_symlink_to_cache().
  • Available years are 2017 until the present.
  • Slow due to API requests.
  • Requires valid API keys of Entsoe, Morph, Quandl, see table below.

Data sources

Time zones

The data is in local time since the Draf Project focuses on the modeling of individual energy hubs. Standard time is used i.e. daylight saving time is ignored. Also see this table of the time zones used.

Installation

Using pip

python -m pip install elmada

NOTE: Read here why you should use python -m pip instead of pip.

From source using conda

For a conda environment including a full editable elmada version do the following steps.

Clone the source repository:

git clone https://github.com/DrafProject/elmada.git
cd elmada

Create an conda environment based on environment.yml and install an editable local Elmada version:

conda env create

Activate the environment

conda activate elmada

Run the tests and ensure that there are no errors

pytest

Usage

import elmada

OPTIONAL: Set your api keys and go live mode:

elmada.set_api_keys(entsoe="YOUR_ENTSOE_KEY", morph="YOUR_MORPH_KEY", quandl="YOUR_QUANDL_KEY")
# NOTE: Api keys are stored in an OS-dependent config directory for later use.

elmada.set_mode("live")

Carbon Emission factors

elmada.get_emissions(year=2019, country="DE", method="MEF_PWL", freq="60min", use_datetime=True)

... returns marginal emission factors calculated by the PWL method with hourly datetime index:

2019-01-01 00:00:00     990.103492
2019-01-01 01:00:00     959.758367
                          ...
2019-12-31 22:00:00    1064.122146
2019-12-31 23:00:00    1049.852079
Freq: 60T, Name: MEFs, Length: 8760, dtype: float64

The method argument of get_emissions() takes strings that consists of two parts seperated by an underscore. The first part is the type of emission factor: grid mix emission factors (XEF) or marginal emission factors (MEF). The second part determines the calculation method: power plant method (PP), piecewise linear method (PWL), or piecewise linear method in validation mode (PWLv).

The first part can be omitted (_PP, _PWL, _PWLv) to return a DataFrame that includes additional information.

elmada.get_emissions(year=2019, country="DE", method="_PWL")

... returns all output from the PWL method:

      residual_load  total_load marginal_fuel  efficiency  marginal_cost         MEFs        XEFs
0          21115.00    51609.75       lignite    0.378432      40.889230   990.103492  204.730151
1          18919.50    51154.50       lignite    0.390397      39.636039   959.758367  164.716687
...             ...         ...           ...         ...            ...          ...         ...
8758       27116.00    41652.00       lignite    0.352109      43.946047  1064.122146  388.542911
8759       25437.75    39262.75       lignite    0.356895      43.356723  1049.852079  376.009477
[8760 rows x 7 columns]

Additionally, XEFs can be calculated from historic fuel type-specific generation data (XEF_EP).

Here is an overview of valid method argument values:

You can plot the carbon emission factors with

elmada.plots.cefs_scatter(year=2019, country="DE", method="MEF_PP")

Wholesale prices

elmada.get_prices(year=2019, country="DE", method="hist_EP")

0       28.32
1       10.07
        ...  
8758    38.88
8759    37.39
Length: 8760, dtype: float64

Possible values for the method argument of get_prices() are:

Merit order

elmada.plots.merit_order(year=2019, country="DE", method="PP")

... plots the merit order:

elmada.get_merit_order(year=2019, country="DE", method="PP")

... returns the merit order as DataFrame with detailed information on individual power plant blocks.

Contributing

Contributions in any form are welcome! To contribute changes, please have a look at our contributing guidelines.

In short:

1. Fork the project and create a feature branch to work on in your fork (git checkout -b new-feature).
2. Commit your changes to the feature branch and push the branch to GitHub (git push origin my-new-feature).
3. On GitHub, create a new pull request from the feature branch.

Citing Elmada

If you use Elmada for academic work please cite this open-access paper published in Applied Energy in 2021.

License

Copyright (c) 2021 Markus Fleschutz

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.