elmada 0.1.0

Dynamic electricity carbon emission factors and prices for Europe

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elmada: Dynamic electricity carbon emission factors and prices for Europe

The open-source Python package elmada provides electricity carbon emission factors and wholesale prices for European countries. The target group includes modelers of distributed energy hubs who need electricity market data (short: elmada), e.g., to evaluate the environmental effect of demand response. elmada is part of the Draf Project but can be used as a standalone package.

Features

• Carbon emission factors (CEF) 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 Applied Energy paper. The data used depends on the method chosen, see scheme below.

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

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

Methodology

With the XEF_EP method, XEFs are calculated by multiplying the share matrix S (fuel type specific share of electricity generation per time step from ENTSO-E) with the intensity vector ε (fuel type specific life cycle carbon emission intensities from Tranberg.2019):

The methods PP, PWL, and PWLv are explained in this Applied Energy paper. Here is an overview:

Data

Geographic scope

In elmada, two-letter country codes (ISO 3166-1 alpha-2) are used.

The countries supported by elmada can be seen in the map below which is the output of elmada.plots.cef_country_map(year=2020, method="XEF_EP").

In the Usage section they are referred to as Europe30. They include:

• 20 countries analyzed in the Applied Energy paper: AT, BE, CZ, DE, DK, ES, FI, FR, GB, GR, HU, IE, IT, LT, NL, PL, PT, RO, RS, SI
• 8 countries with only one reported fossil fuel type: BA, CH, EE, LV, ME, MK, NO, SE
• 2 countries where installed generation capacity data for 2019 were only available after the publication of the Applied Energy paper: BG, SK

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 Applied Energy 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 ENTSO-E, Morph, Quandl, see table below.

Data sources

Description	Local data location	Source	Channel	Involved in
Generation time series & installed generation capacities	.../safe_cache or CACHE_DIR	ENTSO-E	🔌 on-demand-retrieval via EntsoePandasClient (requires valid ENTSO-E API key)	CEFs via EP, PP, PWL, PWLv
Carbon prices (EUA)	.../safe_cache or CACHE_DIR	Sandbag & ICE	🔌 on-demand-retrieval via Quandl (requires valid Quandl API key)	CEFs via PP, PWL, PWLv
Share of CCGT among gas power plants	.../safe_cache or CACHE_DIR	GEO	🔌 on-demand-download via Morph (requires valid Morph API key)	CEFs via PWL, PWLv
(Average) fossil power plants sizes	.../safe_cache or CACHE_DIR	GEO	🔌 on-demand-scraping via BeautifulSoup4	CEFs via PWL, PWLv
German fossil power plant list with efficiencies	.../safe_cache or CACHE_DIR	OPSD	🔌 on-demand-download from here	CEFs via PP, PWL, PWLv
Transmission & distribution losses	.../worldbank	Worldbank	💾 manual download from here	CEFs via PP, PWL, PWLv
Fuel prices for 2015 (+ trends)	.../from_other.py (+ .../destatis)	Konstantin.2017 (+ DESTATIS)	🔢 hard-coded values (+ 💾 manual download from here)	CEFs via PP, PWL, PWLv
Fuel type-specific carbon emission intensities	.../from_other.py & .../tranberg	Quaschning & Tranberg.2019	🔢 hard-coded values	CEFs via EP, PP, PWL, PWLv

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

From source without using conda

For Unix

git clone https://github.com/DrafProject/elmada.git
cd elmada
python3 -m venv env
source env/bin/activate
python -m pip install -e .[dev]

For Windows

git clone https://github.com/DrafProject/elmada.git
cd elmada
py -m venv env
.\env\Scripts\activate
py -m pip install -e .[dev]

Tests

This should always work:

pytest -m="not apikey"

This works only if API keys are set as described below:

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:

method	Return type	Return values	Restriction
XEF_PP	Series	XEFs using PP method	DE
XEF_PWL	Series	XEFs using PWL method	Europe30
XEF_PWLv	Series	XEFs using PWLv method	DE
MEF_PP	Series	MEFs from PP method	DE
MEF_PWL	Series	MEFs using PWL method	Europe30
MEF_PWLv	Series	MEFs using PWLv method	DE
_PP	Dataframe	extended data for PP method	DE
_PWL	Dataframe	extended data for PWL method	Europe30
_PWLv	Dataframe	extended data for PWLv method	DE
XEF_EP	Series	XEFs using fuel type-specific generation data from ENTSO-E	Europe30

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:

method	Description	Restriction
PP	Using the power plant method	DE
PWL	Using piecewise linear method	Europe30
PWLv	Using piecewise linear method in validation mode	DE
hist_EP	Using historic ENTSO-E data	Europe30 without BA, ME, MK
hist_SM	Using historic Smard data	used only as backup for DE, 2015 and 2018

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.

Pre-processed data

The following table describes additional elmada functions that provide pre-processed data. Keyword arguments are for example kw = dict(year=2019, freq="60min", country="DE").

elmada. function call	Return type (Dimensions)	Return value	Usage in elmada	Used within
get_el_national_generation(**kw)	DataFrame (time, fuel type)	National electricity generation	Share matrix S	XEF_EP method
get_el_national_generation(**kw).sum(axis=1)	Series (time)	Total national electricity generation	Proxy for the total load	XEFs calculations
get_residual_load(**kw)	Series (time)	Conventional national generation	Proxy for the residual load (see scheme above)	PP, PWL and PWLv

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.