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Toolset for generating and managing Power Plant Data

Project description


pypi conda pythonversion LICENSE DOI

A toolset for cleaning, standardizing and combining multiple power plant databases.

This package provides ready-to-use power plant data for the European power system. Starting from openly available power plant datasets, the package cleans, standardizes and merges the input data to create a new combining dataset, which includes all the important information. The package allows to easily update the combined data as soon as new input datasets are released.

Map of power plants in Europe

powerplantmatching was initially developed by the Renewable Energy Group at FIAS to build power plant data inputs to PyPSA-based models for carrying out simulations for the CoNDyNet project, financed by the German Federal Ministry for Education and Research (BMBF) as part of the Stromnetze Research Initiative.

What it can do

  • clean and standardize power plant data sets
  • aggregate power plants units which belong to the same plant
  • compare and combine different data sets
  • create lookups and give statistical insight to power plant goodness
  • provide cleaned data from different sources
  • choose between gros/net capacity
  • provide an already merged data set of six different data-sources
  • scale the power plant capacities in order to match country specific statistics about total power plant capacities
  • visualize the data
  • export your powerplant data to a PyPSA or TIMES model


Using pip

pip install powerplantmatching

or conda (as long as the package is not yet in the conda-forge channel)

pip install powerplantmatching entsoe-py --no-deps
conda install pandas networkx pycountry xlrd seaborn pyyaml requests matplotlib geopy beautifulsoup4 cartopy 

Get the Data

In order to directly load the already build data into a pandas dataframe just call

import powerplantmatching as pm

which will parse and store the actual dataset of powerplants of this repository. Setting from_url=False (default) will load all the necessary data files and combine them. Note that this might take some minutes.

The resulting dataset compared with the capacity statistics provided by the ENTSOE SO&AF:

Capacity statistics comparison

The dataset combines the data of all the data sources listed in Data-Sources and provides the following information:

  • Power plant name - claim of each database
  • Fueltype - {Bioenergy, Geothermal, Hard Coal, Hydro, Lignite, Nuclear, Natural Gas, Oil, Solar, Wind, Other}
  • Technology - {CCGT, OCGT, Steam Turbine, Combustion Engine, Run-Of-River, Pumped Storage, Reservoir}
  • Set - {Power Plant (PP), Combined Heat and Power (CHP), Storages (Stores)}
  • Capacity - [MW]
  • Duration - Maximum state of charge capacity in terms of hours at full output capacity
  • Dam Information - Dam volume [Mm^3] and Dam Height [m]
  • Geo-position - Latitude, Longitude
  • Country - EU-27 + CH + NO (+ UK) minus Cyprus and Malta
  • YearCommissioned - Commmisioning year of the powerplant
  • RetroFit - Year of last retrofit
  • projectID - Immutable identifier of the power plant

Where is the data stored?

All data files of the package will be stored in the folder given by pm.core.package_config['data_dir']

Make your own configuration

You have the option to easily manipulate the resulting data modifying the global configuration. Just save the config.yaml file as ~/.powerplantmatching_config.yaml manually or for linux users

wget -O ~/.powerplantmatching_config.yaml

and change the .powerplantmaching_config.yaml file according to your wishes. Thereby you can

  • determine the global set of countries and fueltypes

  • determine which data sources to combine and which data sources should completely be contained in the final dataset

  • individually filter data sources via pandas.DataFrame.query statements set as an argument of data source name. See the default config.yaml file as an example

Optionally you can:

  • add your ENTSOE security token to the .powerplantmaching_config.yaml file. To enable updating the ENTSOE data by yourself. The token can be obtained by following section 2 of the RESTful API documentation of the ENTSOE-E Transparency platform.

  • add your Google API key to the config.yaml file to enable geoparsing. The key can be obtained by following the instructions.


Not available but supported sources:

  • IWPDCY (International Water Power & Dam Country Yearbook)
  • WEPP (Platts, World Elecrtric Power Plants Database)

The merged dataset is available in two versions: The bigger dataset, obtained by


links the entries of the matched power plants and lists all the related properties given by the different data-sources. The smaller, reduced dataset, given by


claims only the value of the most reliable data source being matched in the individual power plant data entry. The considered reliability scores are:

Dataset Reliabilty score

Intergrating new Data-Sources

Let's say you have a new dataset "FOO.csv" which you want to combine with the other data bases. Follow these steps to properly integrate it. Please, before starting, make sure that you've installed powerplantmatching from your downloaded local repository (link).

  1. Look where powerplantmatching stores all data files

    import powerplantmatching as pm
  2. Store FOO.csv in this directory under the subfolder data/in. So on Linux machines the total path under which you store your data file would be: /home/<user>/.local/share/powerplantmatching/data/in/FOO.csv

  3. Look where powerplantmatching looks for a custom configuration file


    If this file does not yet exist on your machine, download the standard configuration and store it under the given path as .powerplantmatching_config.yaml.

  4. Open the yaml file and add a new entry under the section #data config. The new entry should look like this

      reliability_score: 4
      fn: FOO.csv

    The reliability_score indicates the reliability of your data, choose a number between 1 (low quality data) and 7 (high quality data). If the data is openly available, you can add an url argument linking directly to the .csv file, which will enable automatic downloading.

    Add the name of the new entry to the matching_sources in your yaml file like shown below

    #matching config
        - OPSD
        - FOO
  5. Add a function FOO() to the in the powerplantmatching source code. You find the file in your local repository under powerplantmatching/ The function should be structured like this:

    def FOO(raw=False, config=None):
        Importer for the FOO database.
        raw : Boolean, default False
            Whether to return the original dataset
        config : dict, default None
            Add custom specific configuration,
            e.g. powerplantmatching.config.get_config(target_countries='Italy'),
            defaults to powerplantmatching.config.get_config()
        config = get_config() if config is None else config
        df = parse_if_not_stored('FOO', config=config)
        if raw:
            return foo
        df = (df
          .rename(columns){'Latitude': 'lat',
                               'Longitude': 'lon'})
          .loc[lambda df: df.Country.isin(config['target_countries'])]
          .pipe(set_column_name, 'FOO')
        return df

    Note that the code given after df = is just a placeholder for anything necessary to turn the raw data into the standardized format. You should ensure that the data gets the appropriate column names and that any attributes are in the correct format (all of the standard labels can be found in the yaml or by pm.get_config()['target_x'] when replacing x by columns, countries, fueltypes, sets or technologies.

  6. Make sure the FOO entry is given in the configuration


    and load the file
  7. If everything works fine, you can run the whole matching process with


Getting Started

A small presentation of the tool is given in the jupyter notebook

How it works

Whereas single databases as the CARMA, GEO or the OPSD database provide non standardized and incomplete information, the datasets can complement each other and improve their reliability. In a first step, powerplantmatching converts all powerplant dataset into a standardized format with a defined set of columns and values. The second part consists of aggregating power plant blocks together into units. Since some of the datasources provide their powerplant records on unit level, without detailed information about lower-level blocks, comparing with other sources is only possible on unit level. In the third and name-giving step the tool combines (or matches)different, standardized and aggregated input sources keeping only powerplants units which appear in more than one source. The matched data afterwards is complemented by data entries of reliable sources which have not matched.

The aggregation and matching process heavily relies on DUKE, a java application specialized for deduplicating and linking data. It provides many built-in comparators such as numerical, string or geoposition comparators. The engine does a detailed comparison for each single argument (power plant name, fuel-type etc.) using adjusted comparators and weights. From the individual scores for each column it computes a compound score for the likeliness that the two powerplant records refer to the same powerplant. If the score exceeds a given threshold, the two records of the power plant are linked and merged into one data set.

Let's make that a bit more concrete by giving a quick example. Consider the following two data sets

Dataset 1:

Name Fueltype Classification Country Capacity lat lon File
0 Aarberg Hydro nan Switzerland 14.609 47.0444 7.27578 nan
1 Abbey mills pumping Oil nan United Kingdom 6.4 51.687 -0.0042057 nan
2 Abertay Other nan United Kingdom 8 57.1785 -2.18679 nan
3 Aberthaw Coal nan United Kingdom 1552.5 51.3875 -3.40675 nan
4 Ablass Wind nan Germany 18 51.2333 12.95 nan
5 Abono Coal nan Spain 921.7 43.5588 -5.72287 nan


Dataset 2:

Name Fueltype Classification Country Capacity lat lon File
0 Aarberg Hydro nan Switzerland 15.5 47.0378 7.272 nan
1 Aberthaw Coal Thermal United Kingdom 1500 51.3873 -3.4049 nan
2 Abono Coal Thermal Spain 921.7 43.5528 -5.7231 nan
3 Abwinden asten Hydro nan Austria 168 48.248 14.4305 nan
4 Aceca Oil CHP Spain 629 39.941 -3.8569 nan
5 Aceca fenosa Natural Gas CCGT Spain 400 39.9427 -3.8548 nan

where Dataset 2 has the higher reliability score. Apparently entries 0, 3 and 5 of Dataset 1 relate to the same power plants as the entries 0,1 and 2 of Dataset 2. The toolset detects those similarities and combines them into the following set, but prioritising the values of Dataset 2:

Name Country Fueltype Classification Capacity lat lon File
0 Aarberg Switzerland Hydro nan 15.5 47.0378 7.272 nan
1 Aberthaw United Kingdom Coal Thermal 1500 51.3873 -3.4049 nan
2 Abono Spain Coal Thermal 921.7 43.5528 -5.7231 nan

Citing powerplantmatching

If you want to cite powerplantmatching, use the following paper

with bibtex

	title = {Performing energy modelling exercises in a transparent way - {The} issue of data quality in power plant databases},
	volume = {23},
	issn = {2211467X},
	url = {},
	doi = {10.1016/j.esr.2018.11.004},
	language = {en},
	urldate = {2018-12-03},
	journal = {Energy Strategy Reviews},
	author = {Gotzens, Fabian and Heinrichs, Heidi and Hörsch, Jonas and Hofmann, Fabian},
	month = jan,
	year = {2019},
	pages = {1--12}

and/or the current release stored on Zenodo with a release-specific DOI:



The development of powerplantmatching was helped considerably by in-depth discussions and exchanges of ideas and code with

  • Tom Brown from Karlsruhe Institute for Technology
  • Chris Davis from University of Groningen and
  • Johannes Friedrich, Roman Hennig and Colin McCormick of the World Resources Institute


Copyright 2018-2020 Fabian Gotzens (FZ Jülich), Jonas Hörsch (KIT), Fabian Hofmann (FIAS)

powerplantmatching is released as free software under the GPLv3, see LICENSE for further information.

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