General Structure

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

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

Data Sources

• OPSD - Open Power System Data publish their data under a free license

• GEO - Global Energy Observatory, the data is not directly available on the website, but can be obtained from an sqlite scraper

• GPD - Global Power Plant Database provide their data under a free license

• GBPT - `Global Bioenergy Powerplant Tracker by Global Energy Monitor<https://globalenergymonitor.org/projects/global-bioenergy-power-tracker/>`__

• GCPT - Global Coal Powerplant Tracker by Global Energy Monitor

• GGPT - Global Gas Powerplant Tracker by Global Energy Monitor

• GGTPT - Global Geothermal Powerplant Tracker by Global Energy Monitor

• GNPT - Global Nuclear Powerplant Tracker by Global Energy Monitor

• GSPT - Global Solar Powerplant Tracker by Global Energy Monitor

• GWPT - Global Wind Powerplant Tracker by Global Energy Monitor

• CARMA - Carbon Monitoring for Action

• ENTSOe - European Network of Transmission System Operators for Electricity, annually provides statistics about aggregated power plant capacities. Their data can be used as a validation reference. We further use their annual energy generation report from 2010 as an input for the hydro power plant classification. The power plant dataset on the ENTSO-E transparency website is downloaded using the ENTSO-E Transparency API.

• JRC - Joint Research Centre Hydro-power plants database

• IRENA - International Renewable Energy Agency open available statistics on power plant capacities.

• BNETZA - Bundesnetzagentur open available data source for Germany’s power plants

• UBA (Umwelt Bundesamt Datenbank “Kraftwerke in Deutschland)

Not available but supported sources:

• IWPDCY (International Water Power & Dam Country Yearbook)

• WEPP (Platts, World Elecrtric Power Plants Database)

Reliabilty Score

When the matched power plant entries from different sources are combined, the resulting value per column is determined by the most reliable source. The corresponding reliability scores are:

Dataset	Reliabilty score
JRC	6
ESE	6
UBA	5
OPSD	5
OPSD_EU	5
OPSD_DE	5
WEPP	4
ENTSOE	4
IWPDCY	3
GPD	3
GEO	3
BNETZA	3
CARMA	1

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

and

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