Skip to main content

Toolset for generating and managing Power Plant Data

Project description

powerplantmatching

pypi conda pythonversion LICENSE DOI doc pre-commit.ci status Stack Exchange questions

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 combined dataset, which includes all the important information. The package allows to easily update the combined data as soon as new input datasets are released.

You can directly download the current version of the data as a CSV file.

Initially, powerplantmatching was developed by the Renewable Energy Group at FIAS and is now maintained by the Digital Transformation in Energy Systems Group at the Technical University of Berlin to build power plant data inputs to PyPSA-based models for carrying out simulations.

Main Features

  • clean and standardize power plant data sets
  • aggregate power plant 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 gross/net capacity
  • provide an already merged data set of multiple different open 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-based model

Installation

Using pip

pip install powerplantmatching

or conda

conda install -c conda-forge powerplantmatching

Contributing and Support

We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.

Citing powerplantmatching

If you want to cite powerplantmatching, use the following paper

with bibtex

@article{gotzens_performing_2019,
 title = {Performing energy modelling exercises in a transparent way - {The} issue of data quality in power plant databases},
 volume = {23},
 issn = {2211467X},
 url = {https://linkinghub.elsevier.com/retrieve/pii/S2211467X18301056},
 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:

DOI

Licence

Copyright 2018-2022 Fabian Hofmann (EnSys TU Berlin), Fabian Gotzens (FZ Jülich), Jonas Hörsch (KIT),

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

powerplantmatching-0.5.10.tar.gz (716.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

powerplantmatching-0.5.10-py2.py3-none-any.whl (722.0 kB view details)

Uploaded Python 2Python 3

File details

Details for the file powerplantmatching-0.5.10.tar.gz.

File metadata

  • Download URL: powerplantmatching-0.5.10.tar.gz
  • Upload date:
  • Size: 716.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.7

File hashes

Hashes for powerplantmatching-0.5.10.tar.gz
Algorithm Hash digest
SHA256 8030e217ad3e2fc3a54368b4f2d1c61e871ad0c77112351aa55030a3fb2751fe
MD5 2cb2458bb33a6fac7937cf5b51062105
BLAKE2b-256 81c202cec152020903e5c43ddbf587eef895eda6fdbb66b8f94bdb5d2ce13272

See more details on using hashes here.

File details

Details for the file powerplantmatching-0.5.10-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for powerplantmatching-0.5.10-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 d589504f0c4b51ae0330925331809d1155112f4c86ea24b173a41089b2e223e6
MD5 6846d985fc6175b7967ced6a529d2ca1
BLAKE2b-256 c75ce96ffde3235f06658fcd5dd01c279a2ca36f71f8de9dcb4450b0f28d0a2f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page