Model Factsheet

General Information

Name	pandapower
Acronym	pandapower
Methodical Focus	Network calculation
Institution(s)	University of Kassel; Fraunhofer IWES; Kassel
Author(s) (institution, working field, active time period)	Lead developers: Leon Thurner (University of Kassel); Alexander Scheidler (Fraunhofer IWES)
Current contact person	Leon Thurner
Contact (e-mail)	leon.thurner@uni-kassel.de
Website	http://www.pandapower.org/
Logo
Primary Purpose	Detailed modeling of power systems, static and quasi-static power system analysis and optimization including power flow, state estimation, topological analysis and short-circuit current analysis
Primary Outputs	Bus voltages, line currents, component loading, power injections and generation
Support / Community / Forum
Framework
Link to User Documentation	http://pandapower.readthedocs.io
Link to Developer/Code Documentation	https://github.com/lthurner/pandapower
Documentation quality	excellent
Source of funding	development within several projects at University of Kassel / Fraunhofer IWES
Number of developers	less than 50
Number of users	less than 1000

Openness

Open Source
License	BSD 3-clause Clear license
Source code available
GitHub
Access to source code	https://github.com/e2nIEE/pandapower
Data provided	none
Collaborative programming

GitHub Organisation
GitHub Contributions Graph

Software

Modelling software	Python
Internal data processing software	Pandas; numpy; scipy; pypower; networkx
External optimizer
Additional software
GUI

Coverage

Modeled energy sectors (final energy)	electricity
Modeled demand sectors	Households, Industry, Commercial sector
Modeled technologies: components for power generation or conversion	Renewables - Conventional -
Modeled technologies: components for transfer, infrastructure or grid	Electricity distribution, transmission Gas - Heat -
Properties electrical grid	AC load flow, DC load flow
Modeled technologies: components for storage	-
User behaviour and demand side management
Changes in efficiency
Market models	-
Geographical coverage
Geographic (spatial) resolution	-
Time resolution	-
Comment on geographic (spatial) resolution
Observation period	-
Additional dimensions (sector)	-

Mathematical Properties

Model class (optimisation)	-
Model class (simulation)	-
Other
Short description of mathematical model class
Mathematical objective	-
Approach to uncertainty	-
Suited for many scenarios / monte-carlo
typical computation time	more than a day
Typical computation hardware	-
Technical data anchored in the model	-

Model Integration

Interfaces	API for defining networks, running power flow and optimal power flow analysis, running short-circuit calculation, running state estimation, plotting data with matplotlib or plotly
Model file format	possibilities to save grid models in .p (python pickle), .xls, .json
Input data file format	.py
Output data file format	possibilities to save grid models including results in .p (python pickle), .xls, .json
Integration with other models
Integration of other models

References

Citation reference	https://arxiv.org/abs/1709.06743
Citation DOI	-
Reference Studies/Models	"https://www.uni-kassel.de/eecs/fileadmin/datas/fb16/Fachgebiete/energiemanagement/Forschung/20170726_lthurner_preprint.pdf https://arxiv.org/abs/1711.03331"
Example research questions	optimal planning and operation considering the integration of distributed generation in future power systems
Model usage	pandapower is used by universities and scientific institutes all over the world
Model validation	-
Example research questions	optimal planning and operation considering the integration of distributed generation in future power systems
further properties
Model specific properties	Detailed modeling and analysis of power systems with many element models that are otherwise only found in commercial power system analysis tools. Modeling of power systems with nameplate parameters and standard type libraries.

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