edisgo.tools.powermodels_io

Module Contents

Functions

to_powermodels(pypsa_net)	Convert pypsa network to network dictionary format, using the pypower
convert_storage_series(timeseries)
add_storage_from_edisgo(edisgo_obj, psa_net, pm_dict)	Read static storage data (position and capacity) from eDisGo and export to
pypsa2ppc(psa_net)	Converter from pypsa data structure to pypower data structure
ppc2pm(ppc, psa_net)	converter from pypower datastructure to powermodels dictionary,

edisgo.tools.powermodels_io.to_powermodels(pypsa_net)

Convert pypsa network to network dictionary format, using the pypower structure as an intermediate steps

powermodels network dictionary: https://lanl-ansi.github.io/PowerModels.jl/stable/network-data/

pypower caseformat: https://github.com/rwl/PYPOWER/blob/master/pypower/caseformat.py

Parameters:

pypsa_net

Returns:

edisgo.tools.powermodels_io.convert_storage_series(timeseries)

edisgo.tools.powermodels_io.add_storage_from_edisgo(edisgo_obj, psa_net, pm_dict)

Read static storage data (position and capacity) from eDisGo and export to Powermodels dict

edisgo.tools.powermodels_io.pypsa2ppc(psa_net)

Converter from pypsa data structure to pypower data structure

adapted from pandapower’s pd2ppc converter

https://github.com/e2nIEE/pandapower/blob/911f300a96ee0ac062d82f7684083168ff052586/pandapower/pd2ppc.py

edisgo.tools.powermodels_io.ppc2pm(ppc, psa_net)

converter from pypower datastructure to powermodels dictionary,

adapted from pandapower to powermodels converter: https://github.com/e2nIEE/pandapower/blob/develop/pandapower/converter/pandamodels/to_pm.py

Parameters:

ppc

Returns: