def cluster_on_extra_high_voltage(network, busmap, with_time=True):
network_c = Network()
buses = aggregatebuses(network, busmap, {
'x': _leading(busmap, network.buses),
'y': _leading(busmap, network.buses)
})
# keep attached lines
lines = network.lines.copy()
mask = lines.bus0.isin(buses.index)
lines = lines.loc[mask, :]
# keep attached transformer
transformers = network.transformers.copy()
mask = transformers.bus0.isin(buses.index)
transformers = transformers.loc[mask, :]
io.import_components_from_dataframe(network_c, buses, "Bus")
io.import_components_from_dataframe(network_c, lines, "Line")
io.import_components_from_dataframe(network_c, transformers, "Transformer")
if with_time:
network_c.now = network.now
network_c.set_snapshots(network.snapshots)
# dealing with generators
network.generators['weight'] = 1
new_df, new_pnl = aggregategenerators(network, busmap, with_time)
io.import_components_from_dataframe(network_c, new_df, 'Generator')
for attr, df in iteritems(new_pnl):
io.import_series_from_dataframe(network_c, df, 'Generator', attr)
# dealing with all other components
aggregate_one_ports = components.one_port_components.copy()
aggregate_one_ports.discard('Generator')
for one_port in aggregate_one_ports:
new_df, new_pnl = aggregateoneport(network,
busmap,
component=one_port,
with_time=with_time)
io.import_components_from_dataframe(network_c, new_df, one_port)
for attr, df in iteritems(new_pnl):
io.import_series_from_dataframe(network_c, df, one_port, attr)
network_c.determine_network_topology()
return network_c
def init_pypsa_network(time_range_lim):
"""
Instantiate PyPSA network
Parameters
----------
time_range_lim:
Returns
-------
network: PyPSA network object
Contains powerflow problem
snapshots: iterable
Contains snapshots to be analyzed by powerplow calculation
"""
network = Network()
network.set_snapshots(time_range_lim)
snapshots = network.snapshots
return network, snapshots
def cluster_on_extra_high_voltage(network, busmap, with_time=True):
""" Create a new clustered pypsa.Network given a busmap mapping all busids
to other busids of the same set.
Parameters
----------
network : pypsa.Network
Container for all network components.
busmap : dict
Maps old bus_ids to new bus_ids.
with_time : bool
If true time-varying data will also be aggregated.
Returns
-------
network : pypsa.Network
Container for all network components.
"""
network_c = Network()
buses = aggregatebuses(network, busmap, {
'x': _leading(busmap, network.buses),
'y': _leading(busmap, network.buses)
})
# keep attached lines
lines = network.lines.copy()
mask = lines.bus0.isin(buses.index)
lines = lines.loc[mask, :]
# keep attached transformer
transformers = network.transformers.copy()
mask = transformers.bus0.isin(buses.index)
transformers = transformers.loc[mask, :]
io.import_components_from_dataframe(network_c, buses, "Bus")
io.import_components_from_dataframe(network_c, lines, "Line")
io.import_components_from_dataframe(network_c, transformers, "Transformer")
if with_time:
network_c.snapshots = network.snapshots
network_c.set_snapshots(network.snapshots)
# dealing with generators
network.generators.control = "PV"
network.generators['weight'] = 1
new_df, new_pnl = aggregategenerators(network, busmap, with_time)
io.import_components_from_dataframe(network_c, new_df, 'Generator')
for attr, df in iteritems(new_pnl):
io.import_series_from_dataframe(network_c, df, 'Generator', attr)
# dealing with all other components
aggregate_one_ports = components.one_port_components.copy()
aggregate_one_ports.discard('Generator')
for one_port in aggregate_one_ports:
new_df, new_pnl = aggregateoneport(network,
busmap,
component=one_port,
with_time=with_time)
io.import_components_from_dataframe(network_c, new_df, one_port)
for attr, df in iteritems(new_pnl):
io.import_series_from_dataframe(network_c, df, one_port, attr)
network_c.determine_network_topology()
return network_c
def to_pypsa(network, mode, timesteps):
"""
Translate graph based grid representation to PyPSA Network
For details from a user perspective see API documentation of
:meth:`~.grid.network.EDisGo.analyze` of the API class
:class:`~.grid.network.EDisGo`.
Translating eDisGo's grid topology to PyPSA representation is structured
into tranlating the topology and adding time series for components of the
grid. In both cases translation of MV grid only (`mode='mv'`), LV grid only
(`mode='lv'`), MV and LV (`mode=None`) share some code. The
code is organized as follows
* Medium-voltage only (`mode='mv'`): All medium-voltage grid components are
exported by :func:`mv_to_pypsa` including the LV station. LV grid load
and generation is considered using :func:`add_aggregated_lv_components`.
Time series are collected by `_pypsa_load_timeseries` (as example
for loads, generators and buses) specifying `mode='mv'`). Timeseries
for aggregated load/generation at substations are determined individually.
* Low-voltage only (`mode='lv'`): LV grid topology including the MV-LV
transformer is exported. The slack is defind at primary side of the MV-LV
transformer.
* Both level MV+LV (`mode=None`): The entire grid topology is translated to
PyPSA in order to perform a complete power flow analysis in both levels
together. First, both grid levels are translated seperately using
:func:`mv_to_pypsa` and :func:`lv_to_pypsa`. Those are merge by
:func:`combine_mv_and_lv`. Time series are obtained at once for both grid
levels.
This PyPSA interface is aware of translation errors and performs so checks
on integrity of data converted to PyPSA grid representation
* Sub-graphs/ Sub-networks: It is ensured the grid has no islanded parts
* Completeness of time series: It is ensured each component has a time
series
* Buses available: Each component (load, generator, line, transformer) is
connected to a bus. The PyPSA representation is check for completeness of
buses.
* Duplicate labels in components DataFrames and components' time series
DataFrames
Parameters
----------
network : Network
eDisGo grid container
mode : str
Determines grid levels that are translated to
`PyPSA grid representation
<https://www.pypsa.org/doc/components.html#network>`_. Specify
* None to export MV and LV grid levels. None is the default.
* ('mv' to export MV grid level only. This includes cumulative load and
generation from underlying LV grid aggregated at respective LV
station. This option is implemented, though the rest of edisgo does
not handle it yet.)
* ('lv' to export LV grid level only. This option is not yet
implemented)
timesteps : :pandas:`pandas.DatetimeIndex<datetimeindex>` or :pandas:`pandas.Timestamp<timestamp>`
Timesteps specifies which time steps to export to pypsa representation
and use in power flow analysis.
Returns
-------
PyPSA Network
"""
# check if timesteps is array-like, otherwise convert to list (necessary
# to obtain a dataframe when using .loc in time series functions)
if not hasattr(timesteps, "__len__"):
timesteps = [timesteps]
# get topology and time series data
if mode is None:
mv_components = mv_to_pypsa(network)
lv_components = lv_to_pypsa(network)
components = combine_mv_and_lv(mv_components, lv_components)
if list(components['Load'].index.values):
timeseries_load_p_set = _pypsa_load_timeseries(network,
mode=mode,
timesteps=timesteps)
if len(list(components['Generator'].index.values)) > 1:
timeseries_gen_p_min, timeseries_gen_p_max = \
_pypsa_generator_timeseries(
network, mode=mode, timesteps=timesteps)
timeseries_storage_p_min, timeseries_storage_p_max = \
_pypsa_storage_timeseries(
network, mode=mode, timesteps=timesteps)
if list(components['Bus'].index.values):
timeseries_bus_v_set = _pypsa_bus_timeseries(
network, components['Bus'].index.tolist(), timesteps=timesteps)
else:
raise ValueError("Provide proper mode or leave it empty to export "
"entire grid topology.")
# check topology
_check_topology(components)
# create power flow problem
pypsa_network = PyPSANetwork()
pypsa_network.edisgo_mode = mode
pypsa_network.set_snapshots(timesteps)
# import grid topology to PyPSA network
# buses are created first to avoid warnings
pypsa_network.import_components_from_dataframe(components['Bus'], 'Bus')
for k, comps in components.items():
if k is not 'Bus' and not comps.empty:
pypsa_network.import_components_from_dataframe(comps, k)
# import time series to PyPSA network
if len(list(components['Generator'].index.values)) > 1:
import_series_from_dataframe(pypsa_network, timeseries_gen_p_min,
'Generator', 'p_min_pu')
import_series_from_dataframe(pypsa_network, timeseries_gen_p_max,
'Generator', 'p_max_pu')
import_series_from_dataframe(pypsa_network, timeseries_storage_p_min,
'Generator', 'p_min_pu')
import_series_from_dataframe(pypsa_network, timeseries_storage_p_max,
'Generator', 'p_max_pu')
if list(components['Load'].index.values):
import_series_from_dataframe(pypsa_network, timeseries_load_p_set,
'Load', 'p_set')
if list(components['Bus'].index.values):
import_series_from_dataframe(pypsa_network, timeseries_bus_v_set,
'Bus', 'v_mag_pu_set')
_check_integrity_of_pypsa(pypsa_network)
return pypsa_network