etrago.tools package¶

etrago.tools.constraints module¶

Constraints.py includes additional constraints for eTraGo-optimizations

etrago.tools.constraints.read_max_gas_generation(self)[source]¶

Return the values limiting the gas production in Germany

Read max_gas_generation_overtheyear from scenario.egon_scenario_parameters if the table is available in the database and return the dictionnary containing the values needed for the constraints to limit the gas production in Germany, depending of the scenario.

Returns:: arg
Return type:: dict

etrago.tools.constraints.add_ch4_constraints_linopy(self, network, snapshots)[source]¶

Add CH4 constraints for optimization with linopy

Functionality that limits the dispatch of CH4 generators. In Germany, there is one limitation specific for biogas and one limitation specific for natural gas (natural gas only in eGon2035). Abroad, each generator has its own limitation contains in the column e_nom_max.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps considered in the optimization

Return type:

None.

etrago.tools.constraints.add_ch4_constraints(self, network, snapshots)[source]¶

Add CH4 constraints for optimization with pyomo

Functionality that limits the dispatch of CH4 generators. In Germany, there is one limitation specific for biogas and one limitation specific for natural gas (natural gas only in eGon2035). Abroad, each generator has its own limitation contains in the column e_nom_max.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps considered in the optimization

Return type:

None.

etrago.tools.constraints.add_ch4_constraints_nmp(self, network, snapshots)[source]¶

Add CH4 constraints for optimization without pyomo

Functionality that limits the dispatch of CH4 generators. In Germany, there is one limitation specific for biogas and one limitation specific for natural gas (natural gas only in eGon2035). Abroad, each generator has its own limitation contains in the column e_nom_max.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps considered in the optimization

Return type:

None.

etrago.tools.constraints.add_biomass_constraint(self, network, snapshots)[source]¶

etrago.tools.constraints.add_biomass_constraint_linopy(self, network, snapshots)[source]¶

etrago.tools.constraints.snapshot_clustering_daily_bounds(self, network, snapshots)[source]¶

Bound the storage level to 0.5 max_level every 24th hour.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps that will be constrained

Return type:

None

etrago.tools.constraints.snapshot_clustering_daily_bounds_nmp(self, network, snapshots)[source]¶

Bound the storage level to 0.5 max_level every 24th hour.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps that will be constrained

Return type:

None

etrago.tools.constraints.snapshot_clustering_seasonal_storage(self, network, snapshots, simplified=False)[source]¶

Depicts intertemporal dependencies of storage units and stores when using snapshot clustering to typical periods for temporal complexity reduction.

According to:: L. Kotzur et al: ‘Time series aggregation for energy system design: Modeling seasonal storage’, 2018

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (list) – A list of datetime objects representing the timestamps of the snapshots to be clustered.
simplified (bool, optional) – A flag indicating whether to use a simplified version of the model that does not include intra-temporal constraints and variables.

Return type:

None

etrago.tools.constraints.snapshot_clustering_seasonal_storage_hourly(self, network, snapshots)[source]¶

Depicts intertemporal dependencies of storage units and stores when using snapshot clustering to typical periods for temporal complexity reduction.

According to:: L. Kotzur et al: ‘Time series aggregation for energy system design: Modeling seasonal storage’, 2018

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (list) – A list of datetime objects representing the timestamps of the snapshots to be clustered.

Return type:

None

etrago.tools.constraints.snapshot_clustering_seasonal_storage_nmp(self, n, sns, simplified=False)[source]¶

Depicts intertemporal dependencies of storage units and stores when using snapshot clustering to typical periods for temporal complexity reduction.

According to:: L. Kotzur et al: ‘Time series aggregation for energy system design: Modeling seasonal storage’, 2018

Parameters:

n (pypsa.Network) – Overall container of PyPSA
sns (list) – A list of datetime objects representing the timestamps of the snapshots to be clustered.
simplified (bool, optional) – A flag indicating whether to use a simplified version of the model that does not include intra-temporal constraints and variables.

Return type:

None

etrago.tools.constraints.snapshot_clustering_seasonal_storage_hourly_nmp(self, n, sns)[source]¶

Depicts intertemporal dependencies of storage units and stores when using snapshot clustering to typical periods for temporal complexity reduction.

According to:: L. Kotzur et al: ‘Time series aggregation for energy system design: Modeling seasonal storage’, 2018

Parameters:

n (pypsa.Network) – Overall container of PyPSA
sns (list) – A list of datetime objects representing the timestamps of the snapshots to be clustered.

Return type:

None

etrago.tools.constraints.split_dispatch_disaggregation_constraints(self, n, sns)[source]¶

Add constraints for state of charge of storage units and stores when separating the optimization into smaller subproblems while conducting thedispatch_disaggregation in temporally fully resolved network

The state of charge at the end of each slice is set to the value calculated in the optimization with the temporally reduced network to account to ensure compatibility and to reproduce saisonality

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps considered in the optimization

Return type:

None.

etrago.tools.constraints.split_dispatch_disaggregation_constraints_nmp(self, n, sns)[source]¶

etrago.tools.constraints.fixed_storage_unit_soc_at_the_end(n, sns)[source]¶

Defines energy balance constraints for storage units. In principal the constraints states:

previous_soc + p_store - p_dispatch + inflow - spill == soc

etrago.tools.constraints.fixed_storage_unit_soc_at_horizon_end(self, n, sns)[source]¶

Sets soc of seasonal storage units (reservoir) at the end of each horizon to the one from the pre market model allowing a small flexibility

1.01 * soc_pre_market >= soc_market >= 0.99 * soc_pre_market

class etrago.tools.constraints.Constraints(args, conduct_dispatch_disaggregation, apply_on='grid_model', cluster_temporal=None, cluster_ts=None)[source]¶

Bases: object

Methods

functionality(network, snapshots)

Add constraints to pypsa-model using extra-functionality.

functionality(network, snapshots)[source]¶

Add constraints to pypsa-model using extra-functionality. Serveral constraints can be choosen at once. Possible constraints are set and described in the above functions.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
snapshots (pandas.DatetimeIndex) – List of timesteps considered in the optimization

etrago.tools.constraints.add_chp_constraints_nmp(n)[source]¶

Limits the dispatch of combined heat and power links based on T.Brown et. al : Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system, 2018

Parameters:: n (pypsa.Network) – Network container
Return type:: None.

etrago.tools.constraints.add_electrolysis_coupling_constraints(network, snapshots)[source]¶

etrago.tools.constraints.add_chp_constraints_simplyfied(network, snapshots)[source]¶

etrago.tools.constraints.add_chp_constraints(network, snapshots)[source]¶

Limits the dispatch of combined heat and power links based on T.Brown et. al : Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system, 2018

Parameters:

network (pypsa.Network) – Network container
snapshots (pandas.DataFrame) – Timesteps to optimize

Return type:

None.

etrago.tools.constraints.add_chp_constraints_linopy(network, snapshots)[source]¶

Limits the dispatch of combined heat and power links based on T.Brown et. al : Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system, 2018

Parameters:

network (pypsa.Network) – Network container
snapshots (pandas.DataFrame) – Timesteps to optimize

Return type:

None.

etrago.tools.constraints.couple_distribution_links(self, n, snapshots)[source]¶

Couple bidirectional distribution grid links such that capacity expansion is identical in both directions.

This ensures that any additional capacity built between transmission and distribution grids is shared physically, i.e. an expansion of e.g. 10 MW applies to both flow directions equally.

The function automatically detects whether the optimization backend is Pyomo or Linopy based on:

self.args[“method”] ∈ {“pyomo”, “linopy”}

Parameters:

n (pypsa.Network) – The PyPSA network instance.
snapshots (pandas.Index) – Snapshots passed by PyPSA during optimization. (Not used directly but required by the interface.)

Notes

Existing capacities (p_nom) are NOT coupled. Only expansion variables are linked.
Investment costs should only be assigned to one direction of each link pair to avoid double counting.
Link pairs are identified via naming convention:

{bus}_to_distribution_grid {bus}_from_distribution_grid
Only links with carrier == “distribution_grid” are considered.

Raises:: ValueError – If an unknown optimization method is specified.

etrago.tools.distribution_grids module¶

Defines function to seperate transmission grid from distribution grid in eTraGo

etrago.tools.distribution_grids.get_ac_nodes_germany(self)[source]¶

Filters AC buses within Germany

Returns:: Indices of AC buses within Germany.
Return type:: pd.Series

etrago.tools.distribution_grids.distribution_grid_buses_and_links(self, mv_grids, seperate_dg_link=True)[source]¶

Adds buses and links modeling simplified distribution grids. The parametrization is derived from bottom-up simulations of distribution grids with eDisGo/eGo.

Parameters:

mv_grids (pd.DataFrame) – Medium voltage grid districts.
seperate_dg_link (boolean, optional) – Select if distribution grids are modeled as one bi-directional link or as two coupled uni-directional link. The default is True.

Return type:

None.

etrago.tools.distribution_grids.seperate_power_plants(self, egon_power_plants, old_network)[source]¶

Divides power plants by grid level (transmission or distribution grid) and connects them to the corresponding bus.

Parameters:

egon_power_plants (sqlalchemy.ext.declarative.api.DeclarativeMeta) – Pointer to table containing non-aggregated power plants.
old_network (pypsa.Network) – Previous network without adjustments.

Return type:

None.

etrago.tools.distribution_grids.seperate_chp(self, egon_chp_plants, egon_district_heating_areas)[source]¶

Divides CHP plants by grid level (transmission or distribution grid) and connects them to the corresponding bus.

Parameters:

egon_chp_plants (sqlalchemy.ext.declarative.api.DeclarativeMeta) – Pointer to table containing non-aggregated CHP plants.
egon_district_heating_areas (sqlalchemy.ext.declarative.api.DeclarativeMeta) – Pointer to table containing district heating areas.

Return type:

None

etrago.tools.distribution_grids.seperate_demands(self, mv_grids, egon_osm_ind_load_curves_individual, egon_sites_ind_load_curves_individual)[source]¶

Divides electrical loads by grid level (transmission or distribution grid) and connects them to the corresponding bus.

Parameters:

mv_grids (pd.DataFrame) – Medium voltage grid districts.
egon_osm_ind_load_curves_individual – sqlalchemy.ext.declarative.api.DeclarativeMeta Pointer to table containing industrial load curves at OSM areas.
egon_sites_ind_load_curves_individual – sqlalchemy.ext.declarative.api.DeclarativeMeta Pointer to table containing industrial load curves at points.

Return type:

None.

etrago.tools.distribution_grids.seperate_storage_units(self, mv_grids)[source]¶

Divides storage units by grid level (transmission or distribution grid) and connects them to the corresponding bus.

Parameters:: mv_grids (pd.DataFrame) – Medium voltage grid districts.
Return type:: None.

etrago.tools.distribution_grids.add_simplified_distribution_grids(self)[source]¶

Adds simplified distrubution grids to each HV/MV substation in the transmission grid model. Load, generation and storage units are connected to the corresponding grid level based on not-aggregated egon-data results.

Return type:: None.

etrago.tools.extendable module¶

Extendable.py defines function to set PyPSA components extendable.

etrago.tools.extendable.extendable(self, grid_max_D=None, grid_max_abs_D={'110': {'circuits': 2, 'i': 1020, 'wires': 4}, '220': {'circuits': 4, 'i': 1020, 'wires': 4}, '380': {'circuits': 4, 'i': 1020, 'wires': 4}, 'dc': 0}, grid_max_foreign=4, grid_max_abs_foreign=None)[source]¶

Function that sets selected components extendable.

Parameters:

grid_max_D (int, optional) – Upper bounds for electrical grid expansion relative to existing capacity. The default is None.
grid_max_abs_D (dict, optional) – Absolute upper bounds for electrical grid expansion in Germany.
grid_max_foreign (int, optional) – Upper bounds for expansion of electrical foreign lines relative to the existing capacity. The default is 4.
grid_max_abs_foreign (dict, optional) – Absolute upper bounds for expansion of foreign electrical grid. The default is None.

Return type:

None.

etrago.tools.extendable.snommax(i=1020, u=380, wires=4, circuits=4)[source]¶

Function to calculate limitation for capacity expansion.

Parameters:

i (int, optional) – Current. The default is 1020.
u (int, optional) – Voltage level. The default is 380.
wires (int, optional) – Number of wires per line. The default is 4.
circuits (int, optional) – Number of circuits. The default is 4.

Returns:

s_nom_max – Limitation for capacity expansion.

Return type:

float

etrago.tools.extendable.line_max_abs(network, buses, line_max_abs={'110': {'circuits': 2, 'i': 1020, 'wires': 4}, '220': {'circuits': 4, 'i': 1020, 'wires': 4}, '380': {'circuits': 4, 'i': 1020, 'wires': 4}, 'dc': 0})[source]¶

Function to calculate limitation for capacity expansion of lines in network.

Parameters:

network (pypsa.Network object) – Container for all network components.
buses (pypsa.Network buses) – Considered buses in network.
line_max_abs (dict, optional) – Line parameters considered to calculate maximum capacity.

Return type:

None.

etrago.tools.extendable.transformer_max_abs(network, buses)[source]¶

Function to calculate limitation for capacity expansion of transformers in network.

Parameters:

network (pypsa.Network object) – Container for all network components.
buses (pypsa.Network buses) – Considered buses in network.

Return type:

None.

etrago.tools.extendable.extension_preselection(etrago, method, days=3)[source]¶

Function that preselects lines which are extendend in snapshots leading to overloading to reduce nubmer of extension variables.

Parameters:

network (pypsa.Network object) – Container for all network components.
args (dict) – Arguments set in appl.py.
method (str) – Choose method of selection: ‘extreme_situations’ for remarkable timsteps (e.g. minimal resiudual load) ‘snapshot_clustering’ for snapshot clustering with number of days
days (int) – Number of clustered days, only used when method = ‘snapshot_clustering’

Returns:

network – Container for all network components.

Return type:

pypsa.Network object

etrago.tools.extendable.print_expansion_costs(network)[source]¶

Function that prints network and storage investment costs.

Parameters:: network (pypsa.Network object) – Container for all network components.
Return type:: None.

etrago.tools.io module¶

io.py

Input/output operations between powerflow schema in the oedb and PyPSA. Additionally oedb wrapper classes to instantiate PyPSA network objects.

etrago.tools.io.packagename¶

Package containing orm class definitions

Type:: str

etrago.tools.io.temp_ormclass¶

Orm class name of table with temporal resolution

Type:: str

etrago.tools.io.carr_ormclass¶

Orm class name of table with carrier id to carrier name datasets

Type:: str

Notes

A configuration file connecting the chosen optimization method with components to be queried is needed for NetworkScenario class.

class etrago.tools.io.ScenarioBase(engine, session, version=None)[source]¶

Bases: object

Base class to address the dynamic provision of orm classes representing powerflow components from egoio

Parameters:

session (sqla.orm.session.Session) – Handles conversations with the database.
version (str) – Version number of data version control in grid schema of the oedb.

class etrago.tools.io.NetworkScenario(engine, session, scn_name='Status Quo', start_snapshot=1, end_snapshot=20, temp_id=1, scenario_extension=False, **kwargs)[source]¶

Bases: ScenarioBase

Adapter class between oedb powerflow data and PyPSA. Provides the method build_network to generate a pypsa.Network.

Parameters:

scn_name (str) – Scenario name.
method (str) – Objective function.
start_snapshot (int) – First snapshot or timestep.
end_snapshot (int) – Last timestep.
temp_id (int) – Nummer of temporal resolution.

Methods

`build_network`([network])	Core method to construct PyPSA Network object.
`configure_timeindex`()	Construct a DateTimeIndex with the queried temporal resolution, start- and end_snapshot.
`fetch_by_relname`(name)	Construct DataFrame with component data from filtered table data.
`series_fetch_by_relname`(network, name, ...)	Construct DataFrame with component timeseries data from filtered table data.

id_to_source

configure_timeindex()[source]¶: Construct a DateTimeIndex with the queried temporal resolution, start- and end_snapshot.

id_to_source()[source]¶

fetch_by_relname(name)[source]¶

Construct DataFrame with component data from filtered table data.

Parameters:: name (str) – Component name.
Returns:: Component data.
Return type:: pd.DataFrame

series_fetch_by_relname(network, name, pypsa_name)[source]¶

Construct DataFrame with component timeseries data from filtered table data.

Parameters:

name (str) – Component name.
column (str) – Component field with timevarying data.

Returns:

Component data.

Return type:

pd.DataFrame

build_network(network=None, *args, **kwargs)[source]¶: Core method to construct PyPSA Network object.

etrago.tools.io.clear_results_db(session)[source]¶: Used to clear the result tables in the OEDB. Caution! This deletes EVERY RESULT SET!

etrago.tools.io.results_to_oedb(session, network, args, grid='hv', safe_results=False)[source]¶

Return results obtained from PyPSA to oedb

Parameters:

session
network (PyPSA network container) – Holds topology of grid including results from powerflow analysis
args (dict) – Settings from appl.py
grid (str) – Choose voltage-level, currently only ‘hv’ implemented
safe_results (boolean) – If it is set to ‘True’ the result set will be saved to the versioned grid schema eventually apart from being saved to the model_draft by a SQL-script. ONLY set to True if you know what you are doing.

etrago.tools.io.run_sql_script(conn, scriptname='results_md2grid.sql')[source]¶: This function runs .sql scripts in the folder ‘sql_scripts’

etrago.tools.io.extension(self, **kwargs)[source]¶

Function that adds an additional network to the existing network container. The new network can include every PyPSA-component (e.g. buses, lines, links). To connect it to the existing network, transformers are needed.

All components and its timeseries of the additional scenario need to be inserted in the fitting ‘model_draft.ego_grid_pf_hv_extension_’ table. The scn_name in the tables have to be labled with ‘extension_’ + scn_name (e.g. ‘extension_nep2035’).

Until now, the tables include three additional scenarios: ‘nep2035_confirmed’: all new lines and needed transformers planed in the ‘Netzentwicklungsplan 2035’ (NEP2035) that have been confirmed by the Bundesnetzagentur (BNetzA)

‘nep2035_b2’: all new lines and needed transformers planned in the NEP 2035 in the scenario 2035 B2

‘BE_NO_NEP 2035’: DC-lines and transformers to connect the upcomming electrical-neighbours Belgium and Norway Generation, loads and its timeseries in Belgium and Norway for scenario ‘NEP 2035’

Parameters:

network – The existing network container (e.g. scenario ‘NEP 2035’)
session – session-data
overlay_scn_name – Name of the additional scenario (WITHOUT ‘extension_’)
start_snapshot
end_snapshot – Simulation time

Return type:

Network container including existing and additional network

etrago.tools.io.decommissioning(self, **kwargs)[source]¶

Function that removes components in a decommissioning-scenario from the existing network container. Currently, only lines can be decommissioned.

All components of the decommissioning scenario need to be inserted in the fitting ‘model_draft.ego_grid_pf_hv_extension_’ table. The scn_name in the tables have to be labled with ‘decommissioning_’ + scn_name (e.g. ‘decommissioning_nep2035’).

Parameters:

network – The existing network container (e.g. scenario ‘NEP 2035’)
session – session-data
overlay_scn_name – Name of the decommissioning scenario

Return type:

Network container including decommissioning

etrago.tools.io.distance(x0, x1, y0, y1)[source]¶

Function that calculates the square of the distance between two points.

Parameters:

x0 – x - coordinate of point 0
x1 – x - coordinate of point 1
y0 – y - coordinate of point 0
y1 – y - coordinate of point 1

Returns:

distance – square of distance

Return type:

float

etrago.tools.io.calc_nearest_point(bus1, network)[source]¶

Function that finds the geographical nearest point in a network from a given bus.

Parameters:

bus1 (float) – id of bus
network (Pypsa network container) – network including the comparable buses

Returns:

bus0 – bus_id of nearest point

Return type:

float

etrago.tools.io.add_ch4_h2_correspondence(self)[source]¶: Method adding the database table grid.egon_etrago_ch4_h2 to self. It contains the mapping from H2 buses to their corresponding CH4 buses.

etrago.tools.utilities module¶

Utilities.py includes a wide range of useful functions.

etrago.tools.utilities.filter_links_by_carrier(self, carrier, like=True)[source]¶

Parameters:

carrier (list or str) – name of the carriers of interest. Can be a list of carriers or single sting.
like (bool, optional) – When like set to True, the links with carrier names that includes the carrier(s) supplied are returned, Not just exact matches. The default is True.

Returns:

df – Dataframe that contains just links with carriers of the types given in the argument carrier.

Return type:

pandas.DataFrame object

etrago.tools.utilities.buses_of_vlvl(network, voltage_level)[source]¶

Get bus-ids of given voltage level(s).

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
voltage_level (list)

Returns:

List containing bus-ids.

Return type:

list

etrago.tools.utilities.buses_grid_linked(network, voltage_level)[source]¶

Get bus-ids of a given voltage level connected to the grid.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
voltage_level (list)

Returns:

List containing bus-ids.

Return type:

list

etrago.tools.utilities.geolocation_buses(self, apply_on='grid_model')[source]¶

If geopandas is installed: Use geometries of buses x/y(lon/lat) and polygons of countries from RenpassGisParameterRegion in order to locate the buses

Else: Use coordinats of buses to locate foreign buses, which is less accurate.

TODO: Why not alway use geopandas??

Parameters:

etrago (etrago.Etrago) – Transmission grid object
apply_on (str) – State if this function is applied on the grid_model or the market_model. The market_model options can only be used if the method type is “market_grid”.

etrago.tools.utilities.buses_by_country(self, apply_on='grid_model')[source]¶

Find buses of foreign countries using coordinates and return them as Pandas Series

Parameters:

self (Etrago object) – Overall container of PyPSA
apply_on (str) – State if this function is applied on the grid_model or the market_model. The market_model options can only be used if the method type is “market_grid”.

Return type:

None

etrago.tools.utilities.clip_foreign(network)[source]¶

Delete all components and timelines located outside of Germany. If applied after optimization, transborder flows divided by country of origin are added as network.foreign_trade.

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Returns:: network – Overall container of PyPSA
Return type:: :class:`pypsa.Network

etrago.tools.utilities.foreign_links(self)[source]¶

Change transmission technology of foreign lines from AC to DC (links).

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Returns:: network – Overall container of PyPSA
Return type:: :class:`pypsa.Network

etrago.tools.utilities.set_q_national_loads(self, cos_phi)[source]¶

Set q component of national loads based on the p component and cos_phi

Parameters:

network (pypsa.Network) – Overall container of PyPSA
cos_phi (float) – Choose ration of active and reactive power of foreign loads

Returns:

network – Overall container of PyPSA

Return type:

pypsa.Network

etrago.tools.utilities.set_q_foreign_loads(self, cos_phi)[source]¶

Set reative power timeseries of loads in neighbouring countries

Parameters:

etrago (:class:`etrago.Etrago) – Transmission grid object
cos_phi (float) – Choose ration of active and reactive power of foreign loads

Return type:

None

etrago.tools.utilities.connected_grid_lines(network, busids)[source]¶

Get grid lines connected to given buses.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
busids (list) – List containing bus-ids.

Returns:

PyPSA lines.

Return type:

class:`pandas.DataFrame

etrago.tools.utilities.connected_transformer(network, busids)[source]¶

Get transformer connected to given buses.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
busids (list) – List containing bus-ids.

Returns:

PyPSA transformer.

Return type:

class:`pandas.DataFrame

etrago.tools.utilities.load_shedding(self, temporal_disaggregation=False, negative_load_shedding=['Li_ion'], **kwargs)[source]¶

Implement load shedding in existing network to identify feasibility problems

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
temporal_disaggregation (bool) – It is set to False by default.
negative_load_shedding (False, list) – Define if generators for negative load shedding will be created. It is set to False by default. When activated supply a list of carriers e.g. [“AC”, “Li_ion”]

Return type:

None.

etrago.tools.utilities.set_control_strategies(network)[source]¶

Sets control strategies for AC generators and storage units

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Return type:: None.

etrago.tools.utilities.data_manipulation_sh(network)[source]¶

Adds missing components to run calculations with SH scenarios.

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Return type:: None

etrago.tools.utilities.export_to_csv(self, path)[source]¶

Write calculation results to csv-files in path.

Parameters:

network (pypsa.Network) – Overall container of PyPSA
args (dict) – Contains calculation settings of appl.py
path (str or False or None) – Choose path for csv-files. Specify “”, False or None to not do anything.

Return type:

None

etrago.tools.utilities.loading_minimization(network, snapshots)[source]¶

Minimizes the sum of the products of each element in the passive_branches of the model.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
snapshots ('pandas.core.indexes.datetimes.DatetimeIndex') – snapshots to perform the minimization

Return type:

None

etrago.tools.utilities.agg_series_lines(l0, network)[source]¶

Given a pandas DataFrame l0 containing information about lines in a network and a network object, aggregates the data in l0 for all its attributes. Returns a pandas Series containing the aggregated data.

Parameters:

l0 (pandas.DataFrame) – contain information about lines in a network.
network (:class:`pypsa.Network) – Overall container of PyPSA

Returns:

A pandas Series containing aggregated data for the lines in the network.

Return type:

pandas.Series

etrago.tools.utilities.group_parallel_lines(network)[source]¶

Function that groups parallel lines of the same voltage level to one line component representing all parallel lines

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Return type:: None.

etrago.tools.utilities.delete_dispensable_ac_buses(etrago)[source]¶

Function that identifies and delete AC buses without links, transformers, generators, loads, stores or storage_units, which also are connected to just one or two other buses

Parameters:: etrago (etrago object)
Return type:: None.

etrago.tools.utilities.delete_irrelevant_oneports(etrago)[source]¶

etrago.tools.utilities.set_line_costs(self, cost110=230, cost220=290, cost380=85, costDC=375)[source]¶

Set capital costs for extendable lines in respect to PyPSA [€/MVA]

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
args (dict) – containing settings from appl.py
cost110 – capital costs per km for 110kV lines and cables default: 230€/MVA/km, source: costs for extra circuit in dena Verteilnetzstudie, p. 146)
cost220 – capital costs per km for 220kV lines and cables default: 280€/MVA/km, source: costs for extra circuit in NEP 2025, capactity from most used 220 kV lines in model
cost380 – capital costs per km for 380kV lines and cables default: 85€/MVA/km, source: costs for extra circuit in NEP 2025, capactity from most used 380 kV lines in NEP
costDC – capital costs per km for DC-lines default: 375€/MVA/km, source: costs for DC transmission line in NEP 2035

etrago.tools.utilities.set_trafo_costs(self, cost110_220=7500, cost110_380=17333, cost220_380=14166)[source]¶

Set capital costs for extendable transformers in respect to PyPSA [€/MVA]

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
cost110_220 – capital costs for 110/220kV transformer default: 7500€/MVA, source: costs for extra trafo in dena Verteilnetzstudie, p. 146; S of trafo used in osmTGmod
cost110_380 – capital costs for 110/380kV transformer default: 17333€/MVA, source: NEP 2025
cost220_380 – capital costs for 220/380kV transformer default: 14166€/MVA, source: NEP 2025

etrago.tools.utilities.add_missing_components(self)[source]¶

Add a missing transformer at Heizkraftwerk Nord in Munich and a missing transformer in Stuttgart.

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA
Returns:: network – Overall container of PyPSA
Return type:: :class:`pypsa.Network

etrago.tools.utilities.convert_capital_costs(self)[source]¶

Convert capital_costs to fit to considered timesteps

Parameters:: etrago (:class:`etrago.Etrago) – Transmission grid object

etrago.tools.utilities.find_snapshots(network, carrier, maximum=True, minimum=True, n=3)[source]¶

Function that returns snapshots with maximum and/or minimum feed-in of selected carrier.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
carrier (str) – Selected carrier of generators
maximum (bool) – Choose if timestep of maximal feed-in is returned.
minimum (bool) – Choose if timestep of minimal feed-in is returned.
n (int) – Number of maximal/minimal snapshots

Returns:

calc_snapshots – List containing snapshots

Return type:

‘pandas.core.indexes.datetimes.DatetimeIndex’

etrago.tools.utilities.ramp_limits(network)[source]¶

Add ramping constraints to thermal power plants.

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA

etrago.tools.utilities.get_args_setting(self, jsonpath='scenario_setting.json')[source]¶

Get and open json file with scenaio settings of eTraGo args. The settings includes all eTraGo specific settings of arguments and parameters for a reproducible calculation.

Parameters:: json_file (str) – Default: scenario_setting.json Name of scenario setting json file
Returns:: args – Dictionary of json file
Return type:: dict

etrago.tools.utilities.merge_dicts(dict1, dict2)[source]¶

Return a new dictionary by merging two dictionaries recursively.

Parameters:

dict1 (dict) – dictionary 1.
dict2 (dict) – dictionary 2.

Returns:

result – Union of dict1 and dict2

Return type:

dict

etrago.tools.utilities.get_clustering_data(self, path)[source]¶

Import the final busmap and the initial buses, lines and links

Parameters:: path (str) – Name of folder from which to import CSVs of network data.
Return type:: None

etrago.tools.utilities.set_random_noise(self, sigma=0.01)[source]¶

Sets random noise to marginal cost of each generator.

Parameters:

etrago (:class:`etrago.Etrago) – Transmission grid object
seed (int) – seed number, needed to reproduce results
sigma (float) – Default: 0.01 standard deviation, small values reduce impact on dispatch but might lead to numerical instability

etrago.tools.utilities.set_line_country_tags(network)[source]¶

Set country tag for AC- and DC-lines.

Parameters:: network (:class:`pypsa.Network) – Overall container of PyPSA

etrago.tools.utilities.crossborder_capacity_tyndp2020()[source]¶

This function downloads and extracts a scenario datafile for the TYNDP 2020 (Ten-Year Network Development Plan), reads a specific sheet from the file, filters it based on certain criteria, and then calculates the minimum cross-border capacities for a list of European countries. The minimum cross-border capacity is the minimum of the export and import capacities between two countries.

Returns:: Dictionary with cossborder capacities.
Return type:: dict

etrago.tools.utilities.crossborder_capacity(self)[source]¶

Adjust interconnector capacties.

Parameters:

network (:class:`pypsa.Network) – Overall container of PyPSA
method (string) – Method of correction. Options are ‘ntc_acer’ and ‘thermal_acer’. ‘ntc_acer’ corrects all capacities according to values published by the ACER in 2016. ‘thermal_acer’ corrects certain capacities where our dataset most likely overestimates the thermal capacity.

etrago.tools.utilities.set_branch_capacity(etrago)[source]¶

Set branch capacity factor of lines and transformers, different factors for HV (110kV) and eHV (220kV, 380kV).

Parameters:: etrago (:class:`etrago.Etrago) – Transmission grid object

etrago.tools.utilities.check_args(etrago)[source]¶

Function that checks the consistency of etragos input parameters.

Parameters:: etrago (:class:`etrago.Etrago) – Overall container of eTraGo
Return type:: None.

etrago.tools.utilities.drop_sectors(self, drop_carriers)[source]¶

Manually drop secors from network. Makes sure the network can be calculated without the dropped sectors.

Parameters:: drop_carriers (array) – List of sectors that will be dropped. e.g. [‘dsm’, ‘CH4’, ‘H2_saltcavern’, ‘H2_grid’, ‘central_heat’, ‘rural_heat’, ‘central_heat_store’, ‘rural_heat_store’, ‘Li ion’] means everything but AC
Return type:: None.

etrago.tools.utilities.update_busmap(self, new_busmap)[source]¶

Update busmap after any clustering process

Parameters:: new_busmap (dictionary) – busmap used to clusted the network.
Return type:: None.

etrago.tools.utilities.adjust_CH4_gen_carriers(self)[source]¶

Precise the carrier for the generators with CH4 carrier

For the eGon2035 scenario, the generators with carrier CH4 represent the prodution od biogas and methan. In the data model, these two differents types are differenciated only by the marginal cost of the generator. This function introduces a carrier distion (CH4_biogas and CH4_NG) in order to avoid the clustering of these two types of generator together and facilitate the contraint applying differently to each of them.

etrago.tools.utilities.residual_load(network, sector='electricity')[source]¶

Calculate the residual load for the specified sector.

In case of the electricity sector residual load is calculated using all AC loads and all renewable generators with carriers ‘wind_onshore’, ‘wind_offshore’, ‘solar’, ‘solar_rooftop’, ‘biomass’, ‘run_of_river’, and ‘reservoir’.

In case of the central heat sector residual load is calculated using all central heat loads and all renewable generators with carriers ‘solar_thermal_collector’ and ‘geo_thermal’.

Parameters:

network (PyPSA network) – Network to retrieve load and generation time series from, needed to determine residual load.
sector (str) – Sector to determine residual load for. Possible options are ‘electricity’ and ‘central_heat’. Default: ‘electricity’.

Returns:

Dataframe with residual load for each bus in the network. Columns of the dataframe contain the corresponding bus name and index of the dataframe is a datetime index with the corresponding time step.

Return type:

pd.DataFrame

etrago.tools.utilities.manual_fixes_datamodel(etrago)[source]¶

Apply temporal fixes to the data model until a new egon-data run is there

Parameters:: etrago (:class:`Etrago) – Overall container of Etrago
Return type:: None.

etrago.tools.utilities.export_to_shapefile(pypsa_network, shape_files_path=None, srid=4326)[source]¶

Translates all component DataFrames within the pypsa network to GeoDataFrames and saves them to shape files.

Shape files can be used to plot the network in QGIS.

Currently, only the AC network is exported.

Parameters:

pypsa_network (PyPSA network) – PyPSA network as in etrago.network.
shape_files_path (str or None) – If provided, geodataframes are saved as shapefiles to given directory. Default: None.
srid (int) – SRID bus coordinates are given in. Per default WGS84 is assumed. Default: 4326.

Returns:

Dictionary with geodataframes.

Return type:

dict

etrago.tools.utilities.adjust_PtH2_model(self, apply_on='pre_market_model')[source]¶

Adjust the modelling of electrolyzer with waste-heat and O2-utilisation. The method creates a multiple-link-model out of the single links from the dataset (power_to_H2-, PtH2_waste_heat-, PtH2_O2-links) for each location where coupling-product usage is possible. The resulting model consists of a multiple link, additional buses and stores for waste_heat and O2-utilisation, and the connection link to the final heat- and O2-Bus.

Parameters:

etrago (:class:`Etrago) – Overall container of Etrago
apply_on (str)

Returns:

network

Return type:

PyPSA network

etrago.tools.utilities.adjust_chp_model(self, apply_on='pre_market_model')[source]¶

Adjust the modelling of chp plants in foreign countries for eGon100RE

Parameters:

etrago (:class:`Etrago) – Overall container of Etrago
apply_on (str)

Returns:

network

Return type:

PyPSA network

etrago.tools.utilities.levelize_abroad_inland_parameters(self)[source]¶

The method levelize the techno-economic parameters of inland and abroad components of the same carrier. Thus, the favoring of one component is avoided just based on the parameters. The differences in the parameters of the input dataset are caused by a updated source used for the pypsa_eur network and different assumed interest_rates. All necessary parameters of the foreign components are adjusted to the values of the inland components.

Parameters:: etrago (:class:`Etrago) – Overall container of Etrago
Return type:: None