etrago package¶
Subpackages¶
Submodules¶
etrago.appl module¶
This is the application file for the tool eTraGo. Define your connection parameters and power flow settings before executing the function etrago.
-
etrago.appl.etrago(args)[source]¶ The etrago function works with following arguments:
Parameters: db : str
'oedb', Name of Database session setting stored in config.ini of .egoio- gridversion : NoneType or str
'v0.2.11', Name of the data version number of oedb: state'None'for model_draft (sand-box) or an explicit version number (e.g. ‘v0.2.10’) for the grid schema.- method : str
'lopf', Choose between a non-linear power flow (‘pf’) or a linear optimal power flow (‘lopf’).- pf_post_lopf : bool
False, Option to run a non-linear power flow (pf) directly after the linear optimal power flow (and thus the dispatch) has finished.
- start_snapshot : int
1, Start hour of the scenario year to be calculated.
- end_snapshot : int
2, End hour of the scenario year to be calculated.
- solver : str
‘glpk’, Choose your preferred solver. Current options: ‘glpk’ (open-source), ‘cplex’ or ‘gurobi’.
- solver_options: dict
Choose settings of solver to improve simulation time and result. Options are described in documentation of choosen solver.
- model_formulation: str
‘angles’ Choose formulation of pyomo-model. Current options: angles, cycles, kirchhoff, ptdf
- scn_name : str
‘Status Quo’, Choose your scenario. Currently, there are three different scenarios: ‘Status Quo’, ‘NEP 2035’, ‘eGo100’. If you do not want to use the full German dataset, you can use the excerpt of Schleswig-Holstein by adding the acronym SH to the scenario name (e.g. ‘SH Status Quo’).
scn_extension : NoneType or list
None, Choose extension-scenarios which will be added to the existing network container. Data of the extension scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) with the prefix ‘extension_’. Currently there are three overlay networks:
‘nep2035_confirmed’ includes all planed new lines confirmed by the Bundesnetzagentur ‘nep2035_b2’ includes all new lines planned by the Netzentwicklungsplan 2025 in scenario 2035 B2 ‘BE_NO_NEP 2035’ includes planned lines to Belgium and Norway and adds BE and NO as electrical neighbours
- scn_decommissioning : str
None, Choose an extra scenario which includes lines you want to decommise from the existing network. Data of the decommissioning scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) with the prefix ‘decommissioning_’. Currently, there are two decommissioning_scenarios which are linked to extension-scenarios:
‘nep2035_confirmed’ includes all lines that will be replaced in confirmed projects ‘nep2035_b2’ includes all lines that will be replaced in NEP-scenario 2035 B2
- lpfile : obj
False, State if and where you want to save pyomo’s lp file. Options: False or ‘/path/tofolder’.import numpy as np
- csv_export : obj
False, State if and where you want to save results as csv files.Options: False or ‘/path/tofolder’.
- db_export : bool
False, State if you want to export the results of your calculation back to the database.
- extendable : list
[‘network’, ‘storages’], Choose components you want to optimize. Settings can be added in /tools/extendable.py. The most important possibilities:
‘network’: set all lines, links and transformers extendable ‘german_network’: set lines and transformers in German grid
extendable
‘foreign_network’: set foreign lines and transformers extendable ‘transformers’: set all transformers extendable ‘overlay_network’: set all components of the ‘scn_extension’
extendable
- ‘storages’: allow to install extendable storages
(unlimited in size) at each grid node in order to meet the flexibility demand.
- ‘network_preselection’: set only preselected lines extendable,
method is chosen in function call
- generator_noise : bool or int
State if you want to apply a small random noise to the marginal costs of each generator in order to prevent an optima plateau. To reproduce a noise, choose the same integer (seed number).
- minimize_loading : bool
False, …
- ramp_limits : bool
False, State if you want to consider ramp limits of generators. Increases time for solving significantly. Only works when calculating at least 30 snapshots.
- extra_functionality : dict or None
None, Choose extra functionalities and their parameters for PyPSA-model. Settings can be added in /tools/constraints.py. Current options are:
- ‘max_line_ext’: float
Maximal share of network extension in p.u.
- ‘min_renewable_share’: float
Minimal share of renewable generation in p.u.
- ‘cross_border_flow’: array of two floats
Limit cross-border-flows between Germany and its neigbouring countries, set values in p.u. of german loads in snapshots for all countries (positiv: export from Germany)
- ‘cross_border_flows_per_country’: dict of cntr and array of floats
Limit cross-border-flows between Germany and its neigbouring countries, set values in p.u. of german loads in snapshots for each country (positiv: export from Germany)
- ‘max_curtailment_per_gen’: float
Limit curtailment of all wind and solar generators in Germany, values set in p.u. of generation potential.
- ‘max_curtailment_per_gen’: float
Limit curtailment of each wind and solar generator in Germany, values set in p.u. of generation potential.
- ‘capacity_factor’: dict of arrays
Limit overall energy production for each carrier, set upper/lower limit in p.u.
- ‘capacity_factor_per_gen’: dict of arrays
Limit overall energy production for each generator by carrier, set upper/lower limit in p.u.
- ‘capacity_factor_per_cntr’: dict of dict of arrays
Limit overall energy production country-wise for each carrier, set upper/lower limit in p.u.
- ‘capacity_factor_per_gen_cntr’: dict of dict of arrays
Limit overall energy production country-wise for each generator by carrier, set upper/lower limit in p.u.
- network_clustering_kmeans : bool or int
False, State if you want to apply a clustering of all network buses down to only
'k'buses. The weighting takes place considering generation and load at each node. If so, state the number of k you want to apply. Otherwise put False. This function doesn’t work together with'line_grouping = True'.- load_cluster : bool or obj
state if you want to load cluster coordinates from a previous run: False or /path/tofile (filename similar to ./cluster_coord_k_n_result).
- network_clustering_ehv : bool
False, Choose if you want to cluster the full HV/EHV dataset down to only the EHV buses. In that case, all HV buses are assigned to their closest EHV sub-station, taking into account the shortest distance on power lines.
- snapshot_clustering : bool or int
False, State if you want to cluster the snapshots and run the optimization only on a subset of snapshot periods. The int value defines the number of periods (i.e. days) which will be clustered to. Move to PyPSA branch:features/snapshot_clustering
- parallelisation : bool
False, Choose if you want to calculate a certain number of snapshots in parallel. If yes, define the respective amount in the if-clause execution below. Otherwise state False here.
- line_grouping : bool
True, State if you want to group lines that connect the same two buses into one system.
- branch_capacity_factor : dict
{‘HV’: 0.5, ‘eHV’ : 0.7}, Add a factor here if you want to globally change line capacities (e.g. to “consider” an (n-1) criterion or for debugging purposes).
- load_shedding : bool
False, State here if you want to make use of the load shedding function which is helpful when debugging: a very expensive generator is set to each bus and meets the demand when regular generators cannot do so.
- foreign_lines : dict
{‘carrier’:’AC’, ‘capacity’: ‘osmTGmod}’ Choose transmission technology and capacity of foreign lines:
‘carrier’: ‘AC’ or ‘DC’ ‘capacity’: ‘osmTGmod’, ‘ntc_acer’ or ‘thermal_acer’
- comments : str
None
Returns: network : pandas.DataFrame<dataframe>
eTraGo result network based on PyPSA network