Running a flow decomposition#
You can use the module pypowsybl.flowdecomposition in order to run flow decomposition on networks.
Please check out the examples below.
The general idea of this API is to create a decomposition object. Then, you can define contingencies if necessary. Then, you can define XNE and XNEC. XNEC definition requires pre-defined contingencies. Some pre-defined XNE selection adder functions are available. All the adder functions will be united when running a flow decomposition. Finally, you can run the flow decomposition with some flow decomposition and/or load flow parameters.
For detailed documentation of involved classes and methods, please refer to the API reference.
Start by importing the module:
import pypowsybl as pp
First example#
To perform a flow decomposition, you need at least a network. We will define a flow decomposition object, add some contingencies and some monitored lines. Those lines will be mapped to the network when running a flow decomposition. The flow decomposition computation returns a data frame containing the flow decomposition and the reference values. The reference values are the active power flows in AC on the original network and in DC on the compensated network. By default, the compensated network is the same as the original network as the loss compensation is not activated by default. Here are toy examples that do not reflect reality.
>>> network = pp.network.create_eurostag_tutorial_example1_network()
>>> branch_ids = ['NHV1_NHV2_1', 'NHV1_NHV2_2']
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_single_element_contingencies(branch_ids) \
... .add_monitored_elements(branch_ids, branch_ids)
>>> flow_decomposition_dataframe = flow_decomposition.run(network)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
NHV1_NHV2_1 NHV1_NHV2_1 FR BE 302.444049 300.0 0.0 0.0 0.0 0.0 300.0 0.0
NHV1_NHV2_1_NHV1_NHV2_2 NHV1_NHV2_1 NHV1_NHV2_2 FR BE 610.562161 600.0 0.0 0.0 0.0 0.0 600.0 0.0
NHV1_NHV2_2 NHV1_NHV2_2 FR BE 302.444049 300.0 0.0 0.0 0.0 0.0 300.0 0.0
NHV1_NHV2_2_NHV1_NHV2_1 NHV1_NHV2_2 NHV1_NHV2_1 FR BE 610.562161 600.0 0.0 0.0 0.0 0.0 600.0 0.0
Loop flows#
Here is another example with imbricated zones. This example will highlight loop flows from the peripheral areas.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_LOOP_FLOW_WITH_COUNTRIES.uct')))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_monitored_elements(['BLOAD 11 FLOAD 11 1', 'EGEN 11 FGEN 11 1', 'FGEN 11 BGEN 11 1', 'FLOAD 11 ELOAD 11 1'])
>>> flow_decomposition_dataframe = flow_decomposition.run(network)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_es loop_flow_from_fr
xnec_id
BLOAD 11 FLOAD 11 1 BLOAD 11 FLOAD 11 1 BE FR NaN 200.0 0.000000e+00 0.0 0.0 0.0 0.000000e+00 100.0 1.000000e+02
EGEN 11 FGEN 11 1 EGEN 11 FGEN 11 1 ES FR NaN 100.0 -8.526513e-14 0.0 0.0 0.0 3.552714e-14 100.0 -3.552714e-14
FGEN 11 BGEN 11 1 FGEN 11 BGEN 11 1 FR BE NaN 200.0 -1.421085e-13 0.0 0.0 0.0 8.526513e-14 100.0 1.000000e+02
FLOAD 11 ELOAD 11 1 FLOAD 11 ELOAD 11 1 FR ES NaN 100.0 0.000000e+00 0.0 0.0 0.0 0.000000e+00 100.0 0.000000e+00
On this example, the AC load flow does not converge, the fallback to DC load flow is activated by default. This means that the AC reference values are NaNs. For each line where the AC reference is not a number, the rescaling is disabled to prevent NaN propagation.
PST flows#
Network details#
Here is another example with a more complex network containing a phase-shifting transformer (PST). This PST has a non-neutral tap position, thus forcing the flows in a certain direction. This example illustrates the flow decomposition with such network element.
As we cannot set a PST on an interconnection, we set an equivalent null load called ‘BLOAD 11’.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> network.get_generators()
name energy_source target_p min_p max_p min_q max_q rated_s reactive_limits_kind target_v target_q voltage_regulator_on regulated_element_id p q i voltage_level_id bus_id connected
id
FGEN 11_generator OTHER 100.0 -1000.0 1000.0 -1000.0 1000.0 NaN MIN_MAX 400.0 0.0 True FGEN 11_generator NaN NaN NaN FGEN 1 FGEN 1_0 True
BLOAD 12_generator OTHER 100.0 -1000.0 1000.0 -1000.0 1000.0 NaN MIN_MAX 400.0 0.0 True BLOAD 12_generator NaN NaN NaN BLOAD 1 BLOAD 1_1 True
>>> network.get_loads()
name type p0 q0 p q i voltage_level_id bus_id connected
id
BLOAD 12_load UNDEFINED 200.0 0.0 NaN NaN NaN BLOAD 1 BLOAD 1_1 True
>>> network.get_lines()
name r x g1 b1 g2 b2 p1 q1 i1 p2 q2 i2 voltage_level1_id voltage_level2_id bus1_id bus2_id connected1 connected2
id
FGEN 11 BLOAD 12 1 0.5 1.5 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN FGEN 1 BLOAD 1 FGEN 1_0 BLOAD 1_1 True True
FGEN 11 BLOAD 11 1 1.0 3.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN FGEN 1 BLOAD 1 FGEN 1_0 BLOAD 1_0 True True
>>> network.get_buses()
name v_mag v_angle connected_component synchronous_component voltage_level_id
id
FGEN 1_0 NaN NaN 0 0 FGEN 1
BLOAD 1_0 NaN NaN 0 0 BLOAD 1
BLOAD 1_1 NaN NaN 0 0 BLOAD 1
>>> network.get_2_windings_transformers()
name r x g b rated_u1 rated_u2 rated_s p1 q1 i1 p2 q2 i2 voltage_level1_id voltage_level2_id bus1_id bus2_id connected1 connected2
id
BLOAD 11 BLOAD 12 2 0.5 1.5 0.0002 0.00015 400.0 400.0 NaN NaN NaN NaN NaN NaN NaN BLOAD 1 BLOAD 1 BLOAD 1_1 BLOAD 1_0 True True
>>> network.get_phase_tap_changers()
tap low_tap high_tap step_count regulating regulation_mode regulation_value target_deadband regulating_bus_id
id
BLOAD 11 BLOAD 12 2 0 -16 16 33 False FIXED_TAP NaN NaN
Neutral tap position#
Here are the results with neutral tap position.
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_monitored_elements(['FGEN 11 BLOAD 11 1', 'FGEN 11 BLOAD 12 1'])
>>> flow_decomposition_dataframe = flow_decomposition.run(network)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.0 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.0 -5.998523 -5.998523
>>> flow_decomposition_dataframe[[c for c in flow_decomposition_dataframe.columns if ("flow" in c and "reference" not in c)]].sum(axis=1)
xnec_id
FGEN 11 BLOAD 11 1 25.0
FGEN 11 BLOAD 12 1 75.0
dtype: float64
The results are not rescaled to the AC reference by default.
Non neutral tap position#
Here are the results with non-neutral tap position.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> network.update_phase_tap_changers(id="BLOAD 11 BLOAD 12 2", tap=1)
>>> network.get_phase_tap_changers()
tap low_tap high_tap step_count regulating regulation_mode regulation_value target_deadband regulating_bus_id
id
BLOAD 11 BLOAD 12 2 1 -16 16 33 False FIXED_TAP NaN NaN
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_monitored_elements(['FGEN 11 BLOAD 11 1', 'FGEN 11 BLOAD 12 1'])
>>> flow_decomposition_dataframe = flow_decomposition.run(network)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 192.390656 188.652703 29.015809 0.0 163.652703 0.0 -2.007905 -2.007905
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE -76.189072 -88.652703 -87.047428 0.0 163.652703 0.0 6.023714 6.023714
>>> flow_decomposition_dataframe[[c for c in flow_decomposition_dataframe.columns if ("flow" in c and "reference" not in c)]].sum(axis=1)
xnec_id
FGEN 11 BLOAD 11 1 188.652703
FGEN 11 BLOAD 12 1 88.652703
dtype: float64
Note that the reference flow on the 2d branch has changed of sign. As we use it as reference, all the decomposed flows have also changed of sign.
Unmerged X node flows#
To illustrate X node flow, we need a network with unmerged x nodes. Those x nodes might represent HVDCs, outside countries, etc. Merged X nodes will not be considered here.
>>> network = pp.network.load(DATA_DIR.joinpath('19700101_0000_FO4_UX1.uct'))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_interconnections_as_monitored_elements()
>>> flow_decomposition.run(network)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_de loop_flow_from_fr
xnec_id
XBD00011 BD000011 1 + XBD00011 DB000011 1 XBD00011 BD000011 1 + XBD00011 DB000011 1 BE DE 121.821917 124.685261 171.516849 -33.155274 2.951653 0.0 0.226369 -8.994903e-09 -16.854336
XBD00012 BD000011 1 + XBD00012 DB000011 1 XBD00012 BD000011 1 + XBD00012 DB000011 1 BE DE 121.821917 124.685261 171.516849 -33.155274 2.951653 0.0 0.226369 -8.994903e-09 -16.854336
XBF00011 BF000011 1 + XBF00011 FB000011 1 XBF00011 BF000011 1 + XBF00011 FB000011 1 BE FR -775.578124 -764.445217 679.262024 170.472453 7.112098 0.0 -124.052946 -6.713719e-09 31.651588
XBF00021 BF000021 1 + XBF00021 FB000021 1 XBF00021 BF000021 1 + XBF00021 FB000021 1 BE FR -234.032855 -242.462652 169.385837 44.108499 -0.604396 0.0 62.252842 -1.954504e-09 -32.680130
XBF00022 BF000021 1 + XBF00022 FB000022 1 XBF00022 BF000021 1 + XBF00022 FB000022 1 BE FR -234.032855 -242.462652 169.385837 44.108499 -0.604396 0.0 62.252842 -1.954504e-09 -32.680130
XDF00011 DF000011 1 + XDF00011 FD000011 1 XDF00011 DF000011 1 + XDF00011 FD000011 1 DE FR -1156.356167 -1150.629478 906.966302 216.310548 -5.903306 0.0 -0.452738 -2.032061e-08 33.708672
Adder functions#
The flow decomposition algorithm will decompose flow on monitored elements. You need to define those elements. You can either define those elements with specific ids or with automatic functions.
The union of selected elements will be decomposed. For example, if you select the same branch in the same state two times, it will be decomposed only once.
Specific adder functions#
Specific adder functions are based on IDs. When running the flow decomposition, the IDs will be mapped to the network. If an identifiable is not found on the network, a warning will be sent (beware of activated logs) and the corresponding XNEC will be ignored.
With those adder functions, you can create XNEs and/or XNECs. You need to specify contingencies first if required. If you try to create a XNEC with an undefined contingency ID, an error will be raised.
By default, if you add monitored elements with branches and contingencies, it will create all possible valid pairs of branch and states. By default, all the states are base case and all contingency states defined. You can specify which states you want in the base add monitored element function or use a dedicated pre/post contingency function.
Here is an example
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_monitored_elements(['FGEN 11 BLOAD 11 1']) \
... .add_single_element_contingency('FGEN 11 BLOAD 11 1') \
... .add_monitored_elements(['FGEN 11 BLOAD 12 1'], ['FGEN 11 BLOAD 11 1']) \
... .add_multiple_elements_contingency(['FGEN 11 BLOAD 11 1', 'BLOAD 11 BLOAD 12 2']) \
... .add_monitored_elements('FGEN 11 BLOAD 12 1', 'FGEN 11 BLOAD 11 1_BLOAD 11 BLOAD 12 2', pp.flowdecomposition.ContingencyContextType.SPECIFIC)
>>> flow_decomposition.run(network)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.0 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.0 -5.998523 -5.998523
FGEN 11 BLOAD 12 1_FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 11 1 FR BE 116.016179 100.0 115.996062 0.0 0.0 0.0 -7.998031 -7.998031
FGEN 11 BLOAD 12 1_FGEN 11 BLOAD 11 1_BLOAD 1... FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 11 1_BLOAD 11 BLOAD 12 2 FR BE 100.034531 100.0 115.996062 0.0 0.0 0.0 -7.998031 -7.998031
See the API reference for more details about how each specific adder works.
Automatic adder functions#
Automatic adder functions are based on automatic selection processes. With those functions, you can create XNEs and/or XNECs.
Some automatic XNE selection adder functions are available.
5% zonal PTDF criteria#
This adder function will add all branches in the N state that have a zone-to-zone PTDF greater than 5% or that are interconnections. This function adds some non-negligible precomputing to the process.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_5perc_ptdf_as_monitored_elements()
>>> flow_decomposition.run(network)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
BLOAD 11 BLOAD 12 2 BLOAD 11 BLOAD 12 2 BE BE 3.005666 -25.0 28.999015 0.0 -0.0 -1.999508 0.000000 -1.999508
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.000000 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.000000 -5.998523 -5.998523
Interconnections#
This adder function will add interconnections in the N state. Be careful when using this function with large networks.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_interconnections_as_monitored_elements()
>>> flow_decomposition.run(network)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.0 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.0 -5.998523 -5.998523
All branches#
This adder function will add all branches in the N state. Be careful when using this function with large networks.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_all_branches_as_monitored_elements()
>>> flow_decomposition.run(network)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
BLOAD 11 BLOAD 12 2 BLOAD 11 BLOAD 12 2 BE BE 3.005666 -25.0 28.999015 0.0 -0.0 -1.999508 0.000000 -1.999508
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.000000 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.000000 -5.998523 -5.998523
Mixing adder functions#
You can mix everything together as you like.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> parameters = pp.flowdecomposition.Parameters(sensitivity_epsilon=pp.flowdecomposition.Parameters.DISABLE_SENSITIVITY_EPSILON)
>>> flow_decomposition = pp.flowdecomposition.create_decomposition() \
... .add_single_element_contingency('FGEN 11 BLOAD 11 1') \
... .add_monitored_elements(['FGEN 11 BLOAD 12 1', 'BLOAD 11 BLOAD 12 2'], ['FGEN 11 BLOAD 11 1']) \
... .add_multiple_elements_contingency(['FGEN 11 BLOAD 11 1', 'BLOAD 11 BLOAD 12 2']) \
... .add_postcontingency_monitored_elements('FGEN 11 BLOAD 12 1', 'FGEN 11 BLOAD 11 1_BLOAD 11 BLOAD 12 2') \
... .add_interconnections_as_monitored_elements() \
... .add_all_branches_as_monitored_elements()
>>> flow_decomposition.run(network, flow_decomposition_parameters=parameters)
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
BLOAD 11 BLOAD 12 2 BLOAD 11 BLOAD 12 2 BE BE 3.005666 -25.0 28.999015 0.0 -0.0 -1.999508 0.000000 -1.999508
BLOAD 11 BLOAD 12 2_FGEN 11 BLOAD 11 1 BLOAD 11 BLOAD 12 2 FGEN 11 BLOAD 11 1 BE BE 32.000000 -0.0 0.000000 0.0 -0.0 0.000000 0.000000 0.000000
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 25.0 28.999015 0.0 -0.0 0.000000 -1.999508 -1.999508
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 75.0 86.997046 0.0 0.0 0.000000 -5.998523 -5.998523
FGEN 11 BLOAD 12 1_FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 11 1 FR BE 116.016179 100.0 115.996062 0.0 -0.0 0.000000 -7.998031 -7.998031
FGEN 11 BLOAD 12 1_FGEN 11 BLOAD 11 1_BLOAD 1... FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 11 1_BLOAD 11 BLOAD 12 2 FR BE 100.034531 100.0 115.996062 0.0 0.0 0.000000 -7.998031 -7.998031
Note: if one of our xnec is missing, it might be caused by a zero MW DC reference flow, you can show them by reducing the sensitivity-epsilon as bone before. This will be fixed in next versions.
Configuration file#
Inside your config.yml file, you can change the default Configuration of the flow decomposition. Here are the available parameters and their default values:
flow-decomposition-default-parameters:
enable-losses-compensation: False
losses-compensation-epsilon: 1e-5
sensitivity-epsilon: 1e-5
rescale-enabled: False
dc-fallback-enabled-after-ac-divergence: True
sensitivity-variable-batch-size: 15000
The flow decomposition parameters can be overwritten in Python. If you have memory issues, do not hesitate to reduce the sensitivity-variable-batch-size parameter.
>>> network = pp.network.load(str(DATA_DIR.joinpath('NETWORK_PST_FLOW_WITH_COUNTRIES.uct')))
>>> parameters = pp.flowdecomposition.Parameters(enable_losses_compensation=True,
... losses_compensation_epsilon=pp.flowdecomposition.Parameters.DISABLE_LOSSES_COMPENSATION_EPSILON,
... sensitivity_epsilon=pp.flowdecomposition.Parameters.DISABLE_SENSITIVITY_EPSILON,
... rescale_enabled=True,
... dc_fallback_enabled_after_ac_divergence=True,
... sensitivity_variable_batch_size=1000)
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_monitored_elements(['BLOAD 11 BLOAD 12 2', 'FGEN 11 BLOAD 11 1', 'FGEN 11 BLOAD 12 1'])
>>> flow_decomposition_dataframe = flow_decomposition.run(network, parameters)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
BLOAD 11 BLOAD 12 2 BLOAD 11 BLOAD 12 2 BE BE 3.005666 -28.99635 3.008332 0.0 -0.0 -0.001333 0.000000 -0.001333
FGEN 11 BLOAD 11 1 FGEN 11 BLOAD 11 1 FR BE 29.003009 28.99635 29.005675 0.0 0.0 0.000000 -0.001333 -0.001333
FGEN 11 BLOAD 12 1 FGEN 11 BLOAD 12 1 FR BE 87.009112 86.98905 87.017108 0.0 0.0 0.000000 -0.003998 -0.003998
You can also overwrite the Load flow parameters.
>>> network = pp.network.create_eurostag_tutorial_example1_network()
>>> flow_decomposition_parameters = pp.flowdecomposition.Parameters()
>>> load_flow_parameters = pp.loadflow.Parameters()
>>> flow_decomposition = pp.flowdecomposition.create_decomposition().add_monitored_elements(['NHV1_NHV2_1', 'NHV1_NHV2_2'])
>>> flow_decomposition_dataframe = flow_decomposition.run(network, flow_decomposition_parameters, load_flow_parameters)
>>> flow_decomposition_dataframe
branch_id contingency_id country1 country2 ac_reference_flow dc_reference_flow commercial_flow x_node_flow pst_flow internal_flow loop_flow_from_be loop_flow_from_fr
xnec_id
NHV1_NHV2_1 NHV1_NHV2_1 FR BE 302.444049 300.0 0.0 0.0 0.0 0.0 300.0 0.0
NHV1_NHV2_2 NHV1_NHV2_2 FR BE 302.444049 300.0 0.0 0.0 0.0 0.0 300.0 0.0