tsf

Api

transform

Transform an energy system mapping to a tessif energy system.

Internal Functionalities

`_generate_busses`	Generate `Bus` objects out of the `energy_system_dict`.
`_generate_sources`	Generate `Source` objects out of the `energy_system_dict`.
`_generate_sinks`	Generate `Sink` objects out of the `energy_system_dict`.
`_generate_transformers`	Generate `Transformer` objects out of the `energy_system_dict`.
`_generate_chps`	Generate `CHP` objects out of the `energy_system_dict`.
`_generate_storages`	Generate `Storage` objects out of the `energy_system_dict`.

tsf is a tessif package to transform abstract energy system data represented as mapping into ready to simulate energy system model instances.

Mapping like representations are usually returned by utilities part of the parse module.

tessif.transform.mapping2es.tsf._generate_busses(energy_system_dict)[source]

Generate Bus objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionary of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_busses – Generator object yielding the parsed bus objects.
Return type:: Generator

tessif.transform.mapping2es.tsf._generate_sinks(energy_system_dict)[source]

Generate Sink objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_sinks – Generator obeject yielding the parsed sink objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Using the pure_python energy system mapping example

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> sinks = ttsf._generate_sinks(parse.python_mapping(m))
>>> for s in sinks:
...     for name, attr in s.attributes.items():
...         print('{}: {}'.format(name, attr))
accumulated_amounts: {'electricity': MinMax(min=0, max=inf)}
costs_for_being_active: 0
expandable: {'electricity': False}
expansion_costs: {'electricity': 0}
expansion_limits: {'electricity': MinMax(min=0, max=inf)}
flow_costs: {'electricity': 0}
flow_emissions: {'electricity': 0}
flow_gradients: {'electricity': PositiveNegative(positive=11, negative=11)}
flow_rates: {'electricity': MinMax(min=8, max=11)}
gradient_costs: {'electricity': PositiveNegative(positive=0, negative=0)}
initial_status: True
inputs: frozenset({'electricity'})
interfaces: frozenset({'electricity'})
milp: {'electricity': False}
number_of_status_changes: OnOff(on=2, off=1)
status_changing_costs: OnOff(on=2, off=1)
status_inertia: OnOff(on=2, off=1)
timeseries: None
uid: Demand

tessif.transform.mapping2es.tsf._generate_sources(energy_system_dict)[source]

Generate Source objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_sources – Generator obeject yielding the parsed source objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Using the pure_python energy system mapping example

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> sources = ttsf._generate_sources(parse.python_mapping(m))
>>> for s in sources:
...     for name, attr in s.attributes.items():
...         print('{}: {}'.format(name, attr))
accumulated_amounts: {'fuel': MinMax(min=0, max=inf)}
costs_for_being_active: 0
expandable: {'fuel': False}
expansion_costs: {'fuel': 0}
expansion_limits: {'fuel': MinMax(min=0, max=inf)}
flow_costs: {'fuel': 0}
flow_emissions: {'fuel': 0}
flow_gradients: {'fuel': PositiveNegative(positive=42, negative=42)}
flow_rates: {'fuel': MinMax(min=0, max=22)}
gradient_costs: {'fuel': PositiveNegative(positive=1, negative=1)}
initial_status: True
interfaces: frozenset({'fuel'})
milp: {'fuel': False}
number_of_status_changes: OnOff(on=1, off=1)
outputs: frozenset({'fuel'})
status_changing_costs: OnOff(on=1, off=1)
status_inertia: OnOff(on=1, off=1)
timeseries: {'fuel': MinMax(min=0, max=array([10, 22, 22]))}
uid: Gas Station

tessif.transform.mapping2es.tsf._generate_transformers(energy_system_dict)[source]

Generate Transformer objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_transformers – Generator obeject yielding the parsed transformer objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Using the pure_python energy system mapping example

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> transformers = ttsf._generate_transformers(parse.python_mapping(m))
>>> for t in transformers:
...     for name, atr in t.attributes.items():
...         print('{}: {}'.format(
...             name, sorted(atr) if isinstance(atr, frozenset) else atr))
... # frozensets are printed as sorted lists for doctesting consistency
conversions: {('fuel', 'electricity'): 0.42}
costs_for_being_active: 0
expandable: {'electricity': False, 'fuel': False}
expansion_costs: {'electricity': 0, 'fuel': 0}
expansion_limits: {'electricity': MinMax(min=0, max=inf), 'fuel': MinMax(min=0, max=inf)}
flow_costs: {'electricity': 0, 'fuel': 0}
flow_emissions: {'electricity': 0, 'fuel': 0}
flow_gradients: {'electricity': PositiveNegative(positive=10, negative=10), 'fuel': PositiveNegative(positive=50, negative=50)}
flow_rates: {'electricity': MinMax(min=5, max=15), 'fuel': MinMax(min=0, max=50)}
gradient_costs: {'electricity': PositiveNegative(positive=0, negative=0), 'fuel': PositiveNegative(positive=0, negative=0)}
initial_status: True
inputs: ['fuel']
interfaces: ['electricity', 'fuel']
milp: {'electricity': True, 'fuel': False}
number_of_status_changes: OnOff(on=0, off=2)
outputs: ['electricity']
status_changing_costs: OnOff(on=0, off=2)
status_inertia: OnOff(on=0, off=2)
timeseries: {'electricity': MinMax(min=0, max=array([10, 22, 22]))}
uid: Generator

tessif.transform.mapping2es.tsf._generate_chps(energy_system_dict)[source]

Generate CHP objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_chps – Generator obeject yielding the parsed chp objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Store the create_variable_chp example from the hardcoded examples into a hdf5 file and then parse it into a mapping from which the chps are generated.

>>> import tessif.examples.data.tsf.py_hard as tsf_examples
>>> tsf_es = tsf_examples.create_variable_chp()
>>> import os
>>> from tessif.frused.paths import write_dir
>>> output_msg = tsf_es.to_hdf5(
...     directory=os.path.join(write_dir, 'tsf'),
...     filename='chp_es.hdf5',
... )
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> chps = ttsf._generate_chps(parse.hdf5(
...     path=os.path.join(write_dir, 'tsf', 'chp_es.hdf5'),))
>>> for c in chps:
...     for name, atr in c.attributes.items():
...         print('{}: {}'.format(
...             name, sorted(atr) if isinstance(atr, frozenset) else atr))
... # frozensets are printed as sorted lists for doctesting consistency
back_pressure: nan
conversion_factor_full_condensation: {('gas', 'electricity'): 0.5}
conversions: {('gas', 'electricity'): 0.3, ('gas', 'heat'): 0.2}
costs_for_being_active: 0.0
el_efficiency_wo_dist_heat: MinMax(min=nan, max=nan)
enthalpy_loss: MinMax(min=nan, max=nan)
expandable: {'electricity': False, 'gas': False, 'heat': False}
expansion_costs: {'electricity': 0.0, 'gas': 0.0, 'heat': 0.0}
expansion_limits: {'electricity': MinMax(min=0.0, max=inf), 'gas': MinMax(min=0.0, max=inf), 'heat': MinMax(min=0.0, max=inf)}
flow_costs: {'electricity': 3, 'gas': 0, 'heat': 2}
flow_emissions: {'electricity': 2, 'gas': 0, 'heat': 3}
flow_gradients: {'electricity': PositiveNegative(positive=inf, negative=inf), 'gas': PositiveNegative(positive=inf, negative=inf), 'heat': PositiveNegative(positive=inf, negative=inf)}
flow_rates: {'electricity': MinMax(min=0, max=9), 'gas': MinMax(min=0.0, max=inf), 'heat': MinMax(min=0, max=6)}
gradient_costs: {'electricity': PositiveNegative(positive=0.0, negative=0.0), 'gas': PositiveNegative(positive=0.0, negative=0.0), 'heat': PositiveNegative(positive=0.0, negative=0.0)}
initial_status: 1
inputs: ['gas']
interfaces: ['electricity', 'gas', 'heat']
milp: {'electricity': False, 'gas': False, 'heat': False}
min_condenser_load: nan
number_of_status_changes: OnOff(on=0, off=0)
outputs: ['electricity', 'heat']
power_loss_index: nan
power_wo_dist_heat: MinMax(min=nan, max=nan)
status_changing_costs: OnOff(on=0, off=0)
status_inertia: OnOff(on=0, off=0)
timeseries: None
uid: CHP1
back_pressure: False
conversion_factor_full_condensation: {}
conversions: {}
costs_for_being_active: 0.0
el_efficiency_wo_dist_heat: MinMax(min=[0.43, 0.43, 0.43, 0.43], max=[0.53, 0.53, 0.53, 0.53])
enthalpy_loss: MinMax(min=[1.0, 1.0, 1.0, 1.0], max=[0.18, 0.18, 0.18, 0.18])
expandable: {'electricity': False, 'gas': False, 'heat': False}
expansion_costs: {'electricity': 0.0, 'gas': 0.0, 'heat': 0.0}
expansion_limits: {'electricity': MinMax(min=0.0, max=inf), 'gas': MinMax(min=0.0, max=inf), 'heat': MinMax(min=0.0, max=inf)}
flow_costs: {'electricity': 3, 'gas': 0, 'heat': 2}
flow_emissions: {'electricity': 2, 'gas': 0, 'heat': 3}
flow_gradients: {'electricity': PositiveNegative(positive=inf, negative=inf), 'gas': PositiveNegative(positive=inf, negative=inf), 'heat': PositiveNegative(positive=inf, negative=inf)}
flow_rates: {'electricity': MinMax(min=0.0, max=inf), 'gas': MinMax(min=0.0, max=inf), 'heat': MinMax(min=0.0, max=inf)}
gradient_costs: {'electricity': PositiveNegative(positive=0.0, negative=0.0), 'gas': PositiveNegative(positive=0.0, negative=0.0), 'heat': PositiveNegative(positive=0.0, negative=0.0)}
initial_status: 1
inputs: ['gas']
interfaces: ['electricity', 'gas', 'heat']
milp: {'electricity': False, 'gas': False, 'heat': False}
min_condenser_load: [3, 3, 3, 3]
number_of_status_changes: OnOff(on=0, off=0)
outputs: ['electricity', 'heat']
power_loss_index: [0.19, 0.19, 0.19, 0.19]
power_wo_dist_heat: MinMax(min=[8, 8, 8, 8], max=[20, 20, 20, 20])
status_changing_costs: OnOff(on=0, off=0)
status_inertia: OnOff(on=0, off=0)
timeseries: None
uid: CHP2

tessif.transform.mapping2es.tsf._generate_storages(energy_system_dict)[source]

Generate Storage objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_storages – Generator obeject yielding the parsed storage objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Using the pure_python energy system mapping example

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> storages = ttsf._generate_storages(parse.python_mapping(m))
>>> for s in storages:
...     print(s.uid)
...     print(s.input)
...     print(s.output)
...     for name, atr in s.attributes.items():
...         print('{}: {}'.format(
...             name, sorted(atr) if isinstance(atr, frozenset) else atr))
...     # frozensets are printed as sorted lists for doctesting consistency
Battery
electricity
electricity
capacity: 100
costs_for_being_active: 0
expandable: {'electricity': False}
expansion_costs: {'electricity': 0}
expansion_limits: {'electricity': MinMax(min=0, max=inf)}
final_soc: None
fixed_expansion_ratios: {'electricity': True}
flow_costs: {'electricity': 0}
flow_efficiencies: {'electricity': InOut(inflow=0.95, outflow=0.98)}
flow_emissions: {'electricity': 0}
flow_gradients: {'electricity': PositiveNegative(positive=10, negative=10)}
flow_rates: {'electricity': MinMax(min=0, max=10)}
gradient_costs: {'electricity': PositiveNegative(positive=0, negative=0)}
idle_changes: PositiveNegative(positive=0, negative=1)
initial_soc: 10
initial_status: True
input: electricity
interfaces: ['electricity']
milp: {'electricity': False}
number_of_status_changes: OnOff(on=0, off=2)
output: electricity
status_changing_costs: OnOff(on=0, off=2)
status_inertia: OnOff(on=0, off=2)
timeseries: None
uid: Battery

tessif.transform.mapping2es.tsf._generate_connectors(energy_system_dict)[source]

Generate Connector objects out of the energy_system_dict.

Parameters:: energy_system_dict¶ (dict) – Dictionairy of pandas.DataFrame objects keyed by strings. With the key strings representing energy system components. Usually returned by something like the tessif.parse functionalities.
Returns:: generated_connectors – Generator obeject yielding the parsed connector objects.
Return type:: Generator

Examples

Setting spellings.get_from's logging level to debug for decluttering doctest output:

>>> from tessif.frused import configurations
>>> configurations.spellings_logging_level = 'debug'

Using the pure_python energy system mapping example

>>> import tessif.examples.data.tsf.py_mapping.transshipment as tship
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> connectors = ttsf._generate_connectors(
...     parse.python_mapping(tship.mapping))
>>> for c in connectors:
...     for name, atr in c.attributes.items():
...         print('{}: {}'.format(
...             name, sorted(atr) if isinstance(atr, frozenset) else atr))
... # frozensets are printed as sorted lists for doctesting consistency
conversions: {('bus-01', 'bus-02'): 0.9, ('bus-02', 'bus-01'): 0.8}
inputs: ['bus-01', 'bus-02']
interfaces: ['bus-01', 'bus-02']
outputs: ['bus-01', 'bus-02']
timeseries: None
uid: connector

tessif.transform.mapping2es.tsf.infer_timeindex(energy_system_dict)[source]

Try inferring the timeindex from the energy system dictionary. Inferring is done by reading out the timeindex-like column of the timeseries-like spreadsheet (DataFrame) and rounding it according to the temporal resolution.

The timeframe to be simulated is here referred to as “timeindex”.

Warning

For inferring to be succesfull, discrete timeframe length has to be at least 3 (after rounding).

Parameters:: energy_system_dict¶ (dict) – Dictionary containing the energy system data. Typically returned by one of the tessif.parse functionalities
Returns:: timeindex – The time frame inferred from the energy system dictionary.
Return type:: pandas.DatetimeIndex

Example

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> from tessif.frused.paths import example_dir
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> idx = ttsf.infer_timeindex(parse.python_mapping(m))
>>> import pprint
>>> pprint.pprint(idx)
DatetimeIndex(['1990-07-13 00:00:00', '1990-07-13 01:00:00',
               '1990-07-13 02:00:00'],
              dtype='datetime64[ns]', freq='H')

tessif.transform.mapping2es.tsf.transform(energy_system_mapping, **kwargs)[source]

Transform an energy system mapping to a tessif energy system.

Parameters:

energy_system_mapping¶ (dict) – Dictionairy containing the energy system data. Typically returned by one of the tessif.parse functionalities
timeframe¶ (pandas.DatetimeIndex) –
Datetime index representing the evaluated timeframe. Explicitly stating:
- initial datatime (0th element of the pandas.DatetimeIndex)
- number of timesteps (length of pandas.DatetimeIndex)
- temporal resolution (pandas.DatetimeIndex.freq)
For example:
```
idx = pd.DatetimeIndex(
    data=pd.date_range(
        '2016-01-01 00:00:00', periods=11, freq='H'))
```
Note

If not stated it is tried to be inferred.
global_constraints¶ (dict, default={'emissions': float('+inf')}) –
Dictionairy of numeric values mapped to global constraint naming strings.

Recognized constraint keys are:
- emissions
- …
For a more detailed explanation see the user guide’s section: Secondary Objectives
uid¶ (Hashable, default='automatically_transformed') – Hashable unique identifier. Usually a string aka a name.

Returns:

energy_system – The dictionairy transformed oemof energy system, ready for simulation.

Return type:

AbstractEnergySystem

Examples

Using the pure_python energy system mapping example:

>>> from tessif.examples.data.tsf.py_mapping.fpwe import mapping as m
>>> import tessif.parse as parse
>>> import tessif.transform.mapping2es.tsf as ttsf
>>> esys = ttsf.transform(parse.python_mapping(m))
>>> for node in esys.nodes:
...     for k, v in node.attributes.items():
...         print('{} = {}'.format(
...             k, sorted(v) if isinstance(v, frozenset) else v))
... # frozensets are printed as sorted lists for doctesting consistency
...     print(50*'-')
...     print()
inputs = ['Gas Station.fuel']
interfaces = ['Gas Station.fuel', 'Generator.fuel']
outputs = ['Generator.fuel']
timeseries = None
uid = Pipeline
--------------------------------------------------

inputs = ['Battery.electricity', 'Generator.electricity']
interfaces = ['Battery.electricity', 'Demand.electricity', 'Generator.electricity']
outputs = ['Battery.electricity', 'Demand.electricity']
timeseries = None
uid = Power Line
--------------------------------------------------

accumulated_amounts = {'fuel': MinMax(min=0, max=inf)}
costs_for_being_active = 0
expandable = {'fuel': False}
expansion_costs = {'fuel': 0}
expansion_limits = {'fuel': MinMax(min=0, max=inf)}
flow_costs = {'fuel': 0}
flow_emissions = {'fuel': 0}
flow_gradients = {'fuel': PositiveNegative(positive=42, negative=42)}
flow_rates = {'fuel': MinMax(min=0, max=22)}
gradient_costs = {'fuel': PositiveNegative(positive=1, negative=1)}
initial_status = True
interfaces = ['fuel']
milp = {'fuel': False}
number_of_status_changes = OnOff(on=1, off=1)
outputs = ['fuel']
status_changing_costs = OnOff(on=1, off=1)
status_inertia = OnOff(on=1, off=1)
timeseries = {'fuel': MinMax(min=0, max=array([10, 22, 22]))}
uid = Gas Station
--------------------------------------------------

accumulated_amounts = {'electricity': MinMax(min=0, max=inf)}
costs_for_being_active = 0
expandable = {'electricity': False}
expansion_costs = {'electricity': 0}
expansion_limits = {'electricity': MinMax(min=0, max=inf)}
flow_costs = {'electricity': 0}
flow_emissions = {'electricity': 0}
flow_gradients = {'electricity': PositiveNegative(positive=11, negative=11)}
flow_rates = {'electricity': MinMax(min=8, max=11)}
gradient_costs = {'electricity': PositiveNegative(positive=0, negative=0)}
initial_status = True
inputs = ['electricity']
interfaces = ['electricity']
milp = {'electricity': False}
number_of_status_changes = OnOff(on=2, off=1)
status_changing_costs = OnOff(on=2, off=1)
status_inertia = OnOff(on=2, off=1)
timeseries = None
uid = Demand
--------------------------------------------------

conversions = {('fuel', 'electricity'): 0.42}
costs_for_being_active = 0
expandable = {'electricity': False, 'fuel': False}
expansion_costs = {'electricity': 0, 'fuel': 0}
expansion_limits = {'electricity': MinMax(min=0, max=inf), 'fuel': MinMax(min=0, max=inf)}
flow_costs = {'electricity': 0, 'fuel': 0}
flow_emissions = {'electricity': 0, 'fuel': 0}
flow_gradients = {'electricity': PositiveNegative(positive=10, negative=10), 'fuel': PositiveNegative(positive=50, negative=50)}
flow_rates = {'electricity': MinMax(min=5, max=15), 'fuel': MinMax(min=0, max=50)}
gradient_costs = {'electricity': PositiveNegative(positive=0, negative=0), 'fuel': PositiveNegative(positive=0, negative=0)}
initial_status = True
inputs = ['fuel']
interfaces = ['electricity', 'fuel']
milp = {'electricity': True, 'fuel': False}
number_of_status_changes = OnOff(on=0, off=2)
outputs = ['electricity']
status_changing_costs = OnOff(on=0, off=2)
status_inertia = OnOff(on=0, off=2)
timeseries = {'electricity': MinMax(min=0, max=array([10, 22, 22]))}
uid = Generator
--------------------------------------------------

capacity = 100
costs_for_being_active = 0
expandable = {'electricity': False}
expansion_costs = {'electricity': 0}
expansion_limits = {'electricity': MinMax(min=0, max=inf)}
final_soc = None
fixed_expansion_ratios = {'electricity': True}
flow_costs = {'electricity': 0}
flow_efficiencies = {'electricity': InOut(inflow=0.95, outflow=0.98)}
flow_emissions = {'electricity': 0}
flow_gradients = {'electricity': PositiveNegative(positive=10, negative=10)}
flow_rates = {'electricity': MinMax(min=0, max=10)}
gradient_costs = {'electricity': PositiveNegative(positive=0, negative=0)}
idle_changes = PositiveNegative(positive=0, negative=1)
initial_soc = 10
initial_status = True
input = electricity
interfaces = ['electricity']
milp = {'electricity': False}
number_of_status_changes = OnOff(on=0, off=2)
output = electricity
status_changing_costs = OnOff(on=0, off=2)
status_inertia = OnOff(on=0, off=2)
timeseries = None
uid = Battery
--------------------------------------------------

Get the inspected timeframe:

>>> print(esys.timeframe)
DatetimeIndex(['1990-07-13 00:00:00', '1990-07-13 01:00:00',
               '1990-07-13 02:00:00'],
              dtype='datetime64[ns]', freq='H')

Use the automatically generated energy system to transform it into a networkx.Graph to facilitate plotting:

>>> import tessif.visualize.nxgrph as nxv
>>> import matplotlib.pyplot as plt
>>> grph = esys.to_nxgrph()
>>> drawings = nxv.draw_graph(
...     grph, node_color='green', edge_color='pink')
>>> plt.draw()