time_sequential modules

The module provides tools constructing time sequential models using nempy.

Functions:

construct_ramp_rate_parameters(...)	Combine dispatch and ramp rates into the ramp rate inputs compatible with the SpotMarket class.
create_seed_ramp_rate_parameters(...)	Combine historical dispatch and as bid ramp rates to get seed ramp rate parameters for a time sequential model.

nempy.time_sequential.construct_ramp_rate_parameters(last_interval_dispatch, ramp_rates)

Combine dispatch and ramp rates into the ramp rate inputs compatible with the SpotMarket class.

Examples

>>> last_interval_dispatch = pd.DataFrame({
... 'unit': ['A', 'A', 'B'],
... 'service': ['energy', 'raise_reg', 'energy'],
... 'dispatch': [45.0, 50.0, 88.0]})

>>> ramp_rates = pd.DataFrame({
... 'unit': ['A', 'B', 'C'],
... 'ramp_up_rate': [600.0, 1200.0, 700.0],
... 'ramp_down_rate': [600.0, 1200.0, 700.0]})

>>> construct_ramp_rate_parameters(last_interval_dispatch,
...                                ramp_rates)
  unit  initial_output  ramp_up_rate  ramp_down_rate
0    A            45.0         600.0           600.0
1    B            88.0        1200.0          1200.0
2    C             0.0         700.0           700.0

Parameters:

• last_interval_dispatch (pd.DataFrame) –

  Columns:	Description:
  unit	unique identifier of a dispatch unit (as str)
  service	the service being provided, optional, default ‘energy’, (as str)
  dispatch	the dispatch target from the previous dispatch interval, in MW, (as np.float64)

• ramp_rates (pd.DataFrame) –

  Columns:	Description:
  unit	unique identifier for units, (as str)
  ramp_up_rate	the ramp up rate, in MW/h, (as np.float64)
  ramp_down_rate	the ramp down rate, in MW/h, (as np.float64)

Returns:

Columns:	Description:
unit	unique identifier for units, (as str)
initial_output	the output/consumption of the unit at the start of the dispatch interval, in MW, (as np.float64)
ramp_up_rate	the ramp up rate, in MW/h, (as np.float64)
ramp_down_rate	the ramp down rate, in MW/h, (as np.float64)

Return type:

pd.DataFrame

nempy.time_sequential.create_seed_ramp_rate_parameters(historical_dispatch, as_bid_ramp_rates)

Combine historical dispatch and as bid ramp rates to get seed ramp rate parameters for a time sequential model.

Examples

>>> historical_dispatch = pd.DataFrame({
... 'unit': ['A', 'B'],
... 'initial_output': [80.0, 100.0]})

>>> as_bid_ramp_rates = pd.DataFrame({
... 'unit': ['A', 'B'],
... 'ramp_down_rate': [600.0, 1200.0],
... 'ramp_up_rate': [600.0, 1200.0]})

>>> create_seed_ramp_rate_parameters(historical_dispatch,
...                                  as_bid_ramp_rates)
  unit  initial_output  ramp_down_rate  ramp_up_rate
0    A            80.0           600.0         600.0
1    B           100.0          1200.0        1200.0

Parameters:

• historical_dispatch (pd.DataFrame) –

  Columns:	Description:
  unit	unique identifier for units, (as str)
  initial_output	the output/consumption of the unit at the start of the dispatch interval, in MW, (as np.float64)

• as_bid_ramp_rates –

  Columns:	Description:
  unit	unique identifier for units, (as str)
  ramp_up_rate	the ramp up rate, in MW/h, (as np.float64)
  ramp_down_rate	the ramp down rate, in MW/h, (as np.float64)

Returns:

Columns:	Description:
unit	unique identifier for units, (as str)
initial_output	the output/consumption of the unit at the start of the dispatch interval, in MW, (as np.float64)
ramp_up_rate	the ramp up rate, in MW/h, (as np.float64)
ramp_down_rate	the ramp down rate, in MW/h, (as np.float64)

Return type:

pd.DataFrame