battery_optimizer.uk.mel_mil#

Functions

`create_melmil_result_from_dataframe`(...[, trace])
`optimize_mel_mil_request`(request)

Classes

`BMKneePoint`(*, time_from, power_MW[, ...])
`BMKneePointMetadata`(*, result)
`BMPosition`(*, time_from, time_to, power_MW)
`BOA`(*, BOATimepoints)
`BOATimepoint`(*, time_from, power_kW)
`BaselinePoint`(*, time_from, power_kW[, ...])
`EnergyInterval`(*, start_time, end_time, ...)	NESO-style interval carrying an energy value at each of its two ends.
`MDOMDBResult`(*, request_id[, problem_type, ...])
`MELMILResult`(*, request_id[, problem_type, ...])
`PowerInterval`(*, start_time, end_time, ...)	NESO-style interval carrying a power value at each of its two ends.

class battery_optimizer.uk.mel_mil.BMKneePoint(*, time_from, power_MW, energy_MWh=None, energy_slope=False, anchor, metadata=None)#

Bases: BaseModel

Parameters:

time_from (datetime)
power_MW (float)
energy_MWh (float | None)
energy_slope (bool)
anchor (SoEAnchor)
metadata (BMKneePointMetadata | None)

anchor: SoEAnchor#

energy_MWh: float | None#

energy_slope: bool#

metadata: BMKneePointMetadata | None#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

power_MW: float#

time_from: datetime#

class battery_optimizer.uk.mel_mil.BMKneePointMetadata(*, result)#

Bases: BaseModel

Parameters:: result (DataFrame)

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

result: DataFrame#

class battery_optimizer.uk.mel_mil.BMPosition(*, time_from, time_to, power_MW, energy_MWh=None)#

Bases: BaseModel

Parameters:

time_from (datetime)
time_to (datetime)
power_MW (float)
energy_MWh (float | None)

energy_MWh: float | None#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

power_MW: float#

time_from: datetime#

time_to: datetime#

class battery_optimizer.uk.mel_mil.BOA(*, BOATimepoints)#

Bases: BaseModel

Parameters:: BOATimepoints (list[BOATimepoint])

BOATimepoints: list[BOATimepoint]#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class battery_optimizer.uk.mel_mil.BOATimepoint(*, time_from, power_kW)#

Bases: BaseModel

Parameters:

time_from (datetime)
power_kW (float)

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

power_kW: float#

time_from: datetime#

class battery_optimizer.uk.mel_mil.BaselinePoint(*, time_from, power_kW, expected_soc_fraction=None)#

Bases: BaseModel

Parameters:

time_from (datetime)
power_kW (float)
expected_soc_fraction (Annotated[float | None, Ge(ge=0.0), Le(le=1.0)])

expected_soc_fraction: float | None#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

power_kW: float#

time_from: datetime#

class battery_optimizer.uk.mel_mil.EnergyInterval(*, start_time, end_time, start_mwh, end_mwh)#

Bases: BaseModel

NESO-style interval carrying an energy value at each of its two ends.

Parameters:

start_time (datetime)
end_time (datetime)
start_mwh (float)
end_mwh (float)

end_mwh: float#

end_time: datetime#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start_mwh: float#

start_time: datetime#

class battery_optimizer.uk.mel_mil.MDOMDBResult(*, request_id, problem_type=ProblemType.MDO_MDB, asset_id, request_creation_time, result_creation_time, mel, mil, mdo, mdb, request, trace=None)#

Bases: BaseModel

Parameters:

request_id (str)
problem_type (ProblemType)
asset_id (str | int)
request_creation_time (datetime)
result_creation_time (datetime)
mel (list[PowerInterval])
mil (list[PowerInterval])
mdo (list[EnergyInterval])
mdb (list[EnergyInterval])
request (UKRequest | dict)
trace (Any)

asset_id: str | int#

mdb: list[EnergyInterval]#

mdo: list[EnergyInterval]#

mel: list[PowerInterval]#

mil: list[PowerInterval]#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

problem_type: ProblemType#

request: UKRequest | dict#

request_creation_time: datetime#

request_id: str#

result_creation_time: datetime#

trace: Any#

class battery_optimizer.uk.mel_mil.MELMILResult(*, request_id, problem_type=ProblemType.MELMIL, asset_id, request_creation_time, result_creation_time, mel, mil, request, trace=None)#

Bases: BaseModel

Parameters:

request_id (str)
problem_type (ProblemType)
asset_id (str | int)
request_creation_time (datetime)
result_creation_time (datetime)
mel (list[BMPosition])
mil (list[BMPosition])
request (UKRequest | dict)
trace (Any)

asset_id: str | int#

mel: list[BMPosition]#

mil: list[BMPosition]#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

problem_type: ProblemType#

request: UKRequest | dict#

request_creation_time: datetime#

request_id: str#

result_creation_time: datetime#

trace: Any#

class battery_optimizer.uk.mel_mil.PowerInterval(*, start_time, end_time, start_mw, end_mw)#

Bases: BaseModel

NESO-style interval carrying a power value at each of its two ends.

Parameters:

start_time (datetime)
end_time (datetime)
start_mw (float)
end_mw (float)

end_mw: float#

end_time: datetime#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'frozen': False, 'json_encoders': {<class 'datetime.datetime'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function BaseModel.<lambda>>, <class 'pandas._libs.tslibs.timestamps.Timestamp'>: <function BaseModel.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function BaseModel.<lambda>>, <class 'pandas.core.series.Series'>: <function BaseModel.<lambda>>}, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

start_mw: float#

start_time: datetime#

battery_optimizer.uk.mel_mil.create_melmil_result_from_dataframe(request_id, problem_type, asset_id, request_creation_time, mel_df, mil_df, request, trace=None)#

Parameters:

request_id (str)
problem_type (ProblemType)
asset_id (str | int)
request_creation_time (datetime)
mel_df (DataFrame)
mil_df (DataFrame)
request (UKRequest | dict)
trace (Any)

Return type:

MELMILResult

battery_optimizer.uk.mel_mil.optimize_mel_mil_request(request)#