battery_optimizer_app.models#

Functions

get_battery_optimizer_discriminator(request)

Composite discriminator combining country and use_case.

Classes

`APIWrappedAFRREnergyRequest`(*, request_id[, ...])
`APIWrappedBatteryOptimizer`(*, request_id[, ...])
`APIWrappedBodRequest`(*, request_id[, ...])
`APIWrappedMelMilBoaRequest`(*, request_id[, ...])
`APIWrappedMelMilNowRequest`(*, request_id[, ...])
`APIWrappedMelMilRequest`(*, request_id[, ...])
`aFRREnergyPosition`(*, start, volume_MW, ...)
`aFRREnergyPositionMetadata`(*, result)
`aFRREnergyPositions`(*, start, afrr_positive, ...)
`aFRREnergyPremium`(*, start[, ...])
`aFRREnergyResult`(*, request, positions)

class battery_optimizer_app.models.APIWrappedAFRREnergyRequest(*, request_id, problem_type=ProblemType.AFRR_ENERGY, payload, orderbook=None, bucketing_approach=BucketingApproach.whole, blocked_capacities=[], timestamps, premiums=[], premium_lower_bound_per_MWh=10.0, premium_upper_bound_per_MWh=9999.0, premium_multiplier=3.0)#

Bases: APIWrappedBatteryOptimizer

Parameters:

request_id (str)
problem_type (ProblemType)
payload (DERequest)
orderbook (str | None)
bucketing_approach (BucketingApproach)
blocked_capacities (list[EnergyMarketContinuousPositions])
timestamps (list[datetime])
premiums (list[aFRREnergyPremium])
premium_lower_bound_per_MWh (Annotated[float, Ge(ge=0.0)])
premium_upper_bound_per_MWh (Annotated[float, Ge(ge=0.0)])
premium_multiplier (Annotated[float, Ge(ge=0.0)])

ensure_premiums_are_not_null()#

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].

payload: DERequest#

premium_lower_bound_per_MWh: float#

premium_multiplier: float#

premium_upper_bound_per_MWh: float#

premiums: list[aFRREnergyPremium]#

problem_type: ProblemType#

timestamps: list[datetime]#

class battery_optimizer_app.models.APIWrappedBatteryOptimizer(*, request_id, problem_type=ProblemType.PNL, payload, orderbook=None, bucketing_approach=BucketingApproach.whole, blocked_capacities=[])#

Bases: BaseModel

Parameters:

request_id (str)
problem_type (ProblemType)
payload (Annotated[Annotated[UKRequest, Tag(tag=(<Country.UK: 'uk'>, <UseCase.BESS: 'bess'>))] | Annotated[DERequest, Tag(tag=(<Country.DE: 'de'>, <UseCase.BESS: 'bess'>))] | Annotated[EVDERequest, Tag(tag=(<Country.DE: 'de'>, <UseCase.EV: 'ev'>))] | Annotated[EVUKRequest, Tag(tag=(<Country.UK: 'uk'>, <UseCase.EV: 'ev'>))], Discriminator(discriminator=~battery_optimizer_app.models.get_battery_optimizer_discriminator, custom_error_type=None, custom_error_message=None, custom_error_context=None)])
orderbook (str | None)
bucketing_approach (BucketingApproach)
blocked_capacities (list[EnergyMarketContinuousPositions])

aggregate_blocked_capacities()#: Generally, we may be sent blocked capacities with overlapping time ranges, i.e. multuple entries for the same time_from and time_to. Here, we aggregate such entries by summing their volumes.

classmethod avoid_premature_intraday_buckets_check(data)#

blocked_capacities: list[EnergyMarketContinuousPositions]#

bucketing_approach: BucketingApproach#

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].

orderbook: str | None#

payload: get_battery_optimizer_discriminator, custom_error_type=None, custom_error_message=None, custom_error_context=None)]#

problem_type: ProblemType#

reinstate_buckets_check()#

request_id: str#

validate_blocked_capacities_timestamps()#: Validate that blocked_capacities timestamps align with optimization_time_range. Both time_from and time_to must be within the optimization bounds.

class battery_optimizer_app.models.APIWrappedBodRequest(*, request_id, problem_type=ProblemType.BOD, payload)#

Bases: BaseModel

Parameters:

request_id (str)
problem_type (ProblemType)
payload (BodRequest)

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].

payload: BodRequest#

problem_type: ProblemType#

request_id: str#

class battery_optimizer_app.models.APIWrappedMelMilBoaRequest(*, request_id, problem_type=ProblemType.MELMILBOA, payload, boas=None, start=None, baseline)#

Bases: APIWrappedMelMilRequest

Parameters:

request_id (str)
problem_type (ProblemType)
payload (UKRequest)
boas (list[BOA] | None)
start (datetime | None)
baseline (list[BaselinePoint])

baseline: list[BaselinePoint]#

boas: list[BOA] | None#

ensure_soc_level_after_boa_is_given()#

ensure_soc_level_of_boa_is_given()#

property latest_boa_timestamp: 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].

problem_type: ProblemType#

property soc_fraction_after_boa: float#

start: datetime | None#

class battery_optimizer_app.models.APIWrappedMelMilNowRequest(*, request_id, problem_type=ProblemType.MELMILNOW, payload)#

Bases: APIWrappedMelMilRequest

Parameters:

request_id (str)
problem_type (ProblemType)
payload (UKMelMilNowRequest)

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].

payload: UKMelMilNowRequest#

problem_type: ProblemType#

class battery_optimizer_app.models.APIWrappedMelMilRequest(*, request_id, problem_type=ProblemType.MELMIL, payload)#

Bases: BaseModel

Parameters:

request_id (str)
problem_type (ProblemType)
payload (UKRequest)

ensure_asset_strategy_optimization_markets_is_empty()#

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].

payload: UKRequest#

problem_type: ProblemType#

request_id: str#

class battery_optimizer_app.models.aFRREnergyPosition(*, start, volume_MW, base_price_per_MWh, premium_per_MWh, price_per_MWh, market, metadata, blocked_capacities=[])#

Bases: BaseModel

Parameters:

start (datetime)
volume_MW (Annotated[float, Ge(ge=0.0)])
base_price_per_MWh (float)
premium_per_MWh (float)
price_per_MWh (float)
market (MarketType)
metadata (aFRREnergyPositionMetadata | None)
blocked_capacities (list[EnergyMarketContinuousPositions])

base_price_per_MWh: float#

blocked_capacities: list[EnergyMarketContinuousPositions]#

market: MarketType#

metadata: aFRREnergyPositionMetadata | 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].

premium_per_MWh: float#

price_per_MWh: float#

start: datetime#

volume_MW: float#

class battery_optimizer_app.models.aFRREnergyPositionMetadata(*, 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_app.models.aFRREnergyPositions(*, start, afrr_positive, afrr_negative)#

Bases: BaseModel

Parameters:

start (datetime)
afrr_positive (aFRREnergyPosition)
afrr_negative (aFRREnergyPosition)

afrr_negative: aFRREnergyPosition#

afrr_positive: aFRREnergyPosition#

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: datetime#

class battery_optimizer_app.models.aFRREnergyPremium(*, start, afrr_positive_premium_per_MWh=None, afrr_negative_premium_per_MWh=None)#

Bases: BaseModel

Parameters:

start (datetime)
afrr_positive_premium_per_MWh (Annotated[float | None, Ge(ge=-4999.0), Le(le=4999.0)])
afrr_negative_premium_per_MWh (Annotated[float | None, Ge(ge=-4999.0), Le(le=4999.0)])

afrr_negative_premium_per_MWh: float | None#

afrr_positive_premium_per_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].

start: datetime#

class battery_optimizer_app.models.aFRREnergyResult(*, request, positions)#

Bases: BaseModel

Parameters:

request (APIWrappedAFRREnergyRequest)
positions (list[aFRREnergyPositions])

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].

positions: list[aFRREnergyPositions]#

request: APIWrappedAFRREnergyRequest#

battery_optimizer_app.models.get_battery_optimizer_discriminator(request)#

Composite discriminator combining country and use_case. Backward compatible: missing use_case -> BESS.

Parameters:: request (dict)
Return type:: tuple[str, str]