battery_optimizer.de.result#

Classes

`DEMarketPositions`(*, intraday, imbalance, ...)
`DEResult`(*, asset_id, request_id, ...[, ...])
`DESoE`(*, time_from, time_to, soe_target, ...)

class battery_optimizer.de.result.DEMarketPositions(*, intraday, imbalance, epexIDA1, epexDA, fcr, afrr_capacity_pos, afrr_capacity_neg, afrr_energy_pos, afrr_energy_neg)#

Bases: MarketPositions

Parameters:

intraday (list[EnergyMarketContinuousPositions] | DataFrame)
imbalance (list[Imbalance] | DataFrame)
epexIDA1 (list[EnergyMarketAuctionedPositions] | DataFrame)
epexDA (list[EnergyMarketAuctionedPositions] | DataFrame)
fcr (list[CapacityMarketPositions] | DataFrame)
afrr_capacity_pos (list[CapacityMarketPositions] | DataFrame)
afrr_capacity_neg (list[CapacityMarketPositions] | DataFrame)
afrr_energy_pos (list[CapacityMarketPositions] | DataFrame)
afrr_energy_neg (list[CapacityMarketPositions] | DataFrame)

afrr_capacity_neg: list[CapacityMarketPositions] | DataFrame#

afrr_capacity_pos: list[CapacityMarketPositions] | DataFrame#

afrr_energy_neg: list[CapacityMarketPositions] | DataFrame#

afrr_energy_pos: list[CapacityMarketPositions] | DataFrame#

epexDA: list[EnergyMarketAuctionedPositions] | DataFrame#

epexIDA1: list[EnergyMarketAuctionedPositions] | DataFrame#

fcr: list[CapacityMarketPositions] | 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].

class battery_optimizer.de.result.DEResult(*, asset_id, request_id, battery_optimizer_commit_sha, user_id, request_creation_time, result_creation_time, request, market_positions, markets_optimized, markets_already_auctioned, soe, discharge_over_daily_cycle_limit_kWh=None, discharge_over_daily_cycle_limit_count=None, intraday_buckets=None, aggregated_intraday_buckets=None, trace=None, pulp_solver_variables_values_delta_false=None, pulp_solver_variables_values_delta_true=None, optimizer_object=None)#

Bases: Result

Parameters:

asset_id (str | int)
request_id (str)
battery_optimizer_commit_sha (str | None)
user_id (str | int)
request_creation_time (datetime)
result_creation_time (datetime)
request (DERequest)
market_positions (DEMarketPositions)
markets_optimized (set[str] | Dict[datetime, set[str]])
markets_already_auctioned (Dict[datetime, set[str]])
soe (list[SoE] | DataFrame)
discharge_over_daily_cycle_limit_kWh (dict | None)
discharge_over_daily_cycle_limit_count (dict | None)
intraday_buckets (Dict[datetime, IntradayBuckets] | None)
aggregated_intraday_buckets (Dict[datetime, IntradayBuckets] | None)
trace (Any)
pulp_solver_variables_values_delta_false (list[dict] | None)
pulp_solver_variables_values_delta_true (list[dict] | None)
optimizer_object (str | None)

classmethod add_use_case_specific_soe_fields(solved_problem, soe, result)#

See base class Result.add_use_case_specific_soe_fields.

Parameters:

solved_problem (DEBatteryOptimizer)
soe (DataFrame)
result (DataFrame)

Return type:

DataFrame

classmethod get_soe_model()#

Hook method to return the class used for SoE records.

Return type:: type[SoE]

market_positions: DEMarketPositions#

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].

request: DERequest#

class battery_optimizer.de.result.DESoE(*, time_from, time_to, soe_target, soe_target_kwh, max_charge_kW, max_discharge_kW, charge_power_kW, discharge_power_kW, min_soe_with_flex_positive_reservation_kwh, min_soe_with_flex_positive_reservation, max_soe_with_flex_negative_reservation_kwh, max_soe_with_flex_negative_reservation)#

Bases: SoE

Parameters:

time_from (datetime)
time_to (datetime)
soe_target (float)
soe_target_kwh (float)
max_charge_kW (float)
max_discharge_kW (float)
charge_power_kW (Annotated[float, Ge(ge=0.0)])
discharge_power_kW (Annotated[float, Ge(ge=0.0)])
min_soe_with_flex_positive_reservation_kwh (float)
min_soe_with_flex_positive_reservation (float)
max_soe_with_flex_negative_reservation_kwh (float)
max_soe_with_flex_negative_reservation (float)

max_soe_with_flex_negative_reservation: float#

max_soe_with_flex_negative_reservation_kwh: float#

min_soe_with_flex_positive_reservation: float#

min_soe_with_flex_positive_reservation_kwh: 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].