battery_optimizer.uk.ev.result#

Classes

`EVEnergyMarketContinuousPositions`(*, ...[, ...])
`EVSoE`(*, time_from, time_to, soe_target, ...)
`EVUKResult`(*, asset_id, request_id, ...[, ...])

class battery_optimizer.uk.ev.result.EVEnergyMarketContinuousPositions(*, time_from, time_to, volume_sold_MW=None, volume_bought_MW=None, energy_sell_price_per_MWh=None, energy_buy_price_per_MWh=None)#

Bases: EnergyMarketContinuousPositions

Parameters:

time_from (datetime)
time_to (datetime)
volume_sold_MW (float | None)
volume_bought_MW (float | None)
energy_sell_price_per_MWh (float | None)
energy_buy_price_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].

classmethod round_bought_MW_to_nearest_kW(volume_bought_MW)#

classmethod round_sold_MW_to_nearest_kW(volume_sold_MW)#

class battery_optimizer.uk.ev.result.EVSoE(*, time_from, time_to, soe_target, soe_target_kwh, max_charge_kW, max_discharge_kW, charge_power_kW, discharge_power_kW)#

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)])

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].

classmethod validate_charge_power_kW(charge_power_kW, values)#

class battery_optimizer.uk.ev.result.EVUKResult(*, 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, commercial_results_per_settlement_period=None, commercial_results=None, dynamic_services_energy_throughput_buffer=None)#

Bases: UKResult

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 (UKRequest)
market_positions (UKMarketPositions)
markets_optimized (set[str] | Dict[datetime, set[str]])
markets_already_auctioned (Dict[datetime, set[str]])
soe (list[EVSoE] | 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)
commercial_results_per_settlement_period (list[CommercialResults] | DataFrame | None)
commercial_results (list[CommercialResults] | DataFrame | None)
dynamic_services_energy_throughput_buffer (list[DynamicServicesEnergyThroughputBuffer] | DataFrame | None)

classmethod get_intraday_position_model()#

Hook method to return the class used for intraday market positions.

Return type:: type[EnergyMarketContinuousPositions]

classmethod get_soe_model()#

Hook method to return the class used for SoE records.

Return type:: type[EVSoE]

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].

soe: list[EVSoE] | DataFrame#