battery_optimizer.de.ev.battery_optimization#
Classes
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- class battery_optimizer.de.ev.battery_optimization.EVDEBatteryOptimizer(*args, request_id, country=Country.DE, user_id, request_creation_time, elastic_filter=False, already_auctioned_as_constants=True, asset, verbose=False, epsilon=1e-06, commercial_objective=CommercialObjective(commercial_columns=['commodity_revenue', 'commodity_cost']), multiplier_buy_sell_exceed_limit=10.0, upper_bound_risk_increment=None, imbalance=Imbalance(imbalance_cost_per_MWh=1000000.0, min_imbalance_cost_per_MWh=100000.0, imbalance_cost_shaping=True), solve_optimization_problem_max_seconds=90.0, intraday_strategy=IntradayStrategy(name=<IntradayStrategyName.vwap: 'vwap'>, delivery_length=<IntradayDeliveryLength.quarter_hour: 'QuarterHour'>, tradeable_time_window_minutes=NaT), intraday_result_aggregation=IntradayResultAggregation(name=<IntradayResultAggregationName.minmax: 'minmax'>, no_buckets=1), objective=Objective(name=<ObjectiveName.pnl: 'pnl'>), solver_settings=SolverSettings(gapRel=0.01), do_input_for_intraday_bucketing_correct_bucket=True, elastic_soe_constraints=False, global_max_charging_power_kw=None, global_max_discharging_power_kw=None, feature_flags=FeatureFlags(return_pickled_optimizer=False), use_case=UseCase.BESS)#
Bases:
EVDERequest,DEBatteryOptimizer- Parameters:
request_id (str)
country (Country)
request_creation_time (datetime)
elastic_filter (bool)
already_auctioned_as_constants (bool)
asset (EVDEAsset)
verbose (bool)
epsilon (float)
commercial_objective (CommercialObjective)
multiplier_buy_sell_exceed_limit (Annotated[float, Ge(ge=0.0)])
upper_bound_risk_increment (float | dict | UpperBoundRiskIncrement | None)
imbalance (Imbalance)
solve_optimization_problem_max_seconds (float)
intraday_strategy (IntradayStrategy)
intraday_result_aggregation (IntradayResultAggregation)
objective (Objective)
solver_settings (SolverSettings)
do_input_for_intraday_bucketing_correct_bucket (bool)
elastic_soe_constraints (bool)
global_max_charging_power_kw (float | None)
global_max_discharging_power_kw (float | None)
feature_flags (FeatureFlags)
use_case (UseCase)
- add_use_case_specific_constraints()#
Add use case specific constraints to the dataframe.
- Args:
df (pd.DataFrame): DataFrame containing the optimization data.
- Returns:
pd.DataFrame: DataFrame with added use case specific constraints.
- create_minimum_power_constraints(problem, variables)#
- 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].