battery_optimizer.uk.result

Classes

CommercialResults(*, timestamp, ...)
DynamicServicesEnergyThroughputBuffer(*, start)
UKMarketPositions(*, intraday, imbalance, ...)
UKResult(*, asset_id, request_id, ...[, ...])

class battery_optimizer.uk.result.CommercialResults(*, timestamp, delivery_date, third_party_buy_GBP, third_party_sell_GBP, third_party_sum_GBP, dc_high_revenue_GBP, dc_low_revenue_GBP, dc_sum_revenue_GBP, dm_high_revenue_GBP, dm_low_revenue_GBP, dr_high_revenue_GBP, dr_low_revenue_GBP, commodity_buy_GBP, commodity_sell_GBP, commodity_sum_GBP, commodity_buy_MWh, commodity_sell_MWh, pv_revenue_GBP, pv_volume_MWh, sum_mas_GBP, sum_dc_mas_GBP, sum_pv_dc_mas_GBP, imbalance_import_MWh, imbalance_export_MWh)

Bases: BaseModel

Parameters:

• timestamp (datetime)

• delivery_date (date)

• third_party_buy_GBP (float)

• third_party_sell_GBP (float)

• third_party_sum_GBP (float)

• dc_high_revenue_GBP (float)

• dc_low_revenue_GBP (float)

• dc_sum_revenue_GBP (float)

• dm_high_revenue_GBP (float)

• dm_low_revenue_GBP (float)

• dr_high_revenue_GBP (float)

• dr_low_revenue_GBP (float)

• commodity_buy_GBP (float)

• commodity_sell_GBP (float)

• commodity_sum_GBP (float)

• commodity_buy_MWh (float)

• commodity_sell_MWh (float)

• pv_revenue_GBP (float)

• pv_volume_MWh (float)

• sum_mas_GBP (float)

• sum_dc_mas_GBP (float)

• sum_pv_dc_mas_GBP (float)

• imbalance_import_MWh (float)

• imbalance_export_MWh (float)

commodity_buy_GBP: float

commodity_buy_MWh: float

commodity_sell_GBP: float

commodity_sell_MWh: float

commodity_sum_GBP: float

dc_high_revenue_GBP: float

dc_low_revenue_GBP: float

dc_sum_revenue_GBP: float

delivery_date: date

dm_high_revenue_GBP: float

dm_low_revenue_GBP: float

dr_high_revenue_GBP: float

dr_low_revenue_GBP: float

imbalance_export_MWh: float

imbalance_import_MWh: 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].

pv_revenue_GBP: float

pv_volume_MWh: float

sum_dc_mas_GBP: float

sum_mas_GBP: float

sum_pv_dc_mas_GBP: float

third_party_buy_GBP: float

third_party_sell_GBP: float

third_party_sum_GBP: float

timestamp: datetime

class battery_optimizer.uk.result.DynamicServicesEnergyThroughputBuffer(*, start, dcl_MWh_per_MW=0.0, dch_MWh_per_MW=0.0, dml_MWh_per_MW=0.0, dmh_MWh_per_MW=0.0, drl_MWh_per_MW=0.0, drh_MWh_per_MW=0.0, ds_energy_throughput_buffer_low, ds_energy_throughput_buffer_high)

Bases: DSEnergyThroughput

Parameters:

• start (datetime)

• dcl_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• dch_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• dml_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• dmh_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• drl_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• drh_MWh_per_MW (Annotated[float, Ge(ge=0.0)])

• ds_energy_throughput_buffer_low (float)

• ds_energy_throughput_buffer_high (float)

ds_energy_throughput_buffer_high: float

ds_energy_throughput_buffer_low: 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].

class battery_optimizer.uk.result.UKMarketPositions(*, intraday, imbalance, n2ex1h, epex30min, dcl, dch, dml, dmh, drl, drh)

Bases: MarketPositions

Parameters:

• intraday (list[EnergyMarketContinuousPositions] | DataFrame)

• imbalance (list[Imbalance] | DataFrame)

• n2ex1h (list[EnergyMarketAuctionedPositions] | DataFrame)

• epex30min (list[EnergyMarketAuctionedPositions] | DataFrame)

• dcl (list[CapacityMarketPositions] | DataFrame)

• dch (list[CapacityMarketPositions] | DataFrame)

• dml (list[CapacityMarketPositions] | DataFrame)

• dmh (list[CapacityMarketPositions] | DataFrame)

• drl (list[CapacityMarketPositions] | DataFrame)

• drh (list[CapacityMarketPositions] | DataFrame)

dch: list[CapacityMarketPositions] | DataFrame

dcl: list[CapacityMarketPositions] | DataFrame

dmh: list[CapacityMarketPositions] | DataFrame

dml: list[CapacityMarketPositions] | DataFrame

drh: list[CapacityMarketPositions] | DataFrame

drl: list[CapacityMarketPositions] | DataFrame

epex30min: list[EnergyMarketAuctionedPositions] | 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].

n2ex1h: list[EnergyMarketAuctionedPositions] | DataFrame

class battery_optimizer.uk.result.UKResult(*, 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: 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 (UKRequest)

• market_positions (UKMarketPositions)

• 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)

• 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 amend_result_dict(result_dict, solved_problem, delta=False)

Amend the result dictionary with additional information.

Parameters:

• result_dict: The result dictionary to amend.

• solved_problem: The solved optimization problem.

• delta: Whether to use delta values.

Returns:

• The amended result dictionary.

Parameters:

• result_dict (dict)

• solved_problem (UKBatteryOptimizer)

Return type:

dict

commercial_results: list[CommercialResults] | DataFrame | None

commercial_results_per_settlement_period: list[CommercialResults] | DataFrame | None

classmethod create_dataframe_from_solved_problem(solved_problem, delta=False)

Parameters:

solved_problem (UKBatteryOptimizer)

Return type:

dict

classmethod create_melmil_result_from_solved_problem(solved_problem, delta=False, mel_mil=ObjectiveName.mel)

Create Result object from solved BatteryOptimizer problem.

Parameters:

• solved_problem: The solved optimization problem.

• markets: Markets that we want to compute the objective for, especially for the commercial results.

Parameters:

• solved_problem (UKBatteryOptimizer)

• mel_mil (ObjectiveName)

dynamic_services_energy_throughput_buffer: list[DynamicServicesEnergyThroughputBuffer] | DataFrame | None

static get_commercial_results(solved_problem, market_positions, frequency, markets_commercial_results)

Parameters:

• solved_problem (UKBatteryOptimizer)

• market_positions (UKMarketPositions)

static get_dynamic_services_energy_throughput_buffer(solved_problem, delta=False)

Parameters:

solved_problem (UKBatteryOptimizer)

Return type:

list[DynamicServicesEnergyThroughputBuffer]

classmethod get_soe(solved_problem, result)

Return type:

list[SoE]

classmethod get_solved_problem_dataframe_additional_columns()

market_positions: UKMarketPositions

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: UKRequest