battery_optimizer.uk.legacy.legacy_request#

Functions

`datetime_records_as_dataframe`(cls, records)
`is_fraction`(fraction)
`records_as_dataframe`(cls, records)
`string_timedelta_to_pandas_timedelta`(timedelta)

Classes

`BatteryParameters`(*, battery_capacity, ...)
`EFASetting`(*, EFA, DCH, DCL, Market_DCH, ...)
`IndustrialCosts`(*, perspective, AAHEDC, CfD, ...)
`LegacyRequest`(*, name, strategy, local_tz, ...)
`PortfolioOverview`(*, datetime, spot_price, ...)
`StrategyParameters`(*, industrial_costs, ...)
`VolumeWeightedMeanPrices`(*, datetime, ...)

class battery_optimizer.uk.legacy.legacy_request.BatteryParameters(*, battery_capacity, soc_min, soc_max, soc_initial, max_charge_power, charging_efficiency, max_discharge_power, discharging_efficiency, total_battery_cycle, invest_cost, grid_connection_export, grid_connection_import, PV_kWp, activation_likelihood, charged_energy, discharged_energy, cycle_cost)#

Bases: BaseModel

Parameters:

battery_capacity (float)
soc_min (float)
soc_max (float)
soc_initial (float)
max_charge_power (float)
charging_efficiency (float)
max_discharge_power (float)
discharging_efficiency (float)
total_battery_cycle (int)
invest_cost (float)
grid_connection_export (float)
grid_connection_import (float)
PV_kWp (float)
activation_likelihood (float)
charged_energy (float)
discharged_energy (float)
cycle_cost (float)

PV_kWp: float#

activation_likelihood: float#

battery_capacity: float#

charged_energy: float#

charging_efficiency: float#

cycle_cost: float#

discharged_energy: float#

discharging_efficiency: float#

grid_connection_export: float#

grid_connection_import: float#

invest_cost: float#

max_charge_power: float#

max_discharge_power: float#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

soc_initial: float#

classmethod soc_initial_sensible(soc_initial, values)#

soc_max: float#

soc_min: float#

total_battery_cycle: int#

class battery_optimizer.uk.legacy.legacy_request.EFASetting(*, EFA, DCH, DCL, Market_DCH, Market_DCL)#

Bases: BaseModel

Parameters:

EFA (Literal['EFA1', 'EFA2', 'EFA3', 'EFA4', 'EFA5', 'EFA6'])
DCH (float)
DCL (float)
Market_DCH (bool)
Market_DCL (bool)

DCH: float#

DCL: float#

EFA: Literal['EFA1', 'EFA2', 'EFA3', 'EFA4', 'EFA5', 'EFA6']#

Market_DCH: bool#

Market_DCL: bool#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class battery_optimizer.uk.legacy.legacy_request.IndustrialCosts(*, perspective, AAHEDC, CfD, CM, RO, FiT, LLF, TLM, BSUoS, Energy, MgmFee, ImbalRiskChar, TNUoS, DUoS_ch, DUoS_dch)#

Bases: BaseModel

Parameters:

perspective (Literal['customer', 'EON'])
AAHEDC (float)
CfD (float)
CM (float)
RO (float)
FiT (float)
LLF (float)
TLM (float)
BSUoS (float)
Energy (list[float])
MgmFee (float)
ImbalRiskChar (float)
TNUoS (float)
DUoS_ch (list[float])
DUoS_dch (list[float])

AAHEDC: float#

BSUoS: float#

CM: float#

CfD: float#

DUoS_ch: list[float]#

DUoS_dch: list[float]#

Energy: list[float]#

FiT: float#

ImbalRiskChar: float#

LLF: float#

MgmFee: float#

RO: float#

TLM: float#

TNUoS: float#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

perspective: Literal['customer', 'EON']#

class battery_optimizer.uk.legacy.legacy_request.LegacyRequest(*, name, strategy, local_tz, strategy_parameters, lookahead_time='3D', settlement_period='30min', start_time, end_time, portfolio_overview, volume_weighted_mean_prices, perfect_price_forecast, battery_parameters)#

Bases: BaseModel

Parameters:

name (str)
strategy (Literal['uk_battery'])
local_tz (Literal['Europe/London'])
strategy_parameters (StrategyParameters)
lookahead_time (str)
settlement_period (str)
start_time (datetime)
end_time (datetime)
portfolio_overview (list[PortfolioOverview])
volume_weighted_mean_prices (list[VolumeWeightedMeanPrices])
perfect_price_forecast (bool)
battery_parameters (BatteryParameters)

battery_parameters: BatteryParameters#

end_time: datetime#

classmethod ensure_consistent_datetime_index(volume_weighted_mean_prices, values)#

local_tz: Literal['Europe/London']#

lookahead_time: str#

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'frozen': False, 'json_encoders': {<class 'pandas._libs.tslibs.timedeltas.Timedelta'>: <function LegacyRequest.<lambda>>, <class 'pandas.core.frame.DataFrame'>: <function LegacyRequest.<lambda>>}}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str#

perfect_price_forecast: bool#

portfolio_overview: list[PortfolioOverview]#

settlement_period: str#

start_time: datetime#

strategy: Literal['uk_battery']#

strategy_parameters: StrategyParameters#

volume_weighted_mean_prices: list[VolumeWeightedMeanPrices]#

class battery_optimizer.uk.legacy.legacy_request.PortfolioOverview(*, datetime, spot_price, PV_prod_normalized, dch_price, dcl_price, triad_time, optimize_dc_time, optimize_spot_time, spot_bought, spot_sold, dch_marketed, dcl_marketed, DUoS_ch, DUoS_dch, cust_energy_costs)#

Bases: BaseModel

Parameters:

datetime (datetime)
spot_price (float)
PV_prod_normalized (float)
dch_price (float)
dcl_price (float)
triad_time (bool)
optimize_dc_time (bool)
optimize_spot_time (bool)
spot_bought (float)
spot_sold (float)
dch_marketed (float)
dcl_marketed (float)
DUoS_ch (float)
DUoS_dch (float)
cust_energy_costs (float)

DUoS_ch: float#

DUoS_dch: float#

PV_prod_normalized: float#

cust_energy_costs: float#

datetime: datetime#

dch_marketed: float#

dch_price: float#

dcl_marketed: float#

dcl_price: float#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

optimize_dc_time: bool#

optimize_spot_time: bool#

spot_bought: float#

spot_price: float#

spot_sold: float#

triad_time: bool#

class battery_optimizer.uk.legacy.legacy_request.StrategyParameters(*, industrial_costs, DC_settings, battery_name, date_range_freq='3D', cycle_limit=2, power_swing, power_swing_limit, market_access, DC_access, Target_SOC_end, Target_SOC_end_lower_limit, Target_SOC_end_upper_limit)#

Bases: BaseModel

Parameters:

industrial_costs (IndustrialCosts)
DC_settings (list[EFASetting])
battery_name (Literal['Warrington'])
date_range_freq (str)
cycle_limit (int)
power_swing (bool)
power_swing_limit (float)
market_access (bool)
DC_access (bool)
Target_SOC_end (bool)
Target_SOC_end_lower_limit (float)
Target_SOC_end_upper_limit (float)

DC_access: bool#

DC_settings: list[EFASetting]#

Target_SOC_end: bool#

Target_SOC_end_lower_limit: float#

Target_SOC_end_upper_limit: float#

battery_name: Literal['Warrington']#

cycle_limit: int#

date_range_freq: str#

industrial_costs: IndustrialCosts#

market_access: bool#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

power_swing: bool#

power_swing_limit: float#

class battery_optimizer.uk.legacy.legacy_request.VolumeWeightedMeanPrices(*, datetime, volume_weighted_mean_price_eur_mwh)#

Bases: BaseModel

Parameters:

datetime (datetime)
volume_weighted_mean_price_eur_mwh (float)

datetime: datetime#

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

volume_weighted_mean_price_eur_mwh: float#

battery_optimizer.uk.legacy.legacy_request.datetime_records_as_dataframe(cls, records)#

battery_optimizer.uk.legacy.legacy_request.is_fraction(fraction)#

Parameters:: fraction (float)
Return type:: float

battery_optimizer.uk.legacy.legacy_request.records_as_dataframe(cls, records)#

battery_optimizer.uk.legacy.legacy_request.string_timedelta_to_pandas_timedelta(timedelta)#

Parameters:: timedelta (str)
Return type:: Timedelta