Source code for agentlib_flexquant.data_structures.globals

"""Script containing global variables"""

from typing import Literal

# fixed string definitions
PREP_TIME = "prep_time"
MARKET_TIME = "market_time"
FLEX_EVENT_DURATION = "flex_event_duration"
PROFILE_DEVIATION_WEIGHT = "profile_deviation_weight"
PROFILE_COMFORT_WEIGHT = "profile_comfort_weight"
TIME_STEP = "time_step"
PREDICTION_HORIZON = "prediction_horizon"
FlexibilityOffer = "FlexibilityOffer"
FlexibilityDirections = Literal["positive", "negative"]
POWER_ALIAS_BASE = "_P_el_base"
POWER_ALIAS_NEG = "_P_el_neg"
POWER_ALIAS_POS = "_P_el_pos"
STORED_ENERGY_ALIAS_BASE = "_E_stored_base"
STORED_ENERGY_ALIAS_NEG = "_E_stored_neg"
STORED_ENERGY_ALIAS_POS = "_E_stored_pos"
full_trajectory_suffix: str = "_full"
full_trajectory_prefix: str = "_"

# cost function in the shadow mpc. obj_std and obj_flex are to be evaluated according to user definition
SHADOW_MPC_COST_FUNCTION = ("return ca.if_else(self.time < self.prep_time.sym + "
                            "self.market_time.sym, obj_std, ca.if_else(self.time < "
                            "(self.prep_time.sym + self.flex_event_duration.sym + "
                            "self.market_time.sym), obj_flex, obj_std))")


[docs]def return_baseline_cost_function(power_variable: str, comfort_variable: str) -> str: """Return baseline cost function Args: power_variable: name of the power variable comfort_variable: name of the comfort variable Returns: Cost function in the baseline mpc, obj_std is to be evaluated according to user definition """ if comfort_variable: cost_func = ("return ca.if_else(self.in_provision.sym, " "ca.if_else(self.time < self.rel_start.sym, obj_std, " "ca.if_else(self.time >= self.rel_end.sym, obj_std, " f"sum([self.profile_deviation_weight*(self.{power_variable} - self._P_external)**2, " f"self.{comfort_variable}**2 * self.profile_comfort_weight]))),obj_std)") else: cost_func = ("return ca.if_else(self.in_provision.sym, " "ca.if_else(self.time < self.rel_start.sym, obj_std, " "ca.if_else(self.time >= self.rel_end.sym, obj_std, " f"sum([self.profile_deviation_weight*(self.{power_variable} - self._P_external)**2]))),obj_std)") return cost_func