"""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"
FLEXIBILITY_OFFER = "FlexibilityOffer"
LINEAR = 'linear'
CONSTANT = 'constant'
COLLOCATION = 'collocation'
INTEGRATION_METHOD = Literal[LINEAR, CONSTANT]
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 = "_"
shadow_suffix: str = "_shadow"
COLLOCATION_TIME_GRID = 'collocation_time_grid'
# 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} - "
f"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} - "
f"self._P_external)**2]))),obj_std)"
)
return cost_func