Coverage for agentlib_flexquant/data_structures/globals.py: 96%

1"""Script containing global variables""" 

2 

3from typing import Literal 

4 

5# fixed string definitions 

6PREP_TIME = "prep_time" 

7MARKET_TIME = "market_time" 

8FLEX_EVENT_DURATION = "flex_event_duration" 

9PROFILE_DEVIATION_WEIGHT = "profile_deviation_weight" 

10PROFILE_COMFORT_WEIGHT = "profile_comfort_weight" 

11TIME_STEP = "time_step" 

12PREDICTION_HORIZON = "prediction_horizon" 

13FlexibilityOffer = "FlexibilityOffer" 

14FlexibilityDirections = Literal["positive", "negative"] 

15POWER_ALIAS_BASE = "_P_el_base" 

16POWER_ALIAS_NEG = "_P_el_neg" 

17POWER_ALIAS_POS = "_P_el_pos" 

18STORED_ENERGY_ALIAS_BASE = "_E_stored_base" 

19STORED_ENERGY_ALIAS_NEG = "_E_stored_neg" 

20STORED_ENERGY_ALIAS_POS = "_E_stored_pos" 

21full_trajectory_suffix: str = "_full" 

22full_trajectory_prefix: str = "_" 

23 

24# cost function in the shadow mpc. obj_std and obj_flex are to be evaluated according to user definition 

25SHADOW_MPC_COST_FUNCTION = ("return ca.if_else(self.time < self.prep_time.sym + " 

26 "self.market_time.sym, obj_std, ca.if_else(self.time < " 

27 "(self.prep_time.sym + self.flex_event_duration.sym + " 

28 "self.market_time.sym), obj_flex, obj_std))") 

29 

30 

31def return_baseline_cost_function(power_variable: str, comfort_variable: str) -> str: 

32 """Return baseline cost function 

33 

34 Args: 

35 power_variable: name of the power variable 

36 comfort_variable: name of the comfort variable 

37 

38 Returns: 

39 Cost function in the baseline mpc, obj_std is to be evaluated according to user definition 

40 

41 """ 

42 if comfort_variable: 

43 cost_func = ("return ca.if_else(self.in_provision.sym, " 

44 "ca.if_else(self.time < self.rel_start.sym, obj_std, " 

45 "ca.if_else(self.time >= self.rel_end.sym, obj_std, " 

46 f"sum([self.profile_deviation_weight*(self.{power_variable} - self._P_external)**2, " 

47 f"self.{comfort_variable}**2 * self.profile_comfort_weight]))),obj_std)") 

48 else: 

49 cost_func = ("return ca.if_else(self.in_provision.sym, " 

50 "ca.if_else(self.time < self.rel_start.sym, obj_std, " 

51 "ca.if_else(self.time >= self.rel_end.sym, obj_std, " 

52 f"sum([self.profile_deviation_weight*(self.{power_variable} - self._P_external)**2]))),obj_std)") 

53 return cost_func