Add charging strategies

[Omar-Usmani]

Basic, adaptive, price (?), preference (?)

[Omar-Usmani]

Do it via a modulation function
Read that value at each step
Then
Drawn power = modulation * available power

Modulation function
max_
available_power
comes from trip (in define) and run mobility matrix

time_modulation: pd.Dataframe = pd.DataFrame(
index=HOURS_IN_A_DAY, columns=scenario.Locations)
for location_name in scenario.Locations:
time_modulation[location_name] =
scenario.Locations.location_name.time_modulation

def modulation(time_tag: datetime.datetime, location_name: str, available_power: pd.DataFrame, scenario: Box, time_modulation: of.DataFrame, location_price_factor: tu.Dict[str, float], location_desirability: tu.Dict[str, float]) -> float:

modulation: float = 1
strategies: Box = scenario.charging.strategies

modulation *= time_modution.loc[time_tag.hour][location_name]

if strategies.adaptive:
    next_day_start_time_tag: datetime.datetime = xxxx
    power_for_rest_of_day: float = available_power.loc[time_tag:
                                                              next_day_start_time_tag].sum()
    modulation *= available_power.loc[time_tag] 
        / power_for_rest_of_day
  
 if strategies.price:
     modulation *= location_price_factor[location_name]       

 if strategies.desirability:
      modulation *= location_deisrability_factor[location_name]   


 return modulation                                  

[Omar-Usmani]

Possibly add scales such as seasons for time modulation

[Omar-Usmani]

Add adaptive (either at location or at all remaining locations)
Time at location (parked) for adaptive at location and cumulative power