def get_mission(start_time, route, speed):
"""
Calculate position dataFrame at given start time, route and speed
:param start_time: str or Pandas Timestamp, the str input should have format YYYY-MM-DD close the day
:param route: numpy array shape (n,2) list of way points formatted as [lat, lon]
:param speed: int, float or (n) list, speed of platform unit in km/h
:return: Pandas dataFrame
"""
if type(start_time) == str:
start_time = pd.Timestamp(start_time)
position_df = full_day_cut(position_dataframe(start_time, route, speed))
return position_df
def post_run(self, solar_area, wind_area, battery_capacity, dispatch):
print('====== Post simulation run ======')
power_supply = (
self.wind_power_simulation * wind_area
+ self.solar_power_simulation * solar_area
)
post_run_len = len(dispatch) # match length of simulation
supply, load = power_supply[:post_run_len].tolist(), dispatch['Power'].tolist()
if self.config != {}:
battery = Battery(battery_capacity, config=self.config)
else:
battery = Battery(battery_capacity)
battery.run(supply, load)
prop_load = (self.Task.load_demand - self.Task.hotel_load).values
load_demand = self.Task.load_demand.values
hotel_load = self.Task.hotel_load.values
critical_load = self.Task.critical_prop_load + self.Task.critical_hotel_load.values
load_demand_history = np.vstack((load_demand, prop_load, hotel_load, critical_load))
load_demand_history_df = pd.DataFrame(
data=load_demand_history.T,
index=self.Task.mission.df.index,
columns=['Load_demand', 'Prop_load', 'Hotel_load', 'Critical_load'],
)
load_demand_history_df = full_day_cut(load_demand_history_df)
battery_history = battery.battery_history()
battery_history_df = pd.DataFrame(
data=battery_history[:post_run_len].T,
index=self.df[:post_run_len].index,
columns=['SOC', 'Battery', 'Unmet', 'Waste', 'Supply'],
)
results = [
battery_history_df,
load_demand_history_df[:post_run_len],
self.solar[:post_run_len] * solar_area,
self.wind[:post_run_len] * wind_area,
]
result_df = pd.concat(results, axis=1)
return result_df
def post_run(self, solar_area, wind_area, battery_capacity, dispatch):
power_supply = (self.wind_power_simulation * wind_area +
self.solar_power_simulation * solar_area)
post_run_len = len(dispatch) # match length of simulation
supply, load = power_supply[:post_run_len].tolist(
), dispatch['Power'].tolist()
if self.config != {}:
model = Soc_model_variable_load(
Battery(battery_capacity, config=self.config), supply, load)
else:
model = Soc_model_variable_load(Battery(battery_capacity), supply,
load)
prop_load = (self.Task.load_demand[:post_run_len] -
self.Task.hotel_load[:post_run_len]).as_matrix()
load_demand = self.Task.load_demand[:post_run_len].as_matrix()
hotel_load = self.Task.hotel_load[:post_run_len].as_matrix()
load_demand_history = np.vstack((load_demand, prop_load, hotel_load))
load_demand_history_df = pd.DataFrame(
data=load_demand_history[:post_run_len].T,
index=self.Task.mission.df[:post_run_len].index,
columns=['Load_demand', 'Prop_load', 'Hotel_load'],
)
load_demand_history_df = full_day_cut(load_demand_history_df)
battery_history = model.get_battery_history()
battery_history_df = pd.DataFrame(
data=battery_history[:post_run_len].T,
index=self.df[:post_run_len].index,
columns=['SOC', 'Battery', 'Unmet', 'Waste', 'Supply'],
)
results = [
battery_history_df,
load_demand_history_df[:post_run_len],
self.solar[:post_run_len] * solar_area,
self.wind[:post_run_len] * wind_area,
]
result_df = pd.concat(results, axis=1)
return result_df
def result(self, solar_area, wind_area, battery_capacity):
power_supply = (self.wind_power_simulation * wind_area +
self.solar_power_simulation * solar_area)
supply, load = power_supply.tolist(), self.Task.load_demand.tolist()
if self.config is not {}:
model = Soc_model_variable_load(
Battery(battery_capacity, config=self.config), supply, load)
else:
model = Soc_model_variable_load(Battery(battery_capacity), supply,
load)
prop_load = (self.Task.load_demand - self.Task.hotel_load).as_matrix()
load_demand = self.Task.load_demand.as_matrix()
hotel_load = self.Task.hotel_load.as_matrix()
load_demand_history = np.vstack((load_demand, prop_load, hotel_load))
load_demand_history_df = pd.DataFrame(
data=load_demand_history.T,
index=self.Task.mission.df.index,
columns=['Load_demand', 'Prop_load', 'Hotel_load'],
)
load_demand_history_df = full_day_cut(load_demand_history_df)
battery_history = model.get_battery_history()
battery_history_df = pd.DataFrame(
data=battery_history.T,
index=self.df.index,
columns=['SOC', 'Battery', 'Unmet', 'Waste', 'Supply'],
)
results = [
battery_history_df,
load_demand_history_df,
self.solar * solar_area,
self.wind * wind_area,
]
result_df = pd.concat(results, axis=1)
return result_df
def __init__(self, mission):
self.mission = full_day_cut(mission.df)
self.resource_df = None
self.solar_power = 0
self.wind_power = 0
self.battery_energy = 0
