def resource_df(self, **kwargs):
"""
Preview of the optimisation. Return
:return:
"""
route = tuple(self.route.flatten())
return simulation.power_unit_area(self.start_time, route, self.speed,
**kwargs)
def temporal_optimization(
start_time,
route,
speed,
solar_area,
wind_area,
use,
battery_capacity,
depth_of_discharge=1,
discharge_rate=0.005,
battery_eff=0.9,
discharge_eff=0.8,
title=0,
azim=0,
tracking=0,
power_coefficient=0.26,
cut_in_speed=2,
cut_off_speed=15,
technology='csi',
system_loss=0.10,
angles=None,
dataFrame=False,
trace_back=False,
pandas=False,
):
"""
Simulation based optimization for
:param start_time: str or Dataindex start date of journey
:param route: numpy nd array (n,2) [lat, lon] of way points
:param speed: float or list if float is given, it is assumed as constant speed operation mode
otherwise, a list of n with averaged speed should be given
:param wind_area: float area of wind turbine area
:param solar_area: float m^2 area of solar panel area
:param use: float m^2 load demand of the system
:param battery_capacity: float Wh total battery capacity of the renewable energy system
:param title: float degrees title angle of PV panel
:param azim: float degrees azim angle of PV panel
:param tracking: int 0 1 or 2 0 for no tracking, 1 for one axis, 2 for two axis
:param power_coefficient: float power coefficient of wind turbine
:param cut_in_speed: float m/s cut in speed of wind turbine
:param cut_off_speed: float m/s cut off speed of wind turbine
:param technology: optional str 'csi'
:param system_loss: float system lost of the system
:param angles: optional solar angle
:param dataFrame: optional return dataframe or not
:param trace_back: optional in True give all trace back
:return: float lost power supply probability (LPSP)
if trace_back option is on then gives LPSP, SOC, energy history, unmet energy history, water history
"""
# Pack route to immutable object for caching
route = tuple(route.flatten())
solar_power_unit, wind_power_unit = simulation.power_unit_area(
start_time,
route,
speed,
title=title,
azim=azim,
tracking=tracking,
power_coefficient=power_coefficient,
cut_in_speed=cut_in_speed,
cut_off_speed=cut_off_speed,
technology=technology,
system_loss=system_loss,
angles=angles,
dataFrame=dataFrame,
)
solar_power = solar_power_unit * solar_area
wind_power = wind_power_unit * wind_area
power = solar_power + wind_power
SOC, energy_history, unmet_history, waste_history, use_history = soc_model_fixed_load(
power,
use,
battery_capacity,
depth_of_discharge,
discharge_rate,
battery_eff,
discharge_eff,
)
LPSP = 1 - unmet_history.count(0) / len(energy_history)
if trace_back:
if pandas:
all_history = np.vstack((
np.array(power.tolist()),
np.array(waste_history),
np.array(energy_history),
np.array(use_history),
np.array(unmet_history),
np.array(solar_power),
np.array(wind_power),
))
sim_df = pd.DataFrame(
all_history.T,
index=power.index,
columns=[
'Power',
'Waste',
'Battery',
'Use',
'Unmet',
'Solar_power',
'Wind_power',
],
)
return sim_df, LPSP
else:
return (
LPSP,
SOC,
energy_history,
unmet_history,
waste_history,
use_history,
power,
)
else:
return LPSP