"""Process conversion models (external libraries)"""
import logging
import numpy as np
from pandas import DataFrame
logger = logging.getLogger("energia")
try:
from pvlib.location import Location as PVLocation
from pvlib.modelchain import ModelChain as PVModelChain
from pvlib.pvsystem import PVSystem, retrieve_sam
from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
import_all = False
except ImportError:
import_all = True
try:
from windpowerlib import ModelChain as WModelChain
from windpowerlib import WindTurbine
import_all = False
except ImportError:
import_all = True
[docs]
def pvlib(
data: DataFrame,
coord: tuple[float, float],
sam: str = "cecmod",
module_params: str = "Canadian_Solar_Inc__CS5P_220M",
inverter: str = "cecinverter",
inverter_params: str = "ABB__MICRO_0_25_I_OUTD_US_208__208V_",
temperature_params: str = "open_rack_glass_glass",
aoi_model: str = "no_loss",
ac_model: str = "sandia",
spectral_model: str = "no_loss",
) -> list[float] | None:
"""
Calculates solar power output using weather data
Relevant factors include DHI (W/m2), DNI (W/m2), GHI (W/m2),
Temperature (C), Dewpoint (C), Relative humidity (%)
:param data: weather data input with dni, dhi, wind_speed, ghi, air_temperature, dew_point, relative_humidity
:type data: DataFrame
:param coord: latitude and longitude
:type coord: tuple[float, float]
:param sam: Defaults to 'cecmod'.
:type sam: str, optional
:param module_parameters: Defaults to 'Canadian_Solar_Inc__CS5P_220M'.
:type module_parameters: str, optional
:param inverter: Defaults to 'cecinverter'.
:type inverter: str, optional
:param inverter_parameters: Defaults to 'ABB__MICRO_0_25_I_OUTD_US_208__208V_'.
:type inverter_parameters: str, optional
:param temperature_params: Defaults to 'open_rack_glass_glass'.
:type temperature_params: str, optional
:param aoi_model: Defaults to 'no_loss'.
:type aoi_model: str, optional
:param ac_model: Defaults to 'sandia'.
:type ac_model: str, optional
:param spectral_model: Defaults to 'no_loss'.
:type spectral_model: str, optional
:returns: a list with solar power outputs
:rtype: list[float] | None
"""
# data = data.resample('H').mean()
if import_all:
logger.warning(
"⚠ This is an optional feature. Please install pvlib, or pip install energiapy[all] ⚠",
)
return None
modules = retrieve_sam(sam)
module_parameters = modules[module_params]
inverters = retrieve_sam(inverter)
inverter_parameters = inverters[inverter_params]
tparams = TEMPERATURE_MODEL_PARAMETERS["sapm"][temperature_params]
system = PVSystem(
module_parameters=module_parameters,
inverter_parameters=inverter_parameters,
temperature_model_parameters=tparams,
)
location = PVLocation(latitude=coord[0], longitude=coord[1])
mc = PVModelChain(
system,
location,
aoi_model=aoi_model,
ac_model=ac_model,
spectral_model=spectral_model,
)
mc.run_model(weather=data)
# make a list, avoid np.float64
array = np.maximum(0, np.nan_to_num(np.array(mc.results.ac)))
return [float(i) for i in array]
[docs]
def windpowerlib(
data: DataFrame,
roughness_length: float = 0.1,
turbine_type: str = "V100/1800",
hub_height: float = 92,
wind_speed_model: str = "logarithmic",
density_model: str = "ideal_gas",
temperature_model: str = "linear_gradient",
power_output_model: str = "power_coefficient_curve",
density_correction: bool = True,
obstacle_height: float = 0,
observation_height: float = 10,
) -> list[float] | None:
"""
Calculates wind power output using weather data
Relevant factors include wind speeds (m/s), temperature (K), and pressure(Pa)
:param data: weather data input with wind_speed, air_temperature, surface_pressure
:type data: DataFrame
:param roughness_length: Defaults to 0.1.
:type roughness_length: float, optional
:param turbine_type: Defaults to 'V100/1800'.
:type turbine_type: str, optional
:param hub_height: Defaults to 92.
:type hub_height: float, optional
:param wind_speed_model: Defaults to 'logarithmic'.
:type wind_speed_model: str, optional
:param density_model: Defaults to 'ideal_gas'.
:type density_model: str, optional
:param temperature_model: Defaults to 'linear_gradient'.
:type temperature_model: str, optional
:param power_output_model: Defaults to power_coefficient_curve.
:type power_output_model: str, optional
:param density_correction: Defaults to True.
:type density_correction: bool, optional
:param obstacle_height: Defaults to 0.
:type obstacle_height: float, optional
:param observation_height: Defaults to 10.
:type observation_height: float, optional
:returns: a dataframe with hourly wind power outputs
:rtype: list[float] | None
"""
# df_ = df_.dropna()
if import_all:
logger.warning(
"⚠ This is an optional feature. Please install windpowerlib, or pip install energiapy[all] ⚠",
)
return None
data["pressure"] = 100 * data["surface_pressure"]
data["temperature"] = data["air_temperature"] + 273.15
data["roughness_length"] = roughness_length
# data = data.resample('H').mean()
data = DataFrame(
data[["wind_speed", "temperature", "pressure", "roughness_length"]].to_numpy(),
index=data.index,
columns=[
np.array(["wind_speed", "temperature", "pressure", "roughness_length"]),
np.array([observation_height, observation_height, observation_height, 0]),
],
)
# specification of wind turbine where power curve is provided in the
# oedb turbine library
turbine_type_ = {
"turbine_type": turbine_type, # Vestas
"hub_height": hub_height, # in m
}
# initialize WindTurbine object
turbine = WindTurbine(**turbine_type_)
modelchain_data = {
"wind_speed_model": wind_speed_model, # 'logarithmic' (default),
# 'hellman' or
# 'interpolation_extrapolation'
"density_model": density_model, # 'barometric' (default), 'ideal_gas'
# or 'interpolation_extrapolation'
"temperature_model": temperature_model, # 'linear_gradient' (def.) or
# 'interpolation_extrapolation'
# 'power_curve' (default) or
"power_output_model": power_output_model,
# 'power_coefficient_curve'
"density_correction": density_correction, # False (default) or True
"obstacle_height": obstacle_height, # default: 0
"hellman_exp": None,
} # None (default) or None
# initialize ModelChain with own specifications and use run_model method to
# calculate power output
mc_turbine = WModelChain(turbine, **modelchain_data).run_model(data)
return list(mc_turbine.power_output)
