# -*- coding: utf-8 -*-
""" This module is designed for the use with the pvlib, windpowerlib. If you
want to use other libraries you have to adapt the code.
The weather data set has to be a DataFrame with the following columns:
pvlib:
* ghi - global horizontal irradiation [W/m2]
* dni - direct normal irradiation [W/m2]
* dhi - diffuse horizontal irradiation [W/m2]
* temp_air - ambient temperature [°C]
windpowerlib:
* pressure - air pressure [Pa]
* temp_air - ambient temperature [K]
* v_wind - horizontal wind speed [m/s]
* z0 - roughness length [m]
SPDX-FileCopyrightText: 2016-2021 Uwe Krien <krien@uni-bremen.de>
SPDX-License-Identifier: MIT
"""
__copyright__ = "Uwe Krien <krien@uni-bremen.de>"
__license__ = "MIT"
# Python libraries
import os
import logging
# Internal modules
from reegis import config as cfg
# External libraries
import pandas as pd
from windpowerlib.modelchain import ModelChain
from windpowerlib.wind_turbine import WindTurbine
import pvlib
[docs]def get_optimal_pv_angle(lat):
""" About 27° to 34° from ground in Germany.
The pvlib uses tilt angles horizontal=90° and up=0°. Therefore 90° minus
the angle from the horizontal.
"""
return lat - 15
[docs]def create_pvlib_sets():
"""Create pvlib parameter sets from the solar.ini file.
Returns
-------
dict
Examples
--------
>>> pv_set=create_pvlib_sets()['M_LG290G3__I_ABB_MICRO_025_US208'][3]
>>> int(pv_set['surface_azimuth'])
180
>>> for key in sorted(pv_set.keys()):
... print(key)
albedo
inverter_parameters
module_parameters
name
p_peak
surface_azimuth
surface_tilt
"""
# get module and inverter parameter from sandia database
sandia_modules = pvlib.pvsystem.retrieve_sam("sandiamod")
sapm_inverters = pvlib.pvsystem.retrieve_sam("sandiainverter")
pvlib_sets = cfg.get_list("solar", "set_list")
pvsets = {}
for pvlib_set in pvlib_sets:
set_name = cfg.get(pvlib_set, "pv_set_name")
module_name = cfg.get(pvlib_set, "module_name")
module_key = cfg.get(pvlib_set, "module_key")
inverter = cfg.get(pvlib_set, "inverter_name")
azimuth_angles = cfg.get_list(pvlib_set, "surface_azimuth")
tilt_angles = cfg.get_list(pvlib_set, "surface_tilt")
albedo_values = cfg.get_list(pvlib_set, "albedo")
set_idx = 0
pvsets[set_name] = {}
for t in tilt_angles:
if t == "0":
az_angles = (0,)
else:
az_angles = azimuth_angles
for a in az_angles:
for alb in albedo_values:
set_idx += 1
pvsets[set_name][set_idx] = {
"module_parameters": sandia_modules[module_name],
"inverter_parameters": sapm_inverters[inverter],
"surface_azimuth": float(a),
"surface_tilt": t,
"albedo": float(alb),
}
pvsets[set_name][set_idx]["p_peak"] = (
pvsets[set_name][set_idx]["module_parameters"].Impo
* pvsets[set_name][set_idx]["module_parameters"].Vmpo
)
pvsets[set_name][set_idx]["name"] = "_".join(
[
module_key,
inverter[:3],
"tlt{}".format(t[:3].rjust(3, "0")),
"az{}".format(str(a).rjust(3, "0")),
"alb{}".format(str(alb).replace(".", "")),
]
)
logging.debug(
"PV set: {}".format(pvsets[set_name][set_idx]["name"])
)
return pvsets
[docs]def feedin_pv_sets(weather, location, pv_parameter_set):
"""Create a pv feed-in time series from a given weather data set and a
set of pvlib parameter sets. The result of every parameter set will be a
column in the resulting DataFrame.
Parameters
----------
weather : pandas.DataFrame
Weather data set. See module header.
location : pvlib.location.Location
Location of the weather data.
pv_parameter_set : dict
Parameter sets can be created using `create_pvlib_sets()`.
Returns
-------
pandas.DataFrame
"""
df = pd.DataFrame()
for pv_system in pv_parameter_set.values():
if pv_system["surface_tilt"] == "optimal":
tilt = get_optimal_pv_angle(location.latitude)
else:
tilt = float(pv_system["surface_tilt"])
mc = feedin_pvlib(location, pv_system, weather, tilt=tilt)
df[pv_system["name"]] = mc
return df
[docs]def feedin_pvlib(
location,
system,
weather,
tilt=None,
peak=None,
orientation_strategy=None,
installed_capacity=1,
):
"""
Create a pv feed-in time series from a given weather data set and a valid
pvlib parameter set.
Parameters
----------
location : pvlib.location.Location or dict
Location of the weather data.
system : dict
System parameter for the pvlib.
weather : pandas.DataFrame
Weather data set. See file header for more information.
tilt : float
The tilt angle of the surface. This value can also be defined directly
in the system dictionary..
peak : float
Peak power of the pv-module. This value can also be defined directly
in the system dictionary.
orientation_strategy : str
See the pvlib documentation for different strategies.
installed_capacity : float
Overall installed capacity for the given pv module. The installed
capacity is set to 1 by default for normalised time series.
Returns
-------
"""
if tilt is None:
tilt = system["surface_tilt"]
if peak is not None:
system["peak"] = peak
if not isinstance(location, pvlib.location.Location):
location = pvlib.location.Location(**location)
# pvlib's ModelChain
pvsys = pvlib.pvsystem.PVSystem(
inverter_parameters=system["inverter_parameters"],
module_parameters=system["module_parameters"],
surface_tilt=tilt,
surface_azimuth=system["surface_azimuth"],
albedo=system["albedo"],
)
mc = pvlib.modelchain.ModelChain(
pvsys, location, orientation_strategy=orientation_strategy
)
pv_weather = weather.copy()
pv_weather.index = pv_weather.index.shift(-1, freq="30min")
out = mc.run_model(pv_weather)
return (
out.ac.fillna(0)
.clip(0)
.div(system["p_peak"])
.multiply(installed_capacity)
)
[docs]def create_windpowerlib_sets():
"""Create parameter sets for the windpowerlib from wind.ini.
Returns
-------
dict
Examples
--------
>>> wind_set=create_windpowerlib_sets()['ENERCON_82_hub98_2300'][1]
>>> wind_set['hub_height']
98
>>> sorted(list(create_windpowerlib_sets().keys()))[:2]
['ENERCON_127_hub135_7500', 'ENERCON_82_hub138_2300']
>>> for key in sorted(wind_set.keys()):
... print(key)
hub_height
turbine_type
"""
windpowerlib_sets = cfg.get_list("wind", "set_list")
# Only one subset is created but following the pvlib sets it is possible
# to create subsets.
windsets = {}
for windpowerlib_set in windpowerlib_sets:
w_set = {1: cfg.get_dict(windpowerlib_set)}
set_name = w_set[1].pop("set_name")
windsets[set_name] = w_set
return windsets
[docs]def feedin_wind_sets(weather, wind_parameter_set):
"""Create a wind feed-in time series from a given weather data set and a
set of wind parameter sets. The result of every parameter set will be a
column in the resulting DataFrame.
Parameters
----------
weather : pandas.DataFrame
Weather data set. See module header.
wind_parameter_set : dict
Parameter sets can be created using `create_windpowerlib_sets()`.
Returns
-------
pandas.DataFrame
Examples
--------
>>> from reegis import coastdat
>>> fn=os.path.join(os.path.dirname(__file__), os.pardir, 'tests',
... 'data', 'test_coastdat_weather.csv')
>>> wind_parameter_set=create_windpowerlib_sets()
>>> weather=pd.read_csv(fn, header=[0, 1])['1126088']
>>> data_height=cfg.get_dict('coastdat_data_height')
>>> wind_weather=coastdat.adapt_coastdat_weather_to_windpowerlib(
... weather, data_height) # doctest: +SKIP
>>> feedin_wind_sets(wind_weather, wind_parameter_set
... ).sum().sort_index() # doctest: +SKIP
ENERCON_82_hub138_2300 1673.216046
ENERCON_82_hub78_3000 1048.678195
ENERCON_82_hub98_2300 1487.604336
dtype: float64
"""
df = pd.DataFrame()
for set_name, turbine in wind_parameter_set.items():
mc = feedin_windpowerlib(weather, turbine)
df[str(set_name).replace(" ", "_")] = mc
return df
[docs]def feedin_windpowerlib(weather, turbine, installed_capacity=1):
"""Use the windpowerlib to generate normalised feedin time series.
Parameters
----------
turbine : dict or windpowerlib.wind_turbine.WindTurbine
Parameters of the wind turbine (hub height, diameter of the rotor,
identifier of the turbine to get cp-series, nominal power).
weather : pandas.DataFrame
Weather data set. See module header.
installed_capacity : float
Overall installed capacity for the given wind turbine. The installed
capacity is set to 1 by default for normalised time series.
Returns
-------
pandas.DataFrame
Examples
--------
>>> from reegis import coastdat
>>> fn=os.path.join(os.path.dirname(__file__), os.pardir, 'tests',
... 'data', 'test_coastdat_weather.csv')
>>> weather=pd.read_csv(fn, header=[0, 1])['1126088']
>>> turbine={
... 'hub_height': 135,
... 'rotor_diameter': 127,
... 'name': 'E-82/2300',
... 'nominal_power': 4200000,
... 'fetch_curve': 'power_coefficient_curve'}
>>> data_height=cfg.get_dict('coastdat_data_height')
>>> wind_weather=coastdat.adapt_coastdat_weather_to_windpowerlib(
... weather, data_height) # doctest: +SKIP
>>> int(feedin_windpowerlib(wind_weather, turbine).sum()) # doctest: +SKIP
1737
"""
if not isinstance(turbine, WindTurbine):
turbine = WindTurbine(**turbine)
modelchain_data = cfg.get_dict("windpowerlib")
mc = ModelChain(turbine, **modelchain_data)
mcwpp = mc.run_model(weather)
return mcwpp.power_output.div(turbine.nominal_power).multiply(
installed_capacity
)
if __name__ == "__main__":
pass