"""
The ``wake_losses`` module contains functions for modelling wake losses by wind
efficiency curves (reduction of wind speed).
"""
__copyright__ = "Copyright oemof developer group"
__license__ = "GPLv3"
import numpy as np
import pandas as pd
import os
[docs]def reduce_wind_speed(wind_speed, wind_efficiency_curve_name='dena_mean'):
r"""
Reduces wind speed by a wind efficiency curve.
The wind efficiency curves are provided in the windpowerlib and were
calculated in the dena-Netzstudie II and in the work of Knorr
(see [1]_ and [2]_).
Parameters
----------
wind_speed : pandas.Series or numpy.array
Wind speed time series.
wind_efficiency_curve_name : string
Name of the wind efficiency curve. Use
:py:func:`~.get_wind_efficiency_curve` to get all provided wind
efficiency curves. Default: 'dena_mean'.
Returns
-------
reduced_wind_speed : pd.Series or np.array
`wind_speed` reduced by wind efficiency curve.
References
----------
.. [1] Kohler et.al.: "dena-Netzstudie II. Integration erneuerbarer
Energien in die deutsche Stromversorgung im Zeitraum 2015 – 2020
mit Ausblick 2025.", Deutsche Energie-Agentur GmbH (dena),
Tech. rept., 2010, p. 101
.. [2] Knorr, K.: "Modellierung von raum-zeitlichen Eigenschaften der
Windenergieeinspeisung für wetterdatenbasierte
Windleistungssimulationen". Universität Kassel, Diss., 2016,
p. 124
"""
# Get wind efficiency curve
wind_efficiency_curve = get_wind_efficiency_curve(
curve_name=wind_efficiency_curve_name)
# Reduce wind speed by wind efficiency
reduced_wind_speed = wind_speed * np.interp(
wind_speed, wind_efficiency_curve['wind_speed'],
wind_efficiency_curve['efficiency'])
return reduced_wind_speed
[docs]def get_wind_efficiency_curve(curve_name='all'):
r"""
Reads wind efficiency curve(s) specified in `curve_name`.
Parameters
----------
curve_name : str or list
Specifies the curve. Use 'all' to get all curves in a MultiIndex
DataFrame or one of the curve names to retrieve a single curve.
Default: 'all'.
Returns
-------
efficiency_curve : pd.DataFrame
Wind efficiency curve. Contains 'wind_speed' and 'efficiency' columns
with wind speed in m/s and wind efficiency (dimensionless).
If `curve_name` is 'all' or a list of strings a MultiIndex DataFrame is
returned with curve names in the first level of the columns.
Notes
-----
The wind efficiency curves were generated in the "Dena Netzstudie" [1]_ and
in the work of Kaspar Knorr [2]_. The mean wind efficiency curve is an
average curve from 12 wind farm distributed over Germany ([1]_) or
respectively an average from over 2000 wind farms in Germany ([2]_). Curves
with the appendix 'extreme' are wind efficiency curves of single wind farms
that are extremely deviating from the respective mean wind efficiency
curve. For more information see [1]_ and [2]_.
References
----------
.. [1] Kohler et.al.: "dena-Netzstudie II. Integration erneuerbarer
Energien in die deutsche Stromversorgung im Zeitraum 2015 – 2020
mit Ausblick 2025.", Deutsche Energie-Agentur GmbH (dena),
Tech. rept., 2010, p. 101
.. [2] Knorr, K.: "Modellierung von raum-zeitlichen Eigenschaften der
Windenergieeinspeisung für wetterdatenbasierte
Windleistungssimulationen". Universität Kassel, Diss., 2016,
p. 124
Examples
--------
.. parsed-literal::
# Example to plot all curves
fig, ax = plt.subplots() /n
df = get_wind_efficiency_curve(curve_name='all')
for t in df.columns.get_level_values(0).unique():
p = df[t].set_index('wind_speed')['efficiency']
p.name = t
ax = p.plot(ax=ax, legend=True)
plt.show()
"""
possible_curve_names = ['dena_mean', 'knorr_mean', 'dena_extreme1',
'dena_extreme2', 'knorr_extreme1',
'knorr_extreme2', 'knorr_extreme3']
if curve_name == 'all':
curve_names = possible_curve_names
elif isinstance(curve_name, str):
curve_names = [curve_name]
else:
curve_names = curve_name
efficiency_curve = pd.DataFrame(columns=pd.MultiIndex(levels=[[], []],
labels=[[], []]))
for curve_name in curve_names:
if curve_name.split('_')[0] not in ['dena', 'knorr']:
raise ValueError("`curve_name` must be one of the following: " +
"{} but is {}".format(possible_curve_names,
curve_name))
path = os.path.join(os.path.dirname(__file__), 'data',
'wind_efficiency_curves_{}.csv'.format(
curve_name.split('_')[0]))
# Read wind efficiency curves from file
wind_efficiency_curves = pd.read_csv(path)
# Raise error if wind efficiency curve specified in 'curve_name' does
# not exist
if curve_name not in list(wind_efficiency_curves):
msg = ("Efficiency curve <{0}> does not exist. Must be one of the"
"following: {1}.")
raise ValueError(msg.format(curve_name, *possible_curve_names))
# Get wind efficiency curve and rename column containing efficiency
wec = wind_efficiency_curves[['wind_speed', curve_name]]
efficiency_curve[curve_name, 'wind_speed'] = wec['wind_speed']
efficiency_curve[curve_name, 'efficiency'] = wec[curve_name]
if len(curve_names) == 1:
return efficiency_curve[curve_names[0]]
else:
return efficiency_curve