def main():
distance_factors = (None, )
if COMPUTE_CONSTANT_DISTANCE_FACTORS:
distance_factors += DISTANCE_FACTORS
params = product(new_turbine_models() + ('mixed', ), distance_factors)
with Pool(processes=NUM_PROCESSES) as pool:
pool.map(calc_optimal_locations_worker, params)
def savefig_repower_potential():
repower_potentials = []
for distance_factor in DISTANCE_FACTORS:
for turbine_model_new in ('mixed',) + new_turbine_models():
repower_potentials.append(load_repower_potential(turbine_model_new, distance_factor))
plot_repower_potential(*repower_potentials, variable='power_generation')
plt.savefig(FIGURES_DIR / 'repower_potential_power_generation.pdf', bbox_inches='tight')
plot_repower_potential(*repower_potentials, variable='num_turbines')
plt.savefig(FIGURES_DIR / 'repower_potential_num_turbines.pdf', bbox_inches='tight')
def calc_optimal_locations_worker(params):
logging.info("Start calculating repower_potential: %s", params)
turbine_model_new, distance_factor = params
turbine_model_old = ge15_77
if turbine_model_new == 'mixed':
turbine_models = new_turbine_models()
else:
turbine_models = (turbine_model_new, )
power_generation_new = xr.concat([
load_simulated_energy_per_location(turbine_model_new)
for turbine_model_new in turbine_models
],
dim='turbine_model')
is_optimal_location = xr.concat([
load_optimal_locations(turbine_model_new, distance_factor)
for turbine_model_new in turbine_models
],
dim='turbine_model')
power_generation_old = load_simulated_energy_per_location(
turbine_model_old, capacity_scaling=True)
cluster_per_location = load_cluster_per_location(distance_factor)
repower_potential = calc_repower_potential(
power_generation_new=power_generation_new,
power_generation_old=power_generation_old,
is_optimal_location=is_optimal_location,
cluster_per_location=cluster_per_location)
turbine_model_new_fname = (turbine_model_new.file_name
if turbine_model_new != 'mixed' else 'mixed')
repower_potential.attrs['turbine_model_new'] = turbine_model_new_fname
repower_potential.attrs['turbine_model_old'] = turbine_model_old.file_name
repower_potential.attrs['distance_factor'] = (
distance_factor if distance_factor is not None else 0)
df_filename = '' if distance_factor is None else f'_{distance_factor}'
fname = (INTERIM_DIR / 'repower_potential' /
f'repower_potential_{turbine_model_old.file_name}_'
f'{turbine_model_new_fname}{df_filename}.nc')
repower_potential.to_netcdf(fname)
def main():
prevail_wind_direction = load_prevail_wind_direction()
distance_factors = (load_distance_factors(), )
if COMPUTE_CONSTANT_DISTANCE_FACTORS:
distance_factors += DISTANCE_FACTORS
params = product(new_turbine_models(), distance_factors,
[prevail_wind_direction])
# FIXME there is a deadlock because of logging... :-/
# https://codewithoutrules.com/2018/09/04/python-multiprocessing/
# NUM_PROCESSES=1 because need more RAM, 24GB is not enough for 2 processes
with Pool(processes=1) as pool:
pool.map(calc_optimal_locations_worker, params)
def plot_power_curves():
fig, ax = plt.subplots(1, 1, figsize=FIGSIZE)
x = np.linspace(0, 40, num=100)
for turbine_model, color in zip((ge15_77, ) + new_turbine_models(),
TURBINE_COLORS):
linestyle = 'dashed' if turbine_model == ge15_77 else '-'
ax.plot(x,
turbine_model.power_curve(x),
label=turbine_model.name,
color=color,
linestyle=linestyle)
plt.legend()
plt.ylabel('Power generation [kW]')
plt.xlabel('Wind speed [m/s]')
plt.grid()
return fig
def savefig_repower_potential_direction():
repower_potentials = []
for turbine_model_new in new_turbine_models() + ('mixed',):
repower_potentials.append(load_repower_potential(turbine_model_new, distance_factor=None))
_, stacklabels = plot_repower_potential(*repower_potentials, variable='power_generation')
plt.savefig(FIGURES_DIR / 'repower_potential-direction-dependent_power_generation.pdf',
bbox_inches='tight')
plot_repower_potential(load_repower_potential('mixed', distance_factor=None),
variable='power_gain_per_model', stacklabels=stacklabels)
plt.savefig(FIGURES_DIR / 'repower_potential-direction-dependent_power_generation-stacked.pdf',
bbox_inches='tight')
plot_repower_potential(*repower_potentials, variable='num_turbines')
plt.savefig(FIGURES_DIR / 'repower_potential-direction-dependent_num_turbines.pdf',
bbox_inches='tight')
plot_repower_potential(load_repower_potential('mixed', distance_factor=None),
variable='power_generation', relative_ylabel=True, linestyle='-',
legend=False)
plt.savefig(FIGURES_DIR / 'repower_potential_power_generation-relative.pdf', bbox_inches='tight')
def plot_repower_potential(*repower_potentials,
variable='power_generation',
stacklabels=None,
relative_ylabel=False,
linestyle=None,
legend=True):
"""This function plots either expected power generation or total number of installed
turbines."""
fig, ax = plt.subplots(1, 1, figsize=FIGSIZE)
plt.xlabel('Number of repowered turbines')
ylabel = {
'power_generation': 'Annual wind power generation [TWh/yr]',
'power_gain_per_model':
'Additional annual wind power generation [TWh/yr]',
'num_turbines': 'Total number of turbines',
}
factor = {
'power_generation': 1e-3,
'power_gain_per_model': 1e-3,
'num_turbines': 1.,
}
if relative_ylabel:
ylabel = {
'power_generation': 'Wind power generation [%]',
}
factor = {
'power_generation': 100. / repower_potentials[0][variable][0]
}
plt.ylabel(ylabel[variable]) # FIXME make sure that this is GW!
plt.grid(True)
colors = TURBINE_COLORS
turbine_names = ['mixed'] + [t.file_name for t in new_turbine_models()]
turbine_color = dict(zip(turbine_names, colors))
styles = ('--', '--', 'dotted', '-.', '-')
distance_factor_style = dict(zip(DISTANCE_FACTORS + (0, ), styles))
distance_factor_style = {k: v for k, v in distance_factor_style.items()}
distance_factors = []
labels = []
for repower_potential in repower_potentials:
num_new_turbines = repower_potential.num_new_turbines
turbine_model_name = repower_potential.attrs['turbine_model_new']
distance_factor = repower_potential.attrs['distance_factor']
distance_factors.append(distance_factor)
linestyle = linestyle or distance_factor_style[distance_factor]
if turbine_model_name != 'mixed':
turbine_model = getattr(turbine_models, turbine_model_name)
else:
class Turbine:
pass # ok, this a bit dirty...
turbine_model = Turbine()
turbine_model.name = 'Best turbine model per cluster'
linestyle = linestyle or '--'
color = turbine_color[turbine_model_name]
label = turbine_model.name if distance_factor in (2,
0) else '_nolegend_'
if turbine_model_name != 'mixed':
labels.append(label)
if variable == 'power_gain_per_model':
ax.stackplot(num_new_turbines,
factor[variable] * repower_potential[variable],
labels=stacklabels,
colors=TURBINE_COLORS[1:])
else:
ax.plot(num_new_turbines,
factor[variable] * repower_potential[variable],
linestyle=linestyle,
label=label,
color=color)
xlabels = ['{:,.0f}K'.format(x) for x in ax.get_xticks() / 1000]
ax.set_xticklabels(xlabels)
if legend:
loc = 'upper left' if variable != 'num_turbines' else None
legend1 = ax.legend(loc=loc)
dist_factors = [
Line2D([], [],
color='black',
linestyle=distance_factor_style[df],
label=f"Distance factor {df}") for df in distance_factors
if df != 0
]
if legend and dist_factors:
ax.legend(handles=dist_factors, loc='upper right')
ax.add_artist(legend1)
return plt.gca(), labels
import logging
from wind_repower_usa.calculations import calc_simulated_energy
from wind_repower_usa.config import MONTHS, INTERIM_DIR
from wind_repower_usa.load_data import load_wind_speed, load_turbines
from wind_repower_usa.logging_config import setup_logging
from wind_repower_usa.turbine_models import ge15_77, new_turbine_models
setup_logging()
year = 2017
turbines = load_turbines()
turbine_models = [ge15_77] + list(new_turbine_models())
for turbine_model in turbine_models:
# simulates the power generation in the current situation for first turbine
capacity_scaling = turbine_model == ge15_77
scaling_str = '' if not capacity_scaling else '_capacity_scaled'
fname = (INTERIM_DIR / 'simulated_energy_per_location' /
f'simulated_energy_{turbine_model.file_name}{scaling_str}_gwh.nc')
if fname.exists():
logging.info("Skipping %s, file already exists", fname)
continue
logging.info("Calculating simulated energy for %s", turbine_model.name)
# power generation per turbine for one year
wind_speed = load_wind_speed(year, MONTHS)