def plot_coal_fuel_costs():
"""Coal fuel costs"""
# Class containing model data
data = ModelData()
# Get coal DUIDs
mask = data.existing_units['PARAMETERS']['FUEL_TYPE_PRIMARY'].isin(
['COAL'])
# Initialise figures
fig, ax = plt.subplots()
# Generators in order of increasing fuel cost (based on cost in last year)
generator_order = data.existing_units.loc[
mask, 'FUEL_COST'].T.iloc[-1].sort_values().index
# Plot fuel costs for existing units
data.existing_units.loc[mask, 'FUEL_COST'].T.loc[:, generator_order].plot(
ax=ax, cmap='tab20c', alpha=0.9)
# Add legend
ax.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=7,
mode="expand",
borderaxespad=0.,
prop={'size': 6})
# Add axes labels
ax.set_ylabel('Fuel cost (\$/GJ)')
ax.set_xlabel('Year')
# Format axes ticks
ax.xaxis.set_major_locator(MultipleLocator(5))
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(0.5))
ax.yaxis.set_minor_locator(MultipleLocator(0.1))
# Adjust figure placement and size
fig.subplots_adjust(top=0.8, left=0.1, right=0.95)
fig.set_size_inches(6.69291, 6.69291 * 0.8)
# Save figure
fig.savefig(
os.path.join(os.path.dirname(__file__), 'output', 'figures',
'fuel_cost_coal.pdf'))
plt.show()
def plot_wind_build_cost():
"""Plot build costs for candidate wind generators"""
# Class containing model data
data = ModelData()
# Get coal DUIDs
mask = data.candidate_units['PARAMETERS']['FUEL_TYPE_PRIMARY'].isin(
['WIND'])
# Initialise figures
fig, ax = plt.subplots()
# Plot build costs for candidate units
data.candidate_units.loc[mask, 'BUILD_COST'].T.plot(ax=ax,
cmap='tab20c',
alpha=0.9)
# Add legend
ax.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=5,
mode="expand",
borderaxespad=0.,
prop={'size': 6})
# Add axes labels
ax.set_ylabel('Build cost (\$/kW)')
ax.set_xlabel('Year')
# Format axes ticks
ax.xaxis.set_major_locator(MultipleLocator(5))
ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(100))
ax.yaxis.set_minor_locator(MultipleLocator(50))
# Adjust figure placement and size
fig.subplots_adjust(top=0.85, left=0.1, right=0.95)
fig.set_size_inches(6.69291, 6.69291 * 0.6)
# Save figure
fig.savefig(
os.path.join(os.path.dirname(__file__), 'output', 'figures',
f'build_costs_wind.pdf'))
plt.show()
# │ ├── FIELD_ID # │ ├── LABEL # │ │ # │ └── DATEXXXXXXXX # │ : : # │ : └─ BANDXXX # : : # # # ModelData Indexing Format # /GROUP00/DATE20190606/BAND01.tif --> data.G00.D20190606.B01 (Object) # /GROUP00/DATE20190606/CLD.tif --> data.G00.D20190606.CLD (Object) # Example from utils.data import ModelData path = '/Users/benjamindaghir1/Dropbox (GaTech)/CS4641/data' data = ModelData(path) # To create an object for the image /YOUR/PATH/TO/Dropbox (GaTech)/CS4641/data/00/DATE20190606/0_B01_20190606.tif image_object = data.G00.D20190606.B01 # To physically load the image array image_array = image_object.load() # To plot/show the image image_object.show() # To show an rgb version of a group for a specific date. (A combination of bands [B04, B03, B02] => [R, G, B]) date_object = data.G00.D20190606 date_object.show_rgb() # Iterating over all Groups, Dates, and Bands
def plot_solar_capacity_factors(nem_zone='ADE', technology='DAT'):
"""Plot solar capacity factors"""
# Construct solar technology ID based on NEM zone and technology type
tech_id = f'{nem_zone}|{technology}'
# Class containing model data
data = ModelData()
df = pd.read_hdf(
os.path.join(os.path.dirname(__file__), os.path.pardir,
'2_input_traces', 'output', 'dataset.h5'))
df_d = df.loc[:, [('SOLAR', tech_id)]]
# Data for 2020
df_d = df_d.sort_index()
df_d = df_d.loc[df_d.index.year == 2020, :]
# Day of year
df_d['day_of_year'] = df_d.index.dayofyear.values
# Hour in a given day
df_d['hour'] = df_d.index.hour.values
# Adjust last hour in each day - set as 24th hour
df_d['hour'] = df_d['hour'].replace(0, 24)
plt.clf()
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
df_d.pivot(index='day_of_year', columns='hour',
values=('SOLAR', tech_id)).T.plot(ax=ax1,
legend=False,
alpha=0.4,
cmap='viridis')
df_d.pivot(index='day_of_year', columns='hour',
values=('SOLAR', tech_id)).T.plot(ax=ax2,
legend=False,
alpha=0.1,
cmap='viridis')
# Plot traces obtained from k-means clustering
data.input_traces.loc[2020, ('SOLAR', tech_id)].T.plot(ax=ax2,
color='r',
legend=False,
alpha=0.8)
ax1.set_ylabel(f'{tech_id} capacity factor [-]')
ax2.set_ylabel(f'{tech_id} capacity factor [-]')
ax2.set_xlabel('Hour')
# Format axes ticks
ax2.xaxis.set_major_locator(MultipleLocator(5))
ax2.xaxis.set_minor_locator(MultipleLocator(1))
ax1.yaxis.set_major_locator(MultipleLocator(0.2))
ax1.yaxis.set_minor_locator(MultipleLocator(0.05))
ax2.yaxis.set_major_locator(MultipleLocator(0.2))
ax2.yaxis.set_minor_locator(MultipleLocator(0.05))
# Adjust figure placement and size
fig.subplots_adjust(top=0.95, bottom=0.08, left=0.1, right=0.95)
fig.set_size_inches(6.69291, 6.69291)
# Save figure
fig.savefig(
os.path.join(os.path.dirname(__file__), 'output', 'figures',
f"solar_profiles_{tech_id.replace('|', '-')}.pdf"))
plt.show()
def plot_wind_capacity_factors(wind_bubble='YPS'):
"""Plotting wind capacity factors for a given wind bubble"""
# Class containing model data
data = ModelData()
df = pd.read_hdf(
os.path.join(os.path.dirname(__file__), os.path.pardir,
'2_input_traces', 'output', 'dataset.h5'))
df_d = df.loc[:, [('WIND', wind_bubble)]]
# Data for 20202
df_d = df_d.sort_index()
df_d = df_d.loc[df_d.index.year == 2020, :]
# Day of year
df_d['day_of_year'] = df_d.index.dayofyear.values
# Hour in a given day
df_d['hour'] = df_d.index.hour.values
# Adjust last hour in each day - set as 24th hour
df_d['hour'] = df_d['hour'].replace(0, 24)
plt.clf()
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
df_d.pivot(index='day_of_year',
columns='hour',
values=('WIND', wind_bubble)).T.plot(ax=ax1,
legend=False,
alpha=0.4,
cmap='viridis')
df_d.pivot(index='day_of_year',
columns='hour',
values=('WIND', wind_bubble)).T.plot(ax=ax2,
legend=False,
alpha=0.1,
cmap='viridis')
# Plot traces obtained from k-means clustering
data.input_traces.loc[2020, ('WIND', wind_bubble)].T.plot(ax=ax2,
color='r',
legend=False,
alpha=0.8)
ax1.set_ylabel(f'{wind_bubble} capacity factor [-]')
ax2.set_ylabel(f'{wind_bubble} capacity factor [-]')
ax2.set_xlabel('Hour')
# Format axes ticks
ax2.xaxis.set_major_locator(MultipleLocator(5))
ax2.xaxis.set_minor_locator(MultipleLocator(1))
ax1.yaxis.set_major_locator(MultipleLocator(0.2))
ax1.yaxis.set_minor_locator(MultipleLocator(0.05))
ax2.yaxis.set_major_locator(MultipleLocator(0.2))
ax2.yaxis.set_minor_locator(MultipleLocator(0.05))
# Adjust figure placement and size
fig.subplots_adjust(top=0.95, bottom=0.08, left=0.1, right=0.95)
fig.set_size_inches(6.69291, 6.69291)
# Save figure
fig.savefig(
os.path.join(os.path.dirname(__file__), 'output', 'figures',
f'wind_profiles_{wind_bubble}.pdf'))
plt.show()