def getCapacity(folders,
dbs,
switch='fuel',
sector_name='electric',
save_data='N',
create_plots='N',
run_name=''):
# inputs:
# 1) folders - paths containing dbs (list or single string if all in the same path)
# 2) dbs - names of databases (list)
# 3) switch - 'fuel' or 'tech', basis of categorization
# 4) sectorName - name of temoa sector to be analyzed
# 5) saveData - 'Y' or 'N', default is 'N'
# 6) createPlot - 'Y' or 'N', default is 'N'
# 7) run_name - Used for saving results in dedicated folder
#
# outputs:
# 1) capacity - pandas DataFrame holding capacity for each model year
# ==============================================================================
print("Analyzing capacity")
# Save original directory
wrkdir = os.getcwd()
# If only a single db and folder provided, change to a list
if type(dbs) == str and type(folders) == str:
dbs = [dbs]
folders = [folders]
# If a list of folders is provided with one database, only use first folder
elif type(dbs) == str:
dbs = [dbs]
folders = [folders[0]]
# If only a single folder provided, create a list of the same folder
elif type(folders) == str:
fldrs = []
for db in dbs:
fldrs.append(folders)
folders = fldrs
# Create dictionary to hold each capacity_single series
capacity = pd.DataFrame(dtype='float64')
# Iterate through each db
for folder, db in zip(folders, dbs):
capacity_single = SingleDB(folder,
db,
switch=switch,
sector_name=sector_name)
capacity = pd.concat([capacity, capacity_single])
# Reset index (remove multi-level indexing, easier to use in Excel)
capacity = capacity.reset_index()
# Directory to hold results
if save_data == 'Y' or create_plots == 'Y':
tt.create_results_dir(wrkdir=wrkdir, run_name=run_name)
# Save results to Excel
if save_data == 'Y':
# Create savename based on switch
if switch == 'fuel':
savename = 'capacity_by_fuel.csv'
elif switch == 'tech':
savename = 'capacity_by_tech.csv'
# Save
capacity.to_csv(savename)
# Create plots
if create_plots == 'Y':
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'figure.max_open_warning': 0}) # ignore warning
# new figure
plt.figure()
# set aesthetics
sns.set_style("white", {
"font.family": "serif",
"font.serif": ["Times", "Palatino", "serif"]
})
sns.set_context("paper")
sns.set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8})
# wide to long
df2 = pd.melt(capacity,
id_vars=['database', 'scenario', 'fuelOrTech'],
var_name='var',
value_name='value')
# plot
sns.relplot(x='var',
y='value',
hue='database',
data=df2,
kind='line',
col='fuelOrTech',
col_wrap=4)
# save
if switch == 'fuel':
savename = 'capacity_by_fuel.png'
elif switch == 'tech':
savename = 'capacity_by_tech.png'
plt.savefig(savename, dpi=resolution)
# close figure
plt.close()
# Return to original directory
os.chdir(wrkdir)
# return capacity as a dictionary
return capacity
def getCosts(folders,
dbs,
elc_dmd='ELC_DMD',
conversion=0.359971,
save_data='N',
create_plots='N',
run_name=''):
# inputs:
# 1) folders - paths containing dbs (list or single string if all in the same path)
# 2) dbs - names of databases (list)
# 3) elc_dmd - quantity that represents electricity demand
# 4) conversion - converts from cost units per activity to cents/kWH
# default is conversion from M$/PJ to cents/KWh (1E6*100 / (2.778E8))
# 5) save_data - 'Y' or 'N', default is 'N'
# 6) create_plots - 'Y' or 'N', default is 'N'
# 7) run_name - Used for saving results in dedicated folder
#
# outputs:
# 1) yearlyCosts - pandas DataFrame holding yearly_costs
# 2) LCOE - dictionary holding LCOE, calculated wrt first model year
# ==============================================================================
print("Analyzing costs")
# Save original directory
wrkdir = os.getcwd()
# If only a single db and folder provided, change to a list
if type(dbs) == str and type(folders) == str:
dbs = [dbs]
folders = [folders]
# If a list of folders is provided with one database, only use first folder
elif type(dbs) == str:
dbs = [dbs]
folders = [folders[0]]
# If only a single folder provided, create a list of the same folder
elif type(folders) == str:
fldrs = []
for db in dbs:
fldrs.append(folders)
folders = fldrs
# Create a dataframe
yearlyCosts = pd.DataFrame(dtype='float64')
LCOE = pd.DataFrame(dtype='float64')
# Iterate through each db
for folder, db in zip(folders, dbs):
# Access costs
yearlyCosts_single, LCOE_single = SingleDB(folder,
db,
elc_dmd=elc_dmd,
conversion=conversion)
yearlyCosts = pd.concat([yearlyCosts, yearlyCosts_single])
LCOE = pd.concat([LCOE, LCOE_single])
# Reset index (remove multi-level indexing, easier to use in Excel)
yearlyCosts = yearlyCosts.reset_index()
LCOE = LCOE.reset_index()
# Directory to hold results
if save_data == 'Y' or create_plots == 'Y':
tt.create_results_dir(wrkdir=wrkdir, run_name=run_name)
# Save results to CSV
if save_data == 'Y':
yearlyCosts.to_csv('costs_yearly.csv')
LCOE.to_csv('LCOE.csv')
# Plot Results
if create_plots == 'Y':
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'figure.max_open_warning': 0}) # ignore warning
# new figure
plt.figure()
# set aesthetics
sns.set_style("white", {
"font.family": "serif",
"font.serif": ["Times", "Palatino", "serif"]
})
sns.set_context("paper")
sns.set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8})
# wide to long
df2 = pd.melt(yearlyCosts,
id_vars=['database', 'scenario'],
var_name='var',
value_name='value')
# plot
ax = sns.lineplot(x='var', y='value', hue='database', data=df2)
ax.set_xlabel("Year [-]")
ax.set_ylabel("Costs [cents/kWh]")
fig = ax.get_figure()
fig.savefig('costs_yearly.png', dpi=resolution)
# close figure
plt.close()
# Return to original directory
os.chdir(wrkdir)
return yearlyCosts, LCOE
def SingleDB(folder,
db,
switch='fuel',
sectorName='electric',
saveData='N',
createPlots='N',
conversion=277.777778):
# inputs:
# 1) folder - path containing db
# 2) db - name of databas
# 3) switch - 'fuel' or 'tech', basis of categorization
# 4) sectorName - name of temoa sector to be analyzed
# 5) saveData - 'Y' or 'N', default is 'N'
# 6) createPlot - 'Y' or 'N', default is 'N'
# 7) conversion - conversion to GWh, default is 277.778 (from PJ)
# outputs:
# 1) activity - pandas DataFrame holding capacity for each model year
# ==============================================================================
if switch == 'fuel':
savename = 'Results_ActivityTOD_byFuel_' + name(db)
elif switch == 'tech':
savename = 'Results_ActivityTOD_byTech_' + name(db)
# save original folder
origDir = os.getcwd()
# move to folder
os.chdir(folder)
# Connect to Database
con = sqlite3.connect(db)
cur = con.cursor()
# Read from database:
# Select All Efficiencies
qry = "SELECT * FROM Efficiency"
cur.execute(qry)
db_efficiency = cur.fetchall()
# Select All time_of_day
qry = "SELECT * FROM time_of_day"
cur.execute(qry)
db_time_of_day = cur.fetchall()
# Select All time_season
qry = "SELECT * FROM time_season"
cur.execute(qry)
db_time_season = cur.fetchall()
# Select All time_periods
qry = "SELECT * FROM time_periods"
cur.execute(qry)
db_time_periods = cur.fetchall()
# Select All technologies
qry = "SELECT * FROM technologies"
cur.execute(qry)
db_technologies = cur.fetchall()
# Select All Flows
qry = "SELECT * FROM Output_VFlow_Out"
cur.execute(qry)
db_Output_VFlow_Out = cur.fetchall()
# Review db_time_of_day to select timesOfDay
tods = []
for tod in db_time_of_day:
tods.append(str(tod[0]))
# Review db_time_season to select seasons
seasons = []
for season in db_time_season:
seasons.append(str(season[0]))
n_seasons = len(seasons)
# Review db_time_periods to select future periods
years = []
for year, flag in db_time_periods:
if flag == 'f':
years.append(str(year))
n_years = len(years)
# Review db_technologies to select related sector
techs = []
for tech, flag, sector, tech_desc, tech_category in db_technologies:
if sector == sectorName:
if tech not in techs:
techs.append(tech)
# Review db_efficiency to create a dictionary of fuels
d = {}
for input_comm, tech, vintage, output_comm, efficiency, ef_notes in db_efficiency:
if tech in techs:
if tech not in d.keys():
d[tech] = input_comm
# Sort data and assign as columns and rows
if switch == 'fuel':
cols = sorted(set(d.values()))
elif switch == 'tech':
cols = sorted(techs)
rows = sorted(tods)
# Directory to hold results
if save_data == 'Y' or create_plots == 'Y':
tt.create_results_dir(wrkdir=wrkdir, run_name=run_name)
# Create plot
if createPlots == 'Y':
f, a = plt.subplots(n_years, n_seasons, sharex=True, sharey=True)
# Create connection to excel
if saveData == 'Y':
writer = pd.ExcelWriter(savename + '.xls')
# Dictionary to store results
activity = {}
# Fill-in each subplot
for i, year in enumerate(years[:-1]):
for j, season in enumerate(seasons):
# Create appropriate title
titlename = str(year) + ' ' + str(season)
# Create dataframe initialized to zero
df = pd.DataFrame(data=0.0, index=rows, columns=cols)
## Review db_Output_VFlow_Out to fill data frame
for scenario, sector, t_periods, t_season, t_day, input_comm, tech, vintage, output_comm, vflow_out in db_Output_VFlow_Out:
if sector == sectorName:
if str(t_periods) == year and t_season == season:
if switch == 'fuel':
df.loc[t_day, d[tech]] = df.loc[
t_day, d[tech]] + vflow_out * conversion
elif switch == 'tech':
df.loc[t_day, tech] = df.loc[
t_day, tech] + vflow_out * conversion
# Store results
activity[str(year) + '_' + str(season)] = df
# Plot
if createPlots == 'Y':
# Access corresponding subplot
ax = a[i, j]
# Legend
if i == 0 and j == len(seasons) - 1:
df.plot.bar(ax=ax,
stacked=True,
title=titlename,
legend=True)
a[i, j].legend(bbox_to_anchor=(1.7, -0.2), ncol=1)
else:
df.plot.bar(ax=ax,
stacked=True,
title=titlename,
legend=False)
if i == len(years) - 1: # Only bottom of plot
ax.set_xlabel("Year [-]")
if j == 0: # Only leftside of plot
ax.set_ylabel("Activity [GWh]")
# Store to excel
if saveData == 'Y':
sheetname = str(year) + '_' + str(season)
df.to_excel(writer, sheetname)
# Save plot
if createPlots == 'Y':
fig = ax.get_figure()
fig.savefig(savename, dpi=resolution, bbox_inches="tight")
# Save excel file
if saveData == 'Y':
writer.save()
# Return to original directory
os.chdir(origDir)
# Return results
return activity
def getActivityTOD(folders,
dbs,
switch='fuel',
sector_name='electric',
save_data='N',
create_plots='N',
conversion=277.777778,
run_name=''):
# inputs:
# 1) folders - paths containing dbs (list or single string if all in the same path)
# 2) dbs - names of databases (list)
# 3) switch - 'fuel' or 'tech', basis of categorization
# 4) sectorName - name of temoa sector to be analyzed
# 5) saveData - 'Y' or 'N', default is 'N'
# 6) createPlots - 'Y' or 'N', default is 'N'
# 7) conversion - conversion to GWh, default is 277.778 (from PJ)
# 8) run_name - Used for saving results in dedicated folder
# outputs:
# 1) activity
# 2) plots - optional
# 3) Data - optional
# ==============================================================================
print("Analyzing activity by time of day (TOD)")
# Save original directory
wrkdir = os.getcwd()
# If only a single db and folder provided, change to a list
if type(dbs) == str and type(folders) == str:
dbs = [dbs]
folders = [folders]
# If a list of folders is provided with one database, only use first folder
elif type(dbs) == str:
dbs = [dbs]
folders = [folders[0]]
# If only a single folder provided, create a list of the same folder
elif type(folders) == str:
fldrs = []
for db in dbs:
fldrs.append(folders)
folders = fldrs
# Create dataframe to hold each capacity_single series
activity = pd.DataFrame(dtype='float64')
# Iterate through each db
for folder, db in zip(folders, dbs):
activity_single = SingleDB(folder,
db,
switch=switch,
sector_name=sector_name,
conversion=conversion)
activity = pd.concat([activity, activity_single])
# Reset index (remove multi-level indexing, easier to use in Excel)
activity = activity.reset_index()
# Directory to hold results
if save_data == 'Y' or create_plots == 'Y':
tt.create_results_dir(wrkdir=wrkdir, run_name=run_name)
# Save results to CSV
if save_data == 'Y':
# Create savename based on switch
if switch == 'fuel':
savename = 'activityTOD_by_fuel.csv'
else:
savename = 'activityTOD_by_tech.csv'
activity.to_csv(savename)
if create_plots == 'Y':
df = activity.reset_index()
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams.update({'figure.max_open_warning': 0}) # ignore warning
for database in df.database.unique():
# new figure
plt.figure()
# set aesthetics
sns.set_style(
"white", {
"font.family": "serif",
"font.serif": ["Times", "Palatino", "serif"]
})
sns.set_context("talk")
# select relevant database
df2 = df[(df.database == database)]
# plot
sns.relplot(x='tod',
y='value',
hue='fuelOrTech',
row='year',
col='season',
data=df2,
kind='line')
# save
if switch == 'fuel':
savename = 'yearlyActivityTOD_byFuel' + tt.remove_ext(
database) + '.pdf'
else:
savename = 'yearlyActivityTOD_byTech' + tt.remove_ext(
database) + '.pdf'
plt.savefig(savename, dpi=resolution)
# close the figure
plt.close()
# Return to original directory
os.chdir(wrkdir)
return activity
def getEmissions(folders,
dbs,
conversion=1E-6,
save_data='N',
create_plots='N',
run_name=''):
# ==============================================================================
# inputs:
# 1) folders - paths containing dbs (list or single string if all in the same path)
# 2) dbs - names of databases (list)
# 3) conversion - converts from emission units to Mton
# default is conversion from kton to Mton is 1E-6
# 4) save_data - 'Y' or 'N', default is 'N'
# 5) create_plots - 'Y' or 'N', default is 'N'
# 6) run_name - Used for saving results in dedicated folder
#
# outputs:
# 1) yearlyEmissions - pandas DataFrame holding yearly emissions
# 2) avgEmissions - dictionary holding average emissions
# ==============================================================================
print("Analyzing emissions")
# Save original directory
wrkdir = os.getcwd()
# If only a single db and folder provided, change to a list
if type(dbs) == str and type(folders) == str:
dbs = [dbs]
folders = [folders]
# If a list of folders is provided with one database, only use first folder
elif type(dbs) == str:
dbs = [dbs]
folders = [folders[0]]
# If only a single folder provided, create a list of the same folder
elif type(folders) == str:
fldrs = []
for db in dbs:
fldrs.append(folders)
folders = fldrs
# Create a dataframe
yearlyEmissions = pd.DataFrame(dtype='float64')
avgEmissions = pd.DataFrame(dtype='float64')
# Iterate through each db
for folder, db in zip(folders, dbs):
# Move to folder
os.chdir(folder)
# Access costs
yearlyEmissions_single, avgEmissions_single = SingleDB(
folder, db, conversion=conversion)
# Store costs
yearlyEmissions = pd.concat([yearlyEmissions, yearlyEmissions_single])
avgEmissions = pd.concat([avgEmissions, avgEmissions_single])
# Sort data
# yearlyEmissions = yearlyEmissions.sort_index()
# Reset index (remove multi-level indexing, easier to use for processing)
yearlyEmissions = yearlyEmissions.reset_index()
avgEmissions = avgEmissions.reset_index()
# Directory to hold results
if save_data == 'Y' or create_plots == 'Y':
tt.create_results_dir(wrkdir=wrkdir, run_name=run_name)
# Save results to CSV
if save_data == 'Y':
yearlyEmissions.to_csv('emissions_yearly.csv')
avgEmissions.to_csv('emissions_average.csv')
# Plot Results
if create_plots == 'Y':
import matplotlib.pyplot as plt
import seaborn as sns
# new figure
plt.figure()
# set aesthetics
sns.set_style("white", {
"font.family": "serif",
"font.serif": ["Times", "Palatino", "serif"]
})
sns.set_context("paper")
sns.set_style("ticks", {"xtick.major.size": 8, "ytick.major.size": 8})
# wide to long
df2 = pd.melt(yearlyEmissions,
id_vars=['database', 'scenario'],
var_name='var',
value_name='value')
# plot
ax = sns.lineplot(x='var', y='value', hue='database', data=df2)
# yearlyEmissions
ax.set_xlabel("Year [-]")
ax.set_ylabel("Emissions [kton]")
fig = ax.get_figure()
savename = 'emissions_yearly.png'
fig.savefig(savename, dpi=resolution)
# close figure
plt.close()
# Return to original directory
os.chdir(wrkdir)
return yearlyEmissions, avgEmissions