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Separate stp from other locations on co2, ptc plot
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Marisol Garrouste (INL) committed Aug 14, 2023
1 parent d3b3146 commit 6a7266a
Showing 1 changed file with 88 additions and 6 deletions.
94 changes: 88 additions & 6 deletions use_cases/LWR_MeOH_2023/run/pp_SA.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,14 +9,16 @@
locations = ['Braidwood', 'Cooper', 'Davis-Besse', 'Prairie Island', 'South Texas Project']
locations_names = {'braidwood':'Braidwood', 'cooper':'Cooper', 'davis_besse':'Davis-Besse', 'prairie_island':'Prairie Island',
'stp':'South Texas Project'}
locations_names_reg = {'braidwood':'Braidwood', 'cooper':'Cooper', 'davis_besse':'Davis-Besse', 'prairie_island':'Prairie Island'}
locations_names_stp = {'stp':'South Texas\nProject'}
REG_VARIABLES = ['capex', 'elec', 'om', 'synfuels']
reg_variables =['CAPEX', 'Electricity\nprices', 'O&M', 'Synfuels\nprices']
variables = ['CAPEX', 'Electricity\nprices', 'O&M', 'Synfuels\nprices','CO2', 'PTC']
W_VARIABLES = ['co2_high', 'co2_low', 'ptc_000', 'ptc_100', 'ptc_270']
w_variables = ['CO2', 'PTC']
total_var = W_VARIABLES+REG_VARIABLES
ptc_var = ['ptc_000', 'ptc_100', 'ptc_270']
co2_var = ['co2_cost_med','co2_cost_high']
co2_var = ['co2_low','co2_high']
toplot_var = ['ptc_000', 'ptc_100', 'ptc_270','co2_low','co2_high']
variables_names = {'co2_high':r'$CO_2 (\$60/ton)$', 'co2_low':r'$CO_2\; (\$30/ton)$', 'ptc_000':r'$PTC\; (\$0/kg-H_2)$',
'ptc_100':r'$PTC\; (\$1.0/kg-H_2)$','ptc_270':r'$PTC\; (\$2.7/kg-H_2)$',
Expand Down Expand Up @@ -183,13 +185,13 @@ def plot_SA_variable_v2(var_dic):
val_dic = var_dic[var]
co2_df[var+'_value'] = val_dic['value']
co2_df[var+'_sd'] =val_dic['sd']

plt.style.use('seaborn-paper')
fig, ax = plt.subplots(2,1, sharex=True)# figsize=(10,8))

# Error bars
yerr_ptc = ptc_df[['ptc_000_sd', 'ptc_100_sd', 'ptc_270_sd']].to_numpy().T
yerr_co2 = co2_df[['co2_cost_med_sd', 'co2_cost_high_sd']].to_numpy().T
yerr_co2 = co2_df[['co2_low_sd', 'co2_high_sd']].to_numpy().T

# PTC first
ptc_df.plot(ax = ax[0], kind = "bar", y =['ptc_000_value', 'ptc_100_value', 'ptc_270_value'],
Expand All @@ -201,19 +203,99 @@ def plot_SA_variable_v2(var_dic):
ax[0].legend( [r'$PTC\; (\$0/kg-H_2)$',r'$PTC\; (\$1.0/kg-H_2)$',r'$PTC\; (\$2.7/kg-H_2)$'])

# CO2
co2_df.plot(ax = ax[1], kind = "bar", y =['co2_cost_med_value', 'co2_cost_high_value'],
co2_df.plot(ax = ax[1], kind = "bar", y =['co2_low_value', 'co2_high_value'],
yerr=yerr_co2, width=0.3, color=['black', 'grey'],
error_kw=dict(ecolor='black',elinewidth=1, capthick=1, capsize=3))
ax[1].set_xticks(np.arange(len(list(locations_names.keys()))))
ax[1].set_xticklabels(locations_names.values(), rotation=0)
ax[1].set_ylabel('Change in profitability (%)')
ax[1].set_xlabel('')
ax[1].legend( [r'$CO_2 (\$60/ton)$', r'$CO_2\; (\$30/ton)$'])
ax[1].legend( [r'$CO_2\; (\$30/ton)$', r'$CO_2 (\$60/ton)$'])

sns.despine()
fig.tight_layout()
fig.savefig(os.path.join(os.path.dirname(os.path.abspath(__file__)), "SA_results_variable.png"))

def plot_SA_variable_v3(var_dic):
ptc_df = pd.DataFrame()
co2_df = pd.DataFrame()
for var in ptc_var:
val_dic = var_dic[var]
ptc_df[var+'_value'] = val_dic['value']
ptc_df[var+'_sd'] =val_dic['sd']
for var in co2_var:
val_dic = var_dic[var]
co2_df[var+'_value'] = val_dic['value']
co2_df[var+'_sd'] =val_dic['sd']


ptc_df_stp = ptc_df.iloc[-1,:].to_frame().transpose()
co2_df_stp = co2_df.iloc[-1,:].to_frame().transpose()
ptc_df.drop(index=4, inplace=True)
co2_df.drop(index=4, inplace=True)

plt.style.use('seaborn-paper')

# create the subfigures and subplots
fig = plt.figure(figsize=(8, 6), constrained_layout=True)
subfigs = fig.subfigures(1, 2, width_ratios=[4, 1.25], wspace=.05)

# Reg locations
ax = subfigs[0].subplots(2, 1, sharex=True)
# Error bars
yerr_ptc = ptc_df[['ptc_000_sd', 'ptc_100_sd', 'ptc_270_sd']].to_numpy().T
yerr_co2 = co2_df[['co2_low_sd', 'co2_high_sd']].to_numpy().T

# PTC first
ptc_df.plot(ax = ax[0], kind = "bar", y =['ptc_000_value', 'ptc_100_value', 'ptc_270_value'],
yerr=yerr_ptc , width=0.3, color=['red', 'orange', 'green'], error_kw=dict(ecolor='black',elinewidth=1, capthick=1, capsize=3))
ax[0].set_xticks(np.arange(len(list(locations_names_reg.keys()))))
ax[0].set_xticklabels(locations_names_reg.values(), rotation=0)
ax[0].set_ylabel('Change in profitability (%)')
ax[0].set_xlabel('')
ax[0].legend( [r'$PTC\; (\$0/kg-H_2)$',r'$PTC\; (\$1.0/kg-H_2)$',r'$PTC\; (\$2.7/kg-H_2)$'])

# CO2
co2_df.plot(ax = ax[1], kind = "bar", y =['co2_low_value', 'co2_high_value'],
yerr=yerr_co2, width=0.3, color=['black', 'grey'],
error_kw=dict(ecolor='black',elinewidth=1, capthick=1, capsize=3))
ax[1].set_xticks(np.arange(len(list(locations_names_reg.keys()))))
ax[1].set_xticklabels(locations_names_reg.values(), rotation=0)
ax[1].set_ylabel('Change in profitability (%)')
ax[1].set_xlabel('')
ax[1].legend( [r'$CO_2\; (\$30/ton)$', r'$CO_2 (\$60/ton)$'])
sns.despine()


# STP
ax_stp = subfigs[1].subplots(2, 1, sharex=True)
# Error bars
yerr_ptc_stp = ptc_df_stp[['ptc_000_sd', 'ptc_100_sd', 'ptc_270_sd']].to_numpy().T
yerr_co2_stp = co2_df_stp[['co2_low_sd', 'co2_high_sd']].to_numpy().T

# PTC first
ptc_df_stp.plot(ax = ax_stp[0], kind = "bar", y =['ptc_000_value', 'ptc_100_value', 'ptc_270_value'],
yerr=yerr_ptc_stp , width=0.3, color=['red', 'orange', 'green'], error_kw=dict(ecolor='black',elinewidth=1, capthick=1, capsize=3))
ax_stp[0].set_xticks(np.arange(len(list(locations_names_stp.keys()))))
ax_stp[0].set_xticklabels(locations_names_stp.values(), rotation=0)
ax_stp[0].set_ylabel('Change in profitability (%)')
ax_stp[0].set_xlabel('')
ax_stp[0].get_legend().remove()
#ax_stp[0].legend( [r'$PTC\; (\$0/kg-H_2)$',r'$PTC\; (\$1.0/kg-H_2)$',r'$PTC\; (\$2.7/kg-H_2)$'])

# CO2
co2_df_stp.plot(ax = ax_stp[1], kind = "bar", y =['co2_low_value', 'co2_high_value'],
yerr=yerr_co2_stp, width=0.3, color=['black', 'grey'],
error_kw=dict(ecolor='black',elinewidth=1, capthick=1, capsize=3))
ax_stp[1].set_xticks(np.arange(len(list(locations_names_stp.keys()))))
ax_stp[1].set_xticklabels(locations_names_stp.values(), rotation=0)
ax_stp[1].set_ylabel('Change in profitability (%)')
ax_stp[1].set_xlabel('')
ax_stp[1].get_legend().remove()
#ax_stp[1].legend( [r'$CO_2\; (\$30/ton)$', r'$CO_2 (\$60/ton)$'])
sns.despine()

fig.savefig(os.path.join(os.path.dirname(os.path.abspath(__file__)), "SA_results_variable.png"))


def plot_SA_variable(var_dic):
Expand Down Expand Up @@ -251,7 +333,7 @@ def main():
loc_dic, var_dic = load_SA_results_loc()
plot_SA_locations(loc_dic)
plot_SA_one_location(loc_dic, location='stp',type='regular')
plot_SA_variable_v2(var_dic)
plot_SA_variable_v3(var_dic)

if __name__ == "__main__":
main()

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