update README

README.md

@@ -6,6 +6,8 @@ The EOLES-ResIRF Coupling model is a tool for studying integrated decarbonizatio
 
 ## Installation
 
+The current version of the code should be run on Unix.
+
 ### Step 1: Git clone the folder in your computer.
 
 ```bash

eoles/inputs/config/scenarios/scenarios_dr.json

@@ -0,0 +1,24 @@
+{ "trash": {
+    "profile": ["reference", "ProfileFlat"],
+    "capacity_nuc": ["reference", "Nuc-"],
+    "capacity_pv": ["reference", "PV-"],
+    "capacity_offshore": ["reference", "Offshore-"],
+    "woodprices": ["reference", "PriceWood+"],
+    "weather": ["reference", "cold"],
+    "policy_mix": ["reference", "NoPolicy"],
+    "cop": ["reference", "COP+"],
+    "biogas": ["reference", "Biogas-", "Biogas+"],
+    "capacity_ren": ["reference", "Ren-", "Ren+"],
+    "demand": ["reference", "Sufficiency", "Reindustrialisation"],
+    "carbon_budget": ["reference", "CarbonBudget-"]
+},
+      "supply" : {
+        "discount_rate": ["reference", "DiscountRate-", "DiscountRate--", "DiscountRate+", "DiscountRate++"]
+    },
+    "demand" : {
+
+    },
+   "prices": {
+
+  }
+}

eoles/inputs/config/scenarios/scenarios_weather.json

@@ -0,0 +1,7 @@
+{
+    "supply" : {
+      "weather": ["reference", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016"]
+    },
+    "demand" : {},
+   "prices": {}
+}

eoles/inputs/resources.py

@@ -200,6 +200,7 @@
     'Solar PV': color_palette[8],
     'peaking plants': color_palette[5],
     'Peaking Plants': color_palette[5],
+    'Peaking \nPlants': color_palette[5],
     'nuclear': color_palette[7],
     'Nuclear': color_palette[7],
     'natural gas': color_palette[10],

eoles/utils.py

@@ -1556,6 +1556,9 @@ def create_configs_coupling(list_design, config_coupling: dict, config_additiona
 
     config_coupling_update['resirf'] = {}  # we create a dictionary for the ResIRF configuration specs
 
+    if 'discount_rate' in config_additional.keys():
+        config_coupling_update['discount_rate'] = config_additional['discount_rate']
+
     if 'prices' in config_additional.keys():
         config_coupling_update['resirf']['prices'] = config_additional['prices']['resirf']
         config_coupling_update['resirf']['energy'] = config_additional['energy']  # information necessary to update prices afterwards in modif_config_resirf

eoles/write_output.py

@@ -76,7 +76,7 @@
     'pv': 'Solar PV',
     'battery': 'Battery',
     'hydro': 'Hydroelectricity',
-    'peaking plants': 'Peaking Plants',
+    'peaking plants': 'Peaking \nPlants',
     'Generation offshore (TWh)': 'Offshore',
     'Generation onshore (TWh)': 'Onshore',
     'Generation pv (TWh)': 'Solar PV',
@@ -2671,7 +2671,7 @@ def format_ax(ax: plt.Axes, title=None, y_label=None, x_label=None, x_ticks=None
 
 def format_ax_new(ax, y_label=None, title=None, format_x=None,
                   format_y=lambda y, _: y, ymin=None, ymax=None, xinteger=True, xmin=None, x_max=None, loc_title=None,
-                  c_title=None):
+                  c_title=None, y_fontsize=20, x_fontsize=20):
     """
 
     Parameters
@@ -2700,7 +2700,7 @@ def format_ax_new(ax, y_label=None, title=None, format_x=None,
         ax.xaxis.set_major_formatter(plt.FuncFormatter(format_x))
 
     if y_label is not None:
-        ax.set_ylabel(y_label)
+        ax.set_ylabel(y_label, fontsize=y_fontsize)
 
     if title is not None:
         if loc_title is not None:
@@ -3216,7 +3216,7 @@ def plot_blackbox_optimization(dict_optimizer, save_path, two_stage_optim=False)
 
 def waterfall_chart(df, colors=None, rotation=0, save=None, format_y=lambda y, _: '{:.0f}'.format(y), title=None,
                     y_label=None, hline=False, dict_legend=None, total=True, unit='B€', float_precision=0, neg_offset=None,
-                    pos_offset=None, df_max=None, df_min=None):
+                    pos_offset=None, df_max=None, df_min=None, x_fontsize=24, y_fontsize=24, annotate_fontsize=20):
     if isinstance(df, pd.DataFrame):
         df = df.squeeze()
     if dict_legend is not None:
@@ -3289,9 +3289,9 @@ def waterfall_chart(df, colors=None, rotation=0, save=None, format_y=lambda y, _
                 y -= neg_offset
         # if loop > 0:
         if float_precision == 0:
-            ax.annotate("{:+,.0f} {}".format(val, unit), (loop, y), ha="center")
+            ax.annotate("{:+,.0f} {}".format(val, unit), (loop, y), ha="center", fontsize=annotate_fontsize)
         else:
-            ax.annotate("{:+,.1f} {}".format(val, unit), (loop, y), ha="center")
+            ax.annotate("{:+,.1f} {}".format(val, unit), (loop, y), ha="center", fontsize=annotate_fontsize)
         loop += 1
 
     if blank.max() > 0:  # total est True quand on fait les graphes pour les coûts, et False quand on fait les graphes pour les capacités
@@ -3315,7 +3315,7 @@ def waterfall_chart(df, colors=None, rotation=0, save=None, format_y=lambda y, _
     ax.set_ylim(ymax=y_max)
     ax.set_ylim(ymin=y_min)
     ax.set_xlabel('')
-    ax = format_ax_new(ax, format_y=format_y, xinteger=True)
+    ax = format_ax_new(ax, format_y=format_y, xinteger=True, x_fontsize=x_fontsize)
 
     if title is not None:
         if total:
@@ -3336,8 +3336,8 @@ def waterfall_chart(df, colors=None, rotation=0, save=None, format_y=lambda y, _
     #     fig.legend(handles, labels, loc='center left', frameon=False, ncol=1,
     #                bbox_to_anchor=(1, 0.5))
 
-    plt.setp(ax.xaxis.get_majorticklabels(), rotation=rotation)
-    ax.tick_params(axis='both', which='major', labelsize=18)
+    plt.setp(ax.xaxis.get_majorticklabels(), rotation=rotation, fontsize=x_fontsize)
+    ax.tick_params(axis='both', which='major', labelsize=x_fontsize)
 
     save_fig(fig, save=save)
 

postprocessing/process_outputs.py

@@ -33,8 +33,8 @@
 MAPPING = {'Learning+': 'High', 'Learning-': 'Low',
            'Elasticity+': 'High', 'Elasticity-': 'Low',
            'Biogas+': 'High', 'Biogas-': 'Low',
-           'Capacity_ren+': 'High', 'Ren-': 'Low',
-           'Demand+': 'High', 'Sufficiency': 'Low',
+           'Capacity_ren+': 'High', 'Ren-': 'Low', 'Ren+': 'High',
+           'Demand+': 'High', 'Sufficiency': 'Low', 'Reindustrialisation': 'High',
            'PriceGas+': 'High', 'PriceGas-': 'Low',
            'PriceWood+': 'High', 'PriceWood-': 'Low',
            'Policy_mix+': 'High', 'Policy_mix-': 'Low',
@@ -49,7 +49,7 @@
     'learning': 'Technical progress heat-pumps',
     'elasticity': 'Heat-pump price elasticity',
     'biogas': 'Biogas potential',
-    'capacity_ren': 'Renewable capacity',
+    'capacity_ren': 'Renewable potential',
     'demand': 'Other electricity demand',
     'gasprices': 'Gas prices',
     'woodprices': 'Wood prices',
@@ -66,6 +66,7 @@
             'demand', 'carbon_budget', 'gasprices']
 
 
+
 def parse_outputs(folderpath, features, emissions=False):
     """Parses the outputs of the simulations and creates a csv file with the results.
     Return scenarios_complete, and output which has been processed to only include information on the difference between the Ban and the reference scenario."""
@@ -141,8 +142,8 @@ def make_table_summary(data, folderpath):
                    'Consumption Wood fuel (TWh)': 'Consumption Wood',
                    'offshore': 'Offshore capacity',
                    'onshore': 'Onshore capacity',
-                   'pv': 'Solar PV',
-                   'battery': 'Battery',
+                   'pv': 'Solar PV capacity',
+                   'battery': 'Battery capacity',
                    'peaking plants': 'Peaking plants capacity',
                    'methanization': 'Methanization capacity',
                    'pyrogazification': 'Pyrogazification capacity',
@@ -168,6 +169,49 @@ def make_table_summary(data, folderpath):
     return df
 
 
+def make_table_summary_resirf(data1, data2, folderpath):
+    scenarios = {('S0', 'reference'): 'Counterfactual', ('S0', 'Ban'): 'Ban'}
+    selected_keys = [
+    'Stock (Million)',
+    'Surface (Million m2)',
+    'Consumption (TWh)',
+    'Consumption (kWh/m2)',
+    'Consumption PE (TWh)',
+    'Consumption Electricity (TWh)',
+    'Consumption Natural gas (TWh)',
+    'Consumption Oil fuel (TWh)',
+    'Consumption Wood fuel (TWh)',
+    'Consumption Heating (TWh)',
+    'Energy poverty (Million)',
+    'Emission (MtCO2)',
+    'Stock G (Million)',
+    'Stock F (Million)',
+    'Stock E (Million)',
+    'Stock D (Million)',
+    'Stock C (Million)',
+    'Stock B (Million)',
+    'Stock A (Million)',
+    'Stock Heat pump (Million)',
+    'Stock Direct electric (Million)',
+    'Stock Natural gas (Million)',
+    'Stock Oil fuel (Million)',
+    'Stock Wood fuel (Million)',
+    'Stock District heating (Million)',
+    'Energy poverty (Million)'
+]
+    df1 = data1.loc[selected_keys, '2049'].rename({'2049': 2050})
+    df1 = df1.astype('int')
+
+    df2 = data2.loc[selected_keys, '2049'].rename({'2049': 2050})
+    df2 = df2.astype('int')
+
+    df = pd.concat([df1, df2], axis=1)
+    df.to_csv(folderpath / Path('summary_table_resirf.csv'))
+
+    return df
+
+
+
 def get_distributional_data(df):
     """Extracts detailed distributional data, using the knowledge that those distributional data are tuples."""
 
@@ -512,7 +556,7 @@ def frequency_chart_subplot(results1, results2, category_names=None, save_path=N
 
     plt.tight_layout()
 
-def horizontal_stack_bar_plot(df, columns=None, title=None, order=None, save_path=None):
+def horizontal_stack_bar_plot(df, columns=None, title=None, order=None, save_path=None, fontsize=20):
     """
     Create a horizontal stacked bar plot from a DataFrame.
 
@@ -561,7 +605,7 @@ def horizontal_stack_bar_plot(df, columns=None, title=None, order=None, save_pat
     ax.spines['left'].set_visible(False)
 
     # size of x-axis and y-axis ticks
-    ax.tick_params(axis='both', which='major', labelsize=18)
+    ax.tick_params(axis='both', which='major', labelsize=fontsize)
     # size of title
 
     # Remove the x-axis and y-axis titles
@@ -684,6 +728,7 @@ def distributional_plot(df, folder_name=None):
     # scenarios_complete, output, hourly_generation = parse_outputs(folderpath, features=features)
 
     # LOCAL
-    folderpath = Path('assessing_ban/simulations/exhaustive_20240506_195738')  # for cluster use
+    folderpath = Path('assessing_ban/simulations/exhaustive_20240826_120338')  # for cluster use
     features = ['policy_heater', 'policy_insulation', 'learning', 'elasticity', 'biogas', 'capacity_ren', 'demand', 'carbon_budget', 'gasprices']
+    features = ['discount_rate']
     scenarios_complete, output, hourly_generation = parse_outputs(folderpath, features=features)

preprocessing_data.py

@@ -7,33 +7,33 @@
 
 ####### Demand profile #########
 
-# Process csv file to get the demand profile
-# First column is the type of vehicule, second is the hour, last is the demand value in MW
-demand_ev = pd.read_csv('eoles/inputs/demand_data_other/demand_transport2050.csv', index_col=0, header=None).reset_index().rename(columns={0: 'vehicule', 1: 'hour', 2: 'demand'})
-demand_rte = pd.read_csv("eoles/inputs/demand/demand2050_RTE.csv", index_col=0, header=None).squeeze("columns")
-
-adjust_demand = (530 * 1e3 - 580 * 1e3) / 8760  # 580TWh is the total of the profile we use as basis for electricity hourly demand (from RTE), c'est bien vérifié
-demand_rte_rescaled_1 = demand_rte + adjust_demand
-demand_rte_rescaled_2 = demand_rte * (530 / 580)
-
-plt.plot(demand_rte_rescaled_1[0:150], c='red')
-plt.plot(demand_rte_rescaled_2[0:150], c='blue')
-# plt.plot(demand_rte[0:1000], c='green')
-plt.show()
-
-# plot the demand profile for vehicule = 'light'
-
-demand_ev_light = demand_ev.loc[demand_ev.vehicule == 'light']
-demand_ev_light = demand_ev_light.drop(columns=['vehicule'])
-demand_ev_light = demand_ev_light.set_index('hour')
-
-demand_ev_heavy = demand_ev.loc[demand_ev.vehicule == 'heavy']
-demand_ev_heavy = demand_ev_heavy.drop(columns=['vehicule'])
-demand_ev_heavy = demand_ev_heavy.set_index('hour')
-
-demand_ev_bus = demand_ev.loc[demand_ev.vehicule == 'bus']
-demand_ev_bus = demand_ev_bus.drop(columns=['vehicule'])
-demand_ev_bus = demand_ev_bus.set_index('hour')
+# # Process csv file to get the demand profile
+# # First column is the type of vehicule, second is the hour, last is the demand value in MW
+# demand_ev = pd.read_csv('eoles/inputs/demand_data_other/demand_transport2050.csv', index_col=0, header=None).reset_index().rename(columns={0: 'vehicule', 1: 'hour', 2: 'demand'})
+# demand_rte = pd.read_csv("eoles/inputs/demand/demand2050_RTE.csv", index_col=0, header=None).squeeze("columns")
+#
+# adjust_demand = (530 * 1e3 - 580 * 1e3) / 8760  # 580TWh is the total of the profile we use as basis for electricity hourly demand (from RTE), c'est bien vérifié
+# demand_rte_rescaled_1 = demand_rte + adjust_demand
+# demand_rte_rescaled_2 = demand_rte * (530 / 580)
+#
+# plt.plot(demand_rte_rescaled_1[0:150], c='red')
+# plt.plot(demand_rte_rescaled_2[0:150], c='blue')
+# # plt.plot(demand_rte[0:1000], c='green')
+# plt.show()
+#
+# # plot the demand profile for vehicule = 'light'
+#
+# demand_ev_light = demand_ev.loc[demand_ev.vehicule == 'light']
+# demand_ev_light = demand_ev_light.drop(columns=['vehicule'])
+# demand_ev_light = demand_ev_light.set_index('hour')
+#
+# demand_ev_heavy = demand_ev.loc[demand_ev.vehicule == 'heavy']
+# demand_ev_heavy = demand_ev_heavy.drop(columns=['vehicule'])
+# demand_ev_heavy = demand_ev_heavy.set_index('hour')
+#
+# demand_ev_bus = demand_ev.loc[demand_ev.vehicule == 'bus']
+# demand_ev_bus = demand_ev_bus.drop(columns=['vehicule'])
+# demand_ev_bus = demand_ev_bus.set_index('hour')
 # plot only for a subset of hours
 # demand_ev_light.loc[0:100].plot()
 # plt.show()
@@ -179,7 +179,8 @@
 lake_inflows = pd.read_csv('eoles/inputs/hourly_profiles/lake_2000-2019.csv', index_col=0, header=None).reset_index()
 lake_inflows.columns = ["month", "capacity_factor"]
 #
-list_year = [2012]
+year = 2016
+list_year = [year]
 vre_profiles_subset = pd.DataFrame()
 lake_inflows_subset = pd.DataFrame()
 
@@ -203,8 +204,8 @@
 
 lake_inflows_subset = lake_inflows_subset.set_index('month')
 #
-vre_profiles_subset.to_csv('eoles/inputs/hourly_profiles/vre_profiles_2012.csv', header=False)
-lake_inflows_subset.to_csv('eoles/inputs/hourly_profiles/lake_2012.csv', header=False)
+vre_profiles_subset.to_csv(f'eoles/inputs/hourly_profiles/vre_profiles_{year}.csv', header=False)
+lake_inflows_subset.to_csv(f'eoles/inputs/hourly_profiles/lake_{year}.csv', header=False)
 #
 # ############## Estimate run of river values for different years  ######################
 #