several scripts for result evaluation

.gitignore

@@ -99,3 +99,8 @@ output_files
 *.orig
 runme_test_runtime.py
 *.iml
+sensitivity_results_berlin
+sensitivity_results_cologne
+sensitivity_results_hamburg
+sensitivity_results_frankfurt
+sensitivity_results_munich

find_bat_be.py

+import pandas as pd
+
+path = '/home/mce/l2t_results/sensitivity_results_'
+
+mfhs = ['mfh_4'] # 'mfh_1', 'mfh_2', 'mfh_3',#'mfh_4'
+cities = ['cologne', 'munich', 'berlin', 'hamburg', 'frankfurt'] # 'frankfurt',
+years = ['2020', '2023', '2030']
+
+for city in cities:
+    for mfh in mfhs:
+        be_dict = {'aux_price_2020': [],
+                   'aux_price_2023': [],
+                   'aux_price_2030': []}
+        for year in years:
+            df_bat = pd.read_csv(path + city + '/' + mfh + '_' + year + '/data_frame_size_bat.csv').iloc[::-1]
+            #df_bat = df_bat.iloc[::-1]
+            for i in range(len(df_bat['0.10'])):
+                if df_bat['0.10'].iloc[i] > 0:
+                    price = df_bat.iloc[i, 0]
+                    be_dict['aux_price_'+year].append(df_bat.iloc[i, 0])
+                    break
+        be_df = pd.DataFrame(be_dict)
+        be_df.to_csv('/home/mce/l2t_results/bat_break_even/'+mfh+'_'+city+'.csv', index=False)

find_eq.py

+import pandas as pd
+
+path = '/home/mce/l2t_results/sensitivity_results_'
+
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3'] # 'mfh_1', 'mfh_2', 'mfh_3',#'mfh_4'
+cities = ['cologne']#, 'munich', 'berlin', 'hamburg', 'frankfurt'] # 'frankfurt',
+years = ['2020', '2023', '2030']
+prices = ['0.27', '0.31', '0.36', '0.40', '0.45', '0.49', '0.54']
+
+for city in cities:
+    for mfh in mfhs:
+        eq_dict = {'base_price': [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6],
+                   'l2t_price': [float(item) for item in prices],
+                   'aux_price_2020': [],
+                   'aux_price_2023': [],
+                   'aux_price_2030': [],
+                   'pv_2020': [],
+                   'pv_2023': [],
+                   'pv_2030': [],
+                   'bat_2020': [],
+                   'bat_2023': [],
+                   'bat_2030': []}
+        for year in years:
+            df_cost = pd.read_csv(path + city + '/' + mfh + '_' + year + '/data_frame_cost.csv')
+            df_pv = pd.read_csv(path + city + '/' + mfh + '_' + year + '/data_frame_size_pv.csv')
+            df_bat = pd.read_csv(path + city + '/' + mfh + '_' + year + '/data_frame_size_bat.csv')
+            for price in prices:
+                for i in range(len(df_cost[price])):
+                    if df_cost[price].iloc[i] >= 0:
+                        eq_dict['aux_price_'+year].append(df_cost.iloc[i, 0])
+                        eq_dict['pv_' + year].append(df_pv.loc[i, price])
+                        eq_dict['bat_' + year].append(df_bat.loc[i, price])
+                        break
+        eq_df = pd.DataFrame(eq_dict)
+        eq_df.to_csv('/home/mce/l2t_results/equilibria/'+mfh+'_'+city+'.csv', index=False)
\ No newline at end of file

hm_results_to_csv.py

+import pandas as pd
+
+path = '/home/mce/l2t_results/sensitivity_results_frankfurt/'
+
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4'] #['mfh_'+str(i+1) for i in range(4,8)] # 'mfh_1', 'mfh_2', 'mfh_3',
+
+years = [2020, 2023, 2030]
+
+for mfh in mfhs:
+    for y in years:
+        year=str(y)
+        try:
+            df_cost = pd.read_pickle(path+mfh+'_'+year+'/data_frame_cost.pkl').iloc[::-1]
+            df_pv = pd.read_pickle(path + mfh + '_' + year + '/data_frame_size_pv.pkl').iloc[::-1]
+            df_bat = pd.read_pickle(path + mfh + '_' + year + '/data_frame_size_bat.pkl').iloc[::-1]
+            df_cost.to_csv(path+mfh+'_'+year+'/data_frame_cost.csv')
+            df_pv.to_csv(path+mfh+'_'+year+'/data_frame_size_pv.csv')
+            df_bat.to_csv(path+mfh+'_'+year+'/data_frame_size_bat.csv')
+        except:
+            print('no results found for '+mfh+' '+year)
\ No newline at end of file

plot_bat_break_even_cities.py

+import pandas as pd
+import matplotlib.pyplot as plt
+
+years = ['2020', '2023', '2030']
+cities = ['hamburg', 'cologne', 'berlin', 'frankfurt', 'munich']
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4']
+
+colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
+
+
+fig, ax1 = plt.subplots(figsize=(20*0.7, 15*0.7))
+
+df_list_mfhs = [pd.read_csv('/home/mce/l2t_results/bat_break_even/'+mfh+'_'+ 'cologne' +'.csv') for mfh in mfhs]
+#fig, ax1 = plt.subplots(figsize=(20*0.7, 15*0.7))
+#plt.rcParams.update({'font.size': 10})
+plt.subplot(1, 2, 1)
+for i in range(len(mfhs)):
+    if mfhs[i] == 'mfh_4':
+        plt.plot(years, [df_list_mfhs[i].loc[0, 'aux_price_'+year] for year in years], marker='o', color=colors[i], label=str(mfhs[i]))
+    else:
+        plt.plot(years, [df_list_mfhs[i].loc[0, 'aux_price_' + year] for year in years], marker='o', color=colors[i], label=str(mfhs[i]), linestyle='dashed')
+
+#ax1.set_xticklabels(years_3)
+plt.ylabel('Auxiliary electricity price [€/kWh]')
+plt.xlabel('Different MFH scenarios for reference city Berlin')
+plt.legend(['MFH 1', 'MFH 2', 'MFH 3', 'MFH 4'])
+#plt.xlabel('MFH 4')
+#ax1.legend()
+
+df_list_cities = [pd.read_csv('/home/mce/l2t_results/bat_break_even/'+'mfh_4'+'_'+ city +'.csv') for city in cities]
+
+#plt.rcParams.update({'font.size': 10})
+plt.subplot(1, 2, 2)
+for i in range(len(cities)):
+    if cities[i] == 'berlin':
+        plt.plot(years, [df_list_cities[i].loc[0, 'aux_price_'+year] for year in years], marker='o', color=colors[i], label=str(cities[i]))
+    else:
+        plt.plot(years, [df_list_cities[i].loc[0, 'aux_price_' + year] for year in years], marker='o', color=colors[i], label=str(cities[i]), linestyle='dashed')
+
+
+plt.xlabel('Reference scenario MFH 4 for different cities')
+plt.legend(['Hamburg', 'Cologne', 'Berlin', 'Frankfurt', 'Munich'])
+
+plt.tight_layout()
+
+plt.savefig('/home/mce/l2t_results/bat_break_even/bat_break_even_cities.jpeg', dpi=500)
\ No newline at end of file

plot_bat_break_even_mfhs.py

+import pandas as pd
+import matplotlib.pyplot as plt
+
+years = ['2020', '2023', '2030']
+cities = ['hamburg', 'cologne', 'berlin', 'frankfurt', 'munich']
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4']
+
+colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
+df_list_mfhs = [pd.read_csv('/home/mce/l2t_results/bat_break_even/'+mfh+'_'+ 'cologne' +'.csv') for mfh in mfhs]
+fig, ax1 = plt.subplots(figsize=(20*0.7, 15*0.7))
+#plt.rcParams.update({'font.size': 10})
+for i in range(len(mfhs)):
+    if mfhs[i] == 'mfh_4':
+        plt.plot(years, [df_list_mfhs[i].loc[0, 'aux_price_'+year] for year in years], marker='o', color=colors[i], label=str(mfhs[i]))
+    else:
+        plt.plot(years, [df_list_mfhs[i].loc[0, 'aux_price_' + year] for year in years], marker='o', color=colors[i], label=str(mfhs[i]), linestyle='dashed')
+
+#ax1.set_xticklabels(years_3)
+
+ax1.set_ylabel('Auxiliary electricity price [€/kWh]')
+#plt.xlabel('MFH 4')
+ax1.legend()
+
+plt.tight_layout()
+
+plt.savefig('/home/mce/l2t_results/bat_break_even/bat_break_even_mfhs.jpeg', dpi=500)
\ No newline at end of file

plot_eq_cities.py

+import pandas as pd
+import matplotlib.pyplot as plt
+
+years = ['2020', '2023', '2030']
+cities = ['hamburg', 'cologne', 'berlin', 'frankfurt', 'munich']
+years_2 = [year+'_'+city for year in years for city in cities]
+years_3 = years + years + years + years + years
+base_price=['0.30', '0.35', '0.40', '0.45', '0.50', '0.55', '0.60']
+colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
+df_list = [pd.read_csv('/home/mce/l2t_results/equilibria/'+'mfh_4'+'_'+ city +'.csv') for city in cities]
+fig, ax1 = plt.subplots(figsize=(20*0.7, 15*0.7))
+#plt.rcParams.update({'font.size': 10})
+for i in range(len(base_price)):
+    label = True
+    for j in range(len(cities)):
+        if label:
+            plt.plot([year+'_'+cities[j] for year in years], [df_list[j].loc[i, 'aux_price_'+year] for year in years], marker='o', color=colors[i], label=str(base_price[i])+' €/kWh base price')
+            label = False
+        else:
+            plt.plot([year + '_' + cities[j] for year in years], [df_list[j].loc[i, 'aux_price_' + year] for year in years], marker='o', color=colors[i])
+
+ax1.set_xticklabels(years_3)
+ax2 = ax1.twiny()
+fig.subplots_adjust(bottom=0.2)
+
+# Move twinned axis ticks and label from top to bottom
+ax2.xaxis.set_ticks_position("bottom")
+ax2.xaxis.set_label_position("bottom")
+
+# Offset the twin axis below the host
+ax2.spines["bottom"].set_position(("axes", -0.025))
+
+# Turn on the frame for the twin axis, but then hide all
+# but the bottom spine
+ax2.set_frame_on(True)
+ax2.patch.set_visible(False)
+
+for sp in ax2.spines.values():
+    sp.set_visible(False)
+ax2.spines["bottom"].set_visible(True)
+ax2.set_xticks([0.11, 0.305, 0.5, 1-0.305, 1-0.11]) #0.1*(i+1) for i in range(len(cities))
+ax2.set_xticklabels(['Hamburg', 'Cologne', 'Berlin', 'Frankfurt', 'Munich'])
+#ax2.tick_params(axis='x', direction='in', pad=-15)
+#ax2.spines['bottom'].set_position(('outward', 40))
+#ax2.spines['top'].set_color('none')
+ax2.spines['bottom'].set_color('none')
+#ax2.xaxis.set_ticks_position('none')
+#plt.legend()
+#plt.ylabel('Auxiliary electricity price [€/kWh]')
+ax1.set_ylabel('Auxiliary electricity price [€/kWh]')
+#plt.xlabel('MFH 4')
+ax1.legend()
+
+plt.tight_layout()
+
+plt.savefig('/home/mce/l2t_results/equilibria/eq_cities.jpeg', dpi=500)
+#plt.show()
\ No newline at end of file

plot_eq_mfhs

+import pandas as pd
+import matplotlib.pyplot as plt
+
+years = ['2020', '2023', '2030']
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4']
+years_2 = [year+'_'+mfh for year in years for mfh in mfhs]
+years_3 = years + years + years + years + years
+base_price=['0.30', '0.35', '0.40', '0.45', '0.50', '0.55', '0.60']
+colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
+df_list = [pd.read_csv('/home/mce/l2t_results/equilibria/'+mfh+'_'+ 'berlin' +'.csv') for mfh in mfhs]
+fig, ax1 = plt.subplots(figsize=(20*0.7, 15*0.7))
+#plt.rcParams.update({'font.size': 10})
+for i in range(len(base_price)):
+    label = True
+    for j in range(len(mfhs)):
+        if label:
+            plt.plot([year+'_'+mfhs[j] for year in years], [df_list[j].loc[i, 'aux_price_'+year] for year in years], marker='o', color=colors[i], label=str(base_price[i])+' €/kWh base price')
+            label = False
+        else:
+            plt.plot([year + '_' + mfhs[j] for year in years], [df_list[j].loc[i, 'aux_price_' + year] for year in years], marker='o', color=colors[i])
+
+ax1.set_xticklabels(years_3)
+ax2 = ax1.twiny()
+fig.subplots_adjust(bottom=0.2)
+
+# Move twinned axis ticks and label from top to bottom
+ax2.xaxis.set_ticks_position("bottom")
+ax2.xaxis.set_label_position("bottom")
+
+# Offset the twin axis below the host
+ax2.spines["bottom"].set_position(("axes", -0.025))
+
+# Turn on the frame for the twin axis, but then hide all
+# but the bottom spine
+ax2.set_frame_on(True)
+ax2.patch.set_visible(False)
+
+for sp in ax2.spines.values():
+    sp.set_visible(False)
+ax2.spines["bottom"].set_visible(True)
+ax2.set_xticks([0.128, 0.376, 1-0.376, 1-0.128]) #0.1*(i+1) for i in range(len(cities))
+ax2.set_xticklabels(['MFH 1', 'MFH 2', 'MFH 3', 'MFH 4'])
+#ax2.tick_params(axis='x', direction='in', pad=-15)
+#ax2.spines['bottom'].set_position(('outward', 40))
+#ax2.spines['top'].set_color('none')
+ax2.spines['bottom'].set_color('none')
+#ax2.xaxis.set_ticks_position('none')
+#plt.legend()
+#plt.ylabel('Auxiliary electricity price [€/kWh]')
+ax1.set_ylabel('Auxiliary electricity price [€/kWh]')
+#plt.xlabel('MFH 4')
+ax1.legend()
+
+plt.tight_layout()
+
+plt.savefig('/home/mce/l2t_results/equilibria/eq_mfhs.jpeg', dpi=500)
+#plt.show()
\ No newline at end of file

runme.py

@@ -47,13 +47,20 @@ t_start = pd.Timestamp("2019-01-01 00:00:00")  # start time of simulation
 t_horizon = 365 * 24 * 4  # number of time steps to be simulated
 t_step = 1 / 4  # length of a time step in hours
 
-input_profile_dict = {'temperature_1': ['air_temperature', 'input_files/data/temperature/temperature_cologne.csv'],
-                      'irradiance': ['irradiance', 'input_files/data/irradiance/irradiance_cologne.csv'],
+input_profile_dict = {'temperature_berlin': ['air_temperature', 'input_files/data/temperature/temperature_berlin.csv'],
+                      'irradiance_berlin': ['irradiance', 'input_files/data/irradiance/irradiance_berlin.csv'],
+                      'temperature_cologne': ['air_temperature', 'input_files/data/temperature/temperature_cologne.csv'],
+                      'irradiance_cologne': ['irradiance', 'input_files/data/irradiance/irradiance_cologne.csv'],
+                      'temperature_munich': ['air_temperature', 'input_files/data/temperature/temperature_munich.csv'],
+                      'irradiance_munich': ['irradiance', 'input_files/data/irradiance/irradiance_munich.csv'],
+                      'temperature_hamburg': ['air_temperature', 'input_files/data/temperature/temperature_hamburg.csv'],
+                      'irradiance_hamburg': ['irradiance', 'input_files/data/irradiance/irradiance_hamburg.csv'],
+                      'temperature_frankfurt': ['air_temperature', 'input_files/data/temperature/temperature_frankfurt.csv'],
+                      'irradiance_frankfurt': ['irradiance', 'input_files/data/irradiance/irradiance_frankfurt.csv'],
                       'demand_mfh_1': ['elec_demand', 'input_files/data/demand/electricity/mfh_1.csv'],
                       'demand_mfh_2': ['elec_demand', 'input_files/data/demand/electricity/mfh_2.csv'],
                       'demand_mfh_3': ['elec_demand', 'input_files/data/demand/electricity/mfh_3.csv'],
-                      'demand_mfh_4': ['elec_demand', 'input_files/data/demand/electricity/mfh_4.csv'],
-                      'power_factors_1': ['power_factors', 'input_files/data/power_factors/pv_factors_ninja.csv']}
+                      'demand_mfh_4': ['elec_demand', 'input_files/data/demand/electricity/mfh_4.csv']}
 
 #'power_factors_1': ['power_factors', 'input_files/data/power_factors/pv_factors_ninja.csv'],
 #                      'temperature_1': ['air_temperature', 'input_files/data/temperature/temperature_ninja.csv'],
@@ -82,8 +89,14 @@ prosumer_3 = {'lifetime_test': {'config_path': 'input_files/models/prosumer_mode
                                              'elec_cns': ('demand_mfh_3', 'demand_electric', 'perfect_foresight')}}
               }
 
+prosumer_sankey = {'prosumer_sankey': {'config_path': 'input_files/models/prosumer_models/file_mfh_4_2023/0.50_0.27/config.csv',
+                                'topology_path': 'input_files/models/prosumer_models/file_mfh_4_2023/0.50_0.27/',
+                                'profiles': {'pv_roof': [('irradiance_berlin', 'irradiance', 'perfect_foresight'), ('temperature_berlin', 'air_temperature', 'perfect_foresight')],
+                                             'elec_cns': ('demand_mfh_4', 'demand_electric', 'perfect_foresight')}}
+              }
+
 
-def create_scenario_list(mfh, year):
+def create_scenario_list(mfh, year, city):
     scenario_list = []
     for i in np.arange(0.1, 0.61, 0.01):  # auxiliary power price in €/kWh
         for j in np.arange(0.1, 0.61, 0.01):  # l2t power price in €/kWh
@@ -93,7 +106,7 @@ def create_scenario_list(mfh, year):
                     format(i, '.2f')) + '_' + str(format(j, '.2f')),
                 'config_path': 'input_files/models/prosumer_models/file_' + str(mfh) + '_' + str(year) + '/' + str(
                     format(i, '.2f')) + '_' + str(format(j, '.2f')) + '/config.csv',
-                'profiles': {'pv_roof': [('irradiance', 'irradiance', 'perfect_foresight'), ('temperature_1', 'air_temperature', 'perfect_foresight')],
+                'profiles': {'pv_roof': [('irradiance_'+city, 'irradiance', 'perfect_foresight'), ('temperature_'+city, 'air_temperature', 'perfect_foresight')],
                              'elec_cns': ('demand_' + str(mfh), 'demand_electric', 'perfect_foresight')}}})
 
     #scenario_list = [prosumer_1]
@@ -126,22 +139,26 @@ def run_parallel(scenario_list):
     pool.join()
 
 
-mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4'] #['mfh_'+str(i+1) for i in range(4,8)] #
+mfhs = ['mfh_1', 'mfh_2', 'mfh_3', 'mfh_4'] #['mfh_'+str(i+1) for i in range(4,8)] # 'mfh_1', 'mfh_2', 'mfh_3',
 
 years = [2020, 2023, 2030]
+cities = ['berlin']#['hamburg', 'cologne', 'berlin', 'frankfurt', 'munich']
+#optimize_prosumer(prosumer_sankey)
 
-#optimize_prosumer(prosumer_2)
-
+for city in cities:
     for mfh in mfhs:
         for year in years:
-        #run_parallel(create_scenario_list(mfh, year))
-        #if not os.path.exists('sensitivity_results/'):
-        #    os.makedirs('sensitivity_results/')
-        #if not os.path.exists('sensitivity_results/'+str(mfh)+'_'+str(year)+'/'):
-        #    os.makedirs('sensitivity_results/'+str(mfh)+'_'+str(year)+'/')
-        #prepare_data(mfh, year, 'sensitivity_results/'+str(mfh)+'_'+str(year)+'/')
-        plot_all(mfh, year, 'sensitivity_results/'+str(mfh)+'_'+str(year)+'/')
-        #try:
-        #    shutil.rmtree('output_files')
-        #except Exception as e:
-        #    print(f"Error occurred while deleting folder output_files : {str(e)}")
+            if mfh=='mfh_1' and year==2020:
+                break
+            else:
+                run_parallel(create_scenario_list(mfh, year, city))
+                if not os.path.exists('sensitivity_results_'+city+'/'):
+                    os.makedirs('sensitivity_results_'+city+'/')
+                if not os.path.exists('sensitivity_results_'+city+'/'+str(mfh)+'_'+str(year)+'/'):
+                    os.makedirs('sensitivity_results_'+city+'/'+str(mfh)+'_'+str(year)+'/')
+                prepare_data(mfh, year, 'sensitivity_results_'+city+'/'+str(mfh)+'_'+str(year)+'/')
+                plot_all(mfh, year, 'sensitivity_results_'+city+'/'+str(mfh)+'_'+str(year)+'/')
+                try:
+                    shutil.rmtree('output_files')
+                except Exception as e:
+                    print(f"Error occurred while deleting folder output_files : {str(e)}")