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implementing light model

Input_Jahr_2021.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6e554203fe49d3bb990ec181e26aee4d05950ee07dadc980bdbade651962c6ed
-size 1119734
+oid sha256:127195ebc8ff48f366fb9a4c0d32a6e4c33a8bba916f4685523f6ef396374f73
+size 1126639

app.py

@@ -30,21 +30,16 @@ col1.header("Data Input")
 col4.header("Download Results")
 
 # Color dictionary for figures
-color_dict = {'Biomass': 'lightgreen',
-              'Braunkohle': 'brown',
+color_dict = {'Biomasse': 'lightgreen',
+              'Braunkohle': 'red',
               'Erdgas': 'grey',
               'Steinkohle': 'darkgrey',
-              'Erdöl': 'maroon',
-              'RoR': 'aquamarine',
-              'Hydro Water Reservoir': 'azure',
-              'Nuclear': 'orange',
+              'Erdöl': 'brown',
+              'Laufwasser': 'aquamarine',
+              'Kernenergie': 'orange',
               'PV': 'yellow',
               'WindOff': 'darkblue',
-              'WindOn': 'green',
-              'H2': 'crimson',
-              'Laufwasser': 'lightblue',
-              'Batteriespeicher': 'red',
-              'Electrolyzer': 'olive'}
+              'WindOn': 'blue'}
 
 # %%
 with col1:
@@ -91,14 +86,15 @@ def timstep_aggregate(time_steps_aggregate, xr ):
 t = sets_dict['t']
 t_original = sets_dict['t']
 i = sets_dict['i']
-iSto = sets_dict['iSto']
+# iSto = sets_dict['iSto']
 iConv = sets_dict['iConv']
-iPtG = sets_dict['iPtG']
+# iPtG = sets_dict['iPtG']
 iRes = sets_dict['iRes']
-iHyRes = sets_dict['iHyRes']
+# iHyRes = sets_dict['iHyRes']
 
 # Unpack params_dict into the workspace
-l_co2 = params_dict['l_co2']
+# l_co2 = params_dict['l_co2']
+l_co2 = 90
 p_co2 = params_dict['p_co2']
 
 eff_i = params_dict['eff_i']
@@ -107,17 +103,17 @@ c_fuel_i = params_dict['c_fuel_i']
 c_other_i = params_dict['c_other_i']
 c_inv_i = params_dict['c_inv_i']
 co2_factor_i = params_dict['co2_factor_i']
-#c_var_i = params_dict['c_var_i']
+c_var_i = params_dict['c_var_i']
 K_0_i = params_dict['K_0_i']
-e2p_iSto = params_dict['e2p_iSto']
+# e2p_iSto = params_dict['e2p_iSto']
 
 # Sliders and input boxes for parameters
 with col2:
     # Slider for CO2 limit [mio. t]
     l_co2 = st.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label="CO2 Limit [Mio. t]", step=10)
 
-    # Slider for H2 price / usevalue [€/MWH_th]
-    price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Wasserstoffpreis [€/MWh]", step=10)
+    # # Slider for H2 price / usevalue [€/MWH_th]
+    # price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Wasserstoffpreis [€/MWh]", step=10)
 
     for i_idx in c_fuel_i.get_index('i'):
             if i_idx in ['Braunkohle']:
@@ -131,13 +127,15 @@ with col3:
             if i_idx in ['Steinkohle', 'Erdöl','Erdgas']:
                 c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Preis [€/MWh]' , step=10)
 
-    technologies_invest = st.multiselect(label='Technologien für Investitionen', options=i, default=['Braunkohle','Erdgas','Steinkohle','Erdöl','PV','WindOff','WindOn','H2','Laufwasser','Batteriespeicher', 'Elektrolyseur'])
+    technologies_invest = st.multiselect(label='Technologien für Investitionen', options=i, default=['Braunkohle','Erdgas','Steinkohle','Erdöl','PV','WindOff','WindOn','Laufwasser','Kernenergie','Biomasse'])
     technologies_no_invest = [x for x in i if x not in technologies_invest]
 
 # Aggregate time series
 D_t = timstep_aggregate(dt,params_dict['D_t'])
+# D_t sorted descending
+D_t_sorted = D_t.sortby(D_t, ascending = False)
 s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
-h_t = timstep_aggregate(dt,params_dict['h_t'])
+# h_t = timstep_aggregate(dt,params_dict['h_t'])
 t = D_t.get_index('t')
 partial_year_factor = (8760/len(t))/dt
 
@@ -166,25 +164,27 @@ C_inv = m.add_variables(name = 'C_inv', lower = 0)     # Investment costs
 
 K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity
 y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen
-y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
-l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
-w = m.add_variables(coords = [t], name = 'w', lower = 0)          # RES curtailment
-y_curt = m.add_variables(coords = [t,i], name = 'y_curt', lower = 0)
-y_h2 = m.add_variables(coords = [t,i], name = 'y_h2', lower = 0)
+# y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
+# l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
+w = m.add_variables(coords = [t], name = 'w', lower = 0)          
+y_curt = m.add_variables(coords = [t,i], name = 'y_curt', lower = 0) # RES curtailment
+# y_h2 = m.add_variables(coords = [t,i], name = 'y_h2', lower = 0)
 
 ## Objective function
 C_tot = C_op + C_inv
 m.add_objective(C_tot)
 
 ## Costs terms for objective function
-# Operational costs minus revenue for produced hydrogen
-C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt - (y_h2.sel(i = iPtG) * price_h2).sum() * dt  == C_op, name = 'C_op_sum')
+# Operational costs (minus revenue for produced hydrogen)
+# C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt - (y_h2.sel(i = iPtG) * price_h2).sum() * dt  == C_op, name = 'C_op_sum')
+C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt  == C_op, name = 'C_op_sum')
 
 # Investment costs
 C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')
 
 ## Load serving
-loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - y_ch.sum(dims = 'i')) * dt  == D_t.sel(t = t) * dt), name =  'load')
+# loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - y_ch.sum(dims = 'i')) * dt  == D_t.sel(t = t) * dt), name =  'load')
+loadserve_t = m.add_constraints((((y ).sum(dims = 'i') ) * dt  == D_t.sel(t = t) * dt), name =  'load')
 
 ## Maximum capacity limit
 maxcap_i_t = m.add_constraints((y - K <= K_0_i), name = 'max_cap')
@@ -193,34 +193,35 @@ maxcap_i_t = m.add_constraints((y - K <= K_0_i), name = 'max_cap')
 maxcap_invest_i = m.add_constraints((K.sel(i = technologies_no_invest) <= 0), name = 'max_cap_invest')
 
 ## Prevent power production by PtG
-no_power_prod_iPtG_t = m.add_constraints((y.sel(i = iPtG) <= 0), name = 'prevent_ptg_prod')
+# no_power_prod_iPtG_t = m.add_constraints((y.sel(i = iPtG) <= 0), name = 'prevent_ptg_prod')
 
 ## Maximum storage charging and discharging
-maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) - y_ch.sel(i = iSto) - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')
+# maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) - y_ch.sel(i = iSto) - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')
 
 ## Maximum electrolyzer capacity
-ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG) <= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')
+# ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG) <= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')
 
 ## PtG H2 production
-h2_prod_iPtG_t = m.add_constraints(y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG) == y_h2.sel(i = iPtG), name = 'ptg_h2_prod')
+# h2_prod_iPtG_t = m.add_constraints(y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG) == y_h2.sel(i = iPtG), name = 'ptg_h2_prod')
 
 ## Infeed of renewables
 infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) + y_curt.sel(i = iRes) == s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')
 
 ## Maximum filling level restriction storage power plant
-maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')
+# maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')
 
 ## Filling level restriction hydro reservoir
-filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')
+# filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')
 
 ## Filling level restriction other storages
-filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) + (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt - y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')
+# filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) + (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt - y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')
 
 ## CO2 limit
 CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000 , name = 'CO2_limit')
 
 ## set run-of-river power plants capacity limit to 5 GW
 RoR_cap = m.add_constraints(K.sel(i = 'Laufwasser') <= 5000, name = 'RoR_cap')
+Biomass_cap = m.add_constraints(K.sel(i = 'Biomasse') <= 9000, name = 'Biomass_cap')
 
 
 # %%
@@ -346,23 +347,70 @@ with colb1:
     fig    
 
 # %%
-df_charging = m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index()
-fig = px.area(m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index(), y='y_ch', x='t',  title='Speicherbeladung [MWh]', color='i', color_discrete_map=color_dict)
+df_production_pivot = df_production.pivot(index='t', columns='i', values='y')
+# sort columns according to i_with_capacity
+df_production_pivot = df_production_pivot[i_with_capacity]
+co2_factor_i_with_capacity = co2_factor_i.sel(i = i_with_capacity)
+# colour_dict = {i: color_dict[i] for i in i_with_capacity}
+color_dict_with_capacity = {i: color_dict[i] for i in i_with_capacity}
+
+# multiply df_production with co2 factor
+df_production_emissions = df_production_pivot * co2_factor_i_with_capacity
+# unpivot df_production_emissions, sorting by datetime
+df_production_emissions_unpivot = df_production_emissions.reset_index().melt(id_vars='t', var_name='i', value_name='y')
+df_production_emissions_unpivot = df_production_emissions_unpivot.sort_values(by='t')
+# sum up y column in total
+df_production_emissions_total = df_production_emissions_unpivot['y'].sum()
+df_production_emissions_sorted = df_production_emissions_unpivot.sort_values(by='y', ascending=True)
+# sum up cumulated emissions
+df_production_emissions_sorted['cumsum'] = df_production_emissions_sorted['y'].cumsum()
+
+# generate area plot of df_production_emissions_unpivot over t 
+fig = px.area(df_production_emissions_unpivot, y='y', x='t',  title='Co2-Emissionen [t]', color='i', color_discrete_map=color_dict_with_capacity)
 fig.update_traces(line=dict(width=0))
 fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
-
+# fig = px.area(df_production_emissions_unpivot.sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Stromproduktion Lastgang [MW]', color='i', color_discrete_map=color_dict)
+# fig.update_traces(line=dict(width=0))
+# fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
 with colb2:
-    fig
+    fig  
+
 
 # %%
-df_h2_prod = m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index()
-fig = px.area(m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index(), y='y_h2', x='t',  title='Produktion Wasserstoff [MWh_th]', color='i', color_discrete_map=color_dict)
+# generate area plot of df_production_emissions_sorted['cumsum] over t
+x_emissions = np.arange(1, df_production_emissions_sorted['t'].size + 1)
+fig = px.area(df_production_emissions_sorted, y='cumsum', x=x_emissions,  title='Kumulierte Co2-Emissionen [t]', color='i', color_discrete_map=color_dict_with_capacity)
 fig.update_traces(line=dict(width=0))
 fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
 
+with colb2:
+    fig  
+
+# %%
+# df_charging = m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index()
+# fig = px.area(m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index(), y='y_ch', x='t',  title='Speicherbeladung [MWh]', color='i', color_discrete_map=color_dict)
+# fig.update_traces(line=dict(width=0))
+# fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
+
+# with colb2:
+#     fig
+
+# %%
+# define vector x with size (1,size D_t_sorted)
+x = np.arange(1, D_t_sorted.size + 1)
+fig = px.line(y=D_t_sorted, x=x,  title='Lastdauerlinie [€/MWh]', labels={"x": "Stunden im Jahr"})
 with colb2:
     fig
 
+# # %%
+# df_h2_prod = m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index()
+# fig = px.area(m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index(), y='y_h2', x='t',  title='Produktion Wasserstoff [MWh_th]', color='i', color_discrete_map=color_dict)
+# fig.update_traces(line=dict(width=0))
+# fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
+
+# with colb2:
+#     fig
+
 # %%
 ((m.solution['y'] / eff_i) * co2_factor_i * dt).sum()
 # %%
@@ -388,7 +436,7 @@ def disaggregate_df(df):
     #df_repeated = df.iloc[idx_repeat,:].reset_index(drop = True).drop('t', axis = 1)
     df_t_all = pd.DataFrame({"t_all": t_original.to_series(), 't': t.repeat(dt)}).reset_index(drop=True)
 
-    # %%
+    ## %%
     df_output = df.merge(df_t_all,on = 't').drop('t',axis = 1).rename({'t_all':'t'}, axis = 1)
     # last column to first column
     cols = list(df_output.columns)
@@ -408,10 +456,10 @@ with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
     disaggregate_df(df_contr_marg).to_excel(writer, sheet_name='Deckungsbeiträge', index=False)
     disaggregate_df(df_new_capacities).to_excel(writer, sheet_name='Kapazitäten', index=False)
     disaggregate_df(df_production).to_excel(writer, sheet_name='Produktion', index=False)
-    disaggregate_df(df_charging).to_excel(writer, sheet_name='Ladevorgänge', index=False)
+    # disaggregate_df(df_charging).to_excel(writer, sheet_name='Ladevorgänge', index=False)
     disaggregate_df(D_t.to_dataframe().reset_index()).to_excel(writer, sheet_name='Nachfrage', index=False)
     disaggregate_df(df_curtailment).to_excel(writer, sheet_name='Abregelung', index=False)
-    disaggregate_df(df_h2_prod).to_excel(writer, sheet_name='H2 produktion', index=False)
+    # disaggregate_df(df_h2_prod).to_excel(writer, sheet_name='H2 produktion', index=False)
 
 with col4:
     st.download_button(

model_data.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:882997f874bcad2db648bdfb6559637c77e7767b8f7d81311111b01191e84b31
-size 1373467
+oid sha256:3454912d7cfa9b850e8d7651b73c143a7e177d02261d6671a9a8e2d3bc6b81cc
+size 1065854

sourced.py

@@ -34,19 +34,19 @@ def load_data_from_excel(url_excel, write_to_pickle_flag = True):
     i = pd.Index(df_excel.iloc[:, 0], name='i')
 
     df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iConv = pd.Index(df_excel.iloc[0:7, 2], name='iConv')
+    iConv = pd.Index(df_excel.iloc[0:6, 2], name='iConv')       # changed to 6 from 7
 
     df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
     iRes = pd.Index(df_excel.iloc[0:4, 4], name='iRes')
 
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iSto = pd.Index(df_excel.iloc[0:2, 6], name='iSto')
+    # df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    # iSto = pd.Index(df_excel.iloc[0:2, 6], name='iSto')
 
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iPtG = pd.Index(df_excel.iloc[0:1, 8], name='iPtG')
+    # df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    # iPtG = pd.Index(df_excel.iloc[0:1, 8], name='iPtG')
 
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iHyRes = pd.Index(df_excel.iloc[0:1, 10], name='iHyRes')
+    # df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    # iHyRes = pd.Index(df_excel.iloc[0:1, 10], name='iHyRes')
 
     # Parameters
     l_co2 =  pd.read_excel(url_excel, sheet_name='CO2_Cap').iloc[0,0]
@@ -145,21 +145,20 @@ def load_data_from_excel(url_excel, write_to_pickle_flag = True):
     df_excel = df_excel.set_index('i')
     K_0_i = df_excel.iloc[:,0].to_xarray()
 
-    # Energy-to-power ratio storages
-    df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  
-    df_excel = df_excel.rename(columns = {'Speicher':'i', 'Unnamed: 1':'E2P-Ratio'})
-    #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    e2p_iSto = df_excel.iloc[:,0].to_xarray()
-
-    # Inflow for hydro reservoir
-    df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')
-    df_excel = df_excel.rename(columns = {'Zeitschritte':'t', 'Staudamm':'Zufluss'})
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('t')
-    h_t = df_excel.iloc[:,0].to_xarray()
-
+    # # Energy-to-power ratio storages
+    # df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  
+    # df_excel = df_excel.rename(columns = {'Speicher':'i', 'Unnamed: 1':'E2P-Ratio'})
+    # #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    # df_excel = df_excel.fillna(0)
+    # df_excel = df_excel.set_index('i')
+    # e2p_iSto = df_excel.iloc[:,0].to_xarray()
+
+    # # Inflow for hydro reservoir
+    # df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')
+    # df_excel = df_excel.rename(columns = {'Zeitschritte':'t', 'Staudamm':'Zufluss'})
+    # df_excel = df_excel.fillna(0)
+    # df_excel = df_excel.set_index('t')
+    # h_t = df_excel.iloc[:,0].to_xarray()
 
     
     sets_dict = {}
@@ -167,11 +166,11 @@ def load_data_from_excel(url_excel, write_to_pickle_flag = True):
     # Append parameters to the dictionary
     sets_dict['t'] = t
     sets_dict['i'] = i
-    sets_dict['iSto'] = iSto
+    # sets_dict['iSto'] = iSto
     sets_dict['iConv'] = iConv
-    sets_dict['iPtG'] = iPtG
+    # sets_dict['iPtG'] = iPtG
     sets_dict['iRes'] = iRes
-    sets_dict['iHyRes'] = iHyRes
+    # sets_dict['iHyRes'] = iHyRes
     # Append parameters to the dictionary
     params_dict['l_co2'] = l_co2
     params_dict['p_co2'] = p_co2
@@ -186,8 +185,8 @@ def load_data_from_excel(url_excel, write_to_pickle_flag = True):
     params_dict['c_var_i'] = c_var_i
     params_dict['s_t_r_iRes'] = s_t_r_iRes
     params_dict['K_0_i'] = K_0_i
-    params_dict['e2p_iSto'] = e2p_iSto
-    params_dict['h_t'] = h_t
+    # params_dict['e2p_iSto'] = e2p_iSto
+    # params_dict['h_t'] = h_t
 
     if write_to_pickle_flag:
         save_to_pickle(sets_dict, params_dict)