Upload 2 files

translation into german, price duration curve and ror upper limmit

Input_Jahr_2021.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:44b72397bfd8d8a14229f1c1ac883ceabd3412017233ad7999208089d3d44a57
-size 1120098
+oid sha256:6e554203fe49d3bb990ec181e26aee4d05950ee07dadc980bdbade651962c6ed
+size 1119734

app.py

@@ -52,7 +52,7 @@ with col1:
         st.download_button('Download Excel Vorlage', f, file_name='Input_Jahr_2021.xlsx')  # Defaults to 'application/octet-stream'
 
 #url_excel = r'Input_Jahr_2021.xlsx'
-    url_excel = st.file_uploader(label = 'Excel Upload')
+    url_excel = st.file_uploader(label = 'Excel Datei hochladen')
 
 
 if url_excel == None:
@@ -114,24 +114,24 @@ 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)
+    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)
 
     for i_idx in c_fuel_i.get_index('i'):
             if i_idx in ['Braunkohle']:
-                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 + ' Price' , step=10)
+                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)
 
-    dt = st.number_input(label="Length of timesteps [int]", min_value=1, max_value=len(t), value=6, help="Enter only integers between 1 and 8760 (or 8784 for leap years).")
+    dt = st.number_input(label="Zeitliche Auflösung [h]", min_value=1, max_value=len(t), value=6, help="Geben Sie nur ganze Zahlen zwischen 1 und 8760 (oder 8784 für Schaltjahre) ein.")
 
 with col3:
     # Slider for CO2 limit [mio. t]
     for i_idx in c_fuel_i.get_index('i'):
             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 + ' Price' , step=10)
+                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='Technologies for investment', 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','H2','Laufwasser','Batteriespeicher', 'Elektrolyseur'])
     technologies_no_invest = [x for x in i if x not in technologies_invest]
 
 # Aggregate time series
@@ -220,7 +220,7 @@ filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -
 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 = 'RoR') <= 5000, name = 'RoR_cap')
+RoR_cap = m.add_constraints(K.sel(i = 'Laufwasser') <= 5000, name = 'RoR_cap')
 
 
 # %%
@@ -237,7 +237,7 @@ colb1, colb2 = st.columns(2)
 # Assuming df_excel has columns 'All' and 'Capacities'
 
 fig = px.bar((m.solution['K']+K_0_i).to_dataframe(name='K').reset_index(), \
-                y='i', x='K', orientation='h', title='Total Installed Capacities [MW]', color='i')
+                y='i', x='K', orientation='h', title='Installierte Kapazitäten insgesamt [MW]', color='i')
 
 #fig
 
@@ -247,7 +247,7 @@ total_costs_rounded = round(total_costs/1e9, 2)
 df_total_costs = pd.DataFrame({'Total costs':[total_costs]})
 
 with colb1:
-    st.write('Total costs: ' + str(total_costs_rounded) + ' bn. €')
+    st.write('Gesamtkosten: ' + str(total_costs_rounded) + ' Mrd. €')
 
 # %%
 #df_Co2_price = pd.DataFrame({'CO2_Price: ':[float(m.constraints['CO2_limit'].dual.values) * (-1)]})
@@ -257,11 +257,11 @@ df_CO2_price = pd.DataFrame({'CO2 price':[CO2_price]})
 
 with colb2:
     #st.write(str(df_Co2_price))
-    st.write('CO2 price: ' + str(CO2_price_rounded) + ' €/t')
+    st.write('CO2 Preis: ' + str(CO2_price_rounded) + ' €/t')
 
 # %%
 df_new_capacities = m.solution['K'].to_dataframe().reset_index()
-fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='New Capacities [MW]', color='i', color_discrete_map=color_dict)
+fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='Neu installierte Kapazitäten [MW]', color='i', color_discrete_map=color_dict)
 
 with colb1:
     fig
@@ -277,7 +277,7 @@ total_k_sum = df_new_capacities_rounded["K"].sum()
 
 #df_new_capacities_rounded["percentage"] = df_new_capacities_rounded["K"].apply(lambda x: (x/total_k_sum)*100).abs().round(2)
 
-fig = px.pie(df_new_capacities_rounded, names='i', values='K', title='New Capacities [MW] as pie chart',
+fig = px.pie(df_new_capacities_rounded, names='i', values='K', title='Neu installierte Kapazitäten [MW] als Kuchendiagramm',
              color='i', color_discrete_map=color_dict)
 
 with colb1:
@@ -289,7 +289,7 @@ with colb1:
 # %%
 i_with_capacity = m.solution['K'].where( m.solution['K'] > 0).dropna(dim = 'i').get_index('i')
 df_production = m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index()
-fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Production [MW]', color='i', color_discrete_map=color_dict)
+fig = px.area(m.solution['y'].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))
 
@@ -299,7 +299,7 @@ with colb2:
 #Add pie chart of total production per technology type in GWh(divide by 1000)
 df_production_sum = (df_production.groupby('i')['y'].sum() * dt / 1000 ).round(0).sort_values(ascending=False).reset_index()
 
-fig = px.pie(df_production_sum, names="i", values='y', title='Total Production [GWh] as pie chart',
+fig = px.pie(df_production_sum, names="i", values='y', title='Gesamtproduktion [GWh] als Kuchendiagramm',
              color='i', color_discrete_map=color_dict)
 
 with colb2:
@@ -308,18 +308,17 @@ with colb2:
 # %%
 
 df_price = m.constraints['load'].dual.to_dataframe().reset_index()
-#df_price['dual'] = df_price['dual']
 
-# %%
-fig = px.line(df_price, y='dual', x='t',  title='Electricity prices [€/MWh]', range_y=[0,250])
+fig = px.line(df_price, y='dual', x='t',  title='Strompreis [€/MWh]', range_y=[0,350])
 with colb1:
     fig
 
 
 # %%
-df_sorted_price = df_price["dual"].repeat(dt).sort_values(ascending=False).reset_index(drop=True)/int(dt)
+# df_sorted_price = df_price["dual"].repeat(dt).sort_values(ascending=False).reset_index(drop=True)/int(dt)
+df_sorted_price = df_price["dual"].sort_values(ascending=False).reset_index(drop=True)
 
-fig = px.line(y=df_sorted_price, x=df_sorted_price.index,  title='Price duration curve [€/MWh]', labels={"x": "Hours of the year"},range_y=[0,250])
+fig = px.line(y=df_sorted_price, x=df_sorted_price.index,  title='Preisdauerlinie [€/MWh]', labels={"x": "Stunden im Jahr"},range_y=[0,350])
 with colb1:
     fig
 
@@ -331,7 +330,7 @@ df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1)
 
 # %%
 
-fig = px.line(df_contr_marg, y='dual', x='t',title='Contribution margin [€]', color='i', range_y=[0,250], color_discrete_map=color_dict)
+fig = px.line(df_contr_marg, y='dual', x='t',title='Deckungsbeitrag [€]', color='i', range_y=[0,350], color_discrete_map=color_dict)
 with colb2:
     fig
 
@@ -339,7 +338,7 @@ with colb2:
     
 # curtailment
 df_curtailment = m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index()
-fig = px.area(m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index(), y='y_curt', x='t',  title='Curtailment [MWh]', color='i', color_discrete_map=color_dict)
+fig = px.area(m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index(), y='y_curt', x='t',  title='Abregelung [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))
 
@@ -348,7 +347,7 @@ with colb1:
 
 # %%
 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='Storage charging [MWh]', color='i', color_discrete_map=color_dict)
+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))
 
@@ -357,7 +356,7 @@ with colb2:
 
 # %%
 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='Hydrogen production [MWh_th]', color='i', color_discrete_map=color_dict)
+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))
 
@@ -403,22 +402,22 @@ def disaggregate_df(df):
 # Create a Pandas Excel writer using XlsxWriter as the engine
 with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
     # Write each DataFrame to a different sheet
-    disaggregate_df(df_total_costs).to_excel(writer, sheet_name='Total costs', index=False)
-    disaggregate_df(df_CO2_price).to_excel(writer, sheet_name='CO2 price', index=False)
-    disaggregate_df(df_price).to_excel(writer, sheet_name='Prices', index=False)
-    disaggregate_df(df_contr_marg).to_excel(writer, sheet_name='Contribution Margin', index=False)
-    disaggregate_df(df_new_capacities).to_excel(writer, sheet_name='Capacities', index=False)
-    disaggregate_df(df_production).to_excel(writer, sheet_name='Production', index=False)
-    disaggregate_df(df_charging).to_excel(writer, sheet_name='Charging', index=False)
-    disaggregate_df(D_t.to_dataframe().reset_index()).to_excel(writer, sheet_name='Demand', index=False)
-    disaggregate_df(df_curtailment).to_excel(writer, sheet_name='Curtailment', index=False)
-    disaggregate_df(df_h2_prod).to_excel(writer, sheet_name='H2 production', index=False)
+    disaggregate_df(df_total_costs).to_excel(writer, sheet_name='Gesamtkosten', index=False)
+    disaggregate_df(df_CO2_price).to_excel(writer, sheet_name='CO2 Preis', index=False)
+    disaggregate_df(df_price).to_excel(writer, sheet_name='Preise', index=False)
+    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(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)
 
 with col4:
     st.download_button(
-        label="Download Excel workbook Results",
+        label="Download Excel Arbeitsmappe Ergebnisse",
         data=output.getvalue(),
-        file_name="workbook.xlsx",
+        file_name="Arbeitsmappe_Ergebnisse.xlsx",
         mime="application/vnd.ms-excel"
     )