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undergraduate level: added bar plots for investment costs and contribution margin

app.py

@@ -500,7 +500,8 @@ def page_model(): #, write_pickle_from_standard_excel, color_dict):
             ]
 
             # Define default technologies (internal names) — same across all users
-            default_techs = ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer']
+            # default_techs = ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer']
+            default_techs = ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Nuclear', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer']
 
             # Translate default selections for UI (still uses internal list for logic)
             default = [
@@ -686,8 +687,8 @@ def page_model(): #, write_pickle_from_standard_excel, color_dict):
 
             df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df, show= True)
             df_charging = plot_storage_charging(m, iSto, color_dict, colb2, df)
-            # df_filtered = calculate_and_plot_investment_costs(m,df_new_capacities, c_inv_i, color_dict, colb1, df)
-            # df_contr_marg_sum = calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, colb2, df)
+            df_investment = calculate_and_plot_investment_costs(m,df_new_capacities, c_inv_i, color_dict, colb1, df)
+            df_contr_marg_sum = calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, colb2, df)
         else:
             df_total_costs = plot_total_costs(m, colb1, df)
             df_CO2_price = plot_co2_price(m, colb2, df)
@@ -811,7 +812,8 @@ def plot_new_capacities(m, color_dict, col, df):
                      title=df.loc['plot_label_new_capacities', st.session_state.lang], 
                      color='i_en',  # Use the English names for consistent coloring
                      color_discrete_map=color_dict,
-                     labels={'K': '', 'i': ''} # Delete double labeling
+                    #  labels={'K': '', 'i': ''} # Delete double labeling
+                    labels={'i': ''}
                      )
 
     # Hide the legend completely since the labels are already next to the bars
@@ -954,26 +956,6 @@ def plot_electricity_prices(m, dt, col, df, df_residual_load_duration):
         yaxis='y2'  # Link this trace to the secondary y-axis
     ))
     
-    # Layout mit separaten Achsen
-    # fig_duration.update_layout(
-    #     title=df.loc['plot_label_price_duration_curve', st.session_state.lang],
-    #     xaxis=dict(
-    #         title=df.loc['label_hours', st.session_state.lang]  # Common x-axis
-    #     ),
-    #     yaxis=dict(
-    #         title=df.loc['plot_label_price_duration_curve', st.session_state.lang],  # Title for primary y-axis
-    #         range=[-(100/(ax2_max/(ax2_max-ax2_min))-100), 100],  # Primary y-axis range
-    #         titlefont=dict(color='blue'),  # Blue color for primary axis title
-    #         tickfont=dict(color='blue')  # Blue ticks for primary axis
-    #     ),
-    #     yaxis2=dict(
-    #         title=df.loc['plot_label_residual_load', st.session_state.lang],  # Title for secondary y-axis
-    #         range=[ax2_min, ax2_max],  # Secondary y-axis range
-    #         titlefont=dict(color='red'),  # Red color for secondary axis title
-    #         tickfont=dict(color='red'),  # Red ticks for secondary axis
-    #         overlaying='y',  # Overlay secondary axis on primary
-    #         side='right'  # Place secondary y-axis on the right side
-    #     ),
     fig_duration.update_layout(
     title=df.loc['plot_label_price_duration_curve', st.session_state.lang],
     xaxis=dict(
@@ -1580,24 +1562,15 @@ def calculate_and_plot_investment_costs(m, df_new_capacities, c_inv_i, color_dic
     - Displays 'i' in the selected language.
     - Filters out 'Battery storages'.
     
-    Parameters:
-    - df_new_capacities: DataFrame with columns ['i', 'K'] for new capacities.
-    - c_inv_i: xarray DataArray or Series with investment costs indexed by 'i'.
-    - color_dict: Dictionary mapping English technology identifiers (i_en) to colors.
-    - col: Streamlit column to display the plot.
-    - df: Translation/label lookup DataFrame with keys like 'tech_pv', 'tech_windon' etc.
-
     Returns:
     - df_invest_costs: DataFrame with investment costs per technology.
     """
 
-    # Ensure 'i_en' is available for color mapping
-    df_new_capacities = df_new_capacities.copy()
+    # Step 1: Get new capacities from model
+    df_new_capacities = m.solution['K'].round(0).to_dataframe().reset_index()
     df_new_capacities['i_en'] = df_new_capacities['i']
-    i_with_capacity = m.solution['K'].where(m.solution['K'] > 0).dropna(dim='i').get_index('i')
-    available_columns = df_new_capacities.columns.intersection(i_with_capacity)
 
-    # Translate if language is German
+    # Step 2: Translate technology names (if language is German)
     if st.session_state.lang == 'DE':
         tech_mapping_en_to_de = {
             df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE']
@@ -1605,45 +1578,35 @@ def calculate_and_plot_investment_costs(m, df_new_capacities, c_inv_i, color_dic
         }
         df_new_capacities['i'] = df_new_capacities['i_en'].replace(tech_mapping_en_to_de)
 
-    # Filter out 'Battery storages'
-    df_filtered = df_new_capacities[df_new_capacities['i_en'] != 'Battery storages'].copy()
+    # Step 3: Calculate investment cost in bn. EUR
+    c_inv_series = c_inv_i.to_series() if hasattr(c_inv_i, 'to_series') else c_inv_i
+    df_new_capacities['Investment'] = df_new_capacities['i_en'].map(c_inv_series) * df_new_capacities['K']* 1e-9  # Convert to billion EUR
 
-    # # Calculate investment costs
-    df_filtered['Investment'] = df_filtered['K'] * c_inv_i
-    color_dict_with_capacity = {i: color_dict[i] for i in available_columns}
-    # Plot
+    df_investment = df_new_capacities.copy()
+    local_color_dict = color_dict.copy()
+    local_color_dict['Battery storages'] = 'rgba(0,0,0,0)'  # Fully transparent
+    # Step 5: Plot
     fig = px.bar(
-        df_filtered,
+        df_investment,
         y='i',
         x='Investment',
         orientation='h',
         title=df.loc['plot_label_investment_costs', st.session_state.lang],
         color='i_en',
-        color_discrete_map=color_dict_with_capacity,  
-        labels={'i': '', 'Investment': ''}
+        color_discrete_map=local_color_dict,
+        labels={'i': ''},
+        hover_data={'i_en': False,'i': True}  # Show i and investment with 2 decimal places
     )
     fig.update_layout(showlegend=False)
     col.plotly_chart(fig)
 
-    return df_filtered
+    return df_investment
 
 
 def calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, col, df):
     """
     Calculates and plots the contribution margin (Deckungsbeitrag) per technology.
-    Mirrors original working logic, with optional language and coloring.
-
-    Parameters:
-    - m: Optimization model with 'y', 'max_cap', 'load'
-    - i: Full list of technologies (for ordering and reindexing)
-    - iRes: Subset of residual techs (e.g. PV, WindOn)
-    - dt: Time step length in hours
-    - color_dict: Dictionary of colors (keyed by technology names)
-    - col: Streamlit column to display the plot
-    - df: Translation table with tech labels and plot titles
-
-    Returns:
-    - df_contr_marg_sum: DataFrame with contribution margin results
+    Now mirrors visual logic of investment cost plot with transparent color for 'Battery storages'.
     """
 
     # Production data for all technologies
@@ -1658,7 +1621,7 @@ def calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, col, df):
     df_merged = df_merged[['t', 'i', 'y_new']]
     df_contr_marg_sum = df_merged.groupby('i')['y_new'].sum().reset_index()
 
-    # Handle residual technologies with load constraint duals
+    # Handle residual technologies
     df_production_res = m.solution['y'].sel(i=iRes).to_dataframe().reset_index()
     df_price_res = m.constraints['load'].dual.to_dataframe().reset_index()
     df_merged_res = pd.merge(df_production_res, df_price_res, on='t')
@@ -1673,26 +1636,30 @@ def calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, col, df):
     df_contr_marg_sum = df_contr_marg_sum.drop(columns=['multiplied_value'])
     df_contr_marg_sum['y'] = df_contr_marg_sum['y_new'] * -1
 
-    # Translate labels if needed
+    # Language translation and color setup
+    df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
     if st.session_state.lang == 'DE':
         tech_mapping = {
             df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE']
             for tech in i if f'tech_{tech.lower()}' in df.index
         }
-        df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
         df_contr_marg_sum['i'] = df_contr_marg_sum['i_en'].replace(tech_mapping)
     else:
-        df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
+        df_contr_marg_sum['i'] = df_contr_marg_sum['i_en']
 
-    # Reorder by original list
-    # df_contr_marg_sum = df_contr_marg_sum.set_index('i_en').loc[i].reset_index(drop=False)
+    # Sort by full technology list
     if 'i' in df_contr_marg_sum.columns:
         df_contr_marg_sum = df_contr_marg_sum.drop(columns='i')
-
     df_contr_marg_sum = df_contr_marg_sum.set_index('i_en').loc[i].reset_index()
+    df_contr_marg_sum['y'] = df_contr_marg_sum['y'] * 1e-9  # Convert to billion EUR
 
-    # Plot
-    title = df.loc['plot_label_contribution_margin', st.session_state.lang]
+    # Transparent battery color
+    color_dict_local = color_dict.copy()
+    color_dict_local['Battery storages'] = 'rgba(0,0,0,0)'
+    if 'i_en' not in df_contr_marg_sum.columns:
+        df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
+    # Bar plot
+    title = df.loc['plot_label_contribution_margin_undergrad', st.session_state.lang]
     fig = px.bar(
         df_contr_marg_sum,
         y='i',
@@ -1700,25 +1667,18 @@ def calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, col, df):
         orientation='h',
         title=title,
         color='i_en',
-        color_discrete_map=color_dict,
-        labels={'i': '', 'y': ''}
+        color_discrete_map=color_dict_local,
+        labels={'i': ''},
+        hover_data={'i_en': False, 'i': True}
     )
-
-    # Localized legend
-    legend_map = dict(zip(df_contr_marg_sum['i_en'], df_contr_marg_sum['i']))
-    fig.for_each_trace(lambda t: t.update(
-        name=legend_map.get(t.name, t.name),
-        legendgroup=legend_map.get(t.name, t.name),
-        hovertemplate=t.hovertemplate.replace(t.name, legend_map.get(t.name, t.name))
-    ))
-    fig.update_layout(legend_title_text=None)
-
+    fig.update_layout(showlegend=False)
     col.plotly_chart(fig)
 
     return df_contr_marg_sum
 
 
 
+
 def disaggregate_df(df, t, t_original, dt):
     """
     Disaggregates the DataFrame based on the original time steps.

language.csv

@@ -56,8 +56,8 @@ plot_label_load_duration_curve;Lastdauerlinie [MWh];Load duration curve [MWh]
 plot_label_fullload-hours;Volllaststunden [h/a];Full load hours [h/a]
 plot_label_emissions;Emittierte CO2-Emissionen im Jahresverlauf [t];CO2-Emissions [t]
 plot_label_cumulative_emissions;Kumulierte CO2-Emissionen [t];Cumulative CO2-Emissions [t]
-plot_label_investment_costs;Investitionskosten [EUR];Investment costs [EUR]
-plot_label_contribution_margin_undergrad;Deckungsbeitrag [EUR];Contribution Margin [EUR]
+plot_label_investment_costs;Investitionskosten pro Jahr [Mrd. EUR];Investment costs per Year [bn. EUR]
+plot_label_contribution_margin_undergrad;Deckungsbeitr�ge pro Jahr [Mrd. EUR];Contribution Margin [bn. EUR]
 label_electricity_price;Strompreis;Electricity price
 label_time;Zeit;Time
 label_technology;Technologien;Technologies