Update app.py

app.py

@@ -128,7 +128,8 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
                     'type': 'min',
                     'technology': tech,
                     'solution': {g: renewable_capacity[('region1', g)].varValue for g in technologies},
-                    'battery_capacity': battery_capacity.varValue
+                    'battery_capacity': battery_capacity.varValue,
+                    'total_cost': pulp.value(alt_model_min.objective)
                 })
             
             # Maximize capacity of each technology
@@ -149,7 +150,8 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
                     'type': 'max',
                     'technology': tech,
                     'solution': {g: renewable_capacity[('region1', g)].varValue for g in technologies},
-                    'battery_capacity': battery_capacity.varValue
+                    'battery_capacity': battery_capacity.varValue,
+                    'total_cost': pulp.value(alt_model_max.objective)
                 })
 
     return alternative_solutions
@@ -182,28 +184,47 @@ if st.button("Run MGA Optimization"):
     alternative_solutions = optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wind_cost, river_cost, battery_cost, yearly_demand, solar_range, wind_range, river_range, offshore_wind_range, [t / 100 for t in thresholds], selected_technologies)
     
     if alternative_solutions:
-        # 各しきい値ごとの最小と最大のバッテリー容量を収集
+        # コスト積み上げ用データの収集
+        cost_data = []
+        for sol in alternative_solutions:
+            cost_data.append({
+                'threshold': sol['threshold'] * 100,
+                'type': sol['type'],
+                'technology': sol['technology'],
+                'total_cost': sol['total_cost']
+            })
+
+        # コスト積み上げグラフのプロット
+        cost_df = pd.DataFrame(cost_data)
+        fig_cost = px.bar(cost_df, x='threshold', y='total_cost', color='technology', title="Cost Breakdown by Technology and Threshold")
+        fig_cost.update_layout(xaxis_title='Threshold (%)', yaxis_title='Total Cost (¥)')
+
+        # Streamlitでコスト積み上げグラフを表示
+        st.plotly_chart(fig_cost, use_container_width=True)
+
+        # 各しきい値ごとの技術ごとの容量の範囲を表示
         epsilon_values = sorted(list(set(sol['threshold'] * 100 for sol in alternative_solutions)))
-        storage_min = []
-        storage_max = []
-        
-        for epsilon in epsilon_values:
-            capacities = [sol['battery_capacity'] for sol in alternative_solutions if sol['threshold'] * 100 == epsilon]
-            storage_min.append(min(capacities))
-            storage_max.append(max(capacities))
-
-        # 可視化
         fig, ax = plt.subplots()
-        ax.fill_between(epsilon_values, storage_min, storage_max, color='orange', alpha=0.3)  # オレンジ色の塗りつぶし
-        ax.plot(epsilon_values, storage_min, marker='o', color='orange', linestyle='-', linewidth=1.5, label='Min Storage')  # 最小値ライン
-        ax.plot(epsilon_values, storage_max, marker='o', color='orange', linestyle='-', linewidth=1.5, label='Max Storage')  # 最大値ライン
-
+        
+        for tech in selected_technologies:
+            storage_min = []
+            storage_max = []
+            for epsilon in epsilon_values:
+                capacities = [sol['solution'][tech] for sol in alternative_solutions if sol['technology'] == tech and sol['threshold'] * 100 == epsilon]
+                storage_min.append(min(capacities))
+                storage_max.append(max(capacities))
+            
+            # 塗りつぶし範囲とラインプロット
+            ax.fill_between(epsilon_values, storage_min, storage_max, alpha=0.3, label=f"{tech} range")
+            ax.plot(epsilon_values, storage_min, marker='o', linestyle='-', label=f"{tech} Min")
+            ax.plot(epsilon_values, storage_max, marker='o', linestyle='-', label=f"{tech} Max")
+        
         # ラベルとタイトル
         ax.set_xlabel(r'$\epsilon$ [%]')
-        ax.set_ylabel('Storage [GW]')
-        ax.set_title('GHG -100%')
+        ax.set_ylabel('Capacity [GW]')
+        ax.set_title('Capacity Ranges for Each Technology')
         ax.legend()
         ax.grid(True, linestyle='--', alpha=0.7)
-
+        
         # Streamlitにプロットを表示
         st.pyplot(fig)