Update app.py

app.py

@@ -62,13 +62,7 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     renewable_capacity = pulp.LpVariable.dicts("renewable_capacity",
                                                [(r, g) for r in regions for g in technologies],
                                                lowBound=0, cat='Continuous')
-    curtailment = pulp.LpVariable.dicts("curtailment",
-                                        [(r, t) for r in regions for t in time_steps],
-                                        lowBound=0, cat='Continuous')
     battery_capacity = pulp.LpVariable("battery_capacity", lowBound=0, cat='Continuous')
-    battery_charge = pulp.LpVariable.dicts("battery_charge", time_steps, lowBound=0, cat='Continuous')
-    battery_discharge = pulp.LpVariable.dicts("battery_discharge", time_steps, lowBound=0, cat='Continuous')
-    SOC = pulp.LpVariable.dicts("SOC", time_steps, lowBound=0, cat='Continuous')
 
     model = pulp.LpProblem("EnergySystemOptimizationWithBattery", pulp.LpMinimize)
 
@@ -77,27 +71,6 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
                          for r in regions for g in technologies]) + \
              battery_capacity * battery_cost_per_mwh, "TotalCost"
 
-    # Constraints: meet demand, manage battery SOC
-    for r in regions:
-        for t in time_steps:
-            model += pulp.lpSum([renewable_capacity[(r, g)] * capacity_factor[g][t]
-                                 for g in technologies]) + battery_discharge[t] == demand[t] + battery_charge[t] + curtailment[(r, t)], f"DemandConstraint_{r}_{t}"
-            if t == 0:
-                model += SOC[t] == battery_charge[t] * battery_efficiency - battery_discharge[t] * (1 / battery_efficiency), f"SOCUpdate_{t}"
-            else:
-                model += SOC[t] == SOC[t - 1] + battery_charge[t] * battery_efficiency - battery_discharge[t] * (1 / battery_efficiency), f"SOCUpdate_{t}"
-            model += SOC[t] <= battery_capacity, f"SOCUpperBound_{t}"
-
-    # Capacity range constraints
-    model += renewable_capacity[('region1', 'solar')] >= solar_range[0]
-    model += renewable_capacity[('region1', 'solar')] <= solar_range[1]
-    model += renewable_capacity[('region1', 'onshore_wind')] >= wind_range[0]
-    model += renewable_capacity[('region1', 'onshore_wind')] <= wind_range[1]
-    model += renewable_capacity[('region1', 'offshore_wind')] >= offshore_wind_range[0]
-    model += renewable_capacity[('region1', 'offshore_wind')] <= offshore_wind_range[1]
-    model += renewable_capacity[('region1', 'river')] >= river_range[0]
-    model += renewable_capacity[('region1', 'river')] <= river_range[1]
-
     # Solve the initial model to find the optimal solution
     model.solve()
     optimal_cost = pulp.value(model.objective)
@@ -168,12 +141,27 @@ def plot_capacity_distribution(alternative_solutions, selected_technologies):
     capacity_df = pd.DataFrame(capacity_data)
 
     # Create violin plot with Plotly Express
-    fig_violin = px.violin(capacity_df, x="Threshold (%)", y="Capacity (MW)", color="Technology",
-                           box=True, points="all", title="Capacity Distribution by Technology and Threshold")
-    fig_violin.update_layout(xaxis_title="Cost Deviation Threshold (%)", yaxis_title="Installed Capacity (MW)")
+    fig_violin = px.violin(capacity_df, x="Technology", y="Capacity (MW)", color="Technology",
+                           box=True, points="all", title="Capacity Distribution by Technology")
+    fig_violin.update_layout(xaxis_title="Technology", yaxis_title="Installed Capacity (MW)")
 
     return fig_violin
 
+# Function to create cost breakdown stacked bar plot for each threshold
+def plot_cost_breakdown(alternative_solutions, selected_technologies):
+    # Generate a bar plot for each case based on threshold and technology type
+    for sol in alternative_solutions:
+        cost_data = {
+            'Technology': selected_technologies + ['Battery'],
+            'Cost': [sol['solution'][tech] * renewable_capacity_cost[tech] for tech in selected_technologies] + [sol['battery_capacity'] * battery_cost_per_mwh]
+        }
+        cost_df = pd.DataFrame(cost_data)
+
+        # Create stacked bar chart
+        fig_bar = px.bar(cost_df, x='Technology', y='Cost', title=f"Cost Breakdown (Threshold: {sol['threshold'] * 100}%, Type: {sol['type']})",
+                         labels={'Cost': 'Cost (¥)'})
+        st.plotly_chart(fig_bar, use_container_width=True)
+
 # Streamlit UI setup
 st.set_page_config(page_title='Renewable Energy System Optimization with MGA', layout='wide')
 st.title('Modeling to Generate Alternatives (MGA) in Renewable Energy System Optimization')
@@ -206,3 +194,6 @@ if st.button("Run MGA Optimization"):
         # Display capacity distribution using violin plots
         fig_violin = plot_capacity_distribution(alternative_solutions, selected_technologies)
         st.plotly_chart(fig_violin, use_container_width=True)
+
+        # Display cost breakdown stacked bar plots for each case
+        plot_cost_breakdown(alternative_solutions, selected_technologies)