Update app.py

app.py

@@ -71,11 +71,15 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
 
     # Extract necessary data for optimization.
     time_steps = range(len(data['Time']))
+    if 'solar hourly capacity factor' not in data.columns:
+        st.error("Solar data is missing in the retrieved dataset.")
+        return None, None, None
+
     solar_cf = data['solar hourly capacity factor']
-    onshore_wind_cf = data['onshore_wind hourly capacity factor']
-    offshore_wind_cf = data['offshore_wind hourly capacity factor']
-    river_cf = data['river hourly capacity factor']
-    demand_cf = data['demand hourly capacity factor']
+    onshore_wind_cf = data.get('onshore_wind hourly capacity factor', pd.Series([0]*len(data)))
+    offshore_wind_cf = data.get('offshore_wind hourly capacity factor', pd.Series([0]*len(data)))
+    river_cf = data.get('river hourly capacity factor', pd.Series([0]*len(data)))
+    demand_cf = data.get('demand hourly capacity factor', pd.Series([0]*len(data)))
 
     # Define regions and technologies.
     regions = ['region1']
@@ -148,20 +152,25 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     max_SOC = max(SOC_values)
     SOC_normalized = [(soc / max_SOC) * 100 for soc in SOC_values] if max_SOC > 0 else [0] * len(SOC_values)
 
-    # Create a subplot with 3 rows for energy dispatch, state of charge (SOC), and electricity price.
-    ", "Electricity Price Over Time"))
-
-    # Create separate figure for power supply and demand
+    # Create figure for power supply and demand
     fig_supply_demand = go.Figure()
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_solar, mode='lines', stackgroup='one', name='Solar', line=dict(color='#FFD700', width=0)))  # Solar: Gold
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_onshore_wind, mode='lines', stackgroup='one', name='Onshore Wind', line=dict(color='#1F78B4', width=0)))  # Onshore Wind: Blue
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_offshore_wind, mode='lines', stackgroup='one', name='Offshore Wind', line=dict(color='#66C2A5', width=0)))  # Offshore Wind: Light Green
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_river, mode='lines', stackgroup='one', name='Run of River', line=dict(color='#FF7F00', width=0)))  # Run of River: Orange
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=battery_discharge_values, mode='lines', stackgroup='one', name='Battery Discharge', fill='tonexty', line=dict(color='#6A3D9A', width=0)))  # Battery Discharge: Brown
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=battery_charge_values, mode='lines', stackgroup='two', name='Battery Charge', fill='tonexty', line=dict(color='#6A3D9A', width=0)))  # Battery Charge: Purple
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=-demand, mode='lines', stackgroup='two', name='Demand', line=dict(color='black', width=0)))  # Demand: Black
-    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=curtailment_values, mode='lines', stackgroup='two', name='Curtailment', line=dict(color='#aaaaaa', width=0)))  # Curtailment: Grey
-    
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_solar, mode='lines', stackgroup='one', name='Solar'))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_onshore_wind, mode='lines', stackgroup='one', name='Onshore Wind'))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_offshore_wind, mode='lines', stackgroup='one', name='Offshore Wind'))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=supply_river, mode='lines', stackgroup='one', name='River'))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=battery_discharge_values, mode='lines', name='Battery Discharge', line=dict(color='red')))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=battery_charge_values, mode='lines', name='Battery Charge', line=dict(color='blue')))
+    fig_supply_demand.add_trace(go.Scatter(x=data['Time'], y=-demand, mode='lines', name='Demand', line=dict(color='black')))
+
+    # Create figure for state of charge (SOC)
+    fig_soc = go.Figure()
+    fig_soc.add_trace(go.Scatter(x=data['Time'], y=SOC_normalized, mode='lines', name='State of Charge (SOC)'))
+
+    # Create figure for electricity price over time
+    fig_price = go.Figure()
+    fig_price.add_trace(go.Scatter(x=data['Time'], y=price_per_hour, mode='lines', name='Electricity Price'))
+
+    # Layout settings for the figures
     fig_supply_demand.update_layout(
         title_text='Power Supply and Demand',
         yaxis_title='Power dispatch (MW)',
@@ -172,12 +181,8 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
         plot_bgcolor='white',
         xaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
         yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray')
-    )  # Curtailment: Grey
+    )
 
-    # Create separate figure for state of charge (SOC)
-    fig_soc = go.Figure()
-    fig_soc.add_trace(go.Scatter(x=data['Time'], y=SOC_normalized, mode='lines', name='State of Charge (SOC) - Normalized', line=dict(color='black')))
-    
     fig_soc.update_layout(
         title_text='State of Charge (Battery)',
         yaxis_title='State of Charge (%)',
@@ -189,27 +194,9 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
         yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray')
     )
 
-    # Create separate figure for electricity price over time
-    fig_price = go.Figure()
-    fig_price.add_trace(go.Scatter(x=data['Time'], y=price_per_hour, mode='lines', name='Electricity Price', line=dict(color='#FF4500')))
-    
     fig_price.update_layout(
         title_text='Electricity Price Over Time',
-        yaxis_title='Electricity Price (¥/MWh)',
-        font=dict(size=12),
-        margin=dict(l=40, r=40, t=40, b=40),
-        hovermode='x unified',
-        plot_bgcolor='white',
-        xaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
-        yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray')
-    )  # Price: Red-Orange
-
-    # Layout settings for the figure.
-    fig.update_layout(
-        title_text='Optimized result',
-        title_x=0.5,
-        yaxis_title='Power dispatch (MW)',
-        legend_title='Source',
+        yaxis_title='Electricity Price (\u00a5/MWh)',
         font=dict(size=12),
         margin=dict(l=40, r=40, t=40, b=40),
         hovermode='x unified',
@@ -217,11 +204,8 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
         xaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray'),
         yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray')
     )
-    # Update the y-axis titles for each subplot.
-    fig.update_yaxes(title_text="State of Charge (%)", row=2, col=1)
-    fig.update_yaxes(title_text="Electricity Price (¥/MWh)", row=3, col=1)
 
-    return fig, curtailment_values, price_per_hour
+    return fig_supply_demand, fig_soc, fig_price, curtailment_values, price_per_hour
 
 # Streamlit UI for the application
 st.set_page_config(page_title='Renewable Energy System Optimization', layout='wide')
@@ -241,56 +225,19 @@ The visualizations provided help to better understand how different energy sourc
 with st.sidebar:
     st.header('Input Parameters')
     city_code = st.text_input("Enter City Code", value="")
-    solar_cost = st.number_input("Solar Capacity Cost (¥/MW)", value=80.0, help="Estimated average cost of solar capacity per MW")
-    onshore_wind_cost = st.number_input("Onshore Wind Capacity Cost (¥/MW)", value=120.0, help="Estimated average cost of onshore wind capacity per MW")
-    offshore_wind_cost = st.number_input("Offshore Wind Capacity Cost (¥/MW)", value=180.0, help="Estimated average cost of offshore wind capacity per MW")
-    river_cost = st.number_input("River Capacity Cost (¥/MW)", value=100.0, help="Estimated average cost of river (hydro) capacity per MW")
-    battery_cost = st.number_input("Battery Cost (¥/MWh)", value=80.0, help="Estimated average cost of battery storage per MWh")
+    solar_cost = st.number_input("Solar Capacity Cost (\u00a5/MW)", value=80.0, help="Estimated average cost of solar capacity per MW")
+    onshore_wind_cost = st.number_input("Onshore Wind Capacity Cost (\u00a5/MW)", value=120.0, help="Estimated average cost of onshore wind capacity per MW")
+    offshore_wind_cost = st.number_input("Offshore Wind Capacity Cost (\u00a5/MW)", value=180.0, help="Estimated average cost of offshore wind capacity per MW")
+    river_cost = st.number_input("River Capacity Cost (\u00a5/MW)", value=100.0, help="Estimated average cost of river (hydro) capacity per MW")
+    battery_cost = st.number_input("Battery Cost (\u00a5/MWh)", value=80.0, help="Estimated average cost of battery storage per MWh")
     yearly_demand = st.number_input("Yearly Power Demand (TWh/year)", value=15.0, help="Total yearly power demand in TWh")
 
 # Button to trigger optimization
 if st.button('Calculate Optimal Energy Mix'):
-    fig, curtailment_values, price_per_hour = optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wind_cost, river_cost, battery_cost, yearly_demand)
-    if fig:
+    fig_supply_demand, fig_soc, fig_price, curtailment_values, price_per_hour = optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wind_cost, river_cost, battery_cost, yearly_demand)
+    if fig_supply_demand:
         st.plotly_chart(fig_supply_demand, use_container_width=True, height=800)
-    st.plotly_chart(fig_soc, use_container_width=True, height=800)
-    st.plotly_chart(fig_price, use_container_width=True, height=800)
-
-        # Additional analysis and visualizations
-        st.markdown("### Additional Analysis")
-        st.markdown("The following plots provide additional insights into the renewable energy mix, curtailment, and electricity price variations.")
-
-        # Data for additional visualizations
-        renewable_data = {
-            'Solar': solar_cost,
-            'Onshore Wind': onshore_wind_cost,
-            'Offshore Wind': offshore_wind_cost,
-            'Run of River': river_cost
-        }
-        cost_df = pd.DataFrame(list(renewable_data.items()), columns=['Technology', 'Cost (¥/MW)'])
-
-        # Plot cost comparison
-        fig_cost = px.bar(cost_df, x='Technology', y='Cost (¥/MW)', color='Technology', title='Cost Comparison of Different Renewable Technologies', template='plotly_white')
-        st.plotly_chart(fig_cost, use_container_width=True, height=800)
-
-        # Plot curtailment over time
-        curtailment_df = pd.DataFrame({"Time": fig.data[0].x, "Curtailment (MW)": curtailment_values})
-        fig_curtailment = px.line(curtailment_df, x='Time', y='Curtailment (MW)', title='Curtailment Over Time', template='plotly_white')
-        st.plotly_chart(fig_curtailment, use_container_width=True, height=800)
-
-        # Plot electricity price over time
-        price_df = pd.DataFrame({"Time": fig.data[0].x, "Electricity Price (¥/MWh)": price_per_hour})
-        fig_price = px.line(price_df, x='Time', y='Electricity Price (¥/MWh)', title='Electricity Price Variation Over Time', template='plotly_white')
+    if fig_soc:
+        st.plotly_chart(fig_soc, use_container_width=True, height=800)
+    if fig_price:
         st.plotly_chart(fig_price, use_container_width=True, height=800)
-
-        # Correlation analysis of renewable capacity factors
-        if city_code:
-            st.markdown("### Correlation Between Renewable Energy Sources")
-            correlation_matrix = get_renewable_energy_data(city_code)[0].corr()
-            if correlation_matrix is not None:
-                fig_corr = px.imshow(correlation_matrix, title='Correlation Matrix of Renewable Capacity Factors', labels={'color': 'Correlation'}, template='plotly_white')
-                st.plotly_chart(fig_corr, use_container_width=True, height=800)
-            fig_corr = px.imshow(correlation_matrix, title='Correlation Matrix of Renewable Capacity Factors', labels={'color': 'Correlation'}, template='plotly_white')
-            st.plotly_chart(fig_corr, use_container_width=True)
-
-# Note: You may need to adjust initial values or labels to fit your requirements.