Update app.py

app.py

@@ -11,29 +11,18 @@ import numpy as np
 
 # Function to fetch renewable energy data
 def get_renewable_energy_data(city_code):
-    """
-    Fetch renewable energy data from an external source based on a city code.
-    
-    Args:
-    - city_code (str): The code representing the city for which the renewable energy data is to be fetched.
-    Returns:
-    - result_df (DataFrame): A DataFrame containing the energy data with time and capacity factors for each energy type.
-    - error (str): Error message if the data fetching fails.
-    """
     url = f"https://energy-sustainability.jp/_ajax/renewable_energy/get/?code={city_code}"
     response = requests.get(url)
     if response.status_code != 200:
-        return None, "データの取得に失敗しました。"  # Data fetch failed.
+        return None, "データの取得に失敗しました。"
 
     data = response.json()
     if not data:
-        return None, "データが見つかりませんでした。"  # Data not found.
+        return None, "データが見つかりませんでした。"
 
-    # Extract base times from the first energy type.
     base_times = data[next(iter(data))]['x']
     result_df = pd.DataFrame({"Time": base_times})
 
-    # Loop through each energy type and append hourly capacity factor data.
     for energy_type, energy_data in data.items():
         if 'x' in energy_data and 'y' in energy_data:
             values = energy_data['y']
@@ -43,48 +32,21 @@ def get_renewable_energy_data(city_code):
 
 # Function to filter numeric columns
 def filter_numeric_columns(df):
-    """
-    Filters only numeric columns from the provided DataFrame.
-
-    Args:
-    - df (DataFrame): The DataFrame from which to filter numeric columns.
-
-    Returns:
-    - DataFrame: A DataFrame containing only numeric columns.
-    """
     numeric_df = df.select_dtypes(include=['number'])
     return numeric_df
 
 # Function to optimize the energy system
 def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wind_cost, river_cost, battery_cost, yearly_demand):
-    """
-    Optimize the energy system using linear programming to minimize cost while meeting demand with renewable sources and batteries.
-    
-    Args:
-    - city_code (str): The code of the city for which energy data is to be optimized.
-    - solar_cost (float): The cost of solar capacity per MW.
-    - onshore_wind_cost (float): The cost of onshore wind capacity per MW.
-    - offshore_wind_cost (float): The cost of offshore wind capacity per MW.
-    - river_cost (float): The cost of river (hydro) capacity per MW.
-    - battery_cost (float): The cost of battery capacity per MWh.
-    - yearly_demand (float): The total yearly demand in TWh/year.
-    Returns:
-    - fig (Figure): A plotly figure showing the optimized energy dispatch and state of charge (SOC) over time.
-    - curtailment_values (list): List of curtailment values for visualization.
-    - price_per_hour (list): List of estimated electricity price per hour for visualization.
-    """
     data, error = get_renewable_energy_data(city_code)
     if error:
         st.error(error)
         return None, None, None
 
-    # Convert capacity factor columns to numeric values, handling any errors and filling missing data.
     for col in data.columns[1:]:
         data[col] = pd.to_numeric(data[col], errors='coerce')
 
-    data = data.fillna(0)  # Fill missing values with zero.
+    data = data.fillna(0)
 
-    # Extract necessary data for optimization.
     time_steps = range(len(data['Time']))
     solar_cf = data['solar hourly capacity factor']
     onshore_wind_cf = data['onshore_wind hourly capacity factor']
@@ -92,7 +54,6 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     river_cf = data['river hourly capacity factor']
     demand_cf = data['demand hourly capacity factor']
 
-    # Define regions and technologies.
     regions = ['region1']
     technologies = ['solar', 'onshore_wind', 'offshore_wind', 'river']
     capacity_factor = {
@@ -102,15 +63,12 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
         'river': river_cf
     }
 
-    # Cost definitions for renewable capacity and battery.
     renewable_capacity_cost = {'solar': solar_cost, 'onshore_wind': onshore_wind_cost, 'offshore_wind': offshore_wind_cost, 'river': river_cost}
     battery_cost_per_mwh = battery_cost
-    battery_efficiency = 0.9  # Battery efficiency.
+    battery_efficiency = 0.9
     
-    # Scale demand from capacity factor and yearly demand in TWh/year to hourly demand in MW.
     demand = demand_cf * yearly_demand / 100 * 1000 * 1000
 
-    # Define LP variables for renewable capacities, curtailment, battery capacity, charge, discharge, and state of charge (SOC).
     renewable_capacity = pulp.LpVariable.dicts("renewable_capacity",
                                                [(r, g) for r in regions for g in technologies],
                                                lowBound=0, cat='Continuous')
@@ -122,14 +80,12 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     battery_discharge = pulp.LpVariable.dicts("battery_discharge", time_steps, lowBound=0, cat='Continuous')
     SOC = pulp.LpVariable.dicts("SOC", time_steps, lowBound=0, cat='Continuous')
 
-    # Define the optimization problem: minimize the total system cost.
     model = pulp.LpProblem("EnergySystemOptimizationWithBattery", pulp.LpMinimize)
 
     model += pulp.lpSum([renewable_capacity[(r, g)] * renewable_capacity_cost[g]
                          for r in regions for g in technologies]) + \
              battery_capacity * battery_cost_per_mwh, "TotalCost"
 
-    # Constraints to meet demand, manage curtailment, and update state of charge for the battery.
     for r in regions:
         for t in time_steps:
             model += pulp.lpSum([renewable_capacity[(r, g)] * capacity_factor[g][t]
@@ -142,10 +98,8 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
 
             model += SOC[t] <= battery_capacity, f"SOCUpperBound_{t}"
 
-    # Solve the optimization model.
     model.solve()
 
-    # Extract optimized supply and battery values.
     supply_solar = solar_cf * renewable_capacity[('region1', 'solar')].varValue
     supply_onshore_wind = onshore_wind_cf * renewable_capacity[('region1', 'onshore_wind')].varValue
     supply_offshore_wind = offshore_wind_cf * renewable_capacity[('region1', 'offshore_wind')].varValue
@@ -156,25 +110,21 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     SOC_values = [SOC[t].varValue for t in time_steps]
     curtailment_values = [curtailment[(r, t)].varValue for r in regions for t in time_steps]
 
-    # Calculate price per hour based on demand and generation (simplified example).
-    price_per_hour = [100 + 0.05 * demand[t] for t in time_steps]  # Base price + factor for demand.
+    price_per_hour = [100 + 0.05 * demand[t] for t in time_steps]
 
-    # Normalize state of charge (SOC) to a percentage.
     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)
 
-    # Plot energy dispatch.
     fig_energy = go.Figure()
-    fig_energy.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_energy.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_energy.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_energy.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_energy.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_energy.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_energy.add_trace(go.Scatter(x=data['Time'], y=-demand, mode='lines', stackgroup='two', name='Demand', line=dict(color='black', width=0)))  # Demand: Black
-    fig_energy.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_energy.add_trace(go.Scatter(x=data['Time'], y=supply_solar, mode='lines', stackgroup='one', name='Solar', line=dict(color='#FFD700', width=0)))
+    fig_energy.add_trace(go.Scatter(x=data['Time'], y=supply_onshore_wind, mode='lines', stackgroup='one', name='Onshore Wind', line=dict(color='#1F78B4', width=0)))
+    fig_energy.add_trace(go.Scatter(x=data['Time'], y=supply_offshore_wind, mode='lines', stackgroup='one', name='Offshore Wind', line=dict(color='#66C2A5', width=0)))
+    fig_energy.add_trace(go.Scatter(x=data['Time'], y=supply_river, mode='lines', stackgroup='one', name='Run of River', line=dict(color='#FF7F00', width=0)))
+    fig_energy.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)))
+    fig_energy.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)))
+    fig_energy.add_trace(go.Scatter(x=data['Time'], y=-demand, mode='lines', stackgroup='two', name='Demand', line=dict(color='black', width=0)))
+    fig_energy.add_trace(go.Scatter(x=data['Time'], y=curtailment_values, mode='lines', stackgroup='two', name='Curtailment', line=dict(color='#aaaaaa', width=0)))
     
-    # Layout settings for energy dispatch plot.
     fig_energy.update_layout(
         title_text='Power Supply and Demand',
         title_x=0.5,
@@ -188,14 +138,11 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
         yaxis=dict(showgrid=True, gridwidth=0.5, gridcolor='lightgray')
     )
     
-    # Return the figures and other outputs.
     return fig_energy, curtailment_values, price_per_hour
 
-# Streamlit UI for the application
 st.set_page_config(page_title='Renewable Energy System Optimization', layout='wide')
 st.title('Renewable Energy System Optimization')
 
-# Explanation of the project
 st.markdown("""
 ### Overview
 This application is designed to help researchers and policymakers explore and optimize renewable energy systems for a specified region. By inputting cost parameters for different renewable energy sources and energy storage systems, the application determines the optimal mix of resources to meet energy demand while minimizing cost.
@@ -203,7 +150,6 @@ The optimization problem is solved using linear programming, ensuring a balance
 The visualizations provided help to better understand how different energy sources contribute to the overall power supply, how energy storage systems are utilized, and the impact of cost variations on energy prices.
 """)
 
-# Input fields for the user
 with st.sidebar:
     st.header('Input Parameters')
     city_code = st.text_input("Enter City Code", value="")
@@ -214,17 +160,14 @@ with st.sidebar:
     battery_cost = st.number_input("Battery Cost (¥/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:
         st.plotly_chart(fig, 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,
@@ -233,21 +176,17 @@ if st.button('Calculate Optimal Energy Mix'):
         }
         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')
         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")
             energy_data_df, error = get_renewable_energy_data(city_code)
@@ -256,7 +195,6 @@ if st.button('Calculate Optimal Energy Mix'):
             else:
                 numeric_energy_data_df = filter_numeric_columns(energy_data_df)
 
-                # Handling missing values by filling them with 0.
                 numeric_energy_data_df = numeric_energy_data_df.fillna(0)
 
                 correlation_matrix = numeric_energy_data_df.corr()
@@ -265,13 +203,13 @@ if st.button('Calculate Optimal Energy Mix'):
                     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)
 
-        # Plot heatmaps for renewable energy and demand characteristics
         st.markdown("### Heatmap of Renewable Energy and Demand Characteristics")
         hourly_data = numeric_energy_data_df.copy()
-        hourly_data['Date'] = pd.to_datetime(data['Time']).dt.date
-        hourly_data['Hour'] = pd.to_datetime(data['Time']).dt.hour
+        hourly_data['Time'] = pd.to_datetime(data['Time'], errors='coerce')
+        hourly_data['Date'] = hourly_data['Time'].dt.date
+        hourly_data['Hour'] = hourly_data['Time'].dt.hour
         
-        for column in hourly_data.columns[2:]:  # Skipping 'Date' and 'Hour' columns
+        for column in hourly_data.columns[2:]:
             pivot_df = hourly_data.pivot_table(index='Hour', columns='Date', values=column, aggfunc=np.mean)
             fig_heatmap = px.imshow(pivot_df, title=f"Heatmap of {column}", labels={'color': column}, template='plotly_white', aspect='auto')
             st.plotly_chart(fig_heatmap, use_container_width=True, height=800)