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app.py

@@ -17,6 +17,7 @@ import plotly.express as px
 import streamlit as st
 import sourced as src
 import numpy as np
+import tempfile
 
 ## Setting
 write_pickle_from_standard_excel = True
@@ -59,11 +60,16 @@ if url_excel == None:
     with col4:
         st.write('Lauf mit Standarddaten')
 else:
-    sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = False)
-    
+    # sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = False)
+    sets_dict, params_dict= src.load_data_from_excel(url_excel, write_to_pickle_flag = True)
+
     with col4:
         st.write('Lauf mit Nutzerdaten')
 
+# Debugging output to verify that sets_dict is defined
+# st.write(f"sets_dict: {sets_dict}")
+# st.write(f"params_dict: {params_dict}")
+
 # # %%
 
 def timstep_aggregate(time_steps_aggregate, xr ):
@@ -216,7 +222,7 @@ maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iS
 filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) + (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt - y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')
 
 ## CO2 limit
-# l_co2 = 180
+# l_co2 = 50
 CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000 , name = 'CO2_limit')
 
 ## set run-of-river power plants capacity limit to 5 GW
@@ -294,7 +300,7 @@ df_combined = pd.DataFrame({
 })
 
 # Create the integrated plot using Plotly Express
-fig = px.line(df_combined, x='x', y='y', color='label', title='Lastdauerlinie [€/MW]',
+fig = px.line(df_combined, x='x', y='y', color='label', title='Lastdauerlinie [MW]',
               labels={"x": "Stunden im Jahr", "y": "Leistung [MW]"})
 
 # Specific updates for each trace
@@ -310,8 +316,30 @@ with colb2:
 
 # fig.show()
 # %%
+# calculate full load hours
 i_with_capacity = m.solution['K'].where( m.solution['K'] > 0).dropna(dim = 'i').get_index('i')
 df_production = m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index()
+df_capacity = m.solution['K'].sel(i = i_with_capacity).to_dataframe().reset_index()
+df_production_sum = (df_production.groupby('i')['y'].sum() * dt).round(0).reset_index()
+# reorder rows according to i_with_capacity
+df_production_sum = df_production_sum.set_index('i').loc[i_with_capacity].reset_index()
+# df_production_sum['i'] = pd.Categorical(df_production_sum['i'], categories=desired_order, ordered=True)
+
+df_fullload = df_production_sum['y']/df_capacity['K']
+# to dataframe
+df_fullload = df_fullload.to_frame()
+# rename column
+df_fullload.columns = ['fullload']
+df_fullload['i'] = df_production_sum['i']
+# change order of columns
+df_fullload = df_fullload[['i', 'fullload']]
+fig = px.bar(df_fullload, y='i', x=df_fullload['fullload'], orientation='h', title='Volllaststunden [h]', color='i', color_discrete_map=color_dict)
+with colb1:
+    fig
+# fig.show()
+
+
+# %%
 fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Stromproduktion Lastgang [MW]', color='i', color_discrete_map=color_dict)
 fig.update_traces(line=dict(width=0))
 fig.for_each_trace(lambda trace: trace.update(fillcolor = trace.line.color))
@@ -334,26 +362,6 @@ fig = px.line(y=df_sorted_price, x=x_price,  title='Preisdauerlinie [€/MWh]',
 with colb2:
     fig
 
-# %%
-# calculate full load hours
-df_capacity = m.solution['K'].sel(i = i_with_capacity).to_dataframe().reset_index()
-df_production_sum = (df_production.groupby('i')['y'].sum() * dt).round(0).reset_index()
-# reorder rows according to i_with_capacity
-df_production_sum = df_production_sum.set_index('i').loc[i_with_capacity].reset_index()
-# df_production_sum['i'] = pd.Categorical(df_production_sum['i'], categories=desired_order, ordered=True)
-
-df_fullload = df_production_sum['y']/df_capacity['K']
-# to dataframe
-df_fullload = df_fullload.to_frame()
-# rename column
-df_fullload.columns = ['fullload']
-df_fullload['i'] = df_production_sum['i']
-# change order of columns
-df_fullload = df_fullload[['i', 'fullload']]
-fig = px.bar(df_fullload, y='i', x=df_fullload['fullload'], orientation='h', title='Volllaststunden [h]', color='i', color_discrete_map=color_dict)
-with colb1:
-    fig
-# fig.show()
 
 # %%