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app.py

@@ -1,347 +1,424 @@
-# %%
-# -*- coding: utf-8 -*-
-"""
-Spyder Editor
-
-This is a temporary script file.
-"""
-
-
-
-
-from numpy import arange
-import xarray as xr
-import highspy
-from linopy import Model, EQUAL
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-import sourced as src
-st.set_page_config(layout="wide")
-# you can create columns to better manage the flow of your page
-# this command makes 3 columns of equal width
-col1, col2, col3, col4 = st.columns(4)
-col1.header("Data Input")
-col4.header("Download Results")
-
-# Color dictionary for figures
-color_dict = {'Biomass': 'lightgreen',
-              'Lignite': 'brown',
-              'Fossil Gas': 'grey',
-              'Fossil Hard coal': 'darkgrey',
-              'Fossil Oil': 'maroon',
-              'RoR': 'aquamarine',
-              'Hydro Water Reservoir': 'azure',
-              'Nuclear': 'orange',
-              'PV': 'yellow',
-              'WindOff': 'darkblue',
-              'WindOn': 'green',
-              'H2': 'crimson',
-              'Pumped Hydro Storage': 'lightblue',
-              'Battery storages': 'red',
-              'Electrolyzer': 'olive'}
-
-# %%
-with col1:
-    with open('Input_Jahr_2021.xlsx', 'rb') as f:
-        st.download_button('Download Excel Template', f, file_name='Input_Jahr_2021.xlsx')  # Defaults to 'application/octet-stream'
-
-#url_excel = r'Input_Jahr_2021.xlsx'
-    url_excel = st.file_uploader(label = 'Excel Upload')
-
-
-if url_excel == None:
-    url_excel = r'Input_Jahr_2021.xlsx'
-    sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = True)
-    with col4:
-        st.write('Running with standard data')
-else:
-    sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = False)
-    with col4:
-        st.write('Running with user data')
-
-# # %%
-
-def timstep_aggregate(time_steps_aggregate, xr ):
-    return xr.rolling( t = time_steps_aggregate).mean().sel(t = t[0::time_steps_aggregate])
-
-#s_t_r_iRes = timstep_aggregate(6,s_t_r_iRes)
-
-
-
-# %%
-#sets_dict, params_dict= src.load_data_from_excel(url_excel,write_to_pickle_flag=True)
-
-# %%
-
-
-
-#sets_dict, params_dict= load_data_from_excel(url_excel, load_from_pickle_flag = False)
-
-
-dt = 6
-# Unpack sets_dict into the workspace
-t = sets_dict['t']
-i = sets_dict['i']
-iSto = sets_dict['iSto']
-iConv = sets_dict['iConv']
-iPtG = sets_dict['iPtG']
-iRes = sets_dict['iRes']
-iHyRes = sets_dict['iHyRes']
-
-# Unpack params_dict into the workspace
-l_co2 = params_dict['l_co2']
-p_co2 = params_dict['p_co2']
-
-eff_i = params_dict['eff_i']
-c_fuel_i = params_dict['c_fuel_i']
-c_other_i = params_dict['c_other_i']
-c_inv_i = params_dict['c_inv_i']
-co2_factor_i = params_dict['co2_factor_i']
-#c_var_i = params_dict['c_var_i']
-K_0_i = params_dict['K_0_i']
-e2p_iSto = params_dict['e2p_iSto']
-# Aggregate time series
-D_t = timstep_aggregate(dt,params_dict['D_t'])
-s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
-h_t = timstep_aggregate(dt,params_dict['h_t'])
-t = D_t.get_index('t')
-partial_year_factor = (8760/len(t))/dt
-
-# Sliders and input boxes for parameters
-with col2:
-    # Slider for CO2 limit [mio. t]
-    l_co2 = st.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label="CO2 limit [mio. t]", step=50)
-
-    # Slider for H2 price / usevalue [€/MWH_th]
-    price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Hydrogen price [€/MWh]", step=10)
-
-    for i_idx in c_fuel_i.get_index('i'):
-            if i_idx in ['Lignite']:
-                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Price' , step=10)
-
-    dt = st.number_input(label="Length of timesteps [int]", min_value=1, max_value=len(t), value=6, help="Enter only integers between 1 and 8760 (or 8784 for leap years).")
-
-with col3:
-    # Slider for CO2 limit [mio. t]
-    for i_idx in c_fuel_i.get_index('i'):
-            if i_idx in ['Fossil Hard coal', 'Fossil Oil','Fossil Gas']:
-                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Price' , step=10)
-
-    technologies_invest = st.multiselect(label='Technologies for investment', options=i, default=['Lignite','Fossil Gas','Fossil Hard coal','Fossil Oil','PV','WindOff','WindOn','H2','Pumped Hydro Storage','Battery storages'])
-    technologies_no_invest = [x for x in i if x not in technologies_invest]
-
-#time_steps_aggregate = 6
-#= xr_profiles.rolling( time_step = time_steps_aggregate).mean().sel(time_step = time[0::time_steps_aggregate])
-price_co2 = 0
-
-# Aggregate time series
-#D_t = timstep_aggregate(dt,params_dict['D_t'])
-#s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
-#h_t = timstep_aggregate(dt,params_dict['h_t'])
-#t = D_t.get_index('t')
-#partial_year_factor = (8760/len(t))/dt
-
-#technologies_no_invest = st.multiselect(label='Technolgy invest', options=i)
-#technologies_no_invest = ['Electrolyzer','Biomass','RoR','Hydro Water Reservoir','Nuclear']
-# %%
-### Variables
-m = Model()
-
-C_tot = m.add_variables(name = 'C_tot')     # Total costs
-C_op = m.add_variables(name = 'C_op', lower = 0)       # Operational costs
-C_inv = m.add_variables(name = 'C_inv', lower = 0)     # Investment costs
-
-K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity
-y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen
-y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
-l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
-w = m.add_variables(coords = [t], name = 'w', lower = 0)          # RES curtailment
-y_curt = m.add_variables(coords = [t,i], name = 'y_curt', lower = 0)
-y_h2 = m.add_variables(coords = [t,i], name = 'y_h2', lower = 0)
-
-## Objective function
-C_tot = C_op + C_inv
-m.add_objective(C_tot)
-
-## Costs terms for objective function
-# Operational costs minus revenue for produced hydrogen
-C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt - (y_h2.sel(i = iPtG) * price_h2).sum() * dt  == C_op, name = 'C_op_sum')
-
-# Investment costs
-C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')
-
-## Load serving
-loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - y_ch.sum(dims = 'i')) * dt  == D_t.sel(t = t) * dt), name =  'load')
-
-## Maximum capacity limit
-maxcap_i_t = m.add_constraints((y - K <= K_0_i), name = 'max_cap')
-
-## Maximum capacity limit
-maxcap_invest_i = m.add_constraints((K.sel(i = technologies_no_invest) <= 0), name = 'max_cap_invest')
-
-## Prevent power production by PtG
-no_power_prod_iPtG_t = m.add_constraints((y.sel(i = iPtG) <= 0), name = 'prevent_ptg_prod')
-
-## Maximum storage charging and discharging
-maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) + y_ch.sel(i = iSto) - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')
-
-## Maximum electrolyzer capacity
-ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG) <= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')
-
-## PtG H2 production
-h2_prod_iPtG_t = m.add_constraints(y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG) == y_h2.sel(i = iPtG), name = 'ptg_h2_prod')
-
-## Infeed of renewables
-infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) + y_curt.sel(i = iRes) == s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')
-
-## Maximum filling level restriction storage power plant
-maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')
-
-## Filling level restriction hydro reservoir
-filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')
-
-## Filling level restriction other storages
-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
-CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000 , name = 'CO2_limit')
-
-
-# %%
-m.solve(solver_name = 'highs')
-
-st.markdown("---")
-
-colb1, colb2 = st.columns(2)
-
-# %%
-#c_var_i.to_dataframe(name='VarCosts')
-# %%
-# Installed Cap
-# Assuming df_excel has columns 'All' and 'Capacities'
-
-fig = px.bar((m.solution['K']+K_0_i).to_dataframe(name='K').reset_index(), \
-                y='i', x='K', orientation='h', title='Total Installed Capacities [MW]', color='i')
-
-#fig
-
-# %%
-total_costs = float(m.solution['C_inv'].values) + float(m.solution['C_op'].values)
-total_costs_rounded = round(total_costs/1e9, 2)
-df_total_costs = pd.DataFrame({'Total costs':[total_costs]})
-
-with colb1:
-    st.write('Total costs: ' + str(total_costs_rounded) + ' bn. €')
-
-# %%
-#df_Co2_price = pd.DataFrame({'CO2_Price: ':[float(m.constraints['CO2_limit'].dual.values) * (-1)]})
-CO2_price = float(m.constraints['CO2_limit'].dual.values) * (-1)
-CO2_price_rounded = round(CO2_price, 2)
-df_CO2_price = pd.DataFrame({'CO2 price':[CO2_price]})
-
-with colb2:
-    #st.write(str(df_Co2_price))
-    st.write('CO2 price: ' + str(CO2_price_rounded) + ' €/t')
-
-# %%
-df_new_capacities = m.solution['K'].to_dataframe().reset_index()
-fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='New Capacities [MW]', color='i', color_discrete_map=color_dict)
-
-with colb1:
-    fig
-
-# %%
-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()
-fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Production [MWh]', 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))
-
-with colb2:
-    fig
-
-# %%
-
-df_price = m.constraints['load'].dual.to_dataframe().reset_index()
-#df_price['dual'] = df_price['dual']
-
-# %%
-fig = px.line(df_price, y='dual', x='t',  title='Electricity prices [€/MWh]', range_y=[0,250])
-with colb1:
-    fig
-
-# %% 
-
-df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index() 
-df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1)
-
-# %%
-
-fig = px.line(df_contr_marg, y='dual', x='t',title='Contribution margin [€]', color='i', range_y=[0,250], color_discrete_map=color_dict)
-with colb2:
-    fig
-
-# %%
-    
-# curtailment
-df_curtailment = m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index()
-fig = px.area(m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index(), y='y_curt', x='t',  title='Curtailment [MWh]', 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))
-
-with colb1:
-    fig    
-
-# %%
-df_charging = m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index()
-fig = px.area(m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index(), y='y_ch', x='t',  title='Storage charging [MWh]', 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))
-
-with colb2:
-    fig
-
-# %%
-df_h2_prod = m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index()
-fig = px.area(m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index(), y='y_h2', x='t',  title='Hydrogen production [MWh_th]', 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))
-
-with colb2:
-    fig
-
-# %%
-((m.solution['y'] / eff_i) * co2_factor_i * dt).sum()
-# %%
-
-import pandas as pd
-from io import BytesIO
-#from pyxlsb import open_workbook as open_xlsb
-import streamlit as st
-import xlsxwriter
-# %%
-output = BytesIO()
-
-
-# Create a Pandas Excel writer using XlsxWriter as the engine
-with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
-    # Write each DataFrame to a different sheet
-    df_total_costs.to_excel(writer, sheet_name='Total costs', index=False)
-    df_CO2_price.to_excel(writer, sheet_name='CO2 price', index=False)
-    df_price.to_excel(writer, sheet_name='Prices', index=False)
-    df_contr_marg.to_excel(writer, sheet_name='Contribution Margin', index=False)
-    df_new_capacities.to_excel(writer, sheet_name='Capacities', index=False)
-    df_production.to_excel(writer, sheet_name='Production', index=False)
-    df_charging.to_excel(writer, sheet_name='Charging', index=False)
-    D_t.to_dataframe().reset_index().to_excel(writer, sheet_name='Demand', index=False)
-    df_curtailment.to_excel(writer, sheet_name='Curtailment', index=False)
-    df_h2_prod.to_excel(writer, sheet_name='H2 production', index=False)
-
-with col4:
-    st.download_button(
-        label="Download Excel workbook Results",
-        data=output.getvalue(),
-        file_name="workbook.xlsx",
-        mime="application/vnd.ms-excel"
-    )
+# %%
+# -*- coding: utf-8 -*-
+"""
+Spyder Editor
+
+This is a temporary script file.
+"""
+
+
+
+from numpy import arange
+import xarray as xr
+import highspy
+from linopy import Model, EQUAL
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+import sourced as src
+import numpy as np
+
+## Setting
+write_pickle_from_standard_excel = True
+
+
+st.set_page_config(layout="wide")
+# you can create columns to better manage the flow of your page
+# this command makes 3 columns of equal width
+col1, col2, col3, col4 = st.columns(4)
+col1.header("Data Input")
+col4.header("Download Results")
+
+# Color dictionary for figures
+color_dict = {'Biomass': 'lightgreen',
+              'Lignite': 'brown',
+              'Fossil Gas': 'grey',
+              'Fossil Hard coal': 'darkgrey',
+              'Fossil Oil': 'maroon',
+              'RoR': 'aquamarine',
+              'Hydro Water Reservoir': 'azure',
+              'Nuclear': 'orange',
+              'PV': 'yellow',
+              'WindOff': 'darkblue',
+              'WindOn': 'green',
+              'H2': 'crimson',
+              'Pumped Hydro Storage': 'lightblue',
+              'Battery storages': 'red',
+              'Electrolyzer': 'olive'}
+
+# %%
+with col1:
+    with open('Input_Jahr_2021.xlsx', 'rb') as f:
+        st.download_button('Download Excel Template', f, file_name='Input_Jahr_2021.xlsx')  # Defaults to 'application/octet-stream'
+
+#url_excel = r'Input_Jahr_2021.xlsx'
+    url_excel = st.file_uploader(label = 'Excel Upload')
+
+
+if url_excel == None:
+    if write_pickle_from_standard_excel:
+        url_excel = r'Input_Jahr_2021.xlsx'
+        sets_dict, params_dict= src.load_data_from_excel(url_excel, write_to_pickle_flag= True)
+    sets_dict, params_dict = src.load_from_pickle()
+    with col4:
+        st.write('Running with standard data')
+else:
+    sets_dict, params_dict= src.load_data_from_excel(url_excel, load_from_pickle_flag = False)
+    
+    with col4:
+        st.write('Running with user data')
+
+# # %%
+
+def timstep_aggregate(time_steps_aggregate, xr ):
+    return xr.rolling( t = time_steps_aggregate).mean().sel(t = t[0::time_steps_aggregate])
+
+#s_t_r_iRes = timstep_aggregate(6,s_t_r_iRes)
+
+
+
+# %%
+#sets_dict, params_dict= src.load_data_from_excel(url_excel,write_to_pickle_flag=True)
+
+# %%
+
+
+
+#sets_dict, params_dict= load_data_from_excel(url_excel, load_from_pickle_flag = False)
+
+
+# Unpack sets_dict into the workspace
+t = sets_dict['t']
+t_original = sets_dict['t']
+i = sets_dict['i']
+iSto = sets_dict['iSto']
+iConv = sets_dict['iConv']
+iPtG = sets_dict['iPtG']
+iRes = sets_dict['iRes']
+iHyRes = sets_dict['iHyRes']
+
+# Unpack params_dict into the workspace
+l_co2 = params_dict['l_co2']
+p_co2 = params_dict['p_co2']
+
+eff_i = params_dict['eff_i']
+life_i = params_dict['life_i']
+c_fuel_i = params_dict['c_fuel_i']
+c_other_i = params_dict['c_other_i']
+c_inv_i = params_dict['c_inv_i']
+co2_factor_i = params_dict['co2_factor_i']
+#c_var_i = params_dict['c_var_i']
+K_0_i = params_dict['K_0_i']
+e2p_iSto = params_dict['e2p_iSto']
+
+# Sliders and input boxes for parameters
+with col2:
+    # Slider for CO2 limit [mio. t]
+    l_co2 = st.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label="CO2 limit [mio. t]", step=50)
+
+    # Slider for H2 price / usevalue [€/MWH_th]
+    price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Hydrogen price [€/MWh]", step=10)
+
+    for i_idx in c_fuel_i.get_index('i'):
+            if i_idx in ['Lignite']:
+                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Price' , step=10)
+
+    dt = st.number_input(label="Length of timesteps [int]", min_value=1, max_value=len(t), value=6, help="Enter only integers between 1 and 8760 (or 8784 for leap years).")
+
+with col3:
+    # Slider for CO2 limit [mio. t]
+    for i_idx in c_fuel_i.get_index('i'):
+            if i_idx in ['Fossil Hard coal', 'Fossil Oil','Fossil Gas']:
+                c_fuel_i.loc[i_idx] = st.slider(value=int(c_fuel_i.loc[i_idx]), min_value=0, max_value=300, label=i_idx + ' Price' , step=10)
+
+    technologies_invest = st.multiselect(label='Technologies for investment', options=i, default=['Lignite','Fossil Gas','Fossil Hard coal','Fossil Oil','PV','WindOff','WindOn','H2','Pumped Hydro Storage','Battery storages'])
+    technologies_no_invest = [x for x in i if x not in technologies_invest]
+
+# Aggregate time series
+D_t = timstep_aggregate(dt,params_dict['D_t'])
+s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
+h_t = timstep_aggregate(dt,params_dict['h_t'])
+t = D_t.get_index('t')
+partial_year_factor = (8760/len(t))/dt
+
+
+
+#time_steps_aggregate = 6
+#= xr_profiles.rolling( time_step = time_steps_aggregate).mean().sel(time_step = time[0::time_steps_aggregate])
+price_co2 = 0
+
+# Aggregate time series
+#D_t = timstep_aggregate(dt,params_dict['D_t'])
+#s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
+#h_t = timstep_aggregate(dt,params_dict['h_t'])
+#t = D_t.get_index('t')
+#partial_year_factor = (8760/len(t))/dt
+
+#technologies_no_invest = st.multiselect(label='Technology invest', options=i)
+#technologies_no_invest = ['Electrolyzer','Biomass','RoR','Hydro Water Reservoir','Nuclear']
+# %%
+### Variables
+m = Model()
+
+C_tot = m.add_variables(name = 'C_tot')     # Total costs
+C_op = m.add_variables(name = 'C_op', lower = 0)       # Operational costs
+C_inv = m.add_variables(name = 'C_inv', lower = 0)     # Investment costs
+
+K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity
+y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen
+y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
+l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
+w = m.add_variables(coords = [t], name = 'w', lower = 0)          # RES curtailment
+y_curt = m.add_variables(coords = [t,i], name = 'y_curt', lower = 0)
+y_h2 = m.add_variables(coords = [t,i], name = 'y_h2', lower = 0)
+
+## Objective function
+C_tot = C_op + C_inv
+m.add_objective(C_tot)
+
+## Costs terms for objective function
+# Operational costs minus revenue for produced hydrogen
+C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum() * dt - (y_h2.sel(i = iPtG) * price_h2).sum() * dt  == C_op, name = 'C_op_sum')
+
+# Investment costs
+C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')
+
+## Load serving
+loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - y_ch.sum(dims = 'i')) * dt  == D_t.sel(t = t) * dt), name =  'load')
+
+## Maximum capacity limit
+maxcap_i_t = m.add_constraints((y - K <= K_0_i), name = 'max_cap')
+
+## Maximum capacity limit
+maxcap_invest_i = m.add_constraints((K.sel(i = technologies_no_invest) <= 0), name = 'max_cap_invest')
+
+## Prevent power production by PtG
+no_power_prod_iPtG_t = m.add_constraints((y.sel(i = iPtG) <= 0), name = 'prevent_ptg_prod')
+
+## Maximum storage charging and discharging
+maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) + y_ch.sel(i = iSto) - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')
+
+## Maximum electrolyzer capacity
+ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG) <= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')
+
+## PtG H2 production
+h2_prod_iPtG_t = m.add_constraints(y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG) == y_h2.sel(i = iPtG), name = 'ptg_h2_prod')
+
+## Infeed of renewables
+infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) + y_curt.sel(i = iRes) == s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')
+
+## Maximum filling level restriction storage power plant
+maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')
+
+## Filling level restriction hydro reservoir
+filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')
+
+## Filling level restriction other storages
+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
+CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000 , name = 'CO2_limit')
+
+
+# %%
+m.solve(solver_name = 'highs')
+
+st.markdown("---")
+
+colb1, colb2 = st.columns(2)
+
+# %%
+#c_var_i.to_dataframe(name='VarCosts')
+# %%
+# Installed Cap
+# Assuming df_excel has columns 'All' and 'Capacities'
+
+fig = px.bar((m.solution['K']+K_0_i).to_dataframe(name='K').reset_index(), \
+                y='i', x='K', orientation='h', title='Total Installed Capacities [MW]', color='i')
+
+#fig
+
+# %%
+total_costs = float(m.solution['C_inv'].values) + float(m.solution['C_op'].values)
+total_costs_rounded = round(total_costs/1e9, 2)
+df_total_costs = pd.DataFrame({'Total costs':[total_costs]})
+
+with colb1:
+    st.write('Total costs: ' + str(total_costs_rounded) + ' bn. €')
+
+# %%
+#df_Co2_price = pd.DataFrame({'CO2_Price: ':[float(m.constraints['CO2_limit'].dual.values) * (-1)]})
+CO2_price = float(m.constraints['CO2_limit'].dual.values) * (-1)
+CO2_price_rounded = round(CO2_price, 2)
+df_CO2_price = pd.DataFrame({'CO2 price':[CO2_price]})
+
+with colb2:
+    #st.write(str(df_Co2_price))
+    st.write('CO2 price: ' + str(CO2_price_rounded) + ' €/t')
+
+# %%
+df_new_capacities = m.solution['K'].to_dataframe().reset_index()
+fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='New Capacities [MW]', color='i', color_discrete_map=color_dict)
+
+with colb1:
+    fig
+
+# %%
+#add pie chart which shows new capacities
+#round number of new capacities
+df_new_capacities_rounded = m.solution['K'].round(0).to_dataframe()
+#drop all technologies with K<= 0
+df_new_capacities_rounded = df_new_capacities_rounded[df_new_capacities_rounded["K"] > 0].reset_index() 
+
+total_k_sum = df_new_capacities_rounded["K"].sum()
+
+#df_new_capacities_rounded["percentage"] = df_new_capacities_rounded["K"].apply(lambda x: (x/total_k_sum)*100).abs().round(2)
+
+fig = px.pie(df_new_capacities_rounded, names='i', values='K', title='New Capacities [MW] as pie chart',
+             color='i', color_discrete_map=color_dict)
+
+with colb1:
+    fig
+
+
+
+
+# %%
+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()
+fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Production [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))
+
+with colb2:
+    fig
+# %%
+#Add pie chart of total production per technology type in GWh(divide by 1000)
+df_production_sum = (df_production.groupby('i')['y'].sum() * dt / 1000 ).round(0).sort_values(ascending=False).reset_index()
+
+fig = px.pie(df_production_sum, names="i", values='y', title='Total Production [GWh] as pie chart',
+             color='i', color_discrete_map=color_dict)
+
+with colb2:
+    fig
+
+# %%
+
+df_price = m.constraints['load'].dual.to_dataframe().reset_index()
+#df_price['dual'] = df_price['dual']
+
+# %%
+fig = px.line(df_price, y='dual', x='t',  title='Electricity prices [€/MWh]', range_y=[0,250])
+with colb1:
+    fig
+
+
+# %%
+df_sorted_price = df_price["dual"].repeat(dt).sort_values(ascending=False).reset_index(drop=True)/int(dt)
+
+fig = px.line(y=df_sorted_price, x=df_sorted_price.index,  title='Price duration curve [€/MWh]', labels={"x": "Hours of the year"},range_y=[0,250])
+with colb1:
+    fig
+
+# %% 
+
+df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index() 
+df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1)
+
+
+# %%
+
+fig = px.line(df_contr_marg, y='dual', x='t',title='Contribution margin [€]', color='i', range_y=[0,250], color_discrete_map=color_dict)
+with colb2:
+    fig
+
+# %%
+    
+# curtailment
+df_curtailment = m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index()
+fig = px.area(m.solution['y_curt'].sel(i = iRes).to_dataframe().reset_index(), y='y_curt', x='t',  title='Curtailment [MWh]', 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))
+
+with colb1:
+    fig    
+
+# %%
+df_charging = m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index()
+fig = px.area(m.solution['y_ch'].sel(i = iSto).to_dataframe().reset_index(), y='y_ch', x='t',  title='Storage charging [MWh]', 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))
+
+with colb2:
+    fig
+
+# %%
+df_h2_prod = m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index()
+fig = px.area(m.solution['y_h2'].sel(i = iPtG).to_dataframe().reset_index(), y='y_h2', x='t',  title='Hydrogen production [MWh_th]', 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))
+
+with colb2:
+    fig
+
+# %%
+((m.solution['y'] / eff_i) * co2_factor_i * dt).sum()
+# %%
+
+import pandas as pd
+from io import BytesIO
+#from pyxlsb import open_workbook as open_xlsb
+import streamlit as st
+import xlsxwriter
+# %%
+output = BytesIO()
+
+
+# ## 
+
+
+def disaggregate_df(df):
+    
+
+    if not "t" in list(df.columns):
+        return df
+
+    #df_repeated = df.iloc[idx_repeat,:].reset_index(drop = True).drop('t', axis = 1)
+    df_t_all = pd.DataFrame({"t_all": t_original.to_series(), 't': t.repeat(dt)}).reset_index(drop=True)
+
+    # %%
+    df_output = df.merge(df_t_all,on = 't').drop('t',axis = 1).rename({'t_all':'t'}, axis = 1)
+    # last column to first column
+    cols = list(df_output.columns)
+    cols = [cols[-1]] + cols[:-1]
+    df_output = df_output[cols]
+    return df_output.sort_values('t')
+
+
+
+
+# Create a Pandas Excel writer using XlsxWriter as the engine
+with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+    # Write each DataFrame to a different sheet
+    disaggregate_df(df_total_costs).to_excel(writer, sheet_name='Total costs', index=False)
+    disaggregate_df(df_CO2_price).to_excel(writer, sheet_name='CO2 price', index=False)
+    disaggregate_df(df_price).to_excel(writer, sheet_name='Prices', index=False)
+    disaggregate_df(df_contr_marg).to_excel(writer, sheet_name='Contribution Margin', index=False)
+    disaggregate_df(df_new_capacities).to_excel(writer, sheet_name='Capacities', index=False)
+    disaggregate_df(df_production).to_excel(writer, sheet_name='Production', index=False)
+    disaggregate_df(df_charging).to_excel(writer, sheet_name='Charging', index=False)
+    disaggregate_df(D_t.to_dataframe().reset_index()).to_excel(writer, sheet_name='Demand', index=False)
+    disaggregate_df(df_curtailment).to_excel(writer, sheet_name='Curtailment', index=False)
+    disaggregate_df(df_h2_prod).to_excel(writer, sheet_name='H2 production', index=False)
+
+with col4:
+    st.download_button(
+        label="Download Excel workbook Results",
+        data=output.getvalue(),
+        file_name="workbook.xlsx",
+        mime="application/vnd.ms-excel"
+    )
+
+# %%
+
+

sourced.py

@@ -1,205 +1,215 @@
-# %%
-import pandas as pd
-
-import pickle
-
-# Define the file path for the pickle file
-pickle_file_path = 'model_data.pkl'
-
-# Function to save dictionaries to a pickle file
-def save_to_pickle(sets_dict, params_dict):
-    with open(pickle_file_path, 'wb') as file:
-        pickle.dump({'sets': sets_dict, 'params': params_dict}, file)
-
-# Function to load dictionaries from a pickle file
-def load_from_pickle():
-    with open(pickle_file_path, 'rb') as file:
-        data = pickle.load(file)
-    return data['sets'], data['params']
-
-
-
-def load_data_from_excel(url_excel,load_from_pickle_flag = False, write_to_pickle_flag = True):
-
-
-    if load_from_pickle_flag:
-    # Load dictionaries from the pickle file
-        loaded_sets_dict, loaded_params_dict = load_from_pickle()
-        return loaded_sets_dict, loaded_params_dict
-
-    # Timesteps
-    df_excel = pd.read_excel(url_excel, sheet_name='Timesteps_All', header=None)
-    t = pd.Index(df_excel.iloc[:, 0], name='t')
-
-    # Technologies
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    i = pd.Index(df_excel.iloc[:, 0], name='i')
-
-
-
-
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iConv = pd.Index(df_excel.iloc[0:7, 2], name='iConv')
-
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iRes = pd.Index(df_excel.iloc[0:4, 4], name='iRes')
-
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iSto = pd.Index(df_excel.iloc[0:2, 6], name='iSto')
-
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iPtG = pd.Index(df_excel.iloc[0:1, 8], name='iPtG')
-
-
-    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
-    iHyRes = pd.Index(df_excel.iloc[0:1, 10], name='iHyRes')
-
-    # Parameters
-    l_co2 =  pd.read_excel(url_excel, sheet_name='CO2_Cap').iloc[0,0]
-    p_co2 = 0
-    dt = 1
-
-
-    # Demand
-    df_excel= pd.read_excel(url_excel, sheet_name = 'Demand')
-    #df_melt = pd.melt(df_excel, id_vars='Zeit')
-    df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Unnamed: 1':'Demand'})
-    #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('t')
-    D_t = df_excel.iloc[:,0].to_xarray()
-    ## Efficiencies
-    df_excel = pd.read_excel(url_excel, sheet_name = 'Efficiency')
-    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'Efficiency'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    eff_i = df_excel.iloc[:,0].to_xarray()
-
-    ## Variable costs
-    # Fuel costs
-    df_excel = pd.read_excel(url_excel, sheet_name = 'FuelCosts')
-    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'FuelCosts'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    c_fuel_i = df_excel.iloc[:,0].to_xarray()
-    # Apply slider value
-    #c_fuel_i.loc[dict(i = 'Fossil Gas')]  = price_gas
-    #c_fuel_i.loc[dict(i = 'H2')]  = price_h2
-
-    # Other var. costs
-    df_excel = pd.read_excel(url_excel, sheet_name = 'OtherVarCosts')
-    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'OtherVarCosts'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    c_other_i = df_excel.iloc[:,0].to_xarray()
-
-    # Investment costs
-    df_excel = pd.read_excel(url_excel, sheet_name = 'InvCosts')
-    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InvCosts'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    c_inv_i = df_excel.iloc[:,0].to_xarray()*1000*0.1 # kw to MW and annuity factor
-
-    # Emission factor
-    df_excel = pd.read_excel(url_excel, sheet_name = 'EmFactor')
-    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'EmFactor'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    co2_factor_i = df_excel.iloc[:,0].to_xarray()
-
-    ## Calculation of variable costs
-    c_var_i = (c_fuel_i.sel(i = iConv) + p_co2 * co2_factor_i.sel(i = iConv)) / eff_i.sel(i = iConv) + c_other_i.sel(i = iConv)
-
-    # RES capacity factors
-    #df_excel = pd.read_excel(url_excel, sheet_name = 'RES',header=[0,1])
-    #df_excel = pd.read_excel(url_excel, sheet_name = 'RES', index_col=['Timesteps'], columns=['PV', 'WindOn', 'WindOff', 'RoR'])
-    df_excel = pd.read_excel(url_excel, sheet_name = 'RES')
-    df_excel = df_excel.set_index(['Timesteps'])
-    df_test = df_excel
-    df_excel = df_excel.stack()
-    #df_excel = df_excel.rename(columns={'PV', 'WindOn', 'WindOff', 'RoR'})
-    df_test2 = df_excel
-    #df_excel = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    #df_excel = df_excel.fillna(0)
-
-    #df_test = df_excel.set_index(['Timesteps', 'PV', 'WindOn', 'WindOff', 'RoR']).stack([0])
-    #df_test.index = df_test.index.set_names(['t','i'])
-    s_t_r_iRes = df_excel.to_xarray().rename({'level_1': 'i','Timesteps':'t'})
-
-    #s_t_r_iRes = df_excel.iloc[:,0].to_xarray()
-
-    # Base capacities
-    df_excel = pd.read_excel(url_excel, sheet_name = 'InstalledCap')
-    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InstalledCap'})
-    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    K_0_i = df_excel.iloc[:,0].to_xarray()
-
-    # Energy-to-power ratio storages
-    df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  
-    df_excel = df_excel.rename(columns = {'Storage':'i', 'Unnamed: 1':'E2P ratio'})
-    #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('i')
-    e2p_iSto = df_excel.iloc[:,0].to_xarray()
-
-    # Inflow for hydro reservoir
-    df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')
-    df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Hydro Water Reservoir':'Inflow'})
-    df_excel = df_excel.fillna(0)
-    df_excel = df_excel.set_index('t')
-    h_t = df_excel.iloc[:,0].to_xarray()
-
-
-    
-    sets_dict = {}
-    params_dict = {}
-    # Append parameters to the dictionary
-    sets_dict['t'] = t
-    sets_dict['i'] = i
-    sets_dict['iSto'] = iSto
-    sets_dict['iConv'] = iConv
-    sets_dict['iPtG'] = iPtG
-    sets_dict['iRes'] = iRes
-    sets_dict['iHyRes'] = iHyRes
-    # Append parameters to the dictionary
-    params_dict['l_co2'] = l_co2
-    params_dict['p_co2'] = p_co2
-    params_dict['dt'] = dt
-    params_dict['D_t'] = D_t
-    params_dict['eff_i'] = eff_i
-    params_dict['c_fuel_i'] = c_fuel_i
-    params_dict['c_other_i'] = c_other_i
-    params_dict['c_inv_i'] = c_inv_i
-    params_dict['co2_factor_i'] = co2_factor_i
-    params_dict['c_var_i'] = c_var_i
-    params_dict['s_t_r_iRes'] = s_t_r_iRes
-    params_dict['K_0_i'] = K_0_i
-    params_dict['e2p_iSto'] = e2p_iSto
-    params_dict['h_t'] = h_t
-
-    if write_to_pickle_flag:
-        save_to_pickle(sets_dict, params_dict)
-
-    return sets_dict, params_dict
-
-
-# %%
-# # Example usage:
-# url_excel = "Input_Jahr_2021.xlsx"  # Replace with your actual file path
-# limit_co2 = 0.5
-# price_co2 = 50
-# price_gas = 3
-# price_h2 = 5
-
-# sets, params = load_data_from_excel(url_excel,write_to_pickle_flag=True)
-
-# # %%
-# sets, params = load_data_from_excel(url_excel,load_from_pickle_flag=True)
-# # %%
+# %%
+import pandas as pd
+
+import pickle
+
+
+# %%
+# Define the file path for the pickle file
+pickle_file_path = 'model_data.pkl'
+
+# Function to save dictionaries to a pickle file
+def save_to_pickle(sets_dict, params_dict):
+    with open(pickle_file_path, 'wb') as file:
+        pickle.dump({'sets': sets_dict, 'params': params_dict}, file)
+
+# Function to load dictionaries from a pickle file
+def load_from_pickle():
+    with open(pickle_file_path, 'rb') as file:
+        data = pickle.load(file)
+    return data['sets'], data['params']
+
+
+ 
+def load_data_from_excel(url_excel, write_to_pickle_flag = True):
+
+
+
+    # Timesteps
+    df_excel = pd.read_excel(url_excel, sheet_name='Timesteps_All', header=None)
+    t = pd.Index(df_excel.iloc[:, 0], name='t')
+
+    # Technologies
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    i = pd.Index(df_excel.iloc[:, 0], name='i')
+
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    iConv = pd.Index(df_excel.iloc[0:7, 2], name='iConv')
+
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    iRes = pd.Index(df_excel.iloc[0:4, 4], name='iRes')
+
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    iSto = pd.Index(df_excel.iloc[0:2, 6], name='iSto')
+
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    iPtG = pd.Index(df_excel.iloc[0:1, 8], name='iPtG')
+
+    df_excel = pd.read_excel(url_excel, sheet_name='Technologies')
+    iHyRes = pd.Index(df_excel.iloc[0:1, 10], name='iHyRes')
+
+    # Parameters
+    l_co2 =  pd.read_excel(url_excel, sheet_name='CO2_Cap').iloc[0,0]
+    p_co2 = 0
+    dt = 1
+
+    # Demand
+    df_excel= pd.read_excel(url_excel, sheet_name = 'Demand')
+    #df_melt = pd.melt(df_excel, id_vars='Zeit')
+    df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Unnamed: 1':'Demand'})
+    #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('t')
+    D_t = df_excel.iloc[:,0].to_xarray()
+
+    ## Efficiencies
+    df_excel = pd.read_excel(url_excel, sheet_name = 'Efficiency')
+    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'Efficiency'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    eff_i = df_excel.iloc[:,0].to_xarray()
+
+     ## Lifespan
+    df_excel = pd.read_excel(url_excel, sheet_name = 'Lifespan')
+    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'Lifespan'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    life_i = df_excel.iloc[:,0].to_xarray()
+
+    ## Variable costs
+    # Fuel costs
+    df_excel = pd.read_excel(url_excel, sheet_name = 'FuelCosts')
+    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'FuelCosts'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    c_fuel_i = df_excel.iloc[:,0].to_xarray()
+    # Apply slider value
+    #c_fuel_i.loc[dict(i = 'Fossil Gas')]  = price_gas
+    #c_fuel_i.loc[dict(i = 'H2')]  = price_h2
+
+    # Other var. costs
+    df_excel = pd.read_excel(url_excel, sheet_name = 'OtherVarCosts')
+    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'OtherVarCosts'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    c_other_i = df_excel.iloc[:,0].to_xarray()
+
+    # Investment costs
+    df_excel = pd.read_excel(url_excel, sheet_name = 'InvCosts')
+    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InvCosts'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    interest_rate = 0.07
+    annuity_factor_i = (interest_rate * (1 + interest_rate)**life_i) / ((1 + interest_rate)**life_i - 1)
+    c_inv_i = df_excel.iloc[:,0].to_xarray()*1000*annuity_factor_i
+
+    # Emission factor
+    df_excel = pd.read_excel(url_excel, sheet_name = 'EmFactor')
+    df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'EmFactor'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    co2_factor_i = df_excel.iloc[:,0].to_xarray()
+
+    ## Calculation of variable costs
+    c_var_i = (c_fuel_i.sel(i = iConv) + p_co2 * co2_factor_i.sel(i = iConv)) / eff_i.sel(i = iConv) + c_other_i.sel(i = iConv)
+
+    # RES capacity factors
+    #df_excel = pd.read_excel(url_excel, sheet_name = 'RES',header=[0,1])
+    #df_excel = pd.read_excel(url_excel, sheet_name = 'RES', index_col=['Timesteps'], columns=['PV', 'WindOn', 'WindOff', 'RoR'])
+    df_excel = pd.read_excel(url_excel, sheet_name = 'RES')
+    df_excel = df_excel.set_index(['Timesteps'])
+    df_test = df_excel
+    df_excel = df_excel.stack()
+    #df_excel = df_excel.rename(columns={'PV', 'WindOn', 'WindOff', 'RoR'})
+    df_test2 = df_excel
+    #df_excel = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    #df_excel = df_excel.fillna(0)
+
+    #df_test = df_excel.set_index(['Timesteps', 'PV', 'WindOn', 'WindOff', 'RoR']).stack([0])
+    #df_test.index = df_test.index.set_names(['t','i'])
+    s_t_r_iRes = df_excel.to_xarray().rename({'level_1': 'i','Timesteps':'t'})
+
+    #s_t_r_iRes = df_excel.iloc[:,0].to_xarray()
+
+    # Base capacities
+    df_excel = pd.read_excel(url_excel, sheet_name = 'InstalledCap')
+    df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InstalledCap'})
+    df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    K_0_i = df_excel.iloc[:,0].to_xarray()
+
+    # Energy-to-power ratio storages
+    df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  
+    df_excel = df_excel.rename(columns = {'Storage':'i', 'Unnamed: 1':'E2P ratio'})
+    #df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('i')
+    e2p_iSto = df_excel.iloc[:,0].to_xarray()
+
+    # Inflow for hydro reservoir
+    df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')
+    df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Hydro Water Reservoir':'Inflow'})
+    df_excel = df_excel.fillna(0)
+    df_excel = df_excel.set_index('t')
+    h_t = df_excel.iloc[:,0].to_xarray()
+
+
+    
+    sets_dict = {}
+    params_dict = {}
+    # Append parameters to the dictionary
+    sets_dict['t'] = t
+    sets_dict['i'] = i
+    sets_dict['iSto'] = iSto
+    sets_dict['iConv'] = iConv
+    sets_dict['iPtG'] = iPtG
+    sets_dict['iRes'] = iRes
+    sets_dict['iHyRes'] = iHyRes
+    # Append parameters to the dictionary
+    params_dict['l_co2'] = l_co2
+    params_dict['p_co2'] = p_co2
+    params_dict['dt'] = dt
+    params_dict['D_t'] = D_t
+    params_dict['eff_i'] = eff_i
+    params_dict['life_i'] = life_i
+    params_dict['c_fuel_i'] = c_fuel_i
+    params_dict['c_other_i'] = c_other_i
+    params_dict['c_inv_i'] = c_inv_i
+    params_dict['co2_factor_i'] = co2_factor_i
+    params_dict['c_var_i'] = c_var_i
+    params_dict['s_t_r_iRes'] = s_t_r_iRes
+    params_dict['K_0_i'] = K_0_i
+    params_dict['e2p_iSto'] = e2p_iSto
+    params_dict['h_t'] = h_t
+
+    if write_to_pickle_flag:
+        save_to_pickle(sets_dict, params_dict)
+
+    return sets_dict, params_dict
+
+
+# %%
+# # Example usage:
+# url_excel = "Input_Jahr_2021.xlsx"  # Replace with your actual file path
+# limit_co2 = 0.5
+# price_co2 = 50
+# price_gas = 3
+# price_h2 = 5
+
+# sets, params = load_data_from_excel(url_excel,write_to_pickle_flag=True)
+
+# # %%
+# sets, params = load_data_from_excel(url_excel,load_from_pickle_flag=True)
+# # %%
+
+
+if __name__ == "__main__":
+        url_excel = r'Input_Jahr_2021.xlsx'
+        sets_dict, params_dict= load_data_from_excel(url_excel, write_to_pickle_flag= False)