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Input_Jahr_2021.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:630332987718f2aa27e4cd917a37a95ad9d81d4682367982be8301d79445267b
-size 994601
+oid sha256:c92d04350980ce78a44f5ce2cc57fef81682eb12247d80b9f89bed4954302d7d
+size 995396

app.py

@@ -10,198 +10,116 @@ This is a temporary script file.
 
 
 from numpy import arange
-# %%
 import xarray as xr
-# %% 
 import highspy
-# %% 
-import linopy
-
-# %%
 from linopy import Model, EQUAL
-# %%
 import pandas as pd
-#%%
 import plotly.express as px
-##import gurobipy
-
-# %%
 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")
 # %%
+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')
+    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')
 
 # # %%
-# # Slider for gas price [€/MWh_th]
-price_gas = st.slider(value=100, min_value=0, max_value=400, label="Natural gas price [€/MWh]", step=10)
-
-# Slider for CO2 price [€/t]
-price_co2 = st.slider(value=0, min_value=0, max_value=400, label="CO2 price [€/t CO2eq]", step=10)
-
-# Slider for CO2 limit [mio. t]
-limit_co2 = st.slider(value=400, 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)
-
-# %%
+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)
 
 
-#time_steps_aggregate = 6
-#= xr_profiles.rolling( time_step = time_steps_aggregate).mean().sel(time_step = time[0::time_steps_aggregate])
 
-def timstep_aggregate(time_steps_aggregate, xr ):
-    return xr.rolling( t = time_steps_aggregate).mean().sel(t = t[0::time_steps_aggregate])
+# %%
+#sets_dict, params_dict= src.load_data_from_excel(url_excel,write_to_pickle_flag=True)
 
+# %%
 
 
-# %%
-## Define all sets for the model
-# Timesteps
-df_excel= pd.read_excel(url_excel, sheet_name = 'Timesteps_All', header=None)
-t = pd.Index(df_excel.iloc[:,0], name = 't')
-#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')
+#sets_dict, params_dict= load_data_from_excel(url_excel, load_from_pickle_flag = False)
 
-multiselect =  st.multiselect(label = 'Technolgy invest', options=i)
 
+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']
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
-iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')
+# Unpack params_dict into the workspace
+l_co2 = params_dict['l_co2']
+p_co2 = params_dict['p_co2']
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
-iRes = pd.Index(df_excel.iloc[0:4,4], name = 'iRes')
+D_t = timstep_aggregate(dt,params_dict['D_t'])
+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']
+s_t_r_iRes = timstep_aggregate(dt,params_dict['s_t_r_iRes'])
+K_0_i = params_dict['K_0_i']
+e2p_iSto = params_dict['e2p_iSto']
+h_t = timstep_aggregate(dt,params_dict['h_t'])
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
-iSto = pd.Index(df_excel.iloc[0:2,6], name = 'iSto')
+t = D_t.get_index('t')
+partial_year_factor = (8760/len(t))/dt
+# # Slider for gas price [€/MWh_th]
+#price_gas = st.slider(value=100, min_value=0, max_value=400, label="Natural gas price [€/MWh]", step=10)
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
-iPtG = pd.Index(df_excel.iloc[0:1,8], name = 'iPtG')
+# Slider for CO2 price [€/t]
+#price_co2 = st.slider(value=0, min_value=0, max_value=400, label="CO2 price [€/t CO2eq]", step=10)
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
-iHyRes = pd.Index(df_excel.iloc[0:1,10], name = 'iHyRes')
+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)
 
-# %%
-### Parameters
-# CO2 limit (from slider)
-l_co2 = limit_co2*10**6
-p_co2 = price_co2
-
-# length of timesteps
-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()
-D_t = timstep_aggregate(6,D_t)
-
-## 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()
+    # 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)
 
-# 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 = timstep_aggregate(6,s_t_r_iRes)
-#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()
+    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)
 
+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)
 
 
-t = D_t.get_index('t')
+#time_steps_aggregate = 6
+#= xr_profiles.rolling( time_step = time_steps_aggregate).mean().sel(time_step = time[0::time_steps_aggregate])
+price_co2 = 0
 
 
+#technologies_no_invest = st.multiselect(label='Technolgy invest', options=i)
+technologies_no_invest = ['Electrolyzer','Biomass','RoR']
 # %%
 ### Variables
 m = Model()
@@ -216,7 +134,6 @@ y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricit
 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
 
-partial_year_factor = (8760/len(t))
 
 ## Objective function
 C_tot = C_op + C_inv
@@ -224,20 +141,19 @@ m.add_objective(C_tot)
 
 ## Costs terms for objective function
 # Operational costs minus revenue for produced hydrogen
-C_op_sum = m.add_constraints((y.sel(i = c_var_i.get_index('i')) * c_var_i * dt).sum()*partial_year_factor - \
-                             ((y_ch.sel(i = iPtG) * eff_i.sel(i = iPtG)) * price_h2 * dt).sum()*partial_year_factor == C_op, name = 'C_op_sum')
+C_op_sum = m.add_constraints((y * c_fuel_i/eff_i).sum()*dt*partial_year_factor  == 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 * dt).sum(dims = 'i') - (w * dt) - y_ch.sum(dims = 'i')  == D_t.sel(t = t) * dt), name =  'load')
+loadserve_t = m.add_constraints(((y ).sum(dims = 'i') - (w ) - y_ch.sum(dims = 'i')  == D_t.sel(t = t) ), 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 = ['Electrolyzer','RoR','Biomass','H2']) <= 0), name =  'max_cap_invest')
+maxcap_invest_i = m.add_constraints((K.sel(i = technologies_no_invest) <= 0), name =  'max_cap_invest')
 
 ## 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')
@@ -258,49 +174,94 @@ filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(
 filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) + (y.sel(i = iSto) ) * dt - y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')
 
 ## CO2 limit --> ggf. hier auch mit Subset arbeiten (Technologien, die Brennstoff verbrauchen). (JR)
-CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum()* partial_year_factor <= l_co2 , name = 'CO2_limit')
+CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum()* partial_year_factor <= l_co2*1_000_000 , name = 'CO2_limit')
 
 
 # %%
 m.solve(solver_name = 'highs')
 
-# %%
-m.solution['K'].to_dataframe().reset_index()
-
-# %% 
-#((m.solution['y'] / eff_i) * co2_factor_i * dt).sum()*partial_year_factor
-
+st.markdown("---")
 
-# %% 
-#m.solution['C_inv']
+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='TTotal Installed Capacities', color='i')
+                y='i', x='K', orientation='h', title='Total Installed Capacities', color='i')
 
-fig
+#fig
 
 # %%
+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', color='i')
 
-fig
-
+with colb1:
+    fig
 
-i_with_capacity = m.solution['K'].where( m.solution['K'] > 0).dropna(dim = 'i').get_index('i')
 # %%
+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', color='i')
 
-fig
+
+with colb2:
+    fig
+
+
 # %%
+
+df_price = m.constraints['load'].dual.to_dataframe().reset_index()
+df_price['dual'] = df_price['dual']/dt
+
 # %%
-fig = px.line(m.solution['l'].to_dataframe().reset_index(), y='l', x='t',  title='Fillng level', color='i')
+fig = px.line(df_price, y='dual', x='t',  title='Prices')
+with colb1:
+    fig
 
-fig
 
 # %% 
 
-fig = px.line(m.solution['y_ch'].to_dataframe().reset_index(), y='y_ch', x='t',  title='Production', color='i')
-fig
+df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index()
+df_contr_marg['dual'] = df_contr_marg['dual']/dt
+# %%
+
+fig = px.line(m.constraints['max_cap'].dual.to_dataframe().reset_index(), y='dual', x='t',title='contribution margin', color='i')
+with colb2:
+    fig
+
+    
+
+# %%
+df_Co2_price = m.constraints['CO2_limit'].dual.values
+
+
+
+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_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='Capcities', index=False)
+    df_production.to_excel(writer, sheet_name='Capcities', index=False)
+
+
+with col4:
+    st.download_button(
+        label="Download Excel workbook Results",
+        data=output.getvalue(),
+        file_name="workbook.xlsx",
+        mime="application/vnd.ms-excel"
+    )

model_data.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05322da706d8d1acc11be0c013f3992ca967424ecc9f12c41ea92985af49cfa2
+size 1373491

sourced.py

@@ -0,0 +1,205 @@
+# %%
+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)
+# # %%