add basic files

.gitignore

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+myenvEEW
+
+# PowerPoint temporary
+~$*.pptx*

01_InstallPythonEnv.bat

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+REM Check if Python 3.11 is installed
+
+REM Replace "3.x" with the desired Python version (e.g., 3.8, 3.9, etc.)
+set PythonVersion=3.9.0
+set "PythonVersionMain=%PythonVersion:~0,3%"
+set "PythonVersionWithoutDots=%PythonVersionMain:.=%"
+REM Replace "myenv" with the desired name for the virtual environment
+echo %PythonVersionWithoutDots%
+set EnvName= myenvEEW
+
+
+
+if exist %LocalAppData%\Programs\Python\Python39\python.exe (
+  %LocalAppData%\Programs\Python\Python39\python -m venv %EnvName%
+
+) else (
+	  	echo Python 3.9 not found in user app folder.. searching fo install for all users
+  if exist "%ProgramFiles%\Programs\Python39\python.exe" (
+ 		%ProgramFiles%\Programs\Python39\python -m venv %EnvName%
+	) else (
+			echo Python 3.11 is not installed. Installing Python 3.9...
+		REM Download and install Python 3.11.0 in the current directory
+		curl -o python_installer.exe https://www.python.org/ftp/python/3.9.0/python-3.9.0-amd64.exe
+		python_installer.exe \silent InstallAllUsers=0  PrependPath=0 Include_test=0
+		del python_installer.exe
+		%LocalAppData%\Programs\Python\Python39\Python -m venv %EnvName%
+	)
+)
+
+
+REM Activate the virtual environment
+call myenvEEW\Scripts\activate
+
+REM Install required packages
+pip install -r requirements.txt
+
+REM Deactivate the virtual environment
+deactivate
+
+REM This will pause the Command Prompt
+call cmd

README.md

@@ -1,12 +1,2 @@
----
-title: EEW Model 1
-emoji: 👀
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.33.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# EEW_Peak_Load_Model
+Simplified version of the ERD Peak_Load_Model

StartServer.bat

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+call activate
+call streamlit run app.py

activate.bat

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+call myenv/Scripts/activate

app.py

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+# %%
+# -*- 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
+
+# %% price duration curve
+# sort df_price by dual
+df_price_sorted = df_price.sort_values('dual', ascending=False)
+# %% 
+
+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"
+    )

model_data.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:882997f874bcad2db648bdfb6559637c77e7767b8f7d81311111b01191e84b31
+size 1373467

requirements.txt

@@ -0,0 +1,87 @@
+altair==5.1.1
+asttokens==2.4.0
+attrs==23.1.0
+backcall==0.2.0
+blinker==1.6.2
+Bottleneck==1.3.7
+cachetools==5.3.1
+certifi==2023.7.22
+charset-normalizer==3.2.0
+click==8.1.7
+cloudpickle==2.2.1
+colorama==0.4.6
+comm==0.1.4
+dask==2023.9.2
+debugpy==1.8.0
+decorator==5.1.1
+deprecation==2.1.0
+et-xmlfile==1.1.0
+executing==1.2.0
+fsspec==2023.9.2
+gitdb==4.0.10
+GitPython==3.1.37
+highspy==1.5.3
+idna==3.4
+importlib-metadata==6.8.0
+ipykernel==6.25.2
+ipython==8.15.0
+jedi==0.19.0
+Jinja2==3.1.2
+jsonschema==4.19.1
+jsonschema-specifications==2023.7.1
+jupyter_client==8.3.1
+jupyter_core==5.3.1
+linopy==0.2.6
+locket==1.0.0
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+nest-asyncio==1.5.8
+numexpr==2.8.6
+numpy==1.26.0
+openpyxl==3.1.2
+packaging==23.1
+pandas==2.1.1
+parso==0.8.3
+partd==1.4.1
+pickleshare==0.7.5
+Pillow==9.5.0
+platformdirs==3.10.0
+plotly==5.17.0
+prompt-toolkit==3.0.39
+protobuf==4.24.3
+psutil==5.9.5
+pure-eval==0.2.2
+pyarrow==13.0.0
+pydeck==0.8.1b0
+Pygments==2.16.1
+python-dateutil==2.8.2
+pytz==2023.3.post1
+pywin32==306
+PyYAML==6.0.1
+pyzmq==25.1.1
+referencing==0.30.2
+requests==2.31.0
+rich==13.5.3
+rpds-py==0.10.3
+scipy==1.11.2
+six==1.16.0
+smmap==5.0.1
+stack-data==0.6.2
+streamlit==1.27.0
+tenacity==8.2.3
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.3
+tqdm==4.66.1
+traitlets==5.10.0
+typing_extensions==4.8.0
+tzdata==2023.3
+tzlocal==5.0.1
+urllib3==2.0.5
+validators==0.22.0
+watchdog==3.0.0
+wcwidth==0.2.6
+xarray==2023.8.0
+zipp==3.17.0

sourced.py

@@ -0,0 +1,200 @@
+# %%
+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)
+# # %%