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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Input_Jahr_2021.xlsx filter=lfs diff=lfs merge=lfs -text

Input_Jahr_2021.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a163d9c060e9b8c54d09281246eec1cabbce26478374efaec6429602f1f1e0a4
+size 4545951

requirements.txt

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+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

streamlit_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
+import linopy
+import openpyxl
+from linopy import Model, EQUAL
+import pandas as pd
+import plotly.express as px
+##import gurobipy
+
+import streamlit as st
+
+
+# %%
+
+url_excel = r'Input_Jahr_2021.xlsx'
+
+
+
+# %% [markdown]
+#  Read Sets
+
+# %%
+# Define all sets for the model
+
+df_excel= pd.read_excel(url_excel,header=None)
+t = pd.Index(df_excel.iloc[:,0], name = 't')[0:400]
+
+df_excel= pd.read_excel(url_excel,header=None, sheet_name = 'Regionen')
+r = pd.Index(df_excel.iloc[:,0], name='r')
+rr = r.copy() 
+rr.names = ['rr']
+
+df_excel= pd.read_excel(url_excel,header=None, sheet_name = 'Regionen')
+r = pd.Index(df_excel.iloc[:,0], name='r')
+
+df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+i = pd.Index(df_excel.iloc[:,0], name = 'i')
+
+df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+iConv = pd.Index(df_excel.iloc[1:6,2], name = 'iConv')
+
+df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+iRes = pd.Index(df_excel.iloc[0:4,4], name = 'iRes')
+
+df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+iStor = pd.Index(df_excel.iloc[0:1,6], name = 'iStor')
+
+
+
+
+# %%
+
+### Parameter
+dt = 1
+
+df_excel= pd.read_excel(url_excel,sheet_name = 'Nachfrage')
+df_melt = pd.melt(df_excel,id_vars='Zeit')
+
+df_melt = df_melt.rename({'Zeit':'t', 'variable':'r'}, axis= 1)
+df_melt = df_melt.set_index(['t','r'])
+
+xr_D_t_r = df_melt.iloc[:,0].to_xarray()
+
+
+
+
+
+
+# %%
+
+##variable Kosten
+df_excel = pd.read_excel(url_excel, sheet_name = 'Kosten')
+df_excel = df_excel.rename(columns = {'Konventionelle':'i', 'Unnamed: 1':'Costs'})
+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_var_i = df_excel.iloc[:,0].to_xarray()
+
+
+##fixed generation of Res 
+df_excel = pd.read_excel(url_excel, sheet_name = 'EE',header=[0,1])
+
+#Die Tabelle enthällt 2 Überschriften, deshalb die Anpassung mit stack
+df_test = df_excel.set_index([('Zeit', 'Unnamed: 0_level_1')]).stack().stack()
+df_test.index = df_test.index.set_names(['t','i','r'])
+s_t_r_iRes = df_test.to_xarray()
+
+s_t_r_iRes.sel(i = ['RoR']).values = 0.8 * s_t_r_iRes.sel(i = ['RoR']).values
+
+
+#Korrektur: In BE und NL kompletter Zufluss für RoR
+f_t_r_i = 0.2/0.8 * s_t_r_iRes.sel(i = ['RoR'])
+f_t_r_i['i'].values[0] = 'HydroReservior'
+
+f_t_r_i.loc[dict(r='BE')] = f_t_r_i.loc[dict(r='BE')]*0
+f_t_r_i.loc[dict(r='NL')] = f_t_r_i.loc[dict(r='NL')]*0
+
+
+
+## y_max
+df_excel = pd.read_excel(url_excel, sheet_name = 'InstallierteLeistungen', nrows = 12, usecols = 'A:G')
+df_melt2 = pd.melt(df_excel,id_vars='2021 in MW')
+
+df_melt2 = df_melt2.rename({'2021 in MW' : 'i', 'variable' : 'r'}, axis = 1)
+df_melt2 = df_melt2.set_index(['i','r'])
+
+y_max_i_r = df_melt2.iloc[:,0].to_xarray()
+
+
+##l_max
+df_excel = pd.read_excel(url_excel, sheet_name = 'InstallierteLeistungen', skiprows = range(15), nrows = 1, usecols = 'A:G')  
+df_excel = df_excel.rename(columns = {'ReservoirSize (geschätzt)' : 'i'})
+df_melt3 = pd.melt(df_excel, id_vars = 'i')
+df_melt3 = df_melt3.rename({'variable' : 'r'}, axis = 1)
+df_melt3 = df_melt3.set_index(['r','i'])
+l_max_iStor_r = df_melt3.iloc[:, 0].to_xarray()
+
+## inflow of water reservoir
+f_t_r_HydroReservoir = 0.2 * s_t_r_iRes.sel(i = ['RoR']) * y_max_i_r.sel(i = ['RoR'])
+
+
+##transmission capacity between region r and region rr
+df_excel = pd.read_excel(url_excel, sheet_name = 'NTC', skiprows = range (1), nrows = 7, usecols = 'B:H')
+df_melt4 = pd.melt(df_excel, id_vars = 'Unnamed: 1')
+df_melt4 = df_melt4.rename({'Unnamed: 1' : 'rr' , 'variable' : 'r'}, axis = 1)
+df_melt4 = df_melt4.set_index(['r','rr'])
+x_cap_r_rr = df_melt4.iloc[:, 0].to_xarray()
+
+#df_excel.loc[iConv]
+
+
+
+
+# %%
+
+###Variablen
+m = Model()
+k = m.add_variables(coords = [t,r], name = 'k', lower = 0)#Curtailment
+x = m.add_variables(coords = [t,r,rr], name = 'x', lower = 0)
+y = m.add_variables(coords = [t,r,i], name = 'y', lower = 0)
+l = m.add_variables(coords = [t,r,i], name = 'l', lower = 0)
+
+C_op = m.add_variables(name = 'C_op')
+eta_x = 0.001
+############## Start with the model
+
+##objective function
+C_op = (y * c_var_i * dt).sum()
+
+m.add_objective(C_op)
+##load serving
+load_t_r = m.add_constraints((y * dt).sum(dims = 'i') - (k * dt) + \
+                             (-x.sum('rr') + x.sum('r').rename({'rr':'r'}) ) *(1 -eta_x)== xr_D_t_r,name =  'load')
+##maximum capacity limit
+maxcap_i_r_t = m.add_constraints((y <= y_max_i_r),name =  'max_cap')
+##infeed of renewables
+infeed_iRes_r_t = m.add_constraints((y.sel(i = iRes) <= s_t_r_iRes.sel(i = iRes) * y_max_i_r.sel(i = iRes)),name =  'infeed')
+##capacity restriction storage power plant 
+maxcapsto_r_iStor_t = m.add_constraints(l.sel(i = iStor) <= l_max_iStor_r, name = 'max. filling str.')
+##transmission capacity 
+maxcaptrans_r_rr_t = m.add_constraints(x <= x_cap_r_rr, name = 'max. transm. cap.')
+##storage power plant 
+filling_iStor_r_t = m.add_constraints(l.sel(i = iStor).roll(t = -1)  -l.sel(i = iStor) + \
+                                      y.sel(i = iStor) * dt  == f_t_r_i.sel(i = 'HydroReservior') * dt , name = 'filling level')
+
+
+
+
+    
+
+# %%
+
+
+m.solve(solver_name = 'highs')
+
+
+
+
+
+# %% [markdown]
+#  Results
+
+# %%
+
+# Read Objective from solution
+m.objective_value
+
+
+#pd.options.plotting.backend = "plotly"
+# Read dual values and plot 
+df = load_t_r.dual.to_dataframe().reset_index()
+#df['t'] = pd.to_datetime(df['t'])
+
+
+# %%
+fig = px.line(df, x='t', y='dual', color='r' )
+
+tab1, tab2 = st.tabs(["Streamlit theme (default)", "Plotly native theme"])
+with tab1:
+    st.plotly_chart(fig, theme="streamlit", use_container_width=True)
+with tab2:
+    st.plotly_chart(fig, theme=None, use_container_width=True)
+# %%
+
+
+# Read values
+Productionlevels = m.solution['y'].sel(r = 'DE').to_dataframe()
+#Productionlevels