Upload app.py

app.py

@@ -25,196 +25,258 @@ import streamlit as st
 # %%
 
 url_excel = r'Input_Jahr_2021.xlsx'
+url_excel = st.file_uploader()
 
 
+# %%
+# Slider for gas price [€/MWh_th]
+price_gas = st.slider(value=10, min=0, max=400, label="Natural gas price [€/MWh]", step=10)
 
-# %% [markdown]
-#  Read Sets
+# Slider for CO2 price [€/t]
+price_co2 = st.slider(value=80, min=0, max=400, label="CO2 price [€/t CO2eq]", step=10)
 
-# %%
-# Define all sets for the model
+# Slider for CO2 limit [mio. t]
+limit_co2 = st.slider(value=400, min=0, max=750, label="CO2 limit [mio. t]", step=50)
 
-df_excel= pd.read_excel(url_excel,header=None)
-t = pd.Index(df_excel.iloc[:,0], name = 't')[0:400]
+# Slider for H2 price / usevalue [€/MWH_th]
+price_h2 = st.slider(value=100, min=0, max=300, label="Hydrogen price [€/MWh]", step=10)
 
-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']
+# %%
+# Set input file
+url_excel = r'Input_Jahr_2021.xlsx'
 
-df_excel= pd.read_excel(url_excel,header=None, sheet_name = 'Regionen')
-r = pd.Index(df_excel.iloc[:,0], name='r')
+# %% [markdown]
+#  Read Sets
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+# %%
+## 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')[1:168]
+#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')
+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 = 'Technologies')
+iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')
 
-df_excel = pd.read_excel(url_excel, sheet_name = 'Kraftwerke')
+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 = 'Kraftwerke')
-iStor = pd.Index(df_excel.iloc[0:1,6], name = 'iStor')
-
+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
+# CO2 limit (from slider)
+l_co2 = limit_co2.value
+p_co2 = price_co2.value
 
-### Parameter
+# length of timesteps
 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()
-
-
+# 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.value
+c_fuel_i.loc[dict(i = 'H2')]  = price_h2.value
+
+# 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()
 
-# %%
+# 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()
 
-##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')
+# 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')
-c_var_i = df_excel.iloc[:,0].to_xarray()
+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()
 
-##fixed generation of Res 
-df_excel = pd.read_excel(url_excel, sheet_name = 'EE',header=[0,1])
+# %%
+### Variables
+m = Model()
 
-#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()
+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
 
-s_t_r_iRes.sel(i = ['RoR']).values = 0.8 * s_t_r_iRes.sel(i = ['RoR']).values
+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
 
+### Model
 
-#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'
+## Objective function
+C_tot = C_op + C_inv
+m.add_objective(C_tot)
 
-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
+## Costs terms for objective function
+# Operational costs minus revenue for produced hydrogen
+C_op_sum = m.add_constraints((y * c_var_i * dt).sum() - ((y_ch.sel(i = iPtG) / eff_i.sel(i = iPtG)) * price_h2.value * dt).sum() == 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) == D_t.sel(t = t) * dt), name =  'load')
 
-## 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')
+## Maximum capacity limit
+maxcap_i_t = m.add_constraints((y - K <= K_0_i), name =  'max_cap')
 
-df_melt2 = df_melt2.rename({'2021 in MW' : 'i', 'variable' : 'r'}, axis = 1)
-df_melt2 = df_melt2.set_index(['i','r'])
+## 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')
 
-y_max_i_r = df_melt2.iloc[:,0].to_xarray()
+## 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')
 
+## 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) <= s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')
 
-##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()
+## Maximum filling level restriction storage power plant --> Energy-to-Power-Ratio eingeführt. (JR)
+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')
 
-## inflow of water reservoir
-f_t_r_HydroReservoir = 0.2 * s_t_r_iRes.sel(i = ['RoR']) * y_max_i_r.sel(i = ['RoR'])
+## Filling level restriction hydro reservoir --> Ist Kreisbedingung erfüllt? (JR)
+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 --> Ist Kreisbedingung erfüllt? (JR)
+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')
 
-##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()
+## 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() <= l_co2, name = 'CO2_limit')
 
-#df_excel.loc[iConv]
+m.solve(solver_name = 'highs')
 
 
 
+# %% [markdown]
+#  Results
 
 # %%
 
-###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
+m.solve(solver_name = 'highs',presolve = 'off',run_crossover = 'off',solver = 'ipm')
 
 # %%
+#m.solve(solver_name = 'highs', solver = 'ipm', log_file = 'solverlog.txt',run_crossover = 'off' )
 
+# %%
 # Read Objective from solution
 m.objective_value
 
-
-#pd.options.plotting.backend = "plotly"
+pd.options.plotting.backend = "plotly"
 # Read dual values and plot 
-df = load_t_r.dual.to_dataframe().reset_index()
+df = loadserve_t.dual.to_dataframe().reset_index()
 #df['t'] = pd.to_datetime(df['t'])
+df
 
 
 # %%
-fig = px.line(df, x='t', y='dual', color='r' )
+# Read values
+Productionlevels = m.solution['y'].sel(r = 'DE').to_dataframe().reset_index()
+
+df = Productionlevels
+df
 
-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)
 # %%
+#pandas gui
+# Create Line plot
+fig = px.line(df, x=df['t'], y=df['y'], color = df['i'])
+fig
+
+# %%
+
+
 
 
-# Read values
-Productionlevels = m.solution['y'].sel(r = 'DE').to_dataframe()
 #Productionlevels