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Input_Jahr_2021.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:29deba7c045949c344a272c36778075df949d2f030ad240cfb8ed6912f73d8f5
-size 994606
+oid sha256:630332987718f2aa27e4cd917a37a95ad9d81d4682367982be8301d79445267b
+size 994601

app.py

@@ -10,16 +10,21 @@ 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
 
 
@@ -32,10 +37,10 @@ if url_excel == None:
 
 # # %%
 # # Slider for gas price [€/MWh_th]
-price_gas = st.slider(value=10, min_value=0, max_value=400, label="Natural gas price [€/MWh]", step=10)
+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=80, min_value=0, max_value=400, label="CO2 price [€/t CO2eq]", step=10)
+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)
@@ -45,20 +50,29 @@ price_h2 = st.slider(value=100, min_value=0, max_value=300, label="Hydrogen pric
 
 # %%
 
-# %% [markdown]
-#  Read Sets
+
+
+#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])
+
+
 
 # %%
 ## 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')
 #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')
 
+multiselect =  st.multiselect(label = 'Technolgy invest', options=i)
+
 
 df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
 iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')
@@ -78,7 +92,7 @@ iHyRes = pd.Index(df_excel.iloc[0:1,10], name = 'iHyRes')
 # %%
 ### Parameters
 # CO2 limit (from slider)
-l_co2 = limit_co2
+l_co2 = limit_co2*10**6
 p_co2 = price_co2
 
 # length of timesteps
@@ -92,6 +106,7 @@ df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Unnamed: 1':'Demand'})
 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')
@@ -127,7 +142,7 @@ 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()
+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')
@@ -155,6 +170,7 @@ df_test2 = df_excel
 #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
@@ -180,6 +196,9 @@ df_excel = df_excel.fillna(0)
 df_excel = df_excel.set_index('t')
 h_t = df_excel.iloc[:,0].to_xarray()
 
+
+
+t = D_t.get_index('t')
 # %%
 ### Variables
 m = Model()
@@ -194,7 +213,7 @@ 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
 
-### Model
+partial_year_factor = (8760/len(t))
 
 ## Objective function
 C_tot = C_op + C_inv
@@ -202,17 +221,21 @@ 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_var_i * dt).sum() - ((y_ch.sel(i = iPtG) / eff_i.sel(i = iPtG)) * price_h2 * dt).sum() == C_op, name = 'C_op_sum')
+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')
 
 # 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')
+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')
 
 ## 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')
+
 ## 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')
 
@@ -229,17 +252,50 @@ maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iS
 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')
+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() <= l_co2, name = 'CO2_limit')
+CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum()* partial_year_factor <= l_co2 , 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
+
+
+# %% 
+#m.solution['C_inv']
 
 # %%
 # Installed Cap
 # Assuming df_excel has columns 'All' and 'Capacities'
-fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='Installed Capacities', color='i')
+fig = px.bar(m.solution['K'].to_dataframe().reset_index() + K_0_i.to_dataframe().reset_index().values, y='i', x='K', orientation='h', title='Installed Capacities', color='i')
+
+fig
+
+# %%
+fig = px.bar(m.solution['K'].to_dataframe().reset_index(), y='i', x='K', orientation='h', title='New Capacities', color='i')
+
+fig
+
+
+i_with_capacity = m.solution['K'].where( m.solution['K'] > 0).dropna(dim = 'i').get_index('i')
+# %%
+fig = px.area(m.solution['y'].sel(i = i_with_capacity).to_dataframe().reset_index(), y='y', x='t',  title='Production', color='i')
+
+fig
+# %%
+# %%
+fig = px.line(m.solution['l'].to_dataframe().reset_index(), y='l', x='t',  title='Fillng level', color='i')
+
+fig
+
+# %% 
 
+fig = px.line(m.solution['y_ch'].to_dataframe().reset_index(), y='y_ch', x='t',  title='Production', color='i')
 fig