Upload app.py

app.py

@@ -119,7 +119,7 @@ price_co2 = 0
 
 
 #technologies_no_invest = st.multiselect(label='Technolgy invest', options=i)
-technologies_no_invest = ['Electrolyzer','Biomass','RoR']
+technologies_no_invest = ['Electrolyzer','Biomass','RoR','Hydro Water Reservoir']
 # %%
 ### Variables
 m = Model()
@@ -141,13 +141,13 @@ 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*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 ).sum(dims = 'i') - (w ) - y_ch.sum(dims = 'i')  == D_t.sel(t = t) ), name =  'load')
+loadserve_t = m.add_constraints((((y ).sum(dims = 'i') - (w ) - 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')
@@ -156,25 +156,25 @@ maxcap_i_t = m.add_constraints((y - K <= K_0_i), name =  'max_cap')
 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')
+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_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')
 
-## Maximum filling level restriction storage power plant --> Energy-to-Power-Ratio eingeführt. (JR)
+## 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 --> Ist Kreisbedingung erfüllt? (JR)
+## 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 --> Ist Kreisbedingung erfüllt? (JR)
+## 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) ) * 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*1_000_000 , name = 'CO2_limit')
+## 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')
 
 
 # %%
@@ -226,7 +226,7 @@ with colb1:
 # %% 
 
 df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index()
-df_contr_marg['dual'] = df_contr_marg['dual']/dt
+df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1)
 # %%
 
 fig = px.line(m.constraints['max_cap'].dual.to_dataframe().reset_index(), y='dual', x='t',title='contribution margin', color='i')
@@ -236,7 +236,7 @@ with colb2:
     
 
 # %%
-df_Co2_price = pd.DataFrame({'CO2_Price': [float(m.constraints['CO2_limit'].dual.values)]})
+df_Co2_price = pd.DataFrame({'CO2_Price': [float(m.constraints['CO2_limit'].dual.values) * (-1)]})
 
 with colb2:
     st.write('CO2 Price ' + str(df_Co2_price))