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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" | |
| ) | |