# %% # -*- 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" )