diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,1237 +1,1241 @@ -# -*- coding: utf-8 -*- -""" -Energy system optimization model / Investment model - -HEMF EWL: Christopher Jahns, Julian Radek, Hendrik Kramer, Cornelia Klüter, Yannik Pflugfelder -""" - -import numpy as np -import pandas as pd -import xarray as xr -import plotly.express as px -import plotly.graph_objects as go -import streamlit as st -from io import BytesIO -import xlsxwriter -from linopy import Model -import sourced as src -import time - - -# Main function to run the Streamlit app -def main(): - """ - Main function to set up and solve the energy system optimization model, and handle user inputs and outputs. - """ - setup_page() - - settings = load_settings() - - # fill session space with variables that are needed on all pages - if 'settings' not in st.session_state: - st.session_state.df = load_settings() - st.session_state.settings = settings - - if 'url_excel' not in st.session_state: - st.session_state.url_excel = None - - if 'ui_model' not in st.session_state: - st.session_state.url_excel = None - - if 'output' not in st.session_state: - st.session_state.output = BytesIO() - - - setup_sidebar(st.session_state.settings["df"]) - - - - # # Navigation - # pg = st.navigation([st.Page(page_model, title=st.session_state.settings["df"].loc['menu_modell',st.session_state.lang], icon="📊"), - # st.Page(page_documentation, title=st.session_state.settings["df"].loc['menu_doku',st.session_state.lang], icon="📓"), - # st.Page(page_about_us, title=st.session_state.settings["df"].loc['menu_impressum',st.session_state.lang], icon="💬")], - # expanded=True) - - # # # Run the app - # pg.run() - - # Create tabs for navigation - tabs = st.tabs([ - st.session_state.settings["df"].loc['menu_modell', st.session_state.lang], - st.session_state.settings["df"].loc['menu_doku', st.session_state.lang], - st.session_state.settings["df"].loc['menu_impressum', st.session_state.lang] - ]) - - # Load and display content based on the selected tab - with tabs[0]: # Model page - page_model() - with tabs[1]: # Documentation page - page_documentation() - with tabs[2]: # About Us page - page_about_us() - - - - -# Load settings and initial configurations -def load_settings(): - """ - Load settings for the app, including colors and language information. - """ - settings = { - 'write_pickle_from_standard_excel': True, - 'df': pd.read_csv("language.csv", encoding="iso-8859-1", index_col="Label", sep=";"), - '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' - }, - 'colors': { - 'hemf_blau_dunkel': "#00386c", - 'hemf_blau_hell': "#00529f", - 'hemf_rot_dunkel': "#8b310d", - 'hemf_rot_hell': "#d04119", - 'hemf_grau': "#dadada" - } - } - return settings - -# Initialize Streamlit app -def setup_page(): - """ - Set up the Streamlit page with a specific layout, title, and favicon. - """ - st.set_page_config(layout="wide", page_title="Investment tool", page_icon="media/favicon.ico", initial_sidebar_state="expanded") - - -# Sidebar for language and links -def setup_sidebar(df): - """ - Set up the sidebar with language options and external links. - """ - st.session_state.lang = st.sidebar.selectbox("Language", ["🇬🇧 EN", "🇩🇪 DE"], key="foo", label_visibility="collapsed")[-2:] - - st.sidebar.markdown(""" - - """, unsafe_allow_html=True) - - with st.sidebar: - left_co, cent_co, last_co = st.columns([0.1, 0.8, 0.1]) - with cent_co: - st.text(" ") # add vertical empty space - ""+df.loc['menu_text', st.session_state.lang] - st.text(" ") # add vertical empty space - - if st.session_state.lang == "DE": - st.write("Schaue vorbei beim") - st.markdown(r'[Lehrstuhl für Energiewirtschaft](https://www.ewl.wiwi.uni-due.de)', unsafe_allow_html=True) - elif st.session_state.lang == "EN": - st.write("Get in touch with the") - st.markdown(r'[Chair of Management Science and Energy Economics](https://www.ewl.wiwi.uni-due.de/en)', unsafe_allow_html=True) - - st.text(" ") # add vertical empty space - st.image("media/Logo_HEMF.svg", width=200) - st.image("media/Logo_UDE.svg", width=200) - - -# Load model input data -def load_model_input(df, write_pickle_from_standard_excel): - """ - Load model input data from Excel or Pickle based on user input. - """ - if st.session_state.url_excel is None: - if write_pickle_from_standard_excel: - url_excel = r'Input_Jahr_2023.xlsx' - sets_dict, params_dict = src.load_data_from_excel(url_excel, write_to_pickle_flag=True) - sets_dict, params_dict = src.load_from_pickle() - #st.write(df.loc['model_title1.1', st.session_state.lang]) - # st.write('Running with standard data') - else: - url_excel = st.session_state.url_excel - sets_dict, params_dict = src.load_data_from_excel(url_excel, load_from_pickle_flag=False) - st.write(df.loc['model_title1.2', st.session_state.lang]) - - return sets_dict, params_dict - - - -def page_documentation(): - """ - Display documentation and mathematical model details. - """ - - df = st.session_state.settings["df"] - - st.header(df.loc['constr_header1', st.session_state.lang]) - st.write(df.loc['constr_header2', st.session_state.lang]) - - col1, col2 = st.columns([6, 4]) - - with col1: - st.header(df.loc['constr_header3', st.session_state.lang]) - - with st.container(): - - # Objective function - st.subheader(df.loc['constr_subheader_obj_func', st.session_state.lang]) - st.write(df.loc['constr_subheader_obj_func_descr', st.session_state.lang]) - st.latex(r''' \text{min } C^{tot} = C^{op} + C^{inv}''') - - # Operational costs minus revenue for produced hydrogen - st.write(df.loc['constr_c_op', st.session_state.lang]) - st.latex(r''' C^{op} = \sum_{i} y_{t,i} \cdot \left( \frac{c^{fuel}_{i}}{\eta_i} + c_{i}^{other} \right) \cdot \Delta t - \sum_{i \in \mathcal{I}^{PtG}} y^{h2}_{t,i} \cdot p^{h2} \cdot \Delta t''') - - # Investment costs - st.write(df.loc['constr_c_inv', st.session_state.lang]) - st.latex(r''' C^{inv} = \sum_{i} a_{i} \cdot K_{i} \cdot c^{inv}_{i}''') - - # Constraints - st.subheader(df.loc['subheader_constr', st.session_state.lang]) - - # Load-serving constraint - st.write(df.loc['constr_load_serve', st.session_state.lang]) - st.latex(r''' \left( \sum_{i} y_{t,i} - \sum_{i} y_{t,i}^{ch} \right) \cdot \Delta t = D_t \cdot \Delta t, \quad \forall t \in \mathcal{T}''') - - # Maximum capacity limit - st.write(df.loc['constr_max_cap', st.session_state.lang]) - st.latex(r''' y_{t,i} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}''') - - # Capacity limits for investment - st.write(df.loc['constr_inv_cap', st.session_state.lang]) - st.latex(r''' K_{i} \leq 0, \quad \forall i \in \mathcal{I}^{no\_invest}''') - - # Prevent power production by PtG - st.write(df.loc['constr_prevent_ptg', st.session_state.lang]) - st.latex(r''' y_{t,i} = 0, \quad \forall i \in \mathcal{I}^{PtG}''') - - # Prevent charging for non-storage technologies - st.write(df.loc['constr_prevent_chg', st.session_state.lang]) - st.latex(r''' y_{t,i}^{ch} = 0, \quad \forall i \in \mathcal{I} \setminus \{ \mathcal{I}^{PtG} \cup \mathcal{I}^{Sto} \}''') - - # Maximum storage charging and discharging - st.write(df.loc['constr_max_chg', st.session_state.lang]) - st.latex(r''' y_{t,i} + y_{t,i}^{ch} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}^{Sto}''') - - # Maximum electrolyzer capacity - st.write(df.loc['constr_max_cap_electrolyzer', st.session_state.lang]) - st.latex(r''' y_{t,i}^{ch} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}^{PtG}''') - - # PtG H2 production - st.write(df.loc['constr_prod_ptg', st.session_state.lang]) - st.latex(r''' y_{t,i}^{ch} \cdot \eta_i = y_{t,i}^{h2}, \quad \forall i \in \mathcal{I}^{PtG}''') - - # Infeed of renewables - st.write(df.loc['constr_inf_res', st.session_state.lang]) - st.latex(r''' y_{t,i} + y_{t,i}^{curt} = s_{t,r,i} \cdot (K_{0,i} + K_i), \quad \forall i \in \mathcal{I}^{Res}''') - - # Maximum filling level restriction for storage power plants - st.write(df.loc['constr_max_fil_sto', st.session_state.lang]) - # st.latex(r''' l_{t,i} \leq K_{0,i} \cdot e2p_i, \quad \forall i \in \mathcal{I}^{Sto}''') - st.latex(r''' l_{t,i} \leq (K_{0,i} + K_{i}) \cdot \gamma_i^{Sto}, \quad \forall i \in \mathcal{I}^{Sto}''') - - # Filling level restriction for hydro reservoir - st.write(df.loc['constr_fil_hyres', st.session_state.lang]) - st.latex(r''' l_{t+1,i} = l_{t,i} + ( h_{t,i} - y_{t,i}) \cdot \Delta t, \quad \forall i \in \mathcal{I}^{HyRes}''') - - # Filling level restriction for other storages - st.write(df.loc['constr_fil_sto', st.session_state.lang]) - st.latex(r''' l_{t+1,i} = l_{t,i} - \left(\frac{y_{t,i}}{\eta_i} - y_{t,i}^{ch} \cdot \eta_i \right) \cdot \Delta t, \quad \forall i \in \mathcal{I}^{Sto}''') - - # CO2 emission constraint - st.write(df.loc['constr_co2_lim', st.session_state.lang]) - st.latex(r''' \sum_{t} \sum_{i} \frac{y_{t,i}}{\eta_i} \cdot \chi^{CO2}_i \cdot \Delta t \leq L^{CO2}''') - - - with col2: - - symbols_container = st.container() - with symbols_container: - st.header(df.loc['symb_header1', st.session_state.lang]) - st.write(df.loc['symb_header2', st.session_state.lang]) - - st.subheader(df.loc['symb_header_sets', st.session_state.lang]) - st.write(f"$\mathcal{{T}}$: {df.loc['symb_time_steps', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}$: {df.loc['symb_tech', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{Sto}}}}$: {df.loc['symb_sto_tech', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{Conv}}}}$: {df.loc['symb_conv_tech', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{PtG}}}}$: {df.loc['symb_ptg', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{Res}}}}$: {df.loc['symb_res', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{HyRes}}}}$: {df.loc['symb_hyres', st.session_state.lang]}") - st.write(f"$\mathcal{{I}}^{{\\text{{no\_invest}}}}$: {df.loc['symb_no_inv', st.session_state.lang]}") - - - - # Variables section - st.subheader(df.loc['symb_header_variables', st.session_state.lang]) - st.write(f"$C^{{tot}}$: {df.loc['symb_tot_costs', st.session_state.lang]}") - st.write(f"$C^{{op}}$: {df.loc['symb_c_op', st.session_state.lang]}") - st.write(f"$C^{{inv}}$: {df.loc['symb_c_inv', st.session_state.lang]}") - st.write(f"$K_i$: {df.loc['symb_inst_cap', st.session_state.lang]}") - st.write(f"$y_{{t,i}}$: {df.loc['symb_el_prod', st.session_state.lang]}") - st.write(f"$y_{{t, i}}^{{ch}}$: {df.loc['symb_el_ch', st.session_state.lang]}") - st.write(f"$l_{{t,i}}$: {df.loc['symb_sto_fil', st.session_state.lang]}") - st.write(f"$y_{{t, i}}^{{curt}}$: {df.loc['symb_curt', st.session_state.lang]}") - st.write(f"$y_{{t, i}}^{{h2}}$: {df.loc['symb_h2_ptg', st.session_state.lang]}") - - - # Parameters section - st.subheader(df.loc['symb_header_parameters', st.session_state.lang]) - st.write(f"$D_t$: {df.loc['symb_energy_demand', st.session_state.lang]}") - st.write(f"$p^{{h2}}$: {df.loc['symb_price_h2', st.session_state.lang]}") - st.write(f"$c^{{fuel}}_{{i}}$: {df.loc['symb_fuel_costs', st.session_state.lang]}") - st.write(f"$c_{{i}}^{{other}}$: {df.loc['symb_c_op_other', st.session_state.lang]}") - st.write(f"$c^{{inv}}_{{i}}$: {df.loc['symb_c_inv_tech', st.session_state.lang]}") - st.write(f"$a_{{i}}$: {df.loc['symb_annuity', st.session_state.lang]}") - st.write(f"$\eta_i$: {df.loc['symb_eff_fac', st.session_state.lang]}") - st.write(f"$K_{{0,i}}$: {df.loc['symb_max_cap_tech', st.session_state.lang]}") - st.write(f"$\chi^{{CO2}}_i$: {df.loc['symb_co2_fac', st.session_state.lang]}") - st.write(f"$L^{{CO2}}$: {df.loc['symb_co2_limit', st.session_state.lang]}") - # st.write(f"$e2p_{{\\text{{Sto}}, i}}$: {df.loc['symb_etp', st.session_state.lang]}") - st.write(f"$\gamma^{{\\text{{Sto}}}}_{{i}}$: {df.loc['symb_etp', st.session_state.lang]}") - st.write(f"$s_{{t, r, i}}$: {df.loc['symb_res_supply', st.session_state.lang]}") - st.write(f"$h_{{t, i}}$: {df.loc['symb_hyRes_inflow', st.session_state.lang]}") - - # css = float_css_helper(top="50") - # symbols_container.float(css) - - -def page_about_us(): - """ - Display information about the team and the project. - """ - st.write("About Us/Impressum") - - -def page_model(): #, write_pickle_from_standard_excel, color_dict): - """ - Display the main model page for energy system optimization. - - This function sets up the user interface for the model input parameters, loads data, and configures the - optimization model before solving it and presenting the results. - """ - - df = st.session_state.settings["df"] - color_dict = st.session_state.settings["color_dict"] - write_pickle_from_standard_excel = st.session_state.settings["write_pickle_from_standard_excel"] - - - - - # Load data from Excel or Pickle - sets_dict, params_dict = load_model_input(df, write_pickle_from_standard_excel) - - # Unpack sets_dict into the workspace - t = sets_dict['t'] - t_original = 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'] - life_i = params_dict['life_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'] - K_0_i = params_dict['K_0_i'] - e2p_iSto = params_dict['e2p_iSto'] - - # Adjust efficiency for storage technologies - eff_i.loc[iSto] = np.sqrt(eff_i.loc[iSto]) # Apply square root to cycle efficiency for storage technologies - - # Create columns for UI layout - col1, col2 = st.columns([0.30, 0.70], gap="large") - - # Load input data - with col1: - - st.title(df.loc['model_title1', st.session_state.lang]) - - with open('Input_Jahr_2023.xlsx', 'rb') as f: - st.download_button(df.loc['model_title1.3',st.session_state.lang], f, file_name='Input_Jahr_2023.xlsx') # Download button for Excel template - - with st.form("input_file"): - - - st.session_state.url_excel = st.file_uploader(label=df.loc['model_title1.4',st.session_state.lang]) # File uploader for user Excel file - - #st.title(df.loc['model_title4', st.session_state.lang]) - - run_model_excel = st.form_submit_button(df.loc['model_run_info_excel', st.session_state.lang]) #, key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) - #else: - # run_model = st.button(df.loc['model_run_info_gui', st.session_state.lang], key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) - - - - - - # Set up user interface for parameters - with col2: - - st.title(df.loc['model_title3', st.session_state.lang]) - - - - with st.form("input_custom"): - - col1form, col2form, col3form = st.columns([0.25, 0.25, 0.50]) - - # colum 1 form - l_co2 = col1form.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label=df.loc['model_label_co2',st.session_state.lang], step=50) - price_h2 = col1form.slider(value=100, min_value=0, max_value=300, label=df.loc['model_label_h2',st.session_state.lang], step=10) - for i_idx in params_dict['c_fuel_i'].get_index('i'): - if i_idx in ['Lignite']: - params_dict['c_fuel_i'].loc[i_idx] = col1form.slider(value=int(params_dict['c_fuel_i'].loc[i_idx]), - min_value=0, max_value=300, label=df.loc[f'model_label_{i_idx}',st.session_state.lang], step=10) - - # colum 1 form - for i_idx in params_dict['c_fuel_i'].get_index('i'): - if i_idx in ['Fossil Hard coal', 'Fossil Oil', 'Fossil Gas']: - params_dict['c_fuel_i'].loc[i_idx] = col2form.slider(value=int(params_dict['c_fuel_i'].loc[i_idx]), - min_value=0, max_value=300, label=df.loc[f'model_label_{i_idx}',st.session_state.lang], step=10) - - # Create a dictionary to map German names to English names - tech_mapping_de_to_en = { - df.loc[f'tech_{tech.lower()}', 'DE']: df.loc[f'tech_{tech.lower()}', 'EN'] - for tech in sets_dict['i'] if f'tech_{tech.lower()}' in df.index - } - - # Set options and default values based on the selected language - if st.session_state.lang == 'DE': - # German options for the user interface - options = [ - df.loc[f'tech_{tech.lower()}', 'DE'] for tech in sets_dict['i'] if f'tech_{tech.lower()}' in df.index - ] - default = [ - df.loc[f'tech_{tech.lower()}', 'DE'] for tech in ['Lignite', 'Fossil Gas', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer'] - if f'tech_{tech.lower()}' in df.index - ] - else: - # English options for the user interface - options = sets_dict['i'] - default = ['Lignite', 'Fossil Gas', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer'] - - # Multiselect for technology options in the user interface - selected_technologies = col3form.multiselect( - label=df.loc['model_label_tech', st.session_state.lang], - options=options, - default=[tech for tech in default if tech in options] - ) - - # If language is German, map selected German names back to their English equivalents - if st.session_state.lang == 'DE': - technologies_invest = [tech_mapping_de_to_en[tech] for tech in selected_technologies] - else: - technologies_invest = selected_technologies - - # Technologies that will not be invested in (based on English names) - technologies_no_invest = [tech for tech in sets_dict['i'] if tech not in technologies_invest] - - col4form, col5form = st.columns([0.25, 0.75]) - dt = col4form.number_input(label=df.loc['model_label_t',st.session_state.lang], min_value=1, max_value=len(t), value=6, - help=df.loc['model_label_t_info',st.session_state.lang]) - - run_model_manual = col5form.form_submit_button(df.loc['model_run_info_gui', st.session_state.lang]) - - #run_model = st.button(df.loc['model_run_info_gui', st.session_state.lang], key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) - - st.markdown("-------") - - # run_model_manual = True - - if run_model_excel or run_model_manual: - # Model setup - - info_yellow_build = st.info(df.loc['label_build_model', st.session_state.lang]) - - - if run_model_excel: # overwrite with excel values - #sets_dict, params_dict = load_model_input(df, write_pickle_from_standard_excel) - sets_dict, params_dict = src.load_data_from_excel(st.session_state.url_excel, write_to_pickle_flag=True) - - # Unpack sets_dict into the workspace - t = sets_dict['t'] - t_original = 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'] - # life_i = params_dict['life_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'] - K_0_i = params_dict['K_0_i'] - e2p_iSto = params_dict['e2p_iSto'] - - # Adjust efficiency for storage technologies - eff_i.loc[iSto] = np.sqrt(eff_i.loc[iSto]) # Apply square root to cycle efficiency for storage technologies - - - # Time series aggregation for various parameters - D_t = timstep_aggregate(dt, params_dict['D_t'], t) - s_t_r_iRes = timstep_aggregate(dt, params_dict['s_t_r_iRes'], t) - h_t = timstep_aggregate(dt, params_dict['h_t'], t) - t = D_t.get_index('t') - partial_year_factor = (8760 / len(t)) / dt - - m = Model() - - # Define Variables - 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 - y_ch = m.add_variables(coords=[t, i], name='y_ch', lower=0) # Electricity consumption - l = m.add_variables(coords=[t, i], name='l', lower=0) # Storage filling level - y_curt = m.add_variables(coords=[t, i], name='y_curt', lower=0) # RES curtailment - y_h2 = m.add_variables(coords=[t, i], name='y_h2', lower=0) # H2 production - - # Define Objective function - C_tot = C_op + C_inv - m.add_objective(C_tot) - - # Define Constraints - # Operational costs minus revenue for produced hydrogen - 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 - m.add_constraints((K * c_inv_i).sum() == C_inv, name='C_inv_sum') - - # Load serving - m.add_constraints((((y).sum(dims='i') - y_ch.sum(dims='i')) * dt == D_t.sel(t=t) * dt), name='load') - - # Maximum capacity limit - m.add_constraints((y - K <= K_0_i), name='max_cap') - - # Capacity limits for investment - m.add_constraints((K.sel(i=technologies_no_invest) <= 0), name='max_cap_invest') - - # Prevent power production by PtG - m.add_constraints((y.sel(i=iPtG) <= 0), name='prevent_ptg_prod') - - # Prevent charging for non-storage technologies - m.add_constraints((y_ch.sel(i=[x for x in i if x not in iPtG and x not in iSto]) <= 0), name='no_charging') - - # Maximum storage charging and discharging - 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 - 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 - 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 - 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 for storage power plants - 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 for hydro reservoir - 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 for other storages - 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 - m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000, name='CO2_limit') - - # Solve the model - info_yellow_build.empty() - info_green_build = st.success(df.loc['label_build_model', st.session_state.lang]) - info_yellow_solve = st.info(df.loc['label_solve_model', st.session_state.lang]) - - - m.solve(solver_name='highs') - - info_yellow_solve.empty() - info_green_solve = st.success(df.loc['label_solve_model', st.session_state.lang]) - info_yellow_plot = st.info(df.loc['label_generate_plots', st.session_state.lang]) - - - - # Prepare columns for figures - colb1, colb2 = st.columns(2) - - # Generate and display figures - st.markdown("---") - - df_total_costs = plot_total_costs(m, colb1, df) - df_CO2_price = plot_co2_price(m, colb2, df) - df_new_capacities = plot_new_capacities(m, color_dict, colb1, df) - - # Only plot production for technologies with capacity - i_with_capacity = m.solution['K'].where((m.solution['K'] > 0) & (m.solution['i'] != 'Electrolyzer')).dropna(dim='i').get_index('i') - df_production = plot_production(m, i_with_capacity, dt, color_dict, colb2, df) - # df_price = plot_electricity_prices(m, dt, colb2, df) - df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df) - df_residual_load_duration = plot_residual_load_duration(m, dt, colb1, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment) - df_price = plot_electricity_prices(m, dt, colb2, df, df_residual_load_duration) - - df_contr_marg = plot_contribution_margin(m, dt, color_dict, colb1, df) - # df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df) - df_charging = plot_storage_charging(m, iSto, color_dict, colb2, df) - df_h2_prod = plot_hydrogen_production(m, iPtG, color_dict, colb1, df) - - # df_stackplot = plot_stackplot(m) - - # Export results - - st.session_state.output = BytesIO() - - - with pd.ExcelWriter(st.session_state.output, engine='xlsxwriter') as writer: - disaggregate_df(df_total_costs, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_total_costs', st.session_state.lang], index=False) - disaggregate_df(df_CO2_price, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_co2_price', st.session_state.lang], index=False) - disaggregate_df(df_price, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_prices', st.session_state.lang], index=False) - disaggregate_df(df_contr_marg, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_contribution_margin', st.session_state.lang], index=False) - disaggregate_df(df_new_capacities, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_capacities', st.session_state.lang], index=False) - disaggregate_df(df_production, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_production', st.session_state.lang], index=False) - disaggregate_df(df_charging, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_charging', st.session_state.lang], index=False) - disaggregate_df(D_t.to_dataframe().reset_index(), t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_demand', st.session_state.lang], index=False) - disaggregate_df(df_curtailment, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_curtailment', st.session_state.lang], index=False) - disaggregate_df(df_h2_prod, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_h2_production', st.session_state.lang], index=False) - - with col1: - st.title(df.loc['model_title2', st.session_state.lang]) - - st.download_button(label=df.loc['model_title2.1',st.session_state.lang], disabled=(st.session_state.output.getbuffer().nbytes==0), data=st.session_state.output.getvalue(), file_name="workbook.xlsx", mime="application/vnd.ms-excel") - - info_yellow_plot.empty() - info_green_plot = st.success(df.loc['label_generate_plots', st.session_state.lang]) - - time.sleep(1) - - info_green_build.empty() - info_green_solve.empty() - info_green_plot.empty() - - st.stop() - - - - # st.rerun() - - -def timstep_aggregate(time_steps_aggregate, xr_data, t): - """ - Aggregates time steps in the data using rolling mean and selects based on step size. - """ - return xr_data.rolling(t=time_steps_aggregate).mean().sel(t=t[0::time_steps_aggregate]) - -# Visualization functions - -def plot_total_costs(m, col, df): - """ - Displays the total costs. - """ - total_costs = float(m.solution['C_inv'].values) + float(m.solution['C_op'].values) - total_costs_rounded = round(total_costs / 1e9, 2) - with col: - st.markdown( - f"

{df.loc['plot_label_total_costs', st.session_state.lang]} {total_costs_rounded}

", - unsafe_allow_html=True - ) - - df_total_costs = pd.DataFrame({'Total costs':[total_costs]}) - return df_total_costs - -def plot_co2_price(m, col, df): - """ - Displays the CO2 price based on the CO2 constraint dual values. - """ - 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 col: - st.markdown( - f"

{df.loc['plot_label_co2_price', st.session_state.lang]} {CO2_price_rounded}

", - unsafe_allow_html=True - ) - - return df_CO2_price - - -def plot_new_capacities(m, color_dict, col, df): - """ - Plots the new capacities installed in MW as a bar chart and pie chart. - Includes technologies with 0 MW capacity in the bar chart. - Supports both German and English labels for technologies while ensuring color consistency. - """ - # Convert the solution for new capacities to a DataFrame - df_new_capacities = m.solution['K'].round(0).to_dataframe().reset_index() - - # Store the English technology names in a separate column to maintain color consistency - df_new_capacities['i_en'] = df_new_capacities['i'] - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_new_capacities['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_new_capacities['i'] = df_new_capacities['i_en'].replace(tech_mapping_en_to_de) - - # Bar plot for new capacities (including technologies with 0 MW) - fig_bar = px.bar(df_new_capacities, y='i', x='K', orientation='h', - title=df.loc['plot_label_new_capacities', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - color_discrete_map=color_dict, - labels={'K': '', 'i': ''} # Delete double labeling - ) - - # Hide the legend completely since the labels are already next to the bars - fig_bar.update_layout(showlegend=False) - - with col: - st.plotly_chart(fig_bar) - - # Pie chart for new capacities (only show technologies with K > 0 in pie chart) - df_new_capacities_filtered = df_new_capacities[df_new_capacities["K"] > 0] - fig_pie = px.pie(df_new_capacities_filtered, names='i', values='K', - title=df.loc['plot_label_new_capacities_pie', st.session_state.lang], - color='i_en', color_discrete_map=color_dict) - - # Remove English labels (i_en) from the pie chart legend - fig_pie.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - fig_pie.for_each_trace(lambda t: t.update(name=df_new_capacities_filtered['i'].iloc[0] if st.session_state.lang == 'DE' else t.name)) - - with col: - st.plotly_chart(fig_pie) - - return df_new_capacities - - -def plot_production(m, i_with_capacity, dt, color_dict, col, df): - """ - Plots the energy production for technologies with capacity as an area chart. - Supports both German and English labels for technologies while ensuring color consistency. - """ - # Convert the production data to a DataFrame - df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index() - - # Store the English technology names in a separate column to maintain color consistency - df_production['i_en'] = df_production['i'] - - # Convert 't'-column in a datetime format - df_production['t'] = df_production['t'].str.strip("'") - df_production['t'] = pd.to_datetime(df_production['t'], format='%Y-%m-%d %H:%M %z') - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_production['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_production['i'] = df_production['i_en'].replace(tech_mapping_en_to_de) - - # Area plot for energy production - fig = px.area(df_production, y='y', x='t', - title=df.loc['plot_label_production', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - color_discrete_map=color_dict, - labels={'y': '', 't': '', 'i_en': df.loc['label_technology', st.session_state.lang]} # Delete double labeling - ) - - # Update legend labels to display German names instead of English - if st.session_state.lang == 'DE': - fig.for_each_trace(lambda trace: trace.update(name=tech_mapping_en_to_de[trace.name])) - - fig.update_traces(line=dict(width=0)) - fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) - - # # Customize x-axis for better date formatting - # fig.update_layout( - # xaxis=dict( - # tickformat="%d/%m/%Y", # Display months and years in MM/YYYY format - # title='', # No title for the x-axis - # type="date" # Ensure x-axis is treated as a date axis - # ), - # xaxis_tickangle=-45 # Tilt the ticks for better readability - # ) - - with col: - st.plotly_chart(fig) - - # Pie chart for total production - df_production_sum = (df_production.groupby(['i', 'i_en'])['y'].sum() * dt / 1000).round(0).reset_index() - - # If the language is set to German, display German labels, otherwise use English - pie_column = 'i' if st.session_state.lang == 'DE' else 'i_en' - - # Pie chart for total production - fig_pie = px.pie(df_production_sum, names=pie_column, values='y', - title=df.loc['plot_label_total_production_pie', st.session_state.lang], - color='i_en', # Ensure the coloring stays consistent using the 'i_en' column - color_discrete_map=color_dict) - - # Update legend title to reflect the correct language - fig_pie.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - - with col: - st.plotly_chart(fig_pie) - - return df_production - - -def plot_electricity_prices(m, dt, col, df, df_residual_load_duration): - """ - Plots the electricity price and the price duration curve. - Supports both German and English labels for the plot titles and axis labels. - """ - # Convert the dual constraints to a DataFrame - df_price = m.constraints['load'].dual.to_dataframe().reset_index() - - # Convert 't'-column in a datetime format - df_price['t'] = df_price['t'].str.strip("'") - df_price['t'] = pd.to_datetime(df_price['t'], format='%Y-%m-%d %H:%M %z') - - # Line plot for electricity prices - fig_price = px.line(df_price, y='dual', x='t', - title=df.loc['plot_label_electricity_prices', st.session_state.lang], - # range_y=[0, 250], - labels={'dual': '', 't': ''} - ) - with col: - st.plotly_chart(fig_price) - - # Create the price duration curve - df_sorted_price = df_price["dual"].repeat(dt).sort_values(ascending=False).reset_index(drop=True) / int(dt) - df_residual_load_sorted = df_residual_load_duration.sort_values(by='Residual_Load', ascending=False).reset_index(drop=True) - df_axis2 = df_residual_load_sorted['Residual_Load'] - - ax2_max = np.max(df_axis2) - ax2_min = np.min(df_axis2) - - fig_duration = go.Figure() - - # Add primary y-axis trace (Price duration curve) - fig_duration.add_trace(go.Scatter( - x=df_sorted_price.index, - y=df_sorted_price, - mode='lines', - name=df.loc['plot_label_price_duration_curve', st.session_state.lang], # Price duration label - line=dict(color='blue', width=2) # Blue line for primary y-axis - )) - - # Add secondary y-axis trace (Residual load) - fig_duration.add_trace(go.Scatter( - x=df_axis2.index, - y=df_axis2, - mode='lines', - name=df.loc['plot_label_residual_load', st.session_state.lang], # Residual load label - line=dict(color='red', width=2), # Red line for secondary y-axis - yaxis='y2' # Link this trace to the secondary y-axis - )) - - # Layout mit separaten Achsen - fig_duration.update_layout( - title=df.loc['plot_label_price_duration_curve', st.session_state.lang], - xaxis=dict( - title=df.loc['label_hours', st.session_state.lang] # Common x-axis - ), - yaxis=dict( - title=df.loc['plot_label_price_duration_curve', st.session_state.lang], # Title for primary y-axis - range=[-(100/(ax2_max/(ax2_max-ax2_min))-100), 100], # Primary y-axis range - titlefont=dict(color='blue'), # Blue color for primary axis title - tickfont=dict(color='blue') # Blue ticks for primary axis - ), - yaxis2=dict( - title=df.loc['plot_label_residual_load', st.session_state.lang], # Title for secondary y-axis - range=[ax2_min, ax2_max], # Secondary y-axis range - titlefont=dict(color='red'), # Red color for secondary axis title - tickfont=dict(color='red'), # Red ticks for secondary axis - overlaying='y', # Overlay secondary axis on primary - side='right' # Place secondary y-axis on the right side - ), - legend=dict( - x=1, # Positioniert die Legende am rechten Rand - y=1, # Positioniert die Legende am oberen Rand - xanchor='right', # Verankert die Legende am rechten Rand - yanchor='top', # Verankert die Legende am oberen Rand - bgcolor='rgba(255, 255, 255, 0.5)', # Weißer Hintergrund mit Transparenz - bordercolor='black', - borderwidth=1 - ) - ) - - - with col: - st.plotly_chart(fig_duration) - - return df_price - -def plot_residual_load_duration(m, dt, col, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment): - """ - Plots the residual load and corresponding production as a stacked area chart. - Supports both German and English labels for the plot titles and axis labels. - Consistent color coding for technologies using a predefined color dictionary. - """ - - # Extract load data and repeat each value to match the total number of hours in the year - df_load = D_t.values.flatten() - total_hours = len(df_load) * dt # Calculate the total number of hours dynamically - repeated_load = np.repeat(df_load, dt)[:total_hours] # Repeat values to represent each hour - - # Convert production data to DataFrame - df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index() - - # Pivot production data to get technologies as columns and time 't' as index - df_production_pivot = df_production.pivot(index='t', columns='i', values='y') - - # Repeat the pivoted production data to match the number of hours - repeated_index = np.repeat(df_production_pivot.index, dt)[:total_hours] # Create repeated index - df_production_repeated = df_production_pivot.loc[repeated_index].reset_index(drop=True) - - # Create load series with the same index as the repeated production data - df_load_series = pd.Series(repeated_load, index=df_production_repeated.index, name='Load') - - # Combine load with repeated production data - df_combined = df_production_repeated.copy() - df_combined['Load'] = df_load_series - - # Identify renewable technologies from iRes - iRes_list = iRes.tolist() # Convert the Index to a list - - # Calculate renewable generation (only include available technologies in df_combined) - renewable_columns = [col for col in iRes_list if col in df_combined.columns] - df_combined['Renewable_Generation'] = df_combined[renewable_columns].sum(axis=1) if renewable_columns else 0 - - # Create pivot table of curtailment - df_curtailment_pivot = df_curtailment.pivot(index='t', columns='i', values='y_curt') - repeated_index = np.repeat(df_curtailment_pivot.index, dt)[:total_hours] # Create repeated index - df_curtailment_repeated = df_curtailment_pivot.loc[repeated_index].reset_index(drop=True) - df_curtailment_repeated['Sum'] = df_curtailment_repeated.sum(axis=1) - df_combined['Sum_curtailment'] = -df_curtailment_repeated['Sum'] - - # Calculate residual load as the difference between total load and renewable generation - df_combined['Residual_Load'] = df_combined['Load'] - df_combined['Renewable_Generation'] + df_combined['Sum_curtailment'] - - # Sort DataFrame by residual load (descending order) to create the duration curve - df_sorted = df_combined.sort_values(by='Residual_Load', ascending=False).reset_index(drop=True) - - # Identify all technology columns except 'Load', 'Residual_Load', 'Renewable_Generation' - technology_columns = [col for col in df_combined.columns if col not in ['Load', 'Residual_Load', 'Renewable_Generation', 'Sum_curtailment']] - - # Mapping English technology names to German (if desired) - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in technology_columns if f'tech_{tech.lower()}' in df.index - } - else: - tech_mapping_en_to_de = {tech: tech for tech in technology_columns} # Use the original names if not in German - - - # Plotting with Plotly - Creating stacked area chart - fig = go.Figure() - - # Add stacked area traces for each production technology with consistent colors and language-specific names - for tech in technology_columns: - tech_name = tech_mapping_en_to_de.get(tech, tech) # Get the translated name or fallback to the original - fig.add_trace(go.Scatter( - x=df_sorted.index, - y=df_sorted[tech], - mode='lines', - stackgroup='one', # For stacking traces - name=tech_name, - line=dict(width=0.5, color=color_dict.get(tech)) - )) - - # Add residual load trace as a red line - fig.add_trace(go.Scatter( - x=df_sorted.index, - y=df_sorted['Residual_Load'], - mode='lines', - name=df.loc['plot_label_residual_load', st.session_state.lang], # Residual load label in current language - line=dict(color='red', width=2) - )) - - - # Add curtailment trace as a shaded area with a dark yellow tone - fig.add_trace(go.Scatter( - x=df_sorted.index, - y=df_sorted['Sum_curtailment'], - mode='lines', # Line mode for the boundary of the area - name=df.loc['plot_label_sum_curtailment', st.session_state.lang], # Curtailment label in current language - line=dict(color='rgba(204, 153, 0, 1)', width=1.5), # Dark yellow line - fill='tozeroy', # Fill area down to the x-axis - fillcolor='rgba(204, 153, 0, 0.3)' # Semi-transparent dark yellow for the fill - )) - - # Layout settings for the plot - fig.update_layout( - title=df.loc['plot_label_residual_load_curve', st.session_state.lang], - xaxis_title=df.loc['label_hours', st.session_state.lang], - template="plotly_white", - ) - - # Display the plot in Streamlit - with col: - st.plotly_chart(fig) - - return df_combined - - - -def plot_contribution_margin(m, dt, color_dict, col, df): - """ - Plots the contribution margin for each technology. - Supports both German and English labels for titles and axes while ensuring color consistency. - """ - # Convert the dual constraints to a DataFrame - df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index() - - # Adjust the 'dual' values for the contribution margin calculation - df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1) - - # Store the English technology names in a separate column to maintain color consistency - df_contr_marg['i_en'] = df_contr_marg['i'] - - # Convert 't'-column in a datetime format - df_contr_marg['t'] = pd.to_datetime(df_contr_marg['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_contr_marg['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_contr_marg['i'] = df_contr_marg['i_en'].replace(tech_mapping_en_to_de) - - # Plot contribution margin for each technology - fig = px.line(df_contr_marg, y='dual', x='t', - title=df.loc['plot_label_contribution_margin', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - range_y=[0, 250], color_discrete_map=color_dict, - labels={'dual':'', 't':'', 'i_en':''} - ) - - # Update legend to display the correct language - fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - - # For German language, update the legend to show German technology names - if st.session_state.lang == 'DE': - fig.for_each_trace(lambda t: t.update(name=df_contr_marg.loc[df_contr_marg['i_en'] == t.name, 'i'].values[0])) - - # Display the plot - with col: - st.plotly_chart(fig) - - return df_contr_marg - - - - -def plot_curtailment(m, iRes, color_dict, col, df): - """ - Plots the curtailment of renewable energy. - Supports both German and English labels for titles and axes while ensuring color consistency. - """ - # Convert the curtailment solution to a DataFrame - df_curtailment = m.solution['y_curt'].sel(i=iRes).to_dataframe().reset_index() - - # Convert 't'-column in a datetime format - df_curtailment['t'] = pd.to_datetime(df_curtailment['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') - - # Store the English technology names in a separate column to maintain color consistency - df_curtailment['i_en'] = df_curtailment['i'] - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_curtailment['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_curtailment['i'] = df_curtailment['i_en'].replace(tech_mapping_en_to_de) - else: - df_curtailment['i'] = df_curtailment['i_en'] # Use English names if not German - - # Area plot for curtailment of renewable energy - fig = px.area(df_curtailment, y='y_curt', x='t', - title=df.loc['plot_label_curtailment', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - color_discrete_map=color_dict, - labels={'y_curt': '', 't': ''} # Delete double labeling - ) - - # Remove line traces and use fill colors for the area plot - fig.update_traces(line=dict(width=0)) - fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) - - # Update the legend title to reflect the correct language (German or English) - fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - - # For German language, update the legend to show German technology names - if st.session_state.lang == 'DE': - fig.for_each_trace(lambda t: t.update(name=df_curtailment.loc[df_curtailment['i_en'] == t.name, 'i'].values[0])) - - # Display the plot - with col: - st.plotly_chart(fig) - - return df_curtailment - - - -def plot_storage_charging(m, iSto, color_dict, col, df): - """ - Plots the charging of storage technologies. - Supports both German and English labels for titles and axes while ensuring color consistency. - """ - # Convert the storage charging solution to a DataFrame - df_charging = m.solution['y_ch'].sel(i=iSto).to_dataframe().reset_index() - - # Convert 't'-column in a datetime format - df_charging['t'] = pd.to_datetime(df_charging['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') - - # Store the English technology names in a separate column to maintain color consistency - df_charging['i_en'] = df_charging['i'] - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_charging['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_charging['i'] = df_charging['i_en'].replace(tech_mapping_en_to_de) - else: - df_charging['i'] = df_charging['i_en'] # Use English names if not German - - # Area plot for storage charging - fig = px.area(df_charging, y='y_ch', x='t', - title=df.loc['plot_label_storage_charging', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - color_discrete_map=color_dict, - labels={'y_ch': '', 't': ''} # Delete double labeling - ) - - # Remove line traces and use fill colors for the area plot - fig.update_traces(line=dict(width=0)) - fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) - - # Update the legend title to reflect the correct language (German or English) - fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - - # For German language, update the legend to show German technology names - if st.session_state.lang == 'DE': - fig.for_each_trace(lambda t: t.update(name=df_charging.loc[df_charging['i_en'] == t.name, 'i'].values[0])) - - # Display the plot - with col: - st.plotly_chart(fig) - - return df_charging - - - -def plot_hydrogen_production(m, iPtG, color_dict, col, df): - """ - Plots the hydrogen production. - Supports both German and English labels for titles and axes while ensuring color consistency. - """ - # Convert the hydrogen production data to a DataFrame - df_h2_prod = m.solution['y_h2'].sel(i=iPtG).to_dataframe().reset_index() - - # Convert 't'-column in a datetime format - df_h2_prod['t'] = pd.to_datetime(df_h2_prod['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') - - # Store the English technology names in a separate column to maintain color consistency - df_h2_prod['i_en'] = df_h2_prod['i'] - - # Check if the language is German and map English names to German for display - if st.session_state.lang == 'DE': - tech_mapping_en_to_de = { - df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] - for tech in df_h2_prod['i_en'] if f'tech_{tech.lower()}' in df.index - } - # Replace the English technology names with German ones for display - df_h2_prod['i'] = df_h2_prod['i_en'].replace(tech_mapping_en_to_de) - else: - df_h2_prod['i'] = df_h2_prod['i_en'] # Keep English names if not German - - # Area plot for hydrogen production - fig = px.area(df_h2_prod, y='y_h2', x='t', - title=df.loc['plot_label_hydrogen_production', st.session_state.lang], - color='i_en', # Use the English names for consistent coloring - color_discrete_map=color_dict, - labels={'y_h2': '', 't': ''} # Delete double labeling - ) - - # Remove line traces and use fill colors for the area plot - fig.update_traces(line=dict(width=0)) - fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) - - # Update the legend title to reflect the correct language (German or English) - fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) - - # For German language, update the legend to show German technology names - if st.session_state.lang == 'DE': - fig.for_each_trace(lambda t: t.update(name=df_h2_prod.loc[df_h2_prod['i_en'] == t.name, 'i'].values[0])) - - # Display the plot - with col: - st.plotly_chart(fig) - - return df_h2_prod - - - -def disaggregate_df(df, t, t_original, dt): - """ - Disaggregates the DataFrame based on the original time steps. - """ - if "t" not in list(df.columns): - return df - - # Change format of t back - df['t'] = "'" + pd.to_datetime(df['t'], utc=True).dt.tz_convert('Europe/Berlin').dt.strftime('%Y-%m-%d %H:%M %z') + "'" - - df_t_all = pd.DataFrame({"t_all": t_original.to_series(), 't': t.repeat(dt)}).reset_index(drop=True) - df_output = df.merge(df_t_all, on='t').drop('t', axis=1).rename({'t_all': 't'}, axis=1) - df_output = df_output[[df_output.columns[-1]] + list(df_output.columns[:-1])] - # Drop the helping column i_en - df_output = df_output.drop(columns=['i_en'], errors='ignore') - return df_output.sort_values('t') - - -if __name__ == "__main__": +# -*- coding: utf-8 -*- +""" +Energy system optimization model + +HEMF EWL: Christopher Jahns, Julian Radek, Hendrik Kramer, Cornelia Klüter, Yannik Pflugfelder +""" + +import numpy as np +import pandas as pd +import xarray as xr +import plotly.express as px +import plotly.graph_objects as go +import streamlit as st +from io import BytesIO +import xlsxwriter +from linopy import Model +import sourced as src +import time + + +# Main function to run the Streamlit app +def main(): + """ + Main function to set up and solve the energy system optimization model, and handle user inputs and outputs. + """ + setup_page() + + settings = load_settings() + + # fill session space with variables that are needed on all pages + if 'settings' not in st.session_state: + st.session_state.df = load_settings() + st.session_state.settings = settings + + if 'url_excel' not in st.session_state: + st.session_state.url_excel = None + + if 'ui_model' not in st.session_state: + st.session_state.url_excel = None + + if 'output' not in st.session_state: + st.session_state.output = BytesIO() + + + setup_sidebar(st.session_state.settings["df"]) + + + + # # Navigation + # pg = st.navigation([st.Page(page_model, title=st.session_state.settings["df"].loc['menu_modell',st.session_state.lang], icon="📊"), + # st.Page(page_documentation, title=st.session_state.settings["df"].loc['menu_doku',st.session_state.lang], icon="📓"), + # st.Page(page_about_us, title=st.session_state.settings["df"].loc['menu_impressum',st.session_state.lang], icon="💬")], + # expanded=True) + + # # # Run the app + # pg.run() + + # Create tabs for navigation + tabs = st.tabs([ + st.session_state.settings["df"].loc['menu_modell', st.session_state.lang], + st.session_state.settings["df"].loc['menu_doku', st.session_state.lang], + st.session_state.settings["df"].loc['menu_impressum', st.session_state.lang] + ]) + + # Load and display content based on the selected tab + with tabs[0]: # Model page + page_model() + with tabs[1]: # Documentation page + page_documentation() + with tabs[2]: # About Us page + page_about_us() + + + + +# Load settings and initial configurations +def load_settings(): + """ + Load settings for the app, including colors and language information. + """ + settings = { + 'write_pickle_from_standard_excel': True, + 'df': pd.read_csv("language.csv", encoding="iso-8859-1", index_col="Label", sep=";"), + 'color_dict': { + 'Biomass': 'lightgreen', + 'Lignite': 'saddlebrown', + 'Fossil Hard coal': 'chocolate', # Ein Braunton ähnlich Lignite + 'Fossil Oil': 'black', + 'CCGT': 'lightgray', # Hellgrau + 'OCGT': 'darkgray', # Dunkelgrau + 'RoR': 'aquamarine', + 'Hydro Water Reservoir': 'lightsteelblue', + 'Nuclear': 'gold', + 'PV': 'yellow', + 'WindOff': 'darkblue', + 'WindOn': 'green', + 'H2': 'tomato', + 'Pumped Hydro Storage': 'skyblue', + 'Battery storages': 'firebrick', + 'Electrolyzer': 'yellowgreen' + }, + 'colors': { + 'hemf_blau_dunkel': "#00386c", + 'hemf_blau_hell': "#00529f", + 'hemf_rot_dunkel': "#8b310d", + 'hemf_rot_hell': "#d04119", + 'hemf_grau': "#dadada" + } + } + return settings + +# Initialize Streamlit app +def setup_page(): + """ + Set up the Streamlit page with a specific layout, title, and favicon. + """ + st.set_page_config(layout="wide", page_title="Investment tool", page_icon="media/favicon.ico", initial_sidebar_state="expanded") + + +# Sidebar for language and links +def setup_sidebar(df): + """ + Set up the sidebar with language options and external links. + """ + st.session_state.lang = st.sidebar.selectbox("Language", ["🇬🇧 EN", "🇩🇪 DE"], key="foo", label_visibility="collapsed")[-2:] + + st.sidebar.markdown(""" + + """, unsafe_allow_html=True) + + with st.sidebar: + left_co, cent_co, last_co = st.columns([0.1, 0.8, 0.1]) + with cent_co: + st.text(" ") # add vertical empty space + ""+df.loc['menu_text', st.session_state.lang] + st.text(" ") # add vertical empty space + + if st.session_state.lang == "DE": + st.write("Schaue vorbei beim") + st.markdown(r'[Lehrstuhl für Energiewirtschaft](https://www.ewl.wiwi.uni-due.de)', unsafe_allow_html=True) + elif st.session_state.lang == "EN": + st.write("Get in touch with the") + st.markdown(r'[Chair of Management Science and Energy Economics](https://www.ewl.wiwi.uni-due.de/en)', unsafe_allow_html=True) + + st.text(" ") # add vertical empty space + st.image("media/Logo_HEMF.svg", width=200) + st.image("media/Logo_UDE.svg", width=200) + + +# Load model input data +def load_model_input(df, write_pickle_from_standard_excel): + """ + Load model input data from Excel or Pickle based on user input. + """ + if st.session_state.url_excel is None: + if write_pickle_from_standard_excel: + url_excel = r'Input_Jahr_2023.xlsx' + sets_dict, params_dict = src.load_data_from_excel(url_excel, write_to_pickle_flag=True) + sets_dict, params_dict = src.load_from_pickle() + #st.write(df.loc['model_title1.1', st.session_state.lang]) + # st.write('Running with standard data') + else: + url_excel = st.session_state.url_excel + sets_dict, params_dict = src.load_data_from_excel(url_excel, load_from_pickle_flag=False) + st.write(df.loc['model_title1.2', st.session_state.lang]) + + return sets_dict, params_dict + + + +def page_documentation(): + """ + Display documentation and mathematical model details. + """ + + df = st.session_state.settings["df"] + + st.header(df.loc['constr_header1', st.session_state.lang]) + st.write(df.loc['constr_header2', st.session_state.lang]) + + col1, col2 = st.columns([6, 4]) + + with col1: + st.header(df.loc['constr_header3', st.session_state.lang]) + + with st.container(): + + # Objective function + st.subheader(df.loc['constr_subheader_obj_func', st.session_state.lang]) + st.write(df.loc['constr_subheader_obj_func_descr', st.session_state.lang]) + st.latex(r''' \text{min } C^{tot} = C^{op} + C^{inv}''') + + # Operational costs minus revenue for produced hydrogen + st.write(df.loc['constr_c_op', st.session_state.lang]) + st.latex(r''' C^{op} = \sum_{i} y_{t,i} \cdot \left( \frac{c^{fuel}_{i}}{\eta_i} + c_{i}^{other} \right) \cdot \Delta t - \sum_{i \in \mathcal{I}^{PtG}} y^{h2}_{t,i} \cdot p^{h2} \cdot \Delta t''') + + # Investment costs + st.write(df.loc['constr_c_inv', st.session_state.lang]) + st.latex(r''' C^{inv} = \sum_{i} a_{i} \cdot K_{i} \cdot c^{inv}_{i}''') + + # Constraints + st.subheader(df.loc['subheader_constr', st.session_state.lang]) + + # Load-serving constraint + st.write(df.loc['constr_load_serve', st.session_state.lang]) + st.latex(r''' \left( \sum_{i} y_{t,i} - \sum_{i} y_{t,i}^{ch} \right) \cdot \Delta t = D_t \cdot \Delta t, \quad \forall t \in \mathcal{T}''') + + # Maximum capacity limit + st.write(df.loc['constr_max_cap', st.session_state.lang]) + st.latex(r''' y_{t,i} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}''') + + # Capacity limits for investment + st.write(df.loc['constr_inv_cap', st.session_state.lang]) + st.latex(r''' K_{i} \leq 0, \quad \forall i \in \mathcal{I}^{no\_invest}''') + + # Prevent power production by PtG + st.write(df.loc['constr_prevent_ptg', st.session_state.lang]) + st.latex(r''' y_{t,i} = 0, \quad \forall i \in \mathcal{I}^{PtG}''') + + # Prevent charging for non-storage technologies + st.write(df.loc['constr_prevent_chg', st.session_state.lang]) + st.latex(r''' y_{t,i}^{ch} = 0, \quad \forall i \in \mathcal{I} \setminus \{ \mathcal{I}^{PtG} \cup \mathcal{I}^{Sto} \}''') + + # Maximum storage charging and discharging + st.write(df.loc['constr_max_chg', st.session_state.lang]) + st.latex(r''' y_{t,i} + y_{t,i}^{ch} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}^{Sto}''') + + # Maximum electrolyzer capacity + st.write(df.loc['constr_max_cap_electrolyzer', st.session_state.lang]) + st.latex(r''' y_{t,i}^{ch} - K_{i} \leq K_{0,i}, \quad \forall i \in \mathcal{I}^{PtG}''') + + # PtG H2 production + st.write(df.loc['constr_prod_ptg', st.session_state.lang]) + st.latex(r''' y_{t,i}^{ch} \cdot \eta_i = y_{t,i}^{h2}, \quad \forall i \in \mathcal{I}^{PtG}''') + + # Infeed of renewables + st.write(df.loc['constr_inf_res', st.session_state.lang]) + st.latex(r''' y_{t,i} + y_{t,i}^{curt} = s_{t,r,i} \cdot (K_{0,i} + K_i), \quad \forall i \in \mathcal{I}^{Res}''') + + # Maximum filling level restriction for storage power plants + st.write(df.loc['constr_max_fil_sto', st.session_state.lang]) + # st.latex(r''' l_{t,i} \leq K_{0,i} \cdot e2p_i, \quad \forall i \in \mathcal{I}^{Sto}''') + st.latex(r''' l_{t,i} \leq (K_{0,i} + K_{i}) \cdot \gamma_i^{Sto}, \quad \forall i \in \mathcal{I}^{Sto}''') + + # Filling level restriction for hydro reservoir + st.write(df.loc['constr_fil_hyres', st.session_state.lang]) + st.latex(r''' l_{t+1,i} = l_{t,i} + ( h_{t,i} - y_{t,i}) \cdot \Delta t, \quad \forall i \in \mathcal{I}^{HyRes}''') + + # Filling level restriction for other storages + st.write(df.loc['constr_fil_sto', st.session_state.lang]) + st.latex(r''' l_{t+1,i} = l_{t,i} - \left(\frac{y_{t,i}}{\eta_i} - y_{t,i}^{ch} \cdot \eta_i \right) \cdot \Delta t, \quad \forall i \in \mathcal{I}^{Sto}''') + + # CO2 emission constraint + st.write(df.loc['constr_co2_lim', st.session_state.lang]) + st.latex(r''' \sum_{t} \sum_{i} \frac{y_{t,i}}{\eta_i} \cdot \chi^{CO2}_i \cdot \Delta t \leq L^{CO2}''') + + + with col2: + + symbols_container = st.container() + with symbols_container: + st.header(df.loc['symb_header1', st.session_state.lang]) + st.write(df.loc['symb_header2', st.session_state.lang]) + + st.subheader(df.loc['symb_header_sets', st.session_state.lang]) + st.write(f"$\mathcal{{T}}$: {df.loc['symb_time_steps', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}$: {df.loc['symb_tech', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{Sto}}}}$: {df.loc['symb_sto_tech', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{Conv}}}}$: {df.loc['symb_conv_tech', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{PtG}}}}$: {df.loc['symb_ptg', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{Res}}}}$: {df.loc['symb_res', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{HyRes}}}}$: {df.loc['symb_hyres', st.session_state.lang]}") + st.write(f"$\mathcal{{I}}^{{\\text{{no\_invest}}}}$: {df.loc['symb_no_inv', st.session_state.lang]}") + + + + # Variables section + st.subheader(df.loc['symb_header_variables', st.session_state.lang]) + st.write(f"$C^{{tot}}$: {df.loc['symb_tot_costs', st.session_state.lang]}") + st.write(f"$C^{{op}}$: {df.loc['symb_c_op', st.session_state.lang]}") + st.write(f"$C^{{inv}}$: {df.loc['symb_c_inv', st.session_state.lang]}") + st.write(f"$K_i$: {df.loc['symb_inst_cap', st.session_state.lang]}") + st.write(f"$y_{{t,i}}$: {df.loc['symb_el_prod', st.session_state.lang]}") + st.write(f"$y_{{t, i}}^{{ch}}$: {df.loc['symb_el_ch', st.session_state.lang]}") + st.write(f"$l_{{t,i}}$: {df.loc['symb_sto_fil', st.session_state.lang]}") + st.write(f"$y_{{t, i}}^{{curt}}$: {df.loc['symb_curt', st.session_state.lang]}") + st.write(f"$y_{{t, i}}^{{h2}}$: {df.loc['symb_h2_ptg', st.session_state.lang]}") + + + # Parameters section + st.subheader(df.loc['symb_header_parameters', st.session_state.lang]) + st.write(f"$D_t$: {df.loc['symb_energy_demand', st.session_state.lang]}") + st.write(f"$p^{{h2}}$: {df.loc['symb_price_h2', st.session_state.lang]}") + st.write(f"$c^{{fuel}}_{{i}}$: {df.loc['symb_fuel_costs', st.session_state.lang]}") + st.write(f"$c_{{i}}^{{other}}$: {df.loc['symb_c_op_other', st.session_state.lang]}") + st.write(f"$c^{{inv}}_{{i}}$: {df.loc['symb_c_inv_tech', st.session_state.lang]}") + st.write(f"$a_{{i}}$: {df.loc['symb_annuity', st.session_state.lang]}") + st.write(f"$\eta_i$: {df.loc['symb_eff_fac', st.session_state.lang]}") + st.write(f"$K_{{0,i}}$: {df.loc['symb_max_cap_tech', st.session_state.lang]}") + st.write(f"$\chi^{{CO2}}_i$: {df.loc['symb_co2_fac', st.session_state.lang]}") + st.write(f"$L^{{CO2}}$: {df.loc['symb_co2_limit', st.session_state.lang]}") + # st.write(f"$e2p_{{\\text{{Sto}}, i}}$: {df.loc['symb_etp', st.session_state.lang]}") + st.write(f"$\gamma^{{\\text{{Sto}}}}_{{i}}$: {df.loc['symb_etp', st.session_state.lang]}") + st.write(f"$s_{{t, r, i}}$: {df.loc['symb_res_supply', st.session_state.lang]}") + st.write(f"$h_{{t, i}}$: {df.loc['symb_hyRes_inflow', st.session_state.lang]}") + + # css = float_css_helper(top="50") + # symbols_container.float(css) + + +def page_about_us(): + """ + Display information about the team and the project. + """ + st.write("About Us/Impressum") + + +def page_model(): #, write_pickle_from_standard_excel, color_dict): + """ + Display the main model page for energy system optimization. + + This function sets up the user interface for the model input parameters, loads data, and configures the + optimization model before solving it and presenting the results. + """ + + df = st.session_state.settings["df"] + color_dict = st.session_state.settings["color_dict"] + write_pickle_from_standard_excel = st.session_state.settings["write_pickle_from_standard_excel"] + + + + + # Load data from Excel or Pickle + sets_dict, params_dict = load_model_input(df, write_pickle_from_standard_excel) + + # Unpack sets_dict into the workspace + t = sets_dict['t'] + t_original = 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'] + life_i = params_dict['life_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'] + K_0_i = params_dict['K_0_i'] + e2p_iSto = params_dict['e2p_iSto'] + + # Adjust efficiency for storage technologies + eff_i.loc[iSto] = np.sqrt(eff_i.loc[iSto]) # Apply square root to cycle efficiency for storage technologies + + # Create columns for UI layout + col1, col2 = st.columns([0.30, 0.70], gap="large") + + # Load input data + with col1: + + st.title(df.loc['model_title1', st.session_state.lang]) + + with open('Input_Jahr_2023.xlsx', 'rb') as f: + st.download_button(df.loc['model_title1.3',st.session_state.lang], f, file_name='Input_Jahr_2023.xlsx') # Download button for Excel template + + with st.form("input_file"): + + + st.session_state.url_excel = st.file_uploader(label=df.loc['model_title1.4',st.session_state.lang]) # File uploader for user Excel file + + #st.title(df.loc['model_title4', st.session_state.lang]) + + run_model_excel = st.form_submit_button(df.loc['model_run_info_excel', st.session_state.lang]) #, key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) + #else: + # run_model = st.button(df.loc['model_run_info_gui', st.session_state.lang], key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) + + + + + + # Set up user interface for parameters + with col2: + + st.title(df.loc['model_title3', st.session_state.lang]) + + + + with st.form("input_custom"): + + col1form, col2form, col3form = st.columns([0.25, 0.25, 0.50]) + + # colum 1 form + l_co2 = col1form.slider(value=int(params_dict['l_co2']), min_value=0, max_value=750, label=df.loc['model_label_co2',st.session_state.lang], step=50) + price_h2 = col1form.slider(value=100, min_value=0, max_value=300, label=df.loc['model_label_h2',st.session_state.lang], step=10) + for i_idx in params_dict['c_fuel_i'].get_index('i'): + if i_idx in ['Lignite']: + params_dict['c_fuel_i'].loc[i_idx] = col1form.slider(value=int(params_dict['c_fuel_i'].loc[i_idx]), + min_value=0, max_value=300, label=df.loc[f'model_label_{i_idx}',st.session_state.lang], step=10) + + # colum 1 form + for i_idx in params_dict['c_fuel_i'].get_index('i'): + if i_idx in ['Fossil Hard coal', 'Fossil Oil', 'CCGT']: + params_dict['c_fuel_i'].loc[i_idx] = col2form.slider(value=int(params_dict['c_fuel_i'].loc[i_idx]), + min_value=0, max_value=300, label=df.loc[f'model_label_{i_idx}',st.session_state.lang], step=10) + params_dict['c_fuel_i'].loc['OCGT'] = params_dict['c_fuel_i'].loc['CCGT'] + # Create a dictionary to map German names to English names + tech_mapping_de_to_en = { + df.loc[f'tech_{tech.lower()}', 'DE']: df.loc[f'tech_{tech.lower()}', 'EN'] + for tech in sets_dict['i'] if f'tech_{tech.lower()}' in df.index + } + + # Set options and default values based on the selected language + if st.session_state.lang == 'DE': + # German options for the user interface + options = [ + df.loc[f'tech_{tech.lower()}', 'DE'] for tech in sets_dict['i'] if f'tech_{tech.lower()}' in df.index + ] + default = [ + df.loc[f'tech_{tech.lower()}', 'DE'] for tech in ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer'] + if f'tech_{tech.lower()}' in df.index + ] + else: + # English options for the user interface + options = sets_dict['i'] + default = ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer'] + + # Multiselect for technology options in the user interface + selected_technologies = col3form.multiselect( + label=df.loc['model_label_tech', st.session_state.lang], + options=options, + default=[tech for tech in default if tech in options] + ) + + # If language is German, map selected German names back to their English equivalents + if st.session_state.lang == 'DE': + technologies_invest = [tech_mapping_de_to_en[tech] for tech in selected_technologies] + else: + technologies_invest = selected_technologies + + # Technologies that will not be invested in (based on English names) + technologies_no_invest = [tech for tech in sets_dict['i'] if tech not in technologies_invest] + + col4form, col5form = st.columns([0.25, 0.75]) + dt = col4form.number_input(label=df.loc['model_label_t',st.session_state.lang], min_value=1, max_value=len(t), value=6, + help=df.loc['model_label_t_info',st.session_state.lang]) + + run_model_manual = col5form.form_submit_button(df.loc['model_run_info_gui', st.session_state.lang]) + + #run_model = st.button(df.loc['model_run_info_gui', st.session_state.lang], key="run_model_button", help=df.loc['run_model_button_info',st.session_state.lang]) + + st.markdown("-------") + + # run_model_manual = True + + if run_model_excel or run_model_manual: + # Model setup + + info_yellow_build = st.info(df.loc['label_build_model', st.session_state.lang]) + + + if run_model_excel: # overwrite with excel values + #sets_dict, params_dict = load_model_input(df, write_pickle_from_standard_excel) + sets_dict, params_dict = src.load_data_from_excel(st.session_state.url_excel, write_to_pickle_flag=True) + + # Unpack sets_dict into the workspace + t = sets_dict['t'] + t_original = 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'] + # life_i = params_dict['life_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'] + K_0_i = params_dict['K_0_i'] + e2p_iSto = params_dict['e2p_iSto'] + + # Adjust efficiency for storage technologies + eff_i.loc[iSto] = np.sqrt(eff_i.loc[iSto]) # Apply square root to cycle efficiency for storage technologies + + + # Time series aggregation for various parameters + D_t = timstep_aggregate(dt, params_dict['D_t'], t) + s_t_r_iRes = timstep_aggregate(dt, params_dict['s_t_r_iRes'], t) + h_t = timstep_aggregate(dt, params_dict['h_t'], t) + t = D_t.get_index('t') + partial_year_factor = (8760 / len(t)) / dt + + m = Model() + + # Define Variables + 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 + y_ch = m.add_variables(coords=[t, i], name='y_ch', lower=0) # Electricity consumption + l = m.add_variables(coords=[t, i], name='l', lower=0) # Storage filling level + y_curt = m.add_variables(coords=[t, i], name='y_curt', lower=0) # RES curtailment + y_h2 = m.add_variables(coords=[t, i], name='y_h2', lower=0) # H2 production + + # Define Objective function + C_tot = C_op + C_inv + m.add_objective(C_tot) + + # Define Constraints + # Operational costs minus revenue for produced hydrogen + 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 + m.add_constraints((K * c_inv_i).sum() == C_inv, name='C_inv_sum') + + # Load serving + m.add_constraints((((y).sum(dims='i') - y_ch.sum(dims='i')) * dt == D_t.sel(t=t) * dt), name='load') + + # Maximum capacity limit + m.add_constraints((y - K <= K_0_i), name='max_cap') + + # Capacity limits for investment + m.add_constraints((K.sel(i=technologies_no_invest) <= 0), name='max_cap_invest') + + # Prevent power production by PtG + m.add_constraints((y.sel(i=iPtG) <= 0), name='prevent_ptg_prod') + + # Prevent charging for non-storage technologies + m.add_constraints((y_ch.sel(i=[x for x in i if x not in iPtG and x not in iSto]) <= 0), name='no_charging') + + # Maximum storage charging and discharging + 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 + 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 + 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 + 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 for storage power plants + 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 for hydro reservoir + 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 for other storages + 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 + m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2 * 1_000_000, name='CO2_limit') + + # Solve the model + info_yellow_build.empty() + info_green_build = st.success(df.loc['label_build_model', st.session_state.lang]) + info_yellow_solve = st.info(df.loc['label_solve_model', st.session_state.lang]) + + + m.solve(solver_name='highs') + + info_yellow_solve.empty() + info_green_solve = st.success(df.loc['label_solve_model', st.session_state.lang]) + info_yellow_plot = st.info(df.loc['label_generate_plots', st.session_state.lang]) + + + + # Prepare columns for figures + colb1, colb2 = st.columns(2) + + # Generate and display figures + st.markdown("---") + + df_total_costs = plot_total_costs(m, colb1, df) + df_CO2_price = plot_co2_price(m, colb2, df) + df_new_capacities = plot_new_capacities(m, color_dict, colb1, df) + + # Only plot production for technologies with capacity + i_with_capacity = m.solution['K'].where((m.solution['K'] > 0) & (m.solution['i'] != 'Electrolyzer')).dropna(dim='i').get_index('i') + df_production = plot_production(m, i_with_capacity, dt, color_dict, colb2, df) + # df_price = plot_electricity_prices(m, dt, colb2, df) + df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df) + df_residual_load_duration = plot_residual_load_duration(m, dt, colb1, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment) + df_price = plot_electricity_prices(m, dt, colb2, df, df_residual_load_duration) + + df_contr_marg = plot_contribution_margin(m, dt, i_with_capacity, color_dict, colb1, df) + # df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df) + df_charging = plot_storage_charging(m, iSto, color_dict, colb2, df) + df_h2_prod = plot_hydrogen_production(m, iPtG, color_dict, colb1, df) + + # df_stackplot = plot_stackplot(m) + + # Export results + + st.session_state.output = BytesIO() + + + with pd.ExcelWriter(st.session_state.output, engine='xlsxwriter') as writer: + disaggregate_df(df_total_costs, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_total_costs', st.session_state.lang], index=False) + disaggregate_df(df_CO2_price, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_co2_price', st.session_state.lang], index=False) + disaggregate_df(df_price, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_prices', st.session_state.lang], index=False) + disaggregate_df(df_contr_marg, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_contribution_margin', st.session_state.lang], index=False) + disaggregate_df(df_new_capacities, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_capacities', st.session_state.lang], index=False) + disaggregate_df(df_production, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_production', st.session_state.lang], index=False) + disaggregate_df(df_charging, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_charging', st.session_state.lang], index=False) + disaggregate_df(D_t.to_dataframe().reset_index(), t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_demand', st.session_state.lang], index=False) + disaggregate_df(df_curtailment, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_curtailment', st.session_state.lang], index=False) + disaggregate_df(df_h2_prod, t, t_original, dt).to_excel(writer, sheet_name=df.loc['sheet_name_h2_production', st.session_state.lang], index=False) + + with col1: + st.title(df.loc['model_title2', st.session_state.lang]) + + st.download_button(label=df.loc['model_title2.1',st.session_state.lang], disabled=(st.session_state.output.getbuffer().nbytes==0), data=st.session_state.output.getvalue(), file_name="workbook.xlsx", mime="application/vnd.ms-excel") + + info_yellow_plot.empty() + info_green_plot = st.success(df.loc['label_generate_plots', st.session_state.lang]) + + time.sleep(1) + + info_green_build.empty() + info_green_solve.empty() + info_green_plot.empty() + + st.stop() + + + + # st.rerun() + + +def timstep_aggregate(time_steps_aggregate, xr_data, t): + """ + Aggregates time steps in the data using rolling mean and selects based on step size. + """ + return xr_data.rolling(t=time_steps_aggregate).mean().sel(t=t[0::time_steps_aggregate]) + +# Visualization functions + +def plot_total_costs(m, col, df): + """ + Displays the total costs. + """ + total_costs = float(m.solution['C_inv'].values) + float(m.solution['C_op'].values) + total_costs_rounded = round(total_costs / 1e9, 2) + with col: + st.markdown( + f"

{df.loc['plot_label_total_costs', st.session_state.lang]} {total_costs_rounded}

", + unsafe_allow_html=True + ) + + df_total_costs = pd.DataFrame({'Total costs':[total_costs]}) + return df_total_costs + +def plot_co2_price(m, col, df): + """ + Displays the CO2 price based on the CO2 constraint dual values. + """ + 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 col: + st.markdown( + f"

{df.loc['plot_label_co2_price', st.session_state.lang]} {CO2_price_rounded}

", + unsafe_allow_html=True + ) + + return df_CO2_price + + +def plot_new_capacities(m, color_dict, col, df): + """ + Plots the new capacities installed in MW as a bar chart and pie chart. + Includes technologies with 0 MW capacity in the bar chart. + Supports both German and English labels for technologies while ensuring color consistency. + """ + # Convert the solution for new capacities to a DataFrame + df_new_capacities = m.solution['K'].round(0).to_dataframe().reset_index() + + # Store the English technology names in a separate column to maintain color consistency + df_new_capacities['i_en'] = df_new_capacities['i'] + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_new_capacities['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_new_capacities['i'] = df_new_capacities['i_en'].replace(tech_mapping_en_to_de) + + # Bar plot for new capacities (including technologies with 0 MW) + fig_bar = px.bar(df_new_capacities, y='i', x='K', orientation='h', + title=df.loc['plot_label_new_capacities', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + color_discrete_map=color_dict, + labels={'K': '', 'i': ''} # Delete double labeling + ) + + # Hide the legend completely since the labels are already next to the bars + fig_bar.update_layout(showlegend=False) + + with col: + st.plotly_chart(fig_bar) + + # Pie chart for new capacities (only show technologies with K > 0 in pie chart) + df_new_capacities_filtered = df_new_capacities[df_new_capacities["K"] > 0] + fig_pie = px.pie(df_new_capacities_filtered, names='i', values='K', + title=df.loc['plot_label_new_capacities_pie', st.session_state.lang], + color='i_en', color_discrete_map=color_dict) + + # Remove English labels (i_en) from the pie chart legend + fig_pie.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + fig_pie.for_each_trace(lambda t: t.update(name=df_new_capacities_filtered['i'].iloc[0] if st.session_state.lang == 'DE' else t.name)) + + with col: + st.plotly_chart(fig_pie) + + return df_new_capacities + + +def plot_production(m, i_with_capacity, dt, color_dict, col, df): + """ + Plots the energy production for technologies with capacity as an area chart. + Supports both German and English labels for technologies while ensuring color consistency. + """ + # Convert the production data to a DataFrame + df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index() + + # Store the English technology names in a separate column to maintain color consistency + df_production['i_en'] = df_production['i'] + + # Convert 't'-column in a datetime format + df_production['t'] = df_production['t'].str.strip("'") + df_production['t'] = pd.to_datetime(df_production['t'], format='%Y-%m-%d %H:%M %z') + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_production['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_production['i'] = df_production['i_en'].replace(tech_mapping_en_to_de) + + # Area plot for energy production + fig = px.area(df_production, y='y', x='t', + title=df.loc['plot_label_production', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + color_discrete_map=color_dict, + labels={'y': '', 't': '', 'i_en': df.loc['label_technology', st.session_state.lang]} # Delete double labeling + ) + + # Update legend labels to display German names instead of English + if st.session_state.lang == 'DE': + fig.for_each_trace(lambda trace: trace.update(name=tech_mapping_en_to_de[trace.name])) + + fig.update_traces(line=dict(width=0)) + fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) + + # # Customize x-axis for better date formatting + # fig.update_layout( + # xaxis=dict( + # tickformat="%d/%m/%Y", # Display months and years in MM/YYYY format + # title='', # No title for the x-axis + # type="date" # Ensure x-axis is treated as a date axis + # ), + # xaxis_tickangle=-45 # Tilt the ticks for better readability + # ) + + with col: + st.plotly_chart(fig) + + # Pie chart for total production + df_production_sum = (df_production.groupby(['i', 'i_en'])['y'].sum() * dt / 1000).round(0).reset_index() + + # If the language is set to German, display German labels, otherwise use English + pie_column = 'i' if st.session_state.lang == 'DE' else 'i_en' + + # Pie chart for total production + fig_pie = px.pie(df_production_sum, names=pie_column, values='y', + title=df.loc['plot_label_total_production_pie', st.session_state.lang], + color='i_en', # Ensure the coloring stays consistent using the 'i_en' column + color_discrete_map=color_dict) + + # Update legend title to reflect the correct language + fig_pie.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + + with col: + st.plotly_chart(fig_pie) + + return df_production + + +def plot_electricity_prices(m, dt, col, df, df_residual_load_duration): + """ + Plots the electricity price and the price duration curve. + Supports both German and English labels for the plot titles and axis labels. + """ + # Convert the dual constraints to a DataFrame + df_price = m.constraints['load'].dual.to_dataframe().reset_index() + + # Convert 't'-column in a datetime format + df_price['t'] = df_price['t'].str.strip("'") + df_price['t'] = pd.to_datetime(df_price['t'], format='%Y-%m-%d %H:%M %z') + + # Line plot for electricity prices + fig_price = px.line(df_price, y='dual', x='t', + title=df.loc['plot_label_electricity_prices', st.session_state.lang], + # range_y=[0, 250], + labels={'dual': '', 't': ''} + ) + with col: + st.plotly_chart(fig_price) + + # Create the price duration curve + df_sorted_price = df_price["dual"].repeat(dt).sort_values(ascending=False).reset_index(drop=True) / int(dt) + df_residual_load_sorted = df_residual_load_duration.sort_values(by='Residual_Load', ascending=False).reset_index(drop=True) + df_axis2 = df_residual_load_sorted['Residual_Load'] + + ax2_max = np.max(df_axis2) + ax2_min = np.min(df_axis2) + + fig_duration = go.Figure() + + # Add primary y-axis trace (Price duration curve) + fig_duration.add_trace(go.Scatter( + x=df_sorted_price.index, + y=df_sorted_price, + mode='lines', + name=df.loc['plot_label_price_duration_curve', st.session_state.lang], # Price duration label + line=dict(color='blue', width=2) # Blue line for primary y-axis + )) + + # Add secondary y-axis trace (Residual load) + fig_duration.add_trace(go.Scatter( + x=df_axis2.index, + y=df_axis2, + mode='lines', + name=df.loc['plot_label_residual_load', st.session_state.lang], # Residual load label + line=dict(color='red', width=2), # Red line for secondary y-axis + yaxis='y2' # Link this trace to the secondary y-axis + )) + + # Layout mit separaten Achsen + fig_duration.update_layout( + title=df.loc['plot_label_price_duration_curve', st.session_state.lang], + xaxis=dict( + title=df.loc['label_hours', st.session_state.lang] # Common x-axis + ), + yaxis=dict( + title=df.loc['plot_label_price_duration_curve', st.session_state.lang], # Title for primary y-axis + range=[-(100/(ax2_max/(ax2_max-ax2_min))-100), 100], # Primary y-axis range + titlefont=dict(color='blue'), # Blue color for primary axis title + tickfont=dict(color='blue') # Blue ticks for primary axis + ), + yaxis2=dict( + title=df.loc['plot_label_residual_load', st.session_state.lang], # Title for secondary y-axis + range=[ax2_min, ax2_max], # Secondary y-axis range + titlefont=dict(color='red'), # Red color for secondary axis title + tickfont=dict(color='red'), # Red ticks for secondary axis + overlaying='y', # Overlay secondary axis on primary + side='right' # Place secondary y-axis on the right side + ), + legend=dict( + x=1, # Positioniert die Legende am rechten Rand + y=1, # Positioniert die Legende am oberen Rand + xanchor='right', # Verankert die Legende am rechten Rand + yanchor='top', # Verankert die Legende am oberen Rand + bgcolor='rgba(255, 255, 255, 0.5)', # Weißer Hintergrund mit Transparenz + bordercolor='black', + borderwidth=1 + ) + ) + + + with col: + st.plotly_chart(fig_duration) + + return df_price + +def plot_residual_load_duration(m, dt, col, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment): + """ + Plots the residual load and corresponding production as a stacked area chart. + Supports both German and English labels for the plot titles and axis labels. + Consistent color coding for technologies using a predefined color dictionary. + """ + + # Extract load data and repeat each value to match the total number of hours in the year + df_load = D_t.values.flatten() + total_hours = len(df_load) * dt # Calculate the total number of hours dynamically + repeated_load = np.repeat(df_load, dt)[:total_hours] # Repeat values to represent each hour + + # Convert production data to DataFrame + df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index() + + # Pivot production data to get technologies as columns and time 't' as index + df_production_pivot = df_production.pivot(index='t', columns='i', values='y') + + # Repeat the pivoted production data to match the number of hours + repeated_index = np.repeat(df_production_pivot.index, dt)[:total_hours] # Create repeated index + df_production_repeated = df_production_pivot.loc[repeated_index].reset_index(drop=True) + + # Create load series with the same index as the repeated production data + df_load_series = pd.Series(repeated_load, index=df_production_repeated.index, name='Load') + + # Combine load with repeated production data + df_combined = df_production_repeated.copy() + df_combined['Load'] = df_load_series + + # Identify renewable technologies from iRes + iRes_list = iRes.tolist() # Convert the Index to a list + + # Calculate renewable generation (only include available technologies in df_combined) + renewable_columns = [col for col in iRes_list if col in df_combined.columns] + df_combined['Renewable_Generation'] = df_combined[renewable_columns].sum(axis=1) if renewable_columns else 0 + + # Create pivot table of curtailment + df_curtailment_pivot = df_curtailment.pivot(index='t', columns='i', values='y_curt') + repeated_index = np.repeat(df_curtailment_pivot.index, dt)[:total_hours] # Create repeated index + df_curtailment_repeated = df_curtailment_pivot.loc[repeated_index].reset_index(drop=True) + df_curtailment_repeated['Sum'] = df_curtailment_repeated.sum(axis=1) + df_combined['Sum_curtailment'] = -df_curtailment_repeated['Sum'] + + # Calculate residual load as the difference between total load and renewable generation + df_combined['Residual_Load'] = df_combined['Load'] - df_combined['Renewable_Generation'] + df_combined['Sum_curtailment'] + + # Sort DataFrame by residual load (descending order) to create the duration curve + df_sorted = df_combined.sort_values(by='Residual_Load', ascending=False).reset_index(drop=True) + + # Identify all technology columns except 'Load', 'Residual_Load', 'Renewable_Generation' + technology_columns = [col for col in df_combined.columns if col not in ['Load', 'Residual_Load', 'Renewable_Generation', 'Sum_curtailment']] + + # Mapping English technology names to German (if desired) + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in technology_columns if f'tech_{tech.lower()}' in df.index + } + else: + tech_mapping_en_to_de = {tech: tech for tech in technology_columns} # Use the original names if not in German + + + # Plotting with Plotly - Creating stacked area chart + fig = go.Figure() + + # Add stacked area traces for each production technology with consistent colors and language-specific names + for tech in technology_columns: + tech_name = tech_mapping_en_to_de.get(tech, tech) # Get the translated name or fallback to the original + fig.add_trace(go.Scatter( + x=df_sorted.index, + y=df_sorted[tech], + mode='lines', + stackgroup='one', # For stacking traces + name=tech_name, + line=dict(width=0.5, color=color_dict.get(tech)) + )) + + # Add residual load trace as a red line + fig.add_trace(go.Scatter( + x=df_sorted.index, + y=df_sorted['Residual_Load'], + mode='lines', + name=df.loc['plot_label_residual_load', st.session_state.lang], # Residual load label in current language + line=dict(color='red', width=2) + )) + + + # Add curtailment trace as a shaded area with a dark yellow tone + fig.add_trace(go.Scatter( + x=df_sorted.index, + y=df_sorted['Sum_curtailment'], + mode='lines', # Line mode for the boundary of the area + name=df.loc['plot_label_sum_curtailment', st.session_state.lang], # Curtailment label in current language + line=dict(color='rgba(204, 153, 0, 1)', width=1.5), # Dark yellow line + fill='tozeroy', # Fill area down to the x-axis + fillcolor='rgba(204, 153, 0, 0.3)' # Semi-transparent dark yellow for the fill + )) + + # Layout settings for the plot + fig.update_layout( + title=df.loc['plot_label_residual_load_curve', st.session_state.lang], + xaxis_title=df.loc['label_hours', st.session_state.lang], + template="plotly_white", + ) + + # Display the plot in Streamlit + with col: + st.plotly_chart(fig) + + return df_combined + + + +def plot_contribution_margin(m, dt, i_with_capacity, color_dict, col, df): + """ + Plots the contribution margin for each technology. + Supports both German and English labels for titles and axes while ensuring color consistency. + """ + # Convert the dual constraints to a DataFrame + df_contr_marg = m.constraints['max_cap'].dual.sel(i=i_with_capacity).to_dataframe().reset_index() + + # Adjust the 'dual' values for the contribution margin calculation + df_contr_marg['dual'] = df_contr_marg['dual'] / dt * (-1) + + # Store the English technology names in a separate column to maintain color consistency + df_contr_marg['i_en'] = df_contr_marg['i'] + + # Convert 't'-column in a datetime format + df_contr_marg['t'] = pd.to_datetime(df_contr_marg['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_contr_marg['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_contr_marg['i'] = df_contr_marg['i_en'].replace(tech_mapping_en_to_de) + + # Plot contribution margin for each technology + fig = px.line(df_contr_marg, y='dual', x='t', + title=df.loc['plot_label_contribution_margin', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + range_y=[0, 250], color_discrete_map=color_dict, + labels={'dual':'', 't':'', 'i_en':''} + ) + + # Update legend to display the correct language + fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + + # For German language, update the legend to show German technology names + if st.session_state.lang == 'DE': + fig.for_each_trace(lambda t: t.update(name=df_contr_marg.loc[df_contr_marg['i_en'] == t.name, 'i'].values[0])) + + # Display the plot + with col: + st.plotly_chart(fig) + + return df_contr_marg + + + + +def plot_curtailment(m, iRes, color_dict, col, df): + """ + Plots the curtailment of renewable energy. + Supports both German and English labels for titles and axes while ensuring color consistency. + """ + # Convert the curtailment solution to a DataFrame + df_curtailment = m.solution['y_curt'].sel(i=iRes).to_dataframe().reset_index() + + # Convert 't'-column in a datetime format + df_curtailment['t'] = pd.to_datetime(df_curtailment['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') + + # Store the English technology names in a separate column to maintain color consistency + df_curtailment['i_en'] = df_curtailment['i'] + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_curtailment['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_curtailment['i'] = df_curtailment['i_en'].replace(tech_mapping_en_to_de) + else: + df_curtailment['i'] = df_curtailment['i_en'] # Use English names if not German + + # Area plot for curtailment of renewable energy + fig = px.area(df_curtailment, y='y_curt', x='t', + title=df.loc['plot_label_curtailment', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + color_discrete_map=color_dict, + labels={'y_curt': '', 't': ''} # Delete double labeling + ) + + # Remove line traces and use fill colors for the area plot + fig.update_traces(line=dict(width=0)) + fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) + + # Update the legend title to reflect the correct language (German or English) + fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + + # For German language, update the legend to show German technology names + if st.session_state.lang == 'DE': + fig.for_each_trace(lambda t: t.update(name=df_curtailment.loc[df_curtailment['i_en'] == t.name, 'i'].values[0])) + + # Display the plot + with col: + st.plotly_chart(fig) + + return df_curtailment + + + +def plot_storage_charging(m, iSto, color_dict, col, df): + """ + Plots the charging of storage technologies. + Supports both German and English labels for titles and axes while ensuring color consistency. + """ + # Convert the storage charging solution to a DataFrame + df_charging = m.solution['y_ch'].sel(i=iSto).to_dataframe().reset_index() + + # Drop out infinitesimal numbers + df_charging['y_ch'] = df_charging['y_ch'].apply(lambda x: 0 if x < 0.01 else x) + + # Convert 't'-column in a datetime format + df_charging['t'] = pd.to_datetime(df_charging['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') + + # Store the English technology names in a separate column to maintain color consistency + df_charging['i_en'] = df_charging['i'] + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_charging['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_charging['i'] = df_charging['i_en'].replace(tech_mapping_en_to_de) + else: + df_charging['i'] = df_charging['i_en'] # Use English names if not German + + # Area plot for storage charging + fig = px.area(df_charging, y='y_ch', x='t', + title=df.loc['plot_label_storage_charging', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + color_discrete_map=color_dict, + labels={'y_ch': '', 't': ''} # Delete double labeling + ) + + # Remove line traces and use fill colors for the area plot + fig.update_traces(line=dict(width=0)) + fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) + + # Update the legend title to reflect the correct language (German or English) + fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + + # For German language, update the legend to show German technology names + if st.session_state.lang == 'DE': + fig.for_each_trace(lambda t: t.update(name=df_charging.loc[df_charging['i_en'] == t.name, 'i'].values[0])) + + # Display the plot + with col: + st.plotly_chart(fig) + + return df_charging + + + +def plot_hydrogen_production(m, iPtG, color_dict, col, df): + """ + Plots the hydrogen production. + Supports both German and English labels for titles and axes while ensuring color consistency. + """ + # Convert the hydrogen production data to a DataFrame + df_h2_prod = m.solution['y_h2'].sel(i=iPtG).to_dataframe().reset_index() + + # Convert 't'-column in a datetime format + df_h2_prod['t'] = pd.to_datetime(df_h2_prod['t'].str.strip("'"), format='%Y-%m-%d %H:%M %z') + + # Store the English technology names in a separate column to maintain color consistency + df_h2_prod['i_en'] = df_h2_prod['i'] + + # Check if the language is German and map English names to German for display + if st.session_state.lang == 'DE': + tech_mapping_en_to_de = { + df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE'] + for tech in df_h2_prod['i_en'] if f'tech_{tech.lower()}' in df.index + } + # Replace the English technology names with German ones for display + df_h2_prod['i'] = df_h2_prod['i_en'].replace(tech_mapping_en_to_de) + else: + df_h2_prod['i'] = df_h2_prod['i_en'] # Keep English names if not German + + # Area plot for hydrogen production + fig = px.area(df_h2_prod, y='y_h2', x='t', + title=df.loc['plot_label_hydrogen_production', st.session_state.lang], + color='i_en', # Use the English names for consistent coloring + color_discrete_map=color_dict, + labels={'y_h2': '', 't': ''} # Delete double labeling + ) + + # Remove line traces and use fill colors for the area plot + fig.update_traces(line=dict(width=0)) + fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color)) + + # Update the legend title to reflect the correct language (German or English) + fig.update_layout(legend_title_text=df.loc['label_technology', st.session_state.lang]) + + # For German language, update the legend to show German technology names + if st.session_state.lang == 'DE': + fig.for_each_trace(lambda t: t.update(name=df_h2_prod.loc[df_h2_prod['i_en'] == t.name, 'i'].values[0])) + + # Display the plot + with col: + st.plotly_chart(fig) + + return df_h2_prod + + + +def disaggregate_df(df, t, t_original, dt): + """ + Disaggregates the DataFrame based on the original time steps. + """ + if "t" not in list(df.columns): + return df + + # Change format of t back + df['t'] = "'" + pd.to_datetime(df['t'], utc=True).dt.tz_convert('Europe/Berlin').dt.strftime('%Y-%m-%d %H:%M %z') + "'" + + df_t_all = pd.DataFrame({"t_all": t_original.to_series(), 't': t.repeat(dt)}).reset_index(drop=True) + df_output = df.merge(df_t_all, on='t').drop('t', axis=1).rename({'t_all': 't'}, axis=1) + df_output = df_output[[df_output.columns[-1]] + list(df_output.columns[:-1])] + # Drop the helping column i_en + df_output = df_output.drop(columns=['i_en'], errors='ignore') + return df_output.sort_values('t') + + +if __name__ == "__main__": main() \ No newline at end of file