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# -*- 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("""
# <style>
# text-align: center;
# display: block;
# margin-left: auto;
# margin-right: auto;
# width: 100%;
# </style>
# """, 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)
def setup_sidebar(df):
"""
Set up the sidebar with language and level options as two-step selection,
using localized text from the loaded dataframe.
"""
# Step 1: Language selection
lang_choice = st.sidebar.selectbox("Language", ["🇩🇪 DE", "🇬🇧 EN"], key="lang_select", label_visibility="collapsed")
st.session_state.lang = lang_choice[-2:] # 'EN' or 'DE'
# Step 2: Localized level selection
level_options = {
f"🎓 {df.loc['menu_untergraduate', st.session_state.lang]}": "undergraduate",
f"🎓 {df.loc['menu_graduate', st.session_state.lang]}": "graduate"
}
level_choice = st.sidebar.selectbox(df.loc['menu_level', st.session_state.lang], list(level_options.keys()), key="level_select")
st.session_state.level = level_options[level_choice]
# Optional styling and centered sidebar content
st.sidebar.markdown("""
<style>
text-align: center;
display: block;
margin-left: auto;
margin-right: auto;
width: 100%;
</style>
""", 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(" ")
st.markdown(df.loc['menu_text', st.session_state.lang])
st.text(" ")
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])
# 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
}
# 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)
elif 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']
# # 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']
# Set options and default values based on the selected language
# Set core technology list (will later depend on level)
if st.session_state.level == 'undergraduate':
# Exclude specific technologies for undergraduates
excluded_techs = {'Lignite', 'Pumped Hydro Storage', 'Electrolyzer'}
tech_list = [tech for tech in sets_dict['i'] if tech not in excluded_techs]
# tech_list = sets_dict['i'] # same for now
else:
tech_list = sets_dict['i'] # original set
# Localize display labels based on selected language
lang = st.session_state.lang
options = [
df.loc[f'tech_{tech.lower()}', lang] if f'tech_{tech.lower()}' in df.index else tech
for tech in tech_list
]
# Define default technologies (internal names) — same across all users
default_techs = ['Lignite', 'CCGT', 'OCGT', 'Fossil Hard coal', 'Fossil Oil', 'PV', 'WindOff', 'WindOn', 'H2', 'Pumped Hydro Storage', 'Battery storages', 'Electrolyzer']
# Translate default selections for UI (still uses internal list for logic)
default = [
df.loc[f'tech_{tech.lower()}', lang] if f'tech_{tech.lower()}' in df.index else tech
for tech in default_techs if tech in tech_list
]
# 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("---")
if st.session_state.level == "undergraduate":
i_with_capacity = m.solution['K'].where((m.solution['K'] > 0) & (m.solution['i'] != 'Electrolyzer')).dropna(dim='i').get_index('i')
df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df, show = False)
# df_production = plot_production(m, i_with_capacity, dt, color_dict, colb1, df, show = False)
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)
df_residual_load_duration = plot_residual_load_duration(m, dt, colb2, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment, iConv)
df_fullload = calculate_and_plot_fullload_hours(m, dt, color_dict, colb1)
# df_production = plot_production(m, i_with_capacity, dt, color_dict, colb1, df)s
df_price = plot_electricity_prices(m, dt, colb2, df, df_residual_load_duration)
df_production = plot_production(m, i_with_capacity, dt, color_dict, colb1, df)
df_emissions = calculate_and_plot_emissions(m, eff_i, co2_factor_i, dt=1, color_dict=color_dict, col=colb1)
df_emissions_cumulative = calculate_and_plot_cumulative_emissions(m, eff_i, co2_factor_i,dt, color_dict, colb2, df)
# df_residual_load_duration = plot_residual_load_duration(m, dt, colb2, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment, iConv)
df_curtailment = plot_curtailment(m, iRes, color_dict, colb1, df, show= True)
df_charging = plot_storage_charging(m, iSto, color_dict, colb2, df)
# df_filtered = calculate_and_plot_investment_costs(m,df_new_capacities, c_inv_i, color_dict, colb1, df)
# df_contr_marg_sum = calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, colb2, df)
else:
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, iConv)
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"<h3><b>{df.loc['plot_label_total_costs', st.session_state.lang]} {total_costs_rounded}</b></h3>",
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"<h3><b>{df.loc['plot_label_co2_price', st.session_state.lang]} {CO2_price_rounded}</b></h3>",
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)
if st.session_state.level == "graduate":
# 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, show=True):
"""
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
if st.session_state.level == "graduate":
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)
# Set y-axis range conditionally
range_y = [0, 250] if st.session_state.level == "graduate" else None
# 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],
labels={'dual': '', 't': ''}
)
# Apply axis range if needed
if range_y is not None:
fig_price.update_yaxes(range=range_y)
with col:
st.plotly_chart(fig_price)
return df_price
def plot_residual_load_duration(m, dt, col, df, D_t, i_with_capacity, iRes, color_dict, df_curtailment, iConv):
"""
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()
# Sort technology_columns based on the highest index in df_sorted (only for iConv); others are placed at the end
sorted_technology_columns = sorted(
technology_columns,
key=lambda tech: (
tech not in iConv, # Place non-iConv technologies at the end
-df_sorted[df_sorted[tech] != 0].index.max() if tech in iConv and not df_sorted[df_sorted[tech] != 0].empty else float('inf')
)
)
# Add stacked area traces for each production technology with consistent colors and language-specific names
for tech in sorted_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, show=True):
"""
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
if show:
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 calculate_and_plot_fullload_hours(m, dt, color_dict, col):
"""
Calculates full load hours for units with positive capacity and plots the result.
Parameters:
- m: Optimization model object containing solution['K'] (capacity) and solution['y'] (production).
- dt: Time resolution of the production data (e.g., in hours).
- color_dict: Dictionary mapping unit identifiers (i) to colors for plotting.
- col: Streamlit column or panel where the plot should be rendered (e.g., colb1).
"""
# Filter indices with positive capacity
i_with_capacity = m.solution['K'].where(m.solution['K'] > 0).dropna(dim='i').get_index('i')
# Extract production and capacity data
df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index()
df_capacity = m.solution['K'].sel(i=i_with_capacity).to_dataframe().reset_index()
# Sum production and calculate full load hours
df_production_sum = (df_production.groupby('i')['y'].sum() * dt).round(0).reset_index()
df_production_sum = df_production_sum.set_index('i').loc[i_with_capacity].reset_index()
df_fullload = df_production_sum['y'] / df_capacity['K']
df_fullload = df_fullload.to_frame(name='fullload')
df_fullload['i'] = df_production_sum['i']
df_fullload = df_fullload[['i', 'fullload']]
# Plot
fig = px.bar(
df_fullload,
y='i',
x='fullload',
orientation='h',
title='Volllaststunden [h]',
color='i',
color_discrete_map=color_dict
)
col.plotly_chart(fig)
return df_fullload
def calculate_and_plot_emissions(m, eff_i, co2_factor_i, dt, color_dict, col):
"""
Calculates emissions from model output and plots an area chart of emissions over time.
Parameters:
- m: Optimization model with solution['y'] (production) and solution['K'] (capacity).
- eff_i: xarray DataArray of efficiency values indexed by 'i'.
- co2_factor_i: xarray DataArray of CO₂ emission factors indexed by 'i'.
- dt: Time resolution of the data (e.g., in hours).
- color_dict: Dictionary mapping unit identifiers (i) to colors.
- col: Streamlit column or panel for rendering the plot.
Returns:
- df_production_emissions_unpivot: DataFrame with emissions per unit and time.
"""
# Filter technologies with positive capacity
i_with_capacity = m.solution['K'].where(m.solution['K'] > 0).dropna(dim='i').get_index('i')
# Get production data for those technologies
df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index()
# Pivot production data
df_production_pivot = df_production.pivot(index='t', columns='i', values='y')
available_columns = df_production_pivot.columns.intersection(i_with_capacity)
df_production_pivot = df_production_pivot[available_columns]
# Get efficiency and CO₂ factors only for available technologies
df_efficiency = eff_i.sel(i=available_columns)
co2_factor_i_with_capacity = co2_factor_i.sel(i=available_columns)
color_dict_with_capacity = {i: color_dict[i] for i in available_columns}
desired_order = available_columns.tolist()
# Compute emissions
df_production_emissions = df_production_pivot / df_efficiency * co2_factor_i_with_capacity * dt
# Unpivot for plotting
df_production_emissions_unpivot = df_production_emissions.reset_index().melt(
id_vars='t', var_name='i', value_name='y'
)
df_production_emissions_unpivot['i'] = pd.Categorical(
df_production_emissions_unpivot['i'],
categories=desired_order,
ordered=True
)
df_production_emissions_unpivot = df_production_emissions_unpivot.sort_values(by=['t', 'i'])
# Plot
fig = px.area(
df_production_emissions_unpivot,
y='y',
x='t',
title='CO₂-Emissionen [t]',
color='i',
color_discrete_map=color_dict_with_capacity
)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color))
# Display in Streamlit
col.plotly_chart(fig)
return df_production_emissions_unpivot
def calculate_and_plot_cumulative_emissions(m, eff_i, co2_factor_i, dt, color_dict, col, df):
"""
Calculates and plots cumulative CO₂ emissions sorted by time for units with capacity > 0,
with support for multilingual labels and consistent coloring.
Parameters:
- m: Optimization model with 'K' and 'y'
- eff_i: DataArray of efficiencies indexed by 'i'
- co2_factor_i: DataArray of CO₂ factors indexed by 'i'
- dt: Time resolution
- color_dict: Dict mapping English tech names to colors
- col: Streamlit column for plot
- df: DataFrame for label translations (e.g., from Excel)
Returns:
- df_cumulative_emissions_unpivot: Cumulative emissions DataFrame (melted format)
"""
# Step 1: Filter technologies with capacity > 0
i_with_capacity = m.solution['K'].where(m.solution['K'] > 0).dropna(dim='i').get_index('i')
# Step 2: Get production data
df_production = m.solution['y'].sel(i=i_with_capacity).to_dataframe().reset_index()
df_production['i_en'] = df_production['i']
# Translate if needed
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'].unique()
if f'tech_{tech.lower()}' in df.index
}
df_production['i'] = df_production['i_en'].replace(tech_mapping_en_to_de)
# Step 3: Pivot and align
df_production_pivot = df_production.pivot(index='t', columns='i_en', values='y')
available_columns = df_production_pivot.columns.intersection(i_with_capacity)
df_production_pivot = df_production_pivot[available_columns]
# Step 4: Emissions calculation
df_efficiency = eff_i.sel(i=available_columns)
co2_factor_i_with_capacity = co2_factor_i.sel(i=available_columns)
df_emissions = df_production_pivot / df_efficiency * co2_factor_i_with_capacity * dt
# Step 5: Add total and sort
df_emissions['total'] = df_emissions.sum(axis=1)
df_emissions_sorted = df_emissions.sort_values(by='total', ascending=True)
# Step 6: Cumulative sum and cleanup
df_cumsum = df_emissions_sorted.drop(columns='total').cumsum(axis=0)
df_cumsum = df_cumsum.loc[:, (df_cumsum != 0).any(axis=0)]
# Step 7: Unpivot
df_cumulative_emissions_unpivot = df_cumsum.reset_index().melt(
id_vars='t', var_name='i_en', value_name='y'
)
# Step 8: Translate display names (if needed)
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_cumulative_emissions_unpivot['i_en'].unique()
if f'tech_{tech.lower()}' in df.index
}
df_cumulative_emissions_unpivot['i'] = df_cumulative_emissions_unpivot['i_en'].replace(tech_mapping_en_to_de)
else:
df_cumulative_emissions_unpivot['i'] = df_cumulative_emissions_unpivot['i_en']
# Set 0 → NaN for plotting
df_cumulative_emissions_unpivot['y'] = df_cumulative_emissions_unpivot['y'].replace(0, np.nan)
# Step 9: Plot
color_dict_with_capacity = {i: color_dict[i] for i in df_cumulative_emissions_unpivot['i_en'].unique() if i in color_dict}
fig = px.area(
df_cumulative_emissions_unpivot,
y='y',
x='t',
title=df.loc['plot_label_cumulative_emissions', st.session_state.lang],
color='i_en',
color_discrete_map=color_dict_with_capacity,
labels={'i': '', 'y': ''}
)
# Rename legend entries to display translated names
legend_map = dict(zip(
df_cumulative_emissions_unpivot['i_en'],
df_cumulative_emissions_unpivot['i']
))
fig.for_each_trace(
lambda t: t.update(name=legend_map.get(t.name, t.name),
legendgroup=legend_map.get(t.name, t.name),
hovertemplate=t.hovertemplate.replace(t.name, legend_map.get(t.name, t.name)))
)
fig.update_traces(line=dict(width=0))
fig.for_each_trace(lambda trace: trace.update(fillcolor=trace.line.color))
fig.update_layout(legend_title_text=None)
col.plotly_chart(fig)
return df_cumulative_emissions_unpivot
def calculate_and_plot_investment_costs(m, df_new_capacities, c_inv_i, color_dict, col, df):
"""
Calculates and plots investment costs per technology, with multilingual support.
- Uses 'i_en' for color consistency.
- Displays 'i' in the selected language.
- Filters out 'Battery storages'.
Parameters:
- df_new_capacities: DataFrame with columns ['i', 'K'] for new capacities.
- c_inv_i: xarray DataArray or Series with investment costs indexed by 'i'.
- color_dict: Dictionary mapping English technology identifiers (i_en) to colors.
- col: Streamlit column to display the plot.
- df: Translation/label lookup DataFrame with keys like 'tech_pv', 'tech_windon' etc.
Returns:
- df_invest_costs: DataFrame with investment costs per technology.
"""
# Ensure 'i_en' is available for color mapping
df_new_capacities = df_new_capacities.copy()
df_new_capacities['i_en'] = df_new_capacities['i']
i_with_capacity = m.solution['K'].where(m.solution['K'] > 0).dropna(dim='i').get_index('i')
available_columns = df_new_capacities.columns.intersection(i_with_capacity)
# Translate if language is German
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
}
df_new_capacities['i'] = df_new_capacities['i_en'].replace(tech_mapping_en_to_de)
# Filter out 'Battery storages'
df_filtered = df_new_capacities[df_new_capacities['i_en'] != 'Battery storages'].copy()
# # Calculate investment costs
df_filtered['Investment'] = df_filtered['K'] * c_inv_i
color_dict_with_capacity = {i: color_dict[i] for i in available_columns}
# Plot
fig = px.bar(
df_filtered,
y='i',
x='Investment',
orientation='h',
title=df.loc['plot_label_investment_costs', st.session_state.lang],
color='i_en',
color_discrete_map=color_dict_with_capacity,
labels={'i': '', 'Investment': ''}
)
fig.update_layout(showlegend=False)
col.plotly_chart(fig)
return df_filtered
def calculate_and_plot_contribution_margin(m, i, iRes, dt, color_dict, col, df):
"""
Calculates and plots the contribution margin (Deckungsbeitrag) per technology.
Mirrors original working logic, with optional language and coloring.
Parameters:
- m: Optimization model with 'y', 'max_cap', 'load'
- i: Full list of technologies (for ordering and reindexing)
- iRes: Subset of residual techs (e.g. PV, WindOn)
- dt: Time step length in hours
- color_dict: Dictionary of colors (keyed by technology names)
- col: Streamlit column to display the plot
- df: Translation table with tech labels and plot titles
Returns:
- df_contr_marg_sum: DataFrame with contribution margin results
"""
# Production data for all technologies
df_production_all = m.solution['y'].sel(i=i).to_dataframe().reset_index()
# Dual values from max_cap constraint
df_contr_marg = m.constraints['max_cap'].dual.to_dataframe().reset_index()
# Merge and multiply
df_merged = pd.merge(df_production_all, df_contr_marg, on=['t', 'i'])
df_merged['y_new'] = df_merged['y'] * df_merged['dual']
df_merged = df_merged[['t', 'i', 'y_new']]
df_contr_marg_sum = df_merged.groupby('i')['y_new'].sum().reset_index()
# Handle residual technologies with load constraint duals
df_production_res = m.solution['y'].sel(i=iRes).to_dataframe().reset_index()
df_price_res = m.constraints['load'].dual.to_dataframe().reset_index()
df_merged_res = pd.merge(df_production_res, df_price_res, on='t')
df_merged_res['multiplied_value'] = df_merged_res['y'] * df_merged_res['dual']
df_merged_res = df_merged_res[['t', 'i', 'multiplied_value']]
df_contr_marg_res = df_merged_res.groupby('i')['multiplied_value'].sum().reset_index()
df_contr_marg_res['multiplied_value'] = df_contr_marg_res['multiplied_value'] * -dt
# Combine both results
df_contr_marg_sum = pd.merge(df_contr_marg_sum, df_contr_marg_res, on='i', how='left')
df_contr_marg_sum['y_new'] = df_contr_marg_sum['multiplied_value'].combine_first(df_contr_marg_sum['y_new'])
df_contr_marg_sum = df_contr_marg_sum.drop(columns=['multiplied_value'])
df_contr_marg_sum['y'] = df_contr_marg_sum['y_new'] * -1
# Translate labels if needed
if st.session_state.lang == 'DE':
tech_mapping = {
df.loc[f'tech_{tech.lower()}', 'EN']: df.loc[f'tech_{tech.lower()}', 'DE']
for tech in i if f'tech_{tech.lower()}' in df.index
}
df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
df_contr_marg_sum['i'] = df_contr_marg_sum['i_en'].replace(tech_mapping)
else:
df_contr_marg_sum['i_en'] = df_contr_marg_sum['i']
# Reorder by original list
# df_contr_marg_sum = df_contr_marg_sum.set_index('i_en').loc[i].reset_index(drop=False)
if 'i' in df_contr_marg_sum.columns:
df_contr_marg_sum = df_contr_marg_sum.drop(columns='i')
df_contr_marg_sum = df_contr_marg_sum.set_index('i_en').loc[i].reset_index()
# Plot
title = df.loc['plot_label_contribution_margin', st.session_state.lang]
fig = px.bar(
df_contr_marg_sum,
y='i',
x='y',
orientation='h',
title=title,
color='i_en',
color_discrete_map=color_dict,
labels={'i': '', 'y': ''}
)
# Localized legend
legend_map = dict(zip(df_contr_marg_sum['i_en'], df_contr_marg_sum['i']))
fig.for_each_trace(lambda t: t.update(
name=legend_map.get(t.name, t.name),
legendgroup=legend_map.get(t.name, t.name),
hovertemplate=t.hovertemplate.replace(t.name, legend_map.get(t.name, t.name))
))
fig.update_layout(legend_title_text=None)
col.plotly_chart(fig)
return df_contr_marg_sum
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() |