app.py

# %%
# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""




from numpy import arange
import xarray as xr
import highspy
import linopy
import openpyxl
from linopy import Model, EQUAL
import pandas as pd
import plotly.express as px
##import gurobipy

import streamlit as st


# %%

url_excel = r'Input_Jahr_2021.xlsx'
url_excel = st.file_uploader()


# %%
# Slider for gas price [€/MWh_th]
price_gas = st.slider(value=10, min=0, max=400, label="Natural gas price [€/MWh]", step=10)

# Slider for CO2 price [€/t]
price_co2 = st.slider(value=80, min=0, max=400, label="CO2 price [€/t CO2eq]", step=10)

# Slider for CO2 limit [mio. t]
limit_co2 = st.slider(value=400, min=0, max=750, label="CO2 limit [mio. t]", step=50)

# Slider for H2 price / usevalue [€/MWH_th]
price_h2 = st.slider(value=100, min=0, max=300, label="Hydrogen price [€/MWh]", step=10)

# %%
# Set input file
url_excel = r'Input_Jahr_2021.xlsx'

# %% [markdown]
#  Read Sets

# %%
## Define all sets for the model
# Timesteps
df_excel= pd.read_excel(url_excel, sheet_name = 'Timesteps_All', header=None)
t = pd.Index(df_excel.iloc[:,0], name = 't')[1:168]
#t = pd.Index(df_excel.iloc[:,0], name = 't')

# Technologies
df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
i = pd.Index(df_excel.iloc[:,0], name = 'i')
i

df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
iConv = pd.Index(df_excel.iloc[0:7,2], name = 'iConv')

df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
iRes = pd.Index(df_excel.iloc[0:4,4], name = 'iRes')

df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
iSto = pd.Index(df_excel.iloc[0:2,6], name = 'iSto')

df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
iPtG = pd.Index(df_excel.iloc[0:1,8], name = 'iPtG')

df_excel = pd.read_excel(url_excel, sheet_name = 'Technologies')
iHyRes = pd.Index(df_excel.iloc[0:1,10], name = 'iHyRes')

# %%
### Parameters
# CO2 limit (from slider)
l_co2 = limit_co2.value
p_co2 = price_co2.value

# length of timesteps
dt = 1

# Demand
df_excel= pd.read_excel(url_excel, sheet_name = 'Demand')
#df_melt = pd.melt(df_excel, id_vars='Zeit')
df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Unnamed: 1':'Demand'})
#df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('t')
D_t = df_excel.iloc[:,0].to_xarray()

## Efficiencies
df_excel = pd.read_excel(url_excel, sheet_name = 'Efficiency')
df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'Efficiency'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
eff_i = df_excel.iloc[:,0].to_xarray()

## Variable costs
# Fuel costs
df_excel = pd.read_excel(url_excel, sheet_name = 'FuelCosts')
df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'FuelCosts'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
c_fuel_i = df_excel.iloc[:,0].to_xarray()
# Apply slider value
c_fuel_i.loc[dict(i = 'Fossil Gas')]  = price_gas.value
c_fuel_i.loc[dict(i = 'H2')]  = price_h2.value

# Other var. costs
df_excel = pd.read_excel(url_excel, sheet_name = 'OtherVarCosts')
df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'OtherVarCosts'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
c_other_i = df_excel.iloc[:,0].to_xarray()

# Investment costs
df_excel = pd.read_excel(url_excel, sheet_name = 'InvCosts')
df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InvCosts'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
c_inv_i = df_excel.iloc[:,0].to_xarray()

# Emission factor
df_excel = pd.read_excel(url_excel, sheet_name = 'EmFactor')
df_excel = df_excel.rename(columns = {'Conventionals':'i', 'Unnamed: 1':'EmFactor'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
co2_factor_i = df_excel.iloc[:,0].to_xarray()

## Calculation of variable costs
c_var_i = (c_fuel_i.sel(i = iConv) + p_co2 * co2_factor_i.sel(i = iConv)) / eff_i.sel(i = iConv) + c_other_i.sel(i = iConv)

# RES capacity factors
#df_excel = pd.read_excel(url_excel, sheet_name = 'RES',header=[0,1])
#df_excel = pd.read_excel(url_excel, sheet_name = 'RES', index_col=['Timesteps'], columns=['PV', 'WindOn', 'WindOff', 'RoR'])
df_excel = pd.read_excel(url_excel, sheet_name = 'RES')
df_excel = df_excel.set_index(['Timesteps'])
df_test = df_excel
df_excel = df_excel.stack()
#df_excel = df_excel.rename(columns={'PV', 'WindOn', 'WindOff', 'RoR'})
df_test2 = df_excel
#df_excel = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
#df_excel = df_excel.fillna(0)

#df_test = df_excel.set_index(['Timesteps', 'PV', 'WindOn', 'WindOff', 'RoR']).stack([0])
#df_test.index = df_test.index.set_names(['t','i'])
s_t_r_iRes = df_excel.to_xarray().rename({'level_1': 'i','Timesteps':'t'})
#s_t_r_iRes = df_excel.iloc[:,0].to_xarray()

# Base capacities
df_excel = pd.read_excel(url_excel, sheet_name = 'InstalledCap')
df_excel = df_excel.rename(columns = {'All':'i', 'Unnamed: 1':'InstalledCap'})
df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
K_0_i = df_excel.iloc[:,0].to_xarray()

# Energy-to-power ratio storages
df_excel = pd.read_excel(url_excel, sheet_name = 'E2P')  
df_excel = df_excel.rename(columns = {'Storage':'i', 'Unnamed: 1':'E2P ratio'})
#df_excel  = i.to_frame().reset_index(drop=True).merge(df_excel, how = 'left')
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('i')
e2p_iSto = df_excel.iloc[:,0].to_xarray()

# Inflow for hydro reservoir
df_excel = pd.read_excel(url_excel, sheet_name = 'HydroInflow')
df_excel = df_excel.rename(columns = {'Timesteps':'t', 'Hydro Water Reservoir':'Inflow'})
df_excel = df_excel.fillna(0)
df_excel = df_excel.set_index('t')
h_t = df_excel.iloc[:,0].to_xarray()

# %%
### Variables
m = Model()

C_tot = m.add_variables(name = 'C_tot')     # Total costs
C_op = m.add_variables(name = 'C_op', lower = 0)       # Operational costs
C_inv = m.add_variables(name = 'C_inv', lower = 0)     # Investment costs

K = m.add_variables(coords = [i], name = 'K', lower = 0)          # Endogenous capacity
y = m.add_variables(coords = [t,i], name = 'y', lower = 0)          # Electricity production --> für Elektrolyseure ausschließen
y_ch = m.add_variables(coords = [t,i], name = 'y_ch', lower = 0)    # Electricity consumption --> für alles außer Elektrolyseure und Speicher ausschließen
l = m.add_variables(coords = [t,i], name = 'l', lower = 0)          # Storage filling level
w = m.add_variables(coords = [t], name = 'w', lower = 0)          # RES curtailment

### Model

## Objective function
C_tot = C_op + C_inv
m.add_objective(C_tot)

## Costs terms for objective function
# Operational costs minus revenue for produced hydrogen
C_op_sum = m.add_constraints((y * c_var_i * dt).sum() - ((y_ch.sel(i = iPtG) / eff_i.sel(i = iPtG)) * price_h2.value * dt).sum() == C_op, name = 'C_op_sum')

# Investment costs
C_inv_sum = m.add_constraints((K * c_inv_i).sum() == C_inv, name = 'C_inv_sum')

## Load serving
loadserve_t = m.add_constraints(((y * dt).sum(dims = 'i') - (w * dt) == D_t.sel(t = t) * dt), name =  'load')

## Maximum capacity limit
maxcap_i_t = m.add_constraints((y - K <= K_0_i), name =  'max_cap')

## Maximum storage charging and discharging
maxcha_iSto_t = m.add_constraints((y.sel(i = iSto) + y_ch.sel(i = iSto)  - K.sel(i = iSto) <= K_0_i.sel(i = iSto)), name =  'max_cha')

## Maximum electrolyzer capacity
ptg_prod_iPtG_t = m.add_constraints((y_ch.sel(i = iPtG) - K.sel(i = iPtG)<= K_0_i.sel(i = iPtG)), name = 'max_cha_ptg')

## Infeed of renewables
infeed_iRes_t = m.add_constraints((y.sel(i = iRes) - s_t_r_iRes.sel(i = iRes).sel(t = t) * K.sel(i = iRes) <= s_t_r_iRes.sel(i = iRes).sel(t = t) * K_0_i.sel(i = iRes)), name =  'infeed')

## Maximum filling level restriction storage power plant --> Energy-to-Power-Ratio eingeführt. (JR)
maxcapsto_iSto_t = m.add_constraints((l.sel(i = iSto) - K.sel(i = iSto) * e2p_iSto.sel(i = iSto) <= K_0_i.sel(i = iSto) * e2p_iSto.sel(i = iSto)), name = 'max_sto_filling')

## Filling level restriction hydro reservoir --> Ist Kreisbedingung erfüllt? (JR)
filling_iHydro_t = m.add_constraints(l.sel(i = iHyRes) - l.sel(i = iHyRes).roll(t = -1) + y.sel(i = iHyRes) * dt == h_t.sel(t = t) * dt, name = 'filling_level_hydro')

## Filling level restriction other storages --> Ist Kreisbedingung erfüllt? (JR)
filling_iSto_t = m.add_constraints(l.sel(i = iSto) - (l.sel(i = iSto).roll(t = -1) - (y.sel(i = iSto) / eff_i.sel(i = iSto)) * dt + y_ch.sel(i = iSto) * eff_i.sel(i = iSto) * dt) == 0, name = 'filling_level')

## CO2 limit --> ggf. hier auch mit Subset arbeiten (Technologien, die Brennstoff verbrauchen). (JR)
CO2_limit = m.add_constraints(((y / eff_i) * co2_factor_i * dt).sum() <= l_co2, name = 'CO2_limit')

m.solve(solver_name = 'highs')



# %% [markdown]
#  Results

# %%

# %%

m.solve(solver_name = 'highs',presolve = 'off',run_crossover = 'off',solver = 'ipm')

# %%
#m.solve(solver_name = 'highs', solver = 'ipm', log_file = 'solverlog.txt',run_crossover = 'off' )

# %%
# Read Objective from solution
m.objective_value

pd.options.plotting.backend = "plotly"
# Read dual values and plot 
df = loadserve_t.dual.to_dataframe().reset_index()
#df['t'] = pd.to_datetime(df['t'])
df


# %%
# Read values
Productionlevels = m.solution['y'].sel(r = 'DE').to_dataframe().reset_index()

df = Productionlevels
df

# %%
#pandas gui
# Create Line plot
fig = px.line(df, x=df['t'], y=df['y'], color = df['i'])
fig

# %%




#Productionlevels