Extract specific energy flows from results #275

import flixopt as fx
import pandas as pd
import numpy as np
import pathlib
from typing import Dict, List, Union
import plotly.graph_objects as go

if __name__ == '__main__':

    # Data import
    data_import = pd.read_csv(pathlib.Path('C:/import.csv'), index_col=0).sort_index()
    filtered_data = data_import['2024-01-01 00:00:00':'2024-01-01 23:45:00']

    # --- Experiment Options ---
    # Configure options for testing various parameters and behaviors
    check_penalty = False
    excess_penalty = None
    time_indices = None  # Define specific time steps for custom calculations, or use the entire series

    electricity_generation_pv= filtered_data['PV1[kW]'].to_numpy().astype(float) # eingabe  in kW
    electricity_generation_pv= np.where(electricity_generation_pv>0, electricity_generation_pv, 0) 

    timesteps = pd.date_range('2024-01-01', periods= len(electricity_generation_pv), freq= '15min')
    flow_system = fx.FlowSystem(timesteps)  # Create FlowSystem
    
    # --- Define Energy Buses ---
    # Represent node balances (inputs=outputs) for the different energy carriers (electricity, heat, gas) in the system
    flow_system.add_elements(fx.Bus('AC', excess_penalty_per_flow_hour= excess_penalty))

    # --- Define Effects ---
    # Specify effects related to costs, CO2 emissions, and primary energy consumption
    costs = fx.Effect('costs', '€', 'Kosten', is_standard=True, is_objective=True)

    # --- Define Components ---
    battery=fx.Storage(
        'battery',
        charging=fx.Flow('E_Batt_in',bus= 'AC', size= 2500), 
        discharging=fx.Flow('E_Batt_out',bus= 'AC', size=2500),
        capacity_in_flow_hours=fx.InvestParameters(
            fix_effects = 0, # Fixed investment costs if invested. (Attention: Annualize costs to chosen period!)
            specific_effects= 0, # Specific costs, e.g., in €/kW_nominal or €/m²_nominal. Example: {costs: 3, CO2: 0.3} with costs and CO2 representing an Object of class Effect (Attention: Annualize costs to chosen period!)
            optional=False,  # Forced investment
            #minimum_size=500, # Minimum size of 500 kWh
            #maximum_size=1e3,  # Maximum size of 1000 kWh
            fixed_size= 5000  # in kWh
        ),
        initial_charge_state=500 ,  # Initial charge state NOT RELATIVE und muss >= absolute_minimum_charge_state sein
        relative_minimum_charge_state=0.1,
        relative_maximum_charge_state=0.9,
        eta_charge=1,    
        eta_discharge=1,  
        relative_loss_per_hour= 0.00001,
        prevent_simultaneous_charge_and_discharge=True,  # Prevent simultaneous charge/discharge
    )
    
    pv_system=fx.Source('PV', source=fx.Flow(
        'E_el_PV_generation', 
        bus='AC',
        size=1, 
        fixed_relative_profile = electricity_generation_pv)
        )
      
    flow_system.add_elements(costs, battery, pv_system)
        
    # --- Solve Flowsystem ---
    calculation = fx.FullCalculation(
        name= 'Full', 
        flow_system= flow_system, 
        active_timesteps = time_indices
        )
    calculation.do_modeling()        

    calculation.solve(fx.solvers.HighsSolver(
        mip_gap=0.01,
        time_limit_seconds=1200, # timelimit in sec
        # threads = 8, # Number of threads to use.
        # extra_options= '' # Filename for saving the solver log.
        ))
    
    calculation.results.to_file()
    data_e_batt_soc = calculation.results.components['battery|flow_rate']