Re-using of Elements

Früher konnte man Elements beliebig oft verwenden.
Beispiel:
flow_system_ohne_Eigenverbrauch.add_elements(cost_effect, bus_el, battery, source, sink)
flow_system_mit_Eigenverbrauch.add_elements(cost_effect, bus_el, battery, verbraucher, source_sink)

Das scheint nicht mehr zu gehen (mindestens Konflikt mit timeseries-collection).

Es hat bei der Modellerstellung enorme Vorteile. Klar es gibt auch Nachteile für den Zugriff nachher, aber eigentlich nicht, wenn man konsequent mit dem results-Objekt arbeitet.

Wir sollten einfach mal PRO und CONS diskutieren.

Gerne.

Kannst du mal ein Skript teilen damit ich weis was nicht geht?

In meinem ersten Test geht das auf jeden fall.

"""
This script shows how to use the flixOpt framework to model a more complex energy system.
"""

import numpy as np
import pandas as pd
from rich.pretty import pprint  # Used for pretty printing

import flixOpt as fx

if __name__ == '__main__':
    # --- Experiment Options ---
    # Configure options for testing various parameters and behaviors
    check_penalty = False
    excess_penalty = 1e5
    use_chp_with_segments = False
    time_indices = None  # Define specific time steps for custom calculations, or use the entire series

    # --- Define Demand and Price Profiles ---
    # Input data for electricity and heat demands, as well as electricity price
    electricity_demand = np.array([70, 80, 90, 90, 90, 90, 90, 90, 90])
    heat_demand = (
        np.array([30, 0, 90, 110, 2000, 20, 20, 20, 20])
        if check_penalty
        else np.array([30, 0, 90, 110, 110, 20, 20, 20, 20])
    )
    electricity_price = np.array([40, 40, 40, 40, 40, 40, 40, 40, 40])

    # --- Define the Flow System, that will hold all elements, and the time steps you want to model ---
    timesteps = pd.date_range('2020-01-01', periods=len(heat_demand), freq='h')
    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('Strom', excess_penalty_per_flow_hour=excess_penalty),
        fx.Bus('Fernwärme', excess_penalty_per_flow_hour=excess_penalty),
        fx.Bus('Gas', 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)
    CO2 = fx.Effect('CO2', 'kg', 'CO2_e-Emissionen', specific_share_to_other_effects_operation={Costs.label: 0.2})
    PE = fx.Effect('PE', 'kWh_PE', 'Primärenergie', maximum_total=3.5e3)

    # --- Define Components ---
    # 1. Define Boiler Component
    # A gas boiler that converts fuel into thermal output, with investment and on-off parameters
    Gaskessel = fx.linear_converters.Boiler(
        'Kessel',
        eta=0.5,  # Efficiency ratio
        on_off_parameters=fx.OnOffParameters(effects_per_running_hour={Costs.label: 0, CO2.label: 1000}),  # CO2 emissions per hour
        Q_th=fx.Flow(
            label='Q_th',  # Thermal output
            bus='Fernwärme',  # Linked bus
            size=fx.InvestParameters(
                fix_effects=1000,  # Fixed investment costs
                fixed_size=50,  # Fixed size
                optional=False,  # Forced investment
                specific_effects={Costs.label: 10, PE.label: 2},  # Specific costs
            ),
            load_factor_max=1.0,  # Maximum load factor (50 kW)
            load_factor_min=0.1,  # Minimum load factor (5 kW)
            relative_minimum=5 / 50,  # Minimum part load
            relative_maximum=1,  # Maximum part load
            previous_flow_rate=50,  # Previous flow rate
            flow_hours_total_max=1e6,  # Total energy flow limit
            on_off_parameters=fx.OnOffParameters(
                on_hours_total_min=0,  # Minimum operating hours
                on_hours_total_max=1000,  # Maximum operating hours
                consecutive_on_hours_max=10,  # Max consecutive operating hours
                consecutive_on_hours_min=np.array(
                    [1, 1, 1, 1, 1, 2, 2, 2, 2]
                ),  # min consecutive operation hoursconsecutive_off_hours_max=10,  # Max consecutive off hours
                effects_per_switch_on=0.01,  # Cost per switch-on
                switch_on_total_max=1000,  # Max number of starts
            ),
        ),
        Q_fu=fx.Flow(label='Q_fu', bus='Gas', size=200),
    )

    # 2. Define CHP Unit
    # Combined Heat and Power unit that generates both electricity and heat from fuel
    bhkw = fx.linear_converters.CHP(
        'BHKW2',
        eta_th=0.5,
        eta_el=0.4,
        on_off_parameters=fx.OnOffParameters(effects_per_switch_on=0.01),
        P_el=fx.Flow('P_el', bus='Strom', size=60, relative_minimum=5 / 60),
        Q_th=fx.Flow('Q_th', bus='Fernwärme', size=1e3),
        Q_fu=fx.Flow('Q_fu', bus='Gas', size=1e3, previous_flow_rate=20),  # The CHP was ON previously
    )

    # 3. Define CHP with Linear Segments
    # This CHP unit uses linear segments for more dynamic behavior over time
    P_el = fx.Flow('P_el', bus='Strom', size=60, previous_flow_rate=20)
    Q_th = fx.Flow('Q_th', bus='Fernwärme')
    Q_fu = fx.Flow('Q_fu', bus='Gas')
    segmented_conversion_factors = {
        P_el.label: [(5, 30), (40, 60)],  # Similar to eta_th, each factor here can be an array
        Q_th.label: [(6, 35), (45, 100)],
        Q_fu.label: [(12, 70), (90, 200)],
    }

    bhkw_2 = fx.LinearConverter(
        'BHKW2',
        inputs=[Q_fu],
        outputs=[P_el, Q_th],
        segmented_conversion_factors=segmented_conversion_factors,
        on_off_parameters=fx.OnOffParameters(effects_per_switch_on=0.01),
    )

    # 4. Define Storage Component
    # Storage with variable size and segmented investment effects
    segmented_investment_effects = (
        [(5, 25), (25, 100)],  # Investment size
        {
            Costs.label: [(50, 250), (250, 800)],  # Investment costs
            PE.label: [(5, 25), (25, 100)],  # Primary energy costs
        },
    )

    speicher = fx.Storage(
        'Speicher',
        charging=fx.Flow('Q_th_load', bus='Fernwärme', size=1e4),
        discharging=fx.Flow('Q_th_unload', bus='Fernwärme', size=1e4),
        capacity_in_flow_hours=fx.InvestParameters(
            effects_in_segments=segmented_investment_effects,  # Investment effects
            optional=False,  # Forced investment
            minimum_size=0,
            maximum_size=1000,  # Optimizing between 0 and 1000 kWh
        ),
        initial_charge_state=0,  # Initial charge state
        maximal_final_charge_state=10,  # Maximum final charge state
        eta_charge=0.9,
        eta_discharge=1,  # Charge/discharge efficiency
        relative_loss_per_hour=0.08,  # Energy loss per hour, relative to current charge state
        prevent_simultaneous_charge_and_discharge=True,  # Prevent simultaneous charge/discharge
    )

    # 5. Define Sinks and Sources
    # 5.a) Heat demand profile
    Waermelast = fx.Sink(
        'Wärmelast',
        sink=fx.Flow(
            'Q_th_Last',  # Heat sink
            bus='Fernwärme',  # Linked bus
            size=1,
            fixed_relative_profile=heat_demand,  # Fixed demand profile
        ),
    )

    # 5.b) Gas tariff
    Gasbezug = fx.Source(
        'Gastarif',
        source=fx.Flow(
            'Q_Gas',
            bus='Gas',  # Gas source
            size=1000,  # Nominal size
            effects_per_flow_hour={Costs.label: 0.04, CO2.label: 0.3},
        ),
    )

    # 5.c) Feed-in of electricity
    Stromverkauf = fx.Sink(
        'Einspeisung',
        sink=fx.Flow(
            'P_el',
            bus='Strom',  # Feed-in tariff for electricity
            effects_per_flow_hour=-1 * electricity_price,  # Negative price for feed-in
        ),
    )

    # --- Build FlowSystem ---
    # Select components to be included in the flow system
    flow_system.add_elements(Costs, CO2, PE, Gaskessel, Waermelast, Gasbezug, Stromverkauf, speicher)
    flow_system.add_elements(bhkw_2) if use_chp_with_segments else flow_system.add_elements(bhkw)

    pprint(flow_system)  # Get a string representation of the FlowSystem

    flow_system2 = fx.FlowSystem(timesteps)
    flow_system2.add_elements(*list(flow_system.components.values()))
    flow_system2.add_elements(*list(flow_system.buses.values()))
    flow_system2.add_elements(*list(flow_system.effects.effects.values()))

    # --- Solve FlowSystem ---
    calculation = fx.FullCalculation('complex example', flow_system, time_indices)
    calculation.do_modeling()

    calculation.solve(fx.solvers.HighsSolver(0.01, 60))

    calculation2 = fx.FullCalculation('complex example 2', flow_system2, time_indices)
    calculation2.do_modeling()

    calculation2.solve(fx.solvers.HighsSolver(0.01, 60))
    
    assert np.isclose(calculation2.results.objective, calculation.results.objective, rtol=1e-5, atol=0.1)

    # --- Results ---
    # You can analyze results directly or save them to file and reload them later.
    calculation.results.to_file()

    # But let's plot some results anyway
    calculation.results.plot_heatmap('BHKW2(Q_th)|flow_rate')
    calculation.results['BHKW2'].plot_node_balance()
    calculation.results['Speicher'].plot_charge_state()

@baumbude

@baumbude kannst du hier nochmal reinschauen und sagen was nicht funktioniert?

@FBumann

Ja @FBumann , es klappt anscheinend nur nicht, wenn sich die timesteps-Reihe ändert:
Hier Minimalbeispiel:

importt flixOpt as fx
import pandas as pd
import random

type = 'both'
# type = 'first'
# type = 'second'


aSolver = fx.solvers.HighsSolver(0.002, 20)
price_values = [random.randint(-300, 500) for _ in range(25)]

price_1 = pd.DataFrame(price_values, index=pd.date_range(start='2025-01-01', periods=10, freq='h'))
price_2 = pd.DataFrame(price_values, index=pd.date_range(start='2023-01-01', periods=10, freq='h'))

bus_el = fx.Bus('electricity')
cost_effect = fx.Effect('costs', '€', 'Cost', is_standard=True, is_objective=True)

source = fx.Source('DA_market',
          source=fx.Flow(label='Einkauf',
                   bus=bus_el.label,
                   effects_per_flow_hour=price_1,
                   ),
          )

# -----------------------------------------------------
# -------------------- Zeitreihe 1  -------------------

if type in ['both','first']:

    flow_system = fx.FlowSystem(timesteps=price_1.index)
    flow_system.add_elements(cost_effect, bus_el)
    flow_system.add_elements(source)

    # --- Define, model and solve a Calculation ---
    calculation = fx.FullCalculation('Battery_onlyDA_2023', flow_system)
    calculation.do_modeling()
    calculation.solve(aSolver)


# -----------------------------------------------------
# -------------------- Zeitreihe 2  -------------------

if type in ['both','second']:
    flow_system24 = fx.FlowSystem(timesteps=price_2.index)
    source.source.effects_per_flow_hour = -price_2
    flow_system24.add_elements(cost_effect, bus_el)
    flow_system24.add_elements(source)

    # --- Define, model and solve a Calculation ---
    calculation = fx.FullCalculation('Battery_onlyDA_2024', flow_system24)
    calculation.do_modeling()
    calculation.solve(aSolver)

Ist das etwas was wir brauchen?
Dafür müsste man immer die _backup_data aus den timeseries rausholen wenn man Elemente zu einem neuen FlowSystem hinzufügt.

Wäre denkbar umzusetzen.

@baumbude

Ist das etwas was wir brauchen? Dafür müsste man immer die _backup_data aus den timeseries rausholen wenn man Elemente zu einem neuen FlowSystem hinzufügt.

Wäre denkbar umzusetzen.

@baumbude Ich nehms zurück.
Das beisst sich sehr mit dem indexing (active_timesteps).
Das beides möglich zu machen wird sehr schwierig glaube ich.

Closed by #348

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Re-using of Elements #196

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FBumann commented on Mar 25, 2025

FBumann commented on Mar 25, 2025

FBumann commented on Mar 28, 2025

baumbude commented on Apr 2, 2025

FBumann commented on Apr 2, 2025

FBumann commented on Apr 2, 2025

FBumann commented on Oct 13, 2025

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