Re-using of Elements

[baumbude]

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.

[FBumann]

Gerne.

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

[FBumann]

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()

[FBumann]

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

[baumbude]

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)

[FBumann]

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.

[FBumann]

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.

[FBumann]

Closed by #348