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