Calculation Mode comparison
Note: This example relies on time series data. You can find it in the examples folder of the FlixOpt repository.
"""
This script demonstrates how to use the different calcualtion types in the flixOPt framework
to model the same energy system. THe Results will be compared to each other.
"""
import pathlib
from typing import Dict, List, Union
import numpy as np
import pandas as pd
import xarray as xr
from rich.pretty import pprint # Used for pretty printing
import flixopt as fx
if __name__ == '__main__':
# Calculation Types
full, segmented, aggregated = True, True, True
# Segmented Properties
segment_length, overlap_length = 96, 1
# Aggregated Properties
aggregation_parameters = fx.AggregationParameters(
hours_per_period=6,
nr_of_periods=4,
fix_storage_flows=False,
aggregate_data_and_fix_non_binary_vars=True,
percentage_of_period_freedom=0,
penalty_of_period_freedom=0,
)
keep_extreme_periods = True
excess_penalty = 1e5 # or set to None if not needed
# Data Import
data_import = pd.read_csv(pathlib.Path('Zeitreihen2020.csv'), index_col=0).sort_index()
filtered_data = data_import['2020-01-01':'2020-01-2 23:45:00']
# filtered_data = data_import[0:500] # Alternatively filter by index
filtered_data.index = pd.to_datetime(filtered_data.index)
timesteps = filtered_data.index
# Access specific columns and convert to 1D-numpy array
electricity_demand = filtered_data['P_Netz/MW'].to_numpy()
heat_demand = filtered_data['Q_Netz/MW'].to_numpy()
electricity_price = filtered_data['Strompr.€/MWh'].to_numpy()
gas_price = filtered_data['Gaspr.€/MWh'].to_numpy()
# TimeSeriesData objects
TS_heat_demand = fx.TimeSeriesData(heat_demand)
TS_electricity_demand = fx.TimeSeriesData(electricity_demand, agg_weight=0.7)
TS_electricity_price_sell = fx.TimeSeriesData(-(electricity_demand - 0.5), agg_group='p_el')
TS_electricity_price_buy = fx.TimeSeriesData(electricity_price + 0.5, agg_group='p_el')
flow_system = fx.FlowSystem(timesteps)
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),
fx.Bus('Kohle', excess_penalty_per_flow_hour=excess_penalty),
)
# Effects
costs = fx.Effect('costs', '€', 'Kosten', is_standard=True, is_objective=True)
CO2 = fx.Effect('CO2', 'kg', 'CO2_e-Emissionen')
PE = fx.Effect('PE', 'kWh_PE', 'Primärenergie')
# Component Definitions
# 1. Boiler
a_gaskessel = fx.linear_converters.Boiler(
'Kessel',
eta=0.85,
Q_th=fx.Flow(label='Q_th', bus='Fernwärme'),
Q_fu=fx.Flow(
label='Q_fu',
bus='Gas',
size=95,
relative_minimum=12 / 95,
previous_flow_rate=20,
on_off_parameters=fx.OnOffParameters(effects_per_switch_on=1000),
),
)
# 2. CHP
a_kwk = fx.linear_converters.CHP(
'BHKW2',
eta_th=0.58,
eta_el=0.22,
on_off_parameters=fx.OnOffParameters(effects_per_switch_on=24000),
P_el=fx.Flow('P_el', bus='Strom', size=200),
Q_th=fx.Flow('Q_th', bus='Fernwärme', size=200),
Q_fu=fx.Flow('Q_fu', bus='Kohle', size=288, relative_minimum=87 / 288, previous_flow_rate=100),
)
# 3. Storage
a_speicher = fx.Storage(
'Speicher',
capacity_in_flow_hours=684,
initial_charge_state=137,
minimal_final_charge_state=137,
maximal_final_charge_state=158,
eta_charge=1,
eta_discharge=1,
relative_loss_per_hour=0.001,
prevent_simultaneous_charge_and_discharge=True,
charging=fx.Flow('Q_th_load', size=137, bus='Fernwärme'),
discharging=fx.Flow('Q_th_unload', size=158, bus='Fernwärme'),
)
# 4. Sinks and Sources
# Heat Load Profile
a_waermelast = fx.Sink(
'Wärmelast', sink=fx.Flow('Q_th_Last', bus='Fernwärme', size=1, fixed_relative_profile=TS_heat_demand)
)
# Electricity Feed-in
a_strom_last = fx.Sink(
'Stromlast', sink=fx.Flow('P_el_Last', bus='Strom', size=1, fixed_relative_profile=TS_electricity_demand)
)
# Gas Tariff
a_gas_tarif = fx.Source(
'Gastarif',
source=fx.Flow('Q_Gas', bus='Gas', size=1000, effects_per_flow_hour={costs.label: gas_price, CO2.label: 0.3}),
)
# Coal Tariff
a_kohle_tarif = fx.Source(
'Kohletarif',
source=fx.Flow('Q_Kohle', bus='Kohle', size=1000, effects_per_flow_hour={costs.label: 4.6, CO2.label: 0.3}),
)
# Electricity Tariff and Feed-in
a_strom_einspeisung = fx.Sink(
'Einspeisung', sink=fx.Flow('P_el', bus='Strom', size=1000, effects_per_flow_hour=TS_electricity_price_sell)
)
a_strom_tarif = fx.Source(
'Stromtarif',
source=fx.Flow(
'P_el', bus='Strom', size=1000, effects_per_flow_hour={costs.label: TS_electricity_price_buy, CO2: 0.3}
),
)
# Flow System Setup
flow_system.add_elements(costs, CO2, PE)
flow_system.add_elements(
a_gaskessel,
a_waermelast,
a_strom_last,
a_gas_tarif,
a_kohle_tarif,
a_strom_einspeisung,
a_strom_tarif,
a_kwk,
a_speicher,
)
flow_system.plot_network(controls=False, show=True)
# Calculations
calculations: List[Union[fx.FullCalculation, fx.AggregatedCalculation, fx.SegmentedCalculation]] = []
if full:
calculation = fx.FullCalculation('Full', flow_system)
calculation.do_modeling()
calculation.solve(fx.solvers.HighsSolver(0, 60))
calculations.append(calculation)
if segmented:
calculation = fx.SegmentedCalculation('Segmented', flow_system, segment_length, overlap_length)
calculation.do_modeling_and_solve(fx.solvers.HighsSolver(0, 60))
calculations.append(calculation)
if aggregated:
if keep_extreme_periods:
aggregation_parameters.time_series_for_high_peaks = [TS_heat_demand]
aggregation_parameters.time_series_for_low_peaks = [TS_electricity_demand, TS_heat_demand]
calculation = fx.AggregatedCalculation('Aggregated', flow_system, aggregation_parameters)
calculation.do_modeling()
calculation.solve(fx.solvers.HighsSolver(0, 60))
calculations.append(calculation)
# Get solutions for plotting for different calculations
def get_solutions(calcs: List, variable: str) -> xr.Dataset:
dataarrays = []
for calc in calcs:
if calc.name == 'Segmented':
dataarrays.append(calc.results.solution_without_overlap(variable).rename(calc.name))
else:
dataarrays.append(calc.results.model.variables[variable].solution.rename(calc.name))
return xr.merge(dataarrays)
# --- Plotting for comparison ---
fx.plotting.with_plotly(
get_solutions(calculations, 'Speicher|charge_state').to_dataframe(),
mode='line',
title='Charge State Comparison',
ylabel='Charge state',
).write_html('results/Charge State.html')
fx.plotting.with_plotly(
get_solutions(calculations, 'BHKW2(Q_th)|flow_rate').to_dataframe(),
mode='line',
title='BHKW2(Q_th) Flow Rate Comparison',
ylabel='Flow rate',
).write_html('results/BHKW2 Thermal Power.html')
fx.plotting.with_plotly(
get_solutions(calculations, 'costs(operation)|total_per_timestep').to_dataframe(),
mode='line',
title='Operation Cost Comparison',
ylabel='Costs [€]',
).write_html('results/Operation Costs.html')
fx.plotting.with_plotly(
pd.DataFrame(get_solutions(calculations, 'costs(operation)|total_per_timestep').to_dataframe().sum()).T,
mode='bar',
title='Total Cost Comparison',
ylabel='Costs [€]',
).update_layout(barmode='group').write_html('results/Total Costs.html')
fx.plotting.with_plotly(
pd.DataFrame([calc.durations for calc in calculations], index=[calc.name for calc in calculations]), 'bar'
).update_layout(title='Duration Comparison', xaxis_title='Calculation type', yaxis_title='Time (s)').write_html(
'results/Speed Comparison.html'
)