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Improved example_calculation_types.py
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@FBumann
FBumann committed Nov 12, 2024
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298 changes: 116 additions & 182 deletions examples/Ex03_calculation_types/example_calculation_types.py
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"""

import pathlib
from typing import List, Union, Dict

import numpy as np
import pandas as pd

import flixOpt as fx
from flixOpt import OnOffParameters

# Calculation Types
full, segmented, aggregated = True, True, True
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penalty_of_period_freedom=0)
keep_extreme_periods = True


# ###################### Data Import ####################################
# 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'] # Only use data from specific dates
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 = filtered_data.set_index(pd.to_datetime(filtered_data.index)) # Convert index to datetime
datetime_series = np.array(filtered_data.index).astype('datetime64') # Extract the datetime for the flowSystem
filtered_data.index = pd.to_datetime(filtered_data.index)
datetime_series = np.array(filtered_data.index).astype('datetime64')

# Access specific columns
electricity_demand = filtered_data['P_Netz/MW']
heat_demand = filtered_data['Q_Netz/MW']
electricity_price = filtered_data['Strompr.€/MWh']
gas_price = filtered_data['Gaspr.€/MWh']

# Define price bounds
HG_EK_min = 0
HG_EK_max = 100000
HG_VK_min = -100000
HG_VK_max = 0

# Create TimeSeriesData objects, to customize how the Aggregation is performed
# 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')


# Bus Definitions
excess_penalty = 1e5 # or set to None if not needed
Strom = fx.Bus('Strom', excess_penalty_per_flow_hour=excess_penalty)
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# Component Definitions

# 1. Boiler
a_gaskessel = fx.linear_converters.Boiler(
'Kessel',
eta=0.85,
Q_th=fx.Flow(label='Q_th', bus=Fernwaerme),
Q_fu=fx.Flow(
label='Q_fu',
bus=Gas,
size=95,
relative_minimum=12 / 95,
previous_flow_rate=20, # Neededto determin wether the FLow was on
can_be_off=OnOffParameters(effects_per_switch_on=1000)))
a_gaskessel = fx.linear_converters.Boiler('Kessel', eta=0.85,
Q_th=fx.Flow(label='Q_th', bus=Fernwaerme),
Q_fu=fx.Flow(label='Q_fu', bus=Gas, size=95,
relative_minimum=12 / 95, previous_flow_rate=20,
can_be_off=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),
Q_th=fx.Flow('Q_th', bus=Fernwaerme),
Q_fu=fx.Flow(
'Q_fu',
bus=Kohle,
size=288,
relative_minimum=87 / 288,
previous_flow_rate=100))
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),
Q_th=fx.Flow('Q_th', bus=Fernwaerme),
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',
charging=fx.Flow('Q_th_load', size=137, bus=Fernwaerme),
discharging=fx.Flow('Q_th_unload', size=158, bus=Fernwaerme),
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
)
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=Fernwaerme),
discharging=fx.Flow('Q_th_unload', size=158, bus=Fernwaerme))

# 4. Sinks and Sources

# Heat Load Profile
a_waermelast = fx.Sink(
'Wärmelast',
sink=fx.Flow(
'Q_th_Last',
bus=Fernwaerme,
size=1,
relative_maximum=max(TS_heat_demand.data),
fixed_relative_value=TS_heat_demand
)
)
a_waermelast = fx.Sink('Wärmelast',
sink=fx.Flow('Q_th_Last', bus=Fernwaerme, size=1,
relative_maximum=max(TS_heat_demand.data), fixed_relative_value=TS_heat_demand))

# Electricity Feed-in
a_strom_last = fx.Sink(
'Stromlast',
sink=fx.Flow(
'P_el_Last',
bus=Strom,
size=1,
relative_maximum=max(TS_electricity_demand.data),
fixed_relative_value=TS_electricity_demand
)
)
a_strom_last = fx.Sink('Stromlast',
sink=fx.Flow('P_el_Last', bus=Strom, size=1,
relative_maximum=max(TS_electricity_demand.data),
fixed_relative_value=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: gas_price, CO2: 0.3}
)
)
a_gas_tarif = fx.Source('Gastarif',
source=fx.Flow('Q_Gas', bus=Gas, size=1000,
effects_per_flow_hour={costs: gas_price, CO2: 0.3}))

# Coal Tariff
a_kohle_tarif = fx.Source(
'Kohletarif',
source=fx.Flow(
'Q_Kohle',
bus=Kohle,
size=1000,
effects_per_flow_hour={costs: 4.6, CO2: 0.3}
)
)
a_kohle_tarif = fx.Source('Kohletarif',
source=fx.Flow('Q_Kohle', bus=Kohle, size=1000,
effects_per_flow_hour={costs: 4.6, CO2: 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: TS_electricity_price_buy, CO2: 0.3}
)
)
a_strom_einspeisung = fx.Sink('Einspeisung',
sink=fx.Flow('P_el', bus=Strom, size=1000,
effects_per_flow_hour=TS_electricity_price_sell))

# ###################### Flow System Setup ###############################
flow_system = fx.FlowSystem(datetime_series, last_time_step_hours=None)
a_strom_tarif = fx.Source('Stromtarif',
source=fx.Flow('P_el', bus=Strom, size=1000,
effects_per_flow_hour={costs: TS_electricity_price_buy, CO2: 0.3}))

# Add Effects
# Flow System Setup
flow_system = fx.FlowSystem(datetime_series)
flow_system.add_effects(costs, CO2, PE)
flow_system.add_components(a_gaskessel, a_waermelast, a_strom_last, a_gas_tarif, a_kohle_tarif,
a_strom_einspeisung, a_strom_tarif, a_kwk, a_speicher)

# Add Components
flow_system.add_components(
a_gaskessel,
a_waermelast,
a_strom_last,
a_gas_tarif,
a_kohle_tarif,
a_strom_einspeisung,
a_strom_tarif,
a_kwk,
a_speicher
)
# Calculations
kinds = ['Full', 'Segmented', 'Aggregated']
calculations: dict = {key: None for key in kinds}
results: dict = {key: None for key in kinds}

# ###################### Calculations ####################################
calculations = {'Full': None, 'Segmented': None, 'Aggregated': None}
# Full Calculation
if full:
calculation = fx.FullCalculation('fullModel', flow_system, 'pyomo', None)
calculation = fx.FullCalculation('fullModel', flow_system, 'pyomo')
calculation.do_modeling()
calculation.solve(fx.solvers.HighsSolver())
calculations['Full'] = calculation
results['Full'] = calculations['Full'].results()

# Segmented Calculation
if segmented:
calculation = fx.SegmentedCalculation('segModel', flow_system, segment_length, overlap_length)
calculation.do_modeling_and_solve(fx.solvers.HighsSolver())
calculations['Segmented'] = calculation
results['Segmented'] = calculations['Segmented'].results(combined_arrays=True)

# Aggregated 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(
'aggModel', flow_system,
aggregation_parameters= aggregation_parameters)
calculation = fx.AggregatedCalculation('aggModel', flow_system, aggregation_parameters)
calculation.do_modeling()
calculation.solve(fx.solvers.HighsSolver())
calculations['Aggregated'] = calculation


# Compare Results between modeling types
if full and segmented and aggregated:
data = pd.DataFrame({'Full': calculations['Full'].results()['Components']['Speicher']['charge_state'],
'Aggregated': calculations['Aggregated'].results()['Components']['Speicher']['charge_state'],
'Segmented': calculations['Segmented'].results(combined_arrays=True)['Components']['Speicher']['charge_state']},
index = calculations['Full'].system_model.time_series_with_end)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="Compared Charge state of Storage for different Calculation types",
xaxis_title="Time", yaxis_title="Charge state")
fig.write_html('results/Charge State.html')


data = pd.DataFrame({'Full': calculations['Full'].results()['Components']['BHKW2']['Q_th']['flow_rate'],
'Aggregated': calculations['Aggregated'].results()['Components']['BHKW2']['Q_th']['flow_rate'],
'Segmented': calculations['Segmented'].results(combined_arrays=True)['Components']['BHKW2']['Q_th']['flow_rate']},
index=calculations['Full'].system_model.time_series)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="Compared flow_rate of BHKW2__Q_th for different Calculation types",
xaxis_title="Time", yaxis_title="flow rate")
fig.write_html('results/BHKW2 Thermal Power.html')


data = pd.DataFrame({'Full': calculations['Full'].results()['Effects']['costs']['operation']['operation_sum_TS'],
'Aggregated': calculations['Aggregated'].results()['Effects']['costs']['operation']['operation_sum_TS'],
'Segmented':
calculations['Segmented'].results(combined_arrays=True)['Effects']['costs']['operation']['operation_sum_TS']},
index=calculations['Full'].system_model.time_series)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="Compared cost for different Calculation types", xaxis_title="Time", yaxis_title="Costs in €")
fig.write_html('results/Operation Costs.html')


data = pd.DataFrame(data.sum()).T
fig = fx.plotting.with_plotly(data, 'bar')
fig.update_layout(barmode='group',title="Compared Total costs for different Calculation types",
xaxis_title="",yaxis_title="Costs in €")
fig.write_html('results/Total Costs.html')

data = pd.DataFrame({'Full': [calculations['Full'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
'Aggregated': [calculations['Aggregated'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
'Segmented': [calculations['Segmented'].durations.get(key, 0) for key in calculations['Aggregated'].durations]},
index=list(calculations['Aggregated'].durations.keys())).T
fig = fx.plotting.with_plotly(data, 'bar')
fig.update_layout(title="Compared Durations for different Calculation types",
xaxis_title="Calculation type", yaxis_title="Time in seconds")
fig.write_html('results/Speed Comparison.html')

results['Aggregated'] = calculations['Aggregated'].results()


def extract_result(results_data: dict[str, dict], keys: List[str]) -> Dict[str,Union[int, float, np.ndarray]]:
"""
Function to retrieve values from a nested dictionary.
Tries to get the wanted value for eachnkey in the first layer of the dict.
Returns a dict with one key value pair for each dict it found a value in.
"""
def get_nested_value(d, ks):
for k in ks:
if isinstance(d, dict):
d = d.get(k, None)
else:
return None
return d

return {kind: get_nested_value(results_data.get(kind, {}), keys) for kind in results_data.keys()}


if calculations['Full'] is not None:
time_series_used = calculations['Full'].system_model.time_series
time_series_used_w_end = calculations['Full'].system_model.time_series_with_end
else:
time_series_used = calculations['Aggregated'].system_model.time_series
time_series_used_w_end = calculations['Aggregated'].system_model.time_series_with_end

data = pd.DataFrame(extract_result(results, ['Components', 'Speicher', 'charge_state']),
index=time_series_used_w_end)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="Charge State Comparison", xaxis_title="Time", yaxis_title="Charge state")
fig.write_html('results/Charge State.html')

data = pd.DataFrame(extract_result(results, ['Components', 'BHKW2', 'Q_th', 'flow_rate']),
index=time_series_used)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="BHKW2 Q_th Flow Rate Comparison", xaxis_title="Time", yaxis_title="Flow rate")
fig.write_html('results/BHKW2 Thermal Power.html')

data = pd.DataFrame(extract_result(results, ['Effects', 'costs', 'operation', 'operation_sum_TS']),
index=calculations['Full'].system_model.time_series)
fig = fx.plotting.with_plotly(data, 'line')
fig.update_layout(title="Cost Comparison", xaxis_title="Time", yaxis_title="Costs (€)")
fig.write_html('results/Operation Costs.html')

data = pd.DataFrame(extract_result(results, ['Effects', 'costs', 'operation', 'operation_sum_TS']),
index=time_series_used)
data = pd.DataFrame(data.sum()).T
fig = fx.plotting.with_plotly(data, 'bar')
fig.update_layout(title="Total Cost Comparison", yaxis_title="Costs (€)")
fig.write_html('results/Total Costs.html')

duration_data = pd.DataFrame({
'Full': [calculations['Full'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
'Aggregated': [calculations['Aggregated'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
'Segmented': [calculations['Segmented'].durations.get(key, 0) for key in calculations['Aggregated'].durations]},
index=list(calculations['Aggregated'].durations.keys())).T
fig = fx.plotting.with_plotly(duration_data, 'bar')
fig.update_layout(title="Duration Comparison", xaxis_title="Calculation type", yaxis_title="Time (s)")
fig.write_html('results/Speed Comparison.html')

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