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Updated example_calculation_types.py
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‎examples/Ex03_calculation_types/example_calculation_types.py

Lines changed: 54 additions & 165 deletions
Original file line numberDiff line numberDiff line change
@@ -6,12 +6,8 @@
66
"""
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88
import pathlib
9-
import time
10-
11-
import matplotlib.pyplot as plt
129
import numpy as np
1310
import pandas as pd
14-
import yaml
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import flixOpt as fx
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from flixOpt import OnOffParameters
@@ -35,7 +31,7 @@
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# ###################### Data Import ####################################
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data_import = pd.read_csv(pathlib.Path('Zeitreihen2020.csv'), index_col=0).sort_index()
38-
filtered_data = data_import['2020-01-01':'2020-01-15 23:45:00'] # Only use data from specific dates
34+
filtered_data = data_import['2020-01-01':'2020-01-2 23:45:00'] # Only use data from specific dates
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# filtered_data = data_import[0:500] # Alternatively filter by index
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filtered_data = filtered_data.set_index(pd.to_datetime(filtered_data.index)) # Convert index to datetime
@@ -206,185 +202,78 @@
206202
)
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# ###################### Calculations ####################################
209-
210-
list_of_calculations = []
205+
calculations = {'Full': None, 'Segmented': None, 'Aggregated': None}
211206
# Full Calculation
212207
if full:
213208
calculation = fx.FullCalculation('fullModel', flow_system, 'pyomo', None)
214209
calculation.do_modeling()
215210
calculation.solve(fx.solvers.HighsSolver())
216-
list_of_calculations.append(calculation)
211+
calculations['Full'] = calculation
217212

218213
# Segmented Calculation
219214
if segmented:
220215
calculation = fx.SegmentedCalculation('segModel', flow_system, segment_length, overlap_length)
221216
calculation.do_modeling_and_solve(fx.solvers.HighsSolver())
222-
list_of_calculations.append(calculation)
217+
calculations['Segmented'] = calculation
223218

224219
# Aggregated Calculation
225220
if aggregated:
226221
if keep_extreme_periods:
227-
aggregation_parameters.time_series_for_high_peaks = [TS_heat_demand],
222+
aggregation_parameters.time_series_for_high_peaks = [TS_heat_demand]
228223
aggregation_parameters.time_series_for_low_peaks = [TS_electricity_demand, TS_heat_demand]
229224
calculation = fx.AggregatedCalculation(
230225
'aggModel', flow_system,
231226
aggregation_parameters= aggregation_parameters)
232227
calculation.do_modeling()
233228
calculation.solve(fx.solvers.HighsSolver())
234-
list_of_calculations.append(calculation)
235-
229+
calculations['Aggregated'] = calculation
230+
231+
232+
# Compare Results between modeling types
233+
if full and segmented and aggregated:
234+
data = pd.DataFrame({'Full': calculations['Full'].results()['Components']['Speicher']['charge_state'],
235+
'Aggregated': calculations['Aggregated'].results()['Components']['Speicher']['charge_state'],
236+
'Segmented': calculations['Segmented'].results(combined_arrays=True)['Components']['Speicher']['charge_state']},
237+
index = calculations['Full'].system_model.time_series_with_end)
238+
fig = fx.plotting.with_plotly(data, 'line')
239+
fig.update_layout(title="Compared Charge state of Storage for different Calculation types",
240+
xaxis_title="Time", yaxis_title="Charge state")
241+
fig.write_html('results/Charge State.html')
242+
243+
244+
data = pd.DataFrame({'Full': calculations['Full'].results()['Components']['BHKW2']['Q_th']['flow_rate'],
245+
'Aggregated': calculations['Aggregated'].results()['Components']['BHKW2']['Q_th']['flow_rate'],
246+
'Segmented': calculations['Segmented'].results(combined_arrays=True)['Components']['BHKW2']['Q_th']['flow_rate']},
247+
index=calculations['Full'].system_model.time_series)
248+
fig = fx.plotting.with_plotly(data, 'line')
249+
fig.update_layout(title="Compared flow_rate of BHKW2__Q_th for different Calculation types",
250+
xaxis_title="Time", yaxis_title="flow rate")
251+
fig.write_html('results/BHKW2 Thermal Power.html')
252+
253+
254+
data = pd.DataFrame({'Full': calculations['Full'].results()['Effects']['costs']['operation']['operation_sum_TS'],
255+
'Aggregated': calculations['Aggregated'].results()['Effects']['costs']['operation']['operation_sum_TS'],
256+
'Segmented':
257+
calculations['Segmented'].results(combined_arrays=True)['Effects']['costs']['operation']['operation_sum_TS']},
258+
index=calculations['Full'].system_model.time_series)
259+
fig = fx.plotting.with_plotly(data, 'line')
260+
fig.update_layout(title="Compared cost for different Calculation types", xaxis_title="Time", yaxis_title="Costs in €")
261+
fig.write_html('results/Operation Costs.html')
262+
263+
264+
data = pd.DataFrame(data.sum()).T
265+
fig = fx.plotting.with_plotly(data, 'bar')
266+
fig.update_layout(barmode='group',title="Compared Total costs for different Calculation types",
267+
xaxis_title="",yaxis_title="Costs in €")
268+
fig.write_html('results/Total Costs.html')
269+
270+
data = pd.DataFrame({'Full': [calculations['Full'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
271+
'Aggregated': [calculations['Aggregated'].durations.get(key, 0) for key in calculations['Aggregated'].durations],
272+
'Segmented': [calculations['Segmented'].durations.get(key, 0) for key in calculations['Aggregated'].durations]},
273+
index=list(calculations['Aggregated'].durations.keys())).T
274+
fig = fx.plotting.with_plotly(data, 'bar')
275+
fig.update_layout(title="Compared Durations for different Calculation types",
276+
xaxis_title="Calculation type", yaxis_title="Time in seconds")
277+
fig.write_html('results/Speed Comparison.html')
236278

237-
# Segment Plot
238-
if calc_segs and calc_full:
239-
fig, ax = plt.subplots(figsize=(10, 5))
240-
plt.plot(
241-
calc_segs.time_series_with_end,
242-
calc_segs.results_struct.Speicher.charge_state,
243-
'-',
244-
label='chargeState (complete)'
245-
)
246-
for system_model in calc_segs.system_models:
247-
plt.plot(
248-
system_model.time_series_with_end,
249-
system_model.results_struct.Speicher.charge_state,
250-
':',
251-
label='chargeState'
252-
)
253-
plt.legend()
254-
plt.grid()
255-
plt.show()
256-
257-
# Q_th_BHKW Plot
258-
fig, ax = plt.subplots(figsize=(10, 5))
259-
for system_model in calc_segs.system_models:
260-
plt.plot(system_model.time_series, system_model.results_struct.BHKW2.Q_th.val, label='Q_th_BHKW')
261-
plt.plot(calc_full.time_series, calc_full.results_struct.BHKW2.Q_th.val, label='Q_th_BHKW')
262-
plt.legend()
263-
plt.grid()
264-
plt.show()
265-
266-
# Costs Plot
267-
fig, ax = plt.subplots(figsize=(10, 5))
268-
for system_model in calc_segs.system_models:
269-
plt.plot(
270-
system_model.time_series,
271-
system_model.results_struct.global_comp.costs.operation.sum_TS,
272-
label='costs'
273-
)
274-
plt.plot(
275-
calc_full.time_series,
276-
calc_full.results_struct.global_comp.costs.operation.sum_TS,
277-
':',
278-
label='costs (full)'
279-
)
280-
plt.legend()
281-
plt.grid()
282-
plt.show()
283-
284-
# Penalty Variables
285-
print('######### Sum Korr_... (wenn vorhanden) #########')
286-
if calc_agg:
287-
aggregation_element = list(calc_agg.flow_system.other_elements)[0]
288-
for var in aggregation_element.model.variables:
289-
print(f"{var.label_full}: {sum(var.result())}")
290-
291-
# Time Durations
292-
print('')
293-
print('############ Time Durations #####################')
294-
for a_result in list_of_calcs:
295-
print(f"{a_result.label}:")
296-
a_result.listOfModbox[0].describe()
297-
# print(a_result.infos)
298-
# print(a_result.duration)
299-
print(f"costs: {a_result.results_struct.global_comp.costs.all.sum}")
300-
301-
# ##########################################################################
302-
# ###################### Post Processing ################################
303-
304-
import flixOpt.postprocessing as flix_post
305-
306-
list_of_results = []
307-
308-
if do_full_calc:
309-
full = flix_post.flix_results(calc_full.label)
310-
list_of_results.append(full)
311-
costs = full.results_struct.global_comp.costs.all.sum
312-
313-
if do_aggregated_calc:
314-
agg = flix_post.flix_results(calc_agg.label)
315-
list_of_results.append(agg)
316-
costs = agg.results_struct.global_comp.costs.all.sum
317-
318-
if do_segmented_calc:
319-
seg = flix_post.flix_results(calc_segs.label)
320-
list_of_results.append(seg)
321-
costs = seg.results_struct.global_comp.costs.all.sum
322-
323-
# ###### Plotting #######
324-
325-
# Overview Plot
326-
def uebersichts_plot(a_calc):
327-
fig, ax = plt.subplots(figsize=(10, 5))
328-
plt.title(a_calc.name)
329-
330-
plot_flow(a_calc, a_calc.results_struct.BHKW2.P_el.val, 'P_el')
331-
plot_flow(a_calc, a_calc.results_struct.BHKW2.Q_th.val, 'Q_th_BHKW')
332-
plot_flow(a_calc, a_calc.results_struct.Kessel.Q_th.val, 'Q_th_Kessel')
333-
334-
plot_on(a_calc, a_calc.results_struct.Kessel.Q_th, 'Q_th_Kessel', -5)
335-
plot_on(a_calc, a_calc.results_struct.Kessel, 'Kessel', -10)
336-
plot_on(a_calc, a_calc.results_struct.BHKW2, 'BHKW ', -15)
337-
338-
plot_flow(a_calc, a_calc.results_struct.Waermelast.Q_th_Last.val, 'Q_th_Last')
339-
340-
plt.plot(
341-
a_calc.time_series,
342-
a_calc.results_struct.global_comp.costs.operation.sum_TS,
343-
'--',
344-
label='costs (operating)'
345-
)
346279

347-
if hasattr(a_calc.results_struct, 'Speicher'):
348-
plt.step(
349-
a_calc.time_series,
350-
a_calc.results_struct.Speicher.Q_th_unload.val,
351-
where='post',
352-
label='Speicher_unload'
353-
)
354-
plt.step(
355-
a_calc.time_series,
356-
a_calc.results_struct.Speicher.Q_th_load.val,
357-
where='post',
358-
label='Speicher_load'
359-
)
360-
plt.plot(
361-
a_calc.time_series_with_end,
362-
a_calc.results_struct.Speicher.charge_state,
363-
label='charge_state'
364-
)
365-
366-
plt.grid(axis='y')
367-
plt.legend(loc='center left', bbox_to_anchor=(1., 0.5))
368-
plt.show()
369-
370-
# Generate Plots
371-
for a_result in list_of_results:
372-
uebersichts_plot(a_result)
373-
374-
# Component-Specific Plots
375-
for a_result in list_of_results:
376-
# Ensure type hint for better IDE support
377-
a_result: flix_post.flix_results
378-
a_result.plot_in_and_outs('Fernwaerme', stacked=True)
379-
380-
# Penalty Costs
381-
print('## Penalty: ##')
382-
for a_result in list_of_results:
383-
print(f"Kosten penalty Sim1: {sum(a_result.results_struct.global_comp.penalty.sum_TS)}")
384-
385-
# Load YAML File
386-
with open(agg.filename_infos, 'rb') as f:
387-
infos = yaml.safe_load(f)
388-
389-
# Periods Order of Aggregated Calculation
390-
print(f"periods_order of aggregated calc: {infos['calculation']['aggregatedProps']['periods_order']}")

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